ENCLOSURE 3 Email correspondence from Denise Pease (Regional Administration, GSA Northeast and Caribbean Region) to GSA employees Joanna Rosato, Carol Schafer, Carol Diaz, Anne Callahan, and an unknown person containing three attachments (Aug. 24, 2012). The three attachments are as follows: 1. Peace Bridge - Asthma Hospitalization and Residential Exposure to Pollution Study.pdf; 2. Peace Bridge - Harvard Study on Air Toxics Exposure at Peace Bridge.pdf; and, 3. Peace Bridge - Traffic Volumes and Health Care Utilization Before and After Sept. 11th.pdf
Transcript
ENCLOSURE 3
Email correspondence from Denise Pease (Regional Administration, GSA Northeast and Caribbean Region) to GSA employees Joanna Rosato, Carol Schafer, Carol Diaz, Anne Callahan,
and an unknown person containing three attachments (Aug. 24, 2012).
The three attachments are as follows:
1. Peace Bridge - Asthma Hospitalization and Residential Exposure to Pollution Study.pdf; 2. Peace Bridge - Harvard Study on Air Toxics Exposure at Peace Bridge.pdf; and, 3. Peace Bridge - Traffic Volumes and Health Care Utilization Before and After Sept.
11th.pdf
From: Denise Pease (2A) [[email protected]]Sent: Friday, August 24, 2012 5:49 PMTo: Joanna Rosato; Carol Schafer; Carol Diaz (2PR); Anne CallahanCc:Subject: Fwd: Peace Bridge - Asthma StudiesAttachments: Peace Bridge - Asthma Hospitalization and Residential Exposure to Pollution Study.pdf;
Peace Bridge - Harvard Study on Air Toxics Exposure at Peace Bridge.pdf; Peace Bridge -Traffic Volumes and Health Care Utilization Before and After Sept. 11th.pdf
Denise L. PeaseRegional AdministratorUnited States General Services Administration26 Federal Plaza, Suite 18-100New York, New York 10278(212) 264-2600
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Objectives. We conducted a case–control study of adulthood asthma and point-source respirable particulate air pollution with asthma-diagnosed case patients(n=3717) and gastroenteritis-diagnosed control patients (n=4129) to determineeffects of particulate air pollution on public health.
Methods. We used hospitalization data from Buffalo, NY, neighborhoods for a5-year period (1996 through 2000), geographic information systems techniques,the Diggle method, and statistical analysis to compare the locations of case pa-tients and control patients in terms of proximity to different known pollutionsources in the study area.
Results. We found a clustering of asthma cases in close proximity to the PeaceBridge Complex and the freeways and a dose–response relationship indicatinga decreased risk of asthma prevalence the farther an individual resides from thesource of exposure.
Conclusions. These findings provide a basis for the development of new hy-potheses relating to the spatial distribution of asthma prevalence and morbidityin this community. (Am J Public Health. 2004;94:1250–1257)
Geographic Clustering of Adult Asthma Hospitalization and Residential Exposure to Pollution at a United States–Canada Border Crossing | Tonny J. Oyana, PhD, Peter Rogerson, PhD, and Jamson S. Lwebuga-Mukasa, MD, PhD
the United States Environmental ProtectionAgency (EPA) concluded that long-term in-halation exposure to diesel exhaust particlesis likely to pose a lung damage threat, includ-ing a risk for cancer, to humans.22 The studyfurther noted that short-term exposures cancause irritation and inflammatory symptomsof a transient nature.22
In previous studies, we reported an in-creased risk of asthma among residents livingalong the US–Canada border crossing andthe major roadways feeding it. An associationbetween increases in commercial trafficacross the PBC and increases in health careuse for asthma was reported in Lwebuga-Mukasa et al.23 A sharp decrease in trafficafter the September 11, 2001, World TradeCenter terrorist attacks was associated with adecline in health care use for respiratory ill-nesses, which rebounded when traffic recov-ered.7 A house-to-house survey of 214 homesin the area (from 1996–1997) and anotherof 1644 homes (in 2002) found householdsin close proximity to the PBC to have asthmaprevalence rates that were double those ofhouseholds located on Buffalo’s East Side
(J. S. Lwebuga-Mukasa, MD, PhD, unpub-lished data).6 These observations indicatethat increased traffic on Buffalo’s West Sidemay be associated not only with asthma ex-acerbations but also with increased preva-lence in the community.
Cumulative evidence is also emerging fromstudies reviewed in Peterson and Saxon,24
Kane et al.,25 Lwebuga-Mukasa and Dunn-Georgiou,2,3,26 Lwebuga-Mukasa andPszonak,4 and Lin et al.5 showing a higher res-piratory burden on the communities residingin Buffalo’s West Side than on the surround-ing communities. However, there is little focuson the spatial relationships between increasedrisk of asthma and environmental exposure. Inthis study, we investigate the hypothesis thatproximity to the major commercial routes; thePBC; and EPA-designated toxic air releasesites and multiple-emission sites are associatedwith increased asthma risk. Characterizationof environmental and human characteristics ofclusters would provide a basis for identifica-tion of factors contributing to an increasedasthma burden in the community and to thedevelopment of mitigation measures.
Buffalo’s West Side contains the Peace BridgeComplex (PBC), the busiest US–Canadacrossing point for commercial traffic in theeastern United States. Over the past decade,there has been a steady increase in commer-cial traffic primarily owing to the increasedtrade resulting from the North AmericanFree Trade Agreement (NAFTA).1 Increasedasthma prevalence and health care use re-sulting from traffic-related pollution aroundthe US–Canada border crossing point hasbeen a major focus of previous studies.2–7
These studies have provided evidence sup-porting the hypothesis that there is a highrespiratory burden among residents living inclose proximity to the NAFTA corridor.
The PBC is located in a densely populated,urban, and predominantly minority commu-nity. Although the increased traffic throughthe PBC has brought economic prosperity tothe United States, Canada, and Mexico, thehealth effects of NAFTA trade associated withtraffic pollution in the communities along theUS–Canada border have not been extensivelystudied. A recent report on health effects inNAFTA corridors8 concluded that commercialtraffic contributes significantly to air pollutionin all NAFTA corridors, particularly to nitro-gen dioxide and particulate matter, but the re-port did not include the PBC. Diesel exhaustparticles have been shown to worsen respira-tory symptoms and to lead to deterioration inlung function, especially among individualswith preexisting chronic conditions such asasthma.9–12
Previous studies, such as those bySchwartz and Dockery,9,10 Dockery et al.,13
Schwartz,14,15 Schwartz and Morris,16 Styer etal.,17 Norris et al.,18 Wong et al.,19 Samet etal.,20 and Peters et al.,21 have reported an as-sociation between cardiopulmonary morbidityand mortality and exposure to respirable par-ticulate matter. In a groundbreaking study,
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2.5 0 2.5 5 7.5 101.25Kilometers
Major Roads
Sites
Cases
Controls
West Side
Wes
t Sid
e
FIGURE 1—Resident locations of case patients and control patients, network of majorroads, and focus sites in the study area.
METHODS
Study Area and PopulationThe study area covered 156 census tracts
with an estimated population of 529846people in Erie County, according to the 2000population data obtained from the US CensusBureau.27 About 40% of the study populationwas between 18 and 64 years of age. Thestudy area includes inner-city areas character-ized by low socioeconomic status, poor hous-ing conditions, low education levels, high un-employment, and high percentages ofminority population.27 Figure 1 shows resi-dent locations of case patients and control pa-tients, the network of major roads, and focussites in the study area.
Study DesignA cross-sectional study based on case–
control data for adulthood asthma (case pa-tients) and nonrespiratory disease (control pa-tients) for a 5-year period (1996–2000) wasconducted using hospitalization data. Casesubjects (n=3717) and control subjects (n=4129) consisted of asthma patients (Interna-
tional Classification of Diseases, 9th Revision[ICD-9] code 493)28 and gastroenteritis pa-tients (ICD-9 code 558), respectively, residingin Buffalo neighborhoods during the same pe-riod. The study was based on a database thatwas obtained from Kaleida Health Systems, amajor provider of health care in western NewYork. The data were available at individualand zip code levels. Data with residential ad-dresses was separated, processed, and geo-coded for use in spatial analysis and a geo-graphic information systems (GIS). At thecensus tract level, we analyzed cases patientsper population and identified local asthmaclusters. At the zip code level, a comparisonof case patients and control patients was con-ducted to identify which zip codes had an ele-vated risk of asthma. We also conducted afield assessment of the previously identifiedfocus sites.
Data CategoriesThe 3 data categories analyzed in this
study are as follows: hospitalization and out-patient visits for asthma, 1996 through 2000;hospitalization and outpatient visits for gastro-
enteritis, 1996 through 2000; and focussites. The data categories are described in de-tail in the following sections.
Hospitalization and outpatient visits forasthma, 1996 through 2000. The hospitaliza-tion and outpatient visits data were based onthe patient records of kept by Millard Fill-more Health Hospitals, which are divisions ofthe Kaleida Health System, and covered ad-missions from January 1996 to August 2000.Some of these data have been published inLwebuga-Mukasa et al.6 and in Oyana andLwebuga-Mukasa.30 The records containedthe residential addresses of patients and theirinsurance status. The data were vital for theidentification of spatial relationships betweencase address locations and polluting sources.
Hospitalization and outpatient visits for gas-troenteritis, 1996 through 2000. Data onhospitalization and outpatient visits for gas-troenteritis were obtained from the samesource for the same time period. The data-base contained information on the case pa-tients, including the patient’s address and in-surance status. The case patients were alsocategorized into 2 groups: clinical and emer-gency department cases. Gastroenteritis wasused as the control disease. Only cases de-tected from 1996 through 2000 among resi-dents of the study area were included in thecase–control study.
Focus sites. Focus sites were previously ob-tained from the EPA Web site (http://www/epa.gov/enviro/index.html) and mapped aspoint data, as shown in Figure 1. Three focussites identified in Oyana and Lwebuga-Mukasa29 were of primary interest, giventheir statistically significant association withincreased risk for asthma, and 10 additionalsites were also included. Six of these addi-tional sites were test sites used in the valida-tion of the model. The test sites were chosenrandomly, outside of the statistically signifi-cant distance bands. However, the 13 focussites used in this study do not constitute anexhaustive list of possible exposure sites inthe study area.
We conducted a preliminary field assess-ment of the 3 sites (Birge Company, OgradyWinnifred Silver, and Marnap Industries) to fur-ther our understanding of their surroundings inthe study area. A working assumption was de-veloped after finding out that 2 of the 3 focus
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sites had gone out of business, thereby attract-ing new business opportunities and develop-ments in their original locations. We agreedthat the focus sites represented air pollution inthe preceding decade, when exposures mighthave occurred. This assumption seems reason-able for air pollution sources that have been inoperation since the 1980s and whose dispersalis mediated by transport mechanisms (e.g., pre-vailing winds) that have not changed a greatdeal in the past 10 to 20 years.
Three additional sites (Miken Company,Nabisco Company, and Harrison Radiator)that are currently in operation and that haveactive licenses were obtained from the De-partment of Environmental Conservation,Buffalo. We also conducted a preliminaryfield assessment of these 3 sites that con-firmed that the sites were functional.
Analytical TechniquesRigorous analytical methods were applied
to the case–control data to study whetherthere is an association between increased riskof asthma and pollution sources. The Digglemethod was applied to test disease clusteringof asthma around the focus sites.30,31
The Diggle method is a focused cluster-detection approach appropriate for handlingspatial data at the individual level.31,32 Themethod compares the spatial pattern of caselocations with the spatial pattern of controllocations; for instance, using a more commoncontrol disease. The control acts as a nullmodel of no clustering and normally reflectsthe spatial pattern of the population-at-risk.The test is based on maximizing the likeli-hood of the sample of case patients and con-trol patients, which in turn is based on an ex-ponential decline in risk as the squareddistance from the source increases.
GIS techniques combined with statisticalanalysis were used to compare odds ratios(ORs) for the location of case patients andcontrol patients in relation to proximity to dif-ferent pollution sources in the study area. Thechoice of these analytical techniques wasbased on their wide applications in studyingpatterns of disease, prevalence, health careuse, and incidence. These techniques havealso been widely popularized by the develop-ment of the ClusterSeer32 (TerraSeer, Inc,Ann Arbor, Mich) and ARCGIS33 (Environ-
mental Systems Research Institute [ESRI], Inc,Redlands, Calif) software packages, whichhandle large volumes of geographical data.
ClusterSeer Version 1.1.4 (TerraSeer);ArcStreet USA, ARCGIS 8.1.2, andARCVIEW 8.1 (ESRI); and Microsoft Excel(Microsoft, Inc, Redmond, Wash) softwarepackages were used in spatial analysis, GISmapping, and data analysis. All of the datawere compiled and analyzed at the Centerfor Asthma and Environmental Exposure,Kaleida Health Buffalo General Division,University at Buffalo School of Medicine andBiomedical Sciences.
The data were also loaded into ARCStreetUSA to match the physical addresses withgeographical latitudes and longitudes. ARC-Street USA contains the most up-to-datestreet addresses in the United States. Ad-dress matching was based on the Dynamap/Zip+4 Centroids and Correspondences Files(Geographic Data Technology, Inc, Lebanon,NH). Geographic Data Technology providedthis comprehensive street database to ESRI.Case patients’ and control patients’ addresseswere mapped as point data. We had 2340and 2571 case and control patients, respec-tively, with matched addresses. We obtainedan accuracy level of over 90% during the ad-dress-matching exercise. The geocoded datawere processed in ARCGIS 8.1.2 for furtherspatial analysis.
It was assumed that those who livedwithin 1 km of the emission sites and busilytraveled roadways were exposed to vehicleexhaust fumes and pollutants from suspectedsources of pollution, and that those living far-ther away (>2 km) were assumed to be un-exposed. Rijnders et al.34 recommended thatvariables such as degree of urbanization,traffic density, and distance to a nearby high-way or any potential pollution source can beused to estimate exposure to traffic-relatedair pollution. Milligan et al.35 also used a dis-tance of more than 2 km in their study to es-timate exposure resulting from traffic-relatedair pollution.
Epidemiological methods based on ORsand 95% confidence intervals were used tocompute the spatial risk relationships betweencase patients and control patients (using a sig-nificance level of P≤ .05). A 2 � 2 table anal-ysis was conducted to demonstrate the rela-
tionship between 2 dichotomous or binaryvariables (exposed and unexposed groups).
RESULTS
Case–Control DemographicsIn the Kaleida database, there were 3717
patients hospitalized because of asthma.There were 6265 hospital discharges forasthma during the period between 1996 and2000. The majority (80%) of the patientswere adults (aged 17–64 years). Thirty-twopercent were from the city of Buffalo, 3.2%were from the town of Amherst, 3.1% werefrom Williamsville, and 61.7% were fromother places. There was a notable increase inhospital admission between 1996 and 2000,especially in zip codes 14201 and 14213.
In the Kaleida database, there were 4129patients hospitalized because of a nonrespira-tory disease—gastroenteritis. Inpatients consti-tuted 3.9% of the patients, and the remaining96.1% were outpatients. Emergency depart-ment and clinic patients constituted 36.4%and 40.3% of the total, respectively. Thenumber of patients admitted with gastroenter-itis remained constant over this period. An-nual admissions ranged from 800 to 829,with an average of 826 patients admitted an-nually from 1996 to 2000. Zip codes 14201,14213, and 14221 contributed 44% of thepatients during this period.
Table 1 lists the odds ratios from the case–control study at the zip code level between1996 and 2000. A comparison of odds ratiosby zip codes shows certain zip codes with sta-tistically significant increased odds of havingasthma, relative to nonrespiratory disease. Weobserved a positive association between possi-ble exposure and outcome at the 5% signifi-cance level in zip codes 14201, 14213, 14207,and 14204. All of these zip codes that werestatistically significant with odds ratios greaterthan the value 1 are located on Buffalo’sWest Side. In zip codes 14221, 14214, 14217,14150, and 14227, we observed a negativeassociation between possible exposure andoutcome at the 5% significance. Zip codesthat were statistically significant with odds ra-tios less than the value 1 are located furtheraway from the West Side of Buffalo. The re-maining zip codes had statistically nonsignifi-cant results.
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TABLE 1—Exposure Based on Geographic Locations Identified at the Zip Code Level: OddsRatios From a Case–Control Study, 1996–2000
Case Patients (n = 3717) Control Patients (n = 4129)
Asthma Gastroenteritis% Diagnosed Hospitalization % Diagnosed Hospitalization Odds Ratio
Note. Denominators derived from population data from the 1990 US Census; case patients and control patients derived fromhospitalization and outpatient visits for asthma (ICD-9 code 493) and gastroenteritis (ICD-9 code 558) from Kaleidadatabase, 1996–2000.aPositive association between exposure and outcome at the 5% significance level.bNegative association between exposure and outcome at the 5% significance level.
Spatial Analysis of Case–Control StudyThe Diggle method, as used within Clus-
terSeer, was applied to test the null hypothesisof no clustering of case patients in compari-son with a common control disease around afocal point, at α =0.05. GIS was used to de-termine whether spatial associations werebetween emission sites, major roadways, andresidential locations of case patients andcontrol patients. Sites were defined to in-clude the PBC, air, toxic, and multiple re-
leases, as shown in Figure 1. The busily trav-eled roadways were defined according to thetype and volume of traffic on the basis ofdata from the Department of Transportation,as well as on information obtained from theresidences, to include Main Street, Bailey Av-enue, Niagara Street, Seneca Street, Dela-ware Avenue, Interstate 198, Interstate 190,and Route 33 (Figure 1).
Figure 1 shows the increased odds of hav-ing asthma among the residences living in
close proximity to the sites or interstate road-ways. Overall, there was a lower risk ofasthma diagnosis for adults living fartheraway from sites and busily traveled roadways.
We analyzed exposed and unexposed casepatients and control patients living within1 km or farther away from sites and road-ways. Table 2 shows increased odds of havingasthma among the residences living in closeproximity to the sites or roadways—there wasa higher risk of asthma diagnosis for adultswho lived in close proximity to those areas.
Case–control data showed that patients liv-ing along Main Street, Bailey Avenue, NiagaraStreet, Seneca Street, and Interstate 190 allhad increased odds of having asthma. Most ofthe case patients at sites had statistically sig-nificant increased odds of asthma within0.5 km versus more than 2 km away fromthe roadways, but the control patients did not.Apart from Interstate 190, which had statisti-cally significant increased odds of asthmaboth at 0.5 versus 2.0 km and 1 versus 2 km,4 roadways (Main Street, Bailey Avenue, Nia-gara Street, and Seneca Street) were also sta-tistically significant at 1 versus 2 km. Threeroadways (Delaware Avenue, Interstate 198,and Route 33) that carry mostly automobiletraffic did not seem to have much effect whencase patients and control patients were com-pared at 0.5 or 2 km farther away (within500 m or more than 2 km).
The highest odds ratios were observed atair release sites (stationary sources of air pol-lution), with OR=15.77 at 0.5 versus 2 km.Residents living at 0.5 km from air releasesites have 15 times the odds of having asthmaamong all emission sites compared with thoseliving more than 2 km away. The PBC hadOR=4.41 at 0.5 versus 2.0 km, toxic sites hadOR=0.70 at 0.5 versus 2.0 km, and multiplerelease sites had OR=1.93 at 0.5 versus2.0 km.
The evaluation of increased risk of diag-nosed asthma near the focus sites that wereidentified in Oyana and Lwebuga-Mukasa29
was carried out using the Diggle method andthe individual-level data. Figure 2 both showsasthma clusters identified by 2 methods usinggroup-level data36,37 and shows the distribu-tion of asthma cases per 1000 people. Forthis analysis, we have increased the scope ofthe investigation to include test and alterna-
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0 9 121.5Kilometers
Clusters, Besag–Newell method
1–8 (Very low)
9–28 (Low)
29–108 (Medium)
109–233 (High)
234–434 (Very high)
First most likely cluster,Turnbull method
Third most likely cluster,Turnbull method
3 6
Second most likely cluster,Turnbull method
FIGURE 2—Asthma clusters identified by 2 methods and the distribution of asthma casesper 1000 people.
TABLE 2—Spatial Analysis of Case–Control Study Showing Odds Ratios
Main St, Bailey Ave, Niagara St, and Seneca St 1.11 1.36b 0.95, 1.30 1.15, 1.61Delaware Ave 0.65a 0.96 0.51, 0.83 0.76, 1.21
Note. Case patients and control patients derived from hospitalization and outpatient visits for asthma (ICD-9 code 493) andgastroenteritis (ICD-9 code 558) from Kaleida database, 1996–2000.aPositive association between exposure and outcome at the 5% significance level.bNegative association between exposure and outcome at the 5% significance level.
tive sites (geographic coordinates) around thefocus sites to evaluate how the P valuechanged. Our null hypothesis was that thecase and control occurrences have the sameunderlying spatial distribution. The alterna-tive hypothesis was based on case subject lo-cations having different spatial patterns incomparison with the control locations and onthe fact that the density of the case locationswas higher than that of the control near thefocus sites.
Model results for the 13 focus sites showedthat certain geographical areas were signifi-cantly more affected by asthma than others.For instance, our study shows that the chanceof achieving more extreme outcomes (if thenull hypothesis was true) for the model pa-rameters for 4 focus sites (Birge Company,PBC, Ogrady Winnifred Silver, and MikenCompany) located in Buffalo’s West Side hadP≤ .0001. We further observed that focussites located in Buffalo’s West Side have asso-ciations that are very highly significant, andthe effects appear to extend over a large area.However, there were also modest associationsfor 2 focus sites (Marnap Industries, P≤ .053;and Nabisco Company, P≤ .045) located onBuffalo’s East Side, and the effects appear toextend over a small area. There was no evi-dence of association for Harrison Radiator,also located on Buffalo’s East Side, as well asthe other 6 test sites. Overall, the analysis ofcase–control data establishes further evidenceof an association between diagnosed asthmaand 4 focus sites located on Buffalo’s WestSide. Our study also finds modest associationsfor locations within Buffalo’s East Side, espe-cially the Nabisco Company, which manufac-tures Milk-Bone bakery products and releasesrespirable air-borne dust particles. These as-sociations warrant further investigation.
Comparability of Case Patients and Control Patients
Insurance status as a possible confounderwas evaluated during the study period. Acomparison of insurance status for case pa-tients and control patients was conducted toevaluate whether a significant difference ex-isted between asthma and gastroenteritis pa-tients in 34 subcategories of insurance statusreported in the databases, which were identi-fied following these 5 tallies: total number of
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case patients, inpatients, outpatients, emer-gency room use, and clinical case patients.Overall, based on a t test, there was no statis-tical difference in insurance status betweenasthma and gastroenteritis patients in the sub-categories described above. However, therewere some slight differences that were ob-served in 3 subcategories: outpatients(tobserved=1.669, df=66, tcritical=1.668, P≤.049), clinical (tobserved =2.562, P≤ .006), andcase patients diagnosed in 2000 (tobserved =1.694, P≤ .047).
Additional analysis was conducted on thebasis of the 4 categories of benefits (privatebenefit programs, public-sponsored benefitprograms, self-sponsored benefits, and others)that were identified with insurance statusdata. In this particular analysis, it was as-sumed that those patients who were mostlikely to have access to private preventivecare belonged to the private benefit programscategory. Insurance status data for patientsseeking emergency and urgent care in thehospital showed that 34.85% and 30.28% ofthe patients with asthma and gastroenteritisdiagnoses, respectively, were most likely tohave private preventive care as well. The re-maining subcategories showed that public-sponsored benefits were used by 26.99%and 28.41% of the asthma and gastroenteritispatients, self-sponsored benefits constituted20.62% and 22.15% of the asthma and gas-troenteritis patients, and others constituted17.5% and 18.9% of patients with asthmaand gastroenteritis diagnoses, respectively.Overall, the comparison of insurance statusfor patients seeking care in the emergencyroom during the study period shows a com-parable ratio of 1:1 in all 4 subcategories.The insurance status of case patients withasthma diagnosis and diagnosed gastroenteri-tis was therefore comparable, lending supportfor gastroenteritis as an appropriate candidatefor the case–control study.
DISCUSSION
There are 4 major findings from this study:first, the distribution of asthma, after account-ing for spatial variation in the population atrisk, is nonhomogenous; second, areas inwhich case patients have high rates of asthmaappear clustered in proximity to air-polluting
sites, including the PBC and the busily trav-eled roadways supplying it, in addition toEPA-designated toxic air release sites; third,the decrease in asthma prevalence as a func-tion of distance from sites indicates that pollu-tants from the sites are not only associatedwith the worsening of asthma symptoms butmay also play a role in the etiology of asthma;and fourth, further analysis of common fea-tures of pollutants may help elucidate mecha-nisms relating exposures to the genesis ofasthma. To date, studies have focused on high-level exposures and have paid relatively littleattention to local multiple exposures. Identifi-cation of clusters associated with differentsources may provide insights into how mix-tures of pollutants may interact and lead todevelopment of asthma in susceptible individ-uals. The study findings support and expandour earlier observations in Lwebuga-Mukasaand Dunn-Georgiou,2,3 Lwebuga-Mukasa andPszonak,4 Oyana and Lwebuga-Mukasa,29 andLwebuga-Mukasa et al.6 These findings arealso consistent with previous findings thathave been reported in Peterson and Saxon,24
Kane et al.,25 Briggs et al.,38 Donaldson etal.,12 Dockery,39 Loh et al.,40 Lin et al.,5 andLin et al.41 The findings confirm earlier re-ports that indicated that increases in NAFTAtraffic across the PBC were related to in-creases in prevalence and health care use forasthma.23 This study, together with the previ-ous studies, provides a basis for systematic in-vestigation of environmental exposures incommunities and their effect on residents.
Levels of Geographic ResoloutionThis study has benefited from the use of
more than 1 level of geographic resolution.Our spatial analysis has been conducted hereat 2 different levels. At the individual level,we analyzed and compared spatial locationsof case patients and control patients at vary-ing distances from sites. At the zip code level,we computed odds ratios to evaluate whichzip codes were heavily burdened by respira-tory illnesses. In a previous study,29 we ana-lyzed census tract (group)–level data to ob-tain case patients per population size and toidentify local asthma clusters. These differentlevels of geographic resolution enabled us togain more insight into the problem of respira-tory illnesses faced by communities living in
close proximity to the PBC, major roadways,and pollution sites.
Proximity to SitesThis study pinpoints proximity to air re-
lease sites including the PBC as a significantcontributor to increased asthma exacerba-tions in the study area. For the residents liv-ing within 0.5 km from air release sites, theodds of having asthma were 15 times greatercompared with those of patients living morethan 2 km away. We also observed a 4-foldincrease in the odds of having asthma amongresidents living in close proximity to the PBCcompared with those living in nonexposedareas farther away. It is probable that thesesites significantly affect the quality of inhal-able air, which in turn could trigger episodesof airway inflammation among individualswith asthma. It is therefore reasonable to sus-pect that communities living in close proxim-ity to these sites are exposed to high levels ofparticulate emissions42 that contribute to in-creased asthma exacerbations. In addition,this particular finding might explain the highrates of hospitalization and emergency roomuse already reported in previous studies.4,23
Overall, the extreme increase in asthma riskassociated with toxic air release sites pointsto the need to minimize toxic releases be-cause of their potential health risk to resi-dents nearby.
Significant AssociationsSix statistically significant associations of di-
agnosed asthma near the focus sites, in com-parison with the geographical distribution ofgastroenteritis, were found using the Digglemethod. Although 1 focus site located onBuffalo’s East Side did not reach statisticalsignificance, there was modest evidence ofincreased diagnosed asthma in 2 focus siteslocated in this area. Of the 13 sites for whichwe fitted models, we observed a higher in-creased density of case locations in compari-son with control locations on Buffalo’s WestSide than in other areas. These associationsare consistent with those identified usingK-means and nearest-neighborhood hierarchi-cal clustering techniques, the score test ofLawson and Waller, the Bithell score risktest, and Besag and Newell’s methods. ThePBC site was not statistically significant by 1
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method—the score test of Lawson andWaller,29 but all the other methods, includingthe Diggle model, found that particular sitevery highly significant.
The analysis of case–control data estab-lishes further evidence of associations be-tween diagnoses of asthma and the 4 focussites located on Buffalo’s West Side. However,we cannot necessarily attribute all of the ef-fects to these 4 focus sites, because statisti-cally significant P values extend over a largearea. Other possible explanations are ex-plored below. This study also found modestassociations for 2 focus sites located on Buf-falo’s East Side, especially for the NabiscoCompany, which produces grain and flour,and these associations warrant further investi-gation. Current data do not permit determina-tion of whether the increase in diagnosedasthma is related to residents working at thefactory or whether exposure was secondaryto air pollution.
Our study had 3 limitations. First, becausethe hospital data we used were from a singlehospital system, we could not examine otherhospital admissions in the same area. Second,it was not possible to determine why patientswere admitted to the hospitals (i.e., the pa-tient’s original complaint). Third, the data setdid not contain information pertaining to theliving conditions of the patients; this informa-tion could explain other exposures to asthmarisk factors.
Although there is mounting cumulative evi-dence associating increased risk of asthma totraffic-related pollution, it is reasonable to sus-pect the existence of a number of other primerisk factors, such as possible mixtures of pol-lutants and the interaction of local ecologicalfactors that might better explain spatial varia-tions of asthma in the study area. We suspectthat these risk factors are key contributors ofasthma on Buffalo’s West Side, given that pre-vious studies6 have absolved possible con-founders such as exposure to environmentaltobacco smoke, race, and income.
InterpretationsThese interpretations were arrived at on
the basis of the following factors. First, thereis a significant release of diesel exhaust parti-cles42 from a busily traveled state highway inwhich the bulk of commercial traffic serving
the NAFTA trade corridor flows through thiscommunity. In the summer of 2002, a pre-liminary chemical analysis of respirable parti-cles collected from the PBC by Baier, one ofthe research scientists studying traffic-relatedpollution, revealed high ammonium carbon-ates, nitrates, and sulfates in the study area(R. E. Baier, oral communication, August 23,2002). His analysis further revealed a net ad-dition of silicates and iron-bearing particles tothe respirable fraction, which was indepen-dent of wind direction. Baier suggested thatthe net increase of respirable silicate-rich andiron-rich matter was attributable to truckemissions, based on further comparisons tospectra of impacted aerosol samples from aCheektowaga truck stop, where numerousidling trucks were present. These unusuallyhigh levels of silicate-rich and iron-rich matterare residues of burning diesel that may com-bine with local factors and contribute tochronic airway and lung inflammation44–49
and set the stage for airway hyperresponsive-ness, a characteristic of asthma, and worsen-ing of asthma symptoms among individualswho already have the disease. Second, thereis a significant number of manufacturing in-dustries located on Buffalo’s West Side thatmight be emitting respirable particulates. Theevidence of clusters of asthma about thefocus sites we studied illustrates this point.Third, local ecological factors may be impor-tant, including the urban heat island phenom-enon,49,50 residential overcrowding, and mostimportant, meteorological conditions influ-enced by the presence of Lake Erie and theNiagara River, which have an effect on thedispersion of particulate pollutants. There isa low density of vegetation on Buffalo’s WestSide compared with other areas in the studyarea, which causes the area to be warmerthan normal. Finally, the age of the housingunits is a potential confounder that warrantsfurther investigation.
CONCLUSIONS
There are 2 implications of the study find-ings: first, current traffic levels not only con-tribute to asthma and other respiratory dis-ease exacerbations but may also contributeto high asthma prevalence on Buffalo’s WestSide in comparison with other Buffalo com-
munities; and second, identification ofasthma clusters along busily traveled road-ways and the PBC, in addition to other sites,indicates an etiological link between pollu-tants and high asthma prevalence rates.These implications are important for the de-velopment of new hypotheses relating to thespatial distribution of asthma prevalence andmorbidity in this community. Although ourstudy does not provide specific informationpertaining to the chemical compositions ofthe focus sites, it provides evidence about thelocations at which exposures may affect sus-ceptible individuals.
About the AuthorsTonny J. Oyana and Peter Rogerson are with the Depart-ment of Geography, University of Buffalo, Buffalo, NY.Tonny J. Oyana and Jamson S. Lwebuga-Mukasa are withthe Center for Asthma and Environmental Exposure, LungBiology Research Program, Pulmonary and Critical Divi-sion, Department of Internal Medicine, University of Buf-falo School of Medicine and Biomedical Sciences, KaleidaHealth Buffalo General, Buffalo, NY.
Requests for reprints should be sent to Jamson S.Lwebuga-Mukasa, MD, PhD, Center for Asthma and Envi-ronmental Exposure, Lung Biology Research Program, De-partment of Internal Medicine, UB School of Medicine andBiomedical Sciences, Kaleida Health Buffalo General Divi-sion, 100 High St, Buffalo, NY 14203 (e-mail: [email protected]).
This article was accepted February 10, 2003.
ContributorsTonny J. Oyana participated in data processing, geocod-ing, data analysis, and geographic information systems(GIS) modeling and wrote the article. Peter Rogerson ad-vised on data analysis and also contributed to editingthe article. Jamson S. Lwebuga-Mukasa provided thedata sets for disease analysis and guidance on medicalissues and also participated in editing the article. Thespatial and GIS approach used in this study was devel-oped by Tonny J. Oyana in consultation with PeterRogerson and Jameson S. Lwebuga-Mukasa.
AcknowledgmentsThis research was supported in part by the Centersfor Disease Control and Prevention and the TroupFund, Kaleida Health Foundations (grant R01-CCR220259–01).
Human Participant ProtectionAll research reported in this article was approved bythe University of Buffalo human sciences investigationreview board.
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R E S E A R C H R E P O R T
H E A L T HE F F E CTSINSTITUTE
Includes a Commentary by the Institute’s Health Review Committee
Air Toxics Exposure from Vehicle Emissions at a U.S. Border Crossing: Buffalo Peace Bridge StudyJohn Spengler, Jamson Lwebuga-Mukasa, Jose Vallarino, Steve Melly, Steve Chillrud, Joel Baker, and Taeko Minegishi
Number 158July 2011
Air Toxics Exposure from Vehicle Emissions at a U.S. Border Crossing: Buffalo Peace Bridge Study
John Spengler, Jamson Lwebuga-Mukasa, Jose Vallarino, Steve Melly, Steve Chillrud, Joel Baker, and Taeko Minegishi
with a Commentary by the HEI Health Review Committee
Research Report 158
Health Effects Institute Boston, Massachusetts
Trusted Science · Cleaner Air · Better Health
Publishing history: This document was posted at www.healtheffects.org in July 2011.
Citation for document:
Spengler J, Lwebuga-Mukasa J, Vallarino J, Melly S, Chillrud S, Baker J, Minegishi T. 2011. Air ToxicsExposure from Vehicle Emissions at a U.S. Border Crossing: Buffalo Peace Bridge Study. ResearchReport 158. Health Effects Institute, Boston, MA.
Cover paper: made with at least 55% recycled content, of which at least 30% is post-consumer waste; free of acid and elemental chlorine.Text paper: made with l00% post-consumer waste recycled content; acid free; no chlorine used in processing. The book is printed with soy-based inks and is of permanent archival quality.
C O N T E N T S
About HEI v
About This Report vii
Preface ix
HEI STATEMENT 1
INVESTIGATORS’ REPORT by Spengler et al. 5
ABSTRACT 5
INTRODUCTION 6
Assessing Major Sources of Health Risks from Air Toxics 7Assessing Potential Health Risks from Exposure to Mobile-Source Pollution 7
Peace Bridge Border Crossing as a Potential Hot Spot for Mobile-Source Pollution 12
SPECIFIC AIMS 13
METHODS 14
Study Design 14Mobile Monitoring 23Quality Assurance and Quality Control 24
STATISTICAL METHODS AND DATA ANALYSIS 26
RESULTS 27
Overview and Summary Observations 27Contributions of Peace Bridge Plaza Activities to Local Air Contaminants 30
Continuous Measurements at Fixed Sampling Sites and Relationships to Wind Direction and Traffic 67
Mobile Monitoring to Characterize Neighborhood Pollutant Concentrations 78
DISCUSSION AND CONCLUSIONS 90
Comparison of MSATs at the Peace Bridge Plaza and Other Sites 93
Implications of Findings 101Limitations 105
ACKNOWLEDGMENTS 105
REFERENCES 106
APPENDIX A. Data Quality 113
APPENDIX AVAILABLE ON THE WEB 130
ABOUT THE AUTHORS 130
ABBREVIATIONS AND OTHER TERMS 131
Research Report 158
COMMENTARY by the Health Review Committee 133
INTRODUCTION 133
STUDY AIMS 134
STUDY DESIGN AND METHODS 134
Pilot Studies 134Main Sampling Campaigns 134
STATISTICAL METHODS AND DATA ANALYSIS 135
SUMMARY OF RESULTS 137
Characterization of Pollutants in the Peace Bridge Plaza Area 137
Relationship of Traffic at the Peace Bridge to Pollutant Concentrations in Ambient Air 141
Spatial Patterns of Pollutants in a Neighborhood Near the Peace Bridge Plaza 142
The Health Effects Institute is a nonprofit corporation chartered in 1980 as an independent research organization to provide high-quality, impartial, and relevant science on the effects of air pollution on health. To accomplish its mission, the institute
• Identifies the highest-priority areas for health effects research;
• Competitively funds and oversees research projects;
• Provides intensive independent review of HEI-supported studies and related research;
• Integrates HEI’s research results with those of other institutions into broader evaluations; and
• Communicates the results of HEI’s research and analyses to public and private decision makers.
HEI typically receives half of its core funds from the U.S. Environmental Protection Agency and half from the worldwide motor vehicle industry. Frequently, other public and private organizations in the United States and around the world also support major projects or research programs. HEI has funded more than 280 research projects in North America, Europe, Asia, and Latin America, the results of which have informed decisions regarding carbon monoxide, air toxics, nitrogen oxides, diesel exhaust, ozone, particulate matter, and other pollutants. These results have appeared in the peer-reviewed literature and in more than 200 comprehensive reports published by HEI.
HEI’s independent Board of Directors consists of leaders in science and policy who are committed to fostering the public–private partnership that is central to the organization. The Health Research Committee solicits input from HEI sponsors and other stakeholders and works with scientific staff to develop a Five-Year Strategic Plan, select research projects for funding, and oversee their conduct. The Health Review Committee, which has no role in selecting or overseeing studies, works with staff to evaluate and interpret the results of funded studies and related research.
All project results and accompanying comments by the Health Review Committee are widely disseminated through HEI’s Web site (www.healtheffects.org), printed reports, newsletters and other publications, annual conferences, and presentations to legislative bodies and public agencies.
A B O U T T H I S R E P O R T
vii
Research Report 158, Air Toxics Exposure from Vehicle Emissions at a U.S. Border Crossing: Buffalo Peace Bridge Study, presents a research project funded by the Health Effects Institute and conducted by Dr. John Spengler of the Harvard School of Public Health, Boston, Massachusetts, and his colleagues. This report contains three main sections.
The HEI Statement, prepared by staff at HEI, is a brief, nontechnical summary of the study and its findings; it also briefly describes the Health Review Committee’s comments on the study.
The Investigators’ Report, prepared by Spengler and colleagues, describes the scientific background, aims, methods, results, and conclusions of the study.
The Commentary is prepared by members of the Health Review Committee with the assistance of HEI staff; it places the study in a broader scientific context, points out its strengths and limitations, and discusses remaining uncertainties and implications of the study’s findings for public health and future research.
This report has gone through HEI’s rigorous review process. When an HEI-funded study is completed, the investigators submit a draft final report presenting the background and results of the study. This draft report is first examined by outside technical reviewers and a biostatistician. The report and the reviewers’ comments are then evaluated by members of the Health Review Committee, an independent panel of distinguished scientists who have no involvement in selecting or overseeing HEI studies. During the review process, the investigators have an opportunity to exchange comments with the Review Committee and, as necessary, to revise their report. The Commentary reflects the information provided in the final version of the report.
Air toxics comprise a large and diverse group of airpollutants that, with sufficient exposure, are known orsuspected to cause adverse effects on human health,including cancer, effects on the development of organsand tissues, and damage to the respiratory, immune,neurologic, and reproductive systems. These com-pounds are emitted by a variety of indoor and outdoorsources and large numbers of people are exposed tothem. Therefore, the compounds are a cause for publichealth concern, even though the ambient levels are gen-erally low. The low ambient levels are one reason thattools and techniques for assessing specific health effectsof air toxics are very limited.
Air toxics are not regulated by the U.S. Environ-mental Protection Agency (EPA) under the NationalAmbient Air Quality Standards. However, the EPA isrequired under the Clean Air Act and its amendmentsto characterize, prioritize, and address the effects ofair toxics on public health and the environment, and ithas the statutory authority to control and reduce therelease of air toxics. The EPA is also required to regu-late or consider regulating air toxics derived, at least inpar t, from motor vehicles (referred to as mobile-source air toxics [MSATs]) by setting standards forfuels, vehicle emissions, or both. In 2001 the EPA des-ignated 21 high-priority MSATs that needed to bereduced (U.S. EPA 2001a). However, the EPA did nottake any specific regulator y action at that timebecause rules mandating the reduction of sulfur inboth gasoline and diesel fuels as a way to decreaseparticulate matter (PM) in emissions were expectedto result in the reduction of several MSATs as well(U.S. EPA 2000, 2001b). Subsequently, the EPA identi-fied eight MSATs that, based on their emissions andreported toxicity, pose the greatest risk to health —benzene, 1,3-butadiene, formaldehyde, acrolein,
naphthalene, polycyclic organic matter, diesel PM, anddiesel exhaust organic gases — and mandated thereduction of benzene in gasoline and of hydrocarbons(including MSATs) in exhaust (U.S. EPA 2007). In2007, HEI published a critical review of the literatureon exposure to and health effects associated withthese highest-priority MSATs (HEI Air Toxics ReviewPanel 2007).
In trying to understand the potential health effects ofexposure to toxic compounds, scientists often turn firstto evaluating responses in highly exposed populations,such as occupationally exposed workers. However,workers and their on-the-job exposures are not repre-sentative of the general population, and therefore suchstudies may be somewhat limited in value.
Another strategy is to study populations living in “hotspots” — areas that have high concentrations of thesepollutants owing to their proximity to one or moresources. Some hot spots may have sufficiently high pol-lutant concentrations to make them suitable locationsfor studies to determine whether there is a link betweenexposure to air toxics and an adverse health outcome.Such areas offer the potential to conduct health investi-gations in groups that are more representative of thegeneral population. Before health effects studies can beinitiated, however, actual exposures to pollutants —including their spatial and temporal distributions — insuch hot-spot areas must be characterized.
DESCRIPTION OF THE PROGRAM
In Januar y 2003, HEI i ssued a Request forApplications (RFA 03-1) entitled “Assessing Exposureto Air Toxics,” seeking studies aimed at identifying andcharacterizing exposure to air toxics from a variety ofsources in areas or situations where concentrationswere expected to be elevated. The rationale for the
x
Preface
RFA was that understanding exposures in hot spots, aswell as the sources of these exposures, would improveour ability to select the most appropriate sites, popula-tions, and endpoints for subsequent health studies. HEIwas particularly interested in studies that focused onthe high-priority MSATs.
Five studies, including the one by Spengler and col-leagues described in this repor t (Research Repor t158), were funded under this RFA to represent adiversity of possible hot-spot locations and air toxics.The studies are summarized below.
“Assessing Exposure to Air Toxics,” Eric M. Fujita, Desert Research Institute, Reno, Nevada (Principal Investigator)
In this study, presented in HEI Research Report 156(2011) , Fu j i t a and co l leagues measured theconcentrations of PM and MSATs on major Californiafreeways and compared them with correspondingmeasurements obtained at fixed monitoring stations.The diurnal and seasonal variations in concentrationsof selected pollutants and the contribution of diesel-and gasoline-powered vehicles to selected air toxicsand elemental carbon were also determined.
“Measurement and Modeling of Exposure to Air Toxics and Verification by Biomarkers,” Roy M. Harrison, University of Birmingham, Birmingham, United Kingdom (Principal Investigator)
In the study described in HEI Research Report 143(2009), Harrison and colleagues investigated personalexposure to a broad range of air toxics, with the goalof developing detailed personal-exposure models thatwould take various microenvironments into account.Repeated measurements of exposure to selected airtoxics were made for each of 100 healthy nonsmokingadults who resided in urban, suburban, or rural areasof the United Kingdom, among which exposures totraffic were expected to differ ; repeated urine sampleswere also col lected for analysis . Harr ison andcolleagues developed models to predict personalexposure on the basis of microenvironmentalconcentrations and data from time–activity diaries;they then compared measured personal exposurewith modeled estimates of exposure.
“Assessing Personal Exposure to Air Toxics in Camden, New Jersey,” Paul Lioy, Environmental and Occupational Health Sciences Institute, Piscataway, New Jersey (Principal Investigator)
Lioy and colleagues measured ambient and personalexposure concentrations of air toxics and fine PM for107 nonsmoking participants in two neighborhoods ofCamden, New Jersey. One, considered to be a hotspot, had a high density of industrial facilities servicedby truck traffic and nearby busy roads. The other, withno industrial sources but near several highways, wasconsidered an urban reference site. The investigatorscollected four sets of 24-hour personal air samplesfrom the study subjects — in summer and winter andon weekdays and weekend days — and madesimultaneous measurements of ambient pollutantconcentrations at a fixed monitoring site in eachneighborhood. To characterize finer spatial variability inpollutant levels, air toxics levels were measured atmultiple sampling sites in each neighborhood duringthree sampling periods. The investigators also usedmodeling to estimate the contribution of ambientsources to personal exposure.
“Air Toxics Hot Spots in Industrial Parks and Traffic,” Thomas Smith, Harvard School of Public Health, Boston, Massachusetts (Principal Investigator)
The study by Smith and colleagues measured levelsof air toxics and PM in truck cabins and in 15 truckterminals across the United States. The HEI study wasadded to an ongoing study, funded by the NationalCancer Institute, of the relationship between exposureto diesel exhaust and mor tality from lung canceramong dockworkers and truck drivers at more than200 truck terminals in the United States. Smith andcolleagues measured pollutants at upwind anddownwind locations around the perimeter of eachterminal and at loading docks. The degree of variationat different locations and the influence of wind directionwere also evaluated with the goal of identifying thepotential impact of truck terminals on the surroundingareas. Continuous sampling was performed insidedelivery truck cabins during a work shift.
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Preface
“Air Toxics Exposure from Vehicular Emissions at a U.S. Border Crossing,” John Spengler, Harvard School of Public Health, Boston, Massachusetts (Principal Investigator)
The study by Spengler and colleagues, presented inthis repor t, assessed concentrations of MSATssurrounding the plaza adjacent to the Peace Bridge, amajor border crossing between the United States andCanada, located in Buffalo, New York. Three fixedmonitoring sites were used to compare pollutantconcentrations upwind and downwind of the plaza.Meteorologic measurements and hourly counts oftrucks and cars crossing the bridge were used toexamine the relationship between the concentrationsof air toxics and traffic density. To study spatialdistributions of pollutants, members of the investigativeteam used por table instr uments and a GlobalPositioning System to obtain location-specific, time-stamped measurements as they walked along fourroutes in a residential neighborhood near the plaza.
The report on the study of Spengler and colleaguesis the third of the five funded under RFA 03-1 to bepublished. Harrison and colleagues’ report was pub-lished in 2009 and that by Fujita and colleagues in 2011.The two remaining studies have been completed andare currently at varying stages of the publications pro-cess. They are expected to be released in 2011 andearly 2012. HEI is committed to continuing research onair toxics — for example, as part of studies to assessthe health outcomes of air quality actions or studies toevaluate the effects of new technologies and fuels.
REFERENCES
Fujita EM, Campbell DE, Zielinska B, Arnott WP, ChowJC. 2011. Concentrations of Air Toxics in MotorVehicle–Dominated Environments. Research Report156. Health Effects Institute, Boston, MA.
Harrison RM, Delgado-Saborit JM, Baker SJ, Aquilina N,Meddings C, Harrad S, Matthews I, Vardoulakis S,Anderson HR. 2009. Measurement and Modeling ofExposure to Selected Air Toxics for Health EffectsStudies and Verification by Biomarkers. ResearchReport 143. Health Effects Institute, Boston, MA.
HEI Air Toxics Review Panel. 2007. Mobile-Source AirToxics: A Critical Review of the Literature on Exposureand Health Effects. Special Report 16. Health EffectsInstitute, Boston, MA.
Spengler J, Lwebuga-Mukasa J, Vallarino J, Melly S,Chillrud S, Baker J, Minegishi T. 2011. Air Toxics Expo-sure from Vehicle Emissions at a U.S. Border Crossing:Buffalo Peace Bridge Study. Research Repor t 158.Health Effects Institute, Boston, MA.
U.S. Environmental Protection Agency. 2000. Controlof air pollution from new motor vehicles: Tier 2 motorvehicle emissions standards and gasoline sulfur controlrequirements: Final rule. 40 CFR Parts 80, 85, and 86.Fed Regist 65:6698–6870.
U.S. Environmental Protection Agency. 2001a. Controlof emissions of hazardous air pollutants from mobilesources: Final rule. 40 CFR Parts 80 and 86. Fed Regist66:17230–17273.
U.S. Environmental Protection Agency. 2001b. Controlof air pollution from new motor vehicles: Heavy-dutyengine and vehicle standards and highway diesel fuelsulfur control requirements: Final rule. 40 CFR Parts69, 80, and 86. Fed Regist 66:5001–5050.
U.S. Environmental Protection Agency. 2007. Controlof hazardous air pollutants from mobile sources: Finalrule. 40 CFR Par ts 59, 80, 85, and 86. Fed Regist72:8428–8570.
Synopsis of Research Report 158H E I S TAT E M E N T
This Statement, prepared by the Health Effects Institute, summarizes a research project funded by HEI and conducted by Dr. John Spengler,of the Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, and colleagues. Research Report 158contains both the detailed Investigators’ Report and a Commentary on the study prepared by the Institute’s Health Review Committee.
1
Air Toxics Exposure from Vehicle Emissions at a U.S. Border Crossing
BACKGROUND
Motor vehicles and other combustion sourcesemit many air toxics that are either known or sus-pected, with sufficient exposure, to cause adversehealth effects. Characterization of exposure to airtoxics has been challenging, in part, because of thelow ambient levels of individual compounds. HEIhas had a long-standing commitment to improvingmethods for measuring selected air toxics andincreasing our understanding of exposure andhealth effects. In 2003 HEI targeted research to iden-tify and characterize so-called hot spots, areas whereconcentrations of one or more air toxics are expectedto be elevated. Dr. John Spengler and colleagueshypothesized that vehicle-related emissions fromtraffic backed up at the Peace Bridge in Buffalo, NewYork, one of the nation’s busiest border crossings,would result in higher levels of mobile-source airtoxics (MSATs) directly downwind. They proposed astudy to measure levels of air toxics, includingMSATs, upwind and downwind of the plaza adja-cent to the Peace Bridge, to examine the relationbetween traffic at the bridge and pollutant concen-trations in ambient air, and to explore geographicpatterns of ambient air pollutants in this potentialhot spot for mobile-source emissions.
APPROACH
The investigators measured levels of a large num-ber of compounds that might be expected in ex-haust from diesel and gasoline vehicles, includingvolatile organic compounds (VOCs), polycylicaromatic hydrocarbons (PAHs), and nitrogenatedPAHs (NPAHs). Their analyses focused on compar-ing pollutant levels measured at fixed sites on oppo-site sides of the 17-acre plaza adjacent to the PeaceBridge, which comprised the U.S. CustomsBuilding, a customs inspection and holding area,
tollbooths, a duty-free store, the Peace BridgeAuthority Administration Building, and parkingspace. Residential and commercial areas abut theplaza to the east, north, and south, with Lake Erieand the Niagara River to the west.
During pilot studies in July 2004 and January2005, the investigators sampled air at sites upwindand downwind of the plaza and tested a variety ofroutes for mobile monitoring in a neighborhoodnext to the plaza. They collected meteorologic dataand bridge traffic counts by vehicle type (cars,trucks, and buses), for all sampling days. Prevailingwind directions were determined from 10 years ofweather data from the Buffalo Niagara InternationalAirport, which demonstrated that the wind blowsfrom the west side of the plaza (off Lake Erie) about45% of the time (lake winds) and from the east onthe Buffalo side of the plaza (city winds) about 31%of the time. The investigators established two fixedsampling sites for both continuous monitoring andintegrated sampling during the summer 2004 pilotstudy: one to the southwest of the plaza at the GreatLakes Center (GLC site), which they describe as anupwind site, and the other in front of the EpiscopalChurch Home (Chapel site) adjacent to the east sideof the plaza, which they describe as a downwindsite. They also tested routes, equipment, and proto-cols for mobile monitoring of pollutants around thePeace Bridge plaza area, including the neighborhoodof Buffalo directly to the east. The researchers addeda third fixed sampling site in this neighborhood(School site) during the winter 2005 pilot study.
After the two pilot studies, Spengler’s team con-ducted two larger-scale sampling campaigns fea-turing both fixed-site and mobile monitoringcomponents. Samples were collected simulta-neously at all three fixed sampling sites for twoweeks in July 2005 and two weeks in January 2006.The research team collected integrated samples at
Research Report 158
2
these sites and also from collocated real-time contin-uous monitors that took measurements every minute.
The mobile monitoring campaign was designedto assess the levels of selected pollutants in theneighborhood adjacent to the Peace Bridge plaza.Staff members wore backpacks containing air moni-toring equipment and carried GPS (GlobalPositioning System) units while walking along oneof four designated routes in the neighborhood.Pollutant data and GPS coordinates from the mobilemonitoring campaigns were used to create mapsshowing the spatial distribution of pollutants in theneighborhood east of the Peace Bridge plaza.
RESULTS
Spengler and colleagues discuss most of theirresults by comparing sites that were predominantlyupwind and downwind of the Peace Bridge plaza.By considering both the prevailing wind directionand the wind direction measured during the sam-pling campaigns, they were able to draw inferencesabout the likelihood that these measurements wererelated to the volume and composition of traffic tra-versing the Peace Bridge plaza. Approximately twothirds of the sampling days were classified ashaving lake-wind conditions that resulted in theGLC site being directly upwind, the Chapel sitebeing directly downwind, and the School site beingless directly downwind of the plaza.
The investigators compiled a wealth of compar-ative data on several different classes of MSATs —VOCs and carbonyls, elements, PAHs, and NPAHs— and measurements from continuous sampling ofparticulate matter (PM) ! 10 µm and ! 2.5 µm inaerodynamic d iameter (PM 1 0 and PM2 . 5 ,respectively), ultrafine particles (UFPs, defined asparticles < 0.1 µm in aerodynamic diameter),particle-bound PAHs (pPAHs), and gaseouspollutants. For PM10 and PM2.5, and for the fractionof elemental carbon (EC) present in the collectedPM2.5, the mean daytime levels were highest at theChapel site (typically downwind of the plaza), andhigher at the residential School site than at theupwind GLC site.
The investigators created summary categories forselected VOCs and chlorinated compounds. Inweekday 12-hour samples, overall mean and medianlevels of benzene, toluene, ethylbenzene, and xylenes
(BTEX) were highest at the neighborhood School site,followed by the Chapel site, and then the GLC site.Overall mean and median levels of a summarycategory of five chlorinated compounds were verysimilar across the three sites. Median daytimebenzene and formaldehyde levels were lowest by farat the GLC site. Overall mean and median daytimeacetaldehyde levels were highest at the GLC site,nearly as high at the School site, and much lower atthe Chapel site. Acetone levels for all daytime sam-ples were slightly elevated at the GLC site.
Spengler’s team analyzed fixed-site PM2.5 samplesfor 28 different elements, of which only six —calcium, chromium, manganese, iron, copper, andantimony — varied considerably across the threefixed sampling sites. Mean daytime weekday levelsof these were, on average, higher at the Chapel sitethan at the other sites. The authors suggest that thehigher concentrations of these elements at theChapel site were related to emissions from traffic atthe Peace Bridge plaza.
The researchers also noted important contrasts inlevels of PAHs and NPAHs across the three fixedsampling sites. Concentrations of all but a few of thePAH compounds were higher at the Chapel sitewhen the site was downwind of the city of Buffalo(rather than downwind of the bridge), implying thatregional combustion and urban infrastructurecontributed more PAHs than the emissions fromtraffic at the Peace Bridge plaza. Low-molecular-weight PAHs, however, were consistently highest atthe Chapel and School sites. Comparison of medianconcentrations of 14 NPAHs at the GLC and Chapelsites under lake-wind conditions showed that somewere notably higher at the downwind Chapel site,indicating that traffic at the Peace Bridge plaza wasa potential local source for these compounds.
In results f rom 600 hours of continuousmeasurement at each of the three fixed samplingsites, mean UFP counts were substantially higher atthe Chapel site, followed by the School and GLCsites. The relative differences in UFP counts betweenthe sites were similar in winter and summer, with allthree sites recording higher counts in the winter.Concentrations of PM2.5 were relatively uniform atthe three sites, indicating that PM2.5 levels in thestudy area may be dominated by regional airpollution patterns. For both 10-minute average and12-hour average concentrations of PAHs in PM2.5,
Research Report 158
3
the highest mean concentrations were at the Chapelsite, followed by the School and GLC sites. Theseresults indicate an enrichment of PAHs at theChapel and School sites, since the levels of PAHs inPM2.5 varied across the sites while the levels ofPM2.5 did not.
During the mobile monitoring campaigns, UFPand pPAH levels were measured under differentwind conditions at different times of day. Samplingresults under city-wind conditions indicated thatbusy intersections, high-traffic roads, and neighbor-hood combustion sources are likely to have contrib-uted to the UFP and pPAH levels measured by themobile monitoring staff. Under lake-wind condi-tions, mobile monitoring results showed a generalincrease in concentrations of UFPs and pPAHs withdecreasing distance from the Peace Bridge plaza.
The authors performed a source apportionmentanalysis using principal component analysis toanalyze the data for individual elements andpositive matrix factorization for the site-specificPAH measurements from the winter 2005, summer2005, and winter 2006 sampling campaigns. Theprincipal component analysis indicated thatmeasured levels of elements associated with trafficemissions were higher at the Chapel site when it wasdownwind of the Peace Bridge plaza. Positive matrixfactorization analyses, which divided the PAHs intolight, medium, and heavy profiles based on theirmolecular weight, demonstrated relatively higherlevels of the light PAHs at the upwind GLC site,higher levels of the medium PAHs at the downwindChapel and School sites, and relatively uniformdistribution of the heavy PAHs at all three sites.
Overal l , to assess the plausibi l i ty o f arelationship between traffic at the Peace Bridgeplaza and levels of airborne pollutants at the Chapeland GLC sites on either side of the plaza, Spengler’steam compared measurement data obtained withlake winds and with winds blowing over the city ofBuffalo toward the lake. Pairing samples bysampling period and calculating the ratio ofmeasured levels at the Chapel site to those at theGLC site, the authors ranked the results in terms ofEC reflectance (EC-r). The Chapel-to-GLC ratios forEC-r were highest with lake-wind events, whichwould enrich levels of EC-r at the downwind Chapelsite if they were the result of mobile-source emissionsat the plaza. Based on these comparisons and their
overall results, the investigators suggest that thetraffic at the plaza was a source for the higher levels ofcompounds measured at the Chapel site.
INTERPRETATION
In its independent review of the study, the HEIReview Committee considered the methods thatSpengler and his research team used to measure alarge number of pollutants in a variety of ways to besound and generally well implemented. Thesampling campaigns were very well conducted, andthe data were of high quality overall. The mobilemonitoring scheme using GPS units and real-timemeasuring equipment carried in backpacks wasinnovative and successful . However, theinvestigators’ source apportionment analysis wasexploratory and inconclusive, owing to the relativelysmall number of samples available for analysis.
The investigators made some important innova-tions and contributions to study design for researchintended to identify potential hot spots, particularlyfor emissions from traffic and roadway sources. Thestudy demonstrated that a concentration of mobile-source emissions, combined with relatively consis-tent meteorologic conditions, could result in an areaof elevated pollutant concentrations that shifts withchanging wind conditions. Thus the results indicatethat wind speed and wind direction should be con-sidered in the selection of sampling sites in anystudy attempting to characterize measured pol-lutant levels surrounding a putative hot spot.
Whether a hot spot can be defined as a particularlocation with elevated concentrations of pollutantsarising from a local source, as it is in this study, orwhether it should be more broadly defined as anarea where levels of pollutants are excessively highin general (compared with a standard level or withlevels reported in the literature), is debatable. TheReview Committee had two reservations regardingthe investigators’ conclusion that the study areaconstituted a hot spot. First, while differences inmethods, instruments, and other conditions make itdifficult to compare results across studies, theMSAT levels reported in this study were not highrelative to levels measured at other U.S. locations.The authors note this in their comparison of themeasurements for a variety of elements and com-pounds with those in the 2006 TEACH (ToxicsExposure Assessment Columbia and Harvard)
Research Report 158
4
studies in New York City and Los Angeles and the2005 RIOPA (Relationships of Indoor, Outdoor, andPersonal Air) study in Houston and Los Angeles.Second, though meteorologic conditions will affectpollutant levels at any location, the Committeenoted that the elevated concentrations near thePeace Bridge were particularly dependent on winddirection: the neighborhood to the east did not gen-erally experience higher pollutant levels for the55% of the time (40% in the summer) when thewind was not blowing from the Peace Bridge plaza.
The Review Committee decided that differencesof interpretation in the definition a hot spot shouldnot detract from the research effort by Spengler andhis team and their excellent job of characterizingand investigating an area with potentially highemissions of MSATs.
CONCLUSIONS
Although the levels of MSATs in the area near thePeace Bridge in Buffalo may not be high relative tothose in other locations in the United States, thesedata contribute to our understanding of how trafficemissions may result in elevated levels of air toxicsin a local area. Spengler and colleagues devised astudy design and sampling program for character-izing concentrations of air toxics in an area ofpotentially high emissions that included someimportant and innovative features. They selected agood location for a study of traffic-related emissionsby targeting a somewhat geographically isolatedsource of potentially high emissions for intensivesampling and analysis. Finally, their mobile moni-toring program for measuring pollutant levels in alocal neighborhood is technically sound and eco-nomical, and thus will be valuable for studies offine variations in pollutant concentrations in rela-tively small geographic areas.
Air Toxics Exposure from Vehicle Emissions at a U.S. Border Crossing: Buffalo Peace Bridge Study
John Spengler, Jamson Lwebuga-Mukasa, Jose Vallarino, Steve Melly, Steve Chillrud, Joel Baker, and Taeko Minegishi
Harvard School of Public Health, Boston, Massachusetts (J.S., J.V., S.M.); University at Buffalo, State University of New York (J.L.-M.);Lamont-Doherty Earth Observatory, Columbia University, New York, New York (S.C.); Chesapeake Biological Laboratory, Universityof Maryland Center for Environmental Science, Solomons (J.B., T.M.); University of Washington–Tacoma and Center for UrbanWaters, Tacoma (J.B.); Environmental Health and Engineering, Newton, Massachusetts (T.M.)
ABSTRACT
The Peace Bridge in Buffalo, New York, which spans theNiagara River at the east end of Lake Erie, is one of thebusiest U.S. border crossings. The Peace Bridge plaza onthe U.S. side is a complex of roads, customs inspectionareas, passport control areas, and duty-free shops. Onaverage 5000 heavy-duty diesel trucks and 20,000 pas-senger cars traverse the border daily, making the plaza areaa potential “hot spot” for emissions from mobile sources.
In a series of winter and summer field campaigns, wemeasured air pollutants, including many compounds con-sidered by the U.S. Environmental Protection Agency(EPA*) as mobile-source air toxics (MSATs), at three fixedsampling sites: on the shore of Lake Erie, approximately 500m upwind (under predominant wind conditions) of thePeace Bridge plaza; immediately downwind of (adjacent to)the plaza; and 500 m farther downwind, into the communityof west Buffalo. Pollutants sampled were particulate matter(PM) ! 10 µm (PM10) and ! 2.5 µm (PM2.5) in aerodynamicdiameter, elemental carbon (EC), 28 elements, 25 volatile
organic compounds (VOCs) including 3 carbonyls, 52 poly-cyclic aromatic hydrocarbons (PAHs), and 29 nitrogenatedpolycyclic aromatic hydrocarbons (NPAHs). Spatial pat-terns of counts of ultrafine particles (UFPs, particles< 0.1 µm in aerodynamic diameter) and of particle-boundPAH (pPAH) concentrations were assessed by mobile mon-itoring in the neighborhood adjacent to the Peace Bridgeplaza using portable instruments and Global PositioningSystem (GPS) tracking.
The study was designed to assess differences in upwindand downwind concentrations of MSATs, in areas near thePeace Bridge plaza on the U.S. side of the border. The Buf-falo Peace Bridge Study featured good access to monitoringlocations proximate to the plaza and in the community,which are downwind with the dominant winds from thedirection of Lake Erie and southern Ontario. Samples fromthe lakeside Great Lakes Center (GLC), which is upwind ofthe plaza with dominant winds, were used to characterizecontaminants in regional air masses. On-site meteorologicmeasurements and hourly truck and car counts were usedto assess the role of traffic on UFP counts and pPAH con-centrations. The array of parallel and perpendicular resi-dential streets adjacent to the plaza provided a grid onwhich to plot the spatial patterns of UFP counts and pPAHconcentrations to determine the extent to which trafficemissions from the Peace Bridge plaza might extend intothe neighboring community.
For lake-wind conditions (southwest to northwest)12-hour integrated daytime samples showed clear evidencethat vehicle-related emissions at the Peace Bridge plazawere responsible for elevated downwind concentrations ofPM2.5, EC, and benzene, toluene, ethylbenzene, andxylenes (BTEX), as well as 1,3-butadiene and styrene. Thechlorinated VOCs and aldehydes were not differentiallyhigher at the downwind site. Several metals (aluminum,
This Investigators’ Report is one part of Health Effects Institute ResearchReport 158, which also includes a Commentary by the Health Review Com-mittee and an HEI Statement about the research project. Correspondenceconcerning the Investigators’ Report may be addressed to Dr. John Spengler,Exposure Epidemiology and Risk Program, Department of EnvironmentalHealth, Harvard School of Public Health, P.O. Box 15677, Landmark Center406 West, Boston, MA 02215.
Although this document was produced with partial funding by the UnitedStates Environmental Protection Agency under Assistance Award CR–83234701 to the Health Effects Institute, it has not been subjected to theAgency’s peer and administrative review and therefore may not necessarilyreflect the views of the Agency, and no official endorsement by it should beinferred. The contents of this document have not been reviewed by privateparty institutions, including those that support the Health Effects Institute;therefore, it may not reflect the views or policies of these parties, and noendorsement by them should be inferred.
* A list of abbreviations and other terms appears at the end of the Investiga-tors’ Report.
6
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
calcium, iron, copper, and antimony) were two timeshigher at the site adjacent to the plaza as they were at theupwind GLC site on lake-wind sampling days. Other metals(beryllium, sodium, magnesium, potassium, titanium, man-ganese, cobalt, strontium, tin, cesium, and lanthanum)showed significant increases downwind as well. Sulfur,arsenic, selenium, and a few other elements appeared to bemarkers for regional transport as their upwind and down-wind concentrations were correlated, with ratios near unity.Using positive matrix factorization (PMF), we identified thesources for PAHs at the three fixed sampling sites asregional, diesel, general vehicle, and asphalt volatilization.Diesel exhaust at the Peace Bridge plaza accounted forapproximately 30% of the PAHs. The NPAH sources wereidentified as nitrate (NO3) radical reactions, diesel, andmixed sources. Diesel exhaust at the Peace Bridge plazaaccounted for 18% of the NPAHs.
Further evidence for the impact of the Peace Bridgeplaza on local air quality was found when the differencesin 10-minute average UFP counts and pPAH concentra-tions were calculated between pairs of sites and displayedby wind direction. With winds from approximately 160"
through 220", UFP counts adjacent to the plaza were10,000 to 20,000 particles/cm3 higher than those upwindof the plaza. A similar pattern was displayed for pPAHconcentrations adjacent to the plaza, which were between10 and 20 ng/m3 higher than those at the upwind GLC site.Regression models showed better correlation with trafficvariables for pPAHs than for UFPs. For pPAHs, truckcounts and car counts had significant positive correla-tions, with similar magnitudes for the effects of trucks andcars, despite lower truck counts. Examining all traffic vari-ables, including traffic counts and counts divided by windspeed, the multivariate regression analysis had an adjustedcoefficient of determination (R2) of 0.34 for pPAHs, withall terms significant at P < 0.002.
Study staff members traversed established routes in theneighborhood while carrying instruments to record con-tinuous UFP and pPAH values. They also carried a GPS,which was used to provide location-specific time-stampeddata. Analyses using a geographic information system(GIS) demonstrated that emissions at the Peace Bridgeplaza, at times, affected ambient air quality over severalblocks (a few hundred meters). Under lake-wind condi-tions, overall spatial patterns in UFP and pPAH levelswere similar for summer and winter and for morning andafternoon sampling sessions.
The Buffalo Peace Bridge Study demonstrated that a con-centration of motor vehicles resulted in elevated levels ofmobile-source-related emissions downwind, to distances of300 m to 600 m. The study provides a unique data set to
assess interrelationships among MSATs and to ascertain theimpact of heavy-duty diesel vehicles on air quality.
INTRODUCTION
Section 112(b) of the 1990 Clean Air Act Amendmentsdefines 188 chemicals as hazardous air pollutants (HAPs).A HAP is an air pollutant “to which no ambient air qualitystandards are applicable and which in the judgment of theadministrator [of the EPA] causes, or contributes to air pol-lution which may reasonably be anticipated to result in anincrease in mortality or an increase in serious irreversible,or incapacitating reversible, illness.” HAPs are chemicalsthat can adversely affect human health and the environ-ment. These include, but are not limited to, substancesthat are known to be, or may reasonably be anticipated tobe, carcinogenic, mutagenic, teratogenic, or neurotoxic,that cause reproductive dysfunction, or that are acutely orchronically toxic. An adverse environmental effect isdefined as any significant and widespread adverse effect,which may reasonably be anticipated, to wildlife, aquaticlife, or other natural resources, including adverse impactson populations of endangered or threatened species or sig-nificant degradation of environmental quality over broadareas. Table 1 lists the 188 compounds specified as HAPsin 1990, as well as asbestos and lead compounds, whichwere already regulated.
In 1999, the EPA named 33 high-priority air pollutantsin its Integrated Urban Air Toxics Strategy: 32 air toxics onthe Clean Air Act list of 188 HAPs and diesel PM. In 2002,the EPA reported on its National-Scale Air Toxics Assess-ment (NATA) for these 33 pollutants based on 1996 emis-sions (Table 2). The assessment included a nationalinventory of emissions from outdoor sources, estimatedambient concentrations and population exposures acrossthe contiguous United States, and characterization ofpotential public health risks due to inhalation of these airtoxics, including both cancer and noncancer effects. Thegoal of the national-scale assessment was to identify thoseair toxics that are of greatest potential concern in terms ofcontribution to population risk.
Section 20.2 of the Clean Air Act Amendments setsemissions standards for HAPs from vehicles and fuel addi-tives. In its 2001 Mobile Source Air Toxics rule, the EPAidentified 21 MSATs (U.S. EPA 2001). A compound waslisted as an MSAT if it was emitted from mobile sources,and if the EPA Integrated Risk Information System (IRIS)either listed the compound as a potential cancer hazard, or
7
J. Spengler et al.
7
provided an inhalation reference concentration or inges-tion reference dose for the compound.
Since 2001, the EPA has compiled a master list of morethan 1000 compounds from mobile sources including fueland lubricant additives (U.S. EPA 2006a). Table 3 listschemical substances from mobile sources that meet theIRIS criteria. Other substances from mobile sources arenot included in IRIS but are being assessed on an ongoingbasis: for example, cesium, copper, ethanol, platinum,propionaldehyde, and 2,2,4-trimethylpentane. The lattertwo compounds, while not in IRIS, are HAPs and arethus subject to regulation. The MSAT analytes for theBuffalo Peace Bridge Study are listed together in Table 3.Note, however, that we did not speciate chromium IIIand chromium VI or identify compounds of beryllium,lead, or silver.
ASSESSING MAJOR SOURCES OF HEALTH RISKS FROM AIR TOXICS
In the NATA based on 1999 emissions, the EPA includednational and regional assessments of 177 HAPs and dieselPM (U.S. EPA 2007). The assessment focused exclusively onoutdoor exposures and relied extensively on modeled con-centrations. Cancer and noncancer health effects were con-sidered for both national-scale and regional-scale analysesthat used different population-weighted risk criteria. Forcancer, the national criterion was at least 25 million peopleexposed to risk > 10 in 1 million, and the regional criterionwas at least 1 million people exposed to risk > 10 in1 million, or at least 10,000 people exposed to risk > 100 in1 million. For noncancer health effects, the national crite-rion was at least 25 million people with a hazard quotient(level of environmental exposure to the HAP divided bythe level at which no adverse effects are expected) > 1.0,and the regional criterion was at least 10,000 people with ahazard quotient > 1. Table 4 lists those ambient HAPs thatmeet the 1999 NATA criteria for risk to the population,seven of which are considered to have significant contri-butions from mobile sources (U.S. EPA 2006b).
Studies like the California Multiple Air Toxics Expo-sure Study II (MATES-II) have demonstrated the spatialheterogeneity of air pollutants from mobile sources. TheLos Angeles freeway studies of Zhu, Zhang, and col-leagues demonstrated changing particle dynamics andconcentration gradients as airflow crossed and moveddownwind of a congested roadway segment (Zhu et al.2002a, 2004, 2006a, 2006b; Zhang et al. 2004, 2005). Amulticity European study used traffic indicators and dis-tance measures to explain differences among sites for com-pounds related to mobile sources (Brauer et al. 2003).Several studies have now provided land-use and traffic
data to relate mobile-source emissions to health (White etal. 2005).
More recently, the EPA has been developing rules tocontrol compounds emitted from mobile sources, either asdirect exhaust or as evaporative emissions from fuels (U.S.EPA 2007). Based on the 1999 NATA findings, the EPAestimated the percentages that mobile sources contributedto high-risk compounds in ambient air. Table 5, derivedfrom the 1999 NATA assessment that identified the high-risk compounds found in Table 4, lists the seven high-riskcompounds considered to have significant contributionsfrom mobile sources and shows the percentages contrib-uted by all mobile sources and by on-road mobile sources.Although all of the risk from diesel PM and diesel organicgas is attributed to mobile sources, the on-road mobilesources contribute about 38% to the total risk. Both ben-zene and 1,3-butadiene are from predominantly mobilesources, with more than 40% coming from on-road mobilesources.
ASSESSING POTENTIAL HEALTH RISKS FROM EXPOSURE TO MOBILE-SOURCE POLLUTION
Airports, bus depots, toll plazas, congested segments ofroads, and major border crossings, among many other situ-ations, contribute to small-scale variations in exposurefrom MSATs that are not captured in national or regionalassessments of HAP risk. The results of the MATES-IIstudy illustrate this issue (South Coast Air Quality Man-agement District [SCAQMD] 2000). In MATES-II samplerswere deployed in a dense array to capture variation inHAP concentrations within selected communities insouthern California’s Los Angeles Basin. Spatial analysisof MATES-II data showed that concentrations of air toxicswere generally higher near their emission sources. Con-centrations of MSATs such as benzene and 1,3-butadienetended to be higher along freeway corridors and junctions.The levels of air toxics and consequently the cancer risksassociated with these compounds were found to behighest in central and east-central Los Angeles County,where pollution is dominated by mobile sources.
The number of epidemiologic studies documenting asso-ciations between proximity to roadways or traffic exposureand an array of health effects, ranging from diminishedquality of life to increased risk of cardiopulmonary mor-tality, continues to grow (Venn et al. 2000; Shima et al.2003; Hwang et al. 2005; McConnell et al. 2006). Thesestudies are widely reported for countries that have differentmixes of motor vehicles and infrastructure patterns. Japanand European countries have a higher percentage of dieselvehicles than the U.S. and Canada. The proximity of resi-dences to busy roadways also differs among countries, yet
8
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Tabl
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Tabl
e co
ntin
ues
next
pag
e
9
J. Spengler et al.
9
Tabl
e 1
(Con
tinu
ed).
List
of H
azar
dous
Air
Pol
luta
nts
as D
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ed b
y Se
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ting
s in
the
tabl
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e w
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"com
poun
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and
for
glyc
ol e
ther
s, u
nles
s ot
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spec
ifie
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stin
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def
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as
incl
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y un
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ubst
ance
that
con
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med
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whe
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ple
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r C
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c In
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and
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from
the
glyc
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ateg
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d In
clud
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iner
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iber
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issi
ons
from
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ilit
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r pr
oces
sing
gla
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ock,
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(or
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r m
iner
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vera
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eter
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m o
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ss.
e In
clud
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wit
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ore
than
one
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g, a
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poin
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than
or
equa
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100"
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pe o
f ato
m th
at s
pont
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usly
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adio
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.
Tabl
e 2.
Air
Pol
luta
nts
in th
e 19
96 N
atio
nal-
Scal
e A
ir T
oxic
s R
isk
Ass
essm
ent
Ace
tald
ehyd
e C
adm
ium
com
poun
dsEt
hyle
ne d
ibro
mid
eM
anga
nese
com
poun
dsPr
opyl
ene
dich
lori
deA
crol
ein
Car
bon
tetr
achl
orid
eEt
hyle
ne d
ichl
orid
e M
ercu
ry c
ompo
unds
Qui
noli
neA
cryl
onit
rile
Chl
orof
orm
Ethy
lene
oxi
deM
ethy
lene
chl
orid
e1,
1,2,
2-Te
trac
hlor
oeth
ane
Ars
enic
com
poun
dsC
hrom
ium
com
poun
dsFo
rmal
dehy
deN
icke
l com
poun
dsTr
ichl
oroe
thyl
ene
Ben
zene
Cok
e ov
en e
mis
sion
sH
exac
hlor
oben
zene
Perc
hlor
oeth
ylen
eV
inyl
chl
orid
eB
eryl
lium
com
poun
ds1,
3-D
ichl
orop
rope
neH
ydra
zine
Poly
chlo
rina
ted
biph
enyl
s 1,
3-B
utad
iene
Die
sel P
MLe
ad c
ompo
unds
Poly
cycl
ic o
rgan
ic m
atte
r
1010
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
there is a general consistency within the literature on thehealth effects of traffic-related pollution.
A few years ago Spengler and Samet convened a multi-disciplinary workshop to discuss the evidence for traffic-related effects on health and to identify the challengingissues still unresolved (White et al. 2005). The followingpoints were made in the summary of the workshop (White
et al. 2005) and are included here as a condensed appraisalof the difficulty with exposure assessment:
• Studies from throughout the world have reported increased risk of respiratory symptoms and asthma with proximity to or increased concentrations of traffic-related pollution (Murakami et al. 1990; Braun-Fahr-lander et al. 1992; Nitta et al. 1993; Oosterlee et al.
Table 3. Chemical Substances Emitted by Mobile Sources That Are Listed in IRISa,b
Analyzed in This StudyAcenaphthene Cadmium Manganese Acetaldehyde Carbon tetrachloride Methyl tert-butyl ether (MTBE) Acetone Chloroform 2-Methylnaphthalene
Acrolein (2-propenal) Chromium III Naphthalene Anthracene Chromium VI Nickel Antimony Chrysene Pyrene Arsenic, inorganic Dibenz[a,h]anthracene Selenium and compounds
Benzo[k]fluoranthene n-Hexane 1,1,1-Trichloroethane Beryllium and compounds Indeno[1,2,3-c,d]pyrene Vanadium 1,3-Butadiene Lead and compounds (inorganic) Xylenes
Zinc and compounds
Not Analyzed in This StudyAcetophenone 1,2-Dibromoethane 2-Methylphenol Ammonia Dibutyl phthalate 4-MethylphenolBarium and compounds 1,2-Dichlorobenzene Molybdenum Benzaldehyde 2,4-Dinitrophenol Nitrate
a Chemical substances listed in IRIS as known, probable, or possible human carcinogens and/or pollutants for which the EPA has calculated a reference concentration or reference dose. 2,2,4-Trimethylpentane, 2,3,7,8-tetrachlorodibenzo-p-dioxin, 2,4-dinitrophenol polycyclic organic matter, and priopionaldehyde are compounds emitted by mobile sources and are listed in Clean Air Act Amendments section 112(b) but are not listed in IRIS.
b Bold indicates chemical substances that are also listed in Section 112(b) of the Clean Air Act Amendments.
11
J. Spengler et al.
11
1996; van Vliet et al. 1997; Ciccone et al. 1998; Kramer et al. 2000; Venn et al. 2000; Gehring et al. 2002; Brauer et al. 2003; Garshick et al. 2003; Nicolai et al. 2003), and some studies have identified an increased risk of car-diopulmonary and stroke mortality (Hoek et al. 2002; Maheswaran and Elliott 2003). Some evidence also sug-gests that traffic density and proximity may affect pre-natal health (Wilhelm and Ritz 2003; Yang et al. 2003). However, evidence for other outcomes, such as hospital-ization and medication use for asthma and risk of a vari-ety of cancers, has been mixed (Edwards et al. 1994; Duhme et al. 1996; Livingstone et al. 1996; Feychting et al. 1998; English et al. 1999; Harrison et al. 1999; Wilkinson et al. 1999; Morris et al. 2000; Nyberg et al. 2000; Pearson et al. 2000; Buckeridge et al. 2002; Lin et al. 2002; Nafstad et al. 2003; Shima et al. 2003; Reynolds et al. 2004; Zmirou et al. 2004; Gordian et al. 2006). The specific components of the pollution mixture contribut-ing to the adverse health effects, however, are uncertain.
• The primary emissions from mobile sources include nitrogen oxides (NOx), carbon monoxide (CO), and hydrocarbons. Operating engines generally emit exhaust particles as submicrometer-size aerosols (< 1 µm) (Morawska et al. 1998; Ristovski et al. 1998). The pollutants decrease in concentration as they are dispersed in air, and their size distribution shifts, with increasing domination of larger particles at greater
distances from roadways. This dynamic process occurs rapidly (Hitchins et al. 2000; Zhu et al. 2002b; Zhang et al. 2004), and it can be influenced by temper-ature and meteorologic conditions. In addition, types of vehicles, driving behaviors, and vehicle speeds can all affect the characteristics of local pollution from mobile sources (National Research Council 2000).
• Other potentially hazardous vehicle-related combus-tion products are VOCs, carbonyls, and semivolatile organic compounds such as PAHs and NPAHs (U.S. EPA 2002). The stress of traffic noise may also contrib-ute to adverse health responses.
• Another source of air pollution is the resuspension of “road dust” caused by traffic. These predominantly coarse particles can contain a complex mixture of earth-crustal material, asphalt, metals, latex tire frag-ments, debris from the wearing of vehicle parts, and motor oil (Rogge et al. 1993b; Abu-Allaban et al. 2003; Riediker et al. 2004).
While the scientific literature associates vehicle emis-sions with adverse health outcomes, several unansweredquestions about exposure remain. The Buffalo PeaceBridge Study was a detailed examination of air pollutionnear a site of dense vehicle emissions. The selected ana-lytes covered a broad list of elements, VOCs, PAHs, andNPAHs, including most MSATs. The sampling strategy was
Table 4. Pollutants That Meet Criteria for Cancer and Noncancer Risk in 1999 NATA (U.S. EPA 2006b)
a Though diesel PM does not meet the criteria for cancer risk, the 1999 NATA concluded that it was one of the pollutants that posed the greatest relative cancer risk.
b “Others” are pollutants that were not analyzed in the Peace Bridge Study.
c The national criterion for cancer risk is at least 25 million people exposed to risk > 10 in 1 million; for noncancer risk, at least 25 million people exposed to a hazard quotient > 1.
d The regional criterion for cancer risk is at least 1 million people exposed to risk > 10 in 1 million or at least 10,000 people exposed to risk > 100 in 1 million; for noncancer risk, at least 10,000 people exposed to a hazard quotient > 1.
1212
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
designed to differentiate mobile-source emissions ema-nating from the Peace Bridge plaza from the regional airmass, thus identifying and characterizing the complexityof vehicle-related emissions.
PEACE BRIDGE BORDER CROSSING AS A POTENTIAL HOT SPOT FOR MOBILE-SOURCE POLLUTION
International border crossings between the U.S. and itsneighbors channel large volumes of vehicles, particularlydiesel trucks. The Peace Bridge crossing at Buffalo rankedthird among U.S. land ports in terms of handling commer-cial trade between North American Free Trade Agreement(NAFTA) partners in 2000, behind Detroit, Michigan, andLaredo, Texas (U.S. Department of Transportation 2001).Roughly 5000 diesel trucks and 20,000 passenger carstraverse this border daily (Peace Bridge Authority 2005).The heavy traffic, with vehicles idling and accelerating asthey stop for toll collection, passport control, and customsinspection, results in a high concentration of vehicle emis-sions. A segment of Interstate 190 (the Niagara section ofthe New York Thruway) that passes through the PeaceBridge border area also contributes to the traffic pollution.
Before 2005, all the tollbooths, customs areas, the PeaceBridge Authority Administration Building, and a duty-freeshop were located in the Peace Bridge plaza on the U.S. sideof the border, adjacent to west Buffalo neighborhoods. Inearly 2005 toll collection was relocated to the Canadian side,and the tollbooths on the American side were demolished
during June and July 2005. Passport control and customsinspection remained on the U.S. side. The old duty-freeshop was demolished and replaced by a larger one, locatedat the intersection of two streets on the edge of the PeaceBridge plaza.
The EPA defines a hot spot for emissions from mobilesources as a local area where air quality is of concern owingto spatially dense emissions patterns. A variety of situationscan create hot spots. Typically, one associates them withinfrastructure such as truck stops and tollbooths. Someareas might become mobile-source hot spots on a regularbasis, such as converging roads during traffic rush hours;others might become hot spots irregularly, such as whenthere is an accident, or nearby construction, or during anentertainment event. Hot spots need not be associated withhigh-volume traffic. Slowly moving vehicles acceleratingup inclines can increase emissions densities, as engines inhigh torque require fuel-rich conditions, increasing emis-sions of some contaminants.
The Peace Bridge plaza can be considered a mobile-source hot spot on several accounts. Cars and trucks con-verge there, and the passport controls, customs inspection,overnight rest areas for truck drivers, and duty-free shop-ping all are associated with increased idle time for trucks.The ramp to the bridge rises abruptly from the plazadirectly in front of a residential neighborhood, and vehi-cles must accelerate on this slope as they leave the U.S. Onoccasion when congestion builds because of vehiclevolume or increased security, traffic is directed onto BustiAvenue, an adjacent neighborhood road.
Whether the high prevalence of asthma reported in thewest Buffalo community, which abuts the Peace Bridgeplaza, is in any way related to local traffic congestion isunknown. Clustering of cases of chronic respiratory ill-nesses in the community has raised concern that traffic-related pollution may be contributing to the disease preva-lence and morbidity. Nearly 20% of school-age children inthe west Buffalo neighborhood have been reported tosuffer from asthma (Lwebuga-Mukasa and Dunn-Georgiou2000). This is substantially higher than the 5.8% asthmaprevalence rate estimated for the entire country (Eder et al.2006) and is 2.75 times what has been found in the otherBuffalo communities (Lwebuga-Mukasa et al. 2005). Incase-control studies of asthma and nonrespiratory illnesssuch as acute gastroenteritis in Buffalo, asthma cases wereclustered close to the Peace Bridge plaza, and the risk forasthma decreased the farther an individual resided fromthe plaza (Oyana and Lwebuga-Mukasa 2004; Oyana et al.2004). The high rates of respiratory disease prevalence
Table 5. Contributions of Mobile Sources to 1999 NATA High-Risk Pollutants (U.S. EPA 2006b)a
a All compounds, except for diesel PM and diesel exhaust organic gases, were part of the suite of compounds analyzed in the Peace Bridge Study.
b This polycyclic organic matter inventory includes the following 15 compounds, all of which were analyzed in this study: benzo[b]fluoranthene, benz[a]anthracene, indeno[1,2,3-c,d]pyrene, benzo[k]fluoranthene, chrysene, benzo[a]pyrene, dibenz(a,h)anthracene, anthracene, pyrene, benzo(g,h,i)perylene, fluoranthene, acenaphthylene, phenanthrene, fluorene, and acenaphthene.
13
J. Spengler et al.
13
were not fully explained by personal, household, or socio-economic factors (Lwebuga-Mukasa et al. 2004, 2005). Inmultivariate regression analysis of risk factors, residencein the west Buffalo neighborhood was an independent riskfactor for asthma diagnosis (Lwebuga-Mukasa et al. 2004).Furthermore, this area was shown to be downwind ofputative sources of traffic-related pollution emanatingfrom truck traffic (Lwebuga-Mukasa et al. 2005).
This border crossing between the U.S. and Canada willcontinue to be an important corridor for the NAFTA part-ners, as well as for U.S. and Canadian citizens, as it pro-vides access to the shortest routes between New York, NewEngland, and Detroit. By 2021 it is predicted that truckcrossings will increase from the current 1.5 million tomore than 4 million per year (Peace Bridge Authority2000). Since September 11, 2001, increased security hasincreased waiting times at the border crossing, especiallyfor trucks (Tan 2003). There is a high volume of traffic, inparticular diesel trucks, converging on or departing fromthe Peace Bridge plaza and surrounding roads, and thelong idling time at the crossing for these trucks creates anarea of vehicle-dominated pollution that leads to higherexposures to air pollutants in the nearby west Buffalo com-munity. The Buffalo Peace Bridge Study was designed toquantify the spatial and temporal effects of Peace Bridgetraffic on air pollution.
SPECIFIC AIMS
This study was designed to support the overall goal ofHEI’s air toxics program: to provide information that willreduce uncertainties in evaluating the human health risksassociated with exposure to air toxics. Of particular con-cern has been the difficulty of measuring exposure to andeffects of important air toxics at low levels of exposure.Request for Applications (RFA) 03-01 specificallyrequested studies conducted at hot spots, such as hot spotsdominated by mobile sources of air pollution. The PeaceBridge plaza fits this description, as a major bordercrossing immediately adjacent to a residential neighbor-hood, where vehicle congestion could potentially elevateexposures to air toxics. The RFA requested that researcherscompare exposure assessment methods using modelingbased on emissions information (the method employed inNATA) with methods using area monitors. Most emissionsinventories and modeling analyses are of annualized dura-tion, and thus may not adequately reflect exposure eventsof short duration. In addition, the RFA asked for character-ization of the variation in exposures over time and space,as well as identification and characterization of major areasources and mobile sources of air toxics contributing tosuspected hot spots.
The overall hypothesis of the Buffalo Peace BridgeStudy is that the intensity of vehicle-related emissions inthe vicinity of the Peace Bridge plaza on the west side ofBuffalo results in a differential exposure to MSATs, PM,and PAHs, and an increase in exposure to UFPs. Thisreport describes a study carried out by the Harvard Schoolof Public Health (HSPH) and associates from other institu-tions to assess air pollution in the environment near thePeace Bridge plaza.
This study provides extensive field evidence of theimpact of a highly trafficked area (the Peace Bridge plaza)on the potential for exposures to mobile-source pollutantsin a nearby community. A wide range of pollutants,including UFPs and organic and inorganic air toxics, weresampled at different fixed sites upwind and downwind ofthe Peace Bridge plaza. Fixed-site monitoring and mobilemonitoring were used to measure many MSATs and othercontaminants at different distances from the Peace Bridgeplaza. These measurements were used to characterizeupwind and downwind air pollution in an effort to quan-tify gradients in air toxics and other pollutants across thePeace Bridge plaza and into the residential community. Inaddition, meteorologic data from a tower located at theGLC site, 0.5 km west of the Peace Bridge plaza, were usedin the estimates of source contributions. For source esti-mates, we used hourly traffic counts of cars and truckscrossing the bridge that were provided by the Peace BridgeAuthority. Fixed-site and mobile monitoring campaignswere conducted over 2-week periods in both summer andwinter to meet the principal objective of this study, whichwas to characterize the spatial and temporal patterns ofMSATs and other traffic-related pollution across an area1 km by 1.5 km encompassing the U.S. border crossing, apotential hot spot. The analytes included the 12 com-pounds or classes of compounds identified as of interest inthe RFA: acetaldehyde, acrolein, benzene, 1,3-butadiene,ethylbenzene, formaldehyde, n-hexane, MTBE, naphtha-lene, toluene, styrene, and xylene. Diesel exhaust organicsand polycyclic organic matter were addressed by mea-suring particle-phase and gas-phase PAHs, and speciatingthe samples to look for 52 PAHs and 32 NPAHs. Dioxin andfuran compounds were not included in the study design.
To examine the hypothesis that traffic congestion at thePeace Bridge elevates levels of MSATs locally, this studywas designed to:
1. Characterize the ambient levels of air contaminantsincluding MSATs across a series of days to obtainupwind and downwind concentrations in the vicinityof the Peace Bridge plaza.
2. Examine the relation between traffic at the PeaceBridge and pollutant concentrations in ambient air.
3. Explore geographic patterns of ambient air pollutants.
1414
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Field campaigns were staged to obtain samples underdifferent meteorologic conditions during summer andwinter. Integrated measurements, obtained for individualweekdays (12 hours), weekends (48 hours), five weekdays(60 hours), and five weeknights (60 hours), were necessaryto detect many of the MSATs. Continuous measurementswere made to examine the effects of shorter-term varia-tions in traffic and weather. A mobile monitoring systemthat provided continuous measurements and GPS loca-tions was used to map spatial patterns of UFPs and pPAHsacross a series of cases.
METHODS
STUDY DESIGN
The Peace Bridge plaza is on the west side of Buffalo (42"
54$16.80% N, 78" 53$57.00% W). Lake Erie is to the southwest,and the Niagara River starts only a few hundred meterssouth of the Peace Bridge plaza. The U.S. side of the plaza isapproximately 17 acres in size, and until mid 2005 com-prised the U.S. Customs Building, a customs inspectionholding area, tollbooths, a duty-free store, parking, and thePeace Bridge Authority Administration Building.
Figure 1 shows the configuration of the Peace Bridgeplaza and the surrounding area. The northern end of the
plaza connects to the Peace Bridge, where traffic crossesover the Niagara River into Canada by a three-lane inclinedroad. Interstate 190 is to the west of the plaza. To the east ofthe plaza, there is a residential neighborhood known asBuffalo’s west side. A few private homes, a chapel, and anursing home are the most proximate structures along theeast side of Busti Avenue adjacent to the plaza. Extendinginto the neighborhood east of the plaza there are single-and two-family homes and commercial stores, shops, ser-vice stations, auto repair shops, and other communitybuildings. To the south of the plaza is a city park withaccess by feeder roads from Porter Avenue.
The Peace Bridge plaza was selected for study as amobile-source hot spot for several reasons. The PeaceBridge Authority provides hourly traffic counts for alltrucks and passenger vehicles transiting the border. Thereare no major industrial sources of air pollution betweenthe Peace Bridge plaza and the neighboring residentialcommunity. Community relationships developed by ourcolleagues at the University at Buffalo, the State Universityof New York, helped in the establishment of three securefixed sites for sampling. Air flows predominantly from thesouthwest and the west across the plaza to the neighboringcommunity. As shown in the 1992 wind rose pattern(Figure 2) from the Buffalo Niagara International Airport(15 km east-northeast of the Peace Bridge plaza), the mostfrequent wind direction is from the lake and river side of
Figure 1. The study area comprised the Peace Bridge plaza and the threefixed sampling sites (GLC, Chapel, and School), as well as Bird IslandPier and the neighborhood of west Buffalo (boxed), where routes formobile monitoring were located.
Figure 2. Wind rose showing 1992 annual average wind speed anddirection for Buffalo, from the Buffalo Niagara International Airport (15km east-northeast of the Peace Bridge plaza). Source: Buffalo AirportScram Surface Met Data 1992 (available from www.webmet.com; accessedOctober 27, 2010).
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the plaza (45% of the time), with winds blowing from thecity side only 31% of the time. During the summer months,winds at the Peace Bridge plaza are from the lake quadrant60% of the time.
Study Location and Sampling Sites
In the summer of 2004 (June 20–26), we conducted apilot study to test the sampling equipment and studydesign. During this period, two fixed sites for continuousand integrated sampling were established. One was theGLC site, where we could obtain upwind measurementswhen the breezes were from the southwest quadrant. Theother site was in front of the Episcopal Church Home(Chapel site) immediately adjacent to the original out-bound tollbooths. These sites were retained for subsequentmonitoring campaigns. We evaluated several mobile mon-itoring schemes to determine the spatial variability of pol-lutants across and within the community adjacent to thePeace Bridge plaza.
A second pilot study was conducted in winter 2005 (Jan-uary 9–18). At this time we added a third location for fixed-site sampling at the West Side Community Center andSchool (School site), which was used through the remainingcampaigns. During the winter 2005 pilot study, a samplingscheme was established to characterize MSATs and spatialpatterns of UFPs and PAHs around the plaza. The fixed-siteschedule of sampling for 12 hours on weekdays (from7:00 AM to 7:00 PM) was established. Data and analysis inthis report include the samples from the winter 2005 pilotstudy, along with those from the two subsequent field cam-paigns in summer 2005 and winter 2006. During the twopilot studies we were still experimenting with routes,schedules, and equipment for mobile monitoring, so thisreport presents only the mobile monitoring data collectedduring the summer 2005 and winter 2006 campaigns.
Before the summer 2005 campaign, the Peace BridgeAuthority relocated toll collection to the Canadian side ofthe bridge and expanded the tollbooths. The old tollboothson the U.S. side were being demolished during the summer2005 campaign. A new and enlarged duty-free shoppingmall had already replaced the older duty-free shop. Carsand trucks frequently stopped or idled in an area on the eastside of the plaza.
The mobile monitoring campaign was confined to an areaof approximately 2 km2 bounded by the Niagara River to thewest, spanning the Peace Bridge plaza, and extending abouteight city blocks into the residential west side neighborhoodof Buffalo to the east. The residential streets within thestudy area run in a grid pattern from the Peace Bridge plaza,
starting with Busti Avenue and extending 900 m east toNormal Avenue, and extending from Hampshire Street tothe north, down to Connecticut Street to the south. Thewestern boundary of the study area was Bird Island Pier,which at its nearest point is 350 m west of the Peace Bridgeplaza with a north-south orientation. Bird Island Pier iswithin 50 m to 100 m of the U.S. side of the Niagara Riverwhere it passes underneath the Peace Bridge, and it extendsto a point southwest of the GLC site on the shore.
The fixed sampling sites were selected for their locationsrelative to the Peace Bridge plaza with respect to the pre-dominant wind vectors coming out of the southwest, theavailability of electricity, and physical security for theinstruments. The GLC Aquatic Field Station, operated byBuffalo State College, is 560 m southwest of the plaza center.The Chapel site is 80 m directly east of the plaza center,40 m east of the plaza boundary, and 640 m from the GLCsite. Measurements made at the Chapel site reflect trafficemissions emanating from plaza activities and from road-ways farther west when the winds blow from the lake. TheChapel and GLC sites were established to identify MSATsand other compounds associated with plaza activities.
The School site is approximately 600 m east of the PeaceBridge plaza. It is an infrequently used paved parking lotnear the corner of Fargo Avenue and Vermont Street. Thesite is four blocks northeast of the plaza and two blocksnorthwest of Porter Avenue, a commercial route leadingfrom Buffalo to the plaza entrance and interstate highwayaccess. The School site was chosen to represent residentialexposures in the community of west Buffalo. It is central tothe study neighborhood and downwind of the plaza for theprevailing southwest winds off Lake Erie. We learned afterthe site was established that community volunteers weredistributing meals from the community center to people inthe neighborhood. A van or panel truck delivered prepack-aged meals to the school once a day, and about five volun-teers arrived in the morning in their own cars to pick upthe meals for distribution in the community.
Sampling Frequency
At each fixed sampling site 12-hour integrated samplesof ambient air were taken daily from 7:00 AM to 7:00 PM for1 week in January 2005, 2 weeks in July 2005, and 2 weeksin January 2006, to assess the suite of compounds listed inTable 6. The daytime sampling interval included hours ofhigh traffic volume on the bridge. On average, 640 cars and150 trucks crossed the bridge each hour. One duplicatesample and one blank sample were collected daily for eachgroup of compounds. Weekend sampling lasted 48 hours,
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Table 6. Air Pollutants Measured over Integrated Time Periods
a Samples may have been contaminated by solvents used in PAH samples and were voided.
b Levels measured were below method detection limits.
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instead of 12 hours, to reduce the potential of nondetec-tion. To capture nighttime pollutant levels, 60-hour com-posite samples were collected at each site during each ofthe 4 weeks of the summer 2005 and winter 2006 fieldcampaigns from 7:00 PM to 7:00 AM. Between samplingperiods, 60-hour samples were stored at the GLC underrefrigeration to reduce evaporative loss. Daytime 60-hourcomposite samples were also collected. For stable analyteslike PM2.5, EC reflectance (EC-r), and metals, we expectedcorrespondence between the 60-hour composites and thesum of the matching 12-hour samples.
Across the 2-week campaign periods, 10 daytime, oneweekend, and four 60-hour samples were collected at eachsite. In addition, blanks and duplicate samples were col-lected. Table 7 is a summary of the fixed-site samplingdetails for each integrated sampling period.
Meteorologic Data
Meteorologic data for the sampling sites were obtainedfor our study by the GLC Davis Weather Station, operatedby Buffalo State College. The weather station is located atthe GLC near the riverbank and is 6 m above ground level.It is mounted on top of a shortwave radio antenna towerand, except for the Peace Bridge, is the tallest structurewithin a mile in all directions. The Davis Weather Stationreports data for each minute, in contrast to the Buffalo Air-port weather station, which reports data hourly. Weobtained 1-minute values of wind direction, wind speed,temperature, humidity, rainfall, and pressure.
The Davis Weather Station has a wind threshold of 1mph. During the summer 2005 campaign, for the 12-hourdaytime sampling interval the wind was greater than 1 mph(0.44 m/sec) 91.8% of the time and greater than 2 mph (0.9m/sec) 86.8% of the time. Because of the wind speeds mea-sured and the use of 10-minute averages, we do not thinkthis wind threshold is a significant limitation in our anal-ysis. During the summer 2005 campaign, we compared thevalues for wind speed and wind direction obtained fromthe Davis Weather Station with the values reported at theBuffalo Airport. The difference in wind speed between theGLC Davis instrument value and the value reported at theBuffalo Airport for 300 hourly averages was &0.88 mph,with a standard error of 0.10 mph (t = &8.48; P < 0.001).The difference in wind direction values obtained from theGLC Davis Weather Station and at the Buffalo Airport for293 hourly measurements was &10.6" with a standarderror of 1.21" (t = &8.74; P < 0.001). Although the differ-ences in wind speed and wind direction were significant,they were small in magnitude and most likely representactual meteorologic differences between locations. Thevalues at the GLC Davis Weather Station were considered
comparable to the values reported at the Buffalo Airportfor wind speed and wind direction. More information onthis analysis can be found in Appendix A: Data Quality.
Continuous Sampling
Real-time continuous monitors were collocated with theintegrated samplers at the three fixed sites depending onthe availability of equipment. Data were collected everyminute for ozone (O3), NOx, PM2.5, carbon dioxide (CO2),UFPs, and PAHs. All continuous monitors were collocatedupwind and downwind of the Peace Bridge plaza to ensurereliability under similar sampling ranges. These data canbe compared directly with the 1-minute weather measure-ments from the GLC and aggregated to 1-hour periods foranalyses using traffic counts.
Mobile Monitoring Scheme
To understand spatial patterns of air pollution, wedeployed staff carrying monitors in backpacks to traverseestablished routes across the entire study area. Each back-pack contained a continuous particle counter (P-Trak, TSIInc., Shoreview, MN), a continuous PAH monitor (PAS2000CE, EcoChem, League City, TX), and a handheld GPS(eTrex Vista, Garmin, Olathe, KS), as well as a Harvardintegrated personal exposure monitor (PEM), to captureaverage PM2.5 mass, reflectance, and element concentra-tions across the study area.
Four principal routes for mobile monitoring were estab-lished within the study area by grouping sequentialstreets. A discussion on instrumentation, sampling fre-quency, duration, and specific details about sampling pat-terns follows in the Mobile Monitoring section.
Traffic Data
We obtained hourly car and truck counts from the PeaceBridge Authority (2004–2006), which reports counts forbuses, trucks, and passenger cars in each direction oftravel. As buses were a very small fraction of the total traf-fic, we included them in the truck category. Figure 3shows an example of the cyclic nature of truck traffic andtotal traffic for the period of our first pilot study duringsummer 2004. Truck counts were low late at night andthroughout the weekend, a pattern that holds truethroughout the year. Total traffic tended to fall below 200counts per hour from 1:00 AM to 5:00 AM. In contrast, dur-ing peak traffic times, from 10:00 AM to 7:00 PM, countsexceeded 1000 vehicles per hour. Also, three times thenumber of diesel trucks traveled on weekdays during peakhours as on weekends. Although truck counts remainedfairly constant across all seasons, car counts in the winterwere less than half those in the summer. Perhaps most
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Table 7. Fixed-Site Sampling Summary for 12-Hour Weekday, 48-Hour Weekend, and 60-Hour Weekday and Weeknight Samples by Sampling Campaign
a Abbreviations: PAH–C indicates ChemComb PAH samples analyzed at the University of Maryland; Metals indicates PM2.5, EC reflectance, and elements analyzed at LDEO; CARB indicates carbonyl samples (Sep-Pak cartridges) for analysis of formaldehyde, acetone, acetaldehyde, and propionaldehyde by HPLC at HSPH; ACR indicates acrolein samples (XAD-2-[hydroxymethyl]piperidine tubes); VOCt indicates VOC samples in triple-sorbent tubes, analyzed by HSPH. Sampling rates for acrolein and VOCs were higher in winter to capture lower levels. EC/OC indicates ratio of elemental to organic carbon, used to calibrate the regression equation for the calculation of EC-r.
b EC/OC analysis was only performed during the winter 2005 sampling session.
c Samples were collected on weekdays from 7:00 AM to 7:00 PM, or on weeknights from 7:00 PM to 7:00 AM, for 60 hours (5 days).
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central to our specific aims is the fact that diesel trucksmade up between 20% and 50% of the total vehicles cross-ing the bridge during peak traffic times.
Fixed-Site Sampling
Integrated samples for PM2.5, PM10, VOCs, aldehydes,and PAHs were taken at the three fixed sites. Integratedacrolein samples and continuous NOx samples were alsoobtained; data quality issues are discussed further inAppendix A: Data Quality. The methods used to analyzethese samples are described below.
Analytic Methods for Integrated Samples
PM2.5, EC-r, and Multielement Analysis Samples for anal-yses of PM2.5, EC-r, and multiple elements were collectedon Teflon filters with rings (37 mm, 2-µm pore; GelmanSciences). The sampling system consisted of modifiedHarvard impactors. Traditional Harvard impactors useimpactor oil to lubricate the impaction surface (Marple etal. 1987). However, for the multielement analysis this oilpresented the potential for unacceptable contamination.We replaced the inertial impaction heads of the Harvardimpactors with two-stage polyurethane foam (PUF) impac-tion heads, which require no lubrication. Each impactionhead had a 2.5-µm cutoff at 16.7 L/min ± 10% for the12-hour samples and 5 L/min ± 10% for the 60-hour(5 day) samples. A 2.5-µm cut was selected so that a singlesample could be analyzed for PM2.5, EC-r, and multiele-ment analysis. As the purpose of the study was to assessexposure to air toxics emanating from the Peace Bridgeplaza, a PM2.5 cutoff was selected to focus on elements of
combustion origin. The Teflon filters were assembled inthe laboratory on acid-washed filter-holder cassettes, sup-ported by acid-washed drain disks. The assembled filter-holder cassette was placed inside an acid-washed Petridish. Samples were collected for 12 hours, 48 hours, or60 hours. Flows were adjusted to 16.7 L/min before sam-pling for the 12-hour and 48-hour samples, and to 5 L/minbefore sampling, with readjustment daily as needed, forthe 60-hour samples. Final flows were checked at the endof the sampling period. Sampled filters were stored chilledwhen not being analyzed.
PM2.5 filters were weighed before and after sampling ona Mettler microbalance at the HSPH laboratory after beingconditioned in an environment controlled for temperatureand humidity for at least 24 hours (by cracking the Petri-slide cover) and statically discharged via a poloniumsource. Both before and after sampling, filters wereweighed twice; if these two masses were not within 5 µg ofeach other, then the sample was weighed a third time.After the third weighing, the mean of the two masses thatwere within 5 µg of each other was used for calculatingconcentrations. In every batch of 10 samples, the zero,span, and linearity of the balance were checked using astandardized set of calibration weights (class "S" weights).
Before PM2.5 gravimetric analyses, the Teflon filterswere analyzed for reflectance, a measure of filter black-ness. Reflectance measurements were made inside a class100 flow bench using a smoke stain reflectometer (EELModel 43D, Diffusion Systems Ltd). The measurementswere made following the standard operating procedures ofthe European study ULTRA: Exposure and Risk Assess-ment for Fine and Ultrafine Particles in Ambient Air,whereby measurements of five separate locations are madeon each filter (Pekkanen et al. 2000). Because this conven-tional method has the potential for the reflectometer headto touch the active filter area, we designed a new filterholder that prevents any contact of the measurementdevice with the active area of the filter. The filter holderonly touches the outer plastic ring, holding the filter in afixed flat geometry. These modifications enabled us tomake reflectance measurements without a significant riskof contaminating the filter for later multielement analysis.The reflectance measurement is sensitive to the distancebetween the reflectometer head and the filter (which is2.5 mm longer with our filter holder).
EC-r measurements were calculated using concurrentmeasurements from quartz filters (x) according to Method5040, Elemental Carbon (National Institute for Occupa-tional Safety and Health 2003) and reflectance measure-ments from the Teflon filters (y) collected during thesummer 2004 and winter 2005 pilot studies. The regres-sion equation calculated from an independent set of paired
Figure 3. Hourly counts of total traffic and truck traffic on the PeaceBridge (June 20–26, 2004), showing a cyclic pattern with lows late atnight and peaks during the day. Note different scales for y axes.
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samples collected elsewhere in Buffalo shows a similarrelationship (y = 34.5x + 0.023). We used the regressionequation generated with data from both the summer andwinter pilot studies to estimate EC-r values, whereEC (µg/filter) is 3.45 times ln(reflectance blank/reflectancesample) plus 0.023.
After reflectance and PM2.5 gravimetric measurements,the filters were returned to the Petri dish until sample diges-tion took place in preparation for multielement analysis bymagnetic sector high-resolution inductively-coupled-plasma mass spectrometry (HR-ICP-MS) at the Lamont-Doherty Earth Observatory (LDEO), Columbia University,Palisades, New York. All sample handling was done in aclass 100 laminar flow bench. We used 18-Mohm water andOptima or trace-metal-grade acids. All plasticware wasacid-leached and triply rinsed with 18-Mohm water.
The procedures for sample digestion and analysis havebeen standardized for several years (Kinney et al. 2002)and are summarized below. Of the digests in each batch,20% were procedural blanks (acids only). Field blankswere treated as samples. Samples and procedural blanksfrom digestion batches were analyzed on the same day.The supporting ring was cut from filters. The filter wastransferred to a 7-mL perfluoroalkoxy (PFA) vial. Weadded 20 µL of ethanol to wet the filter, then 60 µL of waterand 225 µL of concentrated Optima nitric acid (HNO3).After the ethanol and HNO3 reacted, the vial was sealedand placed in a microwave vessel with 10 mL of 65%HNO3 (outside the vial). The microwave program was runfor 52 minutes (with the samples being held near 200"C forthe last 20 minutes). Then the vials were taken out and10 µL ethanol, 100 µL HNO3, and 40 µL hydrogen fluoridewere added. The vials were returned to the microwave ves-sels with a second 10-mL aliquot of 65% HNO3, and theprogram was run again.
After the digestion, the mass of remaining digest wascalculated gravimetrically. Based on the amountremaining, the digest was diluted with 5 mL of water, or1% HNO3, or 2% HNO3, to make the acid strength of theresulting solution as close to 3% as possible.
Aliquots of standard reference material (SRM) 1648,urban particulate matter, were weighed on a microbalanceand digested several times during the course of the anal-yses. The mass of SRM 1648 digested was similar to thetotal mass of PM2.5 collected on the sample filters. TheSRM aliquots were then digested using the same quantitiesof acids and the same microwave program.
Diluted digests were analyzed using an Axiom (VG-Ele-mental) HR-ICP-MS at LDEO, Columbia University. Data werecollected for all isotopes of interest at the appropriate resolv-ing power (RP) to avoid isobaric interferences. Beryllium,
silver, cadmium, tin, antimony, cesium, lanthanum, plati-num, thallium, and lead, for which interferences are not aproblem, were run at RP 400; sodium, magnesium, alumi-num, sulfur, calcium, scandium, titanium, vanadium, chro-mium, manganese, iron, cobalt, nickel, copper, and zincwere run at RP 3000 to 4300; and potassium, arsenic, andselenium were run at RP > 9300. Indium was added to allsamples, blanks, and standards as an internal drift correc-tor and run in all RPs. External and internal standardswere used to quantify the elements. On each analysis date,several sets of multielement standards were analyzed inboth clean acid-washed blanks and sample matrices.
Three multielement standards were used for theexternal calibration. All were prepared at LDEO from pri-mary, single-element standards acquired from SPEX Cer-t iPrep (Metuchen, NJ) or High-Purity Standards(Charleston, SC). HR-ICP-MS data were reduced in anExcel spreadsheet. Data were drift-corrected using indium,quantified, converted to a mass, and corrected for blanks.Samples below the limit of quantitation according to dailyprocedural blanks were flagged.
PM10 PM10 was collected using 10-L/min Harvardimpactors and measured using gravimetric analysis sim-ilar to that for PM2.5. Blanks for PM10 and PM2.5 were com-bined to increase the number of blanks for determinationsof blank correction and limit of detection (LOD).
Volatile Organic Compounds VOC samples were col-lected on stainless steel, multibed thermal desorptiontubes (TDTs) from Supelco, with the three following adsor-bents: 200 mg of Carbopack B, 230 mg of Carbopack X, and170 mg of Carboxen 1001 (Supelco/Perkin-Elmer),according to EPA Compendium Method TO-17 (U.S. EPA1999). The triple-sorbent tubes were conditioned beforeuse, as described in Method TO-17, by heating them at350"C for 2 hours and passing pure helium gas (50mL/min) through them. In addition, used tubes werereconditioned for 15 minutes after analysis before beingreturned to the field.
Samples were analyzed on a Perkin-Elmer AutomaticThermal Desorber interfaced to a Hewlett Packard gaschromatograph with mass selective detector using EPAMethod TO-17. The thermal desorber’s transfer line con-nects directly to the DB-1 column (J&W Scientific, Folsom,CA) inside the gas chromatography oven. Dry purge andaddition of internal standard were accomplished in onestep. Sample tubes were placed on a spiking device (tubingconnected to an ultra-high-purity nitrogen tank with fit-ting for the tube) with carrier flow of 75 mL/min. A vapor-phase internal standard was injected into the device, and
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the tube was kept in place for 5 to 30 minutes. The vapor-phase internal standard was made from liquid standards insolution (usually methanol) of a known concentration thatare injected as a known volume into a 2-L static dilutionbottle. A volume of vapor was drawn up with a gas-tightsyringe and injected into the injector-port/spiking devicewith flow onto the sample tube. Initial calibration stan-dards were prepared the same way. Drawing different vol-umes yields different masses and thus the different levelsof calibration.
Results for hexane and methylene chloride samples arenot reported due to contamination from the PAH samplingsystem, which contained both of these solvents. The surro-gate spiking solution for the PAH samples was prepared inhexane, and all components including PUF and XAD-2resin were cleaned in methylene chloride before sampling.High levels of these two compounds were occasionallydetected in blanks and samples.
Carbonyls Formaldehyde, acetaldehyde, and acetonewere sampled actively using a commercially availablesampler with silica cartridges coated with 2,4-dinitrophe-nylhydrazine (DNPH) (Waters Associates, Milford, MA).Samples were collected at 100 cm3/min and stored at&4"C. The samples were prepared by desorbing the DNPHderivative from the substrate with 5 mL of acetonitrile.This was done in a vacuum chamber with 12 sample posi-tions, each fitted with a valve to control the extractant flowrate to approximately 1 mL/min. The extracts were col-lected directly into 5-mL volumetric flasks or graduatedtest tubes. After removing the vacuum we diluted the sam-ples to 5 mL with acetonitrile. Aliquots were pipetted intospecial 1-mL amber vials (Whatman mini-prep vials with a0.45-µm filter) for chemical analysis.
Samples were analyzed by high-performance liquidchromatography (HPLC) with ultraviolet detection usingAgilent 1100 series HPLC equipped with a quaternarypumping system, a degassing unit, a 100-positionautosampler, a column compartment with thermostat, andan ultraviolet-visible variable wavelength detector set at360 nm. The mobile phase was a mixture of water, acetoni-trile, and tetrahydrofuran at a flow rate of 1.5 mL/min. A20-µL sample was injected, and the compounds includingthe DNPH derivatives were separated in less than 30 min-utes with a gradient on a Waters reverse-phase Nova-PakC10 column (150 mm ' 3.9-mm internal diameter). Thedata were processed automatically with Agilent ChemSta-tion software.
Acrolein Acrolein samples were analyzed by Assay Tech-nology Laboratory, Pleasanton, California, using Occupational
Safety and Health Administration (OSHA) Method 52,which is specific for acrolein. Additional acrolein sampleswere collected at the Chapel and GLC sites by our collabo-rators at the University of California–Davis using the mistchamber method (Seaman et al. 2006). The sample collec-tion period was 10 minutes per sample. The method uses amist chamber to collect carbonyls by forming water-sol-uble carbonyl-bisulfite adducts. The carbonyls are thenliberated from the bisulfite, derivatized, and quantified bygas chromatography with electron-capture negative ioniza-tion mass spectrometry.
Acrolein samples were also collected by a passive methodbased on the use of dansylhydrazine (DNSH) (Zhang et al.2000). The Personal Aldehydes and Ketones Sampleremploys DNSH-coated solid sorbent to collect aldehydesand ketones (carbonyls). The DNSH-carbonyl derivatives areanalyzed using a sensitive HPLC-fluorescence technique.
PAHs and NPAHs PAHs and NPAHs were sampled usingtwo ChemComb organic samplers (Thermo Fisher Scien-tific, Milford, MA) operating at 16.7 L/min. The Chem-Comb organic sampler consists of a 2.5-µm size-selectiveinlet, a prefired quartz filter, and a glass sleeve containingpurified XAD-2 (Supelpak-2B) sandwiched between twoPUF pads. A third PUF pad was added to 60-hour samplesto evaluate sample breakthrough. The quartz filters wereremoved from the sampler and placed in precleaned glassjars. The glass sleeve containing the PUF pads and XAD-2were capped and wrapped in foil. All samples were storedat 4"C and shipped cold. Filter and PUF/XAD-2 compo-nents were transferred to a Soxhlet apparatus, and the jarsfor filter samples and PUF/XAD-2 sleeves were rinsedwith dichloromethane into the extraction apparatus. Thefilter/PUF/XAD-2/PUF plugs were placed on top of pre-cleaned sodium sulfate and glass wool to prevent XAD-2from cycling into the reflux flask.
Before extraction, the following recovery standardswere added to each filter/PUF/XAD-2/PUF Soxhlet: a mix-ture of acenaphthylene-d8, fluorene-d8, and pyrene-d10and a mixture of 1-nitronaphthalene-d7, 9-nitroan-thracene-d9, and 1-nitropyrene-d9. The samples were con-centrated using a gentle stream of nitrogen and exchangedto hexane to a final volume of approximately 1 mL. Eachconcentrate was transferred to 2-mL vials with Teflon-lined lids. Each analyte was quantified by comparing theresponse of the molecular ion signal with that of the corre-sponding internal standard in the respective selected ionmonitoring window. The mass spectrometer was cali-brated by analyzing mixed standards containing knownmasses of the analytes and internal standards. Analyteidentities were often confirmed by analysis of a secondary
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ion. Instrumental detection limits are defined as threetimes the signal-to-noise ratio of each monitored ion in theregion of each analyte. Method detection limits are definedas three times the average field matrix blank.
After quantification of the PAHs, the remainder of eachsample was fractionated for NPAH analysis using themethod developed by Bamford and Baker (2003). Briefly,each extract was eluted through an aminopropyl solid-phaseextraction cartridge (Sep-Pak, Waters, Milford, MA) using 40mL of a 1:4 dichloromethane-to-hexane solution, concen-trated under nitrogen gas, and exchanged to hexane. Normalphase liquid chromatography was then employed for thefinal cleanup procedure using a 5-µm Chromegabondamino/cyano column (30 cm ' 9.6-mm internal diameter,ES Industries, West Berlin, NJ) with 1:5 dichloromethane-to-hexane solution as the mobile phase. The fractions con-taining NPAHs were retained and concentrated under agentle stream of nitrogen to 1 mL. An NPAH internal stan-dard containing 5-nitroacenaphthene-d9, 2-nitrofluorene-d9,3-nitrofluoranthene-d9, and 6-nitrochrysene-d11 was thenadded to all of the vials before NPAH analysis.
Sample quantification was performed using a gas chro-matograph and mass spectrometer (Models 6890 and 5973,Agilent, Palo Alto, CA) in electron ionization mode andnegative chemical ionization (methane gas) mode for PAHsand NPAHs, respectively (Crimmins and Baker 2006). Thisinstrument was equipped with a programmable tempera-ture vaporizer (PTV, Gerstel, Germany) for injection. ThePAH method employed hot splitless injection (250"C) cou-pled to a DB-5MS gas chromatograph column (30 m ' 0.25-mm internal diameter ' 0.25-µm film thickness, J&W Scien-tific). The oven was held at 40"C during the injection,ramped to 280"C at 10"C/min, and then ramped to 310"C at5"C/min. NPAH analysis was performed using a DB-17MScapillary column (30 m' 0.25-mm internal diameter '0.25-µm film thickness). The oven temperature program was40"C (held for 1.7 minutes during injection), ramped to150"C at 20"C/min, held for 10 minutes, ramped to 220"C at
10"C/min, held for 10 minutes, and finally ramped to 310"Cand held for 15 minutes. The PTV injector (in solvent ventmode) was configured to perform ten 5-µL injections ventingat 100 mL/min at 2 psi for 1.10 minutes. At 1.2 minutes thePTV was ramped at 600"C/min to 280"C.
Cubic regressions of the molecular weight of the targetanalyte against the percentage of each laboratory and fieldsurrogate recovered (eight data points) were calculated foreach PAH sample. Each of the samples, including eachblank, was corrected for recovery using the regressionequation calculated from that sample’s surrogates. NPAHsamples were corrected by molecular weight group of thelaboratory surrogates. All NPAH samples, includingblanks, were corrected for recovery based on the per-centage recovery of the surrogate representing each molec-ular weight group.
Analytic Methods for Continuous Measurements Con-tinuous sampling was done both at the fixed sites (Chapel,GLC, and School) and in the mobile monitoring campaign,with the sampling record shown in Table 8. We measuredPM2.5, UFPs, pPAHs, O3, CO2, and nitric oxide (NO).
Particle-Bound PAHs Continuous measurements of pPAHswere taken using the EcoChem PAS 2000CE, a photoelectricaerosol sensor (PAS) that measures the excitation of sur-face-bound PAHs. It uses a krypton-bromide lamp(excimer laser) that emits radiation at 207 nm, a wave-length specific to the photoelectric threshold of surface-bound PAH. The PAS is limited in that it cannot measurevapor-phase PAHs, cannot provide values for speciatedPAH, and can only detect 4- and 5-ring PAHs adsorbed onparticles with diameters generally less than 2 µm, becauselarger particles are more likely to recapture the ionizedphotons before they reach the electrometer (Wilson et al.1994). The advantages of using the PAS, however, are that itis battery operated, does not require a warm-up period, andcan be operated continuously for 4 to 6 hours. The output of
Table 8. Continuous Sampling Summary for Winter 2005, Summer 2005, and Winter 2006 Campaigns
Analyte Instrument
Valid 10-Minute Average Measurements MobileMonitoring(minutes)GLC Chapel School Total
this monitor is a direct reading of the total concentration ofpPAHs (ng/m3) and is calibrated by the manufacturer. As anadditional check, we collocated all instruments to ensurehigh correlation among instruments.
Ultrafine Particles The P-Trak (Model 8525, TSI Inc.) wasused to measure ambient UFP number concentrations (par-ticles/cm3) within the size range of 0.02 to 1 µm in medianaerodynamic diameter. The P-Trak uses an internal pumpthat draws the air through the instrument. As particlesenter the P-Trak they pass through a saturator tube, whereisopropyl alcohol evaporates into the sample stream, satu-rating the sample with alcohol vapor. The sample thengoes to the cooled condenser tube, where the alcohol vaporsupersaturates and condenses onto the particles,increasing their diameters to more than 10 nm. These par-ticles then pass through a small laser beam. The particlesscatter this light, and an optical system collects andfocuses the light onto a photometer. The instrument thenconverts the light signal into a particle count. The highsensitivity and portable nature of the P-Trak 8525 made itideal for this study. However, a limitation for this instru-ment is that alcohol is typically consumed within 3 hours,and thus long-term sampling requires that the alcoholwick be recharged frequently. P-Traks were zeroed dailywith an external high-efficiency particulate air (HEPA)filter. All instruments were factory calibrated within thepast year, as recommended by the manufacturer. As anadditional check, all instruments were collocated beforewe began sampling. Comparisons of the P-Trak with a gen-eral-purpose condensation particle counter (CPC) suggestthat the P-Trak underreports particle counts when it isclose to combustion sources such as traffic (Zhu et al.2006b). Correlations between the P-Trak and CPC valuesimprove with increasing particle size and distance fromthe road. Studies in Los Angeles by Zhu and colleagues(Zhu et al. 2002a, 2002b, 2004) showed that UFP valuestended to drop off exponentially with distance from majorsources such as roadways.
Particle Mass We measured continuous PM2.5 particlecounts using the DustTrak Aerosol Monitor (TSI Inc.), a por-table, battery-operated laser-photometer. The DustTrak mea-sures particles within the range of 0.001 to 100 mg/m³ and isequipped with a size-selective inlet for PM2.5. Output fromthis optical light-scattering device was adjusted by applyinga correction factor based on daily ratios from measurementsof gravimetric mass concentrations.
Ozone For O3, we used a monitor (Model 202 Ozone Mon-itor, 2B Technologies, Boulder, CO) designed to obtain accu-rate measurement in the mixing ratio range of 0 to
1000 ppbv. The instrument works on the principle ofabsorption of ultraviolet light, and has long been used formeasurements of O3 with high precision and accuracy. TheO3 molecule has an absorption maximum at 254 nm, coinci-dent with the principal emission wavelength of a low-pressure mercury lamp.
Nitric Oxide For NO, we used a monitor (Model 400 NitricOxide Monitor, 2B Technologies) designed to obtain mea-surements in the range of 2 to 2000 ppbv. The technologyemployed by the monitor is based on the quantitative reac-tion of NO with O3: NO + O3 → NO2 + O2 + h(, where h isPlanck’s constant and ( is the frequency of light.
This reaction has long been used as a gas-phase titrationfor the measurement of either NO or O3 in laboratorykinetics experiments. The reaction is stoichiometric; thatis, one O3 molecule is consumed for every NO moleculeoxidized to nitrogen dioxide (NO2) in the reaction. In themonitor, a small concentration of O3 (approximately 3 to5 ppm) is added to the gas sample stream, and the resultingdecrease in concentration of O3 is measured by the absolutemethod of absorption of ultraviolet light. Providing ade-quate time for the reaction to go to completion allows theO3 concentration to decrease to a level that is equal to theoriginal concentration of NO in the gas stream.
Carbon Dioxide We measured CO2 with a CO2 monitor(Model 7001, Telaire, Goleta, CA), which uses a dual-beaminfrared absorption sensor to detect CO2. It has a range of 1to 10,000 ppm and an accuracy of ± 5%.
MOBILE MONITORING
To define spatial patterns of UFPs and pPAHs in theneighborhood, monitors in backpacks were carried by staffwalking along predetermined paths across the entire studyarea. Each backpack contained a P-Trak, a PAS 2000CE, anda handheld GPS (eTrex Vista, Garmin), as well as a HarvardPEM to capture average PM2.5 mass, EC-r, and element con-centrations across the study area. The measurements wererecorded every minute. Location information was matchedto concentration data through their time stamps.
One staff member with a backpack was assigned to eachmobile monitoring route (Figure 4). The first route covers awalking path on Bird Island Pier, which extends into theNiagara River to create a slow-moving channel for smallwatercraft. The river walkway lies roughly 50 to 100 m westof the western edge of Interstate 190 and is representative ofair pollution in the air masses passing over Buffalo from thewest. The other three routes are east of the Peace Bridgeplaza, and each comprises a set of three streets parallel tothe plaza. Over the course of a 3-hour sampling session, astaff member assigned to each route walked those streets in
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a figure-8 pattern. Four zones were created by grouping databy routes: (1) the Bird Island Pier, about 300 m west of thecenter of the Peace Bridge plaza, which represented upwindbackground conditions; (2) the near zone, 90 m to 300 meast of the center of the plaza, comprised Busti Avenue andColumbus Parkway/7th Street, as well as the west side ofNiagara Street; (3) the mid zone, 300 m to 550 m east of thecenter of the plaza, consisted of the east side of NiagaraStreet, as well as Prospect and Fargo Avenues; and (4) thefar zone, 650 m to 900 m east of the plaza, consisted ofWest, Plymouth, and Normal Avenues.
Study staff members wearing backpacks were deployedduring the same time periods on all four routes wheneverfeasible. Sampling duration lasted between 2 and 3 hoursper session. Sampling sessions were consistently held eachmorning and early evening, or from roughly 8:30 AM to11:30 AM and again from 4:30 PM to 7:30 PM. Occasionallysessions would occur midday and on weekends. More than40 hours of data were collected by this method during thesummer 2005 campaign, while the winter 2006 campaignresulted in a total of 42 hours of data. Sampling did not
occur during inclement conditions, such as periods ofheavy rain. During cold weather, backpacks were insulated tokeep instruments at temperatures within recommendedoperating ranges. Each staff member kept a field log on instru-ment checks, location, time, and pertinent observations suchas when a school bus or a person smoking passed by.
The Garmin eTrex Vista GPS is a small cell-phone-sizedunit that provides location accuracy from 3 to 22 m,depending on satellite coverage and reception. It iscapable of recording location, direction, and pace of travel.Locations were recorded every minute and were linked tomeasured concentrations through the time stamp. There-fore, synchronizing instrument times and ensuring thatclock drift was minimal were critical quality control func-tions that occurred daily.
Each backpack contained a PEM (SKC Personal Environ-mental Monitor) with a PM2.5 cutpoint at 10 L/min. A Teflo37-mm filter identical to those used at each fixed samplingsite was the sampling medium. A portable 10-L/min pump(BGI Inc., Waltham, MA) with a lithium battery generatedthe required flow rate to each PEM. For every batch of sam-ples, one field blank was handled and processed identi-cally to the actual sample and was attached to a backpack.
Temperature and relative humidity were measured andstored on HOBO data loggers (Onset Computer Corp., Poc-asset, MA). A HOBO device was placed in every backpackduring each use to identify any periods when the manufac-turer’s suggested operating temperature range for theinstruments recording continuous measurements wereviolated during sampling.
QUALITY ASSURANCE AND QUALITY CONTROL
Strict quality assurance and quality control procedureswere implemented during each sampling period,including the collection of blanks, duplicates, and collo-cated samples in the field. Before any statistical analysis,all data collected were checked against performance cri-teria outlined for this study in our Quality AssuranceProject Plan. These criteria included measures of data cap-ture, accuracy, comparability, and precision.
Devices to measure instrument flow rates in the fieldwere calibrated to a reference standard, and flow tolerancelimits were established and followed. Chain-of-custodyforms were generated for all samples. Duplicates and fieldblanks were tracked with the rest of the samples. The dataquality of each integrated sample type has been summa-rized in data quality report tables that list the LOD, per-centage of samples above the LOD, and the relativeprecision of the duplicate pairs. Additional tables withrecoveries of field spikes for the VOC analyses and surro-gates for the PAH and NPAH analyses are presented inAppendix A: Data Quality.
Figure 4. Mobile monitoring routes. Data points represent GPS pointsrecorded by devices that field staff carried in their backpacks as theywalked the routes.
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Integrated Samples
In Appendix A data quality reports for each analyticsample are presented in tabular form. For each samplingsession the following data quality indicators are presentedfor each target analyte: mean blank value, the LOD calcu-lated from the standard deviation of the field blanks (threetimes standard deviation), the percentage of samples abovethe LOD, and the relative precision (100 ' root meansquare error [RMSE]/!2) calculated from the duplicatepairs. Data quality reports assessing analytic recoveries forVOC, PAH, and NPAH analyses are also presented inAppendix A, and the 60-hour daytime samples are com-pared with the average of the five corresponding 12-hoursamples for VOC, elemental, and PAH analyses.
Treatment of Samples Below the LOD
For all sample types actual values below the LOD wereused in all calculations of summary statistics and statisticaltests. This is known as the instrument-generated data method(Antweiler and Taylor 2008). In summary tables in AppendixB (available on the HEI Web site), negative and zero samplevalues are represented with a dashed line (---). For log trans-formations, zero values and negative values were replacedwith a value at half the instrument detection limit.
Continuous Samples
Appendix A presents the results of the collocation of thePAS 2000CE instruments used in this study. Also inAppendix A is an analysis comparing the PAS 2000CEcontinuous values of pPAHs with the sum of 15 PAHsderived from the 12-hour PUF sampling.
The storage system for O3 data had a software flaw thatwould reset the data logger time stamp to the previous datewhen the instrument was turned off for routine zero, param-eter checks, and downloads. The date on the display readcorrectly, while the date on the data logger returned to thedate it had been set to initially. This resulted in multiplereadings for the same date and time, which made the dataunreliable. The source of the error was not discovered untilthe end of the summer 2005 campaign, when we realizedthat to correct the data logger time stamp, we needed to runthe instrument for 10 minutes and shut it off again. After therestart, the instrument would log the correct date. We havesince learned from other users of the instrument that thissoftware flaw is common to this model. We discovered thatthe manufacturer was aware of the problem yet made noeffort to notify its customers of the software flaw. The O3 datafrom the winter 2006 campaign are not presented becausethe levels are at or near the LOD of the instruments. The CO2instruments had a drift of over 100 ppm, which made detec-tion of any differences between sites questionable. The NOx
monitors did not provide reliable data; these were the firsttwo models produced, and there were issues with zero driftand data logging.
Data Management
Integrated samples were sent to laboratories for analysis.Data were returned to the manager of quality assuranceand quality control, who performed blank corrections, cal-culated relative standard deviations for precision,inspected laboratory-reported recovery rates, and vali-dated samples against control standards in the qualityassurance plan. A data set for each sampling period con-taining all integrated measures was created usingMicrosoft Access software.
Continuous measurements were downloaded at the endof each day or as often as possible to ensure data capture.Files were descriptively named to include the instrumentserial number and type and a location identifier. Thenthese files were read into SAS to remove duplicates, cleanextraneous fields, add site and location information, andmerge the data on the date and time of the sample. P-Trak(UFP) data with values below 500 particles/cm3 and Dust-Trak (PM2.5) data with negative values were deleted fromthe cleaned data set. Data from the fixed-site sampling andfrom mobile monitoring were placed in separate data sets.Ten-minute averages of fixed-site continuous data werecalculated. Only complete 10-minute periods were used indata analyses.
DustTrak continuous data were adjusted using correc-tion factors derived from integrated samples collected atthe fixed sites. For daytime samples the ratio of the Dust-Trak 12-hour mean to the corresponding PM2.5 concentra-tion from the 12-hour integrated sample was used as thecorrection factor. For nighttime samples the average ofthese ratios for the previous-day and the next-day inte-grated samples was used.
GPS coordinates were converted to spatial data setscompatible with GIS software. The GPS devices wouldoccasionally lose contact with one or more satellites,resulting in deterioration of their accuracy. Inaccuratelocations were identified by flagging points that deviatedfrom what would be expected from a typical walking pace,for example, more than 100 m from the location recordedfor the previous minute. Flagged points that veered awayfrom the expected sampling routes were deleted. If fewerthan four points in a row were deleted during a periodwhen the instruments were traveling in a straight line at asteady pace, then they were replaced by points interpo-lated from the previous and next accurate points. After theGPS location data were cleaned, they were linked to the
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corresponding continuous monitoring data by route, date,and time to the nearest minute.
Accurate spatial data sets for streets were obtained from“NYS Streets for Erie County” (New York State GIS Clear-inghouse 2006). The study streets parallel to the PeaceBridge plaza were manually selected and merged by streetname. In order to calculate summary statistics by street,GIS software was used to assign mobile locations within30 m of study area streets, to the nearest street.
All measurements were identified by fixed samplingsite or mobile monitoring route, type of measurement, lati-tude, and longitude, as well as being time-stamped usingofficial government time.
Hourly traffic counts for cars and trucks by direction oftravel were obtained from the Peace Bridge Authority.Meteorologic data recorded every minute were obtainedfrom the GLC Davis Weather Station. These data were aver-aged for 10-minute and 1-hour intervals. Vector additionwas used to average the wind directions.
The Internet-based Hybrid Single-Particle LagrangianIntegrated Trajectory (HYSPLIT) Version 4.8 was used tocalculate 24-hour trajectories of air parcels every 6 hours forthe study sampling periods (National Oceanic and Atmo-spheric Administration [NOAA] 2006). The model outputcontained hourly latitudes and longitudes for each airparcel trajectory. These were converted into arcs in a GIS-compatible format (shapefiles), which could be displayed inrelation to potential pollution sources such as major citiesand to geographic features such as the Great Lakes.
Traffic counts and meteorologic variables were matchedto all ambient measurements by time of day and were aver-aged when necessary. Units for concentrations are reportedas follows: PM2.5, PM10, VOCs, carbonyls, and EC-r inµg/m3; pPAHs, PAHs, and elements in ng/m3; NPAHs inpg/m3; UFPs in particles/cm3; and O3 and NO in ppbv.
STATISTICAL METHODS AND DATA ANALYSIS
The Peace Bridge Plaza Study was designed to identifyMSATs emanating from an area of concentrated traffic.Taking advantage of the geographic location on the eastshore of Lake Erie and the prevalent winds from the westand southwest direction, we structured the analysisaround sets of cases. Analysis focused on contrasting airpollutant concentrations measured at the site that wasupwind during lake-wind conditions (GLC site) with con-centrations measured at the downwind site immediatelyadjacent to the plaza (Chapel site). When the winds blewfrom an easterly direction, the upwind and downwinddesignations for these two sites reversed. Consistent with
the meteorologic records for this area, lake winds weremost prevalent, particularly in the warm season. Thus,there were twice as many “lake-wind” cases as “city-wind”cases. Furthermore, the imbalance in the percentages oflake-wind and city-wind cases between summer andwinter campaigns prevented analysis by season.
Groupings of analytes possibly associated with traffic atthe bridge were established to explore differences betweensites. Pearson product-moment correlations helped toidentify relations among analytes both within and betweensites. A simple t test on log-transformed data establishedsignificant differences among analytes. The correlationstructure for VOCs was obvious for BTEX compounds, soadditional analysis was not done.
Principal component analysis was applied to the ele-mental data to derive linear combinations of the originalvariables with orthogonal eigenvectors. Exploratory anal-yses included 3, 4, 5, and 10 vector spaces, but given thelimited number of cases available for this analysis, thePRINCOMP procedure for principal component analysisin SAS (Version 9.0) provided useful interpretation.
PAH and NPAH data were analyzed by positive matrixfactorization using an EPA model. Windows-based PMFsoftware developed by the EPA (EPA PMF 1.1) is used as areceptor model to solve a least squares problem using themeasured concentration and concentration uncertainties.PMF solves the following equation:
xij = )(gik fkj) + eij (1)
where xij is the concentration of species j in sample i, fkj isthe chemical profile of j species in kth factor, gik is thesource contribution of kth factor in sample i, and eij is themodel-generated error of species j in sample i. This equa-tion is solved to minimize the sum of squares described as:
Q = ))(eij/sij)2 (2)
where sij is the input error estimate of species j in sample i(concentration). EPA PMF 1.1 is set in robust mode to pre-vent the outliers from influencing the fitting of the contribu-tion and profiles (Eberly 2005). Species not detected in eachsample are replaced by one half of the method detectionlimit (MDL), which is three times the average mass of ana-lyte in the field blanks divided by sample volume. Errorestimates are determined based on the following equation:
sij = [(*i xij)2 + (#i DLi)2]1/2 (3)
where *i is the measurement uncertainty for species j (frac-tion), #i is the MDL uncertainty for species j (fraction), andDLi is the MDL for species j (concentration) (Brinkman etal. 2006). The standard deviations of blank and sample
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surrogate recovery values are used for * and #. In thisstudy, equation 3 was modified to incorporate the errorassociated with the quartz filter (QF) and PUF/XAD-2 sam-pling component, in addition to the error associated withvolume measurement in each sample i. The propagation oferror was used to modify the equation to:
where s$ij is the error estimate of mass (QF + PUF/XAD-2)for species j in sample i, QFij is the mass measured on thefilter for species j in sample i, PUF/XAD-2ij is the massmeasured in the PUF/XAD-2 for species j in sample i, +i isthe volume measurement uncertainty for species j, and vijis the volume of species j in sample i. The error estimatefor values below the MDL was multiplied by a factor of 10to down-weight their influence.
The theoretical optimal Q value equals the number ofdata points in the concentration data set. The Q value isused as a guide to determine whether the model is appro-priate for the data and the uncertainties specified trulyreflect the data (Eberly 2005). The optimal number ofsources is a balance between the Q value and the explain-able source profile. As the number of sources increases,the calculated Q value decreases (i.e., is closer to the theo-retical Q value). However, this also increases the numberof unexplainable sources. Each PMF factor is identified byits profile and temporal pattern.
PMF was also applied to the elemental data sets but didnot provide additional information beyond principal com-ponent analysis.
Continuous measurements of pollution variables wereanalyzed as 1-minute, 10-minute, or 1-hour averagesdepending on the circumstance. Because the UFP and pPAHdata, both from fixed-site sampling and from mobile moni-toring, were not normally distributed, we examined differ-ences with nonparametric methods, using both the Wilcoxonrank sum test and the Kruskal-Wallis test for one-way anal-ysis of variance. These tests were used to examine differencesbetween fixed sampling sites, between streets, and betweenzones for periods of time categorized by wind direction.
Wind speed, traffic counts for cars and trucks, and sitedifferences in pollutant concentrations were compiled ashourly averages. Univariate and multivariate analyseswere applied to untransformed and transformed predictorvariables in an attempt to explain variations in differencesof UFPs and pPAHs between the measurements made atthe Chapel site and the GLC site.
GIS software was used for exploratory analysis of the con-tinuous data collected during the summer 2005 and winter2006 campaigns according to the mobile monitoring routes.
ArcGIS 9.2 Geostatistical Analyst Extension was used tocreate surfaces representing UFP concentrations measuredusing P-Traks and pPAH concentrations measured usingPASs. Ordinary kriging using K-Bessel functions,assuming anisotropy, and typically using 50 neighbors(and at least 2 neighbors), was used to estimate surfacesfrom the data points. The results were converted to rasterfiles with 10-m cell size masked to display only the studyarea. A series of figures was created for different condi-tions using the same cutpoints.
RESULTS
OVERVIEW AND SUMMARY OBSERVATIONS
Brief descriptions of concentrations measured at each ofthe fixed sampling sites and the relative differences amongsampling locations and campaigns, and between weekendand weekday values and day and night values, are pro-vided here to orient the reader for the following analyses.Full details of the data are found in Appendix B (availableon the HEI Web site).
PM and Carbon
Across the two winter sampling sessions (N = 14 days)and one summer session (N = 10 days), the 12-hour dailyPM10 mean concentration at the Chapel site (23.8 µg/m3)was only slightly higher than that at the School site(22.6 µg/m3) but almost 4 µg/m3 higher than that at theGLC site (19.1 µg/m3). The PM2.5 means at the three sitesreflected similar proportional differences (15.7 µg/m3,14.6 µg/m3, and 13.4 µg/m3 for the Chapel, School, andGLC sites, respectively). The impact of proximity to PeaceBridge traffic is revealed in comparisons of the medianfraction of EC in PM2.5. EC was 17% of PM2.5 at the Chapelsite, 7.6% at the School site, but only 5.4% at the GLC site.The proportion of coarse fraction particles (PM2.5–10) wasapparently higher at the Chapel site as well: 45% of themedian PM10 concentration was coarse fraction at theChapel site versus 35% at the GLC site.
Integrated 48-hour samples were collected over week-ends and compared with the 12-hour daytime samplesdescribed above. Overall, when the number of truckscrossing the Peace Bridge dropped on weekends, levels ofPM10, PM2.5, and EC were lower. The Chapel and Schoolsites had identical PM10 levels (12 µg/m3), while the GLClevel was lower (9 µg/m3). All three sites had similar PM2.5levels (approximately 7 µg/m3), and only the Chapel siteshowed a higher EC level (0.6 µg/m3 versus 0.4 µg/m3 atthe School and GLC sites).
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During the summer and winter campaigns, we ran inte-grated daytime samplers (7:00 AM to 7:00 PM) for 5 daysand integrated nighttime samplers (7:00 PM to 7:00 AM) for5 nights over each of the 2 weeks. As expected, nighttimePM10 and PM2.5 levels were lower, with the difference pri-marily in the changes in PM2.5. The nighttime EC levelswere only slightly lower.
VOCs and Carbonyls
Details for VOCs and carbonyls are provided in AppendixB (available on the HEI Web site). We created a compositevariable to represent BTEX compounds, BTEX plus (BTEXplus 1,3-butadiene, MTBE, and styrene), as well as a vari-able to represent the sum of six chlorinated compounds.The median values for BTEX and BTEX plus were higher atthe School site than at the Chapel site, and the values at theGLC site were less than half of those at the Chapel site. Thebenzene median concentration was 1.1 µg/m3 at the Chapeland School sites and 0.6 µg/m3 at the GLC site. Concentra-tions of chlorinated compounds were similar across allthree sites, indicating an association with the regional airmass rather than local sources. The ratio of four of the BTEXplus compounds consistent with fuel is 3:2:1:0.1 for tol-uene, m&p-xylene, benzene, and 1,3-butadiene. The overallmedian values of the 67 12-hour samples collected were asfollows: toluene, 2.5 µg/m3; m&p-xylene, 1.2 µg/m3; ben-zene, 1.0 µg/m3; and 1,3-butadiene, 0.1 µg/m3.
For the three reported carbonyls, acetone was moreabundant at the GLC site, with median values higher thanthose at either of the other two sites. Acetaldehyde valueswere similar at the GLC and School sites, but these werehigher than the value at the Chapel site. Formaldehydeshowed a different pattern, with the highest median value atthe School site. Overall, the mean 12-hour concentrationswere formaldehyde, 1.2 µg/m3; acetaldehyde, 4.3 µg/m3;and acetone, 5.9 µg/m3 (Table B.4 in Appendix B). Formal-dehyde showed an interesting seasonal pattern: high sum-mertime levels, particularly at the School site wheresummer levels were five times the winter levels. Con-versely, at the GLC site winter levels were twice thesummer level, which most likely reflects the influence ofurban air at the GLC site in the winter, but not during thesummer sampling period.
Elements
Overall, we analyzed samples for 28 elements using HR-ICP-MS. Of the 20 elements listed in IRIS as compoundsemitted by mobile sources (see Table 3), 13 were detected.The analysis of elements can provide insight into the sourcesinfluencing west Buffalo. The most abundant element wassulfur, about 1.5 µg/m3, followed by several earth-crustal
elements (sodium, magnesium, aluminum, potassium, cal-cium, titanium, iron, and zinc), which were two to threeorders of magnitude lower in abundance than sulfur.
The elements could be broadly grouped into two catego-ries. One category comprised those that had similarmedian concentrations across the three sites, which werethe coal and oil-derived elements (sulfur, arsenic, sele-nium, vanadium, and nickel) and another group of ele-ments (beryllium, sodium, potassium, zinc, silver,cadmium, tin, cesium, and thallium). The other categorycomprised elements that displayed substantial enrichmentat the Chapel site (calcium, chromium, manganese, iron,copper, and antimony); the concentrations of these ele-ments at the School site were intermediate between theconcentrations at the Chapel and GLC sites, which rein-forces the suggestion that they are related to sources at theplaza. It should be noted that the winter 2006 samplesfrom the School site were not analyzed for elements forfinancial reasons.
Concentrations of most elements in this study were sim-ilar to or lower than concentrations reported for a NewYork City study using the same analytic method of HR-ICP-MS (Kinney et al. 2002). As expected, nickel, cobalt, andvanadium concentrations were one or more orders of mag-nitude higher in New York City because of residual oilcombustion. Five elements measured during the summer2005 campaign had concentrations consistently higheracross the three sites than median summer values reportedfor 36 sites in New York City; they are cesium, thallium,selenium, scandium, and arsenic. Because of similarvalues at all three sites, we suspect regional coal or indus-trial sources for these elements in the current study. Theclose proximity to traffic at the Chapel site resulted in con-centrations of magnesium, calcium, and chromium thatwere higher (at that site only) than the median concentra-tions in New York City.
There were no clear trends in daytime-to-nighttimecomparisons. Only the combustion elements vanadium,arsenic, potassium, and selenium showed distinctly ele-vated concentrations during the winter (approximately40%), when the west Buffalo sites experienced more city-wind conditions.
PAHs and NPAHs
Of the PAHs lighter than 2,3,5-trimethylnaphthalene,naphthalene is the most abundant. Levels of naphthalene andmany of the methylated naphthalene compounds were con-sistently higher at the Chapel site, followed by the Schoolsite, and then the GLC site. This pattern was more strikingduring the summer when car traffic was heavier and temper-atures were substantially warmer. Of 14 lower-molecular-weight compounds (naphthalene through acenaphthene in
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Table B.11 in Appendix B), 8 of the compounds’ medianvalues were substantially higher at the Chapel site than atthe GLC site. These include naphthalene, 2-methylnaph-thalene, and biphenyl. The median naphthalene value atthe GLC site was less than 50% of that at the Chapel site,whereas the median naphthalene value at the School sitewas 62% of that at the Chapel site. The median values atthe School site were about 10% and 25% less than thevalues at the Chapel site for 2-methylnaphthalene andbiphenyl, respectively. For the two main sampling cam-paigns, the median naphthalene level at the Chapel sitewas twice as high in the summer (100 ng/m3), when windsfrom the lake sector were most prevalent, as in the winter.Seasonal differences in naphthalene concentrations werenot apparent at the GLC and School sites. (Summary statis-tics for all of the PAH and NPAH samples appear in TablesB.11 through B.14 in Appendix B.)
Three general groupings were identified for the 38 PAHcompounds included in the PMF analysis in the mainreport. These are lightweight compounds characterized byfluorene and phenanthrene, middle-molecular-weightcompounds like the methylated PAH compounds, andheavier-molecular-weight compounds including pyrene,chrysene+triphenylene, and benzo[g,h,i]perylene, amongothers. Concentrations were always lower at the GLC sitethan at the Chapel or School sites; however, they were nothigher at the Chapel site than at the School site. During thesummer 2005 campaign, most of the lightweight and meth-ylated compounds were actually higher at the School site,while compounds heavier than benzo[k]fluoranthene wereroutinely higher at the Chapel site. The pattern was dif-ferent during the winter 2006 campaign. Fluorene andphenanthrene were still higher at the School site, but mostof the methylated PAHs were higher at the Chapel site.
Tunnel and truck-stop studies and emissions tests havecharacterized PAH and NPAH emitted from diesel engines.The profiles vary for many reasons including fuel, enginedesign, emission controls, engine load, and atmosphericconditions. Complicating interpretation of atmosphericsamples are PAHs from other sources, including coal andother biomass combustion, and evaporation from asphalt,tars, and lubricants. Among PAH compounds with molec-ular weights greater than 188, several have been identifiedas probable indicators of diesel emissions: among them arefluorene, phenanthrene, fluoranthene, pyrene, methylatedcompounds, anthracene, and the sum of PAHs with molec-ular weights greater than 202 (chrysene+triphenylene,benzo[b]fluoranthene, indeno[1,2,3-c,d]pyrene, andbenzo[g,h,i]perylene).
When the overall means and medians for these com-pounds across the three fixed sites were examined, the
School site concentrations were slightly higher and theGLC site concentrations were slightly lower than theChapel site concentrations (Table B.11 in Appendix B).Summing the mean values for the methylated compoundsalong with the mean for anthracene shows that the Schoolsite level (20 ng/m3) was actually twice the level of the GLCsite (9.4 ng/m3) and 60% higher than the sum of the meansat the Chapel site (12 ng/m3). Comparing coronene, whichhas been associated with gasoline emissions, across oursites, revealed that the median concentration for the Chapelsite was six times the GLC site median and twice the Schoolsite median. The median values for retene, a reportedmarker for wood (Ramdahl 1983), were similar at theChapel and School sites and slightly lower at the GLC site.
Diesel truck traffic decreased substantially over the week-ends, while car traffic over the bridge did not. The weekendcar traffic was substantially higher during the summer sam-pling session. When all 72 of the 12-hour daytime sampleswere compared with the 9 weekend samples for diesel-related compounds (naphthalene, 2-methylnaphthalene,biphenyl, fluorene, phenanthrene, benzo[b]-flouranthene,chrysene+triphenylene, benzo[g,h,i]perylene, andindeno[1,2,3-c,d]pyrene), most had weekday valuesapproximately twice the weekend median values (TableB.12 in Appendix B). The median value of coronene wasnearly three times the weekday median. The higherweekend median for coronene suggests a higher contribu-tion from gasoline-related PAHs to overall PAH levelsduring the weekend.
The 12-hour integrated samples collected on weekdaysand on weeknights during the summer 2005 campaigncontrast daytime and nighttime conditions. For dieselmarkers (methylated phenanthrene/anthracene, chrysene+triphenylene, and benzo[g,h,i]perylene), the levels mea-sured at the Chapel site were somewhat higher at night(Table B.13 in Appendix B). The number of truck crossingsat night was around 20% of the daytime number, but windspeeds and the height at which pollutants mix in the airdrop off at night, which may have increased their concen-trations near the plaza. In general, the School site showedhigher concentrations for these marker compounds duringthe day. This pattern is consistent with more transportaway from the plaza and more local PAH sources or evapo-rative emissions during the day.
According to a truck-stop sampling study (Minegishi2007) and other studies (Schuetzle et al. 1982; Paputa-Pecket al. 1983; Zielinska et al. 2004; Havey et al. 2006), the fol-lowing NPAHs are possible indicators of diesel emissions:1- and 2-nitronaphthalene, 1- and 2-nitropyrene, 9-nitro-anthracene, and nitrophenanthrene. Detailed analysis inthe current study was restricted to 19 NPAHs that were
3030
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
more reliably and frequently detected. During the summer2005 campaign when the predominant wind direction wasfrom the GLC site across the Peace Bridge plaza to theChapel site (lake winds), NPAHs were about 100 to1000 times less abundant than PAHs overall. Among theNPAHs, four compounds had median concentrations greaterthan 10 pg/m3 at the Chapel and School sites: 1- and 2-nitro-naphthalene, 2- and 3-nitrobiphenyl, 5-nitroacenaphtha-lene, and 9-nitroanthracene (Tables B.14 and B.22 inAppendix B). In addition, at the School site the 2-nitrofluo-ranthene median concentration was greater than 10 pg/m3
and the 2-nitrobiphenyl concentration was greater than200 pg/m3. The median values for NPAHs measured at theGLC site were all less than those at the other two sites.(Winter results are not discussed because winter 2006 sam-ples at the School site were not analyzed for NPAHs.)
The differences in the summer 2005 median concentra-tions of NPAHs across the sites are better indicators ofdiesel emissions near the Chapel site than the differences inmedian PAH concentrations (Table B.14 in Appendix B).Compared with the median values at the GLC site, theChapel site median concentrations were 8 or more timeshigher for both 9-nitrophenanthrene and 1-nitropyrene;approximately 1.9 to 4.9 times higher for 1- and 2-nitronaph-thalene and 2-nitroanthracene; and 1.5 to 2.5 times higherfor 2-nitrofluorene and 9-nitroanthracene. For 9-nitroan-thracene, the School site’s median value was slightlyhigher than the Chapel site’s value. The School site hadhigher median values for 2-nitrobiphenyl, 5-nitroacenaph-thalene, 3-nitrophenanthrene, 2-nitrofluoranthene, and2-nitropyrene.
Five of the six NPAH diesel markers had substantiallyhigher values at the Chapel site. The one exception was2-nitropyrene, for which the median value at the Schoolsite exceeded the value at the Chapel site by more than afactor of two. The median value of 2-nitrofluorene at the
Chapel site was twice the median values at the GLC andSchool sites, suggesting that 2-nitrofluorene might beanother marker for diesel emissions.
CONTRIBUTIONS OF PEACE BRIDGE PLAZA ACTIVITIES TO LOCAL AIR CONTAMINANTS
An objective of the Buffalo Peace Bridge Study was toidentify air contaminants possibly associated with local-ized congestion of traffic, with specific attention to thosecontaminants labeled as MSATs. Our first analyses wereundertaken to broadly identify contaminants found inhigher concentration at the Chapel site next to the PeaceBridge plaza. To help clarify local source impacts, we clas-sified all our 12-hour sampling periods based on persis-tence of wind direction as lake wind, city wind, or otherwind. Summary statistics of all samples collected in thewinter 2005 pilot study and summer 2005 and winter 2006campaigns can be found in Appendix B.
Traffic Data
As expected, hourly traffic data from the Peace BridgeAuthority showed temporal variability, with consistent pat-terns for diurnal, weekday, weekend, holiday, and seasonaltraffic. We first compared the hourly traffic counts during thespecific periods we selected for our field campaigns withannual and seasonal counts (Table 9). Figure 5 displays thecumulative distributions of vehicle counts (total traffic,trucks, and cars) for the three sampling sessions comparedwith seasonal averages for summer 2004 and winter 2004–2005, and annual averages for 2004. The hourly truck andcar counts for the winter 2005 campaign are displayed inFigure 6, summer 2005 in Figure 7, and winter 2006 inFigure 8. As shown in Table 9 and displayed in Figure 5, thecar and total traffic counts during the sampling sessions arerepresentative of the respective seasonal counts. Wilcoxonrank sum tests showed that total traffic during sampling
Table 9. Peace Bridge Authority Hourly Traffic Counts: Annual, Seasonal, and During Sampling Campaigns
Figure 5. Cumulative distributions of hourly counts of total traffic, trucks, and cars on the Peace Bridge during sampling sessions, compared withseasonal and annual data.
3232
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
campaigns did not differ from seasonal counts; however,truck counts during the winter 2005 and 2006 samplingcampaigns were slightly, but not significantly, higher thanthe comparison seasonal (winter) truck count.
Meteorologic Conditions
The general air mass transport across the Buffalo regionwas determined by trajectory analysis. HYSPLIT backwardtrajectories showing the paths of air arriving in Buffalo at mid-night, 6:00 AM, noon, and 6:00 PM are displayed in Figures 9through 14. Also shown are the 10-minute wind vectorsacross the sampling periods as recorded at the GLC meteoro-logic tower. The trajectories and wind vectors are presentedcorresponding to each week in the sampling campaigns: win-ter 2005 (Figures 9 and 10), summer 2005 (Figures 11 and12), and winter 2006 (Figures 13 and 14).
The following criteria were used to classify the 12-hourdaytime monitoring period as being representative of lakewinds, city winds, or other winds. A sampling day was
classified as a lake-wind day when for 75% of the time thewind vector was between 180" and 280". Because windswere more variable when blowing from an easterly direction,a city-wind day was defined less rigidly as a day that had awind vector between 1" and 179" for at least 25% of the time,but did not have wind blowing from the lake for more than5% of the time. Other-wind days either had a strong north-erly component, a vector between 281" and 360", or werequite variable and failed to meet the definition of the othertwo cases. We note that downtown Buffalo at the U.S. shore-line of Lake Erie is actually to the south-southeast of thePeace Bridge plaza. Further, the southern area of Ontario,Canada, is actually west of the plaza. So we defined “lakewinds” to represent conditions when plaza emissions werelikely to affect the adjacent neighborhood defined as ourstudy area. Our classification scheme is generally supportedby radial plots that show the 10-minute differences in pollut-ant concentrations between the GLC site and the Chapel site,by wind vectors (see Figures 34 and 40).
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Figure 9. Backward air mass trajectories and wind directions for the first week of the winter 2005 sampling session.
Figure 10. Backward air mass trajectories and wind directions for the second week of the winter 2005 sampling session.
35
J. Spengler et al.
35
Figure 11. Backward air mass trajectories and wind directions for the first week of the summer 2005 sampling session.
Figure 12. Backward air mass trajectories and wind directions for the second week of the summer 2005 sampling session.
3636
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Figure 13. Backward air mass trajectories and wind directions for the first week of the winter 2006 sampling session.
Figure 14. Backward air mass trajectories and wind directions for the second week of the winter 2006 sampling session.
37
J. Spengler et al.
37
The backward trajectories of the air mass for midnight,6:00 AM, noon, and 6:00 PM were used to characterize therecent history of the air being sampled and to confirm theclassification described above. In addition, days were clas-sified by recent air mass origin in an effort to understandbetter the background concentrations of the constituents ofthe regional background air. Table 10 summarizes the windclassifications and air mass origins for 12-hour weekdaysampling periods during the different campaigns. Out of apossible 24 weekdays during our sampling campaigns, 15had lake-wind conditions, which is consistent with his-toric wind rose data for the Buffalo area.
Considering all the sampling periods for the winter 2005,summer 2005, and winter 2006 field campaigns, we had 24possible 12-hour weekday sampling periods. Table 11,derived from data on air mass trajectories (Figures 9through 14), lists the local wind classification during each12-hour weekday sampling period. In the summer, thewinds were more frequently from the southwest and west;hence, only one summer sampling period was classified ashaving city/other winds.
Overall, we had 15 lake-wind samples and 9 city-windor other-wind samples. These last two categories werecombined for the subsequent analysis and termed as“city/other winds.” We examined analytes by site as wellas between sites, testing for differences and exploring cor-relations. First, all sampling days were combined, andthen they were examined by the classifications discussedabove. Composite variables were created by grouping someof the analytes.
PM2.5 Mass, EC-r, and Sum of All PAHs
At the highest level of aggregation, we examinedupwind-downwind relationships for PM2.5, EC-r, and thesum of PAHs at the GLC and Chapel sites. Table 12 con-trasts the median, minimum, and maximum concentra-tions and the between-site (Chapel-to-GLC) ratios for thesethree composite variables. Concentrations at the Chapelsite were slightly higher than those at the GLC site forcity/other-wind sampling days. The between-site ratios forPM2.5 were higher for lake-wind days than for city/other-wind days. The ratio of the Chapel site median EC-r value to
Table 10. Summary of Local Wind Directions and Air Mass Origins for All 12-Hour Weekday Sampling Periodsa
Season
Local Winds Air Mass Origin
LakeWinds
CityWinds
OtherWinds
Hamilton, ON / Toronto, ON
(NW, N)
Detroit, MI / London, ON
(W)Ohio(SW) Other
Summer 9 1 5 3 2Winter 6 6 2 1 3 8 2
a Values are number of sampling periods. Total = 24.
Table 11. Wind Direction on Days That 12-Hour Weekday Integrated Samples Were Obtained
Winter 2005 Summer 2005 Winter 2006
Date Winds Date Winds Date Winds
January 11 City / other July 18 Lake January 9 LakeJanuary 12 City / other July 19 Lake January 10 LakeJanuary 13 City / other July 20 Lake January 11 LakeJanuary 14 City / other July 21 Lake January 12 Lake
July 22 Lake January 13 City / other
July 25 Lake January 16 City / otherJuly 26 Lake January 17 City / otherJuly 27 City / other January 18 LakeJuly 28 Lake January 19 City / otherJuly 29 Lake January 20 Lake
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
the GLC site median shows levels were 3.42 to 7.42 timeshigher over the 13 lake-wind sampling events (2 lake-winddays had incomplete or missing values for one of the threemeasurements and are not included in Table 12). TheChapel-to-GLC ratio range for EC-r on city/other-wind dayswas 0.95 to 3.86. The between-site ratio for the sum of all
PAHs for lake-wind days ranged from 0.83 to 4.51, with amedian value of 2.67. EC-r was 10% of the PM2.5 at theChapel site under lake-wind conditions. Under city/other-wind conditions, EC-r was 10% of PM2.5 at the GLC site, ascompared with only 2% under lake-wind conditions.Traffic activities between the shoreline and the PeaceBridge plaza increased the PM2.5 in the air by about2 µg/m3, which is almost entirely accounted for by theincrease in EC.
Figure 15 rank orders all 22 sampling days with validmeasurements according to the ratio between EC-r concen-trations at the Chapel and GLC sites, and also shows theChapel-to-GLC ratios for the sum of PAHs. Note thatcity/other-wind days predominate across the lower ratiosand lake-wind days across the higher ratios. In general, forthe sum of PAHs, the lower ratios are to the left whilehigher ratios are to the right. Closer scrutiny of the PAHand NPAH compounds will help clarify which compoundsbehaved more like EC.
VOCs and Aldehydes
Log-transformed values for each of the 19 VOCs and threecarbonyls were tested for significance of differences betweenthe Chapel and GLC sites (Table 13). In addition, three com-posite variables were created: the sum of the BTEX com-pounds; the sum of BTEX and 1,3-butadiene, MTBE, andstyrene (BTEX plus); and the sum of all six chlorinated com-pounds. BTEX, BTEX plus, and all the individual com-pounds except MTBE differed significantly between sites
Table 12. Sum of PAHs, PM2.5, and EC-r Values by Site and Wind Direction, with Between-Site Ratios
Figure 15. Chapel-to-GLC ratios for the sum of PAH measurements andEC-r measurements: 12-hour weekday integrated samples (N = 22).
39
J. Spengler et al.
39
under lake-wind conditions. In addition, the methylatedhexane and pentane compounds were also significantlyhigher at the Chapel site for the analysis of log-transformeddata. These compounds and the BTEX compounds werenot correlated between sites under lake-wind conditions.However, all the BTEX compounds were correlated(Pearson product-moment correlation coefficient r > 0.8)when there were city/other winds. Mobile sourcesappeared to affect both the Chapel and GLC sites undercity/other-wind conditions; however, there must be somedilution of city and plaza mobile-source pollution by thetime the air reaches the GLC site. This is inferred becausethe Chapel-to-GLC mixing ratio for BTEX compounds wasstill greater than one, even for non-lake-wind conditions.
Under lake-wind conditions there was no evidence thatthe mobile sources from the Peace Bridge added to thechlorinated compounds or the carbonyls. Concentrationsat the Chapel and GLC sites were highly correlated forlake-wind conditions but not at all for city-wind condi-tions. One can see in Table 13 that the median ratios for1,1,1-trichloroethane, carbon tetrachloride, and trichlore-thene were close to unity for both city/other-wind andlake-wind days. From the Chapel-to-GLC site ratio forchloroform, it appears that chloroform increased betweenthe GLC and Chapel sites on lake-wind days. Buffalo’sintake station for drinking water is just south of the GLCsite. With southwest (lake) winds, emissions from theplant would miss the GLC site but affect the Chapel site.
Table 13. Statistical Values for Chapel-to-GLC Ratios of VOCs by Wind Directiona
Analyte
Log-Transformed Data Nontransformed Data
(Ln) P (t Test) Pearson r City / Other Winds Lake Winds
a Values in bold: P < 0.05; Pearson r value > 0.8; city/other winds, mean or median ratio less than 1; and lake winds, mean or median ratio greater than 1. City/other winds, N = 7; lake winds, N = 14.
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
There were three days in 2005 (January 12, July 20, andJuly 28) when chloroform was substantially higher at theChapel site than at the GLC site. On each of these daysthere were several hours during the sampling period whenthe winds were from 210!, which would favor transportfrom the water intake station. Table 14 presents themedian, minimum, and maximum concentrations of car-bonyls and chlorinated compounds and Chapel-to-GLCratios for lake-wind and city/other-wind days.
Figure 16 shows the distribution of Chapel-to-GLCratios for concentrations of the VOCs, three carbonyls,PM2.5, and EC-r for city/other-wind and lake-wind days.Many of the nonchlorinated compounds were higher at theChapel site than at the GLC site for city/other-wind days.For the lake-wind days, BTEX and the methylated hexanesand pentanes were clearly elevated at the Chapel site, butchlorinated and carbonyl compounds were not.
Table 14. Chapel and GLC Site Concentrations (µg/m3) of BTEX, BTEX Plus, Formaldehyde, Acetaldehyde, and Chlorinated Compounds by Wind Direction, with Between-Site Ratios
b Formaldehyde values in the summer had only one ratio in which both pairs were above the LOD.
c Values are for the sum of chlorinated compounds: chloroform, 1,1,1-trichloroethane, carbon tetrachloride, trichloroethene, tetrachloroethene, and 1,4-dichlorobenzene.
41
J. Spengler et al.
41
Figure 16. Box and whisker plots of Chapel-to-GLC ratios for VOC, PM2.5, and EC-r values on city-wind days (N = 7 pairs) and lake-wind days (N = 14pairs). Minimum, bottom whisker; 25th percentile, bottom of the box; median, center line in the box; mean, “x”; 75th percentile, top of the box; andmaximum, top whisker. For city/other-wind days all compounds, except chloroform, had at least 5 pairs of samples above LOD (chloroform is not shown).For lake-wind days formaldehyde, 1,4-dichlorobenzene, acetone, and trichloroethene had 5 of 14 sample pairs above LOD. Compounds are ranked by themedian ratio.
4242
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Acrolein
At the Chapel site the highest acrolein level measuredby the University of California–Davis method (Seaman etal. 2006) was 0.46 µg/m3 (Table 15). In the University ofCalifornia–Davis samples, acrolein levels were higher atthe Chapel site than at the GLC site, with the highest levelsbeing found during times of heavy traffic (Figure 17). Con-currently with the University of California–Davis effort,we sampled for acrolein using the Environmental andOccupational Health Sciences Institute (EOSHI) DNSHpassive method (Zhang et al. 2000) on consecutive week-days. We employed a nested sampling strategy in which apair of samples was exposed for 4 days, a second pair wasexposed for 3 days, a third pair was exposed for 2 days,and a final pair was exposed for 1 day. The acrolein levelsin the samples, though higher than the field blanks, werearound the filter detection limit of 0.01 µg/m3 reported byEOSHI. Table 16 presents the results of the DNSH analysis.The levels measured in the current study were comparableto those that Weisel and colleagues (2005) obtained in theRelationships of Indoor, Outdoor, and Personal Air(RIOPA) study using a DNSH-based sampler. Theyreported median concentrations outdoors in Los Angeles,
California; Elizabeth, New Jersey; and Houston, Texas, of0.4, 0.39, and 0.95 µg/m3, respectively. In the currentstudy the range of values obtained using the University ofCalifornia–Davis method was 0.01 to 0.46 µg/m3 and therange obtained using the EOSHI DNSH method was 0.19 to0.96 µg/m3. After assessing the acrolein levels measuredby the University of California–Davis method, we chosenot to analyze any of the acrolein samples collected foranalysis by OSHA Method 52 because the detection limitof the OSHA method, based on the sample volumes col-lected, was 1 µg/m3, which is substantially higher than the
Table 15. University of California–Davis Acrolein Sample Summary (10-Minute Samples), Summer 2005
a We employed a nested sampling strategy: a pair of samples was exposed for 4 days, Monday to Friday; a second pair was exposed for 3 days, Monday to Thursday; a third pair was exposed for 2 days, Monday to Wednesday; and a final pair was exposed for 1 day, Monday to Tuesday.
43
J. Spengler et al.
43
highest reported value obtained using the University ofCalifornia–Davis method.
Elements
Elements analyzed in the 12-hour integrated PM2.5 sam-ples were resolved by HR-ICP-MS. Mean and median ratiosof values at the Chapel site to those at the GLC site wereexamined for city/other-wind and lake-wind days. We cal-culated Pearson correlations for pairs of measurements, aswell as testing for significant difference in the log-trans-formed and nontransformed data (Table 17). Nine elementshad median ratios greater than 1.75 for lake-wind days (alu-minum, calcium, titanium, chromium, iron, cobalt, copper,strontium, and antimony). Of these, only titanium andcobalt had a significant correlation (Pearson r > 0.80 for log-transformed values) between sites on lake-wind days. Oncity/other-wind days, the median ratio was 1.0 ± 0.24 for allelements except chromium (4.26), iron (1.32), and copper(2.23). The values for copper at the two sites were uncorre-lated (Pearson r < 0.80 for log-transformed values) and sig-nificantly different only for lake-wind conditions.
A second set of elements (sodium, magnesium, potas-sium, manganese, nickel, tin, and lanthanum) had signifi-cantly different Chapel-to-GLC ratios on lake-wind days,between 1.25 and 1.75, and on city/other-wind days theyhad ratios near one (1.0 ± 0.16), except nickel had a medianratio of 0.78. Similar to the first set, these elements were sig-nificantly different between sites for the lake-wind days butnot for the city/other-wind days. Neither zinc nor silver dif-fered significantly between sites for either of the classifiedwind conditions.
Elements measured at the two sites that were highly cor-related (Pearson r > 0.80 for log-transformed values) onlake-wind days and did not show enrichment downwindof traffic are most likely associated with the regional airmass as opposed to local sources. Sulfur and arsenic meetthese criteria; their Chapel-to-GLC ratios for city/other-wind days were also near unity, and their correlation coef-ficients were also high (> 0.80). These elements have beenidentified as tracers for fossil fuel.
Figure 18 plots the distributions of Chapel-to-GLC ratiosfor 28 elements, PM2.5, PM10, and EC-r for samples col-lected on days with lake-wind and city/other-wind condi-tions. On city/other-wind days, only for EC-r, chromium,and copper were 75% of the between-site ratios greaterthan unity. EC has many urban sources, and the lower con-centration at the GLC site reflects dilution as air flows fromthe city and plaza toward Lake Erie. The reason for highcopper and chromium ratios for days when winds werenot from the lake is unknown. On lake-wind days, the dis-tributions suggest that the Peace Bridge plaza traffic
resulted in an increase of calcium, iron, zinc, chromium,copper, aluminum, antimony, strontium, cobalt, titanium,manganese, magnesium, EC-r, and PM10. Other elements,although to a lesser extent, also had 75% or more of thepaired ratios greater than unity on lake-wind days.
Principal component analysis was applied to the ele-mental data in an attempt to confirm element groupingsobserved from correlations and ratios of differencesbetween sites. Analysis of 25 elements was carried outusing the PRINCOMP procedure in SAS (Version 9.0).Chromium and thallium were excluded because of the lownumber of samples in which they were detected on lake-wind days. We used this analysis to explore whether thenumber of variables could be logically reduced by clus-tering elements as linear combinations. The first principalcomponent is the best fit to the data as measured by thesum of squared perpendicular distances from each datapoint to the vector.
In our application the next vector is orthogonal to thefirst, and so forth. Principal component analysis was per-formed multiple times on the entire data set (52 observa-tions) for subsets classified by sampling site and winddirection. When five factors or components were allowed,a total of 72% of the variance was explained. One compo-nent contained elements indicative of both coal combus-tion and traffic-related emissions (beryllium, sulfur,potassium, titanium, manganese, iron, cobalt, arsenic,selenium, antimony, and lead). A second component com-prised silver, vanadium, nickel, and to some extentsodium and lead, suggesting residual fuel oil. A third com-ponent had magnesium, aluminum, and calcium, which isindicative of crustal material, but the fourth component(sodium and strontium) and the fifth component (tin andcadmium) were not clearly interpretable. Owing to finan-cial constraints, the combined data set was missing thewinter 2006 elemental analysis from the School site,leading to an imbalance in the days and locations repre-sented. For this reason, and because of the limited numberof sample days, the analysis was restricted to three factorswhen examining samples classified by wind sector. Forlake-winds days the three factors explained 65% of thevariance at the GLC site and 69% of the variance at theChapel site.
Table 18 presents the factor loadings of the principalcomponent analysis for elements measured at the Chapelsite under lake-wind conditions. Factor 1 had moderateloadings of sulfur, arsenic, selenium, cadmium, and tin,which were all negatively associated with the other fac-tors. This factor had the marker elements for coal combus-tion and regional transport. A similar first factor was foundfor the GLC data under lake-wind conditions (not shown).
4444
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Tabl
e 17
. St
atis
tica
l Val
ues
for
Cha
pel-
to-G
LC R
atio
s of
Ele
men
ts b
y W
ind
Dir
ecti
ona
Ana
lyte
Log-
Tran
sfor
med
Dat
aN
ontr
ansf
orm
ed D
ata
(Ln)
P (t
Tes
t)Pe
arso
n r
Pear
son
r C
ity
/ O
ther
Win
dsLa
ke W
inds
Cit
y /
Oth
erW
inds
Lake
Win
dsC
ity
/ O
ther
Win
dsLa
keW
inds
Cit
y /
Oth
erW
inds
Lake
Win
dsM
ean
Rat
ioSD Rat
ioM
edia
nR
atio
Mea
nR
atio
SD Rat
ioM
edia
nR
atio
Be
0.30
0.01
0.23
0.89
0.40
0.89
4.25
8.49
1.08
1.38
0.54
1.24
Na
0.16
0.01
0.35
0.80
0.17
0.76
0.90
0.46
0.94
1.64
1.05
1.36
Mg
0.13
0.00
0.56
0.26
0.86
0.40
0.81
0.40
0.88
1.97
1.22
1.72
Al
0.46
0.01
0.73
0.55
0.76
0.52
1.30
1.08
0.91
2.24
1.39
2.17
S0.
160.
110.
850.
930.
980.
970.
880.
320.
941.
100.
221.
12K
0.13
0.05
0.74
0.77
0.93
0.83
0.80
0.33
0.93
1.18
0.28
1.26
Ca
0.23
0.00
0.49
&0.
080.
480.
351.
211.
260.
923.
823.
812.
94Sc
0.31
0.50
0.93
0.45
0.74
0.73
1.16
0.40
1.20
1.77
1.68
1.00
Ti0.
300.
000.
740.
840.
890.
741.
190.
961.
011.
780.
661.
76V
0.19
0.07
0.41
0.96
0.67
0.91
0.93
0.48
0.95
1.17
0.32
1.17
Cr
0.07
0.01
0.03
0.69
0.06
0.88
6.45
6.61
4.26
2.39
1.53
2.18
Mn
0.23
0.00
0.97
0.72
0.96
0.55
1.09
0.23
1.16
1.50
0.33
1.57
Fe0.
120.
000.
870.
440.
950.
161.
390.
711.
322.
561.
042.
54C
o0.
100.
000.
930.
840.
930.
691.
190.
301.
241.
720.
581.
85N
i0.
370.
120.
430.
710.
170.
711.
601.
350.
781.
691.
941.
28C
u0.
430.
000.
240.
030.
47&
0.09
3.87
3.97
2.23
4.53
6.43
2.45
Zn
0.19
0.10
0.55
0.33
0.53
0.04
2.53
3.50
1.02
1.64
1.10
1.44
As
0.12
0.09
0.89
0.92
0.84
0.94
0.79
0.36
0.84
1.09
0.21
1.08
Se0.
170.
050.
980.
980.
980.
950.
950.
180.
901.
080.
171.
07Sr
0.46
0.00
0.85
0.54
0.80
0.64
1.51
1.60
1.00
2.35
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45
J. Spengler et al.
45
Figure 18. Box and whisker plots of Chapel-to-GLC ratios for 28 elements, PM10, PM2.5, and EC-r on city/other-wind days (N = 8 pairs) and lake-winddays (N = 15 pairs). Minimum, bottom whisker; 25th percentile, bottom of the box; median, center line in the box; mean, “x”; 75th percentile, top of thebox; and maximum, top whisker. Compounds are rank ordered by median ratio.
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Factor 2 at the Chapel site had many of the elements thatappeared to be enriched relative to the GLC site (Figure18), namely, magnesium, calcium, iron, copper, and lead.This factor might be considered to be related to the PeaceBridge plaza traffic. The second factor at the GLC siteunder lake-wind conditions included aluminum, manga-nese, and strontium, elements that might be interpreted asreflecting a transported crustal component. Factor 3 at theChapel site had vanadium, nickel, and sodium loadings,which could be indicators of residual oil combustion. Asimilar factor was not apparent in principal componentanalysis for the GLC site.
Principal component analysis has limited application inthis study, but it does help to confirm the grouped patternsof elements suggested by pairwise correlation and by thevalues for the downwind Chapel site in Table 17. We pre-viously reported that the concentrations of several ele-ments were highly correlated at all three sites. This wasinterpreted as indicative of contaminants more uniformlydistributed in an air mass. Arsenic and sulfur were exam-ples of such transported elements. Edge plots are simplescatter plots of ambient concentrations used to visualizedata for source apportionment (Henry 2003). Plottingsulfur as sulfate against arsenic in an edge plot has provedquite interesting (Figure 19).
We used the wind trajectory analysis shown in Figures 9through 14 to classify the general direction for each12-hour daytime sampling period. As shown in Figure 19,when the air mass came from the southwest, the ratios ofarsenic to sulfate were similar at all three sites. With airmasses from the northwest and east, values also tended toplot along this summertime southwest trend. In contrast,when the wind came from the north, the ratio was very dif-ferent, showing much more enrichment in arsenic withrespect to the relatively low sulfate levels. This suggests that
Table 18. Principal Component Factor Loadings for Elements Measured at Chapel Site Under Lake-Wind Conditionsa
b Percentage of the variance explained by the factor.
Figure 19. Edge plot of ratios of arsenic to sulfate concentrations with air mass trajectories from different directions. Data for the north, east, andnorthwest trajectories are for all sites and seasons; data for the southwest trajectory are presented by site and season.
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the northerly air masses have a much more distant and dis-tinct source for arsenic and sulfate. When the wind camefrom the southwest in wintertime, there was little evidencefor an association between arsenic and sulfate at either ofthe two sites monitored, consistent with power plant coalcombustion being much less prevalent in winter.
PAHs and NPAHs
Analyses of PAHs and NPAHs are particularly chal-lenging. These compounds are emitted from a variety ofsources, including vehicles and construction equipment,combustion of fossil and biomass fuels from stationarysources, and evaporation from surface materials. Many ofthem undergo chemical transformations, some of whichare dependent on other constituents in the atmospheresuch as hydroxyl radicals or nitrogen compounds, or onthe presence or absence of sunlight. Parsing our BuffaloPeace Bridge data by site, season, and wind direction pro-vides some insight into the behavior of PAH and NPAHcompounds, but the analysis is limited in statistical power.We first examined the relationship between PAH andNPAH values at the Chapel site and at the GLC site, thenapplied PMF to a subset of the PAH and NPAH data fromall three sites. In support of our study, the Chesapeake Bio-logical Laboratory (CBL) collected 12-hour high-volumedaytime and nighttime PAH samples (obtained usingGraseby Andersen high-volume samplers) at the Chapel,GLC, and School sites for 1 week during the summer 2005campaign. The samples were added to the Harvard datasets to enhance the interpretation of PMF output andexplore diurnal differences. The primary differencesbetween our data sets and those of CBL were that CBL’sanalysis excluded PAHs with molecular weight below 188and had greater sensitivity and temporal resolution due tolarger sampling volumes. The source-related PAH profilesderived for PMF analysis had been collected earlier andwere restricted to compounds with molecular weightabove 188 because the high-volume samplers CBL used tocollect supplemental samples for specific sources and atthe study site did not contain XAD-2 resin or deuteratedsurrogates, which are necessary for collecting the lighter-molecular-weight compounds. The CBL Peace Bridge sam-ples were compared with samples that CBL collected fromother locations using the same method and analyte list.The CBL samples were higher-volume samples that pro-vided higher resolution for diurnal analysis at all sites.
Between-Site Comparison of PAHs by Wind DirectionFigure 20 presents the distributions of Chapel-to-GLCratios for the PAH compounds with at least five pairs ofobservations for city/other-wind conditions, and Figure 21
presents results for lake-wind conditions. The compoundsare rank ordered by the median ratios. Note that for all buta few compounds PAH concentrations were higher at theChapel site when winds were classified as city/other. Onlyazulene, 2,2,5-trimethylnaphthalene, and 1-methylnaph-thalene had ratios close to or less than one. This findingsuggests that many PAHs are added to air masses as theypass over an urbanized area. Median ratios for compoundsthat are likely from diesel emissions ranged from 1.1 to 1.4,with naphthalene and benzo[a]pyrene in the middle at1.25, for city/other-wind conditions. Plaza and highwaytraffic activity added to urban background levels of manyPAHs, but when the wind flow was from the urban areaacross the plaza to the GLC site, the between-site ratioswere lower, suggesting possible dilution. In general, theratios were larger for lake-wind conditions. All of themethylated compounds (lower molecular weights) weresubstantially higher at the Chapel site for lake winds.Because of the previously noted limitation of the high-volume samplers, data for these lighter-weight compoundsare not often reported. Naphthalene, an MSAT compoundassociated with diesel emissions, was up to 10 timeshigher at the Chapel site than at the GLC site during lake-wind events. The concentrations at the two sites were cor-related (at 0.95) for city/other winds and uncorrelated (at0.01) for lake winds. Another PAH associated with dieselemissions, benzo[a]pyrene, showed a similar pattern.
Table 19 shows the correlations and median ratios forselect PAHs. Several PAHs related to diesel combustion showa pattern of nonsignificant correlations between sites andhigher between-site median ratios with lake winds (benzo[a]-anthracene, chrysene+triphenylene, benzo[a]pyrene,indeno[1,2,3-c,d]pyrene, dibenz[a,h+a,c]anthracene, andbenzo[g,h,i]perylene), while winds classified as city/othercarried these compounds over both sites, and thus thebetween-site correlation values were 0.80 or higher.Among the few exceptions were 1,3-dimethylnaphthaleneand 1,4-dimethylnaphthalene, which had higher between-site correlations for lake winds than for city/other winds.These compounds had substantially higher median ratiosfor lake winds. These observations suggest that these PAHcompounds are present in the background air but havestrong local sources at the plaza. Minegishi (2007) alsoidentified benz[a]anthracene, chrysene+triphenylene,benzo[a]pyrene, benzo[g,h,i]perylene, and to a lesserextent dibenz[a,h+a,c,]anthracene as mobile-source pol-lutants. Other studies of diesel emissions in which directexhaust measurements were made also have shown thepresence of these compounds to varying degrees (Zielinskaet al. 2004; Shah et al. 2005). Retene has been associatedwith biomass combustion, but concentrations do not
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Figure 20. Box and whisker plots of Chapel-to-GLC ratios for PAHs and sum of PAHs (N = 8 pairs) with city/other winds. Minimum, bottom whisker; 25thpercentile, bottom of the box; median, center line in the box; mean, “x”; 75th percentile, top of the box; and maximum, top whisker. All compounds haveat least five pairs of samples above the LOD. 3-Methylchloanthrene, 9-methylanthracene, 3,6-dimethylphenanthrene, benzo[b]fluorene, naphthacene,4-methylchrysene, dimethylbenz[a]anthracene, perylene, dibenz[a,h+a,c]anthracene, anthranthrene, and coronene are not shown. Compounds are orderedby the median ratio.
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Figure 21. Box and whisker plots of Chapel-to-GLC ratios for PAHs and sum of PAHs (N = 14 pairs) with lake winds. Minimum, bottom whisker; 25thpercentile, bottom of the box; median, center line in the box; mean, “x”; 75th percentile, top of the box; and maximum, top whisker. All compounds haveat least five pairs of samples above the LOD. 3-Methylchloanthrene, 9-methylanthracene, 3,6-dimethylphenanthrene, benzo[b]fluorene, naphthacene,4-methylchrysene, dimethylbenz[a]anthracene, and anthranthrene are not shown. Compounds are ordered by the median ratios.
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
appear correlated between sites for either wind condition.Coronene, a reported marker for gasoline combustion,showed high correlation between sites for both wind con-ditions. Although only five pairs of samples obtainedunder city/other-wind conditions were available, theChapel-to-GLC ratio of 1.46 indicates that concentrationswere higher at the Chapel site because gasoline in theurban air was diluted somewhat as the air mass movedaway from the city. The median ratio of 2.04 for lake-windconditions, with between-site correlations of 0.8, suggeststhat the level of coronene, which is in the background air,is also enriched by local plaza traffic.
Concentrations of PAHs in city air that are not related tovehicles, as well as MSATs arising from activities at theplaza, were diluted up to a factor of four between theChapel and GLC sites. For example, overall the concentra-tion of 1-methylfluorene was twice as high at the Chapelsite as at either the School or GLC site under lake-windconditions. Yet when the wind came from the plaza to thelake (city/other-wind conditions), the values at the Chapeland GLC sites were similar and highly correlated.
The ratios of median PAH values on lake-wind days tothose on city/other-wind days are displayed for the Chapel
and GLC sites in Figure 22; the Chapel site ratios for thetwo wind conditions are rank ordered, with the GLC siteratios superimposed. The heavier-molecular-weight com-pounds identified with diesel emissions were equally ormore abundant in air from an urban direction (Table B.20 inAppendix B). Ratios of median city/other-wind concentra-tions to median lake-wind concentrations reported at theChapel site were: chrysene+triphenylene 2.5; benzo[b]fluo-ranthene 4.8; benzo[k]fluoranthene 5.0; benzo[e]pyrene 5.0;benzo[a]pyrene 5.0; perylene 2.0; indeno[1,2,3-c,d]pyrene4.8; benzo[g,h,i]perylene 3.5; and coronene 3.3. However,over a third of the compounds had higher concentrations(ratios < 1) in the air coming from the lake and passing overthe Peace Bridge plaza: acenaphthene 0.95; 2,3,5-trimethyl-naphthalene 0.86; fluorene 0.62; 1-methylfluorene 0.64;dibenzothiophene 0.34; phenanthrene 0.48; anthracene 0.61;2-methyldibenzothiophene 0.67; 4-methyldibenzothiophene0.66; 2-methylphenanthrene 0.69; 2-methylanthracene 0.51;4,5-methylenephenanthrene 0.54; 1-methylanthracene 0.43;1-methylphenanthrene 0.49; 9,10-dimethylanthracene 0.45;fluoranthene 0.38; and pyrene 0.58.
At the GLC site, unlike the Chapel site, more of the com-pounds had higher median concentrations with city/otherwinds. However, there were some exceptions, including
Table 19. Statistical Values for Chapel-to-GLC Ratios of PAHs by Wind Directiona
PAH
Log-Transformed Data Nontransformed Data
(Ln) P Value (t Test) Pearson r City / Other Winds Lake Winds
a Values in bold: P < 0.05; Pearson r > 0.8; city/other winds, mean or median ratio less than 1; and lake winds, mean or median ratio greater than 1.
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2,3,5-trimethylnaphthalene, retene, benzo[b]fluorene, anda few others. All the compounds with ratios of medianlake-wind concentrations to city/other-wind concentra-tions greater than 1 at the GLC site also had ratios greaterthan 1 at the Chapel site, suggesting that although plazaemissions might have added to the concentrations at theChapel site, the compounds were already in the air mass.
Analysis by Edge Plots and Ratio-to-Ratio Plots Certainratios between the compounds in emissions are character-istic of specific sources. In an edge plot a straight line repre-senting the ratio between two emitted compounds atdifferent concentrations is plotted for each source. When asingle source is contributing to ambient concentrations,data will fall along the source line. When two sources arecontributing, the ambient data will fall between the twosource lines. Data falling outside the source lines indicate asource is present that is not accounted for. The closer theambient data fall to the line representing the emission ratefor a specific source, the stronger the contribution from thatsource. Diesel, coke, and gasoline emissions have specificratios of benzo[e]pyrene and indeno[1,2,3-c,d]pyrene.Figure 23 shows edge plots of benzo[e]pyrene and
indeno[1,2,3-c,d]pyrene concentrations by season (summerand winter) and by sampling site paired with wind direc-tion. The signal from coke emissions was stronger in thewinter, when there were more city-wind conditions than inthe summer. A strong diesel signal was present at the Chapelsite on lake-wind days compared with city-wind days. Thesame signal was not apparent at the GLC or School sites.Concentrations of benzo[e]pyrene and indeno[1,2,3-c,d]-pyrene were higher on city-wind days than they were onlake-wind days. Edge plots are more difficult to interpretwhen a mixture of multiple sources is present.
Ratio-to-ratio plots include a third dimension of infor-mation that produces more easily interpretable data (Rob-inson et al. 2008). In Figure 24 a ratio-to-ratio comparisonof (benzo[g,h,i]perylene ' 1000)/EC and (indeno[1,2,3-c,d]pyrene ' 1000)/EC shows the strong diesel signal atthe Chapel site during lake-wind days. At the other twosites, the effect of wind direction was not as distinct, andthe data for the GLC and School sites for both wind direc-tions were similar to those for the Chapel site during city-wind days, suggesting that the dilution of the diesel signalwas the dominant process occurring with distance fromthe plaza.
Figure 22. Ratios of median lake-wind PAH concentrations to median city/other-wind PAH concentrations measured at the Chapel and GLC sites.Compounds are rank ordered by Chapel site values.
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Figure 23. Edge plots of benzo[e]pyrene and indeno[1,2,3-c,d]pyrene by season (summer and winter), and by sampling site paired with wind direction.The plots at the bottom are magnifications of the low values in the top plots.
Figure 24. Ratio-to-ratio plots of (benzo[g,h,i]perylene ! 1000)/EC and (indeno[1,2,3-c,d]pyrene ! 1000)/EC. The plot on the right is a magnification ofthe low values in the plot on the left.
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NPAH Analyses by Wind Direction NPAHs in ambientair have been associated with adverse health effects (Areyet al. 1988; IARC 1989; Gupta et al. 1996). NPAHs arebyproducts of combustion (Arey et al. 1987) or are formedthrough the oxidation of PAHs in the atmosphere (Arey1998). Individual NPAHs are specific in origin to eithercombustion or oxidation and thus may help identify pri-mary and secondary aerosol sources (Cecinato et al. 1996;Crimmins and Baker 2006). The truck-stop study byMinegishi (2007) identified 9-nitroanthracene and 1-nitro-pyrene as emissions from diesel engines. Figure 25 showsthe distributions of Chapel-to-GLC ratios of NPAH concen-trations for lake-wind days. The data confirm the presenceof these two compounds at the Chapel site. The medianratios suggest nearly a 10-fold increase for 1-nitropyreneand 9-nitrophenanthrene at the Chapel site. Most of theNPAHs for which there were sufficient paired measure-ments had 75% of the Chapel-to-GLC ratios above unity. Afew compounds, 2-nitropyrene and 9-nitroanthraceneamong them, had little differences in their concentrationsbetween the Chapel and GLC sites for lake-wind conditions,implying that these were not related to mobile sources.
Table 20 lists the NPAH samples above the LOD for bothlake-wind and city/other-wind conditions. For 5-nitro-acenaphthalene, 9-nitroanthracene, 3-nitrophenanthrene,and 2-nitrofluoranthene, all 20 samples (10 at each site)taken with a lake wind blowing had levels above the LOD,but none of the 8 samples taken under city/other-wind con-ditions had detectable levels. Compounds detected pre-dominantly in air from the lake and not in air from the cityor other directions are noted in Table 20. Other compoundsthat were detected primarily in lake-wind directions were1,3-dinitronaphthalene (18 of 20 samples were detectedwith lake winds and 0 of 8 with city/other winds), 2-nitrof-luorene (17 of 20 samples with lake winds and 0 of 8 withcity/other winds), 3-nitrophenanthrene (20 of 20 sampleswith lake winds and 1 of 8 with city/other winds), and 1-nitropyrene (18 of 20 samples with lake winds and 1 of 8with city/other winds). The only NPAH compoundbehaving contrary to the others was 3-nitrofluoranthene: itwas detected at both sites only when the wind came fromthe city/other direction and was not detected at either sitewhen the wind came from the lake.
Diurnal PAH and NPAH Concentrations The diurnalvariations in PAH and NPAH concentrations in Buffaloduring summer 2005 (July 20–26) are summarized inTable 21, which shows daytime and nighttime 12-hour geo-metric mean concentrations in samples collected by CBLusing high-volume Graseby Andersen samplers. Generally,the total PAH concentration measured was higher duringthe day and the total NPAH concentration was elevated at
night at all three fixed sampling sites. The total PAH geo-metric mean daytime concentration varied between sam-pling sites, with the School site having the highestconcentration (84 ng/m3), then the Chapel site (34 ng/m3),and then the GLC site (11 ng/m3). Figure 26 shows a uniquediurnal pattern of PAHs at the School site, where the totalconcentration was higher during the day. No similar consis-tent trend was observed at the GLC or Chapel site. Similarly,the highest NPAH geometric mean nighttime concentrationwas at the School site (317 pg/m3), and the Chapel site mean(233 pg/m3) was higher than the GLC site mean (117 pg/m3).NPAH showed a strong diurnal pattern, with the nighttimeconcentration twice as high as that in the daytime. This pat-tern was relatively consistent among sampling sites.
Positive Matrix Factorization Analysis PMF was per-formed on the seasonal PAH data collected in the winter2005, summer 2005, and winter 2006 sampling sessions.The analysis was applied separately among the samplingsites to characterize the different sources at each site. Spe-cies that were not detected in at least 80% of the sampleswere removed from the analysis. Seventeen PAHs were usedin an analysis with an additional 2% uncertainty error toachieve a realistic Q value. The minimal value for the addi-tional uncertainty error that resulted in the calculatedQ value closest to the theoretical optimal Q value was 2%.The optimal number of sources for the winter 2005, summer2005, and winter 2006 data for each site appeared to be twoor three. The GLC site was characterized by two factors, butthree factors were necessary to sufficiently characterize thesources at the Chapel and School sites.
Figure 25. Box and whisker plots of Chapel-to-GLC ratios for NPAHs (N =10 pairs) with lake winds. Minimum, bottom whisker; 25th percentile,bottom of the box; median, center line in the box; mean, “x”; 75thpercentile, top of the box; and maximum, top whisker.
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Table 20. NPAH Samples above the LOD at the Chapel and GLC Sites by Wind Direction
1,3-Dinitropyrene 0 0 0 0 0 01,6-Dinitropyrene 0 0 0 0 0 06-Nitrobenzo[a]pyrene 0 1 1 3 4 71-Nitrobenzo[e]pyrene 1 0 1 4 6 103-Nitrobenzo[e]pyrene 0 0 0 1 2 3a NPAHs primarily detected on lake-wind days at both sites.
b NPAH primarily detected on city/other-wind days at both sites.
Table 21. Total PAH and NPAH Concentrations Measured at the Three Sampling Sites, Using Graseby Andersen High-Volume Samplers, During July 20–26, 2005a
a Values are the geometric means of 6 samples (except the value for the GLC night samples is the geometric mean of 5 samples).
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Figure 26. Diurnal pattern of total PAH and NPAH concentrations at the GLC, School, and Chapel sites during July 2005, from data obtained usingGraseby Andersen high-volume samplers.
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
The two PAH profiles resolved by PMF were similar forthe GLC, School, and Chapel sites. One of the sources ispredominantly loaded with lighter-molecular-weightPAHs (light PAH profile), and the other source is enrichedwith heavier-molecular-weight PAHs (heavy PAH profile)(Figure 27). The temporal patterns of the light and heavy
PAH profiles were consistent among the sampling sites(Figure 28). The light PAH profile was dominant in thesummer (July), and the heavy PAH profile in the winter(January). The two sources resolved by PMF characterizethe summer and winter profiles common among the sam-pling sites.
Figure 27. Light PAH profile and heavy PAH profile based on lighter-molecular-weight and heavier-molecular-weight PAHs, resolved by two-sourcePMF analysis of seasonal data (winter 2005, summer 2005, and winter 2006 samples). (Figure continues next page)
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Figure 27 (Continued).
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Figure 28. Temporal patterns of light PAH profile and heavy PAH profile concentrations resolved by two-source PMF analysis. (Figure continues next page)
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Figure 28 (Continued).
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
In the three-source PMF analysis, a medium PAH profilewas resolved in addition to the light and heavy profiles(Figure 29). The light PAH profile is dominated by fluoreneand phenanthrene, which have molecular weights of 166and 178, respectively. The medium profile is dominatedby the methylated compounds, with molecular weightsranging from 190 to 198. In the heavy profile, molecular
weights are 216 or greater. The light and heavy PAH pro-files resolved from the GLC, School, and Chapel sites wererelatively similar, but the medium profile varied amongthe sampling sites. As in the two-source PMF analysis, thelight PAH profile was dominated by lighter-molecular-weight PAHs, especially phenanthrene and fluorene,which suggests volatilization (from ground surfaces).
Figure 29. Light, medium, and heavy PAH profiles resolved by three-source PMF analysis of PAH data. (Figure continues on next two pages)
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Phenanthrene, fluoranthene, and pyrene were included inthe heavy PAH profile to determine their associations tothe heavier PAHs, such as chrysene+triphenylene,benzo[b]fluoranthene, and benzo[g,h,i]perylene, whichrepresent combustion sources. Though the medium pro-files differed among the sampling sites, all were enriched
with methylated PAHs. The profile patterns from theSchool and Chapel sites were similar, but there wereapproximately four times the number of individual speciesat the School site. The medium PAH profile represents vol-atilization from surface materials nearby (asphalt pavingand roofing).
Figure 29 (Continued).
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
The temporal pattern showed that the light PAH profilewas more abundant at the GLC site compared with theSchool and Chapel sites (Figure 30). At all of the samplingsites, the light profile had higher concentrations in thesummer than in the winter. The correlation betweenambient temperature and the PAH concentration indicatedthat the lighter-molecular-weight PAHs were positively
correlated with warmer temperature (P < 0.01). This sug-gests that the higher concentration of lighter-molecular-weight PAHs in the summer sampling session was due tovolatilization.
The medium PAH profile was not prevalent at the GLCsite, but was a dominant source at the School and Chapelsites during the summer sampling session (Figure 30). The
Figure 29 (Continued).
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concentration of the medium profile at the School site wasapproximately five times greater than that at the Chapel site.The medium profile, like the light profile, was characterizedby lighter-molecular-weight PAHs. The medium PAH pro-file is enriched with methylated PAHs, and its abundance atthe School site indicates that volatilization from surfacematerial near the School site is a possible PAH source.
The heavy PAH profile was relatively abundant in thewinter sampling sessions at all three sites (Figure 30). Theambient temperature was negatively correlated with theconcentration of heavier-molecular-weight PAHs (P < 0.05).The heavier-molecular-weight PAHs are common tracers ofcombustion sources (Greenberg et al. 1981; Rogge et al.1993a, Miguel et al. 1998; Kado et al. 2000; Kavouras et al.
Figure 30. Temporal pattern of the light, medium, and heavy PAH profiles in winter 2005, summer 2005, and winter 2006, resolved by three-sourcePMF analysis. (Figure continues on next two pages)
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
2001). As seen in Figures 5 through 8, the number of vehi-cles on the Peace Bridge in the winter was half of that inthe summer. However, with a lower atmospheric boundarylayer and less mixing of compounds in the air, vehicleemissions could contribute to the heavier-molecular-weight PAHs in the winter. Also, increased use of heatingappliances during colder temperatures could be a source
of oil, wood, and coal combustion (Freeman and Cattel1990; Rogge et al. 1997; Simcik et al. 1999; Kavouras et al.2001). With the limited number of PAH species in thisanalysis, the winter source could not be specified.
Figure 30 (Continued).
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Figure 30 (Continued).
Comparisons with PAH and NPAH Levels in Other Cities The PAH and NPAH concentrations measured inBuffalo were compared with those collected in other citiesduring summer and winter sampling sessions (Figure 31).Fluoranthene had the lowest molecular weight of the PAHsavailable in the comparison database. It was selected to rep-resent PAHs found primarily in the gas phase (light profile).
Chrysene+triphenylene were selected to represent PAHsthat occur in both the gas and particle-bound phases(medium profile), and benzo[g,h,i]perylene was selected torepresent PAHs found in the particle phase (heavy profile).The three compounds selected for NPAH analysis werethose that occurred in all of the data sets.
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Air Toxics from Vehicle Emissions at a U.S. Border Crossing
The mean of PAH concentrations measured in the winterat Buffalo was an order of magnitude less than means ofconcentrations measured in Birmingham, United Kingdom(Harrison et al. 1996) and in Baltimore, Maryland (Crim-mins 2006). In the summer, PAH concentrations measuredin Buffalo were similar to those reported in Baltimore. The
concentration of fluoranthene during the summer wasapproximately 3-fold higher at the School site than in eitherBaltimore or Birmingham. Seasonal patterns varied amonglocations, possibly due to meteorologic differences. ThePAH concentrations were higher during the winter than inthe summer in Birmingham. In Baltimore, the volatile PAHs
Figure 31. Comparison of selected PAHs and NPAHs measured at the three sampling sites in Buffalo (GLC, School, Chapel) during the summer andwinter with those reported in other locations: Buffalo, means of 15 samples measured in January 2005 and 2006 and 10 samples (9 for School site) in July2005 (this study); Baltimore, geometric means of 69 samples measured in summer 2002 and winter 2003 (Crimmins 2006); Birmingham, means of55 samples collected in February and August 1992 (Harrison et al. 1996); Los Angeles and Riverside, means of four 5-day samples collected at differenttime intervals in August 2002 and four collected in January 2003 (Reisen and Arey 2005).
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(e.g., fluoranthene) had a higher concentration during thesummer, and the semivolatile PAHs (e.g., benzo[a]pyreneand benzo[g,h,i]perylene) were more abundant in thewinter, which was a similar trend to that in Buffalo. Themean PAH concentration in Buffalo was within the rangeof concentrations in other urban areas.
For NPAHs, the mean concentration of 9-nitroan-thracene in Buffalo was comparable to the concentrationsin Los Angeles and Riverside, California (Reisen and Arey2005), but the mean concentrations of 2-nitrofluorantheneand 1-nitropyrene were 10 to 100 times less during bothsummer and winter (Figure 31). The seasonal patterns ofNPAHs were different among locations. The concentrationof 2-nitrofluoranthene in Riverside was twice as high inthe summer as in the winter. In Baltimore (Crimmins 2006)the 9-nitroanthracene concentration was three timesgreater in the summer, and the 1-nitropyrene concentra-tion in the winter was 7-fold above the concentration mea-sured in the summer. In Buffalo the concentrations of all ofthe NPAHs were higher during the summer. The NPAHconcentrations in Buffalo were relatively lower than thosein the other urban areas, which could be because there isless production of secondary NPAHs (e.g., 2-nitrofluoran-thene) in the atmosphere and there are fewer combustionsources that emit primary NPAHs (e.g., 1-nitropyrene).
The PAH and NPAH concentrations measured in Buffalowere compared with concentrations in other studies thatcharacterized diurnal differences. In general, the indi-vidual PAH concentration did not change dramaticallybetween day and night at any of the locations (Figure 32).The day and night PAH concentrations measured at theChapel and GLC sites were comparable to those measuredin Los Angeles and Riverside, California (Reisen and Arey2005). The concentrations of benzo[a]pyrene andbenzo[g,h,i]perylene at the School site were most similarto those that were measured in samples collected during aphotochemical smog episode in Torrance, California,which is 20 km south of central Los Angeles (Arey et al.1987). The concentration of NPAHs was typically higher atnight, but there were differences in individual species con-centrations at different locations (Figure 32). In Buffalo theconcentration of 9-nitroanthracene increased three toseven times at night, and in Riverside, California, the con-centration of 2-nitrofluoranthene was twice as high atnight (Reisen and Arey 2005). The concentration of1-nitropyrene did not differ significantly between day andnight at any of the locations. The higher NPAH concentra-tion during the night could be due to NPAH production viagas-phase reaction with NO3 radicals, to a lower atmo-spheric boundary layer, or to less photodegradation.
CONTINUOUS MEASUREMENTS AT FIXED SAMPLING SITES AND RELATIONSHIPS TO WIND DIRECTION AND TRAFFIC
This section focuses on the continuous measurements ofPM2.5, UFPs, and pPAHs collected at the fixed samplingsites. Attempts to obtain continuous measurements of O3,NO, and CO2 were less successful. The NO device was thefirst edition from 2B Technologies and had problems withdata storage. The O3 device, also from 2B Technologies,developed problems that required factory repairs, sosimultaneous measurements upwind and downwind ofthe Peace Bridge plaza were limited. Although the CO2instruments worked reasonably well, simultaneous mea-surements recorded at the GLC and Chapel sites also werelimited; therefore, in this report we chose to focus on theparticle measurements. In the integrated sample analysis,the “other-wind” cases were combined with “city-wind”cases because of the limited number of 12-hour city-windcases. It was not necessary to include the other-wind casesfor the continuous samples because the data set wasreduced to 10-minute sampling periods and the number ofcity-wind cases was sufficient to conduct the analysis.
The P-Trak was used for UFP measurements. Eventhough the manufacturer claims that the P-Trak respondsto particles as small as 20 nm, comparisons of particlecounts made at the Peace Bridge plaza by Clarkson Univer-sity during the summer 2004 pilot study showed strongcorrelations between counts by a CPC (Model 2007, TSIInc.) and by the engine exhaust particle spectrometer(EEPS, Model 39090, TSI Inc.). The EEPS continuouslymeasures number concentration in the particle size rangeof 6 to 500 nm (Ogulei et al. 2007). From one of theClarkson researchers, Tim McAuley (personal communica-tion, March 2007), we learned that the CPC was responsiveto particles as small as 15 nm, but the EEPS showed that atlocations very close to combustion sources a majority ofthe particles counted will be smaller than this. At a loca-tion near our Chapel site with a truck idling, the CPCnumber counts tracked with the EEPS number counts butwere always lower by 10% to 50% (data not shown). In theabsence of local mobile sources, the CPC counts werelower than the EEPS number counts, even for regional airmasses. Zhu and coworkers (2006b) reported on compari-sons between the P-Trak and CPCs for their Los Angelesfreeway studies. Consistent with McAuley’s point, the LosAngeles group claimed that the P-Trak underreported par-ticle number counts (25% of CPC with r 2 = 0.68 whenwithin 30 m of a freeway). Upwind of the freeway and far-ther downwind of the freeway there was less under-counting by the P-Trak and correlations increased (Zhu etal. 2006b). Zhu and colleagues were able to demonstrate
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Figure 32. Comparison of selected PAHs and NPAHs measured at the three sampling sites in Buffalo (GLC, School, Chapel) during the daytime andnighttime with those reported in three California locations: Buffalo, means of 4 samples measured during the daytime (7:00 AM to 7:00 PM) in July 2004and during the nighttime (7:00 PM to 7:00 AM) in July 2005 (this study); Torrance, means of 6 filter samples measured during the daytime (6:00 AM to 6:00 PM)and the nighttime (6:00 PM to 6:00 AM) in February 1986 (Arey et al. 1987); Los Angeles and Riverside, means of 5-day sampling at 4 time intervals duringthe daytime (7:00 AM to 6:30 PM) and nighttime (7:00 PM to 6:30 AM) in August 2002 (Reisen and Arey 2005).
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that for particles between 20 nm and 50 nm, the ratio of P-Trak counts to CPC counts ranged from 20% to 50% (Zhuet al. 2006b). For particles larger than 60 nm, the ratioapproached unity. Thus, although the P-Trak instrumentswhen operated side-by-side are within 1000 particles/cm3
of each other, comparative interpretation of particlenumber counts among locations will depend on the particlesize distributions of fresh and aging aerosols. Only duringthe final preparation of this report did the number counts byparticle size made by Clarkson researchers become avail-able. These detailed data from EEPS measurements upwindand downwind of the Peace Bridge plaza are relevant to theinterpretation of our UFP measurements.
First, we explored the between-site differences in UFPs,pPAHs, and PM2.5 by wind direction and wind speed.Then, for a select portion of the data, we looked at theinfluence of traffic on the differences in the UFPs andpPAHs between the Chapel site measurements and theGLC site measurements.
Ultrafine Particle Counts
More than 600 hours of UFP measurements were madeat each of the three sites. Table 22 provides the statisticaldistribution of the 10-minute UFP counts for each site. Wedecided to use 10-minute averages for the fixed-site con-tinuous measurements, rather than complicate the anal-ysis with lags adjusted for wind speed. With a wind speed
of 5 m/sec, which is typical for the study area, the time forwind to travel between sites is less than 2 minutes. UFPcounts at the Chapel site were two to three times those atthe GLC site over the lower half of the distribution, but thedifference diminished above the 75th percentile. UFPcounts at the GLC site were less than those at the Schoolsite for all of the distribution except the higher percentiles.
The UFP counts were substantially higher during the win-tertime than the summertime (Table 23) (P < .001 for eachsite). The wintertime means for both the GLC and the Schoolsites were twice the summer means. For the Chapel site, thesummer mean was approximately 70% of the winter mean.
The paired differences in UFP counts were calculatedfor combinations of the fixed sampling sites and displayedas cumulative frequency distributions (Figure 33). UFPcounts at the GLC site exceeded counts at the Chapel siteonly 20% of the time, and rarely were the differencesgreater than 10,000 particles/cm3. In contrast, UFP countsat the Chapel site exceeded counts at the GLC site by10,000 particles/cm3 or more about 25% of the time, andby 20,000 particles/cm3 or more 10% of the time. UFPcounts at the Chapel site were greater than counts at theSchool site more than 80% of the time. The shape of thecumulative frequency distribution of the differencesbetween the Chapel and School sites was similar to thatbetween the Chapel and GLC sites for the portion wherethe Chapel site had higher concentrations. The Chapel siteexperienced substantially higher UFP counts than eitherthe GLC or School sites. Across the lower end of the distri-bution, a different pattern was observed. The School siterarely exceeded the Chapel site by more than 5000 parti-cles/cm3, suggesting fewer local UFP sources. The twosites are in west Buffalo and experience similar well-mixed particle pollution with winds from the city. Withwinds from the lake, each site experiences the regionalbackground air as well as pollution from sources betweenthe shoreline and the site; under lake-wind conditions,UFP counts would be reduced from the levels observed atthe plaza through dilution and coagulation of the particlesas they are transported to the School site.
Table 22. Ten-Minute Average UFP Counts at Fixed Sampling Sites for Winter 2005, Summer 2005, and Winter 2006a
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
The long tail of negative readings in the cumulative fre-quency distribution of differences between the Chapel andGLC site UFP counts indicates that, on occasion, the GLCsite experienced local emissions. To examine the differ-ence in more detail, we created a radial plot. Figure 34shows the difference in 10-minute average UFP countsbetween the Chapel and GLC sites plotted by the windvector over the entire sampling period. Between approxi-mately 145" and 315" the count was always higher at the
Chapel site than at the GLC site. For winds from about 5" to135" (city-wind conditions) the count was typically higherat the GLC site than at the Chapel site. If there were no UFPsources between the Chapel site and the shore of Lake Erie,one would expect that with winds from the city, the GLCsite would have lower UFP counts due to dilution andcoagulation of particles.
With city winds between 100" and 135" the UFP countsat the GLC site were pronouncedly higher than those at the
Figure 33. Cumulative frequency distributions of the differences in 10-minute average UFP counts between fixed sampling sites.
Figure 34. Radial plots of differences in UFP concentrations (particles/cm3) between the Chapel and School, School and GLC, and Chapel and GLCsites, by wind direction. From a data set of average wind direction and concentration data for 10-minute periods during winter 2005, summer 2005, andwinter 2006 sampling sessions, the average of the 10-minute average differences in concentration was calculated for each wind direction degree.
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Chapel site. When examining the temporal record, wenoted a period between midday on January 15 through latemorning on January 17, 2006, when UFPs at the GLC siteoften exceeded those at the other two sites. The air masstrajectory and wind data for this period show that on Jan-uary 15 local surface winds and air mass movement werefrom the north. By late morning on January 15, winds hadshifted to north-northeast, and they had moved easterly bythat evening. With easterly winds, UFP counts increased atall sites, with substantial increases recorded at the GLCsite. We speculate that the GLC was downwind of theInterstate 190 highway and the Peace Bridge plaza duringtimes when winds were from the northeast quadrant andwas possibly downwind of some local source when windswere from the 90" to 110" direction. As winds shifted fur-ther, first to 150" and then from the southwest quadrant,UFPs decreased at all three sites.
Moving hourly averages were created for each fixedsampling site from the 10-minute average UFP counts.Figure 35 shows a plot of the 1-hour averages for thesummer 2005 campaign, and Figure 36 shows the same forthe winter 2006 campaign. Wintertime particle counts, asnoted, were typically higher than summertime counts. A
diurnal pattern was apparent for periods of time, particu-larly during the summer. The overnight decrease in UFPcounts was more distinct nearer the bridge. There were afew times in the summer (on July 23 and 27, 2005) and alonger period in the winter (January 15–17, 2006) whenUFP counts at the three sites appeared more correlated. Itwas during this winter period that the GLC site had higherUFP counts than the School and Chapel sites, which hadsimilar UFP counts. The air mass arriving over Buffaloduring these summer periods was from Ontario to the north,with surface winds from the north-northeast and northeast.The air mass trajectories for January 15 and 16 were verysimilar. In contrast, from early morning on January 9through January 10, winds were consistently from the westor west-southwest for about 36 hours, with the air massarriving in Buffalo after crossing southern Ontario and LakeErie. UFP counts were low at the GLC site, reached 20,000 to30,000 particles/cm3 at the School site, and were 40,000 tomore than 80,000 particles/cm3 at the Chapel site. Theseobservations suggest that in typical urban air UFP valuesmight be 15,000 to 20,000 particles/cm3, but local sources oftraffic (cars and trucks) might double those levels as mea-sured by the P-Trak. If the P-Trak values were adjusted to
Figure 35. UFP counts (hourly moving average) at the three sampling sites during the summer 2005 sampling session.
Figure 36. UFP counts (hourly moving average) at the three sampling sites during the winter 2006 sampling session.
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CPC values as described by Zhu and colleagues (2006b), thelocal number counts for particles of 10 nm and smallermight be three to four times higher.
To explore further the relationship of UFP counts to thePeace Bridge plaza traffic, we created scatter plots com-paring 10-minute average values at the Chapel site versusthe GLC site. Figure 37 displays a plot of data from allpaired readings and a plot of data restricted to readingsobtained under lake-wind conditions. The two plotsclearly show that restricting the data to those obtainedwhen the winds were from the southwest sector greatlyreduced the number of times when UFP counts at the GLC
site exceeded those at the Chapel site. In Figure 38, thelake-wind cases were divided among wind speeds greaterthan and equal to or less than 3.1 m/sec. With the higherwind speeds, only a few 10-minute UFP counts at the GLCsite exceeded those at the Chapel site.
Particle-Bound PAHs
Mean pPAH concentrations were highest at the Chapelsite. The ratio of the pPAH concentrations (medians andmeans) among the GLC, School, and Chapel sites wasapproximately 1:2:4. Table 24 provides summary statisticson pPAHs at the three sampling sites. Like UFP counts, pPAH
Table 24. Ten-Minute Average pPAH Concentrations (ng/m3) at Fixed Sampling Sites
Figure 37. UFP counts at the Chapel site versus those at the GLC site: alldata and data obtained under lake-wind conditions (wind direction,180"–280").
Figure 38. UFP counts at the Chapel site versus those at the GLC siteobtained during lake winds of low speed (! 3.1 m/sec) and higher speed(> 3.1 m/sec).
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concentrations were higher in the winter than in the summer.Further, the daytime values at the Chapel and School siteswere higher than the nighttime values (Table 25).
Figure 39 is the cumulative frequency distribution of thebetween-site differences in pPAH concentrations. The 95thpercentile for the difference between the School site andthe GLC site was about 9 ng/m3, and the differencebetween the Chapel and GLC sites exceeded this valueabout 30% of the time. The pPAH values were higher at theChapel site than at the other two sites more than 70% ofthe time. To complete this analysis, we prepared radialplots of the differences in pPAH concentrations betweenthe individual sites (Figure 40). The pPAHs were slightlyelevated at the School site in comparison with the GLC sitefor wind directions from about 135" through 215".Although the difference was not as pronounced as the dif-ferences between the Chapel and GLC sites, the patternswere similar. The small increase in pPAH concentrationfrom the GLC site to the School site may or may not be dueto the traffic at the Peace Bridge plaza; other sourcesbetween these two sites may contribute to the difference.For city-wind directions, the School and GLC sites hadequivalent levels of pPAHs. As shown in Figure 40, for thenorth-to-east wind sector, the Chapel and School siteswere equivalent in pPAH concentrations. For the east-to-southeast wind sector, pPAHs were slightly higher at theSchool site than at the Chapel site. However, for winddirections from about 160" to 340", the PAHs were higher at
the Chapel site. The southwest sector shows the same con-sistent pattern as seen on the previous radial plots forUFPs (see Figure 34).
Next, differences between the Chapel and GLC siteswere explored by wind sector and two categories of windspeed. Figure 41 shows the scatter plot of simultaneouspPAH measurements made at the Chapel and GLC sitesunder lake-wind conditions. There was a substantial
Table 25. Twelve-Hour Average pPAH Concentrations (ng/m3) for Day versus Night, by Season
Figure 39. Cumulative frequency distributions of the differences in pPAH 10-minute average concentrations between sampling sites.
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reduction in the number of 10-minute values at the GLCsite that were greater than those at the Chapel site. As withUFPs, the number of 10-minute pPAH values at the GLCsite that exceeded those at the Chapel site was reducedwith higher wind speeds, as indicated in Figure 42, whichclassifies the lake-wind conditions into two categories ofwind speeds.
Figure 43 shows the temporal variations in pPAHs for thesummer 2005 campaign, and Figure 44 for the winter 2006campaign. Some diurnal and weekend-versus-weekday pat-terns were apparent. We would have expected to see higherpPAH concentrations at the Chapel site starting on Monday,January 16, except it was a national holiday (birthday ofMartin Luther King, Jr.). For most of the previous 2 days, the
Figure 40. Radial plots of differences between pPAH concentrations (ng/m3) at the Chapel and School, School and GLC, and Chapel and GLC sites, bywind direction. From a data set of 10-minute average wind directions and pPAH concentrations for winter 2005, summer 2005, and winter 2006 samplingsessions, the average difference of the 10-minute average concentrations was calculated for each wind direction degree.
Figure 41. Scatter plot of all pPAH concentrations from the summer 2005and winter 2006 sampling sessions for the Chapel site versus the GLCsite; and scatter plot of concentrations measured under lake-windconditions (wind direction, 180"–280").
Figure 42. Scatter plot of pPAH concentrations from the summer 2005 andwinter 2006 sampling sessions for the Chapel site versus the GLC sitemeasured during lake winds of low speed (! 3.1 m/sec); and scatter plot ofconcentrations measured under lake winds of higher speed (> 3.1 m/sec).
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wind flow had been from Canada to the north. By Monday,winds had shifted and were blowing from the northeastquadrant. The Chapel site was not affected by air that cameacross the Peace Bridge plaza until Wednesday, January 18.On Tuesday, January 17, one can observe elevated pPAHlevels at all three sites (Figure 45).
Continuous PM2.5 Measurements
The laser-based nephelometer used to collect PM2.5measurements operated continuously at each fixed sam-pling site for more than 600 hours. The instrument is sub-ject to interference from humidity, which causes smallparticles to agglomerate. To correct for this interference,each 12-hour daytime integrated PM2.5 measurement wasused to adjust the instrument’s electronic output for thatperiod. The mean of the values obtained for mass gravi-metric measurements during the previous and followingsampling periods was used to adjust the values for periodswhen we were not making these measurements.
Table 26 provides the statistical summary for the 10-minute average PM2.5 (µg/m3) concentrations, whileFigure 46 shows the cumulative frequency distribution ofthe differences in DustTrak PM2.5 measurements betweensites for summer and winter periods. During the summer,the differences between the Chapel and GLC sites werewithin ± 5 µg/m3 about 80% of the time; however, a smallfraction of the Chapel site PM2.5 concentrations did exceedthe corresponding GLC values by more than 10 µg/m3.Although the GLC and the School sites had similar meanvalues, the tails of the distribution for the differences inthe summer suggest the influence of local sources at bothsites, leading to more extreme differences. During thewinter, PM2.5 values at both the Chapel and School siteswere more likely to exceed the GLC values than during thesummer. The Chapel site exceeded the simultaneouslyrecorded GLC site PM2.5 levels by more than 5 µg/m3 25%of the time versus 20% of the time in the summer. TheSchool site PM2.5 levels in the winter were greater than the
Figure 43. One-hour moving average pPAH concentrations during the summer 2005 sampling session for the three sampling sites.
Figure 44. One-hour moving average pPAH concentrations during the winter 2006 sampling session for the three sampling sites.
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GLC site levels 80% of the time versus only 45% of thetime during the summer (Figure 46).
UFP and pPAH Data Analysis by Wind Factors and Traffic Counts
To understand the mechanism by which traffic-relatedUFPs and pPAHs were transported, we stratified 1-houraverage wind directions by wind-angle increments of10 degrees. For robustness in data analysis, we includedthe data for wind directions between &30" and +30" ofevery wind-angle increment of 10 degrees. The coefficientof determination (R2) was computed between the UFP con-centrations measured from each of the three fixed sam-pling sites and the hourly traffic counts categorized byvehicle types reported by the Peace Bridge Authority. Wewere not able to find a clear relationship between Peace
Bridge traffic counts and UFP concentrations at the Chapelsite when the wind direction was from the southwest towest (Figure 47). The same procedure was applied topPAH analysis. We found that the Chapel site had thehighest variability when the wind was blowing from thewest. Figure 47 demonstrates the highest variabilitybetween pPAH concentration and total truck counts at theChapel site when the wind was blowing from averagedvector directions of 190" to 230", and we attributed this toemissions from diesel trucks southwest of the Chapel site.
Analysis by Wind Speed and Direction Ogulei and col-leagues (2007), in another study of this location, found thatthe highest average UFP concentrations were recorded atthe Chapel site and the lowest at the GLC site. A number ofphysical and chemical factors affect the dispersion and
Figure 45. Ten-minute average pPAH concentrations and wind directions for selected days in winter 2006.
Table 26. Statistical Summary of 10-Minute Average PM2.5 (µg/m3) Measurements from DustTrak
dilution of UFPs as the air mass moves away from the PeaceBridge. Seasonal changes and meteorologic conditions playan important part in particle emissions. As shown by Hus-sein and colleagues (2004), UFP concentrations vary season-ally. Hussein and colleagues (2006) observed that UFPconcentrations in Helsinki, Finland, were higher duringwinter than summer. Among the meteorologic factors influ-encing UFP concentrations, low wind speed has been foundto be important (Hussein et al. 2006; Kerminen et al. 2007).
Hence, we stratified the continuous data in our study bywind speeds equal to or less than 1.8 m/sec and greater than1.8 m/sec and divided the data into three different wind sec-tors of city winds (1" through 179"), lake winds (180"
through 280"), and other winds (281" through 359") based onthe 1-hour vector average wind directions. The data werethen separated by sampling session. Note that the 12-hourintegrated data were assigned to wind sectors using10-minute vector average wind directions and considering
Figure 46. Cumulative frequency distributions of the differences in 10-minute average PM2.5 concentrations between sampling sites, for the summer2005 and winter 2006 sampling sessions.
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not only the direction, but also the length of time that thewind came from that direction. The mean, standard devia-tion, and median were computed for hourly average UFPconcentrations at each sampling site (GLC, Chapel, andSchool sites), and the variance in concentrationsexplained by traffic counts was compared for all vehicles,cars, and trucks.
We observed a systematic pattern during both summerand winter: when the wind was blowing from southwest towest (lake winds), the UFP concentrations at the Chapelsite were the highest and background concentrations at theupwind GLC site were lowest. The traffic-related particlesmoved from the major roads (Niagara Thruway) and thePeace Bridge toward the downwind location at the Chapelsite, and the average concentrations measured at theSchool site, located 520 m away from the Chapel site, werelower than the values at the Chapel site but higher thanthose at the GLC site. Total pollutant concentrations havebeen found to gradually decrease with distance from thePeace Bridge (Ogulei et al. 2007), and this may be becausethe particles are diluted by mixing before they reach theSchool site. Analyses by Hussein and colleagues (2006)have shown that low wind speed and stable atmosphericstratification can reduce the transport of local pollution.
The total UFP concentrations at the Chapel site wereslightly higher when the wind speed was equal to or lessthan 1.8 m/sec, and the average winter UFP concentrationwas greater than the average summer concentration by afactor of 1.5 (Table 27). We observed a similar pattern forpPAH concentrations at the Chapel site when the wind wasblowing from the lake. The 1-hour average pPAH concentra-tion was greater by a factor of 1.3 when the wind speed wasequal to or less than 1.8 m/sec, and the average pPAH con-centration was higher during winter than summer by a factorof 1.3. Studies carried out in Finland found that aerosol par-ticle concentrations were elevated with low wind speed andlow ambient temperatures (Hussein et al. 2006).
Analysis by Traffic Counts After stratifying the 1-houraverage counts of all vehicles, cars, and trucks on thePeace Bridge by wind speed (1.8 m/sec) and season, weexamined the relationship between traffic counts and thetotal UFP concentration and pPAH concentration when theprevailing wind direction was from the southwest to west(lake winds). In Table 28, R1
2 is the coefficient of determina-tion between the total traffic counts and UFP concentration.R2
2 represents the variability in the UFP concentrationexplained by the total number of trucks, and R3
2 is a linearregression between the total number of cars and the UFPconcentration. The same procedure was applied to pPAHanalysis. According to Table 28 the highest variability wasexplained by the relationship between truck counts and UFPconcentration at the Chapel site during the summer with alow wind speed (R2
2 = 0.96). At the School site, the UFP con-centration showed a strong relationship to the total trafficcount (R1
2 = 0.63) and the total number of cars (R32 = 0.66)
during summer with a low wind speed. For the pPAH anal-ysis, we found a strong relationship between the totalnumber of trucks and the pPAH concentration at the Chapelsite during summer with a low wind speed (R2
2 = 0.61).
MOBILE MONITORING TO CHARACTERIZE NEIGHBORHOOD POLLUTANT CONCENTRATIONS
Mobile monitoring was used to characterize pollutantconcentrations across the 10-block residential neighbor-hood adjacent to the Peace Bridge plaza. Previous studieshave explored the use of portable equipment to recordreal-time particle concentrations with GPS tracking of thelocations. A study in the Dudley Square neighborhood ofBoston established the feasibility of profiling pollutantconcentrations over a complex network of urban streets(Levy et al. 2003).
Figure 47. Coefficient of determination (R2) for the Chapel site UFP con-centrations and pPAH concentrations versus car, truck, and total trafficcounts reported by the Peace Bridge Authority. R2 was computed betweenthe pollutant concentrations for each average wind direction stratified bywind-angle increment of 10" ± 30" and the hourly traffic counts catego-rized by vehicle types.
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Table 27. UFP and pPAH Concentrations Stratified by Sampling Session, Wind Direction, and Wind Speed (1.8 m/sec)
SchoolCity NA NA NA 12,646 6,497 10,376 8.6 6.5 7.1 5.3 2.4 5.2Lake NA NA NA 10,991 6,663 9,560 6.4 4.4 4.9 4.7 1.9 4.3Other NA NA NA 10,105 4,813 9,339 32.2 7.0 32.2 4.4 2.2 3.7
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The summer 2004 pilot study convinced us that a move-able platform containing many instruments and a meteoro-logic station was not feasible for the Peace Bridge plaza area,even in warm weather. Moving the platform proved to beawkward on uneven sidewalks, and we could obtain air pol-lution data for only a limited number of locations with thissystem. After comparing the advantages of data collectionby staff members using handheld UFP counters, instru-ments to measure PM2.5 and pPAHs, and a GPS, we aban-doned the moveable platform concept in the winter 2005pilot study. In the first pilot study we had been contrastingair pollution profiles of different streets and intersections inwest Buffalo. By the second pilot study, we were convincedthat a survey of the neighborhood was feasible.
A schedule was established to deploy three staff mem-bers, equipped as described above, across the network ofstreets parallel to the Peace Bridge plaza and extending in agrid to the east-northeast, and a fourth staff member to theBird Island Pier, approximately 50 m to 100 m off theeastern bank of the Niagara River. The Bird Island Pier mon-itoring route started in a public park northeast of the PeaceBridge and extended under the bridge to a point west of theGLC fixed sampling site. With lake winds or Canadianwinds from the southwest or northwest sectors, measure-ments along Bird Island Pier reflected upwind conditions.
The staff members collected UFP and pPAH data acrossthe network of streets (see Figure 4). In our analysis we
pooled all data first by street and then by neighborhoodzone (Bird Island Pier, near zone, mid zone, and far zone)to determine whether there were significant spatial differ-ences in UFP and pPAH concentrations.
Mobile monitoring was conducted during the summer2005 and winter 2006 field campaigns. We first exploredthe data to determine if there might be overall differencesbetween the morning and afternoon mobile monitoringsessions or between seasons. Table 29 provides a summaryof the morning and afternoon mobile monitoring sessionsfor UFPs and pPAHs, by season; the data were split toexamine Bird Island Pier monitoring separate from moni-toring on the neighborhood streets east of the Peace Bridgeplaza. Table 30 shows results for the individual streets.The Wilcoxon rank sum test (two sided) for differences inthe mean ranked score does not require assumptions aboutthe form of the distribution. The Kruskal-Wallis test exam-ines the equality of the medians but does not assume anormal distribution. The mean of UFP concentrations atBird Island Pier was significantly lower (P < 0.01) than themean UFP concentrations measured on any of the eightstreets to the east of the plaza.
We made between-street comparisons for the pPAH dataas well (Table 30). Again, as expected, the mean of pPAHconcentrations on Busti Avenue was significantly higherthan that on Bird Island Pier. Proceeding east, the next fourstreets (Columbus Parkway/7th Street, Niagara Street,
Table 28. Multiple Linear Regression of Traffic Counts by Vehicle Type and Lake-Wind UFP and pPAH Concentrations Stratified by Wind Speed and by Season
a R12 is the coefficient of determination between the UFP or pPAH concentrations and all vehicles; R2
2, all trucks; R32, all cars.
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Prospect Avenue, and Fargo Avenue) all had significantlyhigher mean pPAH concentrations than Bird Island Pier. Forthe next street, West Avenue, the difference in mean pPAHconcentrations was barely significant at P = 0.04 (though thedifference in median values remained significant at P < 0.01).For the next two streets (Plymouth Avenue and NormalAvenue), the differences in pPAH means compared withBird Island Pier mean were not significant. These findings
might indicate that the effects of activities related to thePeace Bridge plaza and highway emissions are discernibleabout 500 m to 700 m into the neighborhood.
We also compared UFP counts on Busti Avenue, theroad next to the Peace Bridge plaza, with counts collectedwhile traversing the streets to the east of it. For this com-parison, the results obtained with the Wilcoxon andKruskal-Wallis tests of significance were similar, so only
Table 29. Mobile Monitoring Summary
Measurements /Time of Day Pollutant
Summer Winter
HoursMeanValue SD Hours
MeanValue SD
All MeasurementsMorning UFPs (particles/cm3) 53 12,007 8,055 80 22,792 21,434
a Significantly different from Bird Island Pier value at P < 0.01 (Wilcoxon rank sum test, two sided).
b Significantly different from Busti Avenue value at P < 0.01 (Wilcoxon rank sum test, two sided).
c The difference between median pPAH concentrations at West Avenue and Bird Island Pier was significant at P < 0.01.
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the Kruskal-Wallis test results are reported. We also exam-ined the upper tail of the distribution using Q-Reg STATAto test differences at the 90th percentile (Table 31). MedianUFP counts on Busti Avenue were significantly greaterthan UFP counts on all other streets.
The median pPAH concentrations were significantly dif-ferent between Busti Avenue and all of the other streets (P< 0.01) except for Columbus Parkway/7th Street, the firststreet east of Busti Avenue. Like the UFP values, the street-averaged pPAH values generally decreased with distancefrom the Peace Bridge plaza.
Correlation Between UFP and pPAH Measurements
We examined the relationship between the continuousUFP and pPAH measurements, believing that both mightserve as proxies for the spatial and temporal impact of dieselemissions. In Table 32, Pearson correlation coefficients werederived for all paired UFP and pPAH data collected on BirdIsland Pier, on each of the eight neighborhood streets, and byzone (near, mid, and far). Overall, the correlations were low,but some were statistically significant. A subset of the datawas defined for measurements made under lake-wind condi-tions to see if correlations strengthened, but these correla-tions were not substantially higher.
For Bird Island Pier, the UFP and pPAH values were sig-nificantly correlated (P < 0.01), and the correlation coeffi-cient increased from 0.11 to 0.27 when the data wererestricted to those obtained under lake-wind conditions(Table 32). During mobile monitoring, staff had noted occa-sional increases in UFP counts and pPAH values when ves-sels would pass by on the Niagara River. No correlationsbetween UFP and pPAH values were observed for BustiAvenue, Columbus Parkway/7th Street, and Niagara Street,
or for the combined data in the near zone. Prospect Avenue(mid zone) is a low-traffic residential street, east of NiagaraStreet, which is a commercial route with mixed zoning.Prospect Avenue’s UFP and pPAH measurements werestrongly correlated (P < 0.01). However, the correlation coef-ficient was lower in the lake-wind data. Measurements wereuncorrelated for Fargo Street, the next street east of Prospect.However, for the combined data collected on these two mid-zone streets, there was a modest correlation between theUFP and pPAH measurements. Of the three streets in the farzone of the neighborhood, two (West Avenue and PlymouthAvenue) had a significant correlation (P < 0.01) between
Table 31. Test of Differences in Upper Tail of UFP and pPAH Distributions by Street
a Check mark indicates significant difference from Busti Avenue value at P < 0.01 (Kruskal-Wallis test).
Table 32. Correlations between pPAH and UFP Measurements by Street and by Zonea,b
Location Overall Lake Winds
Bird Is. Pier 0.11 0.27
Busti 0.015 !0.012Columbus 0.035 0.030Niagara 0.042 0.055Near Zone 0.027 0.026
Prospect 0.26 0.17Fargo 0.058 0.025Mid Zone 0.17 0.11
West 0.21 0.24Plymouth 0.30 0.13Normal 0.085 0.050Far Zone 0.17 0.16
a Values are Pearson correlation coefficients.
b Bold indicates P < 0.01.
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these two measures. In the combined data for these three far-zone streets, the correlation was significant, albeit modest.
The continuous PAH monitor (PAS 2000CE) has consid-erable signal noise, particularly at the lower concentra-tions. Therefore, we examined the data that were above themedian pPAH measurements from the neighborhoodstreets in the near zone, and found that the correlation wassignificant at P < 0.0001.
Examination of UFP and pPAH Data by Neighborhood Zones
Figure 48 shows the cumulative distribution of the1-minute UFP measurements (particles/cm3) collectedduring the summer 2005 and winter 2006 mobile moni-toring campaigns by street. Figure 49 presents the distribu-tions classified by neighborhood zone and, along withTable 33 (a statistical summary), shows that the streets
Figure 48. Cumulative frequency distribution of 1-minute UFP data collected during summer 2005 and winter 2006 mobile monitoring campaigns, clas-sified by street.
Figure 49. Cumulative frequency distribution of 1-minute UFP data collected during summer 2005 and winter 2006 mobile monitoring campaigns, classi-fied by neighborhood zone.
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closest to the plaza (the near zone) had UFP counts consis-tently higher across the entire distribution. The distinctionwas not as great between the near and mid zones as it wasfor the other pairings. The median value for Bird IslandPier (background) was about 6000 particles/cm3, which is
half of the median value for the far zone but a third of themedian value for the near zone.
The pPAH data, displayed as cumulative distributionsclassified by street (Figure 50), by neighborhood zone(Figure 51), and by percentile values (Table 34), show a
Table 33. One-Minute UFP Measurements (Particles/cm3) Collected on Weekdays During Summer 2005 and Winter 2006 Mobile Monitoring Campaignsa
a Sampling periods included rush hours and did not include weekends.
Figure 50. Cumulative frequency distribution of 1-minute pPAH data collected during summer 2005 and winter 2006 mobile monitoring campaigns, clas-sified by street.
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somewhat different picture than the UFP data. For pPAHs,the data collected on streets in the near zone, close to thePeace Bridge plaza, were distinct from those collected inthe mid zone across the entire distribution. For example,
the relative difference in measurements was greater at the90th percentile than at the 50th percentile. The distributionfor the neighborhood streets in the far zone resembled theBird Island Pier distribution for much of the data up to the
Figure 51. Cumulative frequency distribution of 1-minute pPAH data collected during summer 2005 and winter 2006 mobile monitoring campaigns, clas-sified by neighborhood zone.
Table 34. One-Minute pPAH Measurements (ng/m3) Collected on Weekdays During Summer 2005 and Winter 2006 Mobile Monitoring Campaignsa
a Sampling periods included rush hours and did not include weekends.
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70th percentile; the distribution for higher concentrationsdid not follow this pattern. Plymouth Avenue and NormalAvenue have public bus routes, with an extra route onschool days. Other public buses transit the study area onNiagara Street.
In addition to carrying the continuous monitoringequipment, each staff member wore a PEM to collect anintegrated PM2.5 sample. The ratio between exposures toelements on backpack routes in the near, mid, and farzones and values on the Bird Island Pier (background)route are shown in Figure 52 for the summer 2005 mobile
monitoring campaign. Values are the arithmetic mean offour samples obtained by staff while walking the routes,and each sample was a composite of several route-days.Typically, the exposures measured on the mid-zone routewere less than those on the near-zone or far-zone routes.For some elements related to mobile sources, the expo-sures were higher on the near-zone route (nickel, chro-mium, strontium, and antimony), but for others theexposures were higher on the far-zone route (calcium, tita-nium, iron, copper, and lead).
Spatial Patterns of UFPs and pPAHs in West Buffalo
To generate patterns of neighborhood levels of UFPs andpPAHs, we combined all the data from both the summer2005 and winter 2006 mobile monitoring campaigns in thewest Buffalo neighborhood, and then separated themaccording to whether they were obtained under lake-windor city/other wind conditions (see Table 33 for UFP par-ticle counts and Table 34 for pPAH concentrations).
Figure 53 displays the summer and winter UFP counts(particles/cm3) under lake-wind conditions. Counts were
Figure 52. Ratio of exposures to elements on the mobile monitoringroutes in the near, mid, and far zones to values on the Bird Island Pierroute (background). Values are the means of data from 4 samplers worn bystaff in summer 2005 while walking the routes. Each sample is acomposite of several route-days.
Figure 53. Spatial pattern of combined summer and winter UFPmeasurements (N = 10,808) made under lake-wind conditions in westBuffalo using P-Trak.
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Figure 54. Spatial pattern of combined summer and winter UFPmeasurements (N = 3980) made under city-wind conditions in westBuffalo using P-Trak.
Figure 55. Spatial pattern of UFP measurements (N = 2478) made onwinter mornings under lake-wind conditions in west Buffalo using P-Trak.
elevated along Busti Avenue adjacent to the PeaceBridge plaza. Higher readings occurred at a busy inter-section at the northwest end of Niagara Street. Figure 54represents seasonally pooled UFP data under city-windconditions. In general, the particle counts were elevatedacross the study region with city winds. Traffic alongBusti Avenue and Niagara Street and around the plazaappeared to contribute to the UFP levels. An area of ele-vated particle counts on the southeast side of Niagara
Street, between Connecticut Street and Porter Avenue, isnear D’Youville College, where a dormitory was underconstruction during the summer 2005 campaign.
The wintertime UFP data obtained under lake-windconditions show a decreasing gradient with distance intothe neighborhood (away from the Peace Bridge plaza),with higher counts more narrowly confined near BustiAvenue for the morning sampling sessions (Figure 55).The spatial profile of afternoon UFP data (Figure 56)
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shows higher concentrations extending over much of theneighborhood.
Figure 57 displays the wintertime morning UFP valuesunder city-wind conditions. The spatial pattern is similar tothat for combined UFP data shown in Figure 54, with themost elevated UFP counts north of the Peace Bridge plaza.
The pPAH concentrations (ng/m3) are displayed spatiallyin the next series of figures. The seasonally aggregated data
plotted by wind sector ArcGIS are shown in Figure 58 (lakewinds) and Figure 59 (city winds). Although the noisylower-end signal from the PAS 2000CE instrument used tocollect PAH data resulted in contour lines that are not assmooth as they were for the UFPs, these figures do illustratethe probable transport of diesel-derived PAHs into the neigh-borhood. Under city-wind conditions, there were areas ofelevated pPAHs from other sources. At the southeast end of
Figure 56. Spatial pattern of UFP measurements (N = 2373) madeon winter afternoons under lake-wind conditions in west Buffalousing P-Trak.
Figure 57. Spatial pattern of UFP measurements (N = 2331) madeon winter mornings under city-wind conditions in west Buffalousing P-Trak.
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Busti Avenue, where it intersects with Porter Street, there isa fast-food restaurant that may have contributed to theincreased level of pPAHs in the air.
The final two figures in this series show the patterns ofpPAH concentrations measured on winter mornings underlake-wind and city-wind conditions in west Buffalo.Focusing on one season and time of day reduces the noise inthe data. The contour lines are somewhat smoother for thesetwo cases, as the lake-wind condition was defined to
include a rather large set of wind vectors. Figure 60 showsthat under lake-wind conditions, pPAH concentrationswere higher due east of the Peace Bridge plaza. A persistentwest wind could fetch along the bridge and across the plazato yield this pattern of pPAHs. Figure 61 shows that undercity-wind conditions, pPAH concentrations were slightlyhigher in the vicinity of Niagara Street, and the highestpPAH concentrations were in patches in the south cornerand along the northeast side of the neighborhood grid.
Figure 58. Spatial pattern of combined summer and winter pPAHmeasurements (N = 12,821) made under lake-wind conditions in westBuffalo using a PAS.
Figure 59. Spatial pattern of combined summer and winter pPAHmeasurements (N = 4637) made under city-wind conditions in westBuffalo using a PAS.
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DISCUSSION AND CONCLUSIONS
Each year tens of thousands of cars, trucks, and busesuse the Peace Bridge at one of the busiest internationalborder crossings in the United States. Idling vehicles, stop-and-go traffic for passport control, customs inspections,and use of duty-free shops are responsible for motorvehicle emissions near the plaza on the New York side ofthe bridge. The type of vehicular activity at the PeaceBridge results in emissions that are unlike emissions mea-sured by dynamometer tests, along roadways or in tunnels.Vehicles transit the Peace Bridge plaza slowly, with fre-quent stops and starts. In addition, vehicles are usually inlow gear, particularly heavy-duty vehicles, as they slowdown while descending from the bridge, stopping for pass-port control and inspection, or are accelerating up theincline to the bridge as they leave the United States.
The Peace Bridge plaza is considered a hot spot formobile-source emissions. Measurements at the Chapel site
on Busti Avenue, adjacent to the plaza on the northeastside, clearly indicated elevated concentrations of PM2.5,EC-r, and vehicle-related VOCs, metals, PAHs, NPAHs,UFPs, and pPAHs when meteorologic conditions weresuch that the air flowed from Lake Erie.
More than 62% of the total daily heavy-duty truck trafficcrossed the bridge during the daytime 12-hour weekdaysampling periods. The Chapel site concentrations rangedfrom 1.5 to 10 times the concentrations at the upwind GLCsite, located 560 m to the southwest of the Peace Bridgeplaza at the shore of Lake Erie and the start of the NiagaraRiver. Under lake-wind conditions concentrations mea-sured at the GLC site reflected the regional air mass, whilethe Chapel site concentrations reflected regional air withthe addition of emissions from the Peace Bridge plaza andsurrounding roadways.
Table 35 groups the different types of analytes by theratio of Chapel median concentrations to GLC median con-centrations under lake-wind conditions. The ratios are
Figure 60. Spatial pattern of pPAH measurements (N = 2978) made onwinter mornings under lake-wind conditions in west Buffalo using aPAS.
Figure 61. Spatial pattern of pPAH measurements (N = 2522) made onwinter mornings under city-wind conditions in west Buffalo using a PAS.
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grouped into four categories to indicate a gradient in thestrength of the Peace Bridge plaza as a source of enrichment:, 1.5 but < 2.0 enriched; , 2.0 but < 2.5 strongly enriched; ,2.5 but < 3.0 substantially enriched; , 3.0 very enriched.
Other analytes appeared to be enriched even though theirratios of median concentrations for the Chapel and GLCsites were less than 1.5. For example, PM2.5 was, on average,30% higher at the Chapel site under lake-wind conditions.
Higher levels of fine PM and EC have been associated withdiesel exhaust in several studies. Schauer and coworkers(1996) applied source apportionment techniques to PM sam-ples collected in Los Angeles. Although they reported mul-tiple sources of organic aerosol, diesel exhaust was a majorcontributor to PM2.5 in downtown Los Angeles. Miguel andcolleagues (1998) compared PAHs with particle and blackcarbon concentrations collected in the Caldecott Tunnelsnear Berkeley, California. Trucks were restricted to twobores, while a third bore permitted only cars. Black carbonlevels in the truck-influenced samples were five times thelevels in the car-only samples, with chemical mass balanceanalysis associating 93% of the black carbon with heavy-duty diesel trucks. In a multicity study in Europe, wherediesel-fueled personal vehicles are a more substantial frac-tion of the fleet than in the U.S., Brauer and colleagues(2003) examined the variation in air pollutant measures,
including an indicator for EC, and found reasonable pre-diction of measurements by geographic variations in trafficdensity and distance from high-volume roadways.
Many studies have consistently associated benzene, tol-uene, ethylbenzene, m&p-xylene, and o-xylene with emis-sions from gasoline-fueled vehicles; so universal are thesereports that verifying references are not needed. This studyshowed that in addition to the compounds methylatedhexane and pentane, which are consistently associatedwith vehicle emissions, 1,3-butadiene and styrene werealso enriched downwind of the plaza.
Analysis of PM by HR-IPC-MS identified 11 elements(including 8 metals) that were clearly enriched in down-wind samples. These elements were not necessarily fromvehicle tailpipe emissions. Air passing over the plaza alsomight have carried a resuspended complex mixture of roaddust, which could contain abraded asphalt, oil-coated par-ticles, latex tire fragments, pollens, sand, salt, soil, andengine-wear and brake-wear debris. Vehicles in tunnel androadway traffic can disperse road dust by the friction oftheir tires on the road surface and the microturbulence ofdisplaced air; however, with the slower-moving traffic atthe plaza, these factors would be less effective in resus-pending settled debris.
Table 35. Analytes Enriched at the Downwind Chapel Site Relative to the Upwind GLC Site, with Lake Winds
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Dynamometers have been used to identify metals origi-nating from brake, tire, and engine wear that are notrelated to tailpipe emissions. Schauer and colleagues(2006) found that iron and barium were an order of magni-tude higher than other elements. The next most abundantelements were zinc, manganese, and copper. Interestingly,in the test cycles featuring the most extreme vehicle accel-eration and deceleration, most elements had higher emis-sion rates than during the more standard federal drivingtest cycles. During the test with aggressive braking andacceleration, emission rates of chromium and strontiumwere equivalent to those of zinc and copper. Other ele-ments reported as being emitted during the wear test werevanadium, molybdenum, and to a lesser extent antimony.They noted that this type of driving probably volatilizedmaterial from the hot surfaces of brakes and tires, whichthen condensed to smaller-size particles.
Schauer and colleagues (2006) also reported on sam-pling conducted in the Howell Tunnel (Seattle, WA) onweekdays and weekends to contrast traffic with higher andlower proportions of heavy-duty trucks (approximately7% on weekdays and 2% on weekends). They noted thatin the weekday PM10 emissions the ratio of EC to totalmetals was about three times that in weekend emissions.Emissions of many crustal elements were higher on week-ends (sodium, magnesium, aluminum, silicon, calcium,and iron), most likely from resuspension. Emissions ofsome “wear” elements (copper, zinc, antimony, andbarium) were also higher.
Schauer and colleagues (2006) pointed out that dustfrom brake housing comprises debris from pad and rotorwear. The composition of brake pads varies considerably,coming in two basic types: semimetallic and low-metallicversions. The wear of rotors contributes iron and copper,whereas the pads mostly contain barium as barium sulfate.In roadway situations the distinction is less important;tunnel studies indicated that brake dust is a substantialcomponent of the resuspended road dust. Schauer and col-leagues (2006) suggested that tire wear is a negligible con-tributor of trace metals compared with other sources. Thismay be the case in general, but the correlation betweentraffic activities and zinc concentrations in airborne PM, aswell as roadside soil samples, suggests otherwise. Piersonand Brachaczek (1974) first reported the associationbetween traffic activities and zinc. Twenty years laterAhlbom and Duus (1994) estimated that about 10% of theparticulate zinc in Swedish cities came from tire wear.Fauser (1999) showed that organic zinc might be a markerfor tire-generated PM, and Fauser and colleagues (2002)reported that 20% of the total zinc in samples of airborneparticles collected in Copenhagen was from tire wear.
Organic zinc may be a marker for tire wear, but ele-mental zinc comes from other vehicle sources as well.Metals in tailpipe emissions are primarily from lubricatingoils that contain antiwear agents and surfactants, alongwith engine-wear debris. Okada and colleagues (2003)demonstrated that the metals in diesel PM emissions camefrom lubricating oils. Zinc was a measurable component inboth gasoline and diesel tailpipe emissions, as reported bySchauer and colleagues (2006). Schauer and coworkers(2006) showed that the EC fraction of PM2.5 from dieselemissions ranged from 70% to 84%, whereas it was only17% to 28% of the PM2.5 mass from gasoline emissions.Zinc, calcium, and iron were significant components ofthe PM2.5 mass, along with lesser amounts of copper,molybdenum, and lead.
In its Commentary on the report by Schauer and col-leagues (Schauer et al. 2006, p. 84), HEI’s Health ReviewCommittee states: “They [Schauer and colleagues] con-clude that much of the lighter crustal elements (sulfur,magnesium, aluminum, potassium, calcium, iron, and tita-nium), as well as the trace elements strontium, cadmium,and lead, were attributable to road dust. They also con-cluded that brake dust contributed to a larger portion ofthe roadway emissions of iron, chromium, manganese,copper, zinc, strontium, antimony, and barium. The inves-tigators report that diesel-vehicle tailpipe emissions con-tributed to a large percentage of PM10, vanadium, andcadmium and a smaller amount of calcium, chromium,zinc, strontium, and lead. The combination of gasoline-vehicle tailpipe emissions and tire dust were shown toaccount for a large percentage of the roadway emission ofvanadium, chromium, zinc, strontium, lead, molybdenum,and silver.”
Measurements made at the Chapel site on lake-winddays showed that the PM2.5 mass had substantially ele-vated concentrations of many metals and elements associ-ated with vehicle-related emissions, such as iron, copper,and chromium, and with brake dust, such as manganese,copper, zinc, strontium, and antimony (barium was notmeasured). Elevated levels of EC, along with calcium andother trace elements (strontium, chromium, and zinc), sug-gest the contribution of diesel-vehicle tailpipe emissions.Calcium, magnesium, aluminum, and iron are alsomarkers for road dust. It was difficult to attribute vana-dium, silver, and lead concentrations to plaza emissions,although on lake-wind days both silver and lead werehigher in the Chapel PM2.5 (> 75% of cases) than in theGLC PM2.5. Exploratory principal component analysis forthe Chapel site revealed five components, including a dom-inant first component (factor 1 in Table 18) that containedclear markers for regional transport (sulfur, selenium, and
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arsenic) and some trace metals associated with vehicle emis-sions (tin and antimony). The analysis reasonably separateda component associated with iron, copper, magnesium, cal-cium, and lead (factor 2) from two other components. A thirdcomponent (factor 3) also distinguished vanadium, nickel,and sodium from the other two components. More samplingevents would strengthen these observations.
Several PAHs and NPAHs were elevated in the air down-wind of the plaza. Although these and other compoundswere also present in the urban air of Buffalo, the Chapel-to-GLC ratios for lake-wind days indicate local sources. Truck-stop, tunnel, and emissions tests, as reported by Minegishi(2007), have confirmed the association of some of thesecompounds with heavy-duty diesel emissions. Analysisprofiles were dominated by benzo[b]fluoranthene,benzo[a]pyrene, indeno[1,2,3-c,d]pyrene, chrysene+triphe-nylene, as well as many methylated PAH compounds that
are characteristic of diesel exhaust. Naphthalene, a lighter-molecular-weight PAH also associated with diesel emis-sions, was elevated downwind of the plaza, as was coro-nene, a PAH associated with gasoline emissions.
1-Nitropyrene was substantially elevated at the down-wind Chapel site and highly correlated with truck counts. Itappears that several other locally generated PAHs reactedquickly with hydroxyl and NO3 radicals to form NPAH com-pounds. Several nitrogenated derivatives of naphthalene,phenanthrene, and benz[a]anthracene were elevated aswell. These parent compounds, along with 9-nitrophenan-threne and 3-nitrophenathrene, 1-nitronaphthalene,2-nitronaphthalene, and 1,3-dinitronaphthalene, appear tobe good markers for diesel exhaust, along with 1-nitropy-rene (Collier et al. 1995; Nasrin et al. 1995; Rhead andPemberton 1996).
COMPARISON OF MSATS AT THE PEACE BRIDGE PLAZA AND OTHER SITES
We sampled 43 of the 93 compounds identified in theIRIS list of MSATs (Table 3). Hexane could not be measuredreliably because of field contamination. Acrolein was notpresent above the limits of detection. Chromium III andchromium VI were not differentiated from elemental chro-mium. Figures 62, 63, and 64 provide an overall summaryfor MSAT elements, PAHs, and VOCs, respectively, as mea-sured at the GLC, Chapel, and School sites under lake-windconditions. In addition to the VOC MSATs, Figure 64includes the sum of BTEX compounds, the sum of BTEXplus compounds, and the chlorinated compounds.
Of the MSAT elements, manganese, zinc, strontium,antimony, and lead were elevated at the Chapel site, as dis-cussed previously. All the vehicle-related VOC com-pounds, with the possible exception of MTBE, showedconsistently higher concentrations at the site downwind ofthe Peace Bridge plaza, while none of the chlorinatedMSAT VOCs did. Formaldehyde tended to be higher at theChapel site with lake-wind conditions.
Most of the concentration distributions for the MSATPAHs were higher at the Chapel site. This was not the casefor some compounds identified as diesel-related, notablypyrene and fluoranthene. Yet 1-nitropyrene and 2-nitroflu-oranthene were substantially higher at the Chapel site thanat the GLC site.
Very few studies of traffic hot spots were as comprehen-sive as the Peace Bridge Study in terms of the number oftarget analytes. Furthermore, in this study with the loca-tion of the Peace Bridge at the eastern edge of Lake Erieand the prevailing wind flow, the upwind site (GLC) wasnot influenced by local sources. We compared 12-hourdaytime integrated measurements collected during
Figure 62. Box and whisker plots of concentrations of MSAT elements atthe Chapel (C), GLC (G), and School (S) sites for all data (N = 24) and fordata obtained on lake-wind days (N = 15). Minimum, bottom whisker;25th percentile, bottom of the box; median, center line in the box; mean,“x”; 75th percentile, top of the box; and maximum, top whisker.
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Figure 63. Box and whisker plots of concentrations of MSAT PAHs at the Chapel (C), GLC (G), and School (S) sites for all data (N = 24) and for dataobtained on lake-wind days (N = 14). Minimum, bottom whisker; 25th percentile, bottom of the box; median, center line in the box; mean, “x”; 75thpercentile, top of the box; and maximum, top whisker.
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Figure 64. Box and whisker plots of concentrations of MSAT VOCs, BTEX, and BTEX plus compounds, chlorinated compounds, and carbonyls at theChapel (C), GLC (G), and School (S) sites for all data (N = 24) and for data obtained on lake-wind days (N = 14). Minimum, bottom whisker; 25thpercentile, bottom of the box; median, center line in the box; mean, “x”; 75th percentile, top of the box; and maximum, top whisker.
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summer and winter with measurements from a variety ofstudies conducted in different settings, for different pur-poses, and using different protocols. Nevertheless, it isimportant to place the Peace Bridge measurements in con-text. For PM2.5, we compared our values with values that theEPA reported for selected cities in 2005 (U.S. EPA 2005). Themetropolitan areas used as references were those reported inthe 2007 article linking cardiovascular mortality and mor-bidity in postmenopausal women to particulate air pollution(Miller et al. 2007). Annual average PM2.5 concentrationsranged from 5 µg/m3 in Honolulu, Hawaii, to 21.4 µg/m3 inPittsburgh, Pennsylvania; in the Buffalo–Niagara Falls area,the annual average was 14.7 µg/m3. The mean PM2.5 con-centrations across each of our sites ranged from 13.4 µg/m3
at the GLC site to a high of 15.7 µg/m3 at the Chapel site. Themean at the School site (14.6 µg/m3), in a residential neigh-borhood of west Buffalo, was consistent with the overallmetropolitan average.
As shown in Figure 65, we compared the Buffalo PeaceBridge VOC and carbonyl data with data collected andanalyzed by similar methods in the Toxics ExposureAssessment Columbia and Harvard (TEACH) study in LosAngeles (Sax et al. 2006), as well as the Boston ExposureAssessment in Microenvironments (BEAM) study (Dodson2007). Levels measured in the Buffalo Peace Bridge Studywere generally comparable to levels measured in theBEAM study in metropolitan Boston and lower than levelsmeasured in the TEACH study in Los Angeles.
Elements measured in the Buffalo Peace Bridge Studywere compared with ambient 48-hour concentrations sam-pled in the TEACH study in New York City (Sax et al.2006). Collaborator Dr. Steve Chillrud, of LDEO atColumbia University, was responsible for the analysis byHR-ICP-MS in both studies. Figure 66 compares the PeaceBridge summer 2005 and winter 2006 data with theTEACH New York City summer and winter data. Elements
Figure 65. Box and whisker plots of VOC concentrations at the Chapel, GLC, and School sites in this study and outdoor concentrations from the TEACHstudy in Los Angeles and the BEAM study in Boston. Minimum, bottom whisker; 25th percentile, bottom of the box; median, center line in the box; mean,“x”; 75th percentile, top of the box; and maximum, top whisker.
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are rank ordered by mean ratios based on the Chapel site.Comparison of the data from the two studies shows thatmore sulfur and thallium were found in Buffalo than inNew York City. These elements were correlated to eachother in mass concentrations, suggesting a similar source.Potential sources of thallium have been reported to includecoal combustion, cement factories or resuspension ofcement dust, and electronics industries.
The Peace Bridge PAH data were compared with PAHmeasurements taken by the Integrated Atmospheric Depo-sition Network on the shores of the Great Lakes from twomonitoring stations: Eagle Harbor, Michigan (on LakeSuperior), which represents regional background levels,and Sturgeon Point, New York (on Lake Erie), which is rela-tively near Buffalo and is affected by PAHs from the metro-politan Buffalo area (Cortes et al. 2000). Sturgeon Point datawere compared with the overall means of the Peace Bridgedata set, while the Eagle Harbor regional background datawere compared with the GLC (upwind) data on lake-winddays. There was good agreement between the data for theprimarily particle-bound PAHs, those PAHs starting atbenz[a]anthracene and including heavier compounds, atthe Peace Bridge and the other two sites (Figure 67). For thethree other PAHs to the left of benz[a]anthracene (pyrene,fluoranthene, and phenanthrene) in Figure 67, which are
primarily gas-phase PAHs with some particle-bound com-ponent, levels were higher in the Peace Bridge samples.
It is clear that many MSATs were added to the air as itpassed over the plaza from the west, as evidenced by con-trasting MSAT levels at the downwind site. When windsblow from the lakefront across the plaza, as they do 60% ofthe time, they are unimpeded for the most part. Windvelocities are higher during the day, when truck traffic isthe highest, than at night. The mean wind speed for lake-wind conditions during our campaigns was 4.83 m/sec, incontrast to 2.64 m/sec for city/other-wind conditions. Eventhough vehicle traffic through the Peace Bridge plaza issubstantial, benzene concentrations — a good marker forgasoline emissions in outdoor settings — were less thanhalf the concentrations reported in Los Angeles andHouston for the RIOPA study (Weisel et al. 2005), butslightly higher than outdoor levels measured across metro-politan Boston in the BEAM study (Dodson 2007). Severalchlorinated compounds, such as trichloroethene, chloro-form, and tetrachloroethene, were higher in the TEACHLos Angeles urban samples than in samples obtained nearthe Peace Bridge plaza. In contrast, as an informal checkon analytic methods, we measured carbon tetrachloride, aubiquitous compound in the atmosphere with regionalrather than local sources, and found similar concentra-tions at all three study sites (Chapel, GLC, and School).
The situation was quite different for elements. Levels ofseveral elements were actually higher in Buffalo than inNew York City. Some of the MSAT metals (chromium,arsenic, manganese, and scandium) were higher in theBuffalo samples during the summer than they were in NewYork City ambient samples in the summer, but not duringthe winter. Concentrations of many of the metals in thisstudy, particularly those identified with vehicles (iron,zinc, vanadium, tin, copper, and calcium), were lowerthan concentrations found in New York City during theTEACH study (Sax et al. 2006).
Differences in the sampling and analytic methods usedmake it problematic to compare PAH measurements in thePeace Bridge Study with values obtained elsewhere. How-ever, the study by Choi (2006) of eight heavier-molecular-weight PAHs and pyrene in Krakow, Poland, offers anopportunity for comparison because similar methods wereused to capture PAHs in both particle and gaseous phases.Table 36 presents the mean values for the nine PAHs avail-able from the Krakow study and mean data for the PeaceBridge Study. The Krakow measurements were from48-hour integrated samples taken outside homes or apart-ments of participants living in the metropolitan area,whereas the Peace Bridge samples were 12-hour integratedsamples obtained during weekdays. Nevertheless, except
Figure 66. Seasonal comparison of median element concentrations inBuffalo (this study) and in New York City (TEACH study, in which N = 36for both winter and summer).
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Figure 67. Left: Mean PAH concentrations at the GLC site under lake-wind conditions (N = 14), in the Peace Bridge Study samples overall (N = 67), andin samples taken by the Integrated Atmospheric Deposition Network at Eagle Harbor and Sturgeon Point (N = 100; analyte detection, 42%–100%). Right:Reported relative standard deviations of measurements (SD/mean) in these sample sets. Eagle Harbor is on the shore of Lake Superior and representsregional background levels; data were from November 1990 through December 1997. Sturgeon Point is on the shore of Lake Erie, in the Buffalo area; datawere from December 1991 through December 1997. (Data are from Cortes et al. 2000.)
Table 36. Ambient Concentrations (ng/m3) of Eight Heavy-Molecular-Weight PAHs and Pyrene at the Peace Bridge and in Krakow, Poland
Sum of eight PAHs (above) 72 0.78 0.58 70 43.06 7.02
Pyrene 72 4.67 7.84 70 12.45 2.10
a Peace Bridge data are for the three fixed sampling sites combined.
b Krakow data are from Choi (2006).
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for pyrene, the Peace Bridge levels are only 3% or less of theKrakow levels. For pyrene, the median value is 1.6 ng/m3;the School site experienced one measurement of pyrenefour times the next-highest measurement. For some PAHsfrom diesel emissions, such as indeno[1,2,3-c,d]pyrene,chrysene+triphenylene, and benzo[g,h,i]perylene, thePeace Bridge levels range from 2% to 3% of the Krakowvalues. These compounds showed spatial differenceswithin the Peace Bridge Study consistent with localsources. For the PAHs that are primarily coal-derived,there were no spatial differences across the Buffalo sites,and the levels are less than 2% of those in Krakow. Pyrene,for which diesel emissions are a substantial contributionin the Peace Bridge Study, has a mean value at the Chapelsite of 2.82 ng/m3, which is 23% of the Krakow mean. Theprimary source of these PAHs in Krakow was coal combus-tion, with outdoor levels showing distinct seasonal andspatial patterns (Choi et al. 2008a).
Schauer and colleagues (2003) reported on historic andmore recent PAH levels in Munich, Germany. The pyrenelevels from year-round sampling at a traffic-oriented site in2001–2002 were about 25% less than the Peace Bridgeplaza levels, but other compounds, such as benzo[g,h,i]-perylene, indeno[1,2,3-c,d]pyrene, and benz[a]anthracene,were comparable. Schauer and colleagues (2003) alsoreported that PAH concentrations observed in Munichwere similar to levels reported in metropolitan areas in theU.S. (Baltimore), Australia (Melbourne), Finland (Hels-inki), and Hong Kong. Similar to the Buffalo–Krakow com-parison, contemporary PAH levels in Munich were lowerthan values in Seoul, Naples, London, and Manchester byfactors of 6 to 20 (Schauer et al. 2003).
The Peace Bridge Study utilized a novel approach foracquiring temporally and spatially resolved air pollutionprofiles in the community. Staff equipped with continu-ously recording monitors and GPS instruments walked theneighborhood near the Peace Bridge plaza. During periodswhen the winds were from the west (lake winds), residen-tial streets experienced pPAH concentrations and UFPcounts two to five times the upwind levels measured alongBird Island Pier, a jetty in the Niagara River that passesunder the Peace Bridge. The distributions of concentra-tions show a clear gradient by distance from the plaza.
The association of elevated UFP counts and higher con-centrations of pPAHs with heavy-duty vehicles passingthrough the plaza was previously reported by ClarksonUniversity researchers (Ogulei et al. 2007), who creativelyused size-resolved particle counts to identify seven inde-pendent source profiles: fresh tailpipe diesel exhaust,local street diesel traffic, aged or evolved diesel particles,spark-ignition gasoline emissions, background urban
emissions, heavy-duty diesel agglomerates, and secondaryor transported material. Whereas our P-Trak instrumentsonly provided total UFP counts per cubic centimeter, theClarkson mobile van had an EEPS to measure concentra-tions of submicron-size particles (5.6 to 560 nm) at a timeresolution of seconds.
Figure 68 is a map showing locations where theClarkson mobile van parked to record concentrations ofsubmicron-size particles. The reported distances are mea-sured to an arbitrary center point of the Peace Bridge. Thewind direction was generally southwesterly over the sam-pling period. The Clarkson study upwind (GLC) and R1sites are collocated with the GLC and Chapel sites of thePeace Bridge Study. In addition, Clarkson sites R2, R3, andR4 are within the neighborhood of our Peace Bridge Study.Ogulei and colleagues (2007) operated the mobile vanduring the period of our summer 2004 pilot study.Figure 69 shows the size profiles of particles that theClarkson researchers measured during this time period,when the winds were consistently from the southwest andwest. The elevated number of small particles at the R1 siteindicates the presence of fresh diesel emissions. Zhu andhis colleagues (Zhu et al. 2002a, 2002b) showed that par-ticle counts shift to larger sizes as the distance from road-ways with heavy traffic increases. Consistent with thisfinding the Clarkson data for R2 and R3 sites showed anelevation in counts of particles in the very small sizeranges (< 20 nm) and higher number counts than the R1site in the larger particle size range of 50 to 60 nm. Statis-tical analysis by Ogulei and colleagues (2007) found thatthe site-specific size-resolved particle counts could beattributed to the seven factors reflecting source profiles.
Table 37 indicates that in the Clarkson analysis (Oguleiet al. 2007), fresh diesel particles (factor 1) and aged/pro-cessed diesel particles (factor 3) accounted for 54% of theparticle counts at the Chapel (R1) site and 30% at the R2and R3 sites. By the time the air traveled to the R4 site,some 800 m downwind of the Peace Bridge plaza, the freshdiesel contribution had dropped to less than half of what itwas at the closer sites, but the aged/processed diesel par-ticle contribution had increased from what it was at the R2and R3 sites by nearly as much.
From the results of our mobile monitoring campaigns,we could infer an influence of plaza-related diesel emis-sions a few hundred meters downwind into the neighbor-hood. The neighborhood has residential streets and somecommercial streets, and additional emissions from mobileand stationary sources make it difficult to discern an influ-ence of bridge-related congestion. The case study pre-sented by Ogulei and colleagues (2007) shows promisingresults to support the possibility of attributing particle
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Figure 68. Map showing the location and sampling sites used in the Clarkson University study. The reported distances are measured to an arbitrary centerpoint of the Peace Bridge, marked with a star on the map. The wind direction was generally southwesterly over the sampling period. Adapted with permis-sion from Ogulei et al. 2007, and from the Air and Waste Management Association.
Figure 69. Average number concentrations (particles/cm3) measured atthe six Clarkson University sampling sites on June 24, 2004, when windsconsistently blew from the southwest. Main Street measurements weremade on June 23, 2004. For completeness the entire particle size rangemeasured is shown (6–500 nm). Adapted with permission from Ogulei etal. 2007, and from the Air and Waste Management Association.
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contributions to specific or generic sources. In the traffic andweather conditions prevailing during their discrete sam-pling times, they found submicron-size particles influencedstrongly by diesel emissions that reflect the agglomerate sizerange of 50 to 70 nm from more aged (not fresh) sources atdistances of 800 m (R4 site) into the neighborhood.
In a meta-analysis by Zhou and Levy (2007), our HSPHcolleagues reviewed published reports and governmentdocuments that presented spatial measurements of air pol-lution near roadways or mobile-source hot spots. Fromthese studies they concluded that the effects could be dis-cerned at distances on the order of 100 to 400 m for EC orPM mass concentration, and 100 to 300 m for UFP counts.For studies reporting on NO2, spatial effects were seen to adistance of 500 m. The street-by-street comparisons of UFPsand pPAHs in the Peace Bridge Study, together with the GISanalysis of the data collected for both summer and winterdays with lake winds, suggest that plaza-associated trafficemissions were still discernible 400 m to 500 m into thecommunity. Our findings are consistent with the findings ofthe meta-analysis (Zhou and Levy 2007). However, the spa-tial measurements and GIS analysis of pPAH concentrationsin our Peace Bridge Study indicate that mobile-source pol-lutants perhaps extended farther into the west Buffaloneighborhood than 500 m.
IMPLICATIONS OF FINDINGS
Air pollution clearly has distinct spatial and temporalvariability. The 2003 RFA issued by HEI called for an exam-ination of hot spots where traffic congestion might elevateexposures. There are many examples of permanent, transi-tory, or episodic hot spots, including ports, truck stops, con-struction sites, airports, shopping and sports centers, and avariety of roadway configurations. The distinguishing con-dition is the concentration of mobile sources (constructionequipment, ships, aircraft, etc.) that leads to spatially dis-tinct patterns of pollution concentrations.
California’s MATES-II study (SCAQMD 2000) demon-strated the spatial heterogenicity of mobile-source air pol-lution and the importance of diesel sources as contributorsto the risks associated with air toxics. The Los Angelesfreeway studies of Zhang, Zhu, and their colleagues(Zhang et al. 2004, 2005; Zhu et al. 2004, 2006a) have dem-onstrated that particle dynamics and concentration gradi-ents change as air flows across and downwind ofcongested freeways. A multicity study in Europe foundthat traffic indicators and distance measures were stronglypredictive of NO2 and carbon measurements (Brauer et al.2003). Using a sophisticated time- and size-resolved par-ticle counter, Ogulei and colleagues (2007) were able todistinguish “fresh” diesel emissions in proximity to thePeace Bridge plaza in west Buffalo.
In their meta-analysis, Zhou and Levy (2007) reviewedmany studies of mobile-source air pollution to determinethe spatial extent of pollution impacts. Of course, theextent to which concentrations of mobile sources can bedistinguished downwind would vary depending on sourceconfiguration, meteorology, and analytic factors associatedwith equipment, study design, and laboratory methods.Zhou and Levy (2007) concluded that the spatial extentover which distinct mobile sources might be discernedranged from 100 m to 500 m downwind. Dilution, coagula-tion, transformation, and the addition of other sourceseventually make it difficult in an urban setting to establishwith certainty that a mobile-source hot spot has great spa-tial influence.
In 2004 we responded to the HEI RFA on hot spots witha proposal to study air pollution in a neighborhood of westBuffalo, adjacent to the Peace Bridge plaza. Thousands ofcars and trucks cross over the Niagara River at the easternedge of Lake Erie each day. Vehicles stop for tolls, customsinspection, and duty-free shopping at this busy interna-tional border crossing between the U.S. and its northern
Table 37. Average Source Contributions from All of the Resolved Factors
Source: Adapted with permission from Ogulei et al. 2007, and from the Air and Waste Management Association.
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neighbor. NAFTA has increased the volume of traffic, andsince September 11, 2001, security concerns haveextended wait times for vehicles at the bridge. During ourstudy period, 2005–2006, tollbooths were moved to theCanadian side (where there is more room) to alleviate con-gestion on the U.S. side.
The Peace Bridge plaza was an almost ideal location fora hot spot study, as the bridge authority counted and clas-sified all vehicles every hour. Winds prevailed from thesouthwest quadrant 45% of the time (more in summer),which provided a long fetch across Lake Erie to the west ofthe plaza. The plaza was approximately 500 m from thelakeshore, where a site was established at the Great LakesCenter to monitor background pollutant levels. Dr. JamsonLewbuga-Mukasa had excellent relationships within thecommunity because of his long-term asthma studies. Wearranged for secure monitoring sites directly across thestreet from the plaza at an Episcopal church (Chapel site)and at a community center school (School site) that is cen-tral to the neighboring community and 520 m from theChapel site. In addition, the streets of west Buffalo formed aconvenient parallel and perpendicular grid for mobile mon-itoring along the downwind axis of a southwest wind flow.
Through two pilot studies we established a comprehen-sive study design. All three sites had instruments to obtainintegrated samples capturing 12-hour daytime concentra-tions of 10 targeted VOCs, 4 carbonyls, 28 elements,30 PAHs, and 13 NPAHs, along with PM10, PM2.5, and EC.Though the daytime weekday periods accounted for morethan 80% of the overall truck traffic, we also obtained inte-grated weekend and nighttime samples. Continuous moni-toring instruments recorded values of PM2.5, pPAHs,UFPs, and several gases (O3, NOx, and CO2) every minute.
Our staff were equipped with portable instruments and aGPS to examine spatial patterns of UFPs and pPAHsupwind and across the grid of streets downwind (usually) ofthe Peace Bridge plaza. Two-week mobile monitoring cam-paigns were conducted in summer 2005 and winter 2006.
Air mass trajectories and local wind measurements wereuseful in characterizing the meteorologic conditions influ-encing local transport conditions. For sampling periodscharacterized as having lake-wind conditions (themajority), contaminants associated with mobile sourceswere identified by comparison between sites, particularlycontrasting the Chapel site to the GLC site on the lake-shore, where the meteorologic tower is located.
The Chapel site, as expected, had substantially higherconcentrations of MSATs (as well as other mobile-sourcecontaminants) than the GLC site with southwest winds.Chapel-to-GLC ratios of 2 to 10 were found for EC, BTEXVOCs, acrolein, several metals, PAHs, and NPAHs.
Although the levels of many MSATs increased in the airpassing over the plaza, their absolute concentrations wereless than or comparable to pollution in other urban loca-tions. Formaldehyde levels, for example, were comparableto levels reported in Boston but one fourth of levels reportedin Los Angeles. Benzene levels were half the Los Angeleslevels, with 1,3-butadiene levels equal to those in Boston.Acrolein levels measured during summer days and immedi-ately downwind of the plaza were comparable to otherurban measurements in the U.S. Only a few elements werepresent at levels higher than the average levels reported forNew York City or elsewhere, and these were not MSATs.
Several PAHs and NPAHs substantially increased in theair measured adjacent to the plaza. Benzo[a]pyrene con-centrations, which had a Chapel-to-GLC ratio of 5, werehigher across all three fixed sampling sites with citywinds. Overall values were about one half of thebenzo[a]pyrene levels reported for Elizabeth, New Jersey(Naumova et al. 2002), and for Quebec (Sanderson andFarant 2004), and one seventh of the New York City levels(Choi et al. 2008b). Although residents near the plazaclearly experience higher vehicle-related pollution thanthey would without the Peace Bridge, the concentrationswe measured are comparable to or less than those found inmany urban settings. The good ventilation (higher windspeeds and lack of upwind sources) with airflow off LakeErie provides some benefit in diluting MSATs emitted frombridge traffic.
The Peace Bridge plaza and the adjacent neighborhoodrepresent a classic mobile-source hot spot. Congestion of car,bus, and truck traffic results in a persistent source of emis-sions. Persistent southwest to northwest winds increase PMand EC. We have presented evidence that many compoundsemitted directly from cars and heavy-duty vehicles weresubstantially elevated immediately downwind of the plaza.For example, at the Chapel site the mean benzene concentra-tion was only 1.1 µg/m3 overall, but on days when the windswere from Lake Erie, it was twice as high as the backgroundlevel recorded at the (upwind) GLC site. Similar associationswere observed for all BTEX compounds, as well as styreneand 1,3-butadiene.
The impact of the Peace Bridge traffic on BTEX com-pounds at the School site, only 600 m west of the plazacenter but surrounded by a network of residential and com-mercial streets, was difficult to distinguish. Results for otherindicators of gasoline emissions, such as coronene, azulene,and 1-methylfluorene, indicate a greater impact of mobilesources at the Chapel site. The two NPAHs showing themost enrichment at the Chapel site (with reference to theGLC site) under lake-wind conditions were 1-nitropyreneand 9-nitrophenanthrene; however, their concentrations at
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the School site were less than half the mean concentra-tions over the summer campaign.
A different pattern was seen for secondary pollutants,such as formaldehyde, which had higher concentrations atthe School site than at either the GLC site or Chapel site.Furthermore, we inferred the influence of PAH sources onthe School site that were not prominent at the Chapel siteor GLC site.
The mobile UFP and pPAH monitoring with GPS was aninnovation of our project. It demonstrated how concentra-tion contours can be mapped in urban areas to show sub-block spatial patterns of pollution. We contrasted the UFPand pPAH levels across the west Buffalo streets to those atBird Island Pier in the Niagara River, which is upwindfrom the plaza under lake-wind conditions. Across all spa-tial case studies based on the mobile monitoring results,the median difference between UFP counts for BustiAvenue (the street adjacent to the plaza) and Bird IslandPier (upwind from the plaza) was 15,000 particles/cm3.The difference at the 90th percentile was 25,000 parti-cles/cm3. The 50th and 90th percentile spread betweenthese two locations was even more distinct for pPAHs. Theratio between the differences at the 90th percentile and the50th percentile was 5 (8 ng/m3 for the 50th percentile dif-ference and 42 ng/m3 for the 90th percentile). Spatial plotsshowed that the UFP concentration dropped by half acrossa distance of about eight city blocks from the plaza, whilethe pPAH concentration decreased by two thirds across thesame distance. Statistical analysis showed that at least to adistance of six blocks (600–700 m) from the plaza, UFPand pPAH levels were different from levels monitoredalong Bird Island Pier.
Radial plots of differences in 10-minute average UFP andpPAH values between pairs of sites support the contentionthat mobile sources at the plaza influenced pollution levelsat least 600 m downwind. For all wind directions fromabout 60" to 340", both UFP and pPAH levels were consis-tently higher at the Chapel site than at either the GLC site (asexpected) or the School site. The differences between thevalues at the Chapel and School sites are not as great asthose between either of these sites paired with the GLC site,but they are discernible (particularly for UFPs).
The results of our mobile monitoring and geographicanalysis appear consistent with the size-resolved particlecounts obtained using an EEPS in the Clarkson mobile van(Ogulei et al. 2007). The EEPS measurements by Ogulei andcolleagues (2007) demonstrated that the P-Trak device wasnot responding to particles in the size range below 20 nm,where fresh tailpipe diesel exhaust dominates. A similarpoint was made by Zhu and colleagues (2006b). Perhaps theundercounting of very small UFPs by the P-Trak, which is
more problematic near the Peace Bridge plaza, is an expla-nation why, at least qualitatively, the geographic analysisprovides more resolution of the pPAH data than the UFPdata. The issues about the particle-size response ranges ofdifferent types of equipment used in field studies need fur-ther attention. There are obvious trade-offs among prac-tical considerations of probability, cost, and data analysis.While the study by Ogulei and coworkers (2007) providesvaluable insight with 15 hours of data, our Peace BridgePlaza Study provides spatially and temporally resolvedinformation on the distribution of pPAHs.
Birmili and colleagues (2008) have reported the results ofdetailed time- and size-resolved particle measurements inBerlin, Germany. They used two sites characterized as nearand distant background urban sites to compare roadsidemonitoring and plotted the concentration of particles by sizerelative to the average background concentration. Severalinteresting features of that study are pertinent to our study.The three size classes of particles (11 nm, 17 nm, and 26 nm)were clearly enriched by 8- to 10-fold in the morning (6:00AM) and by 5- to 7-fold in the evening (7:00 PM). The P-Traksused for UFP measurements in our study most likely werenot responding to these freshly produced vehicle-exhaustparticles and would have underestimated the true particlecounts near the source, leading to an underestimate of thespatial gradients.
Birmili and colleagues (2008) did show that for particlesbetween 41 nm and 96 nm the roadside values were 4 to 6times the background counts and displayed a temporalpattern similar to that for the smaller particles. Concentra-tions of larger particles (229–352 nm), while twice as highnear the motorway during high-traffic times of the day, didnot display the same temporal pattern as concentrations ofsmaller particles.
Observations and methodologies developed from theseBuffalo studies need to be considered in the context ofexposure and health risk. Observed associations betweenasthma and proximity to the Peace Bridge plaza by Lwe-buga-Mukasa and colleagues (2002) and Oyana and col-leagues (2004) were based on geographic correlations, aswere other studies reporting associations between healtheffects and proximity to roadway traffic (White et al. 2005;HEI Air Toxics Review Panel 2007). However, the extent towhich differential exposures to size- and composition-resolved particle pollution would alter the reported associ-ations with traffic remains uncertain.
Several studies support the observations of increasedincidence of asthma and asthma symptoms near mobile-source hot spots, typically defined geographically or byNO2 concentrations. The Southern California Children’sHealth Study is now a 12-year follow-up of 5500 children
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with chronic exposure to air pollution (McConnell et al.2006). Adverse effects were reported for those whose resi-dences were closer to major roadways. Asthma prevalenceand school absences decreased with the distance of thechild’s residence from a major roadway. An increase in theprobability of asthma was most distinct for the first 100 maway from the road but still significant to nearly 200 m.
From this same southern California study, Gauderman andcolleagues (2005) showed that childhood asthma increasedby 89% with closer residential proximity to freeways. McCo-nnell and colleagues (2006) showed that treatment for cur-rent asthma was 2.5 times higher and wheezing was 2.7times higher in the group at high risk of traffic exposure.
In an asthma study of children living in Oakland, Cali-fornia, Kim and colleagues (2004) reported a 5% to 8%increase in risk of acute respiratory symptoms when thechild’s school was close to traffic. More recently Kim andcolleagues (2008) reported on an expanded study in theSan Francisco Bay area. This cross-sectional study of 1080children was designed to examine the possible associationof current asthma and other respiratory symptoms with thedistance of their residences from high-traffic roadways.Relevant to the west Buffalo situation, this San FranciscoBay area study included urbanized areas with coastal loca-tions characterized by good regional air quality. Childrenwhose residences were in the highest quintile of exposurehad approximately twice the adjusted odds for currentasthma compared with children whose residences were inthe lowest quintile. Children living within 75 m of aheavily trafficked roadway had the highest risk.
Gauderman and colleagues (2007) examined lunggrowth for a subset of 1500 children over an 8-year period.At 18 years of age, the forced expiratory volume in1 second (FEV1) of children who lived within 500 m of afreeway was 5.5% less over 8 years than the FEV1 for chil-dren living more than 1500 m from a freeway. At age 18, bythis measure of lung performance, these children were7.5% behind “non-freeway exposed children.” Effects ofintermediate distances (500–1000 m and 1000–1500 m)were consistent with an exposure-response function. It isnoteworthy that even children living 500 m to 1000 mfrom a freeway had 5.5% lower FEV1, a difference that wasstatistically significant (P < 0.05) by age 18.
In our study, the UFP and pPAH concentration profilesnear the Peace Bridge plaza and the difference in concentra-tions of MSATs, PM, and EC between the GLC and Chapelsites are consistent with effects of mobile sources on thewest Buffalo neighborhood. The literature on traffic-relatedhealth effects includes associations with increased cardio-vascular, developmental, and cancer risks. Mills and col-leagues (2007) demonstrated ST-segment depression and
ischemic burden in 20 subjects when they were exposed todilute diesel exhaust during 15-minute intervals of exer-cise-induced stress as compared with breathing clean fil-tered air. In a European study, Peters and colleagues (2004)showed a consistent association between time spent incars, on public transport, or on motorcycles and anincreased risk of myocardial infarction. This case-cross-over study examined activities before onset of myocardialinfarction in 691 subjects who survived for at least24 hours. Exposure to traffic within 1 hour of the eventhad an odds ratio of 2.92 (confidence interval, 2.22–3.83).Exposure to vehicle exhaust was associated with a 36%increase in low birthweights and a 27% increase in prema-ture births in a Los Angeles County study by Wilhelm andRitz (2005). Earlier, Ritz and colleagues (2002) had associ-ated a 3-fold increase in cardiac birth defects with traffic-related pollutants in a southern California study. PAHs aremutagens as well as carcinogens in humans and may playa role in both prenatal and postnatal reproductive anddevelopmental outcomes (Perera et al. 1998; Dejmek et al.2000; Perera et al. 2003). Choi and colleagues (2008b) asso-ciated prenatal exposures to PAHs with a risk of intrau-terine growth restriction, including small size forgestational age and preterm delivery, among African Amer-icans (but not Dominicans) in New York City. Adversebirth outcomes were also reported by Choi and colleagues(2006) in a study conducted in Krakow, Poland. We com-pared the concentrations of eight heavy-molecular-weightPAHs measured at the Chapel site with concentrationsreported for New York City and Krakow (Choi 2006; Choiet al. 2008b). As expected, wintertime coal combustionwas the dominant source of PAHs in Krakow. For the sumof the eight heavy-molecular-weight PAHs, the Krakowconcentration (43.06 ng/m3) was more than 50 times themean Chapel concentration (0.78 ng/m3) and more than 10times the New York City concentration (3.2 ng/m3). Theindividual PAH concentrations at the Chapel site averagedbetween one fifth and one half of the New York City levels.
We conducted intensive field campaigns to provide acomprehensive assessment of many MSATs. The purposewas to characterize the influence of a permanent mobile-source hot spot on neighborhood air pollution. To ourknowledge this study is among only a few comprehensiveexaminations of VOCs, carbonyls, PAHs, and NPAHs, alongwith particle mass and EC, conducted at mobile-source hotspots. The study also demonstrated the use of portable airmonitors and GIS analysis to assess spatial and temporalvariability in air pollution across a community. Although itis reasonable to infer from published reports a health risk toa community chronically and differentially exposed tomobile-source air pollutants, this study provides only a
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partial examination of long-term exposures. Since the studyperiod, changes have occurred in the configuration of theplaza, the neighborhood, fuel composition, vehicle controls,and fleet composition that could moderate the influence ofthe Peace Bridge plaza on the community. With these quali-fications in mind, we nevertheless recommend that ambientair monitoring, as well as respiratory and cardiovascularhealth studies, be conducted in west Buffalo.
LIMITATIONS
The UFP counts and pPAH levels were elevated andmore uniformly spatially distributed when the wind camefrom the east of our study neighborhood (city-wind condi-tions). There are many urban sources for pPAHs and UFPs,which complicates any analysis of traffic-related hot spotsor plumes in this area.
The risk associated with traffic congestion at the PeaceBridge crossing for this community cannot be adequatelyassessed with the results of our field campaigns. Ambientconcentrations for these MSATs would be substantiallylower in the absence of the plaza. Actual exposures andthe corresponding risks will depend on meteorologic con-ditions, the penetration of pollutants into dwellings, and acomplex set of demographic factors.
Although the two pilot studies and two field campaignscaptured the predominant wind and traffic profiles for thestudy area, our sampling sessions only covered a fewweeks in summer and winter. Combining the data for threeof the sampling sessions only gave us 24 weekdays, with9 classified as city/other-wind days and 15 classified aslake-wind days. This number of cases allowed for only lim-ited statistical analysis, even though inferences could bemade because of the distinction between results at theupwind and downwind sites. In addition, the nighttime dataare limited. To maximize resources we focused on 12-hourdaytime samples. Although the measured concentrationswere much lower at night than during the day for all pollut-ants, lower nighttime wind speeds may have revealed night-to-night variations for those pollutants, as well as differentspatial patterns. In our limited nighttime data, NPAHs weremuch more prevalent than they were in the daytime.
Acrolein levels in the vicinity of the Peace Bridge werelower than expected. In the first (summer 2004) pilot study,we sampled for acrolein passively using the EOSHI DNSH-based method. None of our acrolein samples were signifi-cantly different from the field blanks. We switched to anactive system, OSHA Method 52 for acrolein, for the second(winter 2005) pilot study and two subsequent field cam-paigns. The detection limits for this method were 2.8 µg/m3
for a 12-hour sample and 0.5 µg/m3 for a 60-hour sample.During the summer 2005 sampling session, University of
California–Davis colleagues working on a sensitive acroleinsampling method offered to collect samples alongside oursamples at the Chapel and GLC sites using their mistchamber method (Seaman et al. 2006), which has an ana-lytic acrolein detection limit of 0.04 µg/m3. For these10-minute samples, the highest value at the Chapel site was0.46 µg/m3 and the average value measured at the Chapelsite was 0.28 µg/m3 (10 times lower than the detection limitfor the 12-hour OSHA Method 52 samples). The averageconcentration measured by the University of California–Davis method at the GLC site was < 0.04 µg/m3.
This study focused on measurements of toxic pollutantswithout linking them to health effects. It identified manytraffic-related toxic pollutants that penetrated deep intothe nearby community. Except for UFP counts, most othermeasurements were integrated over time and may notreveal short-term spikes that might be of clinical or healthconsequence, because a short-term exposure may trigger aprolonged adverse response that lasts for hours or days in asusceptible individual. Furthermore, sensitized individ-uals may respond to very low levels of pollutants thatwould normally not bother nonsensitized persons. It isalso apparent that numerous pollutants are involved; thecombined health effects of chronic exposure to such mix-tures, especially during periods of lung development andgrowth (up to 20 years of age), are unknown. When com-paring pollutant levels and implying health effects, it isimportant to take into account the individual suscepti-bility factors: for example, in Los Angeles, the predomi-nant Latino population is of Mexican descent, a group inwhich asthma prevalence is low. On Buffalo’s west side,the Latino population is predominantly of Puerto Ricandescent, a group that, for unknown reasons, has highasthma rates that are well documented. We do not yet haveany information on how traffic-related pollutants interactwith or affect other environmental exposures that alsoimpact the respiratory or cardiovascular health of nearbyresidents. We recommend mechanistic studies of the fac-tors and pathways that contribute to inception of asthmaand maintenance of high asthma morbidity in Buffalo’swest-side community.
ACKNOWLEDGMENTS
The Buffalo Peace Bridge Study was successful becauseof the assistance and generosity of many. Steven Vermetteat Buffalo State College arranged access to the Great LakesCenter (GLC), where the late Captain John J. Friedhoff andCaptain Caleb P. Basiliko allowed us to use rooms, labora-tories, refrigerators, and computers; this space was our
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staging area for the field study. Our upwind lakeshore sitewas located on the GLC property, and we used their mete-orologic data collected on site. The manager of the Epis-copal Church Home provided us with secure locationsimmediately adjacent to the plaza. The manager of theSouthwest Community Center allowed us to locate ourthird fixed-monitoring site in the neighborhood.
Major Scott Newberg (U.S. Army), while on assignmentat HSPH, made significant contributions to our study. Heassembled equipment and built the sampling platform; hedesigned and managed the mobile monitoring componentof the study; and he supervised the team of field staffthroughout the three campaigns, including cold wintersand hot summers. Scott was always on duty, and we arevery grateful for his invaluable participation.
The intensity of field work was shared by collaboratorsfrom Clarkson University. Joining us for summer samplingwere Professors Phil Hopke, Andrea Ferro, and PeterJacque, along with their graduate students, led by TimMcAuley. Also during one summer campaign, ProfessorThomas Cahill of the University of California–Davis sent ateam led by Vincent Seaman to measure acrolein. Theirdetailed short-term measurements confirmed that locallevels of acrolein were below the detection limits of ouranalytic methods.
A field study is only as successful as the reliability of thelaboratory analyses. We were fortunate to have the collabora-tion of Professor Joel Baker, who provided technical adviceand interpretation of the PAH and NPAH analyses performedby Bernie Crimmins and Taeko Minegishi at the ChesapeakeBiological Laboratory, University of Maryland Center forEnvironmental Science. The HR-ICP-MS elemental analysiswas done at Lamont-Doherty Earth Observatory, ColumbiaUniversity, by Dr. Jamie Ross under the direction of Dr. SteveChillrud, who provided insightful interpretation of the ele-mental data. Dr. Jim Zhang of the Environmental and Occu-pational Health Institute at Rutgers University provided uswith analysis of aldehyde passive samplers.
The Organic Chemistry Laboratory at HSPH did an out-standing job with the VOC and aldehyde measurements,and the gravimetric and reflectance analyses. We areindebted to Larisa Altshul, Brian LaBrecque, Scott Fors-berg, and Beatriz Vinas for their analytic skills.
We thank Professor Louise Ryan, Professor Chris Paciorek,and Subha Guha of the HSPH Department of Biostatisticsfor their advice and input on the study design and dataanalysis. Eunice Lee, a master’s student in the Exposure,Epidemiology and Risk Program at HSPH, performed addi-tional analysis of the UFP and pPAH measurements at theChapel site to explore relationships with wind speed,wind direction, and traffic counts.
We recognize the important in-kind contributions ofmany who generously loaned us equipment that allowedus to simultaneously outfit three fixed sites and equip fourstaff making neighborhood surveys of UFPs and pPAHs.These are Luisa Molina of MIT, George Allen of NortheastStates for Coordinated Air Use Management (NESCAUM),Andrea Ferro and Phil Hopke of Clarkson University, JalalGhaemghami of the Boston Public Health Commission,and Petros Koutrakis of HSPH. HSPH engineer Steve Fer-guson, on whom we have come to rely in all of our fieldstudies, gave us critical advice on sampling system design.Others at HSPH who provided review and constructivecomments are Drs. Gary Adamkiewicz, Robin Dodson, andMiranda Loh.
The Peace Bridge Authority provided traffic counts andassisted us with access to the plaza and bridge, and weespecially thank Ron Rienas for coordinating these activi-ties with his staff.
Paulette (Wydro) Niewczyk and Dr. Jamson Lwebuga-Mukasa at the University at Buffalo arranged for our ter-rific Buffalo field staff, whose early morning and lateevening work shifts on hot summer and snowy winter daysdid not diminish their enthusiastic and reliable help.
We acknowledge the $25,000 contribution by the AkiraYamaguchi Endowment at HSPH in support of laboratoryanalysis and methods development. In addition, HarvardNational Institute of Environmental Health SciencesCenter for Environmental Health Grant P30 ES00002 pro-vided support for our HSPH laboratory equipment mainte-nance, basic quality assurance, and methods development.
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APPENDIX A. Data Quality
INTEGRATED SAMPLES
For each analytic sample, data quality reports are pre-sented in tabular form. The reports present the followingindicators of data quality for each target analyte in eachsampling session: mean blank value, the LOD calculatedfrom the standard deviation of the field blanks (3 ' SD), of
the total samples the percentage that were above the LOD,and the relative precision (100 ' RMSE/!2) calculatedfrom the duplicate pairs. The data quality reports are pre-sented in Table A.1 for PM2.5, PM10, and reflectance anal-ysis; Table A.2 for carbonyls; and Table A.3 for VOCs. Anadditional data quality indicator is presented in Table A.4
Table A.1. PM2.5 and PM10 Data Quality Statisticsa
a LOD is 3 times SD of blanks; relative precision is 100 ' RMSE/!2.
114114
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Tabl
e A
.2.
Car
bony
l Dat
a Q
uali
ty S
tati
stic
sa
Car
bony
l
Win
ter
2005
Sum
mer
200
5W
inte
r 20
06
Mea
n B
lank
V
alue
(µg)
LOD
(µ
g) S
ampl
es>
LOD
(%)
Mea
n B
lank
V
alue
(µg)
LOD
(µ
g) S
ampl
es>
LOD
(%)
Mea
n B
lank
V
alue
(µg)
LOD
(µ
g) S
ampl
es>
LOD
(%)
Form
alde
hyde
00
100
0.1
0.08
850.
000.
0388
Ace
tald
ehyd
e0.
030.
0810
00.
160.
1195
0.00
0.01
88A
ceto
ne0.
020.
0610
00.
822.
8314
0.02
0.11
75
a LO
D is
3 ti
mes
SD
of
blan
ks.
Tabl
e A
.3.
VO
C D
ata
Qua
lity
Sta
tist
icsa
Win
ter
2005
Sum
mer
200
5W
inte
r 20
06
VO
CLO
D(n
g)
Sam
ples
> LO
D(%
)
Dup
Pair
s>
LOD
Rel
Pre
(%
)LO
D(n
g)
Sam
ples
> LO
D(%
)
Dup
Pair
s>
LOD
Rel
Pre
(%)
LOD
(ng)
Sam
ples
> LO
D(%
)
Dup
Pair
s>
LOD
Rel
Pre
(%)
1,3-
But
adie
ne1.
0227
.60
0.00
100
933
2.09
9811
34M
TB
E0.
0086
.23
420.
0057
989
1.21
3511
32B
enze
ne1.
1096
.63
103.
0810
09
292.
0698
1118
Tolu
ene
0.10
100.
03
242.
2510
09
371.
5298
1117
Ethy
lben
zene
0.94
96.6
315
0.00
100
924
0.10
9811
18m
&p-
Xyl
ene
0.48
82.8
316
0.86
100
924
0.43
9811
17St
yren
e0.
0779
.33
270.
0090
948
0.80
9811
34o-
Xyl
ene
0.00
89.7
314
0.00
100
924
0.11
9811
17
Chl
orof
orm
0.00
24.1
10.
0055
362
0.17
9811
461,
1,1-
Tric
hlor
oeth
ane
0.00
82.8
37
0.00
100
932
0.21
9811
23C
arbo
n te
trac
hlor
ide
0.00
82.8
35
0.00
100
932
0.09
9811
252-
Met
hylh
exan
e0.
0096
.63
90.
0010
09
250.
1798
1134
2,3-
Dim
ethy
lpen
tane
0.00
93.1
315
0.00
100
927
0.17
9811
423-
Met
hylh
exan
e0.
0096
.63
120.
0010
09
280.
2598
1145
Tric
hlor
oeth
ene
0.12
62.1
12.
2610
02
790.
1598
1119
2,2,
4-Tr
imet
hylp
enta
ne0.
0096
.63
5023
.423
974
1.76
9811
15
Met
hylc
yclo
hexa
ne0.
0096
.63
110.
0098
927
0.13
9811
27Te
trac
hlor
oeth
ene
0.00
79.3
327
0.00
503
300.
0798
1112
1,4-
Dic
hlor
oben
zene
0.36
72.4
331
0.00
381
620.
3898
1117
a LO
D is
3 ti
mes
SD
of b
lank
s. R
el P
re is
rel
ativ
e pr
ecis
ion:
100
' R
MSE
/!2.
115
J. Spengler et al.
115
for the VOC analysis containing the percentage recoveriesof the VOC field spikes for each sampling session. In TableA.5, VOC data for the 60-hour daytime samples are com-pared with the average of the five corresponding 12-hourdaily samples per week by site.
Table A.6 presents the data quality statistics for thewinter 2005, summer 2005, and winter 2006 elementalanalysis. In Table A.7, the elemental data for the 60-hourdaytime samples are compared with the average of the fivecorresponding 12-hour daily samples per week and site.Table A.8 presents the data quality report for the winter2005, summer 2005, and winter 2006 PAH analyses, andTable A.9 presents the data quality report for the NPAHanalyses. NPAH analysis was not performed on the winter2006 samples. Table A.10 contains descriptive statistics of
the laboratory and field surrogates of the PAHs and NPAHsfor the winter 2005, summer 2005, and winter 2006campaigns. Cubic regressions of molecular weight of thetarget analyte against percentage recoveries of each labora-tory and field surrogate (eight data points) were calculatedfor each PAH sample. The PAH samples, including blanks,were each corrected for recovery from the regression equa-tion calculated from that sample’s surrogates. NPAH sam-ples were corrected by molecular weight group of thelaboratory surrogates. All NPAH samples, includingblanks, were corrected for recovery based on the per-centage recovery of the surrogate representing each molec-ular weight group. In Table A.11, the PAH data for the60-hour daytime samples are compared with the average ofthe five corresponding 12-hour daily samples per weekand site.
Table A.4. Mean Percentage Recovery of VOC Field Spikesa
a Tubes were spiked before departure on the field trip, taken to the field, stored with the samples, and returned to the lab and analyzed with the samples. These quality control samples were collected to document that there was no 1,3-butadiene loss in the actual VOC samples. All compounds were recovered within the range of - 30% to 100%. Recoveries between 70% and 130% are considered acceptable. a-Pinene and d-limonene were not included in the spike mixture. All compounds were spiked with 49.75 ng except for dichloropropene, which was spiked with 199.00 ng.
116116
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Tabl
e A
.5.
VO
C C
once
ntra
tion
(ng/
m3 )
in 6
0-H
our
Day
tim
e (5
-Day
) Wee
kday
Sam
ples
Com
pare
d w
ith
Ave
rage
Con
cent
rati
on o
f the
Fiv
e 12
-Hou
r Sa
mpl
es fo
r th
e W
eek
by F
ield
Sam
plin
g Si
te in
Sum
mer
200
5
VO
C
Cha
pel
(Jul
y 18
, 200
5)C
hape
l(J
uly
25, 2
005)
GLC
(Jul
y 18
, 200
5)Sc
hool
(Jul
y 18
, 200
5)Sc
hool
(Jul
y 25
, 200
5)R
elat
ive
Prec
isio
na12
-Hou
r5-
Day
12-H
our
5-D
ay12
-Hou
r5-
Day
12-H
our
5-D
ay12
-Hou
r5-
Day
1,3-
But
adie
ne14
4.05
61.3
213
8.05
89.7
743
.95
18.6
098
.98
77.8
793
.88
100.
3216
.6M
TB
E15
.88
0.00
14.6
311
.48
5.44
3.28
23.1
810
.04
15.9
79.
1728
.6B
enze
ne1,
286.
0114
16.4
913
83.4
817
49.5
644
8.43
623.
401,
128.
4310
43.0
110
70.7
014
67.4
07.
2To
luen
e2,
826.
0535
41.4
430
09.4
929
67.3
189
4.39
1311
.35
3,14
2.57
4464
.38
4344
.92
5556
.55
8.7
Ethy
lben
zene
559.
3947
4.92
578.
2353
5.34
164.
7115
4.69
443.
5537
9.89
493.
2657
1.99
4.5
m&
p-X
ylen
e1,
875.
9715
18.4
719
26.7
617
91.9
246
0.27
468.
821,
467.
7112
78.9
516
50.8
020
24.6
95.
5o-
Xyl
ene
716.
5660
8.22
741.
3868
6.87
185.
8520
1.07
598.
1449
8.86
649.
0978
0.08
5.1
Styr
ene
136.
4580
.68
114.
1490
.70
27.1
833
.60
96.8
189
.78
130.
2714
6.65
9.5
Chl
orof
orm
15.7
535
.77
27.2
217
.07
7.47
22.5
162
.47
49.5
058
.43
17.7
322
.8Tr
ichl
oroe
than
e10
1.47
93.4
091
.35
97.8
887
.21
65.5
198
.10
89.1
998
.06
107.
474.
2C
arbo
n te
trac
hlor
ide
600.
2850
9.06
600.
7848
8.82
537.
6338
7.14
494.
0648
9.40
614.
3255
2.75
5.8
2-M
ethy
lhex
ane
476.
4037
0.54
431.
2944
9.47
191.
3482
.63
356.
3242
1.34
495.
4245
4.45
6.5
2,3-
Dim
ethy
l-pe
ntan
e21
3.51
202.
0120
8.84
227.
4789
.48
64.0
315
3.61
406.
8622
5.03
211.
4118
.1
3-M
ethy
lhex
ane
487.
3142
5.88
477.
1852
4.48
218.
3912
0.88
389.
8472
1.61
609.
8052
1.63
11.5
Tric
hlor
ethe
ne31
.78
77.4
110
.83
30.5
749
.13
49.7
750
.55
96.7
810
9.95
50.8
622
.82,
2,4-
Trim
ethy
l-pe
ntan
e43
4.00
648.
2310
45.2
767
0.39
199.
5624
1.01
279.
7145
4.26
726.
6869
8.80
12.3
Met
hylc
yclo
hexa
ne15
3.03
155.
1514
7.76
172.
9860
.13
68.6
813
7.54
198.
7014
9.66
169.
817.
01,
4-D
ichl
orob
enze
ne3.
6049
.14
23.7
598
.92
12.0
867
.62
275.
1929
6.65
592.
4498
6.26
24.0
Form
alde
hyde
856
1916
7497
3911
3,84
644
336
5132
865
.5A
ceta
ldeh
yde
12,3
4115
2041
0673
18,5
2565
52,
766
259
3448
1215
67.8
Ace
tone
17,4
1156
215
0524
122
,726
019
,185
1889
3979
3670
.0
a R
elat
ive
prec
isio
n is
100
' R
MSE
/!2.
117
J. Spengler et al.
117
Tabl
e A
.6.
Dat
a Q
uali
ty S
tati
stic
s fo
r El
emen
ts (N
= 4
3)a
Elem
ent
Win
ter
2005
Sum
mer
200
5W
inte
r 20
06
Mea
n B
lank
Val
ue (n
g)
< LO
DD
upPa
irs
> LO
DR
el P
re
(%)
Mea
n B
lank
Val
ue
(ng)
< LO
DD
upPa
irs
> LO
D
Mea
n B
lank
Val
ue (n
g)
< LO
D
N%
N%
Rel
Pre
(%
)N
%
Be
0.5
2366
336
.70.
0212
286
70.
011
5N
a 11
01
36
11.6
140
716
85.
368
00
Mg
290
06
21.4
210
010
411
15
Al
720
3394
036
024
564
29.6
211
5
S 23
00
06
5.8
140
00
101.
960
00
K
360
00
618
.111
01
210
3.5
381
5C
a 14
07
206
3079
01
210
1311
00
0Sc
0.
025
146
43.6
0.32
4298
00.
0615
75
Ti42
00
630
.912
00
010
3.8
7.7
420
V8.
1 5
146
8.5
2.9
49
93.
40.
500
0C
r 22
2777
07.
1 31
723
9.5
2019
95M
n4.
0 1
36
13.7
4.0
00
103.
30.
821
5
Fe
240
00
622
.942
09
218
5.9
661
5C
o 0.
062
66
21.4
0.07
00
105.
20.
051
5N
i 21
926
672
.36.
7 31
723
219.
9 16
80C
u 23
2057
211
5.5
2017
405
74.9
3.4
15
Zn
450
2467
310
3.2
170
2865
116
.269
210
As
1610
276
74.9
1.6
00
106.
30.
911
5Se
1.
8 6
176
14.4
2.8
716
73.
80.
750
0Sr
0.
530
06
18.7
5.8
3070
215
.70.
390
0
Ag
0.31
00
612
.10.
2535
812
16.3
0.07
525
Cd
0.63
26
620
.80.
8013
304
9.1
0.06
15
Sn
0.54
00
618
.11.
9 0
010
4.6
0.33
15
Sb
0.20
00
66.
90.
410
010
20.
301
5
Cs
0.00
00
66.
70.
0815
355
5.4
0.01
15
La0.
210
06
15.8
0.48
1023
87.
10.
090
0T
l0
0.02
00
104.
80.
022
10Pb
6.
1 1
36
8.2
7.1
00
1014
.21.
9 1
5
a R
el P
re is
rel
ativ
e pr
ecis
ion:
100
' R
MS
E/!
2. O
nly
ana
lyte
s w
ith
both
sam
ple
s in
the
dupl
icat
e pa
ir a
bove
the
LO
D w
ere
incl
uded
in th
e re
lati
ve p
reci
sion
cal
cula
tion
; dup
lica
te p
airs
in w
inte
r 20
06 w
ere
not a
naly
zed.
118118
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
a LOD is 3 times SD of blanks. Rel Pre is relative precision: 100 ' RMSE/!2. Only analytes with both samples in the duplicate pair above the LOD were included in the relative precision calculation.
120120
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Table A.8 (Continued). PAH Data Quality Statisticsa
a LOD is 3 times SD of blanks. Rel Pre is relative precision: 100 ' RMSE/!2.Only analytes with both samples in the duplicate pair above the LOD were included in the relative precision calculation.
121
J. Spengler et al.
121
Table A.8 (Continued). PAH Data Quality Statisticsa
a LOD is 3 times SD of blanks. Rel Pre is relative precision: 100 ' RMSE/!2. Only analytes with both samples in the duplicate pair above the LOD were included in the relative precision calculation.
122122
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Table A.8 (Continued). PAH Data Quality Statisticsa
a LOD is 3 times SD of blanks. Rel Pre is relative precision: 100 ' RMSE/!2. Only analytes with both samples in the duplicate pair above the LOD were included in the relative precision calculation.
a LOD is 3 times SD of blanks. Rel Pre is relative precision: 100 ' RMSE/!2. Only analytes with both samples in the duplicate pair above the LOD were included in the relative precision calculation.
124124
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Table A.10. Surrogate Recovery Statistics for PAH and NPAH Samples
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
Tabl
e A
.11
(Con
tinu
ed).
Com
pari
son
of P
AH
Con
cent
rati
on (n
g/m
3 ) in
60-
Hou
r D
ayti
me
(5-D
ay) W
eekd
ay S
ampl
es to
the
Ave
rage
Con
cent
rati
on o
f the
Fi
ve 1
2-H
our
Sam
ples
for
the
Wee
k by
Fix
ed S
ampl
ing
Site
in S
umm
er 2
005a
PAH
Cha
pel
(Jul
y 18
, 200
5)C
hape
l(J
uly
25, 2
005)
GLC
(Jul
y 18
, 200
5)G
LC(J
uly
25, 2
005)
Scho
ol(J
uly
18, 2
005)
Rel
ativ
e Pr
ecis
ion
12-H
our
5-D
ay12
-Hou
r5-
Day
12-H
our
5-D
ay12
-Hou
r5-
Day
12-H
our
5-D
ay
4,5-
Met
hyle
neph
enan
thre
ne0.
380.
070.
390.
090.
380.
060.
320.
140.
690.
4329
.61-
Met
hyla
nthr
acen
e2.
0 1.
7 1.
8 1.
8 1.
4 1.
0 0.
740.
754.
3 5.
9 10
.81-
Met
hylp
hena
nthr
ene
1.6
1.3
1.3
1.4
1.2
0.75
0.69
0.58
4.5
6.2
12.9
9-M
ethy
lant
hrac
ene
0.02
0.03
0.02
0.03
0.02
0.02
0.02
0.02
0.01
0.03
16.0
9,10
-Dim
ethy
lant
hrac
ene
0.38
0.28
0.32
0.19
0.23
0.16
0.13
0.12
0.50
0.74
13.7
Fluo
rant
hene
173.
7 5.
3 4.
6 13
7.4
123.
8 79
130
25.8
Pyre
ne7.
3 2.
4 3.
1 2.
6 6.
1 3.
3 5.
2 2.
0 31
5226
.63,
6-D
imet
hylp
hena
nthr
ene
0.02
0.05
0.02
0.03
0.01
0.01
0.01
0.01
0.02
0.09
37.1
Ben
zo[a
]flu
oren
e0.
100.
050.
080.
050.
110.
060.
080.
060.
320.
5725
.6
Ret
ene
0.09
0.06
0.07
0.05
0.15
0.07
0.05
0.06
0.15
0.20
15.0
Ben
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]flu
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040.
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080.
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060.
040.
190.
3827
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yclo
pent
a[c,
d]py
rene
0.02
0.01
0.04
0.01
0.02
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0.01
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0.17
0.06
46.6
Ben
z[a}
anth
race
ne0.
060.
020.
060.
030.
030.
010.
020.
020.
110.
1621
.0C
hrys
ene+
Trip
heny
lene
0.11
0.03
0.12
0.08
0.09
0.04
0.09
0.04
0.66
0.84
14.0
Nap
htha
cene
0.01
0.00
0.01
0.00
0.01
0.00
0.01
0.0
0.01
0.00
47.7
4-M
ethy
lchr
ysen
e0.
0 0.
0 0.
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0 0.
000.
0 0.
000.
0 0.
0 0.
0087
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enzo
[b]f
luor
anth
ene
0.05
0.01
0.06
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0.03
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0.07
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zo[k
]flu
oran
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.9
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ethy
lben
z[a]
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race
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0 0.
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0 0.
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0 0.
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0 0.
0 10
5.9
Ben
zo[e
]pyr
ene
0.03
0.01
0.06
0.02
0.03
0.01
0.03
0.01
0.06
0.04
27.7
Ben
zo[a
]pyr
ene
0.03
0.01
0.04
0.02
0.02
0.01
0.02
0.01
0.0
0.02
33.7
Pery
lene
0.00
0.0
0.01
0.0
0.01
0.0
0.0
0.0
0.0
0.0
84.1
3-M
ethy
l-ch
loan
thre
ne0.
040.
0 0.
050.
0 0.
0 0.
0 0.
0 0.
0 0.
0 0.
0 10
0.9
Inde
no[1
,2,3
-c,d
]pyr
ene
0.04
0.02
0.04
0.02
0.05
0.01
0.04
0.02
0.08
0.04
25.4
Dib
enz[
a,h+
a,c]
anth
race
ne0.
000.
0 0.
020.
0 0.
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000.
0 0.
0 0.
0 0.
0 90
.4B
enzo
[g,h
,i]pe
ryle
ne0.
070.
020.
090.
060.
030.
020.
040.
020.
080.
0423
.4A
nthr
anth
rene
0.0
0.0
0.01
0.0
0.00
0.0
0.0
0.0
0.0
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111.
8C
oron
ene
0.02
0.00
0.02
0.01
0.01
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0.0
0.00
0.0
0.01
42.7
a R
elat
ive
prec
isio
n is
100
! R
MSE
/!2.
Dup
lica
te p
airs
in w
inte
r 20
06 w
ere
not a
naly
zed.
In
win
ter
2006
, 60-
hour
sam
ples
wer
e no
t ana
lyze
d d
ue to
bud
get l
imit
atio
ns.
127
J. Spengler et al.
127
TREATMENT OF SAMPLES BELOW THE LOD
For all sample types actual values of samples below theLOD were used in all calculations of summary statisticsand statistical tests. This is known as the instrument-gen-erated data method (Antweiler and Taylor 2008). In sum-mary tables of Appendix B, negative and zero values werereplaced with a dashed line (---). For log transformations,zero values and negative values were replaced with a valueat half of the instrument detection limit.
ACROLEIN SAMPLES
Based upon an assessment of the acrolein levels measuredby the University of California–Davis method, we chose notto analyze any of the acrolein samples collected by OSHAMethod 52. The detection limit of OSHA Method 52, basedon the sample volumes collected, was 1 µg/m3, which is sub-stantially higher than the highest value reported by the Uni-versity of California–Davis of 0.46 µg/m3.
CONTINUOUS SAMPLES
The results of the collocation of the PAS 2000CE instru-ments used in our study are presented in Table A.12. Theresults from multiple instruments were compared. In twosessions we collected a total of 374 minutes of data fromcollocated instruments. Levels measured during this exer-cise were below the reported analytic sensitivity; theinstruments were responding to PAH concentrations in therange of 1 to 10 ng/m3, with a coefficient of variation ofapproximately 35%. Most values obtained during the col-location exercises were at or below the analytic sensitivityof the instrument of 10 ng/m3; however, the mean rangeswere 2.4 ng/m3 for summer 2005 and 3 ng/m3 for winter
2006 on an instrument with a sensitivity of 1 ng/m3. Twoinstruments borrowed from other researchers for thesummer 2005 campaign had not arrived on site at the timethe collocation exercise was performed. The summer 2005collocation was conducted at the GLC site, and the winter2006 collocation was conducted at the Chapel site.
We also compared the PAS 2000CE continuous values ofpPAHs and the sum of 15 PAHs derived from the 12-hourPUF sampling. These 15 PAHs were a subset of all of thePAHs measured and represent heavier-molecular-weightcompounds more likely to be in the particle-bound phaseversus the gas phase. Analysis was restricted to data col-lected at the GLC and School sites to avoid ambiguities of“fresh” PAHs generated by mobile sources near the Chapelsite. The correlation coefficient between PAS 2000CE andthe sum of 15 PAHs was 0.64. The ratio of PAS to PUF (15PAHs) was approximately 8.
The PAS 2000CE only samples pPAHs, while both theparticle-bound phase and the gas phase are captured in theintegrated samples. Separate analyses can be performed onthe integrated sample filter and PUF to separate these frac-tions, but this would double the sampling cost. To addressthe correspondence between the integrated and contin-uous PAH methods, we compared the average PAS 2000CEvalues at the School and GLC sites with the sum of valuesfor compounds primarily found in the particle phase(high-molecular-weight PAHs) with the integrated Chem-Comb samples: chrysene+triphenylene, naphthacene,4-methylchrysene, benzo[b]fluoranthene, benzo[k]fluoran-thene, dimethylbenz[a}anthracene, benzo[e]pyrene,benzo[a]pyrene, perylene, 3-methylchloanthrene,indeno[1,2,3-c,d]pyrene, dibenz-[a,h+a,c]anthracene,benzo[g,h,i]perylene, anthranthrene, and coronene. Some
Table A.12. PAH Results (ng/m3) from Collocated PAH Instruments
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
of the high-molecular-weight PAHs listed above occur inthe gas phase as well as the particle-bound phase; in addi-tion, some lower-molecular-weight PAHs that are preva-lent in higher concentrations than the high-molecular-weight PAHs may have a small fraction in the particle-bound phase. The closer (fresher) the aerosol is to itssource, the higher its gas-phase portion. For this reason,the Chapel site values were left out of this exercise. TheChapel site is closer to local traffic sources than the othertwo sites, and this means there was a much greater impacton the portion of PAHs in the semivolatile fraction of thegas phase that is not detected by the PAS than at the othertwo sites. By averaging over the entire sampling session, theday-to-day variation in the ratio of compounds in the gasphase to compounds in the particle phase can be smoothedout. Figure A.1 displays the average high-molecular-weight
PAH concentration (in ng/m3) at the GLC and School sitescombined, for the three sampling sessions (winter 2005,summer 2005, and winter 2006), against the correspondingaverage PAS PAH reading.
Figure A.2 presents a comparison of high-molecular-weight PAHs in the integrated samples to the mean PASreading for the corresponding time periods for all sampleswhose trajectories were primarily over water for the winterand summer periods. This included most lake-wind daysand some days when the air mass trajectories were overLake Huron to the northwest. Our intention was to focuson conditions where most of the high-molecular-weightPAHs would be found in the particle-bound phase, whichis detected by the PAS 2000CE, rather than in the gasphase, which the instrument does not detect. Both plotsshow a distinct relationship between the PAS 2000CE andthe integrated PAH samples.
EVALUATION OF DAVIS WEATHER STATION AT THE GLC
During the summer 2005 campaign, we compared dataon wind speed and direction obtained from the DavisWeather Station at the GLC with values reported at theBuffalo Airport. Figure A.3 plots the wind speed (mph)reported at the Buffalo Airport against the wind speedreported by the GLC Davis Weather Station. Table A.13shows the regression output from the data plotted inFigure A.3. Figure A.4 is a histogram showing the distribu-tion of differences in wind speed between the valuesreported at the GLC and the Buffalo Airport. For 300hourly averages, the difference in wind speed valuesbetween the GLC Davis Weather Station and the BuffaloAirport was &0.88 mph, with a standard error of 0.10 mph,and the t value was &8.48, which had a P value < 0.001.
Figure A.1. Comparison of the mean concentration of high-molecular-weight PAHs (ng/m3) for the GLC and School sites overall against the cor-responding mean PAS PAH reading. By averaging over all of the samplingsessions, the day-to-day variation in the ratio between compounds in thegas phase and compounds in the particle can be smoothed out.
Figure A.2. Comparison of high-molecular-weight PAH concentrations(ng/m3) in the integrated 12-hour samples and the mean PAS samples forthe corresponding time periods (winter and summer sampling sessions)for all samples where air mass trajectories were primarily over water.
Figure A.3. Comparison of wind speeds (mph) between the Buffalo Airportand the GLC Davis Weather Station for summer 2005 sampling session.
129
J. Spengler et al.
129
Figure A.5 plots the wind direction (degrees) reported atthe Buffalo Airport against the wind direction reported bythe GLC Davis Weather Station. Table A.14 shows theregression output from the data plotted in Figure A.5.Figure A.6 is a histogram showing the distribution of dif-ferences in wind direction between the values reported atthe GLC Davis Weather Station and the Buffalo Airport.The difference in wind direction obtained from the GLC
Davis Weather Station and the values reported at the Buf-falo Airport for 293 hourly measurements was &10.6" witha standard error of 1.21", and the t value was &8.74 with aP value < 0.001. Although the differences in wind speedand wind direction were significant, they were small inmagnitude. The GLC Davis Weather Station values wereconsidered comparable to the values reported at the Buf-falo Airport for wind speed and wind direction.
Table A.13. Summary Regression Output for the Comparison of Wind Speeds (mph) between the Buffalo Airport and the GLC Davis Weather Station for the Summer 2005 Sampling Session
Regression Statistics Multiple R 0.94R 2 0.89Adjusted R 2 0.89Standard error 1.80Observations 300
Figure A.4. Histogram of wind speed differences (mph) between theBuffalo Airport and the GLC site for summer 2005 sampling session.
Figure A.5. Comparison of wind direction (degrees) between the BuffaloAirport and the GLC Davis Weather Station for summer 2005 samplingsession (seven pairs of measurements with one value greater than 0 andthe other less than 360 have been removed).
130130
Air Toxics from Vehicle Emissions at a U.S. Border Crossing
APPENDIX AVAILABLE ON THE WEB
Appendix B contains supplemental material notincluded in the printed report. It is available on the HEIWeb site http://pubs.healtheffects.org.
Appendix B. Overview, Summary, and Comparison of Inte-grated Data
ABOUT THE AUTHORS
John D. Spengler, Ph.D., is Akira Yamaguchi Professor ofEnvironmental Health and Human Habitation at the Har-vard University School of Public Health. Over the past 30years, he has conducted research in the areas of personalmonitoring, air pollution health effects, aerosol character-ization, indoor air pollution, and air pollution meteorology.
Jamson Lwebuga-Mukasa, M.D., Ph.D., is founder, presi-dent, and CEO of the Respiratory and Environmental Expo-sure Consultants LLC, Getzville, New York. At the time ofthe HEI study, Dr. Lwebuga-Mukasa was associate professorof medicine at the School of Medicine and Biomedical Sci-ences, University at Buffalo, State University of New York,and director of the Pulmonary and Critical Care Division atBuffalo General Hospital; he also founded and served asdirector of the Center for Asthma and Environmental Expo-sure at Buffalo General Hospital. Dr. Lwebuga-Mukasa has
Table A.14. Summary Regression Output for the Comparison of Wind Direction (Degrees) between the Buffalo Airport and the GLC Davis Weather Station for the Summer 2005 Sampling Session
Regression StatisticsMultiple R 0.97R 2 0.93Adjusted R 2 0.93Standard error 20.64Observations 293
Figure A.6. Histogram of differences in wind speed directions (degrees)between the Buffalo Airport and the GLC Davis Weather Station forsummer 2005 sampling session.
131
J. Spengler et al.
131
published work on the high prevalence of and morbiditydue to asthma and other chronic respiratory illnessesamong residents of communities close to Buffalo, NewYork, and Fort Erie, Ontario, a NAFTA corridor. His cur-rent research focuses on the role of combustion-generatedparticles in human diseases.
Jose Vallarino, M.S., is a project manager working with Dr.Spengler at the Harvard School of Public Health.
Steve Melly, M.A., M.S., is a data analyst/data managerand GIS specialist working with Dr. Spengler at the Har-vard School of Public Health.
Steve Chillrud, Ph.D., is Lamont Research Professor atLamont-Doherty Earth Observatory of Columbia Univer-sity and codirector of Columbia’s Exposure AssessmentCore Facility, which supports Columbia’s National Insti-tute for Environmental Health Sciences Center for Envi-ronmental Health in Northern Manhattan. Dr. Chillrud isknown for his work on the analysis and geochemistry ofpollutants in air and water and has collaborated withpublic health investigators on a variety of exposure assess-ment projects.
Joel Baker, Ph.D., is Port of Tacoma Chair in EnvironmentalScience, science director of the Center for Urban Waters,and professor at the University of Washington–Tacoma. Hisresearch team studies the environmental behaviors ofanthropogenic organic chemicals in surface waters and theatmosphere, including air-sea exchange, bioaccumulation,and organic aerosol source characterization.
Taeko Minegishi, M.S., is currently a staff scientist at Envi-ronmental Health and Engineering, Inc., in Newton, Mas-sachusetts. In 2007, she completed her master’s degreeresearch, under the supervision of Dr. Joel Baker, at theUniversity of Maryland.
ABBREVIATIONS AND OTHER TERMS
BEAM Boston Exposure Assessment in Microenvironments study
BTEX benzene, toluene, ethylbenzene, and xylenes
BTEX plus BTEX plus 1,3-butadiene, MTBE, and styrene
CBL Chesapeake Biological Laboratory
CO carbon monoxide
CO2 carbon dioxide
CPC condensation particle counter
DNPH 2,4-dinitrophenylhydrazine
DNSH dansylhydrazine
EC elemental carbon
EC-r elemental carbon estimated by reflectance analysis
EEPS engine-exhaust-particle spectrometer
EOSHI Environmental and Occupational Health Sciences Institute
EPA Environmental Protection Agency (U.S.)
FEV1 forced expiratory volume in 1 sec
GIS geographic information system
GLC Great Lakes Center
GPS Global Positioning System
HAP hazardous air pollutant
HEPA high-efficiency particulate air
HNO3 nitric acid
HPLC high-performance liquid chromatography
HR-ICP-MS high-resolution inductively-coupled-plasma mass spectrometry
Research Report 158, Air Toxics Exposure from Vehicle Emissions at a U.S. Border Crossing: Buffalo Peace Bridge Study, J. Spengler et al.
INTRODUCTION
Motor vehicles and other combustion sources emitmany air toxics that are either known or suspected, withsufficient exposure, to cause adverse health effects. Char-acterization of exposure to air toxics has been challenging,in part, because of the low ambient levels of individualcompounds.
HEI has had a long-standing commitment to improvingmethods for measuring selected air toxics and increasingour understanding of exposure and health effects. Theaccompanying Preface to Research Report 158 describes theactions that the U.S. Environmental Protection Agency(EPA*) has taken to regulate emissions of air toxics ingeneral and of mobile-source air toxics (MSATs)specifically. It also describes HEI’s rationale for issuingRequest for Applications (RFA) 03-1, “Assessing Exposureto Air Toxics,” which was to support research to identify andcharacterize exposure to air toxics at so-called hot spots,areas where concentrations of one or more air toxics areexpected to be elevated. HEI’s objective through this RFAwas to identify hot-spot areas (and populations that live insuch areas) that may be appropriate for health effects studies.
In response to RFA 03-1, Dr. John Spengler, of the Har-vard School of Public Health in Boston, Massachusetts,submitted an application entitled “Air Toxics Exposurefrom Vehicular Emissions at a U.S. Border Crossing.”Spengler, along with colleagues from other institutions,proposed measuring levels of air toxics and EPA criteria airpollutants in the area surrounding the Peace Bridge in Buf-falo, New York, as a potential hot spot for mobile-sourceemissions.
During the study period, approximately 5000 truckscrossed the border between the United States and Canadadaily at this location, each requiring customs and passportinspection. Increased security after the terrorist attacks ofSeptember 11, 2001, lengthened the waiting time for vehi-cles at the border, especially for trucks, which often idledwhile in traffic or during layovers near the bridge. Theinvestigators proposed measuring ambient levels of airtoxics, including those of polycyclic aromatic hydrocar-bons (PAHs), a variety of metals, aldehydes, benzene, buta-diene, methyl tert-butyl ether (MTBE), and styrene, as wellas particulate matter with aerodynamic diameter ! 2.5 µm(PM2.5), black carbon, carbon monoxide, nitric oxide, andozone. Spengler and colleagues hypothesized that thevehicle-related emissions from traffic backed up aroundthe plaza adjacent to the bridge on the U.S. side of theborder would result in higher exposures in the communitydirectly downwind of the plaza than in upwind areas, con-stituting an air toxics hot spot.
On the strength of the location proposed for study as apotential hot spot and Spengler’s extensive experience withair toxics measurement, the RFA 03-1 Review Panel rankedthe application second among the 17 that HEI received. TheResearch Committee requested some modifications to thisinitial proposal, and they agreed to approve the study forfunding after review of the revised proposal, with the stipu-lation that a site visit by Research Committee members takeplace in conjunction with the pilot phase of the project.
During the summer pilot study in July 2004 and thewinter pilot study in January 2005, the investigators sam-pled air at sites upwind and downwind of the plaza adja-cent to the Peace Bridge and tested routes for mobilemonitoring. The Research Committee visited the site inAugust 2004 to audit the first pilot sampling campaign forthe feasibility of the investigators’ methods and the qualityof results and approved funding for the main phase of sam-pling. Using their experience and data from the two pilotstudies, Spengler’s team planned and conducted cam-paigns in summer 2005 and winter 2006 with fixed-sitesampling, and they collected information on meteorologicconditions and bridge traffic counts for all sampling days.
This Commentary is intended to aid HEI sponsors andthe public by highlighting the strengths and limitations ofthe study and discussing the HEI Health Review Com-mittee’s interpretation of the results.
Dr. John Spengler’s 2-year study, “Air Toxics Exposure from VehicularEmissions at a U.S. Border Crossing,” began in April 2004. Total expendi-tures were $861,426. The draft Investigators’ Report from Spengler and col-leagues was received for review in June 2007. A revised report, received inApril 2009, was accepted for publication in June 2009. During the reviewprocess, the HEI Health Review Committee and the investigators had theopportunity to exchange comments and to clarify issues in both the Investi-gators’ Report and the Review Committee’s Commentary.
This document has not been reviewed by public or private party institu-tions, including those that support the Health Effects Institute; therefore, itmay not reflect the views of these parties, and no endorsements by themshould be inferred.
* A list of abbreviations and other terms appears at the end of the Investiga-tors’ Report.
134
Commentary on Investigators’ Report by Spengler et al.
STUDY AIMS
Spengler and his research team hypothesized that thetraffic backed up on the U.S. side of the Peace Bridge resultsin high levels of motor vehicle emissions, which then blowwith prevailing winds into the nearby residential areas.They argued that these emissions, particularly emissionsfrom diesel-powered trucks waiting for customs inspection,may locally elevate levels of MSATs, creating a hot spot ofemissions and exposure. To explore this hypothesis, theauthors pursued three specific aims:
1. Characterize the ambient levels of air contaminantsincluding MSATs across a series of days to obtainupwind and downwind concentrations in the vicinityof the Peace Bridge plaza.
2. Examine the relation between traffic at the PeaceBridge and pollutant concentrations in ambient air.
3. Explore geographic patterns of ambient air pollutants.
STUDY DESIGN AND METHODS
The study area is centered on the Peace Bridge plaza inBuffalo, on the U.S. side of one of the nation’s busiestborder crossings. At the beginning of the study, the 17-acreplaza comprised the U.S. Customs Building, a customsinspection and holding area, tollbooths, a duty-free store,the Peace Bridge Authority Administration Building, andparking space. Residential and commercial areas abut theplaza to the east, north, and south, with Lake Erie and theNiagara River to the west. The Commentary Figure showsthe study area. Typical weekly traffic counts on the PeaceBridge are shown in Figure 3 of the Investigators’ Report.
PILOT STUDIES
To establish fixed sampling sites representative ofdownwind and upwind conditions, assess wind andweather patterns relevant to the transport of pollutants andMSATs, and investigate routes for mobile monitoring,Spengler’s team conducted pilot studies in summer 2004and winter 2005.
Prevailing wind directions were determined from 10 yearsof weather records obtained from the Buffalo Niagara Inter-national Airport, northeast of the plaza. The authors’analysis of these data demonstrated that the wind blowsfrom the west side of the plaza, off Lake Erie and the Nia-gara River, about 45% of the time (lake winds) and fromthe east on the Buffalo side of the plaza (city winds) about
31% of the time. A representative wind rose for this areamay be seen in Figure 2 of the Investigators’ Report.
Based on the wind analysis, the investigators establishedtwo fixed sampling sites, with integrated sampling equip-ment and real-time continuous monitoring equipment (tak-ing readings every minute), during the summer 2004 pilotstudy: one to the southwest of the plaza at the Great LakesCenter (GLC site), which they describe as an upwind site,and the other in front of the Episcopal Church Home(Chapel site), adjacent to the east side of the plaza, whichthey describe as a downwind site. They also tested routes,equipment, and protocols for mobile monitoring intendedto determine the spatial variability of pollutant levelsaround the Peace Bridge plaza area, including the Buffaloneighborhood to the east and downwind of the plaza.
During the winter 2005 pilot study, the researchers addeda third fixed sampling site in the neighborhood to the east ofthe plaza (School site), to reflect potential pollutant expo-sure of the residents. They also established the integratedsampling schedule of 12 hours per weekday and weeknight,with 48-hour weekend samples, which was used in sub-sequent campaigns, and they continued to experiment todetermine the best routes, schedules, and equipment fortheir mobile monitoring program to characterize the spatialvariation of MSAT levels near the plaza.
MAIN SAMPLING CAMPAIGNS
After the two pilot studies, Spengler’s team conductedtwo larger-scale sampling campaigns featuring both fixed-site and mobile monitoring components. The teamsimultaneously collected samples at all three fixed sam-pling sites for two weeks in July 2005 and two weeks inJanuary 2006. Before their summer 2005 campaign, thetollbooths in the Peace Bridge plaza were relocated to theCanadian side of the bridge, which changed the locationand number of idling vehicles, and this should beconsidered when comparing data from the pilot studieswith those from the main sampling campaigns.
The three locations used for fixed-site sampling in thewinter 2005 pilot study, the GLC site, the Chapel site, andthe School site (shown on the map in the CommentaryFigure), were retained for the main sampling campaigns insummer 2005 and winter 2006Ǥ Weather information forthe fixed sampling locations was collected at the GLC siteusing a Davis Weather Station, and the prevailing winddirection at the GLC site was assumed to be representativeof conditions in the entire Peace Bridge plaza area duringthe sampling campaigns. The research team obtained
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hourly counts of passenger vehicles, buses, and truckscrossing the bridge in each direction from the Peace BridgeAuthority. An overview of the sampling dates and locationsand the elements and compounds sampled is presented inCommentary Table 1.
A summary of the sampling instruments and equipmentand analytical methods for each type of sample collectedduring the study is presented in Commentary Table 2; theyare described in more detail in the Investigators’ Report.The research team collected 12-hour integrated samples onweekdays (7:00 AM to 7:00 PM) and weeknights (7:00 PM to7:00 AM) and 48-hour integrated samples over eachweekend during the campaigns. In addition, two 60-hourcomposite samples were collected over five 12-hourweekdays and five 12-hour weekday nights for each week ofeach sampling campaign. When available, real-timecontinuous monitors collecting data every minute were alsoplaced at the three fixed sampling sites. These instrumentsmeasured ozone (O3), nitrogen oxides (NOx), PM2.5, carbondioxide (CO2), ultrafine particles (UFPs, particles < 0.1 µmin aerodynamic diameter), and particle-bound PAHs(pPAHs). Problems with the equipment and data storagelimited the available sampling data for O3 and NOx, in
particular, and instrument downtime limited the amount ofdirectly comparable data for upwind and downwindmeasurements for all measured species.
Each of the main two-week sampling campaigns includeda mobile monitoring component. Dr. Spengler designed themobile monitoring campaign to assess the spatial variabilityof levels of selected pollutants in the neighborhood adjacentto the Peace Bridge plaza. Researchers from Harvard Schoolof Public Health and the University at Buffalo, State Univer-sity of New York, recruited staff to wear backpacks containingair monitoring equipment while walking along designatedroutes near the plaza. The staff also carried GPS (Global Posi-tioning System) units that logged their progress along theroutes during sampling. The four designated mobilemonitoring routes are displayed in Investigators’ ReportFigure 4, and a summary of the mobile sampling equipmentand the sampled species is presented in Commentary Table 2.
STATISTICAL METHODS AND DATA ANALYSIS
Primary analysis of the sampling data focused oncomparing and contrasting pollutant levels measured atthe GLC and Chapel sites, on opposite sides of the PeaceBridge plaza. According to the prevailing winds in thearea, the GLC site was typically upwind of the plaza andthe Chapel site was typically downwind, as determinedfrom weather data before the study and verified during thesampling campaigns. Sampling data from these two siteswere further compared with data from the School siterepresenting pollutant levels in the residential area fartherdownwind of the Peace Bridge plaza, and results for all threesites were also stratified by season (summer and winter).
The investigators note that they selected statisticalmethods for summarizing data according to the forms of thedata distributions. Thus they report measures of centraltendency and data variability according to whether thedistributions of measured values for a given pollutant werenormally or lognormally distributed, log-transformed,nonparametric, and so forth. This accounts for the variety ofdifferent statistics for different measured species ofpollutants reported. Detailed information on how datafrom integrated sampling were handled is presented inAppendix A of the Investigators’ Report, along with dataquality statistics.
The investigators were most interested in the levels ofMSATs, which they expected to be elevated as a result ofthe traffic congestion in the Peace Bridge plaza area. Theymeasured levels of a large number of compounds that
Commentary Figure. The study area comprised the Peace Bridge plazaand the three fixed sampling sites (GLC, Chapel, and School), as well asBird Island Pier and the neighborhood of west Buffalo (boxed), whereroutes for mobile monitoring were located. (This map appears as Figure 1in the Investigators’ Report.)
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might be expected to be in exhaust from diesel and gasolinevehicles, including volatile organic compounds (VOCs),PAHs, and nitrogenated PAHs (NPAHs). The Investigators’Report presents extensive comparisons of data fromsamples designated as upwind or downwind of the plaza atthe time of measurement. Traffic counts from the PeaceBridge were matched to all ambient air measurements.Where appropriate, traffic counts were averaged to matchthe interval of the integrated pollutant samples. Furthercomparisons were made to assess the degree of correlationamong measurements of selected elements and compounds(and summed values for specific groupings of compounds),typically using Pearson correlations or t-test statistics onlog-transformed data. Strongly correlated levels mayindicate emissions from a common source.
Measurements of a variety of elements from the fixed-sitesampling were further analyzed by principal componentanalysis. In this analysis various species are groupedaccording to how their levels tend to increase and decreasetogether over time, possibly indicating a common source orsources. Spengler’s team used principal component anal-ysis to evaluate the levels of groups of measured elements inrelation to traffic counts for passenger cars and dieselvehicles (trucks and buses) and to wind conditions during
sampling. Industrial and residential combustion are otherpotential sources for groups of elements measured at thethree fixed sampling sites. The results of principal compo-nent analyses were compared between sites to explore thedifferences in source profiles and wind conditions. Theteam also employed a statistical procedure called positivematrix factorization (PMF) to examine the patterns inmeasured levels of different groups of PAHs, NPAHs, andelements relative to a source measurement (traffic counts atthe Peace Bridge). PMF analysis of the elemental data setsdid not yield additional information beyond that providedby the principal component analysis.
Spengler’s team used the real-time mobile monitoringdata to assess the distribution of pollutants on four routes:three along streets in the neighborhood adjacent to anddownwind of the Peace Bridge plaza, and one along BirdIsland Pier to the west and upwind of the plaza. GPScoordinates were converted to spatial data sets using geo-graphic information system (GIS) software, merged with astandard data set of streets, then linked to measurementsmade by study staff during the summer and wintercampaigns, to determine levels of criteria air pollutants andMSATs at specific times and locations on the routes. Theywere thus able to map pollutant levels along the routes.
Commentary Table 1. Overview of Sampling Campaigns
Monitoring Type / Pollutants Measureda Campaign (Sampling Dates) Locations
a See Table 6 of the Investigators’ Report for a complete list of elements, VOCs, PAHs, and NPAHs measured.
b See Figure 4 of the Investigators’ Report for a map of the mobile monitoring routes.
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SUMMARY OF RESULTS
Spengler and his team discuss most of their results bydrawing comparisons between measurements at sites thatwere predominantly upwind and downwind relative tothe Peace Bridge plaza. Accounting for wind direction,they drew inferences about the likelihood that thesemeasurements were related to the volume and compositionof traffic traversing the Peace Bridge plaza. Whencomparing measurements around the study area, they alsonote which of the measured species are typically associatedwith traffic emissions, particularly diesel emissions.
Notable sampling results are discussed here, and specificsummary statistics for these results appear in CommentaryTable 3. The authors provide further comparative analysesof differences related to the individual sampling campaign,season, time of day, and wind direction, as warranted, in thetext of the Investigators’ Report.
CHARACTERIZATION OF POLLUTANTS IN THE PEACE BRIDGE PLAZA AREA
Spengler and his team made extensive measurementsduring this study. Of greatest interest are the comparativesummary statistics for measurements of several different
Commentary Table 2. Overview of Sampling Methods
Monitoring Type / Equipment Pollutant Analysis Method
Modified Harvard impactor with 2-stage PUF impaction heads, Teflon filters
PM2.5 Gravimetric analysisEC-r EEL smoke stain reflectometerElements Magnetic sector high-resolution inductively-coupled-plasma mass
spectrometry (HR-ICP-MS)
Stainless steel, multibed thermal desorption tubes with Carbopack B, Carbopack X, and Carboxen 1001
VOCs Gas chromatography with automatic thermal desorber (EPA Method TO-17)
DNPH-coated silica cartridges Carbonyls High-performance liquid chromatography with ultraviolet detection
Mist chamber Acrolein Gas chromatography using OSHA Method 52 for acrolein
ChemComb Organic Sampler with 2.5-µm inlet, prefired quartz filter, and glass sleeve with Supelpak-2B between two PUF pads
PAHs Concentration in hexane; identification and quantification using gas chromatography with mass spectroscopy in electron ionization mode
NPAHs Extraction from PAH concentrate using aminopropyl solid-phase extraction cartridge and liquid chromatography; gas chromatography with mass spectroscopy in negative chemical ionization mode
TSI P-Trak Model 8525 UFPs Laser-photometer with isopropyl alcohol saturation and condensation
EchoChem PAS 2000CE pPAHs Photoelectric aerosol sensor with krypton-bromide excimer laser
2B Technologies Model 202 Ozone Monitor
O3 Ultraviolet light absorption
2B Technologies Model 400 Nitric Oxide Monitor
NO Ultraviolet light absorption of O3 (before and after reaction with NO in sample stream)
Mobile
TSI P-Trak UFPs Laser-photometer with isopropyl alcohol saturation and condensation
EchoChem PAS 2000CE pPAHs Photoelectric aerosol sensor with krypton-bromide excimer laser
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classes of MSATs — particulate matter, VOCs andcarbonyls, elements, PAHs, and NPAHs — and measure-ments from continuous sampling of PM, UFPs, pPAHs, andgaseous pollutants. They report and compare mean12-hour daytime levels for each fixed sampling site for thewinter 2005, summer 2005, and winter 2006 campaignscombined. For both PM10 and PM2.5, the mean daytimelevels were highest at the Chapel site (typically downwindof the plaza), and higher at the residential School site thanat the upwind GLC site. The fraction of elemental carbon(EC) present in the collected PM2.5 followed the samepattern as the PM2.5 concentrations, with the median ECfraction at the Chapel site exceeding those at the Schooland GLC sites by a large margin (Commentary Table 3). Theauthors note that these results are consistent with motorvehicle emissions, and with their hypothesis that traffic atthe Peace Bridge plaza is a significant source of pollutionin the study area.
The investigators combined measurements for classes ofVOCs, carbonyls, and aldehydes into composite variablesfor a comparative analysis of study results. These summary
categories are presented in Table 13 of the Investigators’Report. Approximately two thirds of the sampling dayswere classified as having lake-wind conditions that resultedin the GLC site being directly upwind, the Chapel site beingdirectly downwind, and the School site being less directlydownwind of the Peace Bridge plaza. The investigators pro-pose, therefore, that the higher levels of compoundsmeasured at the Chapel site relative to the other sites insummary comparisons indicate that traffic at the plaza wasa source for these pollutants.
BTEX, BTEX Plus, and Chlorinated Compounds
The authors created composite variables to representbenzene, toluene, ethylbenzene, and xylenes (BTEX), theBTEX compounds plus 1,3-butadiene, MTBE, and styrene(BTEX plus), and the sum of five chlorinated compounds.Overall mean and median levels of BTEX in weekday12-hour samples were highest at the School site, followedby the Chapel site, and then the GLC site (Table B.5 inAppendix B, available on the HEI Web site). The mean andmedian levels of BTEX plus were similar to the BTEX
PM10 (µg/m3) Mean 23.8 22.6 19.1 PM2.5 (µg/m3) Mean 15.7 14.6 13.4 EC-r (% in PM2.5) Mean 17.0 7.6 5.4
VOCs (µg/m3)BTEX Median 6.2 7.1 2.6BTEX plus Median 6.4 7.3 2.8 Benzene Median 1.1 1.1 0.62Chlorinated compounds Median 0.99 1.2 0.85Formaldehyde Median 0.64 0.85 0.50Acetaldehyde Median 1.1 1.5 1.6Acetone Median 1.5 1.9 2.4
Elements (ng/m3)Ca Mean 79.0 40.0 47.0 Cr Mean 0.72 1.1 0.19 Mn Mean 2.3 1.9 1.9 Fe Mean 88.0 55.0 57.0Cu Mean 5.6 3.4 1.7 Sb Mean 0.79 0.45 0.52
a All summary statistics in this table were calculated using the combined results of sampling campaigns in winter 2005, summer 2005, and winter 2006.
b Values appear in Appendix B of the Investigators’ Report (available on the HEI Web site): PM10 and PM2.5 in Table B.1, VOCs in Table B.5, and elements in Table B.7. The mean percentages of EC-r in PM2.5 appear in the Investigators’ Report Results.
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levels. Overall mean and median levels of the chlorinatedcompounds category were similar across all three fixedsampling sites, indicating, as the authors suggest, that thecomposition of the regional air mass, and not localsources, was predominantly responsible for the levels ofthese compounds.
Benzene, Formaldehyde, and Acetaldehyde
Median daytime benzene levels were lowest by far at theGLC site, with measurements at the School and Chapelsites demonstrating similar and higher levels. Overallmean and median daytime formaldehyde levels werehighest at the School site, followed by the Chapel site, andthen the GLC site. The authors note also that formaldehydelevels at the School site were five times as high in thesummer as they were in the winter, while at the GLC site,winter levels were twice as high as summer levels. Thisseasonal variation highlights the importance of photo-chemical reactions for pollutant levels, as was noted inHEI’s report on Mobile-Source Air Toxics (HEI Air ToxicsReview Panel 2007). Acetaldehyde levels were highest atthe GLC site, nearly as high at the School site, and muchlower at the Chapel site. The authors note that wind pat-terns and urban area emissions likely resulted in thehigher measured levels of acetaldehydes at the GLC sitethan at the other two sites in the winter, but not in thesummer. Acetone levels for all daytime samples werefairly similar across all three sites, although they wereslightly elevated at the GLC site. Overall, the patterns ofmeasured levels for these VOCs present a mixed picture ofpotential sources, with downwind benzene and formalde-hyde levels appearing to be associated with activities atthe Peace Bridge plaza, and with acetone and acetaldehydelevels deriving from other urban sources.
Elements
Spengler’s team analyzed fixed-site PM2.5 samples for 28different elements. Of the elements detected, the summarylevels of 22 elements did not vary importantly across thethree fixed sampling sites, implying either regionalemissions or natural sources. Elements with little or no vari-ation in their concentrations between sites included thecrustal elements sodium, magnesium, aluminum, potas-sium, titanium, and zinc; elements associated with fossilfuel (coal and oil) combustion, sulfur, arsenic, selenium,vanadium, and nickel; and the elements beryllium, silver,cadmium, tin, cesium, and thallium. Summary levels forthe 12-hour weekday daytime samples of these elements arefound in Table B.7 (Appendix B). Although the elementsrelated to fossil fuel combustion could be derived from the
diesel emissions of truck traffic, the authors hypothesizethat in this study the dominant sources for these elementsmay have been regional, such as electrical power plantsand emissions from the New York City area.
Mean daytime weekday levels of elements that were, onaverage, present at higher levels at the Chapel site than atthe other sites are presented in Commentary Table 3. Theseelements are calcium, chromium, manganese, iron,copper, and antimony. As antimony, manganese, andhexavalent chromium are MSATs, and iron and coppercould also be derived from mobile sources, the authorssuggest that the higher levels at the downwind Chapel siteappear to be related to emissions from traffic transiting thePeace Bridge plaza during the sampling campaigns(although iron is also present in particulate matter fromcrustal sources). Levels of these elements at the School sitewere either similar to those at the GLC site, or intermediatebetween those at the Chapel and GLC sites, furtherimplicating traffic emissions at the plaza as a possiblesource of these elements given predominant wind patternsduring the sampling campaigns. Thus the overall resultsfor analysis of elements in PM2.5 samples are suggestive ofpossible traffic sources, but not conclusive.
PAHs
Spengler’s team noted important contrasts in levels ofPAHs and NPAHs across the three fixed sampling sites, aswell as some important seasonal differences. They dividedthe 38 detected PAH compounds included in the PMF anal-ysis into three groups by molecular weight: lightweightcompounds, such as fluorene; medium-molecular-weightcompounds, including many methylated PAHs; and PAHsheavier than benzo[k]fluoranthene, including pyrene.Results for the 12-hour weekday daytime sampling for PAHsare presented in Table B.11 (Appendix B).
The investigators found that for all but a few of the PAHcompounds, concentrations at the Chapel site were highestwhen winds were classified as “city/other winds” — that is,when the site was downwind of the city of Buffalo andupwind of the Peace Bridge plaza (Figures 20–22 in theInvestigators’ Report). For compounds typically associatedwith combustion emissions, such as naphthalene andbenzo[a]pyrene, city-wind conditions produced overallmean concentration ratios between the Chapel and GLCsites ranging from 1.1 to 1.4 (although the authors note thatunder lake-wind conditions concentrations of light PAHslike naphthalene were up to ten times as high at the Chapelsite). These findings imply that regional traffic emissions,fossil fuel combustion, and such urban infrastructural fea-tures as asphalt surfaces and tar-based roofing materials
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could contribute nearly as much PAH to the ambient air astraffic emissions from the Peace Bridge plaza itself undercommon wind and weather conditions in the area.
The light PAHs, such as naphthalene and methylatednaphthalene compounds, were consistently highest at theChapel site, followed by the School site and GLC site(Table B.11 in Appendix B). This pattern of downwindenrichment, which indicates that traffic emissions at theplaza were a source, was more pronounced in the summer,when traffic levels were higher and temperatures werewarmer. Other areal sources of PAH, such as emissionsfrom roofing materials and roadway asphalt, coal andbiomass combustion, and tars and lubricants, may havecomplicated these interpretations.
NPAHs
Overall concentrations of 14 NPAHs at the GLC andChapel sites under lake-wind conditions are compared inInvestigators’ Report Figure 25. Median ratios formeasurements at these sites demonstrate that the levels ofsome of the NPAHs at the Chapel site were nearly tenfoldthose at the GLC site. This enrichment, along with theknown relationship between diesel combustion and thesecompounds, indicates that traffic at the Peace Bridge plazamay be a major local source for these compounds.3-Nitrofluoranthene is the sole exception since it wasdetected at both sites only during winds classified ascity/other, implying a city-based source for this compound.
Daytime and nighttime mean PAH and NPAHconcentrations measured during one week in summer 2005using Graseby Andersen high-volume samplers revealedsome important contrasts, as shown in Investigators’ ReportTable 21. For all PAHs combined and for all NPAHscombined, concentrations were highest at the neighborhoodSchool site, followed by the Chapel site adjacent to the PeaceBridge plaza. At the School site, the PAH concentrationswere higher during the day, while the NPAH concentrationswere higher at night. At both the Chapel and GLC sites, PAHconcentrations lacked any consistent diurnal trends, whileNPAH concentrations were consistently higher at night.
Continuous UFP Measurements
Results from 600 hours of 10-minute average UFPcounts at each of the three fixed sampling sites in thewinter 2005, summer 2005, and winter 2006 campaignscombined are presented in Investigators’ Report Table 22.A seasonal comparison of measurements from the summer2005 and winter 2006 campaigns i s shown inInvestigators’ Report Table 23. The mean UFP count wassubstantially higher at the Chapel site, followed by theSchool and GLC sites. The relative differences between the
UFP counts at the sites were similar in winter and summermeasurements, with all three sites recording higher UFPcounts in the winter. As with any UFP measurements, thetendency for extremely small particles to combine intolarger agglomerated particles at some distance from thesource must be considered. Such agglomeration is knownto result in lower number counts in smaller UFP sizeranges and lower overall UFP number counts at increasingdistances from the source, as discussed in the text. There-fore, overall UFP number counts would be expected to behigher at the Chapel site near the Peace Bridge plaza andlower at the more distant School and GLC sites, even iflake-wind and city/other-wind conditions were equallyrepresented in the sampling campaigns. This pattern isapparent in the sampling data, since the nearest down-wind site (Chapel) recorded the highest number counts ofUFPs, followed by the more distant downwind site(School) and the upwind GLC site. The differencesbetween UFP number counts measured at the Chapel andSchool sites could be due, in part, to this particle agglom-eration phenomenon.
Continuous PM2.5 Measurements
Concentrations of PM2.5 are summarized as 10-minuteaverages in Investigators’ Report Table 26. In contrast tothe UFP count results, the mean PM2.5 concentrations, aswell as the percentile distributions and standarddeviations for the data, are relatively uniform at the threesites. This pattern indicates that the PM2.5 levels measuredin the study area may be dominated by well-documentedregional air pollution patterns and not the local sourcesassociated with either the Peace Bridge plaza or the city ofBuffalo.
Continuous pPAH Measurements
Ten-minute average concentrations of pPAHs in PM2.5 atthe three sampling sites, shown in Investigators’ ReportTable 24, demonstrate a similar pattern to the UFP counts.Although the GLC site registered the highest maximumconcentration, it had the lowest mean concentration,indicating that a few high readings dominated the summarystatistics for pPAHs at the GLC site. The highest meanconcentration was at the Chapel site, followed by the Schooland GLC sites. Twelve-hour average pPAH concentrationsfor daytime and nighttime sampling, displayed inInvestigators’ Report Table 25, demonstrate the samepattern, with substantially higher mean concentrationsrecorded both day and night at the Chapel site than at theSchool and GLC sites. It is important to note that thesemeasurements are concentrations of pPAH in PM2.5 andreflect an enrichment of pPAH in those samples, since
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PM2.5 levels did not vary between the three sites. Theseresults support the investigators’ hypothesis that there is arelationship between diesel-engine traffic and downwindexposures, since pPAH concentrations in PM2.5 werehighest at the site that was most frequently downwind ofthe expected sources.
Continuous Gaseous Pollutant Measurements
Spengler’s team encountered difficulties with thecontinuous sampling equipment used to obtain NOx andO3 measurements. Owing to flawed software for O3 datastorage, which was not discovered until the end of thesummer 2005 sampling campaign, most of the data werenot reliable, and there were insufficient data for O3 fromthe summer 2005 campaign for analysis. The O3 data fromthe winter 2006 campaign were not reported because thelevels were frequently at or near the limit of detection. TheNOx monitors were prototypes with serious reliabilityissues and did not provide reliable data.
RELATIONSHIP OF TRAFFIC AT THE PEACE BRIDGE TO POLLUTANT CONCENTRATIONS IN AMBIENT AIR
Spengler’s team compared measurement data obtainedwith lake winds (the predominant wind direction) andwith winds blowing over the city of Buffalo toward thelake, or from other directions, to assess the plausibility of arelationship between traffic at the Peace Bridge plaza andlevels of airborne pollutants at the Chapel and GLC sites.They further applied models from principal componentanalysis for elements and PMF models for PAH and NPAHcompounds in order to s ta t is t ica l ly assess th isrelationship. Out of a total of 24 possible 12-hour samplingperiods, 15 samples were collected under lake-wind con-ditions, and 9 under city/other-wind conditions,consistent with historical prevailing wind patterns for thearea. The investigators believe that the proportionalnumber of samples in each wind-type category wassufficient for contrasting measured pollutant levels duringdifferent wind conditions at the three fixed sampling sites.
Pairing samples by sampling period and calculating theratio of measured levels at the Chapel site to those at theGLC site, the authors ranked the results for EC reflectance(EC-r) and also plotted those for the total PAHs (sum of allPAH concentrations), for the 22 sampling periods with validmeasurements in Investigators’ Report Figure 15 (see alsoTable 12). The highest Chapel-to-GLC ratios for EC-r (rightend of the x axis) are associated with lake-wind events,which would enrich levels of EC-r at the Chapel site if theywere the result of mobile-source emissions at the plaza. Thelower ratios are predominantly for city/other-wind events,which would enrich the levels of EC-r at the GLC site and
thus lower the Chapel-to-GLC ratios. In general, the mea-surements for the sum of PAHs show the same trend ofhigher Chapel-to-GLC ratios with lake winds. Ratios forthe levels of elements measured at the Chapel and GLCsites, shown in Investigators’ Report Figure 18,demonstrate a pattern of higher levels of calcium, iron,zinc, chromium, copper, aluminum, antimony, strontium,cobalt, titanium, manganese, magnesium, and EC-r at theChapel site on lake-wind days, consistent with the investi-gators’ hypothesis.
An analysis of correlation between levels of VOCs,aldehydes, and chlorinated compounds for samples fromthe Chapel and GLC sites was less conclusive overall(Table 13 in the Investigators’ Report). Levels of BTEXcompounds at the two sites were not correlated for lake-wind events, but highly correlated for city/other-windevents. Levels of the BTEX plus summary category at thetwo sites, and of all but one individual component of thatcategory (MTBE), were strongly correlated with winddirection: higher levels at the Chapel site were associatedwith lake-wind events, and higher levels at the GLC sitewere associated with city/other-wind events. Comparisonsbetween the two sites did not show any enrichment ofchlorinated compounds or carbonyls downwind of thePeace Bridge.
Principal component analysis using measurements of 25elements in the complete data set, classified by samplingsite and wind direction, resulted in the grouping of ele-ments with similarly varying levels into five factors orcomponents. One component contained elementsassociated with both coal combustion and traffic-relatedemissions. Another component included, among other ele-ments, nickel and vanadium, which are associated withresidual fuel oil combustion. A third componentcontained magnesium, aluminum, and calcium, which areindicative of crustal material, while the fourth and fifthcomponents were not easily associated with knownsources. Specific results of this analysis for the Chapel siteunder lake-wind conditions are presented in Investigators’Report Table 18. The principal component analyses indi-cated that measured levels of a group of air toxics associ-ated with traffic emissions are higher at the Chapel sitewhen it is downwind of the Peace Bridge plaza (relative tolevels measured when it is upwind and to levels of othercomponents), suggesting that traffic emissions are a sub-stantial source of air toxics in the study area.
PMF was applied to site-specific PAH results from thewinter 2005, summer 2005, and winter 2006 samplingsessions. The analysis divided the PAHs into light,medium, and heavy profiles based on molecular weight(shown in Figures 29 and 30 of the Investigators’ Report).
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The light PAH profile was more abundant at the GLC sitethan at the School or Chapel sites. The medium PAHprofile was not common at the GLC site, but was dominantat the School and Chapel sites in the summer and aboutfive times higher at the School site. Heavy PAHs wererelatively abundant at all sites in the winter and could beassociated with traffic sources or with the combinedeffects of low temperature (lack of volatilization of PAHs tolighter profiles) and increased coal and oil combustion forindustrial and residential heating. These patterns of PAHconcentrations across sites that vary with PAH weightclass and sampling campaign season indicate the presenceof a complex mixture of PAH sources in the urban environ-ment, seasonal changes in sources, and varying interac-tions of PAHs with weather conditions.
SPATIAL PATTERNS OF POLLUTANTS IN A NEIGHBORHOOD NEAR THE PEACE BRIDGE PLAZA
Spengler’s team developed an innovative mobile moni-toring scheme for measuring neighborhood exposures. TheUFP and pPAH levels, measured under different windconditions at different times of day, are depicted on maps ofthe neighborhood study area in Investigators’ ReportFigures 53 through 61. Busy intersections and possiblecombustion sources in the neighborhood probablycontributed to the UFP and pPAH levels, as indicated bysampling results under city-wind conditions. Theinvestigators suggest that the maps showing UFP and pPAHlevels verify the general association between higherconcentrations and distance from the Peace Bridge plazaunder lake-wind conditions (pPAH levels on winter morn-ings being one exception).
HEI HEALTH REVIEW COMMITTEE EVALUATION
The HEI Health Review Committee undertook an inde-pendent and detailed review of the final report by Spen-gler and colleagues and noted the following points.
METHODS
The Review Committee considered the methods thatSpengler and his research team used to measure pollutantsin a variety of ways to be sound and generally well imple-mented, and they were satisfied with the large number ofelements and compounds that were measured. The pilotstudies provided valuable background for designing aneffective sampling campaign for the main study.
The Review Committee noted that the samplingcampaigns were well designed and conducted, and thedata were of high quality overall. They also commentedthat the mobile monitoring scheme using GPS units andreal-time measuring equipment carried in backpacks tostudy spatial distributions of pollutants near the PeaceBridge plaza was innovative and successful. This effortcomplemented the fixed-site sampling campaigns by morefinely evaluating potential source impacts on local varia-tions in pollutant levels, particularly within a residentialneighborhood adjacent to the Peace Bridge plaza.
STATISTICAL ANALYSIS METHODS
The Review Committee found that the investigators’ teamused appropriate methods to compare and contrast ambientpollutant levels at the three sites. However, the Committeeviewed the investigators’ source apportionment analysis aslargely exploratory, given the relatively small number ofsamples available for analysis. They expressed concern thatthe investigators provided little justification for the choiceof principal component analysis for the elemental data andPMF for the PAH and NPAH data, as opposed to using otheravailable methods.
STUDY DESIGN
The sampling campaigns and data sets were thoroughand comprehensive. The Committee felt, however, that theoverall quantity of data for any given element or compoundor sampled species was still too limited to produce adequatestatistical power for more advanced analyses such ascomplicated regressions or source apportionment studies.It was also difficult to fully combine data sets between thepilot studies and the main sampling campaigns becausethe Peace Bridge plaza was under construction and thetollbooth area was relocated to the Canadian side of thebridge before the summer 2005 campaign. This furtherreduced the size of the data set for any given statisticalanalysis, limiting the investigators’ ability to make statisti-cally sophisticated comparisons that would supplementtheir characterization of the air quality around the plaza.
Aside from these concerns, the Review Committee feltthat the study design was sound and that the study waswell conducted. Moreover, they noted that the study madesome important innovations and contributions to studydesign for research intended to identify potential hot spotsof emissions, particularly from traffic and roadwaysources. Innovations included the use of multiple years ofdata on prevailing wind patterns to determine the locationof fixed sampling sites relative to the Peace Bridge plaza
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before the study began (instead of sampling in convenientlocations and recording wind directions post hoc), and theuse of study staff walking fixed routes for intensive localmonitoring of specific air pollutants in the adjacentneighborhood. Collection of both continuous and inte-grated samples at the same location was another strengthof the study.
RESULTS
The Review Committee believed that Spengler’s teammade a thorough assessment of the concentrations ofMSATs surrounding the Peace Bridge plaza. They felt thatthe resulting measurements were sound and that the sum-mary measurements by season and sampling campaignadequately described the air quality in the area. The sum-mary results for upwind and downwind locations, as wellas for the individual sites, are therefore the best indicatorsof relationships between traffic at the plaza and downwindexposure and are the most important results from thisstudy. The movement of the tollbooths, however, poten-tially reduced the overall mean MSAT levels that were cal-culated across the time period before and after the plazareconstruction. The Review Committee noted that anyconclusions about the relationship between traffic andpollutant levels beyond exploration of patterns of winddirection, traffic composition, and measured levels ofselected traffic-related elements and compounds shouldbe interpreted with caution.
The innovative mobile monitoring program provided anassessment of pollutant levels in the nearby neighborhood,and the Committee generally supported the value of theseGIS-based results. The Committee did express some con-cern that local traffic on the streets, particularly the busythoroughfares that had the highest pollutant concentra-tions, may have had a greater influence on exposure levelsmeasured by the study staff than the Peace Bridge plazatraffic itself, especially for species known to decay at ashort distance from the emission source. Nonetheless, thisassessment of traffic and pollutant concentrations isimportant to understanding the air quality in neighbor-hoods that are usually downwind of the Peace Bridgeplaza, where local residents have reported adverse healtheffects (Lwebuga-Mukasa et al. 2004). The measurementdata set that Spengler and his team developed for thisstudy is important to understanding local contributions toselected MSATs in areas where people live and work com-pared with exposures in an area primarily used for recre-ation (the Bird Island Pier route). This study alsodemonstrated that a concentration of mobile-source emis-sions, combined with relatively consistent meteorology,
could result in areas with elevated concentrations that aredependent on wind conditions. Thus the results of thisstudy indicate that wind speed and direction should beconsidered in any study characterizing a putative hot spot.
In the final analysis, the Review Committee and the inves-tigators were somewhat at odds about whether the PeaceBridge plaza constituted a hot spot of MSAT emissions andpotentially high community exposure. According toSpengler and his colleagues, the Peace Bridge plaza, at thetime the study was conducted, met the definition of a hotspot as a location with pollutant concentrations higher thanthose of the surrounding area. The conclusions to theInvestigators’ Report state that “The Peace Bridge plaza andthe adjacent neighborhood represent a classic mobile-sourcehot spot. Congestion of car, bus, and truck traffic results in apersistent source of emissions. Persistent southwest tonorthwest winds increase PM and EC. We have presentedevidence that many compounds emitted directly from carsand heavy-duty vehicles were substantially elevatedimmediately downwind of the plaza.”
While the Review Committee took note of the authors’interpretation, it had two reservations regarding this con-clusion. First, although differences in methods, instru-ments, and other conditions make it difficult to compareresults across studies, the levels of MSATs reported in thisstudy were not high relative to levels of MSATs measuredat other congested U.S. locations. The authors note this intheir comparison of the measurements for a variety of ele-ments and compounds with those in the TEACH (ToxicsExposure Assessment Columbia and Harvard) studies inNew York City and Los Angeles (Sax et al. 2006) and theRIOPA (Relationships of Indoor, Outdoor, and PersonalAir) study in Houston and Los Angeles (Weisel et al. 2005).Second, though meteorologic conditions will affect pol-lutant levels at any location, the Committee noted that theelevated concentrations near the Peace Bridge were particu-larly dependent on wind direction: because the wind direc-tion was from Lake Erie and the Niagara River 45% of thetime (60% in the summer), the community did not experi-ence higher levels of pollutants the other 55% of the time(40% in the summer) when the wind direction changed.Thus, in view of the relatively low elevations of MSAT pol-lutants, and varying meteorologic conditions at differenttimes of the year, the Committee did not agree with desig-nating the Peace Bridge site an unqualified hot spot.
The Committee ultimately concluded that determiningwhether an area is a hot spot is difficult. The subtle differ-ences of opinion on the characterization of an air toxics hotspot notwithstanding, Spengler and his team have done anexcellent job of characterizing and investigating an area withpotentially elevated emissions of MSATs.
144
Commentary on Investigators’ Report by Spengler et al.
CONCLUSIONS
The Review Committee noted that the Buffalo PeaceBridge Study by Spengler and his colleagues makes valu-able contributions to HEI’s hot-spot assessment program inseveral areas. First and foremost, their presentation of howto thoroughly characterize levels of air toxics surroundinga putative hot spot for mobile-source air pollution emis-sions is exemplary.
Spengler’s team devised a study design and samplingscheme for characterizing air pollutant levels in an area ofpotentially high emissions that included important andinnovative features. Their scheme for selecting samplingsites based on historic prevailing wind conditions relativeto a putative pollution source strengthened their overallability to attribute measured MSAT levels to traffic at thePeace Bridge plaza. They also selected an appropriatestudy location for a traffic-related emissions study by tar-geting a somewhat geographically isolated source of poten-tially high emissions for intensive analysis.
Although the overall levels of MSATs in this study areado not seem to be high relative to other locations in theUnited States, the investigators’ data sets do contribute toour understanding of how traffic emissions may result inelevated levels of airborne MSATs in a local area. Finally,their mobile monitoring program for evaluating air toxicslevels in a local neighborhood was both innovative andpotentially valuable for future studies of variability inlevels of air toxics at small geographic scales in inhabitedareas, and it further adds to the field by demonstrating eco-nomical methods for such assessments.
Relocation of the tollbooths to the Canadian side of thePeace Bridge and other changes that altered the traffic andidling patterns in the plaza made comparisons between theindividual sampling campaigns in this study difficult.Subsequent construction to move the traffic away from theresidential neighborhood and increase capacity for truckinspections has likely altered the present-day patterns ofMSAT concentrations in the study area relative to thosereported in this study. Furthermore, emissions from motorvehicles are changing as a result of new fuels and newemission-control technologies. Meanwhile, the PeaceBridge remains one of the most heavily traveled bordercrossings in the United States. The data collected in thisstudy may provide important baseline information forassessing the effects of changes in infrastructure and trafficpatterns on MSAT concentrations in the Peace Bridgeplaza area.
ACKNOWLEDGMENTS
The Health Review Committee thanks the ad hocreviewers for their help in evaluating the scientific merit ofthe Investigators’ Report. The Committee is also grateful toDebra Kaden for her oversight of the study, to Kate Adamsfor her assistance in preparing its Commentary, to Genev-ieve MacLellan for science editing of this Report and itsCommentary, and to Suzanne Gabriel, Barbara Gale, FredHowe, Bernard Jacobson, Flannery Carey McDermott, andRuth Shaw for their roles in preparing this ResearchReport for publication.
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HEI Air Toxics Review Panel. 2007. Mobile-Source AirToxics: A Critical Review of the Literature on Exposureand Health Effects. HEI Special Report 16. Health EffectsInstitute, Boston, MA.
Lwebuga-Mukasa JS, Oyana TJ, Wydro P. 2004. Risk factorsfor asthma prevalence and chronic respiratory illnessesamong residents of different neighborhoods of Buffalo,New York neighborhoods. J Epidemiol Community Health58:951–957.
Sax SN, Bennett DH, Chillrud SN, Ross J, Kinney PL, Spen-gler JD. 2006. A cancer risk assessment of inner-city teen-agers living in New York City and Los Angeles. EnvironHealth Perspect 114:1558–1566.
Weisel CP, Zhang J, Turpin B, Morandi MT, Colome S,Stock TH, Spektor DM, and Others. 2005. Part I: Collectionmethods and descriptive analyses. In: Relationships ofIndoor, Outdoor, and Personal Air (RIOPA). ResearchReport 130. Health Effects Institute, Boston, MA.
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RELATED HEI PUBLICATIONS: AIR TOXICS AND RISK ASSESSMENT
145
Research Reports160 Personal and Ambient Exposures to Air Toxics in Camden, New Jersey P. Lioy 2011
156 Concentrations of Air Toxics in Motor Vehicle–Dominated Environments E.M. Fujita 2011
153 Improved Source Apportionment and Speciation of Low-Volume J.J. Schauer 2010Particulate Matter Samples
150 Mutagenicity of Stereochemical Configurations of 1,3-Butadiene Epoxy Metabolites R.Q. Meng 2010in Human Cells
149 Development and Application of a Sensitive Method to Determine Concentrations T.M. Cahill 2010of Acrolein and Other Carbonyls in Ambient Air
144 Genotoxicity of 1,3-Butadiene and Its Epoxy Intermediates V.E. Walker 2009
143 Measurement and Modeling of Exposure to Selected Air Toxics for Health Effects R.M. Harrison 2009Studies and Verification by Biomarkers
133 Characterization of Metals Emitted from Motor Vehicles J.J. Schauer 2006
132 An Updated Study of Mortality among North American Synthetic Rubber E. Delzell 2006Industry Workers
130 Relationships of Indoor, Outdoor, and Personal Air (RIOPA)
Part I. Collection Methods and Descriptive Analyses C.P. Weisel 2005
Part II. Analyses of Concentrations of Particulate Matter Species B.J. Turpin 2007
116 Biomarkers in Czech Workers Exposed to 1,3-Butadiene: A Transitional R.J. Albertini 2003Epidemiologic Study
115 Validation and Evaluation of Biomarkers in Workers Exposed to Benzene in China Q. Qu 2003
113 Benzene Metabolism in Rodents at Doses Relevant to Human Exposure K.W. Turteltaub 2003from Urban Air
108 Case–Cohort Study of Styrene Exposure and Ischemic Heart Disease G.M. Matanoski 2002
103 Characterization and Mechanisms of Chromosomal Alterations Induced by Benzene D. Eastmond 2001in Mice and Humans
101 Respiratory Epithelial Penetration and Clearance of Particle-Borne Benzo[a]pyrene P. Gerde 2001
92 1,3-Butadiene: Cancer, Mutations, and Adducts 2000
Part I. Carcinogenicity of 1,2,3,4-Diepoxybutane R.F. Henderson
Part II. Roles of Two Metabolites of 1,3-Butadiene in Mediating Its L. Recioin Vivo Genotoxicity
Part III. In Vivo Mutation of the Endogenous hprt Genes of Mice and Rats by V.E. Walker1,3-Butadiene and Its Metabolites
Continued
RELATED HEI PUBLICATIONS: AIR TOXICS AND RISK ASSESSMENT
PrincipalNumber Title Investigator Date*
* Reports published since 1998. Copies of these reports can be obtained from the Health Effects Institute and many are available at pubs.healtheffects.org.
146
Part IV. Molecular Dosimetry of 1,3-Butadiene I.A. Blair
Part V. Hemoglobin Adducts as Biomarkers of 1,3-Butadiene Exposure J.A. Swenbergand Metabolism
87 Development of Liquid Chromatography–Electrospray Ionization–Tandem A.A. Melikian 1999Mass Spectrometry Methods for Determination of Urinary Metabolites of Benzenein Humans
84 Evaluation of the Potential Health Effects of the Atmospheric Reaction Products A.J. Grosovsky 1999of Polycyclic Aromatic Hydrocarbons
HEI Communications10 Improving Estimates of Diesel and Other Emissions for Epidemiologic Studies 2003
7 Diesel Workshop: Building a Research Strategy to Improve Risk Assessment 1999
6 A Partnership to Examine Emerging Health Effects: EC/HEI Workshop on 1,3-Butadiene 1999
HEI Program SummariesResearch on Air Toxics 1999
HEI Special Reports17 A Critical Review of the Health Effects of Traffic-Related Air Pollution 2010
16 Mobile-Source Air Toxics: A Critical Review of the Literature on Exposure and Health Effects 2007
Research Directions to Improve Estimates of Human Exposure HEI Diesel Epidemiology 2002and Risk from Diesel Exhaust Working Group
Part I. Report of the Diesel Epidemiology Working Group
Part II. Investigators’ Reports
Cancer Risk from Diesel Emissions Exposure in Central and Eastern Europe: P. BoffettaA Feasibility Study
Cancer Risk from Diesel Exhaust Exposure in the Canadian Railroad M.M. FinkelsteinIndustry: A Feasibility Study
Quantitative Assessment of Lung Cancer Risk from Diesel Exhaust Exposure E. Garshickin the US Trucking Industry: A Feasibility Study
Measurement of Diesel Aerosol Exposure: A Feasibility Study D.B. Kittelson
Measuring Diesel Emissions Exposure in Underground Mines: B. ZielinskaA Feasibility Study
Diesel Emissions and Lung Cancer: Epidemiology and Quantitative HEI Diesel Epidemology 1999Risk Assessment Expert Panel
H E I B OA R D, C O M M I T T E E S , a n d S TA F F
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H E A L T HE F F E CTSINSTITUTE
Number 158
July 2011
Air Toxics Exposure from Vehicle Emissions at a U.S. Border Crossing: Buffalo Peace Bridge StudyJohn Spengler, Jamson Lwebuga-Mukasa, Jose Vallarino, Steve Melly, Steve Chillrud, Joel Baker, and Taeko Minegishi
Number 158July 2011
JOURNAL OF ASTHMA Vol. 40, No. 8, pp. 855-864, 2003
ORIGINAL ARTICLE
Traffic Volumes and Respiratory Health Care Utilization Among Residents in Close Proximity to the Peace Bridge
Before and After September 11, 2001
J. S. Lwebuga-Mukasa, M.D., Ph.D.," S. J. Ayirookuzhi, M.D.,I and A. Hyland2
[Center for Asthma and Environmental Exposure, Lung Biology ReSearch Program, Pulmonary and Critical Care Division, Department of Internal Medicine,
University at Buffalo School of Medicine and Biomedical Sciences, Kaleida Health Buffalo General Division, Buffalo, New York, USA
'Division of Cancer Prevention, Roswell Park Cancer Institute, Buffalo, New York, USA
ABSTRACT
A recent study based on data over a 10-year period (1991-2000) showed a positive association between health care utilization and prevalence of asthma, and commercial traffic at a U.S.—Canada border crossing. We wanted to determine whether decreases in total traffic would also be associated with decreases in health care utilization for respiratory illnesses. Following September 11, 2001, there was a 50% drop in total traffic at the Peace Bridge border crossing point between Buffalo, New York and Fort Erie, Ontario, Canada. To investigate the impact of such a traffic decline on health care utilization for respiratory illnesses, weekly respiratory admissions to Kaleida Health System, Western New York's largest health care provider were analyzed according to ICD9Clvl classification and compared with total weekly traffic volumes for 3-month periods in 2000 and 2001 (August, September, and October). The total number of patients admitted to hospital or seen in emergency departments for respiratory illnesses during the 3-month periods of both years was 5288. A 50% drop in total traffic following Labor Day and September 11, 2001, from week 4 to week 7 was found to be statistically significant (p = 0.031) when a one-way ANOVA was performed. Likewise, the drop in total respiratory cases approached statistical significance (p = 0.052) when a one-way ANOVA was conducted. The results suggest
tan
*Correspondence: 3. S. Lwebuga-Mukasa, M.D., Ph.D., Associate Professor of Medicine, Director, Center for Asthma and Environmental Exposure, Lung Biology Research Program, Pulmonary and Critical Care Division, Department of Internal Medicine, Kaleida Health Buffalo General Division, 100 High St., Buffalo, NY 14203, USA; Fax: (716) 859-3620; E-mail: jlwebuga@ acsu.buffalo.edu.
an association between decrease in traffic volumes with decrease in health care utilization for respiratory diseases. These results suggest that current levels of traffic may be impacting on the respiratory health of residents in the nearby community.
Key Words: Traffic-related pollution; Asthma; Respiratory health; North American Free Trade Agreement (NAFTA); Health impacts; Health care utilization; US—Canada border crossing points; Peace Bridge; Trade corridors.
INTRODUCTION
Located in residential Buffalo's west side, the Peace Bridge Plaza Complex (PBC) is the busiest U.S.—Canada border crossing for commercial traffic in the northeastern United States (1). The Peace Bridge Complex consists of U.S. Customs Inspection Station, Bridge toll booth plaza, and a duty-free shop. The community provides a unique clinical setting in which up to 7,000 trucks and 26,000 automobiles are processed daily by the U.S. Customs Station and Peace Bridge toll booths plaza (2). The Peace Bridge connects Fort Erie, Ontario, Canada, to Buffalo, NY, U.S.A. Since the implementation of the North American Free Trade Agreement (NAFTA) in 1994, commercial traffic crossing the Peace Bridge has grown at an annual rate of 9% (3). In contrast, auto-mobile traffic has decreased slightly (4). Buffalo's west side has been found to have a high asthma prevalence (4) and health care utilization for asthma (4,5). A previous communication also suggests a link between asthma prevalence and health care utilization for asthma, and commercial traffic volume (4). An association has also been found between proximity to the PBC and high asthma prevalence (2).
Traffic-related pollution has been shown to be related to an increase in health care utilization for a number of acute respiratory illnesses, including upper respiratory diseases such as sinusitis, otitis, bronchitis; lower respiratory diseases such as pneumonia and in-fluenza, and airway diseases such as asthma and chro-nic obstructive pulmonary disease (COPD) (5-11). Residents of border communities in close proximity to major USA—Canada trade corridors are exposed to increased commercial traffic-related pollution (1). How-ever, health impacts of traffic-related pollution have not been fully evaluated. We had initially reported admission rates for asthma and bronchitis to be a major health problem in the study region (12); Buffalo's lower west side had an admission rate that was two-fold higher than surrounding communities. We have subsequently investigated home environmental and personal factors and found that although they play important roles in triggering asthma, they did not fully account for the higher asthma prevalence among resi-
dents living in Buffalo's west side ( 2) . Rather, the study (2) found that living in close proximity to the Peace Bridge Plaza Complex (PBC) was associated with a high asthma prevalence. Hence, a need was felt to investigate the role of traffic volume in this region as a contributing factor to adverse respiratory health outcomes.
In a orevious communication, we reported an association of increased health care utilization due to asthma with increases in commercial traffic through Buffalo's west side following implementation of the North American Free Trade Agreement (NAFTA) (4). The study found that the zip code areas closest to the traffic corridor were the most severely affected (4). The study also found a cluster of households with in-creased asthma prevalence in close proximity to the PBC. Criteria for causality include presence of out-come when exposure is present and absence of outcome when exposure is absent. In the light of this background, this study sought to investigate the relationship between an acute decrease in traffic volumes and a decrease in health care utilization for respiratory illnesses. Studies of health impacts over a short time period provided a unique opportunity that minimized the confounding of personal factors such as smoking, household triggers, or socio-economic status.
Following, September 11, 2001, there was a dra-matic decrease in commercial and automobile traffic crossing the U.S.—Canada border through the Peace Bridge. The current study examines the impact of de-creased traffic on health care utilization for respiratory conditions among residents of the community border-ing the trade corridor.
METHODS AND PROCEDURES
The study population consisted of all the residents of zip codes 14201-14204, 14206-14216, and 14222 (See map of study area, Figure 1). The same study area had been included in a previous study that found an association between traffic volume and health care utilization for asthma (4). The total population of this study area was 279,362 according to the 2000 U.S.
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857 Traffic Volumes and Respiratory Health Care Utilization
Figure 1. Map showing postal zip codes of study area. (Go to www.dekker.com to view this figure in color.)
census data. The Peace Bridge is located in 14213, and most of the traffic passes through the lower west side, which is comprised of 14201-14204. Hospital utiliza-tion data were obtained from Kaleida Health System, which is the largest health care provider in western New York and within the City of Buffalo, with a market share of 42%.
Data Categories
The following categories of data were analyzed: 1) commercial and automobile traffic across the Peace Bridge, 2000-2001; 2) hospital utilization data of 2000 and 2001; and 3) U.S. census data for the region for the year 2000 was obtained from the U.S. census bureau. Each data category is described in detail below.
Traffic Data Across the Peace Bridge During August, September, and
October 2000-2001
Traffic data for the Peace Bridge Plaza for the period of August, September, and October were ob-tained from the Public Bridge Authority, Buffalo, NY,
for both 2000 and 2001. Weekly traffic counts of cases were taken from Saturday to Friday instead of daily counts because the number of cases was otherwise too small, Hence, August 1-4, 2000, and October 28-31, 2000, were not included. Likewise, August 1-3, 2001, and October 27-31, 2001, were omitted. Data were collected for trucks, buses, and automobiles. Trucks and buses were considered together as commer-cial traffic because of the greater contribution they make to air pollution from diesel engine exhaust. Air-monitoring data were unavailable because of lack of air-monitoring facilities on Buffalo's west side. Hence, traffic volume was used as a surrogate for air moni-toring. Moreover, central air monitoring may not capture localized high levels of pollution in certain neighborhoods located at varying distances from the source of air pollution (1,13).
Hospital Utilization Data of 2000 and 2001
Electronic hospital discharge data for respiratory were obtained from Kaleida Health System, Western New York's largest health care provider with 42% of regional market share.
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The database included data from the five com-ponent hospitals of the Kaleida Health System for the two study years of 2000 and 2001. A 3-month study period from August to October of each year was se-lected to take into account seasonal and natural va-riability in health care utilization rates. The data analyzed included only respiratory cases as defined by ICD9CM codes 460-519.99. Upper respiratory disease was classified as ICD9CM codes, which included 460-466 and 470-478 and included conditions such as pharyngitis, otitis, and sinusitis. Lower respiratory disease, which included conditions such as pneumonia and influenza, was classified as ICD9CM codes, which included 480-487 and 510-519.99. Airway disease was defined as conditions that arc included in ICD9CM codes 490-496 and included asthma and COPD exacerbations. ICD9CM codes from 500 to 508 were not included because we considered them to be either respiratory diseases, which are not directly triggered by air pollution (e.g., asbestosis and coal workers pneumoconiosis). Although the existence of the prior condition could worsen with exposure to pollutants, available data did not permit determina-tion whether that was the reason for which the patient was seen.
US Census Data
Census data for the year 2000, for population by zip code in the study area were obtained from the U.S. census bureau at www.uscensus.gov.
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Data Analysis
The data were analyzed with MS-Excel 2000 (Microsoft, Inc.) and SPSS version 10.1 (SPSS Inc, Chicago, IL). Descriptive graphs were generated with MS-Excel. The study period was divided into three periods: weeks 1-4 (before traffic drop), weeks 5-8 (right after traffic drop), and weeks 9-12 (far beyond traffic drop). The difference in total traffic, commercial traffic, total respiratory, airway diseases, lower respira-tory, and upper respiratory cases was calculated for corresponding weeks in 2000 and 2001. A one-way ANOVA was conducted to detect statistically significant differences in means of variables for corresponding periods in 2000 and 2001 with SPSS. Two-sided statistical tests were performed with a 5% level of sig-nificance. All population rates were calculated per 10,000 persons.
RESULTS
Traffic Volume 2000 and 2001: August, September, and October
The total number of vehicles was counted as the total number of automobiles, buses, and trucks, and a line graph for the time period was plotted (Figure 2). Si-milarly, graphs were plotted for trucks and buses together as well as for trucks alone, buses alone, and automobiles alone (data not shown). In the current study, we included
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automobiles in our total traffic volume analysis because they constituted the bulk of traffic volume in terms of gross numbers and also constituted the bulk of traffic decline following September 11, 2001. The drop in commercial traffic (25%) was less dramatic than the drop in total traffic (50%) from week 4 to week 7. The drop in traffic might have been due to cross-border restrictions on travel in the aftermath of the World Trade Center attack as well a general fear among the public to travel in an environment of heightened cross-border security.
Figure 2 shows a precipitous decline in total traffic volume in the week of September 11, to 64% of the previous week. Traffic volumes reached the lowest point in the third week of September after which it started to rise. Week five included U.S. Labor Day weekend, which explains the decrease in commercial traffic volume for 2000. The traffic volume stabilized in October, although at a consistently lower level than the previous year.
When the volume of commercial traffic was considered (data not shown), there was a decline in
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Figure 4. Airway disease cases for August, September and October of 2000 and 2001 by the week (excluding clinic cases). (Go to www.dekkercom to view this figure in color.)
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traffic volume to 69% of the previous years in the week of September 11, 2001. Again, a recovery in traffic volume was not seen till the third week of September 2001, although levels generally remained consistently lower than the previous years.
Health Care Utilization Data from the Kaleida Health System Database
The total number of respiratory disease cases seen for the entire study period excluding those seen in the clinics was 5288. Of the total, 41% were males and the
Lwebuga-Mukasa, Ayirookuzhi, and Hyland
remaining were females. The mean age of this sample was 26.6 years with one standard deviation of 24.75 years. The racial breakdown of the patient population for respiratory cases is as follows: 45% African Ame-rican, 21% Hispanic, 28% white, and the remaining 6% others. During the 2000 study period, the total number of cases was 5735, whereas it dropped to 5458 during the 2001 study period. Of these patients, emergent cases (not seen in clinics) were 2671 in 2000 and 2617 in 2001. The gender distribution of patients for both years in the study period was comparable, with 60% of cases being female patients. Health care utilization by
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Figure 5. (a) Lower respiratory cases for August, September, and October of 2000 and 2001 by the week (excluding clinics). (b) Upper respiratory cases for August, September, and October of 2000 and 2001 by the week (excluding clinics). (Go to www.dekker.coni to view this figure in color.)
MARCEL DEKKER,
970 Madison Avenue, New York, New York 10016
Traffic Volumes and Respiratory Health Care Utilization
Latino Hispanics increased from 18.88% in 2000 to 23.78% in 2001, whereas there was a slight decline in utilization for African Americans from 45.65% to 44.8% and for whites from 29.05% to 25.91%.
Data for health care utilization for respiratory ill-nesses were plotted for all respiratory diseases together. Cases evaluated in clinics were excluded because we wanted to focus on emergent cases (Figure 3). The usual pattern shows an increase in health care utilization from the late summer, to fall and winter. This is reflected in the 2000 study period. A line graph of health care utilization seen in the study pe-riod of both years showed a decreased number of res-piratory cases during the month of September 2001, which remained consistently lower than the previous year's September. The levels for the two years did not begin to approximate until the beginning of October. The number of total cases, excluding those seen at the clinics, dropped to as low as 75% of the previous year's level.
Line graphs were plotted for each of three ca-tegories of respiratory diseases based on ICD9CM codes: airway disease, lower respiratory disease, and upper respiratory disease. The graph for airway disease (Figure 4), which included conditions such as asthma, COPD, and allied conditions, showed an overall climb in the number of cases in the study months in 2001, but
861
the expected peak in September, which is representa-tive of the natural variability, was not seen. In fact, from the 6th to the 10th week, the number of cases was far less than what was seen during 2000, whereas during the period on either side of this period, the number of cases in both the years approximate each other. In the last week of September 2001, the number of cases was only 75% of what was seen in the pre-vious year where it peaked at 74 cases compared to 99 of the previous year.
A line graph of lower respiratory disease (Figure 5a) showed that during the month of September, there was consistently a lower number of cases in 2001 than in 2000 and that this was not the case for August and October where numbers for the 2 years were more equal. During the sixth week of the study, which is the second week of September, the number of cases was only 72% of the previous year.
Figure 5b shows line graphs for admissions for upper respiratory disease. The number of cases in 2001 for weeks 5 and 7 was lower than in the previous year. The number of cases in weeks 1-4 were comparable for the 2 years. In the 8 to 11-week segment, there was an overshoot of cases for 2001 compared to 2000.
The above patterns suggest that a decrease in traffic volume was associated with a decrease in health care utilization for respiratory cases during September 2001.
Table 1. Results of one-way ANOVA comparing difference in means for corresponding periods in 2001 and 2000 for the variables in the study.
Period Period Mean difference p Value p Value of model
1. Total respiratory cases Weeks 5 to 8 Weeks 1 to 4 -40.25 0.052** Weeks 5 to 8 Weeks 9 to 12 -45.75 0.029* 0.023* Weeks 9 to 12 Weeks 1 to 4 5.50 0.925
2. Airway disease cases Weeks 5 to 8 Weeks I to 4 -15.50 0.343 Weeks 5 to 8 Weeks 9 to 12 -17.50 0.266 0.240 Weeks 9 to 12 Weeks 1 to 4 2.00 0.980
3. Lower respiratory cases Weeks 5 to 8 Weeks 1 to 4 -10.50 0.066** Weeks 5 to 8 Weeks 9 to 12 - 3.50 0.670 0.073** Weeks 9 to 12 Weeks 1 to 4 - 7.00 0.242
4. Upper respiratory cases Weeks 5 to 8 Weeks 1 to 4 - 15.75 0.354 Weeks 5 to 8 Weeks 9 to 12 -25.25 0.101 0.114 Weeks 9 to 12 Weeks 1 to 4 9.50 0.666
5. Total traffic volumes Weeks 5 to 8 Weeks 1 to 4 -36807.25 0.031* Weeks 5 to 8 Weeks 9 to 12 - 7884.75 0.790 0.030* Weeks 9 to 12 Weeks 1 to 4 - 28922.50 0.088**
6. Commercial traffic volumes Weeks 5 to 8 Weeks 1 to 4 - 2463.00 0.274 Weeks 5 to 8 Weeks 9 to 12 - 811.00 0.852 0.291 Weeks 9 to 12 Weeks 1 to 4 - 1651.25 0.533
Weeks 1 to 4: Before traffic drop. Weeks 5 to 8: Right after traffic drop. Weeks 9 to 12: Far beyond traffic drop. *P value of <0.05 indicates statistical significance by convention. **P value approaches but does not attain statistical significance.
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Statistical Analysis
Statistical analysis using one-way ANOVA was performed, and results are shown in Table 1. The drop in total traffic was statistically significant (p = 0.031), and the drop in total respiratory cases approached sta-tistical significance (p = 0.052). The one-way ANOVA evaluated the drop from weeks 1 to 4 to weeks 5 to 8 (i.e., the period right after the traffic drop compared to the period before the traffic drop), Comparison of weeks 5-8 segment with weeks 9-12 segment for total respiratory cases attained p value of 0.029. Figure 6 shows a plot of means of the one-way ANOVA com-paring difference in total respiratory cases by period for corresponding weeks in 2001 and 2000. Similar analysis was conducted for subcategories of respira-tory disease (Table 1), and an association approaching statistical significance was obtained for lower respira-tory diseases which include pneumonia and influenza (p = 0.073). Association between the drop in upper and lower respiratory cases could not be found with acceptable p values, and this may be due to the small number of cases in subcategories of respiratory di-seases, which is a limiting factor in the study.
DISCUSSION
This is the first demonstration of an association between decrease in traffic and a decline in health care utilization for respiratory diseases in close proximity to
Period (weeks 1-4, weeks 5-8, weeks 9-12)
Figure 6. Plot of means of one-way ANOVA comparing the difference in total respiratory cases for corresponding weeks in 2001 and 2000 and the difference in traffic in the corres-ponding period. (Go to www.dekkercom to view this figure in color,)
Lwebuga-Mukasa, Ayirookuzhi, and Hyland
this U.S.—Canada border crossing point. A previous report had revealed an association between increased traffic and increased health care utilization (4). Com-bined, the two studies link variations in current traffic volume with health care utilization.
The current study contributes to a growing list of published studies that have reported an association between traffic volume and health care utilization. A unique attribute of the study is the relatively short pe-riod during which traffic volume and health care utilization were studied. The short period minimizes confounders such as personal habits, smoking, socio-economic status, or demographic shifts in the study population. Previous studies have found an association between traffic volume and respiratory health effects (6-12). Two studies from Japan (6,9) found that housewives who lived within 20 meters of the road had more respiratory symptoms than their peers who lived farther away. Another study found that respiratory symptoms in 4 to 11-year-old children were double that seen in their peers who lived farther away (10). Similarly, studies by Wjst (6), Brunekreef (11), and Ciccone (12) reported an association between increased respiratory symptoms and children living in close proximity to busily traveled roadways. These studies have shown that in the absence of particulate moni-toring, traffic volumes could be used as a surrogate instead. It must be noted, however, that certain stu-dies have failed to find an association between asthma and busily traveled roadways. For example, Livingstone and coworkers (14), who used case-matched controls including additional specific information about cases, did not find an association between traffic and health care utilization.
In a separate report (4), we found a positive asso-ciation between increasing annual commercial traffic volumes and increasing rates of health care utilization that was statistically significant (p < 0.05) in zip codes around the Peace Bridge Plaza Complex between 1991 and 1996. The study found that there was an annual excess of 230.2 adult hospital discharges due to asthma in the same time period, which were associated with the increase in annual commercial traffic volumes in the same study area shown in Figure 1 (4). The effect was seen to be greatest in communities closest to the PBC and the major roadways supplying it and decreased as distance from the PBC increased. These findings showing increasing traffic volumes were associated with an increase in health care utilization are compli-mented by the current study, which showed an association between decrease in traffic and decrease in health care utilization for all respiratory cases.
The current study has a number of limitations. Traffic volume data do not take into account longer
MARCEL DEKKER, INC. 170 Madison Avenue, New York, New 'York 10016
T a I.
Traffic Volumes and Respiratory Health Care Utilization 863
waiting periods, which were required for border security checks. This may have contributed to failure to achieve statistical significance. Measurement of air quality pol-lutant levels would overcome this limitation. Second, health care utilization data do not provide information on personal data (e.g., exposure to environmental trig-gers, tobacco smoke, home environment, or genetic factors). In addition, history data of individuals who moved into the west side were not available, so the initial susceptibility of the target population was not known to environmental triggers as well as the duration of residence in the study area. Third, using zip code of the residence may not take into account the duration of time spent at the workplace assuming, that there will always be people who work at a distance from their workplace. However, the converse is also true that people who come to the study area including the west side to work could have been excluded from the study if their area of residence was outside the study region.
In this study, traffic volumes were used as a surrogate for air particulate monitoring because the air particulate data for the time period of study were not available to us. However, as mentioned before, central air quality-monitoring stations may not always capture local trends. Furthermore, susceptible individuals may become symptomatic at levels below the U.S. Envi-ronmental Protection Agency (EPA) accepted levels. Insufficient data did not permit statistical analysis using multivariate regression analysis to be conducted, which would have adjusted for possible confounding variables such as temperature or precipitation. Data were not available on the cost of ill health to measure the financial loss borne by the community. It is possi-ble that susceptible people would move away from an area they associate with their illness. However, the observation that changes in health care utilization oc-curred over short periods of time argues against changes in demographics or socioeconomic susceptibility fac-tors. Finally, analysis based on health care utilization misses out on a large number of residents who are affected but choose to treat themselves without going to a clinic or emergency department to be hospitalized. Prospective studies are needed in the future in which personal pollutant exposures are measured and related to respiratory outcomes and costs, including over-the-counter drug costs and social costs.
The strength of the study lies in its ability to demonstrate temporality. Buffalo's west side provides a model for future research both here and at other border crossing points. The studies (1,2,4,12) raise an intriguing possibility that traffic-related pollutants may not only be associated with exacerbation of respira-tory conditions but may also play an etiological role in asthma.
ABBREVIATIONS
NAFTA
North American Free Trade Agreement PBC
Peace Bridge Plaza Complex COPD
Chronic obstructive pulmonary disease EPA
Environmental Protection Agency
ACKNOWLEDGMENTS
This research was supported in part by a grant ROI-CCR220259 from the Centers for Disease Control and Prevention, and EMAP grant from the Environ-mental Society Institute at the University of Buffalo to J.L-M. All research in this study was approved by the University of Buffalo (UB) Human Investigation Review Board, which oversees all human investiga-tions at UB in accordance with national and institu-tional guidelines for the protection of human subjects.
REFERENCES
1. Lwebuga-Mukasa JS, Dunn-Gcorgiou E. Health implications of Peace Bridge Plaza Complex: traffic related pollution effects on an American community at a US—Canada gateway. J Air Waste Management Assoc. Symposium Proceedings "En-gineering Solutions to Indoor Air Quality Pro-blems", July 17-19, 2000; 4767-503.
2. Lwebuga-Mukasa JS, Wojcik R, Dunn-Georgiou E, Johnson C. Home environmental factors asso-ciated with asthma prevalence in two Buffalo inner-city neighbourhoods. J Health Care Poor Underserved 2002; 13(2).
3. Canadian Trucking Alliance. Trade, Transport Infrastructure, and Canadian Prosperity. Novem-ber 1, 1999. At http://www.tradecorridor.com/ trade2.pdf.
4. Lwebuga-Mukasa JS, Oyana T, Thenappan A, Ayirookuzhi SJ. Association between traffic vol-ume and health care utilization for asthma among residents at a US—Canada border crossing point. (in press).
5. Wijst M, Reitmer P, Dold S, Wulff A, Nicolai T, Nicolai T, Von Loeffelholz-Colberg, Von Mutius E. Road traffic and adverse effects on respiratory health in children. Brit Med I 1993; 307(6904): 596-600.
6. Guo YL, Lin YC, Sung FC. Traffic-related air pollution positively associated with prevalence of asthma in middle school Taiwan. Environ Health Perspect 1999; 107:1001.
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