ent 368 (2006) 781–794www.elsevier.com/locate/scitotenv
Science of the Total Environm
Characterization of fine particle sources in theGreat Smoky Mountains area
Eugene Kim a,⁎, Philip K. Hopke b
a Department of Civil and Environmental Engineering, Clarkson University, Box 5708, Potsdam, NY 13699, USAb Department of Chemical Engineering, Clarkson University, Box 5708, Potsdam, NY 13699, USA
Received 11 August 2005; received in revised form 1 February 2006; accepted 26 February 2006Available online 19 April 2006
Keywords: Southeastern US; PM2.5; The Great Smoky Mountains; Source apportionment; Positive matrix factorization; Thermal optical method;Carbon fractions
1. Introduction
The Great Smoky Mountains area often experiencespoor visibility in summer due to the high concentrationof biogenic aerosols emitted from the forest as well as theanthropogenic aerosols originating from major combus-tion sources (Ames et al., 2000; Day et al., 2000;Hand et al., 2000; Malm et al., 2000a,b). In recent source
apportionment studies of ambient PM2.5 (particulatematter≤2.5 μm in aerodynamic diameter) composition-al data including eight separate carbon fractionsmeasured in the northeastern and midwestern US, gaso-line vehicle emissions were separated from diesel emis-sions and three different types of secondary sulfates wereresolved (Kim and Hopke, 2004a,b; Kim et al., 2005).These studies reported that the Great Smoky Mountainsarea is a potential source area for secondary sulfate.
Previously, particle measurements have been con-ducted in the Great Smoky Mountains area (Andrews
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et al., 2000; Olszyna et al., 2005; Tanner et al., 2004a,b).A factor analysis was applied to the particle numberconcentrations measured in the Great Smoky Mountains(Cheng and Tanner, 2002). However, advanced sourceapportionment studies utilizing temperature resolvedparticulate carbon fractions have not been conducted forthe Great Smoky Mountains area.
In the present study, the major sources of PM2.5 inGreat Smoky Mountains area were identified and theircontributions to the PM2.5 concentrations were estimat-ed by the advanced source apportionment method, po-sitive matrix factorization (PMF) (Paatero, 1997). Thetemporal and seasonal trends as well as the fractionalcarbon profiles of the identified sources were discussed.The directions and locations of potential sources weresuggested by a combination of conditional probabili-ty function and potential source contribution functionanalyses.
2. Methods
2.1. Sample collection and chemical analysis
PM2.5 samples were collected Wednesdays andSaturdays (Mar. 1988–Aug. 2000) and on every thirdday (Sep. 2000–Dec. 2003) at the InteragencyMonitoring of Protected Visual Environments (IM-PROVE, Malm et al., 1994) monitoring site locatedat Look Rock Ridge adjacent to the Great SmokyMountains National Park, TN (latitude: 35.6334,longitude: −83.9417, elevation 820 m). The monitoringsite is situated 13 and 40 km south of Maryville andKnoxville, TN, respectively.
Teflon, Nylon, and quartz filters were used to collectPM2.5 samples. The detailed filter analyses methods arereported in Cahill et al. (1986) and Malm et al. (1994).The quartz filter was analyzed by the IMPROVE/TOR(thermal optical reflectance) method (Chow et al., 1993)for eight carbon fractions. Organic carbon (OC)fractions were volatilized by four temperature steps(OC1 at 120 °C, OC2 at 250 °C, OC3 at 450 °C, andOC4 at 550 °C) in a helium environment. Pyrolyzed OC(OP) was oxidized at 550 °C in a mixture of 2% oxygenand 98% helium environment until the reflectancereturns to its original intensity. Then elemental carbon(EC) fractions are measured in the oxidizing environ-ment (EC1 at 550 °C, EC2 at 700 °C, and EC3 at850 °C).
Samples for which PM2.5 concentrations or eightcarbon fractions were not available were excluded fromthis analysis (7.5%). Samples in which the PM2.5
concentration error flag was not ‘NM’ (normal) were
also excluded (8.1%). A total of 84.4% of the original datawas used for this study. SO4
2− analyzed via ionchromatography was not included and only S analyzedvia X-ray fluorescence (XRF) was used in this studybecause they showed good correlations (slope=3.2±0.05,r2=0.96). OP was subtracted from EC1 and utilized as anindependent variable in this study since the reported EC1concentration in IMPROVE/TOR method includes OPconcentration. Thus, EC1 in this study did not include OP.Sodium (Na) was not included in this study since itsconcentrations and method detection limit values wereunrealistically high after the analytical method waschanged from particle-induced X-ray emission (PIXE) toXRF in December 2001. To obtain reasonable model fit,the samples collected on December 24, 1994 in whichNO3
−, Se, and Znmass concentrations were unusually highwere excluded. A total of 1442 samples collected betweenMarch 1988 and December 2003 and 30 species includingPM2.5 were used in this study. Table 1 shows a summary ofPM2.5 species.
2.2. Source apportionment
PMF provides source profiles and source contribu-tions without prior knowledge of PM2.5 sources. Thedetailed receptor modeling and PMF equations arespecified in Paatero (1997) and Kim et al. (2003). Theapplication of PMF requires the estimated uncertaintiesfor each of the measured data. The uncertainty esti-mation provides a useful tool to decrease the weight ofmissing and below detection limit data in the solution.To assign measured data and the associated uncertain-ties as input data to the PMF, the procedure of Polissar etal. (1998) was used. Species that have Signal/Noise (S/N) ratios (Table 1) between 0.2 and 2 were consideredweak variables and their estimated uncertainties wereincreased by a factor of three to reduce their weight inthe solution as recommended by Paatero and Hopke(2003).
In addition to the standard uncertainty estimation, inorder to take the temporal variability into account, largeruncertainties were used to decrease the weight of somespecific variables in the model fit (Paatero and Hopke,2003). In this study, it was found necessary to increasethe estimated uncertainties of Al, Ca, Cu, NO3
−, S, and Tiby a factor of three. The estimated uncertainties of OC1were increased by a factor of two to down-weight theinfluence of the known positive artifact from theadsorption of gaseous OC (Pankow and Mader, 2001).The estimated uncertainties of EC1 were increased by afactor of three to account for the additional uncertaintyfrom the subtraction of OP.
Table 1Summary of PM2.5 and 29 species mass concentrations used for PMF analysis
a Below method detection limit.b Signal/noise ratio.c Negative and zero values were excluded for the arithmetic mean calculations.d Data below the limit of detection were replaced by half of the reported detection limit values for the geometric mean calculations.e Not available (infinite S/N ratio caused by no BDL value).
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To obtain the mass apportionment without the usualmultiple regression (Kim and Hopke, 2004a,b), themeasured PM2.5 mass concentrations were included asan independent variable in the PMF modeling (Kim andHopke, 2004a). Then, PMF apportions a PM2.5
contribution for each source according to its temporalvariation. The estimated uncertainties of the PM2.5
concentrations were set at four times their values so thatthe large uncertainties decreased their weight in themodel fit.
PMF uses non-negativity constraints on the factors todecrease rotational ambiguity that produces an infinitenumber of possible combinations of source contribu-tions and profiles in the multivariate receptor modelingproblem (Henry, 19873). Also, the parameter FPEAK is
used to control the rotations (Lee et al., 1999; Paatero etal., 2002). By setting a non-zero value of FPEAK, theroutine is forced to add one source contribution vector toanother and subtract the corresponding source profilefactors from each other, and thereby yield rotatedsolutions. PMF was run with different FPEAK values todetermine the range within which the scaled residualsremain relatively constant (Paatero et al., 2002). Theoptimal solution should lie in this FPEAK range. In thisway, subjective bias was reduced to a large extent. Thefinal PMF solutions were determined by experimentswith different numbers of sources, and different FPEAKvalues with the final choice based on the evaluation ofthe resulting source profiles as well as the quality of thespecies fits. The global optimum of the PMF solutions
Table 2Average source contributions a (%) to PM2.5 mass concentrations
Average source contribution
Secondary sulfate I 55.4 (1.3)b
Secondary sulfate II 15.8 (0.2)Gasoline vehicle 12.5 (0.3)Airborne soil 6.1 (0.3)Industry 5.1 (0.1)Secondary nitrate 2.0 (0.1)Secondary sulfate III 1.8 (0.03)Diesel emissions 1.3 (0.03)a Based on PMF predicted mass concentrations.b Standard error.
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was tested by changing the initial values used in theiterative fitting process (Paatero, 1997; Paatero et al.,2002).
2.3. Conditional probability function analysis
The conditional probability function (Kim andHopke, 2004c) estimates the local source directionsutilizing the time resolved wind directions coupled withthe source contribution estimates from PMF. The CPFestimates the probability that a given source contribu-tion from a given wind direction will exceed apredetermined criterion. CPF is defined as
CPF ¼ mDu
nDuð1Þ
where mΔθ is the number of occurrence from windsector Δθ that exceeded the criterion, and nΔθ is thetotal number of data from the same wind sector. Thesame daily contribution was assigned to each hour of agiven day to match to the hourly wind data. In thisstudy, 24 sectors were used (Δθ=15°). Calm winds(b1 m sec− 1) were excluded from this analysis due tothe isotropic behavior of wind vane under calm winds.From the tests with several different percentiles of thecontribution from each source, the criterion of upper25% was chosen to clearly show the directionality of thesources. The sources are likely to be located in thedirections that have high conditional probability func-tion values.
2.4. Potential source contribution function analysis
The potential source contribution function (PSCF)(Ashbaugh et al., 1985; Hopke et al., 1995) estimates thelikely locations of the regional sources for the secondaryparticles using the source contributions calculated fromPMF and backward trajectories calculated using theHybrid Single Particle Lagrangian Integrated Trajectory(HYSPLIT) model with gridded meteorological data(NCEP/NCAR Reanalysis) (Draxler and Rolph, 2003;Rolph, 2003). PSCF is a conditional probability that anair parcel that passed through an area had a concentra-tion exceeding the threshold criterion upon arrival at themonitoring site. The sources are likely to be located inthe area that has high PSCF values. The details areshown in previous studies (Kim and Hopke, 2005).
In this study, the average contribution of each sourcewas used for the threshold criterion. Five-day backwardtrajectories starting at 12:00 at height of 500 m abovethe ground level were computed using the verticalmixing model producing 120 hourly trajectory end
points per sample. The geophysical region covered bythe trajectories was divided into 57,014 grid cells of0.5°×0.5° latitude and longitude so that there are anaverage of 3 trajectory end points per cell.
To minimize the effect of small number of end pointsin a cell that results in high PSCF values with highuncertainties, an arbitrary weight function W(nij) wasapplied to down-weight the PSCF values for the cell inwhich the total number of end points was less than threetimes the average number of the end points per cell(Hopke et al., 1995; Polissar et al., 2001a).
W ðnijÞ ¼1:0 9bnij0:7 3bnijV90:4 2bnijV30:2 nijV2
8>><>>:
ð2Þ
where nij is the total number of end points located in theijth cell.
3. Results and discussion
An eight-source model and a value of FPEAK=−0.3provided the most physically reasonable source profilesin a variety of source number solutions and FPEAKvalues. The secondary sulfate III and diesel emissionswere not extracted in a seven-source model. Thesecondary sulfate III profile was separated into twounreasonable S-high sources in the nine-source model.
The average contributions of each source to the PM2.5
mass concentrations are summarized in Table 2. Thereconstructed PM2.5 mass concentrations were estimatedby the sum of the contributions from PMF resolvedsources. The comparisons between the reconstructed andmeasured PM2.5 mass concentrations show that theresolved sources effectively reproduce the measuredvalues and account for most of the variation in the PM2.5
mass concentrations (slope=0.77±0.01 and r2=0.90).The PMF deduced source profiles (value±standard
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deviation) and contributions are presented in Figs. 1 and2, respectively. In Fig. 3, the elemental compositions ofsources are compared. Monthly and annual variationsof source contributions are shown in Figs. 4 and 5,respectively.
Three different types of secondary sulfate particleswere identified as seen in previous studies when theeight carbon fractions were included in the analyses(Kim and Hopke, 2004a,b; Kim et al., 2005). Secondarysulfate I is characterized by its high concentration of S.Secondary sulfate II shows a strong association betweenS and OP. Secondary sulfate III shows an associationbetween S and Se. As reported in the previous study,carbon and tracer elements were also associated withsecondary particles in the present study (Liu et al.,2003). Secondary sulfate I has the highest contributionto PM2.5 mass concentrations in the Great SmokyMountains area (55%). Secondary sulfates II and IIIaccount for 16% and 2% of the PM2.5 mass concentra-
Fig. 1. Source profiles deduced from PM2.5 sa
tion, respectively. Secondary sulfate II has higher OCconcentration than the other types of the secondarysulfate particles as shown in Fig. 3.
Previously, high photochemistry activity sulfate(higher S/Se) has been separated from the lowphotochemistry sulfate (lower S/Se) with seasonaldifferences of the S/Se concentrations (Poirot et al.,2001; Polissar et al., 2001b; Song et al., 2001; Kim andHopke, 2004a,b; Kim et al., 2005). As shown in Fig. 4,the secondary sulfate I shows a strong summer-highseasonal variation that is associated with the highphotochemical activity in summer. PMF separated sec-ondary sulfate III with its lower S/Se concentration inthis study (Fig. 1). Secondary sulfate III shows summer-low seasonal variations with the highest contribution inApril (Fig. 4). Secondary sulfate II has a weak summer-high seasonal variation. The observed seasonal varia-tions may be due to the variation in source strength, intransport efficiency, or in atmospheric chemistry.
mples (prediction±standard deviation).
Fig. 2. Time series plot of source contributions.
Fig. 3. The comparison of elemental compositions of sources deduced by PMF.
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Fig. 4. Monthly variations of source contributions (mean±95% distribution).
Fig. 5. Annual variations of source contributions (mean±95% distribution).
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Fig. 6. Potential source contribution function plot. (a) Secondarysulfate I. (b) Secondary sulfate II. (c) Secondary sulfate III.
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Based on the measurements between 1988 and 1999 inthe southeastern US., Malm et al. (2002) repor-ted statistically insignificant changes in the SO4
2− concen-trations over the period, but decreasing trend until 1995–1996 and increasing trend afterward. In this study, as shownin Fig. 5, the annual variation of secondary sulfate I showsdecreasing trend in 1995 and increasing trend in 1996.Secondary sulfate II shows weak decreasing trend in itsannual average contributions. Secondary sulfate III doesnot show clear long term trends.
Fig. 6 shows the PSCF plots for the three types ofsecondary sulfate particles in which PSCF values aredisplayed in terms of a color scale. Potential sourceareas and pathways where the secondary sulfates wereformed are likely to be located in the high PSCF valueareas. As shown in Fig. 6(a), the PSCF plot of secondarysulfate I shows influence of the coal-fired power plantsin Ohio River Valley, Tennessee Valley, Mississippi, andAlabama. The West Virginia and western NorthCarolina areas might be pathways where the secondarysulfates were produced. The high PSCF value areas inthe ocean could show the influence of the circulation ofemissions that had moved out of the field of view of thePSCF analysis and then back into the domain. Similarbehavior has been observed in prior PSCF studies(Hopke et al., 1995). In Fig. 6(b), the PSCF plot ofsecondary sulfate II shows a circular tail from theYucatan, Mexico and Guatemala area that are likely tobe related to the organic carbon emissions from biogenicsources, such as wildfires that then give rise to second-ary organic particles. Also, the potential source areas ofthe secondary sulfate II include Houston area wheresignificant petrochemical industries are situated. ThePSCF plot of secondary sulfate III in Fig. 6(c) showshigh potential areas in the southern Ontario, Canada.However, the detailed nature of this area is uncertain. Thereare also areas of potential influence in northeastern Texaswhere the power plants use low S coals.
Secondary sulfate II could be in part the result of thehigh photochemical reaction enhanced by organiccompound originated from biogenic and anthropogenicsources. An association between the wood smokeparticles and OP formation in the thermal analysis hasbeen reported (Chow et al., 2004). Thus, the high OPconcentration in the secondary sulfate II could be relatedto the organic compounds produced by forest fires. Itcould also be secondary OC produced from the oxidationof volatile precursors thatwere or becomewater soluble inthe atmosphere since an association between the watersoluble OC and OP formation in the thermal analysis hasbeen reported (Yu et al., 2002). In addition, high OPconcentration could be in part the result of heterogeneous
acid-catalyzed reactions between the acidic sulfate andvolatile organic compounds which lead to secondaryorganic particle formation (Jang et al., 2003). Sulfateparticles can also provide a surface onto which semi-volatile organic compounds can condense. There areseveral possible mechanisms for the formation of thesecondary sulfate II and more studies are needed tounderstand the exact nature of these particles.
Fig. 7. Potential source contribution function plot for the secondarynitrate.
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Tanner et al. (2004b) reported from their carbonisotopic analyses that about 75% of PM2.5 carbonconcentration collected in the Great Smoky Mountainsarea in summer originated from biogenic sources. Themass fraction of the secondary sulfate II, 22%, in thethree types of secondary sulfates is higher than those in
Fig. 8. Conditional probability function plots for
the urban areas (Washington, DC: 18%; Atlanta, GA:11%) and other rural areas (Brigantine, NJ: 11%;Bondville, IL: 14%) indicating the contributions fromthe local biogenic precursors emitted in the region.Therefore, the secondary sulfate II is likely a combina-tion of local and regional influences of the biogenic aswell as anthropogenic secondary particles.
Secondary nitrate particle is identified by its highconcentration of NO3
−. It accounts for 2% (0.2 μg/m3) ofthe PM2.5 mass concentration. Secondary nitrate hasseasonal variation with higher values in winter as shownin Figs. 2 and 4 indicating the enhanced formation ofsecondary nitrate particles under lower temperature andhigher relative humidity environments. This seasonalvariation is consistent with previous studies (Kim andHopke, 2004a,b; Kim et al., 2004a,b, 2005). Figs. 2 and5 show the increased contributions of secondary nitratefrom the winter of 2000.
PSCF plots for the secondary nitrate contributions areshown in Fig. 7. Potential source areas include south-western Ontario, Canada, Minnesota, northeastern Kan-sas, and Oklahoma where NH3 emission is high fromfertilizer use and animal husbandry in the U.S. farm belt(Goebes et al., 2003). The regional source area for the
the highest 25% of the mass contributions.
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secondary nitrate shown that the PSCF plot point more toNH3 source regions than to likely NO3
− source areas. Thisresult would suggest that some of the secondary nitrate islocally produced with transported ammonia.
Gasoline vehicle and diesel emissions were separatedwith their high carbon concentrations whose abun-dances differ between two sources. Gasoline vehicleemissions have high concentrations of the OC fractions.In contrast, diesel emissions were tentatively identifiedbased upon the high concentration of EC. Gasolinevehicles and diesel emissions account for 13% (1.5 μg/m3) and 1% (0.2 μg/m3) of the PM2.5 mass concentra-tion, respectively. Gasoline vehicles and diesel emis-sions do not have clear seasonal variation and annualtrends in Figs. 4 and 5. A difference from previousstudies (Kim and Hopke, 2004a; Kim et al., 2004a,b)was that diesel emissions did not show clear weekday/weekend variation in the Great Smoky Mountains areasince there was only a very weak source strength in this
Fig. 9. The comparison of fractional carbon profiles of gasoline vehicle emresults are excerpted from Kim and Hopke (2004a), Kim and Hopke (200respectively.
rural location. Gasoline vehicle emissions have thehighest OC fractions among eight sources extracted inthis study (Fig. 3). CPF values for gasoline vehicle anddiesel emissions plotted in polar coordinates in Fig. 8 donot show clear source directionality to these sources.
In Figs. 9 and 10, the PMF extracted fractionalcarbon profiles of gasoline vehicle and diesel emissionsare compared with previous studies showing similarcarbon profiles to those estimated in Washington, DC(Kim and Hopke, 2004a), Brigantine, NJ (Kim andHopke, 2004b), Atlanta, GA (Kim et al., 2004a),Bondville, IL (Kim et al., 2005), and Seattle, WA(Kim et al., 2004b) studies when the eight carbonfractions were included in the analyses. Gasoline vehicleemissions have large amounts of OC3 and OC4. Dieselemissions contain high concentrations of EC1. Thesesimilar fractional carbon profiles support that there areseparable gasoline and diesel emission profiles acrossthe US. However, Shah et al. (2004) reported that diesel
issions. Washington, DC, Brigantine, Atlanta, Bondville, and Seattle4b), Kim et al. (2004a), Kim et al. (2005), and Kim et al. (2004b),
Fig. 10. The comparison of fractional carbon profiles of diesel emissions. Washington, DC, Brigantine, Atlanta, Bondville, and Seattle results areexcerpted from Kim and Hopke (2004a), Kim and Hopke (2004b), Kim et al. (2004a), Kim et al. (2005), and Kim et al. (2004b), respectively.
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engines operating at very slow speeds and in stop and gotraffic produce OC/EC ratios that were similar to typicalgasoline vehicle emissions. There was significantlymore EC than OC in the diesel emissions only undermore continuous motion at higher speeds (transient andcruise). Therefore, the diesel emission profile that wasextracted by PMF may represent only diesel vehicleemissions moving at reasonable speed in fluid trafficconditions. Diesel emissions emitted from stop and gotraffic could be apportioned into the gasoline emissioncategory, and the separation of two motor vehiclesources needs to be confirmed with more studiesutilizing additional information such as detailed organictracers (Schauer et al., 1996; Schauer and Cass, 2000).
Airborne soil is represented by Si, Fe, Ca, Al and K(Watson and Chow, 2001; Watson et al., 2001) contrib-uting 6% (0.7 μg/m3) to the PM2.5 mass concentration.These crustal particles could be contributed by wind-blown and traffic resuspended soil. The airborne soilshows seasonal variationwith higher concentrations in the
dry summer season, especially July as shown in Figs. 2and 4. Airborne soil does not show a year-to-year con-tribution trend (Fig. 5). The CPF plot in Fig. 8 showsstrong contribution of airborne soil from southeast. TheHYSPLITmodel (Draxler and Rolph, 2003; Rolph, 2003)was used to calculate the air mass backward trajectorieswith starting height of 500 m above sea level using thevertical mixing model for days with higher impacts ofairborne soil in Fig. 2. The higher airborne soilcontributions on April 22, 1995, April 11, 1998, andApril 19, 2001 were likely to be caused by Asian duststorm (Kim and Hopke, 2004b; NASA, 2001), and thehigher impacts on August 5, 1989, July 8, 1992, and July5, 1995 were likely caused by a Saharan dust storm(NASA, 2002) according to the backward trajectoriesshown in Fig. 11.
Mixed industry contribution is identified in the GreatSmoky Mountains area by carbon fractions, Fe, K, Pb,and Zn contributing 5% (0.6 μg/m3) to the PM2.5 massconcentration. This source does not have clear seasonal
Fig. 11. Backward trajectories arriving at the monitoring site are calculated from NOAA Air Resources Laboratory for days with high impacts ofairborne soil. (a) April 22, 1995, April 11, 1998, and April 19, 2001. (b) August 5, 1989, July 8, 1992, and July 5, 1995.
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trend. Fig. 5 shows a decreasing trend in the estimatedimpacts from this source. In Fig. 8, the CPF plot showsstrong contribution of this source from north whereMaryville and Knoxville are situated.
4. Conclusions
PM2.5 compositional data collected from a IM-PROVE monitoring site in the Great Smoky Mountainsarea between 1988 and 2003 were analyzed throughPMF. Similar to the previous studies when the eightcarbon fractions were included in the analyses, threedifferent subtypes of secondary sulfate particles wereidentified and those are likely a combination of local andregional influences of the biogenic as well as anthropo-genic secondary particles. Three types of secondarysulfate particles contributed the most to the PM2.5 (73%)among eight main sources extracted by PMF. Secondarynitrate particles contributed 2% to the PM2.5 massconcentration. The PSCF plot of secondary nitrateshows NH3 source regions suggesting that some of the
secondary nitrate particle is locally produced withtransported ammonia.
Gasoline vehicle emissions were tentatively sepa-rated from diesel emissions with their differentabundance of carbon fractions accounting for 13%and 1% of the PM2.5 mass concentration, respectively.The fractional carbon profiles of gasoline and dieselemissions are similar to those identified in other areasacross the US. PMF also extracted the airborne soil aswell as the mixed industry contributions accountingfor 6% and 5% of the PM2.5 mass concentration,respectively. Several inter-continental dust stormevents were identified by backward trajectories. Theannual industry contributions show decreasing trendsin the estimated impacts.
Acknowledgments
This work was supported by the United StatesEnvironmental Protection Agency (US EPA)'s theScience to Achieve Results (STAR) program under
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Grant R8310780. Although the research described inthis article has been funded by the US EPA, the viewsexpressed herein are solely those of the authors and donot represent the official policies or positions of the USEPA. The authors gratefully acknowledge the NOAAAir Resources Laboratory (ARL) for the provision of theHYSPLIT transport and dispersion model and READYwebsite (http://www.arl.noaa.gov/ready.html) used inthis publication.
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