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IntroductionMethodsResults
Conclusions
The Dependence of Indoor PAH
Concentrations on Outdoor PAHs and
Traffic Volume in an Urban Residential
Environment
B. Rey de Castro, Sc.D.
WestatRockville, Maryland USA
November 4, 2009
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Outline
1 Introduction
2 MethodsMonitoring SiteMeasurementsImputation of Missing Values
3 ResultsExploratory AnalysisTime Series Models
4 Conclusions
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Outline
1 Introduction
2 MethodsMonitoring SiteMeasurementsImputation of Missing Values
3 ResultsExploratory AnalysisTime Series Models
4 Conclusions
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
PAH Health Risks
PAHs among Mobile Source Air Toxics
Potential population at risk: 17.8 million residences
Toxicity: Cancer
18th Century scrotal cancer among chimney sweepsLung cancer from occupational exposures
Toxicity: Neurodevelopment
Low birthweightRespiratory deficitsChromosomal degradationDiminished cognition
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Monitoring SiteMeasurementsImputation of Missing Values
Outline
1 Introduction
2 MethodsMonitoring SiteMeasurementsImputation of Missing Values
3 ResultsExploratory AnalysisTime Series Models
4 Conclusions
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Monitoring SiteMeasurementsImputation of Missing Values
Monitoring Site
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Monitoring SiteMeasurementsImputation of Missing Values
Monitoring Site
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Monitoring SiteMeasurementsImputation of Missing Values
Monitoring Site
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Monitoring SiteMeasurementsImputation of Missing Values
Baltimore Traffic Study Objectives
Sustained, continuous monitoring: 12 months
High temporal resolution: 10-minute intervals
Simultaneous monitoring of traffic & covarying factors
Control expected autocorrelation: time series analysis
Conclude long-term characteristics of PAH exposure
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Monitoring SiteMeasurementsImputation of Missing Values
Measurements
PAHs
EcoChem PAS 2000Selective ionization of particle-bound PAHsAlternating indoor-outdoor 5-minute samplingCombined into 10-minute observations
Traffic
Pneumatic counter5-minute counts
Weather
Rooftop weather station (30-minute)NWS airport measurements (60-minute)
All data transformed to 10-minute observational interval
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Monitoring SiteMeasurementsImputation of Missing Values
Imputation of Missing Values
Linear regression with reference data
Predictions substituted for missing values
Add pseudorandom variate to reduce bias
Yimpute = Ypredict + N(0, σ2)
N = 52,560
July 1, 2002 to June 30, 2003
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Exploratory AnalysisTime Series Models
Outline
1 Introduction
2 MethodsMonitoring SiteMeasurementsImputation of Missing Values
3 ResultsExploratory AnalysisTime Series Models
4 Conclusions
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Exploratory AnalysisTime Series Models
Variability over Time
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Exploratory AnalysisTime Series Models
Workday vs. Non-Workday
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Exploratory AnalysisTime Series Models
Temperature & Dew Point
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Exploratory AnalysisTime Series Models
Mixing Height & Wind Speed
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Exploratory AnalysisTime Series Models
Models With Autocorrelation
Indoor PAHTraffic, outdoor PAHs, wind speed, wind direction,temperature, dew point, season, workdayARMA[3,3] autocorrelation
Yt,in = µin +
p∑i=1
βiXi ,t +MA(1 : 3)
AR(1 : 3)(144)(1008)+ εi ,in
Outdoor PAHTraffic, wind speed, wind direction, temperature, dewpoint, season, workdayARMA[1,1] autocorrelation
Yt,out = µout +
p∑i=1
βiXi ,t +MA(1)
AR(1)(144)(1008)+ εi ,out
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Exploratory AnalysisTime Series Models
Indoor Parameters: Treemap Visualization
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Exploratory AnalysisTime Series Models
Outdoor Parameters: Treemap Visualization
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Exploratory AnalysisTime Series Models
Wind Direction: Outdoor vs. Indoor
Indoor PAHs, SW–S–SE: 0.59 – 1.16 ng/m3Outdoor PAHs, WSW–S–NE: 0.95 – 9.78 ng/m3
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Outline
1 Introduction
2 MethodsMonitoring SiteMeasurementsImputation of Missing Values
3 ResultsExploratory AnalysisTime Series Models
4 Conclusions
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Conclusions
1 Indoor PAHs depend on both traffic volume & outdoorPAHs
2 Outdoor PAHs depend on traffic volume
3 Observed diminished effect of traffic volume in afternoon
4 Season (Spring & Summer 2003) was strongest predictorof indoor & outdoor PAHs
5 Contributions from wind direction differ between indoor &outdoor PAHs
6 Meteorology & workday had significant effects
7 Autocorrelation was significant
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Conclusions
1 Indoor PAHs depend on both traffic volume & outdoorPAHs
2 Outdoor PAHs depend on traffic volume
3 Observed diminished effect of traffic volume in afternoon
4 Season (Spring & Summer 2003) was strongest predictorof indoor & outdoor PAHs
5 Contributions from wind direction differ between indoor &outdoor PAHs
6 Meteorology & workday had significant effects
7 Autocorrelation was significant
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Conclusions
1 Indoor PAHs depend on both traffic volume & outdoorPAHs
2 Outdoor PAHs depend on traffic volume
3 Observed diminished effect of traffic volume in afternoon
4 Season (Spring & Summer 2003) was strongest predictorof indoor & outdoor PAHs
5 Contributions from wind direction differ between indoor &outdoor PAHs
6 Meteorology & workday had significant effects
7 Autocorrelation was significant
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Conclusions
1 Indoor PAHs depend on both traffic volume & outdoorPAHs
2 Outdoor PAHs depend on traffic volume
3 Observed diminished effect of traffic volume in afternoon
4 Season (Spring & Summer 2003) was strongest predictorof indoor & outdoor PAHs
5 Contributions from wind direction differ between indoor &outdoor PAHs
6 Meteorology & workday had significant effects
7 Autocorrelation was significant
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Conclusions
1 Indoor PAHs depend on both traffic volume & outdoorPAHs
2 Outdoor PAHs depend on traffic volume
3 Observed diminished effect of traffic volume in afternoon
4 Season (Spring & Summer 2003) was strongest predictorof indoor & outdoor PAHs
5 Contributions from wind direction differ between indoor &outdoor PAHs
6 Meteorology & workday had significant effects
7 Autocorrelation was significant
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Conclusions
1 Indoor PAHs depend on both traffic volume & outdoorPAHs
2 Outdoor PAHs depend on traffic volume
3 Observed diminished effect of traffic volume in afternoon
4 Season (Spring & Summer 2003) was strongest predictorof indoor & outdoor PAHs
5 Contributions from wind direction differ between indoor &outdoor PAHs
6 Meteorology & workday had significant effects
7 Autocorrelation was significant
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Conclusions
1 Indoor PAHs depend on both traffic volume & outdoorPAHs
2 Outdoor PAHs depend on traffic volume
3 Observed diminished effect of traffic volume in afternoon
4 Season (Spring & Summer 2003) was strongest predictorof indoor & outdoor PAHs
5 Contributions from wind direction differ between indoor &outdoor PAHs
6 Meteorology & workday had significant effects
7 Autocorrelation was significant
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Acknowledgements
Patrick N. Breysse Timothy J. BuckleyJana N. Mihalic Alison S. Geyh
Lu Wang
EPA grant
On SlideShare: http://cli.gs/BTSpahIndoor
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Summary: Quantitative
Indoor PAHs
0.57 ng/m3 per 100 vehicles every 10 minutes0.16 ng/m3 per ng/m3 outdoor PAHCombination of fresh and aged PAHs
Outdoor PAHs
3.17 ng/m3 per 100 vehicles every 10 minutes
Season (Spring & Summer 2003) was strongest predictor
Indoor PAHs: 9.27 – 9.99 ng/m3Outdoor PAHs: 9.26 – 9.78 ng/m3
Workday
Indoor PAHs: 1.64 ng/m3Outdoor PAHs: 3.01 ng/m3
[email protected] Indoor PAHs @ ISES 2009
IntroductionMethodsResults
Conclusions
Summary: Quantitative
MeteorologyIndoor PAHs
Wind speed: -0.38 ng/m3 per m/sTemperature: -2.48 ng/m3 per 5 CDew point: 1.87 ng/m3 per 5 C
Outdoor PAHs
Wind speed: -0.79 ng/m3 per m/sTemperature: -3.45 ng/m3 per 5 CDew point: 2.77 ng/m3 per 5 C
[email protected] Indoor PAHs @ ISES 2009