The Dependence of Indoor PAH Concentrations on Outdoor PAHs and Traffic Volume in an Urban Residential Environment
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The document presents the results of a study on indoor PAH concentrations and their relationship to outdoor PAH levels and traffic volume. Key findings include: 1) Indoor PAH levels depend on both traffic volume and outdoor PAH concentrations. 2) Outdoor PAH levels depend on traffic volume. 3) Season was the strongest predictor of indoor and outdoor PAH levels, with higher levels observed in spring and summer.
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The Dependence of Indoor PAH Concentrations on Outdoor PAHs and Traffic Volume in an Urban Residential Environment
- 1. Introduction Methods Results Conclusions The Dependence of Indoor PAH Concentrations on Outdoor PAHs and Traffic Volume in an Urban Residential Environment B. Rey de Castro, Sc.D. Westat Rockville, Maryland USA March 25, 2010 reyDecastro@westat.com Indoor PAHs @ Gradient
- 2. Introduction Methods Results Conclusions Outline 1 Introduction 2 Methods Monitoring Site Measurements Imputation of Missing Values 3 Results Exploratory Analysis Time Series Models 4 Conclusions reyDecastro@westat.com Indoor PAHs @ Gradient
- 3. Introduction Methods Results Conclusions Outline 1 Introduction 2 Methods Monitoring Site Measurements Imputation of Missing Values 3 Results Exploratory Analysis Time Series Models 4 Conclusions reyDecastro@westat.com Indoor PAHs @ Gradient
- 4. Introduction Methods Results 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 sweeps Lung cancer from occupational exposures Toxicity: Neurodevelopment Low birthweight Respiratory deficits Chromosomal degradation Diminished cognition reyDecastro@westat.com Indoor PAHs @ Gradient
- 5. Introduction Monitoring Site Methods Measurements Results Imputation of Missing Values Conclusions Outline 1 Introduction 2 Methods Monitoring Site Measurements Imputation of Missing Values 3 Results Exploratory Analysis Time Series Models 4 Conclusions reyDecastro@westat.com Indoor PAHs @ Gradient
- 6. Introduction Monitoring Site Methods Measurements Results Imputation of Missing Values Conclusions Monitoring Site reyDecastro@westat.com Indoor PAHs @ Gradient
- 7. Introduction Monitoring Site Methods Measurements Results Imputation of Missing Values Conclusions Monitoring Site reyDecastro@westat.com Indoor PAHs @ Gradient
- 8. Introduction Monitoring Site Methods Measurements Results Imputation of Missing Values Conclusions Monitoring Site reyDecastro@westat.com Indoor PAHs @ Gradient
- 9. Introduction Monitoring Site Methods Measurements Results Imputation of Missing Values Conclusions 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 reyDecastro@westat.com Indoor PAHs @ Gradient
- 10. Introduction Monitoring Site Methods Measurements Results Imputation of Missing Values Conclusions Measurements PAHs EcoChem PAS 2000 Selective ionization of particle-bound PAHs Alternating indoor-outdoor 5-minute sampling Combined into 10-minute observations Traffic Pneumatic counter 5-minute counts Weather Rooftop weather station (30-minute) NWS airport measurements (60-minute) All data transformed to 10-minute observational interval reyDecastro@westat.com Indoor PAHs @ Gradient
- 11. Introduction Monitoring Site Methods Measurements Results Imputation of Missing Values Conclusions Imputation of Missing Values Linear regression with reference data Predictions substituted for missing values Add pseudorandom variate to reduce bias Yimpute = Ypredict + N(0, σ 2 ) N = 52,560 July 1, 2002 to June 30, 2003 reyDecastro@westat.com Indoor PAHs @ Gradient
- 12. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Outline 1 Introduction 2 Methods Monitoring Site Measurements Imputation of Missing Values 3 Results Exploratory Analysis Time Series Models 4 Conclusions reyDecastro@westat.com Indoor PAHs @ Gradient
- 13. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Variability over Time reyDecastro@westat.com Indoor PAHs @ Gradient
- 14. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Workday vs. Non-Workday reyDecastro@westat.com Indoor PAHs @ Gradient
- 15. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Temperature & Dew Point reyDecastro@westat.com Indoor PAHs @ Gradient
- 16. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Mixing Height & Wind Speed reyDecastro@westat.com Indoor PAHs @ Gradient
- 17. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Models With Autocorrelation Indoor PAH Traffic, outdoor PAHs, wind speed, wind direction, temperature, dew point, season, workday ARMA[3,3] autocorrelation p MA(1 : 3) Yt,in = µin + βi Xi,t + + t,in i=1 AR(1 : 3) × AR(144) × AR(1008) Outdoor PAH Traffic, wind speed, wind direction, temperature, dew point, season, workday ARMA[1,1] autocorrelation p MA(1) Yt,out = µout + βi Xi,t + + t,out i=1 AR(1) × AR(144) × AR(1008) reyDecastro@westat.com Indoor PAHs @ Gradient
- 18. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Indoor Parameters: Treemap Visualization reyDecastro@westat.com Indoor PAHs @ Gradient
- 19. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Outdoor Parameters: Treemap Visualization reyDecastro@westat.com Indoor PAHs @ Gradient
- 20. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Wind Direction: Outdoor vs. Indoor Indoor PAHs, SW–S–SE: 0.59 – 1.16 ng/m3 Outdoor PAHs, WSW–S–NE: 0.95 – 9.78 ng/m3 reyDecastro@westat.com Indoor PAHs @ Gradient
- 21. Introduction Methods Results Conclusions Outline 1 Introduction 2 Methods Monitoring Site Measurements Imputation of Missing Values 3 Results Exploratory Analysis Time Series Models 4 Conclusions reyDecastro@westat.com Indoor PAHs @ Gradient
- 22. Introduction Methods Results Conclusions Conclusions 1 Indoor PAHs depend on both traffic volume & outdoor PAHs reyDecastro@westat.com Indoor PAHs @ Gradient
- 23. Introduction Methods Results Conclusions Conclusions 1 Indoor PAHs depend on both traffic volume & outdoor PAHs 2 Outdoor PAHs depend on traffic volume reyDecastro@westat.com Indoor PAHs @ Gradient
- 24. Introduction Methods Results Conclusions Conclusions 1 Indoor PAHs depend on both traffic volume & outdoor PAHs 2 Outdoor PAHs depend on traffic volume 3 Observed diminished effect of traffic volume in afternoon reyDecastro@westat.com Indoor PAHs @ Gradient
- 25. Introduction Methods Results Conclusions Conclusions 1 Indoor PAHs depend on both traffic volume & outdoor PAHs 2 Outdoor PAHs depend on traffic volume 3 Observed diminished effect of traffic volume in afternoon 4 Season (Spring & Summer 2003) was strongest predictor of indoor & outdoor PAHs reyDecastro@westat.com Indoor PAHs @ Gradient
- 26. Introduction Methods Results Conclusions Conclusions 1 Indoor PAHs depend on both traffic volume & outdoor PAHs 2 Outdoor PAHs depend on traffic volume 3 Observed diminished effect of traffic volume in afternoon 4 Season (Spring & Summer 2003) was strongest predictor of indoor & outdoor PAHs 5 Contributions from wind direction differ between indoor & outdoor PAHs reyDecastro@westat.com Indoor PAHs @ Gradient
- 27. Introduction Methods Results Conclusions Conclusions 1 Indoor PAHs depend on both traffic volume & outdoor PAHs 2 Outdoor PAHs depend on traffic volume 3 Observed diminished effect of traffic volume in afternoon 4 Season (Spring & Summer 2003) was strongest predictor of indoor & outdoor PAHs 5 Contributions from wind direction differ between indoor & outdoor PAHs 6 Meteorology & workday had significant effects reyDecastro@westat.com Indoor PAHs @ Gradient
- 28. Introduction Methods Results Conclusions Conclusions 1 Indoor PAHs depend on both traffic volume & outdoor PAHs 2 Outdoor PAHs depend on traffic volume 3 Observed diminished effect of traffic volume in afternoon 4 Season (Spring & Summer 2003) was strongest predictor of indoor & outdoor PAHs 5 Contributions from wind direction differ between indoor & outdoor PAHs 6 Meteorology & workday had significant effects 7 Autocorrelation was significant reyDecastro@westat.com Indoor PAHs @ Gradient
- 29. Introduction Methods Results Conclusions Acknowledgements Patrick N. Breysse Timothy J. Buckley Jana N. Mihalic Alison S. Geyh Lu Wang EPA grant On SlideShare: http://cli.gs/BTSpahIndoorGradient B. Rey de Castro, Sc.D. 410-929-3583 reyDecastro@westat.com Indoor PAHs @ Gradient
- 30. Introduction Methods Results Conclusions Summary: Quantitative Indoor PAHs 0.57 ng/m3 per 100 vehicles every 10 minutes 0.16 ng/m3 per ng/m3 outdoor PAH Combination 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/m3 Outdoor PAHs: 9.26 – 9.78 ng/m3 Workday Indoor PAHs: 1.64 ng/m3 Outdoor PAHs: 3.01 ng/m3 reyDecastro@westat.com Indoor PAHs @ Gradient
- 31. Introduction Methods Results Conclusions Summary: Quantitative Meteorology Indoor PAHs Wind speed: -0.38 ng/m3 per m/s Temperature: -2.48 ng/m3 per 5 C Dew point: 1.87 ng/m3 per 5 C Outdoor PAHs Wind speed: -0.79 ng/m3 per m/s Temperature: -3.45 ng/m3 per 5 C Dew point: 2.77 ng/m3 per 5 C reyDecastro@westat.com Indoor PAHs @ Gradient