The Longitudinal Dependence of Indoor PAH Concentration on Outdoor PAH and Traffic Volume in an Urban Residential Environment
deCastro B.R.1, Breysse P.N.2, Buckley T.J.3, Wang L.4, Mihalic J.N.2, Geyh A.S.2 1Westat, Rockville, MD. 2Johns Hopkins Bloomberg School of Public Health, Department of Environmental Health Sciences, Baltimore, MD. 3Ohio State University School of Public Health, Division of Environmental Health Sciences, Columbus, OH. 4Johns Hopkins Bloomberg School of Public Health, Department of Epidemiology, Baltimore, MD. The influence of traffic volume and ambient outdoor PAH on indoor PAH exposure was quantified at the Baltimore Traffic Study site, an unoccupied attached 2nd-floor apartment in an inner-city neighborhood “hot spot" surrounded by urban roadways that together carry over 150,000 vehicles per day. Monitoring of outdoor and indoor particle-bound PAH and traffic volume was conducted continously for 12 months at 10-minute intervals (n = 52,560). Time-series modeling accounted for complex and extensive autocorrelation. Vehicle count (0.57 [±SE=0.04] ng/m3 per 100 vehicles every ten minutes) and outdoor PAH (0.16 [±0.001] ng/m3 per ng/m3 outdoor PAH) are statistically significant predictors of indoor PAH, in addition to a mean background indoor exposure without indoor sources of 9.07 ng/m3. Spring 2003 (9.99 [±0.67] ng/m3) and Summer 2003 (9.27 [±1.27] ng/m3) are associated with the greatest increases in indoor PAH, relative to Summer 2002. An additional 1.64 [±0.27] ng/m3 is attributable to work days. Winds from the SW-S-NE quarter, which would have entrained PAH from Baltimore’s densely trafficked central business district and a nearby interstate highway, contribute significantly to indoor PAH (0.31 – 1.16 ng/m3). Dew point, outdoor temperature, and wind speed are also statistically significant predictors. Indoor PAH’s short-term autocorrelation is ARMA[3,3], where lag 3 indicates that PAH concentrations are correlated for up to 30 minutes. Significant autoregressive correlation at lags 144 and 1008 indicate autocorrelations at diurnal and weekly cycles, respectively. In a separate time series model, it was established that outdoor PAH itself depends at a statistically significant on vehicle count at a rate of 3.17 [±0.11] ng/m3 per 100 vehicles every ten minutes. Conclusion: local indoor & outdoor exposure to PAH from mobile sources is substantially modified by meteorologic and temporal conditions, including atmospheric transport processes. PAH concentration also demonstrates statistically significant autocorrelation at several timescales.
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The Longitudinal Dependence of Indoor PAH Concentration on Outdoor PAH 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 November 4, 2009 reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 6. Introduction Monitoring Site Methods Measurements Results Imputation of Missing Values Conclusions Monitoring Site reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 7. Introduction Monitoring Site Methods Measurements Results Imputation of Missing Values Conclusions Monitoring Site reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 8. Introduction Monitoring Site Methods Measurements Results Imputation of Missing Values Conclusions Monitoring Site reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 13. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Variability over Time reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 14. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Workday vs. Non-Workday reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 15. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Temperature & Dew Point reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 16. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Mixing Height & Wind Speed reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 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 + + i,in i=1 AR(1 : 3)(144)(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 + + i,out i=1 AR(1)(144)(1008) reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 18. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Indoor Parameters: Treemap Visualization reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 19. Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Outdoor Parameters: Treemap Visualization reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 22. Introduction Methods Results Conclusions Conclusions 1 Indoor PAHs depend on both traffic volume & outdoor PAHs reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009
- 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/BTSpahIndoor reyDecastro@westat.com 240-453-2947 reyDecastro@westat.com Indoor PAHs @ ISES 2009
- 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 @ ISES 2009
- 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 @ ISES 2009