The Longitudinal Dependence of Indoor PAH Concentration on Outdoor PAH and Traffic Volume in an Urban Residential Environment
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The Longitudinal Dependence of Indoor PAH Concentration on Outdoor PAH and Traffic Volume in an Urban Residential Environment

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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 ...

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 The Longitudinal Dependence of Indoor PAH Concentration on Outdoor PAH and Traffic Volume in an Urban Residential Environment Presentation Transcript

  • Introduction Methods Results Conclusions Coda 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 April 12, 2010 reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Outline 1 Introduction 2 Methods Monitoring Site Measurements Imputation of Missing Values 3 Results Exploratory Analysis Time Series Models 4 Conclusions 5 Coda reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Outline 1 Introduction 2 Methods Monitoring Site Measurements Imputation of Missing Values 3 Results Exploratory Analysis Time Series Models 4 Conclusions 5 Coda reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda 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 @ US EPA
  • Introduction Methods Monitoring Site Results Measurements Conclusions Imputation of Missing Values Coda Outline 1 Introduction 2 Methods Monitoring Site Measurements Imputation of Missing Values 3 Results Exploratory Analysis Time Series Models 4 Conclusions 5 Coda reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Monitoring Site Results Measurements Conclusions Imputation of Missing Values Coda Monitoring Site reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Monitoring Site Results Measurements Conclusions Imputation of Missing Values Coda Monitoring Site reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Monitoring Site Results Measurements Conclusions Imputation of Missing Values Coda Monitoring Site reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Monitoring Site Results Measurements Conclusions Imputation of Missing Values Coda 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 @ US EPA
  • Introduction Methods Monitoring Site Results Measurements Conclusions Imputation of Missing Values Coda 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 @ US EPA
  • Introduction Methods Monitoring Site Results Measurements Conclusions Imputation of Missing Values Coda 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 @ US EPA
  • Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Coda Outline 1 Introduction 2 Methods Monitoring Site Measurements Imputation of Missing Values 3 Results Exploratory Analysis Time Series Models 4 Conclusions 5 Coda reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Coda Variability over Time reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Coda Workday vs. Non-Workday reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Coda Temperature & Dew Point reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Coda Mixing Height & Wind Speed reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Coda 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) reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Coda 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 @ US EPA
  • Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Coda Indoor Parameters: Treemap Visualization reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Coda Outdoor Parameters: Treemap Visualization reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Exploratory Analysis Results Time Series Models Conclusions Coda 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 @ US EPA
  • Introduction Methods Results Conclusions Coda Outline 1 Introduction 2 Methods Monitoring Site Measurements Imputation of Missing Values 3 Results Exploratory Analysis Time Series Models 4 Conclusions 5 Coda reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Conclusions 1 Indoor PAHs depend on both traffic volume & outdoor PAHs reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Conclusions 1 Indoor PAHs depend on both traffic volume & outdoor PAHs 2 Outdoor PAHs depend on traffic volume reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda 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 @ US EPA
  • Introduction Methods Results Conclusions Coda 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 @ US EPA
  • Introduction Methods Results Conclusions Coda 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 @ US EPA
  • Introduction Methods Results Conclusions Coda 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 @ US EPA
  • Introduction Methods Results Conclusions Coda 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 @ US EPA
  • Introduction Methods Results Conclusions Coda Acknowledgements Johns Hopkins Bloomberg School of Public Health Patrick N. Breysse Timothy J. Buckley Jana N. Mihalic Alison S. Geyh EPA grant On SlideShare: http://cli.gs/BTSpahIndoorEPA reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Outline 1 Introduction 2 Methods Monitoring Site Measurements Imputation of Missing Values 3 Results Exploratory Analysis Time Series Models 4 Conclusions 5 Coda reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Other Work The Longitudinal Dependence of Black Carbon Concentration on Traffic Volume in an Urban Environment. JAWMA, 2008 reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Other Work The Longitudinal Dependence of Black Carbon Concentration on Traffic Volume in an Urban Environment. JAWMA, 2008 New Haven air pollution reduction and public health indicators. Prepared under contract to the US EPA, 2008 reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Other Work The Longitudinal Dependence of Black Carbon Concentration on Traffic Volume in an Urban Environment. JAWMA, 2008 New Haven air pollution reduction and public health indicators. Prepared under contract to the US EPA, 2008 Gastrointestinal illness associated with water exposure. Prepared under contract to the US EPA, 2007. reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Other Work A Method for Obtaining Microenvironment Exposure Weights From a Straightforward Statistical Model of Time-Location Data. [under review at JESEE]. reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Other Work A Method for Obtaining Microenvironment Exposure Weights From a Straightforward Statistical Model of Time-Location Data. [under review at JESEE]. Estrogenic Activity of Polychlorinated Biphenyls Present in Human Tissue and the Environment. ES&T, 2006 reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Other Work A Method for Obtaining Microenvironment Exposure Weights From a Straightforward Statistical Model of Time-Location Data. [under review at JESEE]. Estrogenic Activity of Polychlorinated Biphenyls Present in Human Tissue and the Environment. ES&T, 2006 The Statistical Performance of an MCF-7 Cell Culture Assay Evaluated Using Generalized Linear Mixed Models and a Score Test. Statistics in Medicine, 2007 reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda Contact B. Rey de Castro, Sc.D. Baltimore, Maryland USA rey.decastro@comcast.net 410-929-3583 reyDecastro@westat.com Indoor PAHs @ US EPA
  • Introduction Methods Results Conclusions Coda 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 @ US EPA
  • Introduction Methods Results Conclusions Coda 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 @ US EPA