the longitudinal dependence of indoor pah concentration on outdoor pah and traffic volume in an...
DESCRIPTION
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.TRANSCRIPT
IntroductionMethodsResults
ConclusionsCoda
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
April 12, 2010
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Outline
1 Introduction
2 MethodsMonitoring SiteMeasurementsImputation of Missing Values
3 ResultsExploratory AnalysisTime Series Models
4 Conclusions
5 Coda
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Outline
1 Introduction
2 MethodsMonitoring SiteMeasurementsImputation of Missing Values
3 ResultsExploratory AnalysisTime Series Models
4 Conclusions
5 Coda
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Monitoring SiteMeasurementsImputation of Missing Values
Outline
1 Introduction
2 MethodsMonitoring SiteMeasurementsImputation of Missing Values
3 ResultsExploratory AnalysisTime Series Models
4 Conclusions
5 Coda
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Monitoring SiteMeasurementsImputation of Missing Values
Monitoring Site
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Monitoring SiteMeasurementsImputation of Missing Values
Monitoring Site
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Monitoring SiteMeasurementsImputation of Missing Values
Monitoring Site
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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
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IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Exploratory AnalysisTime Series Models
Outline
1 Introduction
2 MethodsMonitoring SiteMeasurementsImputation of Missing Values
3 ResultsExploratory AnalysisTime Series Models
4 Conclusions
5 Coda
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Exploratory AnalysisTime Series Models
Variability over Time
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IntroductionMethodsResults
ConclusionsCoda
Exploratory AnalysisTime Series Models
Workday vs. Non-Workday
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IntroductionMethodsResults
ConclusionsCoda
Exploratory AnalysisTime Series Models
Temperature & Dew Point
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IntroductionMethodsResults
ConclusionsCoda
Exploratory AnalysisTime Series Models
Mixing Height & Wind Speed
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IntroductionMethodsResults
ConclusionsCoda
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)× AR(144)× AR(1008)+εt,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)× AR(144)× AR(1008)+εt,out
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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)× AR(144)× AR(1008)+εt,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)× AR(144)× AR(1008)+εt,out
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IntroductionMethodsResults
ConclusionsCoda
Exploratory AnalysisTime Series Models
Indoor Parameters: Treemap Visualization
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IntroductionMethodsResults
ConclusionsCoda
Exploratory AnalysisTime Series Models
Outdoor Parameters: Treemap Visualization
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IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Outline
1 Introduction
2 MethodsMonitoring SiteMeasurementsImputation of Missing Values
3 ResultsExploratory AnalysisTime Series Models
4 Conclusions
5 Coda
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Acknowledgements
Johns Hopkins Bloomberg School of Public Health
Patrick N. Breysse Timothy J. BuckleyJana N. Mihalic Alison S. Geyh
EPA grant
On SlideShare: http://cli.gs/BTSpahIndoorEPA
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Outline
1 Introduction
2 MethodsMonitoring SiteMeasurementsImputation of Missing Values
3 ResultsExploratory AnalysisTime Series Models
4 Conclusions
5 Coda
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Other Work
The Longitudinal Dependence of Black CarbonConcentration on Traffic Volume in an UrbanEnvironment. JAWMA, 2008
New Haven air pollution reduction and public healthindicators. Prepared under contract to the US EPA, 2008
Gastrointestinal illness associated with water exposure.Prepared under contract to the US EPA, 2007.
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Other Work
The Longitudinal Dependence of Black CarbonConcentration on Traffic Volume in an UrbanEnvironment. JAWMA, 2008
New Haven air pollution reduction and public healthindicators. Prepared under contract to the US EPA, 2008
Gastrointestinal illness associated with water exposure.Prepared under contract to the US EPA, 2007.
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Other Work
The Longitudinal Dependence of Black CarbonConcentration on Traffic Volume in an UrbanEnvironment. JAWMA, 2008
New Haven air pollution reduction and public healthindicators. Prepared under contract to the US EPA, 2008
Gastrointestinal illness associated with water exposure.Prepared under contract to the US EPA, 2007.
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Other Work
A Method for Obtaining Microenvironment ExposureWeights From a Straightforward Statistical Model ofTime-Location Data. [under review at JESEE].
Estrogenic Activity of Polychlorinated Biphenyls Presentin Human Tissue and the Environment. ES&T, 2006
The Statistical Performance of an MCF-7 Cell CultureAssay Evaluated Using Generalized Linear Mixed Modelsand a Score Test. Statistics in Medicine, 2007
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Other Work
A Method for Obtaining Microenvironment ExposureWeights From a Straightforward Statistical Model ofTime-Location Data. [under review at JESEE].
Estrogenic Activity of Polychlorinated Biphenyls Presentin Human Tissue and the Environment. ES&T, 2006
The Statistical Performance of an MCF-7 Cell CultureAssay Evaluated Using Generalized Linear Mixed Modelsand a Score Test. Statistics in Medicine, 2007
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Other Work
A Method for Obtaining Microenvironment ExposureWeights From a Straightforward Statistical Model ofTime-Location Data. [under review at JESEE].
Estrogenic Activity of Polychlorinated Biphenyls Presentin Human Tissue and the Environment. ES&T, 2006
The Statistical Performance of an MCF-7 Cell CultureAssay Evaluated Using Generalized Linear Mixed Modelsand a Score Test. Statistics in Medicine, 2007
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
Contact
B. Rey de Castro, Sc.D.Baltimore, Maryland USA
[email protected] Indoor PAHs @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA
IntroductionMethodsResults
ConclusionsCoda
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 @ US EPA