The Longitudinal Dependence of Indoor PAH Concentration on Outdoor PAH and Traffic Volume in an...

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

reyDecastro@westat.com Indoor PAHs @ US EPA

ConclusionsCoda

Outline

1 Introduction

2 MethodsMonitoring SiteMeasurementsImputation of Missing Values

3 ResultsExploratory AnalysisTime Series Models

4 Conclusions

5 Coda

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Outline

1 Introduction

4 Conclusions

5 Coda

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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

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Monitoring SiteMeasurementsImputation of Missing Values

Outline

1 Introduction

4 Conclusions

5 Coda

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Monitoring Site

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Monitoring Site

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Monitoring Site

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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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Measurements

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

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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

ConclusionsCoda

Exploratory AnalysisTime Series Models

Outline

1 Introduction

4 Conclusions

5 Coda

ConclusionsCoda

Variability over Time

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Workday vs. Non-Workday

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Temperature & Dew Point

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Mixing Height & Wind Speed

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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

ConclusionsCoda

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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Indoor Parameters: Treemap Visualization

ConclusionsCoda

Outdoor Parameters: Treemap Visualization

ConclusionsCoda

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

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Outline

1 Introduction

4 Conclusions

5 Coda

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

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Conclusions

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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

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Outline

1 Introduction

4 Conclusions

5 Coda

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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.

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Other Work

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Other Work

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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

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Other Work

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Other Work

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Contact

B. Rey de Castro, Sc.D.Baltimore, Maryland USA

rey.decastro@comcast.net410-929-3583

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

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

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