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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Indoor PAH Concentrations Depend on Outdoor Levels & Traffic
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
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