Application and validation of a line-source dispersion model to estimate small scale traffic-related particulate matter concentrations across the conterminous US

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Abstract

Numerous studies document increased health risks from exposure to traffic and traffic-related particulate matter (PM). However, many studies use simple exposure metrics to represent traffic-related PM, and/or are limited to small geographic areas over relatively short (e.g., 1 year) time periods. We developed a modeling approach for the conterminous US from 1999 to 2011 that applies a line-source Gaussian plume dispersion model using several spatially and/or temporally varying inputs (including daily meteorology) to produce high spatial resolution estimates of primary near-road traffic-related PM levels. We compared two methods of spatially averaging traffic counts: spatial smoothing generalized additive models and kernel density. Also, we evaluated and validated the output from the line-source dispersion modeling approach in a spatio-temporal model of 24-h average PM < 2.5 μm (PM2.5) elemental carbon (EC) levels. We found that spatial smoothing of traffic count point data performed better than a kernel density approach. Predictive accuracy of the spatio-temporal model of PM2.5 EC levels was moderate for 24-h averages (cross-validation (CV) R2 = 0.532) and higher for longer averaging times (CV R2 = 0.707 and 0.795 for monthly and annual averages, respectively). PM2.5 EC levels increased monotonically with line-source dispersion model output. Predictive accuracy was higher when the spatio-temporal model of PM2.5 EC included line-source dispersion model output compared to distance to road terms. Our approach provides estimates of primary traffic-related PM levels with high spatial resolution across the conterminous US from 1999 to 2011. Spatio-temporal model predictions describe 24-h average PM2.5 EC levels at unmeasured locations well, especially over longer averaging times.

Original languageEnglish (US)
Pages (from-to)741-754
Number of pages14
JournalAir Quality, Atmosphere and Health
Volume11
Issue number6
DOIs
StatePublished - Jul 1 2018

Fingerprint

Particulate Matter
particulate matter
Carbon
carbon
smoothing
Meteorology
spatial resolution
line source
traffic
Health risks
meteorology
health risk
modeling
plume
road
Health
prediction

All Science Journal Classification (ASJC) codes

  • Pollution
  • Atmospheric Science
  • Management, Monitoring, Policy and Law
  • Health, Toxicology and Mutagenesis

Cite this

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title = "Application and validation of a line-source dispersion model to estimate small scale traffic-related particulate matter concentrations across the conterminous US",
abstract = "Numerous studies document increased health risks from exposure to traffic and traffic-related particulate matter (PM). However, many studies use simple exposure metrics to represent traffic-related PM, and/or are limited to small geographic areas over relatively short (e.g., 1 year) time periods. We developed a modeling approach for the conterminous US from 1999 to 2011 that applies a line-source Gaussian plume dispersion model using several spatially and/or temporally varying inputs (including daily meteorology) to produce high spatial resolution estimates of primary near-road traffic-related PM levels. We compared two methods of spatially averaging traffic counts: spatial smoothing generalized additive models and kernel density. Also, we evaluated and validated the output from the line-source dispersion modeling approach in a spatio-temporal model of 24-h average PM < 2.5 μm (PM2.5) elemental carbon (EC) levels. We found that spatial smoothing of traffic count point data performed better than a kernel density approach. Predictive accuracy of the spatio-temporal model of PM2.5 EC levels was moderate for 24-h averages (cross-validation (CV) R2 = 0.532) and higher for longer averaging times (CV R2 = 0.707 and 0.795 for monthly and annual averages, respectively). PM2.5 EC levels increased monotonically with line-source dispersion model output. Predictive accuracy was higher when the spatio-temporal model of PM2.5 EC included line-source dispersion model output compared to distance to road terms. Our approach provides estimates of primary traffic-related PM levels with high spatial resolution across the conterminous US from 1999 to 2011. Spatio-temporal model predictions describe 24-h average PM2.5 EC levels at unmeasured locations well, especially over longer averaging times.",
author = "Jeffrey Yanosky and Jared Fisher and Duanping Liao and Donghyun Rim and {Vander Wal}, {Randy Lee} and Groves, {William Arthur} and Puett, {Robin C.}",
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AU - Yanosky, Jeffrey

AU - Fisher, Jared

AU - Liao, Duanping

AU - Rim, Donghyun

AU - Vander Wal, Randy Lee

AU - Groves, William Arthur

AU - Puett, Robin C.

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N2 - Numerous studies document increased health risks from exposure to traffic and traffic-related particulate matter (PM). However, many studies use simple exposure metrics to represent traffic-related PM, and/or are limited to small geographic areas over relatively short (e.g., 1 year) time periods. We developed a modeling approach for the conterminous US from 1999 to 2011 that applies a line-source Gaussian plume dispersion model using several spatially and/or temporally varying inputs (including daily meteorology) to produce high spatial resolution estimates of primary near-road traffic-related PM levels. We compared two methods of spatially averaging traffic counts: spatial smoothing generalized additive models and kernel density. Also, we evaluated and validated the output from the line-source dispersion modeling approach in a spatio-temporal model of 24-h average PM < 2.5 μm (PM2.5) elemental carbon (EC) levels. We found that spatial smoothing of traffic count point data performed better than a kernel density approach. Predictive accuracy of the spatio-temporal model of PM2.5 EC levels was moderate for 24-h averages (cross-validation (CV) R2 = 0.532) and higher for longer averaging times (CV R2 = 0.707 and 0.795 for monthly and annual averages, respectively). PM2.5 EC levels increased monotonically with line-source dispersion model output. Predictive accuracy was higher when the spatio-temporal model of PM2.5 EC included line-source dispersion model output compared to distance to road terms. Our approach provides estimates of primary traffic-related PM levels with high spatial resolution across the conterminous US from 1999 to 2011. Spatio-temporal model predictions describe 24-h average PM2.5 EC levels at unmeasured locations well, especially over longer averaging times.

AB - Numerous studies document increased health risks from exposure to traffic and traffic-related particulate matter (PM). However, many studies use simple exposure metrics to represent traffic-related PM, and/or are limited to small geographic areas over relatively short (e.g., 1 year) time periods. We developed a modeling approach for the conterminous US from 1999 to 2011 that applies a line-source Gaussian plume dispersion model using several spatially and/or temporally varying inputs (including daily meteorology) to produce high spatial resolution estimates of primary near-road traffic-related PM levels. We compared two methods of spatially averaging traffic counts: spatial smoothing generalized additive models and kernel density. Also, we evaluated and validated the output from the line-source dispersion modeling approach in a spatio-temporal model of 24-h average PM < 2.5 μm (PM2.5) elemental carbon (EC) levels. We found that spatial smoothing of traffic count point data performed better than a kernel density approach. Predictive accuracy of the spatio-temporal model of PM2.5 EC levels was moderate for 24-h averages (cross-validation (CV) R2 = 0.532) and higher for longer averaging times (CV R2 = 0.707 and 0.795 for monthly and annual averages, respectively). PM2.5 EC levels increased monotonically with line-source dispersion model output. Predictive accuracy was higher when the spatio-temporal model of PM2.5 EC included line-source dispersion model output compared to distance to road terms. Our approach provides estimates of primary traffic-related PM levels with high spatial resolution across the conterminous US from 1999 to 2011. Spatio-temporal model predictions describe 24-h average PM2.5 EC levels at unmeasured locations well, especially over longer averaging times.

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