Modeling Spatial Patterns of Traffic-Related Air Pollutants in Complex Urban Terrain

Modeling Spatial Patterns of Traffic-Related Air Pollutants in Complex Urban Terrain

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Modeling Spatial Patterns of Traffic-Related Air Pollutants in Complex Urban Terrain

dc.contributor.author	Zwack, Leonard M.
dc.contributor.author	Paciorek, Christopher Joseph
dc.contributor.author	Spengler, John D.
dc.contributor.author	Levy, Jonathan Ian
dc.date.accessioned	2012-05-28T15:30:25Z
dc.date.issued	2011
dc.identifier.citation	Zwack, Leonard M., Christopher Joseph Paciorek, John D. Spengler, and Jonathan Ian Levy. 2011. Modeling spatial patterns of traffic-related air pollutants in complex urban terrain. Environmental Health Perspectives 119(6): 852-859.	en_US
dc.identifier.issn	0091-6765	en_US
dc.identifier.issn	1552-9924	en_US
dc.identifier.uri	http://nrs.harvard.edu/urn-3:HUL.InstRepos:8808827
dc.description.abstract	Background: The relationship between traffic emissions and mobile-source air pollutant concentrations is highly variable over space and time and therefore difficult to model accurately, especially in urban settings with complex terrain. Regression-based approaches using continuous real-time mobile measurements may be able to characterize spatiotemporal variability in traffic-related pollutant concentrations but require methods to incorporate temporally varying meteorology and source strength in a physically interpretable fashion. Objective: We developed a statistical model to assess the joint impact of both meteorology and traffic on measured concentrations of mobile-source air pollutants over space and time. Methods: In this study, traffic-related air pollutants were continuously measured in the Williamsburg neighborhood of Brooklyn, New York (USA), which is affected by traffic on a large bridge and major highway. One-minute average concentrations of ultrafine particulate matter (UFP), fine particulate matter [\(\leq 2.5 \mu m\) in aerodynamic diameter \((PM_{2.5})\)], and particle-bound polycyclic aromatic hydrocarbons were measured using a mobile-monitoring protocol. Regression modeling approaches to quantify the influence of meteorology, traffic volume, and proximity to major roadways on pollutant concentrations were used. These models incorporated techniques to capture spatial variability, long- and short-term temporal trends, and multiple sources. Results: We observed spatial heterogeneity of both UFP and \(PM_{2.5}\) concentrations. A variety of statistical methods consistently found a 15–20% decrease in UFP concentrations within the first 100 m from each of the two major roadways. For \(PM_{2.5}\), temporal variability dominated spatial variability, but we observed a consistent linear decrease in concentrations from the roadways. Conclusions: The combination of mobile monitoring and regression analysis was able to quantify local source contributions relative to background while accounting for physically interpretable parameters. Our results provide insight into urban exposure gradients.	en_US
dc.language.iso	en_US	en_US
dc.publisher	National Institute of Environmental Health Sciences	en_US
dc.relation.isversionof	doi:10.1289/ehp.1002519	en_US
dc.relation.hasversion	http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3114822	en_US
dash.license	LAA
dc.subject	mobile measurements	en_US
dc.subject	mobile sources	en_US
dc.subject	regression	en_US
dc.subject	spatial variation	en_US
dc.subject	ultrafine particles	en_US
dc.subject	urban air quality	en_US
dc.title	Modeling Spatial Patterns of Traffic-Related Air Pollutants in Complex Urban Terrain	en_US
dc.type	Journal Article	en_US
dc.description.version	Version of Record	en_US
dc.relation.journal	Environmental Health Perspectives	en_US
dash.depositing.author	Paciorek, Christopher Joseph
dc.date.available	2012-05-28T15:30:25Z
dash.affiliation.other	SPH^Biostatistics	en_US