Document Detail


Predicting traffic-related air pollution in Los Angeles using a distance decay regression selection strategy.
MedLine Citation:
PMID:  19540476     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Land use regression (LUR) has emerged as an effective means of estimating exposure to air pollution in epidemiological studies. We created the first LUR models of nitric oxide (NO), nitrogen dioxide (NO2) and nitrogen oxides (NOX) for the complex megalopolis of Los Angeles (LA), California. Two-hundred and one sampling sites (the largest sampling design to date for LUR estimation) for two seasons were selected using a location-allocation algorithm that maximized the potential variability in measured pollutant concentrations and represented populations in the health study. Traffic volumes, truck routes and road networks, land use data, satellite-derived vegetation greenness and soil brightness, and truck route slope gradients were used for predicting NOX concentrations. A novel model selection strategy known as "ADDRESS" (A Distance Decay REgression Selection Strategy) was used to select optimized buffer distances for potential predictor variables and maximize model performance. Final regression models explained 81%, 86% and 85% of the variance in measured NO, NO2 and NOX concentrations, respectively. Cross-validation analyses suggested a prediction accuracy of 87-91%. Remote sensing-derived variables were significantly correlated with NOX concentrations, suggesting these data are useful surrogates for modeling traffic-related pollution when certain land use data are unavailable. Our study also demonstrated that reactive pollutants such as NO and NO2 could have high spatial extents of influence (e.g., > 5000 m from expressway) and high background concentrations in certain geographic areas. This paper represents the first attempt to model traffic-related air pollutants at a fine scale within such a complex and large urban region.
Authors:
Jason G Su; Michael Jerrett; Bernardo Beckerman; Michelle Wilhelm; Jo Kay Ghosh; Beate Ritz
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2009-06-21
Journal Detail:
Title:  Environmental research     Volume:  109     ISSN:  1096-0953     ISO Abbreviation:  Environ. Res.     Publication Date:  2009 Aug 
Date Detail:
Created Date:  2009-07-06     Completed Date:  2009-07-17     Revised Date:  2013-06-02    
Medline Journal Info:
Nlm Unique ID:  0147621     Medline TA:  Environ Res     Country:  United States    
Other Details:
Languages:  eng     Pagination:  657-70     Citation Subset:  IM    
Affiliation:
Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, USA.
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MeSH Terms
Descriptor/Qualifier:
Air Pollution / analysis*
Environmental Monitoring / methods*,  statistics & numerical data
Humans
Inhalation Exposure / analysis*
Los Angeles
Models, Theoretical*
Nitrogen Oxides / analysis
Particle Size
Particulate Matter / analysis*
Prognosis
Regression Analysis
Seasons
Vehicle Emissions / analysis*
Grant Support
ID/Acronym/Agency:
R01 ES010960-01/ES/NIEHS NIH HHS; R01 ES010960-02/ES/NIEHS NIH HHS; R01 ES010960-03/ES/NIEHS NIH HHS; R01 ES010960-03S1/ES/NIEHS NIH HHS
Chemical
Reg. No./Substance:
0/Nitrogen Oxides; 0/Particulate Matter; 0/Vehicle Emissions
Comments/Corrections

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