Predicting traffic-related air pollution in Los Angeles using a distance decay regression selection strategy

• Published in: Environmental research Vol. 109; no. 6; pp. 657 - 670
• Main Authors: Su, Jason G., Jerrett, Michael, Beckerman, Bernardo, Wilhelm, Michelle, Ghosh, Jo Kay, Ritz, Beate
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01-08-2009; Elsevier
• Subjects: Air; Air pollution; Air Pollution - analysis; Analysis methods; Applied sciences; Atmospheric pollution; Biological and medical sciences; Environmental Monitoring - methods; Environmental Monitoring - statistics & numerical data; Environmental pollutants toxicology; Exact sciences and technology; GIS; Humans; Inhalation Exposure - analysis; Land use; Land use regression; Los Angeles; Mathematical models; Medical sciences; Models, Theoretical; Nitrogen dioxide; Nitrogen oxides; Nitrogen Oxides - analysis; Particle Size; Particulate Matter - analysis; Pollutants; Pollution; Prognosis; Regression; Regression Analysis; Remote sensing; Seasons; Strategy; Toxicology; Traffic; Vehicle Emissions - analysis; California; United States; North America; America; Los Angeles; USA, California, Los Angeles; GIS; Land use regression; Traffic; Nitrogen oxides; Air pollution; Los Angeles; Remote sensing; Human; Nitrogen oxide; Urban environment; Methodology; Prediction; Urban road traffic; Regression model; Geographic information system; Distance; Public health
• ISSN: 0013-9351; 1096-0953
• DOI: 10.1016/j.envres.2009.06.001

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 (NO
2) and nitrogen oxides (NO
X
) 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 NO
X
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, NO
2 and NO
X
concentrations, respectively. Cross-validation analyses suggested a prediction accuracy of 87–91%. Remote sensing-derived variables were significantly correlated with NO
X
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 NO
2 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.