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

Abstract

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.

Fig. 1

The Los Angeles Study Area.

Fig. 2

Distance decay curves of correlation between selected spatial covariates and measured air pollution concentrations (a) for traffic volumes-total vehicle miles traveled, (b) for NO, (c) for NO₂ and (d) for NO_x.

Fig. 2

Distance decay curves of correlation between selected spatial covariates and measured air pollution concentrations (a) for traffic volumes-total vehicle miles traveled, (b) for NO, (c) for NO₂ and (d) for NO_x.

Fig. 3

The semivariograms of NO and NO₂ based on measurements from the 201 monitoring sites.

Fig. 4

Tasseled-cap greenness (a) and soil brightness (b).

Fig. 5

The distance decay of NO concentrations further away from highway (Al and A2) based on the 201 monitoring sites in the LA metropolitan area.

Fig. 6

The distance decay of NO₂ concentrations further away from highway (Al and A2) based on the 201 monitoring sites in the LA metropolitan area.

Fig. 7

Model predictions of natural log-transformed NO, NO₂ and NO_x (a, b and c) and corresponding cross-validation results (d, e and f).

Fig. 8

Model prediction surfaces of NO (a), NO₂ (b, c) and NO_x (d) through an ADDRESS selection process. The difference between 5b and 5c is that 5c represents the modeling result omitting highway buffer distance 11 km as a predictor.