A land use regression model for predicting ambient fine particulate matter across Los Angeles, CA

Abstract

Land use regression (LUR) models have been used successfully for predicting local variation in traffic pollution, but few studies have explored this method for deriving fine particle exposure surfaces. The primary purpose of this method is to develop a LUR model for predicting fine particle or PM_2.5 mass over the five county metropolitan statistical area (MSA) of Los Angeles. PM_2.5 includes all particles with diameter less than or equal to 2.5 microns. In the Los Angeles MSA, 23 monitors of PM_2.5 were available in the year 2000. This study uses GIS to integrate data regarding land use, transportation and physical geography to derive a PM_2.5 dataset covering Los Angeles. Multiple linear regression was used to create the model for predicting the PM_2.5 surface. Our parsimonious model explained 69% of the variance in PM_2.5 with three predictors: (1) traffic density within 300 m, (2) industrial land area within 5000 m, and (3) government land area within 5000 m of the monitoring site. These results suggest the LUR method can refine exposure models for epidemiologic studies in a North American context.