Combining Land-Use Regression and Chemical Transport Modeling in a Spatio-temporal Geostatistical Model for Ozone and PM _2.5

Abstract

Assessments of long-term air pollution exposure in population studies have commonly employed land use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatio-temporal LUR model with spatial smoothing to estimate spatio-temporal variability of ozone (O ₃) and particulate matter with diameter less than 2.5 μm (PM _2.5) from 2000 to 2008 in the Los Angeles Basin. The observations include over nine years’ data from more than 20 routine monitoring sites and specific monitoring data at over 100 locations to provide more comprehensive spatial coverage of air pollutants. Our composite modeling approach outperforms separate CTM and LUR models in terms of root mean square error (RMSE) assessed by 10-fold cross-validation in both temporal and spatial dimensions, with larger improvement in the accuracy of predictions for O ₃ (RMSE [ppb] for CTM: 6.6, LUR: 4.6, composite: 3.6) than for PM _2.5 (RMSE [μg/m ³] CTM: 13.7, LUR: 3.2, composite: 3.1). Our study highlights the opportunity for future exposure assessment to make use of readily available spatio-temporal modeling methods and auxiliary gridded data that takes chemical reaction processes into account to improve the accuracy of predictions in a single spatio-temporal modeling framework.

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