Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model.

Abstract

The short-term and acute health effects of fine particulate matter less than 2.5 μm (PM2.5) have highlighted the need for exposure assessment models with high spatiotemporal resolution. Here, we utilize satellite, meteorologic, atmospheric, and land-use data to train a random forest model capable of accurately predicting daily PM2.5 concentrations at a resolution of 1 × 1 km throughout an urban area encompassing seven counties. Unlike previous models based on aerosol optical density (AOD), we show that the missingness of AOD is an effective predictor of ground-level PM2.5 and create an ensemble model that explicitly deals with AOD missingness and is capable of predicting with complete spatial and temporal coverage of the study domain. Our model performed well with an overall cross-validated root mean squared error (RMSE) of 2.22 μg/m3 and a cross-validated R2 of 0.91. We illustrate the daily changing spatial patterns of PM2.5 concentrations across our urban study area made possible by our accurate, high-resolution model. The model will facilitate high-resolution assessment of both long-term and acute PM2.5 exposures in order to quantify their associations with related health outcomes.