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

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.