Geng et al.: Random forest models for PM2.5 speciation using MISR data

Random forest models were developed to predict ground-level daily PM2.5 speciation concentrations in California from MISR fractional AODs and other supporting data such as ground measurements, chemical transport model simulations, land use variables and meteorological fields. Sensitivity tests were also conducted to explore the influence of variable selection on model performance. Results shows that fractional AODs and total AOD have similar predicting power in estimating PM2.5 species if there are sufficient ground measurements and predictor data to support the most sophisticated model structure. Otherwise, models using fractional AODs outperform those with total AOD. PM2.5 speciation concentrations are more sensitive to land use variables than other supporting data such as CTM simulations and meteorological information.

This article was published in Environmental Research Letters in March 2020.


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