Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach.

TLDR: A random forest model incorporating aerosol optical depth data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM2.5 concentrations over the conterminous United States in 2011 is developed.

Abstract: To estimate PM25 concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM25 concentrations over the conterminous United States in 2011 Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability Our results achieve an overall cross-validation (CV) R2 value of 080 Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 178 and 283 μg/m3, respectively, indicating a good agreement between CV predictions and observations The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales In addition, the