Wei et al. (2022) Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous United States, v1 (2000 – 2016)

The Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., 2000-2016, v1.0 dataset contains predictions of air pollution concentrations at the ZIP Code-level for 2000-2016. An ensemble framework consisting of three machine-learning models (Random Forest, Gradient Boosting, and Neural Network) was implemented to estimate the daily concentrations of fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) in grid cells at 1-km resolution. Prediction variables included air monitoring data, satellite aerosol optical depth, meteorological conditions, chemical transport model simulations, and land-use variables. Annual predictions represent the average daily predictions in each grid cell for each year.