Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 - 2016)

An ensemble modeling framework was used to assess NO2 levels with high accuracy, which combined estimates from three machine learning models (neural network, random forest, and gradient boosting), with a generalized additive model.

Predictor variables included NO2 column concentrations from satellites, land-use variables, meteorological variables, predictions from two chemical transport models, GEOS-Chem and the U.S. Environmental Protection Agency (EPA) CommUnity Multiscale Air Quality Modeling System (CMAQ), along with other ancillary variables. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell.