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- Silvern, Rachel, et al. Show all 13 Authors
- Environment international 2019 v.130 pp. 104909
- adverse effects; aerosols; algorithms; artificial intelligence; data collection; geographical variation; land use; meteorological parameters; model validation; models; particulates; prediction; remote sensing; standard deviation; uncertainty; United States
- ... Various approaches have been proposed to model PM2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM2.5 at a resolution of 1 ...