Machine learning derived daily PM2.5 concentration estimates from by County, ZIP code, and census tract in 11 western states 2008-2018

We created daily concentration estimates for fine particulate matter (PM_2.5) at the centroids of each county, ZIP code, and census tract across the western US, from 2008-2018. These estimates are predictions from ensemble machine learning models trained on 24-hour PM_2.5 measurements from monitoring station data across 11 states in the western US. Predictor variables were derived from satellite, land cover, chemical transport model (just for the 2008-2016 model), and meteorological data. Ten-fold spatial and random CV R² were 0.66 and 0.73, respectively, for the 2008-2016 model and 0.58 and 0.72, respectively for the 2008-2018 model. Comparing areal predictions to nearby monitored observations demonstrated overall R² of 0.68 for the 2008-2016 model and 0.58 for the 2008-2018 model, but we observed higher R² (> 0.80) in many urban areas. These data can be used to understand spatiotemporal patterns of, exposures to and health impacts of PM_2.5 in the western US where PM_2.5 levels have been heavily impacted by wildfire smoke over this time period.