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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 R 2  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 R 2  of 0.70 for the 2008–2016 model and 0.58 for the 2008–2018 model, but we observed higher R 2  (>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.

Daily PM2.5 concentration estimates by county, ZIP code, and census tract in 11 western states 2008–2018