Daily, Monthly, and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grid (2000 – 2016)

This dataset contains many files; use the Tree view below or Dataverse API directory index to get a condensed overview of what is available. See the README-PM25.pdf file for more information. Overview This dataset includes daily predictions of ambient PM2.5 across the contiguous U.S. from 2000 to 2016. These predictions were produced by a geographically-weighted ensemble model that combined predictions from fitted neural network, random forest, and gradient boosting machine learners. The overall 10-fold cross-validated R2 values were 0.86 for daily predictions and 0.89 for annual predictions. Daily predictions and monthly and yearly aggregates are available in RDS and plaintext formats, and the prediction grid is available as CSV and GeoPackage files. Example R code is provided to facilitate reading and common merge operations. When using this dataset, please also cite the Related Publication below, which further details the data sources and processes used to produce these predictions. This is a reupload and slight format change of data that was previously available on SEDAC / EarthData under the name "Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000 – 2016)". Bulk downloads wget and other download programs can be used to bulk download files using the Dataverse API directory index: wget --recursive --execute robots=off --no-host-directories --span-hosts --content-disposition 'https://dataverse.harvard.edu/api/datasets/:persistentId/dirindex?persistentId=doi:10.7910/DVN/58C6HG'