Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species.

ABSTRACT: Accurate spatiotemporal air pollution fields are essential for health impact and epidemiologic studies. There are an increasing number of studies that have combined observational data with spatiotemporally complete air pollution simulations. Land-use, speciated gaseous and particulate pollutant concentrations and chemical transport modeling are fused using a random forest approach to construct daily air quality fields for 12 pollutants (CO, NOx, NO₂, SO₂, O₃, PM_2.5, PM₁₀, and PM_2.5 constituents: SO₄^2-, NO₃^-, NH₄⁺, EC and OC) between 2005 and 2014 for the continental United States with little spatial or temporal bias. R² ranged from 0.45 to 0.96, dep

SUBMITTER: Senthilkumar N 

PROVIDER: S-EPMC10735214 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature