Multivariate Spatial Prediction of Air Pollutant Concentrations with INLA.

ABSTRACT: Estimates of daily air pollution concentrations with complete spatial and temporal coverage are important for supporting epidemiologic studies and health impact assessments. While numerous approaches have been developed for modeling air pollution, they typically only consider each pollutant separately. We describe a spatial multipollutant data fusion model that combines monitoring measurements and chemical transport model simulations that leverages dependence between pollutants to improve spatial prediction. For the contiguous United States, we created a data product of daily concentration for 12 pollutants (CO, NOx, NO₂, SO₂, O₃, PM₁₀, and PM_2.5 species EC, OC, NO₃, NH₄, SO₄) during the period 2005 to

SUBMITTER: Gong W 

PROVIDER: S-EPMC9187197 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature