Development of longitudinal datasets (2000-2020) with high spatiotemporal resolution for air pollution exposure assessment in Canada.

ABSTRACT: We developed datasets intended to aid and inform, health and epidemiological studies in Canada by providing highly resolved spatiotemporal concentrations for regulated air pollutants (fine particulate matter PM_2.5, nitrogen dioxide NO₂, and ozone O₃) across Canada. Daily estimates were generated at various spatial resolutions for the years 2000 through 2020. The datasets are based on simulations of the US EPA's Community Multiscale (CMAQ) model at 12 km horizontal resolution. In an effort to increase the accuracy and spatial resolution of the exposure estimates, especially in complex urban environments, we used statistical and machine learning methods to downscale CMAQ outputs to finer resolutions. Downscaling relies on raw CMAQ results, high-resolution lan

SUBMITTER: Alhusban A 

PROVIDER: S-EPMC12197882 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature