A Bayesian ensemble approach to combine PM_2.5 estimates from statistical models using satellite imagery and numerical model simulation.

ABSTRACT: Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) has been linked to various adverse health outcomes. PM_2.5 arises from both natural and anthropogenic sources, and PM_2.5 concentrations can vary over space and time. However, the sparsity of existing air quality monitors greatly restricts the spatial-temporal coverage of PM_2.5 measurements, potentially limiting the accuracy of PM_2.5-related health studies. Various methods exist to address these limitations by supplementing air quality monitoring measurements with additional data. We develop a method to combine PM_2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical mode

SUBMITTER: Murray NL 

PROVIDER: S-EPMC7048623 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature