A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM_2.5 Concentrations across Great Britain.

ABSTRACT: Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM_2.5) levels across Great Britain between 2008-2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM_2.5 series using co-located PM₁₀ measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospher

SUBMITTER: Schneider R 

PROVIDER: S-EPMC7116547 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature