Hour by Hour PM_2.5 Mapping Using Geostationary Satellites.

ABSTRACT: This study estimates ground-level fine particulate matter (PM_2.5) concentrations using geostationary satellites-derived Aerosol Optical Depth (AOD) and radiance measurements and meteorological parameters from the High-Resolution Rapid Refresh (HRRR) model, with AirNow PM_2.5 measurements over the contiguous United States (CONUS). A Deep Neural Network (DNN) was adopted and compared with other machine learning (ML) models (i.e., Random Forest and Light Gradient-Boosting Machine) to estimate surface PM_2.5 concentrations. The DNN model (without the tropospheric emissions: monitoring of pollution (TEMPO); 1 year) estimated PM_2.5 with an interquartile range (IQR) of 4.32 μg/m³, and outperformed ML models, with up to 44.68% better index of ag

SUBMITTER: Park S 

PROVIDER: S-EPMC12439241 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature