Hour by Hour PM2.5 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 agreement (IOA) and 45.28% smaller relative root-mean-square error (rRMSE), particularly in high PM_2.5 cases. The hourly estimated PM_2.5 closely matched the observed PM_2.5 in both temporal trend and spatial distribution across the eastern CONUS. ML modeling was further enhanced to include TEMPO Level 1b (L1b) data. The DNN model with TEMPO improved performance, with an 8% higher R² and a 25% lower rRMSE than the DNN model without TEMPO. The more significant improvement was seen during high smoke events using the TEMPO data. For the first time, we demonstrate the use of TEMPO L1b spectrally resolved radiances data to capture high PM_2.5 concentrations during the wildfire events, enhancing our understanding of PM_2.5 dynamics. This study provides a framework to integrate data from multiple geostationary satellites with HRRR model outputs to estimate surface air quality at high temporal resolution.

This study provides enhanced PM_2.5 monitoring estimated through deep neural networks, particularly during wildfire events, and supporting public health responses.