A Deep Learning Framework for Satellite-Derived Surface PM2.5 Estimation: Enhancing Spatial Analysis in the United States

Abstract Monitoring fine particulate matter (PM 2.5 ) is crucial for evaluating air quality and its effects on public health. However, the limited distribution of monitoring stations presents a challenge in accurately assessing air pollution, especially in areas distant from these stations. To address this challenge, our study introduces a two-step deep learning approach for estimating daily gap-free surface PM 2.5 concentrations across the contiguous United States (CONUS) from 2018 to 2022, with a spatial resolution of 4 km. In the first phase, we employ a depthwise-partial convolutional neural network (DW-PCNN) to fill gaps between surface PM 2.5 stations, utilizing aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the second phase, we integrate the PM 2.5 grids imputed by the DW-PCNN with meteorological and anthropogenic variables into a deep convolutional neural network (Deep-CNN) to further enhance the accuracy of our estimation. This enables us to estimate gap-free surface PM 2.5 concentrations accurately, evidenced by Pearson’s correlation coefficient R of 0.92 and an index of agreement (IOA) of 0.96 in 10-fold cross validation. We also introduce a grid-based method for calculating PM 2.5 design values (DVs), providing a continuous spatial representation of PM 2.5 DV that enhances the traditional station-based approach. Our grid-based DV representations offer a comprehensive perspective on air quality, facilitating more detailed analysis. Furthermore, our model’s ability to provide spatiotemporally consistent, gap-free PM 2.5 data addresses the issue of missing values, supporting health impact research, policy formulation, and the accuracy of environmental assessments.