Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical <i>a Priori</i> Information in Deep Learning.

ABSTRACT: Global fine particulate matter (PM_2.5) assessment is impeded by a paucity of monitors. We improve estimation of the global distribution of PM_2.5 concentrations by developing, optimizing, and applying a convolutional neural network with information from satellite-, simulation-, and monitor-based sources to predict the local bias in monthly geophysical a priori PM_2.5 concentrations over 1998-2019. We develop a loss function that incorporates geophysical a priori estimates and apply it in model training to address the unrealistic results produced by mean-square-error loss functions in regions with few monitors. We introduce novel spatial cross-validation for air quality to examine the importance of considering spatial properties. We address the s

SUBMITTER: Shen S 

PROVIDER: S-EPMC11092969 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature