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Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information
Siyuan Shen1, Chi Li1, Aaron van Donkelaar1
1Department of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States.
ACS ES&T Air
|May 16, 2024
Summary
Improving global fine particulate matter (PM2.5) assessment, this study uses a novel deep learning model. It enhances PM2.5 concentration estimates, even with limited ground monitors, for better air quality monitoring.
Area of Science:
- Environmental science
- Atmospheric chemistry
- Data science
Background:
- Global assessment of fine particulate matter (PM2.5) is limited by sparse monitor networks.
- Accurate PM2.5 data is crucial for public health and environmental policy.
Purpose of the Study:
- To improve the estimation of global PM2.5 concentration distribution.
- To develop a robust deep learning model for PM2.5 prediction using diverse data sources.
Main Methods:
- Developed and optimized a convolutional neural network integrating satellite, simulation, and monitor data.
- Introduced a novel loss function and spatial cross-validation for air quality.
- Incorporated geophysical a priori PM2.5 estimates to enhance model performance in data-sparse regions.
Main Results:
- The model accurately predicts local bias in monthly PM2.5 concentrations from 1998-2019.
- Estimates show high consistency with ground-based monitor data globally and regionally.
- The model demonstrates robustness, maintaining high accuracy (R² = 0.73) even with only 1% of monitors for training.
Conclusions:
- The developed deep learning approach significantly improves global PM2.5 estimation.
- Geophysical a priori data integration enhances model performance, especially in areas with few monitors.
- This method offers a reliable solution for comprehensive air quality assessment worldwide.
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