Estimating PM2.5 Exposures and Cardiovascular Disease Risks in the Yangtze River Delta Region Using a Spatiotemporal Convolutional Approach to Fill Gaps in Satellite Data
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Region
2.2. Data Sets
2.2.1. Ground-Based PM2.5 Measurements
2.2.2. Satellite Data Products
2.2.3. Meteorological Data
2.2.4. Population Data
2.3. Methods
2.3.1. Data Preprocessing and Matching
2.3.2. Imputation Method for Missing Values
2.3.3. Model Implementation
Random Forest (RF) Model
Gradient-Boosting Regression (GBR) Model
Extreme Gradient-Boosting (XGBoost) Regression Model
Support Vector Regression (SVR) Model
3. Results
3.1. Performance of Different Machine-Learning Models
3.2. Site-Specific Performances of TOAR-Based and AOD-Based Models
3.3. Spatiotemporal Distributions of PM2.5 Concentration
3.4. PM2.5 and Cardiovascular Disease
3.5. The Impact of Pixel-Count (Sampling) Differences on TOAR-Based and AOD-Based Models
4. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Machine-Learning Algorithms | TOAR-Based Model | AOD-Based Model | ||||
---|---|---|---|---|---|---|
R2 | RMSE (μg m−3) | Slope | R2 | RMSE (μg m−3) | Slope | |
RF (gap-filled data) | 0.75 | 18.30 | 0.72 | 0.71 | 16.72 | 0.68 |
RF (original data) | 0.75 | 18.85 | 0.72 | 0.64 | 15.89 | 0.60 |
GBR (gap-filled data) | 0.71 | 20.42 | 0.73 | 0.67 | 17.79 | 0.68 |
XGBoost (gap-filled data) | 0.73 | 19.44 | 0.71 | 0.69 | 17.45 | 0.66 |
SVR (gap-filled data) | 0.69 | 20.95 | 0.68 | 0.65 | 18.85 | 0.65 |
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Hussain, M.J.; Seong, M.; Shahid, B.; Bai, H. Estimating PM2.5 Exposures and Cardiovascular Disease Risks in the Yangtze River Delta Region Using a Spatiotemporal Convolutional Approach to Fill Gaps in Satellite Data. Toxics 2025, 13, 392. https://doi.org/10.3390/toxics13050392
Hussain MJ, Seong M, Shahid B, Bai H. Estimating PM2.5 Exposures and Cardiovascular Disease Risks in the Yangtze River Delta Region Using a Spatiotemporal Convolutional Approach to Fill Gaps in Satellite Data. Toxics. 2025; 13(5):392. https://doi.org/10.3390/toxics13050392
Chicago/Turabian StyleHussain, Muhammad Jawad, Myeongsu Seong, Behjat Shahid, and Heming Bai. 2025. "Estimating PM2.5 Exposures and Cardiovascular Disease Risks in the Yangtze River Delta Region Using a Spatiotemporal Convolutional Approach to Fill Gaps in Satellite Data" Toxics 13, no. 5: 392. https://doi.org/10.3390/toxics13050392
APA StyleHussain, M. J., Seong, M., Shahid, B., & Bai, H. (2025). Estimating PM2.5 Exposures and Cardiovascular Disease Risks in the Yangtze River Delta Region Using a Spatiotemporal Convolutional Approach to Fill Gaps in Satellite Data. Toxics, 13(5), 392. https://doi.org/10.3390/toxics13050392