Estimating PM<sub>2.5</sub> Exposures and Cardiovascular Disease Risks in the Yangtze River Delta Region Using a Spatiotemporal Convolutional Approach to Fill Gaps in Satellite Data
Accurate estimation of ambient PM<sub>2.5</sub> concentrations is crucial for assessing air quality and health risks, particularly in regions with limited ground-based monitoring. Satellite-retrieved data products, such as top-of-atmosphere reflectance (TOAR) and aerosol optical depth (A...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Toxics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2305-6304/13/5/392 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Accurate estimation of ambient PM<sub>2.5</sub> concentrations is crucial for assessing air quality and health risks, particularly in regions with limited ground-based monitoring. Satellite-retrieved data products, such as top-of-atmosphere reflectance (TOAR) and aerosol optical depth (AOD), are widely used for PM<sub>2.5</sub> estimation. However, complex atmospheric conditions cause retrieval gaps in TOAR and AOD products, limiting their reliability. This study introduced a spatiotemporal convolutional approach to fill sampling gaps in TOAR and AOD data from the Himawari-8 geostationary satellite over the Yangtze River Delta (YRD) in 2016. Four machine-learning models (random forest, extreme gradient boosting, gradient boosting, and support vector regression) were used to estimate hourly PM<sub>2.5</sub> concentrations by integrating gap-filled and original TOAR and AOD data with meteorological variables. The random forest model trained on gap-filled TOAR data yielded the highest predictive accuracy (R<sup>2</sup> = 0.75, RMSE = 18.30 μg m<sup>−3</sup>). Significant seasonal variations in PM<sub>2.5</sub> estimates were found, with TOAR-based models outperforming AOD-based models. Furthermore, we observed that a substantial portion of the YRD population in non-attainment areas is at risk of cardiovascular disease due to chronic PM<sub>2.5</sub> exposure. This study suggests that TOAR-based models offer more reliable PM<sub>2.5</sub> estimates, enhancing air-quality assessments and public health-risk evaluations. |
|---|---|
| ISSN: | 2305-6304 |