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...
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MDPI AG
2025-05-01
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| author | Muhammad Jawad Hussain Myeongsu Seong Behjat Shahid Heming Bai |
| author_facet | Muhammad Jawad Hussain Myeongsu Seong Behjat Shahid Heming Bai |
| author_sort | Muhammad Jawad Hussain |
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| description | 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. |
| format | Article |
| id | doaj-art-2c70d0b70cc745b480d7d179d8e6d003 |
| institution | DOAJ |
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| series | Toxics |
| spelling | doaj-art-2c70d0b70cc745b480d7d179d8e6d0032025-08-20T03:12:05ZengMDPI AGToxics2305-63042025-05-0113539210.3390/toxics13050392Estimating 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 DataMuhammad Jawad Hussain0Myeongsu Seong1Behjat Shahid2Heming Bai3Research Center for Intelligent Information Technology, Nantong University, Nantong 226019, ChinaDepartment of Mechatronics and Robotics, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaFaculty of Media and Communication Studies, University of Central Punjab, Lahore 54590, PakistanResearch Center for Intelligent Information Technology, Nantong University, Nantong 226019, ChinaAccurate 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.https://www.mdpi.com/2305-6304/13/5/392PM<sub>2.5</sub>aerosol optical depthtop of atmospheric reflectancecardiovascular diseasesmachine-learning |
| spellingShingle | Muhammad Jawad Hussain Myeongsu Seong Behjat Shahid Heming Bai 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 Toxics PM<sub>2.5</sub> aerosol optical depth top of atmospheric reflectance cardiovascular diseases machine-learning |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | PM<sub>2.5</sub> aerosol optical depth top of atmospheric reflectance cardiovascular diseases machine-learning |
| url | https://www.mdpi.com/2305-6304/13/5/392 |
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