Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance
Abstract Aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance are two useful sources of satellite data for estimating surface PM2.5 concentrations. Comparison of PM2.5 estimates between these two approaches remains to be explored. In this study, satellite observations of TOA reflectan...
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| Format: | Article |
| Language: | English |
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Springer
2020-10-01
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| Series: | Aerosol and Air Quality Research |
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| Online Access: | https://doi.org/10.4209/aaqr.2020.05.0257 |
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| author | Heming Bai Zhi Zheng Yuanpeng Zhang He Huang Li Wang |
| author_facet | Heming Bai Zhi Zheng Yuanpeng Zhang He Huang Li Wang |
| author_sort | Heming Bai |
| collection | DOAJ |
| description | Abstract Aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance are two useful sources of satellite data for estimating surface PM2.5 concentrations. Comparison of PM2.5 estimates between these two approaches remains to be explored. In this study, satellite observations of TOA reflectance and AOD from the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite in 2016 over Yangtze River Delta (YRD) and meteorological data are used to estimate hourly PM2.5 based on four different machine learning algorithms (i.e., random forest, extreme gradient boosting, gradient boosting regression, and support vector regression). For both reflectance-based and AOD-based approaches, our cross validated results show that random forest algorithm achieves the best performance, with a coefficient of determination (R2) of 0.75 and root-mean-square error (RMSE) of 18.71 µg m−3 for the former and R2 = 0.65 and RMSE = 15.69 µg m−3 for the later. Additionally, we find a large discrepancy in PM2.5 estimates between reflectance-based and AOD-based approaches in terms of annual mean and their spatial distribution, which is mainly due to the sampling difference, especially over northern YRD in winter. Overall, reflectance-based approach can provide robust PM2.5 estimates for both annual mean values and probability density function of hourly PM2.5. Our results further show that almost all population lives in non-attainment areas in YRD using annual mean PM2.5 from reflectance-based approach. This study suggests that reflectance-based approach is a valuable way for providing robust PM2.5 estimates and further for constraining health impact assessments. |
| format | Article |
| id | doaj-art-17aba7105d02447983fc925f00fb4838 |
| institution | Kabale University |
| issn | 1680-8584 2071-1409 |
| language | English |
| publishDate | 2020-10-01 |
| publisher | Springer |
| record_format | Article |
| series | Aerosol and Air Quality Research |
| spelling | doaj-art-17aba7105d02447983fc925f00fb48382025-02-09T12:21:38ZengSpringerAerosol and Air Quality Research1680-85842071-14092020-10-0121211710.4209/aaqr.2020.05.0257Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere ReflectanceHeming Bai0Zhi Zheng1Yuanpeng Zhang2He Huang3Li Wang4Research Center for Intelligent Information Technology, Nantong UniversityDepartment of Surveying Engineering, Heilongjiang Institute of TechnologyResearch Center for Intelligent Information Technology, Nantong UniversitySchool of Atmospheric Sciences, Nanjing UniversityResearch Center for Intelligent Information Technology, Nantong UniversityAbstract Aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance are two useful sources of satellite data for estimating surface PM2.5 concentrations. Comparison of PM2.5 estimates between these two approaches remains to be explored. In this study, satellite observations of TOA reflectance and AOD from the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite in 2016 over Yangtze River Delta (YRD) and meteorological data are used to estimate hourly PM2.5 based on four different machine learning algorithms (i.e., random forest, extreme gradient boosting, gradient boosting regression, and support vector regression). For both reflectance-based and AOD-based approaches, our cross validated results show that random forest algorithm achieves the best performance, with a coefficient of determination (R2) of 0.75 and root-mean-square error (RMSE) of 18.71 µg m−3 for the former and R2 = 0.65 and RMSE = 15.69 µg m−3 for the later. Additionally, we find a large discrepancy in PM2.5 estimates between reflectance-based and AOD-based approaches in terms of annual mean and their spatial distribution, which is mainly due to the sampling difference, especially over northern YRD in winter. Overall, reflectance-based approach can provide robust PM2.5 estimates for both annual mean values and probability density function of hourly PM2.5. Our results further show that almost all population lives in non-attainment areas in YRD using annual mean PM2.5 from reflectance-based approach. This study suggests that reflectance-based approach is a valuable way for providing robust PM2.5 estimates and further for constraining health impact assessments.https://doi.org/10.4209/aaqr.2020.05.0257PM2.5TOA reflectanceSatellite remote sensingMachine learning |
| spellingShingle | Heming Bai Zhi Zheng Yuanpeng Zhang He Huang Li Wang Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance Aerosol and Air Quality Research PM2.5 TOA reflectance Satellite remote sensing Machine learning |
| title | Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance |
| title_full | Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance |
| title_fullStr | Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance |
| title_full_unstemmed | Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance |
| title_short | Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance |
| title_sort | comparison of satellite based pm2 5 estimation from aerosol optical depth and top of atmosphere reflectance |
| topic | PM2.5 TOA reflectance Satellite remote sensing Machine learning |
| url | https://doi.org/10.4209/aaqr.2020.05.0257 |
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