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Open AccessArticle
The Reconstruction of FY-4A and FY-4B Cloudless Top-of-Atmosphere Radiation and Full-Coverage Particulate Matter Products Reveals the Influence of Meteorological Factors in Pollution Events
by
Zhihao Song
Zhihao Song 1,2,
Lin Zhao
Lin Zhao 1,2,
Qia Ye
Qia Ye 1,2,
Yuxiang Ren
Yuxiang Ren 1,2,
Ruming Chen
Ruming Chen 1,2 and
Bin Chen
Bin Chen
Bin Chen is a Professor at
the College of Atmospheric Sciences, Lanzhou University. He received his [...]
Bin Chen is a Professor at
the College of Atmospheric Sciences, Lanzhou University. He received his B.Sc.
and Ph.D. degrees from Lanzhou University in June 2007 and June 2012,
respectively, and went on a two-year exchange at Scripps Institution of
Oceanography, University of California, San Diego, USA, in October 2010. He has won
the National Natural Science Award (Second Class Prize). His main research
directions include (1) satellite remote sensing and radiation climate effects
(atmospheric aerosols, atmospheric pollutants); (2) research on transmission
characteristics and weather situations of synergistic pollution of atmospheric
aerosols and atmospheric pollutants; (3) the application of machine learning and
deep learning to atmospheric aerosols and atmospheric environments; (4) research
on epidemic big data and machine learning prediction and impacts.
1,2,*
1
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
2
Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(18), 3363; https://doi.org/10.3390/rs16183363 (registering DOI)
Submission received: 2 July 2024
/
Revised: 3 September 2024
/
Accepted: 9 September 2024
/
Published: 10 September 2024
Abstract
By utilizing top-of-atmosphere radiation (TOAR) data from China’s new generation of geostationary satellites (FY-4A and FY-4B) along with interpretable machine learning models, near-surface particulate matter concentrations in China were estimated, achieving hourly temporal resolution, 4 km spatial resolution, and 100% spatial coverage. First, the cloudless TOAR data were matched and modeled with the solar radiation products from the ERA5 dataset to construct and estimate a fully covered TOAR dataset under assumed clear-sky conditions, which increased coverage from 20–30% to 100%. Subsequently, this dataset was applied to estimate particulate matter. The analysis demonstrated that the fully covered TOAR dataset (R2 = 0.83) performed better than the original cloudless dataset (R2 = 0.76). Additionally, using feature importance scores and SHAP values, the impact of meteorological factors and air mass trajectories on the increase in PM10 and PM2.5 during dust events were investigated. The analysis of haze events indicated that the main meteorological factors driving changes in particulate matter included air pressure, temperature, and boundary layer height. The particulate matter concentration products obtained using fully covered TOAR data exhibit high coverage and high spatiotemporal resolution. Combined with data-driven interpretable machine learning, they can effectively reveal the influencing factors of particulate matter in China.
Share and Cite
MDPI and ACS Style
Song, Z.; Zhao, L.; Ye, Q.; Ren, Y.; Chen, R.; Chen, B.
The Reconstruction of FY-4A and FY-4B Cloudless Top-of-Atmosphere Radiation and Full-Coverage Particulate Matter Products Reveals the Influence of Meteorological Factors in Pollution Events. Remote Sens. 2024, 16, 3363.
https://doi.org/10.3390/rs16183363
AMA Style
Song Z, Zhao L, Ye Q, Ren Y, Chen R, Chen B.
The Reconstruction of FY-4A and FY-4B Cloudless Top-of-Atmosphere Radiation and Full-Coverage Particulate Matter Products Reveals the Influence of Meteorological Factors in Pollution Events. Remote Sensing. 2024; 16(18):3363.
https://doi.org/10.3390/rs16183363
Chicago/Turabian Style
Song, Zhihao, Lin Zhao, Qia Ye, Yuxiang Ren, Ruming Chen, and Bin Chen.
2024. "The Reconstruction of FY-4A and FY-4B Cloudless Top-of-Atmosphere Radiation and Full-Coverage Particulate Matter Products Reveals the Influence of Meteorological Factors in Pollution Events" Remote Sensing 16, no. 18: 3363.
https://doi.org/10.3390/rs16183363
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