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https://doi.org/10.5194/acp-2020-1004
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-1004
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Oct 2020

07 Oct 2020

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This preprint is currently under review for the journal ACP.

Estimating daily full-coverage and high-accuracy 5-km ambient particulate matters across China: considering their precursors and chemical compositions

Yuan Wang1, Qiangqiang Yuan1,4,5, Tongwen Li2, Siyu Tan1, and Liangpei Zhang3,5 Yuan Wang et al.
  • 1School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China
  • 2School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China
  • 3The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, 430079, China
  • 4The Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, Hubei, 430079, China
  • 5The Collaborative Innovation Center for Geospatial Technology, Wuhan, Hubei, 430079, China

Abstract. The ambient concentrations of particulate matters (PM2.5 and PM10) are significant indicators for monitoring the air quality relevant to living conditions. Most of the existing approaches for the estimation of PM2.5 and PM10 employed the remote sensing Aerosol Optical Depth (AOD) products as the main variate. Nevertheless, the coverage of missing data is generally large in AOD products, which can cause inconvenience to the researchers. To efficiently address this issue, our study explores a novel approach using the datasets of the precursors and chemical compositions for PM2.5 and PM10 instead of AOD products. Specifically, the daily full-coverage ambient concentrations of PM2.5 and PM10 are estimated at 5-km (0.05°) spatial girds across China based on Sentinel-5P and GEOS-FP. In this paper, the Light Gradient Boosting Machine is exploited to train the estimation models, which will fully fuse the multi-source data. For comparison, the Deep Blue AOD product from VIIRS is adopted in a similar framework as a baseline (AOD-based). The validation results show that the ambient concentrations are well estimated through the proposed approach, with the sample-based Cross-Validation R2s and RMSEs of 0.93 (0.9) and 8.982 (17.604) μg/m3 for PM2.5 (PM10), respectively. Meanwhile, the proposed approach achieves better performance than the AOD-based in different cases (e.g., overall and seasonal). Compared to the related previous works over China, the estimation accuracy of our method is also satisfactory. Furthermore, all the variates of the precursors and chemical compositions for PM2.5 and PM10 positively contribute to the estimation in the proposed approach, as expected. With regard to the mapping, the estimated results through the proposed approach present consecutive spatial distribution and can exactly express the seasonal variations of PM2.5 and PM10. It is concluded that the full-coverage estimated results in our study are conducive to the researches on PM2.5 and PM10 over the regions where the AOD values are missing.

Yuan Wang et al.

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Short summary
Estimating ambient PM2.5 and PM10 considering their precursors and chemical compositions instead of AOD products; Both remote sensing (Sentinel-5P) and assimilated data (GEOS-FP) are adopted; Sample-based Cross-Validation R2s and RMSEs are 0.93 (0.9) and 8.982 (17.604) μg/m3 for PM2.5 (PM10), respectively; Achieving better performance compared to the baseline (AOD-based) in different cases (e.g., overall and seasonal).
Estimating ambient PM2.5 and PM10 considering their precursors and chemical compositions instead...
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