سابِق|تال

Estimation of near-surface O3 concentration based on TROPOMI NO2， CO and HCHO reconstruction data

  • role： First author第一作者
  • Affiliation：

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

  • Email：chenxj@cumt.edu.cn
  • Introduction：陈小娟，研究方向为大气环境遥感。E-mail： chenxj@cumt.edu.cn
CHEN Xiaojuan,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

  • Email：qinkai@cumt.edu.cn
  • Introduction：秦凯，研究方向为大气环境遥感。E-mail： qinkai@cumt.edu.cn
QIN Kai*,  
  • Affiliation：

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

COHEN Jason,  
  • Affiliation：

    School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

HE Qin

ملخص

Satellite remote sensing data represented by TROPOMI atmospheric composition products show good potential in the estimation of near-surface O3 concentrations. Given the limitations of cloud and inversion algorithms, data for TROPOMI atmospheric composition products are lacking, resulting in low coverage of estimation results. Therefore, the DINEOF method was used to reconstruct the missing cells of TROPOMI NO2, CO, and HCHO original data products and estimate the maximum daily 8 h average O3 concentration (MDA8 O3) of Chinese mainland high coverage from 2019 to 2021 based on XGBoost. In this study, three schemes to improve the coverage of O3 model estimation results are compared. Scheme 1 reconstructs the missing cells of TROPOMI NO2, CO, and HCHO original data products based on the DINEOF method and uses the reconstructed data to model and estimate O3. Scheme 2 is based on TROPOMI NO2, CO, and HCHO original data products, null values are assigned to their missing cells, and only other characteristic variables are entered to model and estimate O3. Scheme 3 uses a combination of modeling results containing TROPOMI NO2, CO, and HCHO original data products and modeling results that do not contain TROPOMI NO2, CO, and HCHO original data products but with other characteristic variables. Experiments show that the results of scheme 1 are the best; its 10-fold cross-validation results are R2=0.86 and RMSE=15.86 μg/m3. The model accuracy is basically the same as scheme 2 and higher than that of scheme 3, and the model accuracy in the reconstruction region is the highest (training set R2=0.82, RMSE=15.57 μg/m3). When O3 heavy pollution occurs in the reconstruction region (concentration greater than 160 μg/m3), the underestimation of the high value of the model can be remarkably improved, and the spatial distribution of the results is more reasonable. The average coverage of the near-surface MDA8 O3 estimated in scheme 1 increased from 33.6% to 97.2% from 2019 to 2021. Using TROPOMI NO2, CO, and HCHO refactor data products to model and estimate O3 can improve model performance and coverage of model results.

مفهوم

remote sensing;near-surface O3;TROPOMI;DINEOF;XGBoost;Data refactoring

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