Preprints
https://doi.org/10.5194/essd-2023-76
https://doi.org/10.5194/essd-2023-76
08 Mar 2023
 | 08 Mar 2023
Status: this discussion paper is a preprint. It has been under review for the journal Earth System Science Data (ESSD). The manuscript was not accepted for further review after discussion.

Sequential spatiotemporal distribution of PM2.5, SO2 and Ozone in China from 2015 to 2020

Yufeng Chi, Yu Zhan, Kai Wang, and Hong Ye

Abstract. Currently, in the modeling of various atmospheric pollutants, the simulation of independent trace gases (SO2 and O3) is constrained by the insufficient resolution of key remote sensing products, resulting in insufficient simulation reliability. In this study, spatial sampling and parameter convolution are combined to optimize LightGBM by utilizing ground observations, remote sensing products, meteorological data, assistance data, and random ID. Through the above techniques and an sequentialsimulation of air pollutants, we produce seamless daily 1-km-resolution products of PM2.5, SO2 and O3 for most parts of China from 2015 to 2020. Through random sampling, random site sampling, area-specific validation, comparisons of different models, and a cross-sectional comparison of different studies, we verified that our simulations of the spatial distribution of multiple atmospheric pollutants are reliable and effective. The CV of the random sample yielded an R2 of 0.88 and an RMSE of 9.91 µg/m3 for PM2.5, an R2 of 0.89 and an RMSE of 4.62 µg/m3 for SO2, and an R2 of 0.91 and an RMSE of 6.88 µg/m3 for O3. Combined with the SHapley Additive exPlanations (SHAP) approach, the roles of different parameters in the simulation process were clarified, and the positive role of parameter convolution was confirmed. Our dataset was used to assess the changes in the Air Pollution Index (API) in China before and after the outbreak of COVID-19, and the results indicate that these changes were relatively small huge, suggesting that the epidemic control measures in 2020 were effective. The study demonstrates that the multipollutant datasets produced with the proposed models are of great value for long-term, large-scale, and regional-scale air pollution monitoring and prediction, as well as population health evaluation. The datasets are available at https://doi.org/10.5281/zenodo.7533813 (Chi et al. 2023a), https://doi.org/10.5281/zenodo.7547774 (Chi et al. 2023b), https://doi.org/10.5281/zenodo.7312179 (Chi et al. 2023c), https://doi.org/10.5281/zenodo.7580714 (Chi et al. 2023d), https://doi.org/10.5281/zenodo.7580720 (Chi et al. 2023e), https://doi.org/10.5281/zenodo.7580726 (Chi et al. 2023f).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Yufeng Chi, Yu Zhan, Kai Wang, and Hong Ye

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-76', Anonymous Referee #1, 22 Mar 2023
  • RC2: 'Comment on essd-2023-76', Anonymous Referee #2, 27 Mar 2023
  • RC3: 'Comment on essd-2023-76', Anonymous Referee #3, 17 Apr 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-76', Anonymous Referee #1, 22 Mar 2023
  • RC2: 'Comment on essd-2023-76', Anonymous Referee #2, 27 Mar 2023
  • RC3: 'Comment on essd-2023-76', Anonymous Referee #3, 17 Apr 2023
Yufeng Chi, Yu Zhan, Kai Wang, and Hong Ye

Data sets

Spatial distribution of various air pollutants in China at 1 km(SO2 2018-03-21:2020-12-31) Yufeng Chi https://doi.org/10.5281/zenodo.7580714

Yufeng Chi, Yu Zhan, Kai Wang, and Hong Ye

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Short summary
A data set of regional spatial distribution of PM2.5, SO2 and Ozone in China for 6 years from 2015 to 2020 is provided. The time resolution of the data is 1d, the spatial resolution is about 1 km, and the cross-validation R2 is about 0.9. Data sharing is on the zenodo platform. This data can be directly used to visualize the distribution of regional air pollutants, and can also be used for data analysis, ecological applications, etc.
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