Sequential spatiotemporal distribution of PM2.5, SO2 and Ozone in China from 2015 to 2020
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).
Yufeng Chi et al.
Status: open (until 03 May 2023)
- RC1: 'Comment on essd-2023-76', Anonymous Referee #1, 22 Mar 2023 reply
- RC2: 'Comment on essd-2023-76', Anonymous Referee #2, 27 Mar 2023 reply
Yufeng Chi et al.
Spatial distribution of various air pollutants in China at 1 km(SO2 2018-03-21:2020-12-31) https://doi.org/10.5281/zenodo.7580714
Yufeng Chi et al.
Viewed (geographical distribution)
Chi et al. developed a method to estimate 1-km PM2.5, SO2, and O3 across China during 2015-2020. They claimed the new-developed dataset showed satisfied performance. Overall, the topic is very interesting and high-resolution air quality dataset is very useful for health effect assessment. Unfortunately, the method suffered from serious flaws because no strong 1-km proxy (variable) was applied to train the model for SO2 and O3. The robustness of 1-km SO2 and O3 dataset might remain high uncertainty. Moreover, the novelty of the dataset in this manuscript compared with CHAP and TAP remained high uncertainty. Therefore, I did not recommend the manuscript for publication on ESSD in the current form. However, I can support the publication if the authors could make a significant revision and provide sufficient proof. The detailed comments are as follows: