TY  - Data
T1  - Spatial distribution of various air pollutants in China at 1 km(PM2.5 2015-01-01:2018-03-18)
A1  - None
A1  - None
DO  - 10.5281/zenodo.7533813
PY  - 2024
DA  - 2024-08-29
PB  - National Cryosphere Desert Data Center
AB  - 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.
DB  - NCDC
UR  - http://www.ncdc.ac.cn/portal/metadata/4b3fc46e-5ecd-4422-ab78-e7685866ee63
ER  -