Estimation of the ground-level SO2 concentration in eastern China based on the LightGBM model and Himawari-8 TOAR

Abstract

Sulfur dioxide (SO2) is one of the main pollutants in China’s atmosphere, but the spatial distribution of ground-based SO2 monitors is too sparse to provide a complete coverage. Therefore, obtaining a high spatial resolution of SO2 concentration is of great significance for SO2 pollution control. In this study, based on the LightGBM machine learning model, combined with the top-of-atmosphere radiation (TOAR) of Himawari-8 and additional data such as meteorological factors and geographic information, a high temporal and spatial resolution TOAR-SO2 estimation model in eastern China (97–136°E, 15–54°N) is established. TOAR and meteorological factors are the two variables that contribute the most to the model, and both of their feature importance values exceed 30%. The TOAR-SO2 model has great performance in estimating ground-level SO2 concentrations with 10-fold cross validation R2 (RMSE) of 0.70 (16.26 μg/m3), 0.75 (12.51 μg/m3), 0.96 (2.75 μg/m3), 0.97 (2.16 μg/m3), and 0.97 (1.71 μg/m3) when estimating hourly, daily, monthly, seasonal, and annual average SO2. Taking North China as main study area, the annual average SO2 is estimated. The concentration of SO2 in North China showed a downward trend since 2016 and decreased to 15.19 μg/m3 in 2020. The good agreement between ground measured and model estimated SO2 concentrations highlights the capability and advantage of using the model to monitor spatiotemporal variations of SO2 in Eastern China.

First estimation of hourly full-coverage ground-level ozone from Fengyun-4A satellite using machine learning

“…sampling, exclusive feature bundling, the leafwise tree growth strategy with depth restriction, etc. Many researchers utilize this model to predict and analyse atmospheric pollutants, including PM 2.5(Zhong et al 2021, Wei et al 2021b, Ma et al 2022, NO 2(Kang et al 2021), O 3(Kang et al 2021), and SO 2(Xu et al 2023). In comparison to eXtreme Gradient Boosting (XGBoost), one of GBDT, LightGBM demonstrates superior, computational speed and memory consumption.…”