| Title: | High-resolution mapping of regional VOCs using the enhanced space-time extreme gradient boosting machine (XGBoost) in Shanghai |
| Author(s): | Lu B; Meng X; Dong S; Zhang Z; Liu C; Jiang J; Herrmann H; Li X; |
| Address: | "Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, PR China. Leibniz-Institut fur Tropospharenforschung (IfT), Permoserstr. 15, 04318 Leipzig, Germany. Department of Environmental Science & Engineering, Fudan University, Shanghai 200438, PR China; Institute of Eco-Chongming (IEC), Shanghai 200241, China. Electronic address: lixiang@fudan.edu.cn" |
| DOI: | 10.1016/j.scitotenv.2023.167054 |
| ISSN/ISBN: | 1879-1026 (Electronic) 0048-9697 (Linking) |
| Abstract: | "The accurate estimation of highly spatiotemporal volatile organic compounds (VOCs) is of great significance to establish advanced early warning systems and regulate air pollution control. However, the estimation of high spatiotemporal VOCs remains incomplete. Here, the space-time extreme gradient boost model (STXGB) was enhanced by integrating spatiotemporal information to obtain the spatial resolution and overall accuracy of VOCs. To this end, meteorological, topographical and pollutant emissions, was input to the STXGB model, and regional hourly 300 m VOCs maps for 2020 in Shanghai were produced. Our results show that the STXGB model achieve good hourly VOCs estimations performance (R(2) = 0.73). A further analysis of SHapley Additive exPlanation (SHAP) regression indicate that local interpretations of the STXGB models demonstrate the strong contribution of emissions on mapping VOCs estimations, while acknowledging the important contribution of space and time term. The proposed approach outperforms many traditional machine learning models with a lower computational burden in terms of speed and memory" |
| Keywords: | Machine learning SHapley Additive exPlanations (SHAP) Volatile organic compounds XGBoost; |
| Notes: | "PublisherLu, Bingqing Meng, Xue Dong, Shanshan Zhang, Zekun Liu, Chao Jiang, Jiakui Herrmann, Hartmut Li, Xiang eng Netherlands 2023/09/16 Sci Total Environ. 2023 Sep 13; 905:167054. doi: 10.1016/j.scitotenv.2023.167054" |
