| Title: | A novel machine learning method for evaluating the impact of emission sources on ozone formation |
| Author(s): | Cheng Y; Huang XF; Peng Y; Tang MX; Zhu B; Xia SY; He LY; |
| Address: | "Laboratory of Atmospheric Observation Supersite, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, 518055, China. Laboratory of Atmospheric Observation Supersite, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, 518055, China. Electronic address: huangxf@pku.edu.cn" |
| DOI: | 10.1016/j.envpol.2022.120685 |
| ISSN/ISBN: | 1873-6424 (Electronic) 0269-7491 (Linking) |
| Abstract: | "Ambient ozone air pollution is one of the most important environmental challenges in China today, and it is particularly significant to identify pollution sources and formulate control strategies. In present study, we proposed a novel method of positive matrix factorization-SHapley Additive explanation (PMF-SHAP) for evaluating the impact of emission sources on ozone formation, which can quantify the main emission sources of ozone pollution. In this method, we first used the PMF model to identify the source of volatile organic compounds (VOCs), and then quantified various emission sources using a combination of machine learning (ML) models and the SHAP algorithm. The R(2) of the optimal ML model in this method was as high as 0.96, indicating that the prediction performance was excellent. Furthermore, we explored the impact of different emission sources on ozone formation, and found that ozone formation in Shenzhen was more affected by VOCs, of which vehicle emission sources may have the greatest impact. Our results suggest that the appropriate combination of traditional models with ML models can well address environmental pollution problems. Moreover, the conclusions obtained based on the PMF-SHAP method were different from the traditional ozone formation potential (OFP) results, providing valuable clues for related mechanism studies" |
| Keywords: | *Ozone/toxicity Machine Learning *Air Pollution *Volatile Organic Compounds Environmental Pollution Emission sources Ozone Shap VOCs; |
| Notes: | "MedlineCheng, Yong Huang, Xiao-Feng Peng, Yan Tang, Meng-Xue Zhu, Bo Xia, Shi-Yong He, Ling-Yan eng England 2022/11/19 Environ Pollut. 2023 Jan 1; 316(Pt 2):120685. doi: 10.1016/j.envpol.2022.120685. Epub 2022 Nov 15" |
