Zhang Lei, Wang Lili, Ji Dan, Xia Zheng, Nan Peifan, Zhang Jiaxin, Li Ke, Qi Bing, Du Rongguang, Sun Yang, Wang Yuesi, Hu Bo
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China.
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Zhejiang Key Laboratory of Ecological and Environmental Big Data (2022P10005), Zhejiang Ecological and Environmental Monitoring Center, Hangzhou 310012, China; Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Sci Total Environ. 2024 Apr 20;922:171295. doi: 10.1016/j.scitotenv.2024.171295. Epub 2024 Feb 27.
Megacity Hangzhou, located in eastern China, has experienced severe O pollution in recent years, thereby clarifying the key drivers of the formation is essential to suppress O deterioration. In this study, the ensemble machine learning model (EML) coupled with Shapley additive explanations (SHAP), and positive matrix factorization were used to explore the impact of various factors (including meteorology, chemical components, sources) on O formation during the whole period, pollution days, and typical persistent pollution events from April to October in 2021-2022. The EML model achieved better performance than the single model, with R values of 0.91. SHAP analysis revealed that meteorological conditions had the greatest effects on O variability with the contribution of 57 %-60 % for different pollution levels, and the main drivers were relative humidity and radiation. The effects of chemical factors on O formation presented a positive response to volatile organic compounds (VOCs) and fine particulate matter (PM), and a negative response to nitrogen oxides (NOx). Oxygenated compounds (OVOCs), alkenes, and aromatic of VOCs subgroups had higher contribution; additionally, the effects of PM and NOx were also important and increased with the O deterioration. The impact of seven emission sources on O formation in Hangzhou indicated that vehicle exhaust (35 %), biomass combustion (16 %), and biogenic emissions (12 %) were the dominant drivers. However, for the O pollution days, the effects of biomass combustion and biogenic emissions increased. Especially in persistent pollution events with highest O concentrations, the magnitude of biogenic emission effect elevated significantly by 156 % compared to the whole situations. Our finding revealed that the combination of the EML model and SHAP analysis could provide a reliable method for rapid diagnosis of the cause of O pollution at different event scales, supporting the formulation of control measures.
特大城市杭州位于中国东部，近年来经历了严重的臭氧污染，因此明确其形成的关键驱动因素对于抑制臭氧恶化至关重要。在本研究中，结合了夏普利值法（SHAP）的集成机器学习模型（EML）以及正定矩阵因子分解法，用于探究2021年4月至2022年10月整个期间、污染日以及典型持续污染事件中各种因素（包括气象、化学成分、源）对臭氧形成的影响。EML模型的表现优于单一模型，R值为0.91。SHAP分析表明，气象条件对臭氧变化的影响最大，不同污染水平下的贡献为57%-60%，主要驱动因素是相对湿度和辐射。化学因素对臭氧形成的影响对挥发性有机化合物（VOCs）和细颗粒物（PM）呈正响应，对氮氧化物（NOx）呈负响应。VOCs子组中的含氧化合物（OVOCs）、烯烃和芳烃贡献较高；此外，PM和NOx的影响也很重要，且随着臭氧恶化而增加。杭州七种排放源对臭氧形成的影响表明，机动车尾气（35%）、生物质燃烧（16%）和生物源排放（12%）是主要驱动因素。然而，对于臭氧污染日，生物质燃烧和生物源排放的影响增加。特别是在臭氧浓度最高的持续污染事件中，与整体情况相比，生物源排放效应的幅度显著提高了156%。我们的研究结果表明，EML模型和SHAP分析相结合可以为不同事件尺度下臭氧污染成因的快速诊断提供可靠方法，支持控制措施的制定。
