Zhang Keran, Chen Qiaoling, Hong Youwei, Ji Xiaoting, Chen Gaojie, Lin Ziyi, Zhang Feng, Wu Yu, Yi Zhigang, Zhang Fuwang, Zhuang Mazhan, Chen Jinsheng
Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; College of JunCao Science and Ecology, Fujian Agriculture and Forest University, Fuzhou, 350002, China.
Fujian Key Laboratory of Atmospheric Ozone Pollution Prevention, Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
Environ Pollut. 2025 Feb 1;366:125491. doi: 10.1016/j.envpol.2024.125491. Epub 2024 Dec 7.
Elucidating the meteorology and emissions contribution of O variation is a crucial issue for implementing effective measures for O pollution control. We quantified the impacts of meteorology and emissions on O variability during spring and autumn from 2019 to 2022, using multi-year continuous observations. A machine learning (ML)-based de-weathering model revealed that meteorology accounted for a greater proportion of O variability (71.9% in spring and 57.4% in autumn) compared to emissions (28.1% and 42.6%, respectively). In spring, relative humidity (RH, 22.8%) and wind speed (WS, 13.7%) were key drivers, contributing to O decreases and increases, respectively. During autumn, temperature (T, 10.8%) and surface solar radiation (SSR, 9.45%) were the dominant factors, both contributing to O production. We assessed the O formation sensitivity based on VOCs emissions sources and evaluated the importance of emission by O production rate (P(O)) calculated from box model and the positive matrix factorization (PMF) model. Vehicle emissions and solvent use were identified as the major contributors to O formation from 2019 to 2022 and reducing them would be beneficial for O pollution control. This study elucidates the relative roles of meteorological conditions and anthropogenic emissions in O variability and key insights for formulating future O control policies.
阐明臭氧（O₃）变化的气象学和排放贡献对于实施有效的O₃污染控制措施至关重要。我们利用多年连续观测数据，量化了2019年至2022年春季和秋季气象学和排放对O₃变异性的影响。基于机器学习（ML）的去气象化模型显示，与排放（分别为28.1%和42.6%）相比，气象学在O₃变异性中占更大比例（春季为71.9%，秋季为57.4%）。在春季，相对湿度（RH，22.8%）和风速（WS，13.7%）是关键驱动因素，分别导致O₃减少和增加。在秋季，温度（T，10.8%）和地表太阳辐射（SSR，9.45%）是主要因素，两者都促进O₃生成。我们基于挥发性有机化合物（VOCs）排放源评估了O₃生成敏感性，并通过箱式模型和正定矩阵因子分解（PMF）模型计算的O₃生成率（P（O₃））评估了排放的重要性。车辆排放和溶剂使用被确定为2019年至2022年O₃生成的主要贡献者，减少这些排放将有利于O₃污染控制。本研究阐明了气象条件和人为排放在O₃变异性中的相对作用以及对制定未来O₃控制政策的关键见解。
