| 河南省PM2.5-O3双高复合污染的特征及影响因素 |
| 摘要点击 1353 全文点击 208 投稿时间：2024-02-19 修订日期：2024-05-20 |
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| 中文关键词 河南省 复合污染 气象条件 排放 潜在源贡献分析（PSCF） 浓度权重轨迹分析法（CWT） |
| 英文关键词 Henan Province composite pollution meteorological conditions emissions potential source contribution function（PSCF） concentration weighted trajectory（CWT） |
| 作者 | 单位 | E-mail | | 王梦珂 | 南京信息工程大学大气物理学院, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044 | 245517927@qq.com | | 曹乐 | 南京信息工程大学大气物理学院, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044 | le.cao@nuist.edu.cn | | 徐力强 | 南京信息工程大学大气物理学院, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044 | | | 王宇曦 | 南京信息工程大学大气物理学院, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044 | | | 孔海江 | 河南省气象台, 郑州 450003 | | | 高萌萌 | 鹤壁市气象局, 鹤壁 458000 | | | 秦阳 | 河南省气象台, 郑州 450003 | | | 孟凯 | 河北省气象科学研究所, 石家庄 050021
河北省气象与生态环境重点实验室, 石家庄 050021 | | | 赵天良 | 南京信息工程大学大气物理学院, 中国气象局气溶胶与云降水重点开放实验室, 南京 210044 | |
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| 中文摘要 |
| 河南省大气中的PM2.5-O3双高复合污染对该地区的经济、社会和生活都有重要影响,且呈现出比单一污染更加复杂的性质. 因此,使用河南省17个地级市的空气质量数据、地面气象数据、边界层高度和其他污染物（CO）浓度、排放源清单等,深入分析了河南省2014年5月至2023年12月PM2.5-O3双高复合污染事件的时空分布、形成原因和影响因素,并结合PSCF和CWT方法揭示了复合污染的可能源区. 结果表明：①由于政府对PM2.5污染的管控以及疫情影响,2014~2023年河南省双高复合污染事件总体呈现下降趋势,但2022年疫情后的复工复产,复合污染发生频次又出现反弹. 2018年前,由于严重的PM2.5污染,臭氧浓度是决定复合污染发生与否的主要因素. 2018年后,PM2.5浓度的下降使其重要性上升. 特别是河南省5~9月PM2.5浓度的下降,是近年来复合污染发生频次降低的主要原因. ②复合污染的月际分布呈现出双峰型,在4月和10月达到峰值,分别对应于臭氧上升、PM2.5下降和臭氧下降、PM2.5上升的两个时期,而7~8月的雨季对复合污染有减轻作用. ③河南省复合污染的严重程度为：北部>中部>南部,地形对复合污染的严重程度也有一定影响. ④复合污染发生时的气象条件为：温度、比湿和CO浓度适中,介于PM2.5污染和臭氧污染之间,低风速和低边界层高度. ⑤2022年河南省发生复合污染期间,除省内东部和北部城市的污染物排放和输送外,省外东部（安徽省）和东北部（山东省）也是其污染的主要源区. 以上源区的污染物传输,叠加河南省内的排放,是造成该时期河南省双高污染的重要原因. |
| 英文摘要 |
| The PM2.5-O3 composite pollution in Henan Province has notable impacts on the economy, society, and daily life of the region, exhibiting a more complex nature than single pollution. Therefore, this study utilized air quality data, ground-based meteorological data, boundary layer height, CO concentration, emission inventory, and other information from 17 cities in Henan Province to investigate the spatial-temporal distribution, formation causes, and influencing factors of PM2.5-O3 composite pollution from May 2014 to December 2023. The PSCF and CWT methods were used to identify possible source areas of composite pollution. The results showed that： ① Due to the government's control of PM2.5 pollution and the impact of the epidemic, the PM2.5-O3 composite pollution events in Henan Province from 2014 to 2023 showed an overall downward trend, however, rebounded during the resumption of work and production after the epidemic in 2022. Before 2018, O3 concentration was the major factor determining the occurrence of composite pollution due to severe PM2.5 pollution. After 2018, the decrease in PM2.5 concentration increased its importance, especially the decrease in PM2.5 concentration from May to September in Henan Province, which was the primary reason for the reduction in composite pollution frequency in recent years. ② The monthly distribution of composite pollution showed a bimodal pattern, with peaks in April and October, corresponding to two periods when O3 increased while PM2.5 decreased and O3 decreased while PM2.5 increased, respectively, whereas the rainy season in July and August had a mitigating effect on composite pollution. ③ The severity of composite pollution in Henan Province was in the order of north > central > south and the topography also had a certain impact on the severity of composite pollution. ④ The meteorological conditions during the occurrence of composite pollution were moderate temperature, specific humidity, and CO concentration between PM2.5 pollution and O3 pollution, with low wind speed and low boundary layer height. ⑤ During the period of compound pollution in Henan Province, in addition to the transportation of pollutants from cities inside the province, especially the eastern and northern parts of the province, the eastern area （Anhui Province） and northeastern area （Shandong Province） to Henan Province were the major source areas of pollutants. Emissions from the source area, combined with emissions from cities inside Henan Province, were important causes for the PM2.5-O3 pollution during this period. |
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