| 长三角地区PM2.5区域性污染时空变化特征 |
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| 引用本文： | 张懿华. 长三角地区PM2.5区域性污染时空变化特征[J]. 环境科学研究, 2022, 35(1): 1-10. DOI: 10.13198/j.issn.1001-6929.2021.09.03 |
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| 作者姓名： | 张懿华 |
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| 作者单位： | 上海市环境监测中心, 上海 200235 |
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| 基金项目： | 国家重点研发计划项目(No.2018YFC0213804)；;上海市科学技术委员会科技攻关项目(No.20dz1204000)~~； |
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| 摘 要： | 为定量分析长三角地区PM2.5区域性污染的变化特征，建立适用于长三角地区的PM2.5区域污染划分标准，基于2015—2020年长三角地区41个城市日均ρ(PM2.5)开展区域污染变化趋势研究，并针对长三角PM2.5重度区域污染开展了时空变化以及网络特征分析. 结果表明:①2015—2020年长三角三省一市年均ρ(PM2.5)降幅均在25%以上，城市ρ(PM2.5)分布呈北高南低的特征，南北城市之间ρ(PM2.5)差异较大，ρ(PM2.5)最高值与最低值相差35~46 μg/m3. ②2015—2020年长三角PM2.5区域污染天数比例为16.9%~35.9%，以轻度污染为主，不同年份中度和重度污染天数比例差异较大，且主要出现在秋冬季，轻度、中度和重度污染天数均呈波动下降趋势. ③与2015年相比，2019年和2020年PM2.5区域污染天数分别减少了38和69 d，且PM2.5重度区域污染持续天数和重度及以上污染城市数量均呈减少趋势. ④PM2.5重度区域污染日，长三角城市之间表现出较强的污染关联性，并可划分为4个子群. 以连云港市为代表的子群1位于长三角地区北部，PM2.5污染相对较重，受长三角区域内输送影响较小，但对区域内其他城市有一定的输送影响；以宁波市为代表的子群2和以南京市为代表的子群4受长三角区域内输送影响较大，并指示了东路沿海和中路两条污染传输通道；以安庆市为代表的子群3位于内陆地区，污染独立性相对较强，受长三角区域内输送影响较小，同时对长三角其他城市影响也较小. 研究显示，长三角地区PM2.5污染改善显著，但重度区域污染尚未消除，中北部城市的联防联控将对改善PM2.5区域污染起积极作用.
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| 关 键 词： | PM2.5 区域污染 时空变化 长三角地区 社会网络分析 |
| 收稿时间： | 2021-06-09 |
Spatial-Temporal Characteristics of PM2.5 Regional Pollution in Yangtze River Delta Region |
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| ZHANG Yihua. Spatial-Temporal Characteristics of PM2.5 Regional Pollution in Yangtze River Delta Region[J]. Research of Environmental Sciences, 2022, 35(1): 1-10. DOI: 10.13198/j.issn.1001-6929.2021.09.03 |
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| Authors: | ZHANG Yihua |
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| Affiliation: | Shanghai Environmental Monitoring Center, Shanghai 200235, China |
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| Abstract: | A PM2.5 regional pollution classification method was established in the Yangtze River Delta (YRD) Region to quantitatively analyze the spatial-temporal characteristics of PM2.5 regional pollution. The variation of PM2.5 regional pollution was analyzed based on the daily PM2.5 concentrationof 41 cities in the YRD region from 2015 to 2020. And the spatial-temporal characteristics and network features of heavy regional pollutions were analyzed. The results show that: (1) The annual average PM2.5 concentration in Jiangsu Province, Anhui Province, Zhejiang Province and Shanghai decreased by more than 25% from 2015 to 2020. The PM2.5 concentration was higher in north and lower in south and exhibited great difference between northern and southern cities. The difference between the maximum and minimum values was in the range of 35-46 μg/m3. (2) The proportion of PM2.5 pollution days in the region was 16.9%-35.9% from 2015 to 2020, most of which were lightly polluted days. There were big differences in the proportion of moderately polluted days and heavily polluted days in different years. The moderately polluted and heavily polluted days mainly occurred in autumn and winter. The number of lightly polluted, moderately polluted and heavily polluted days showed a fluctuating downward trend from 2015 to 2020. (3) Compared with 2015, the PM2.5 regional pollution days in 2019 and in 2020 decreased by 38 and 69 d, respectively. Both the duration and the amount of heavily polluted and severely polluted cities decreased during the heavy regional pollution days. (4) The PM2.5 pollutions among the cities in the YRD showed a strong correlation during the heavy regional pollution days. The cities in the YRD could be divided into four subgroups. Subgroup Ⅰ Cites represented by Lianyungang were located in the north of the YRD, with high PM2.5 concentrations. Subgroup Ⅰ was less influenced by intraregional transport, but influenced other cities in the YRD. Intraregional transportation was important for Subgroup Ⅱ and Subgroup Ⅳ represented by Ningbo and Nanjing, respectively, indicating the east coastal and middle transportation pathways in the YRD. Subgroup Ⅲ represented by Anqing City was less influenced by the intraregional transportation and influenced other cities in the YRD less. The improvement of PM2.5 pollution was significant in the YRD Region, but heavy regional pollution was not eliminated. Cooperative prevention and control in northern and central cities will help further improve PM2.5 regional pollution. |
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