| 2017~2021年苏皖鲁豫交界区域PM2.5和O3时空变化特征及影响因素 |
| 摘要点击 1480 全文点击 132 投稿时间：2023-04-26 修订日期：2023-06-20 |
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| 中文关键词 PM2.5 臭氧（O3） 时空分布 相关性 气象因素 |
| 英文关键词 PM2.5 ozone （O3） spatial-temporal correlation meteorological elements |
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| 中文摘要 |
| 苏皖鲁豫交界区域是长三角和京津冀及周边两大大气污染治理重点区域的连接带,揭示该区域PM2.5和O3污染特征对推动区域大气污染联防联控有着重要意义.基于2017~2021年苏皖鲁豫交界区域22个地市的国家空气环境监测网络观测数据,探讨了该区域PM2.5和O3浓度的时空变化特征及气象影响.结果表明：①2017~2021年区域PM2.5浓度呈现逐年下降趋势,PM2.5浓度月均值呈现“U型”分布,冬季PM2.5浓度仍维持高位.O3-8h-90%浓度呈现波动下降趋势,O3-8h-90%浓度月均值变化呈“M型”分布,夏秋季O3污染程度未有好转.②与2017年相比,2021年PM2.5-O3复合污染天数减少了52 d,但PM2.5污染仍占主导地位.③PM2.5和O3污染区域主要集中在区域中部和北部城市,且中部城市PM2.5和O3污染程度均改善显著.④采用Moran's I指数和LISA指数分析了区域PM2.5和O3-8h-90%浓度的全局和局部空间自相关性,PM2.5和O3-8h-90%浓度均具有空间相关性,PM2.5浓度主要表现为高值-高值聚集或低值-低值聚集现象,且高值-高值聚集有从中部向西部转移的现象,2020年和2021年O3-8h-90%浓度表现为高值-高值聚集或低值-低值聚集现象.⑤结合气象要素数据,利用KZ滤波方法量化排放源与气象条件对区域PM2.5和O3-8h浓度的贡献,两者主要受到污染物排放影响,贡献率分别为101.0%和99.3%,表明污染物减排是驱动区域空气质量改善的主要因素.此外,气象条件对PM2.5浓度的贡献在一、四季度为正值,二、三季度为负值,而对O3-8h浓度的影响则反之,且气象条件对不同城市PM2.5和O3-8h浓度的影响程度存在较大差异. |
| 英文摘要 |
| The border area of Jiangsu, Anhui, Shandong, and Henan （SWLY） is the connecting zone of two key air pollution control regions： the Yangtze River Delta Region and Beijing-Tianjin-Hebei and its surrounding areas. Revealing the characteristics of PM2.5 and ozone pollution in the region is of great significance for promoting the regional joint prevention and control of air pollution. The spatial-temporal characteristics and influencing factors of PM2.5 and ozone were discussed based on the observation data obtained from the national air environmental monitoring network of 22 cities in the SWLY region from 2017 to 2021. ① The annual average PM2.5 concentration showed a year-round decrease trend during 2017-2021, and the monthly average concentration showed a “U-shaped” distribution, but the PM2.5 concentration remained high in winter. The O3-8h-90% concentration showed a fluctuating decrease trend during 2017-2021, the monthly average concentration showed an “M-shaped” distribution, and O3 pollution had not improved in summer and autumn. ② Compared with that in 2017, PM2.5-O3 co-pollution days in 2021 decreased by 52d, but PM2.5 pollution was still predominant in the region. ③ The PM2.5 and O3 pollution areas mainly occurred in the central and northern cities of the region, but the pollution of both PM2.5 and O3 in the central cities improved significantly. ④ The global and local spatial auto-correlation of PM2.5 and O3 concentrations were analyzed using Moran's I and LISA. The results showed that the spatial correlation of both PM2.5 and O3 concentrations occurred in the region, and the spatial aggregation of PM2.5 was characterized by “high-high” or “low-low” aggregations, and the phenomenon of “high-high” aggregation was shifted from the middle to the western cites. The spatial aggregation of O3 was characterized by “high-high” or “low-low” aggregations in 2020 and 2021. ⑤ By combining meteorological data and using the Kolmogorov-Zurbenko filter, the influence of emissions and meteorological conditions on the PM2.5 and O3 concentrations was quantified. The results showed that they were mainly affected by emissions, with the contributions of 101.0% and 99.3%, respectively, indicating that emission reduction actions were the decisive factor in the improvement in regional air quality. In addition, the influence of meteorological conditions on PM2.5 concentration was positive in the first and fourth quarters and negative in the second and third quarters. Conversely, the influence on O3 concentration showed the opposite, and the influence of meteorological conditions on the concentration of PM2.5 and O3 concentration in different cities was quite different. |
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