黄河流域城市PM2.5时空分异特征及污染物解析

分析揭示黄河流域城市PM_2.5时空分异特征，对打赢大气污染防治攻坚战，推动黄河流域空气污染跨区域协同治理机制的建立和完善，以及流域绿色高质量发展具有重要意义.本文以中国空气质量在线监测分析平台456个监测站点的PM_2.5浓度监测数据为基础，运用莫兰指数和标准差椭圆方法分析黄河流域70个城市2015—2021年PM_2.5的时空分异特征、演变格局，并基于皮尔逊相关系数分析法对其污染源进行解析.结果表明：(1)PM_2.5浓度的月度、季节变化特征明显.月均浓度呈底部宽缓的“U”型分布，12月或1月达到最大值；冬季平均浓度最高、春秋季次之、夏季最低，冬季浓度是夏季的1.9～2.6倍；年均PM_2.5浓度整体趋降，且表现为下游>中游>上游的空间分异性.(2)PM_2.5的空间聚集表现为上游“低—低”集聚、下游“高—高”集聚、中游城市的空间聚集特征不显著，空间正相关集聚的城市数量以先增后减的趋势变化，负相关集聚特征的城市较少.(3)PM_2.5

Spatial-Temporal Variation Characteristics and Pollutants Analysis of Urban PM2.5 in the Yellow River Basin

Analyzing and revealing the spatial and temporal characteristics of PM_2.5 in cities of the Yellow River Basin is of great significance for winning the battle against air pollution and promoting the establishment and improvement of the cross-regional cooperative control mechanism of air pollution and the green high-quality development. Based on the PM_2.5 concentration monitoring data of 456 monitoring stations, this paper analyzed the spatial and temporal variation characteristics and evolution pattern of PM_2.5 in 70 cities in the Yellow River Basin from 2015 to 2021 by using the Moran index and standard deviation ellipse method, and identified the pollution sources by Pearson correlation coefficient analysis method. The results showed that: (1) PM_2.5 concentration reflected monthly and seasonal changes. The monthly average concentration showed a ‘U-shaped’ distribution with a wide and slow bottom, reaching the maximum in December or January. In terms of seasons, the average concentration in winter was the highest (1.9-2.6 times that of summer), followed by spring and autumn, and the lowest in summer. The annual average concentration decreased in general, showing a spatial differentiation of downstream > midstream > upstream. (2) The spatial aggregation of PM_2.5 was characterized by ‘low-low’ agglomeration in the upstream, ‘high-high’ agglomeration in the downstream, and insignificant spatial aggregation in the midstream. The number of cities with positive spatial correlation agglomeration increased first and then decreased, while the number of cities with negative spatial correlation agglomeration was relatively small. (3) The spatial distribution pattern of PM_2.5 generally followed a ‘northwest-southeast’ geographical spatial trend, and the concentration distribution showed the characteristics of geographical spatial decentralization and gradual reduction of distribution range. (4) PM_2.5 pollution in upstream cities was complex and diverse, mainly including PM₁₀, NO₂, CO and SO₂. PM₁₀, NO₂ and CO were the main sources in midstream cities, and PM₁₀ and CO were the main sources in downstream cities. The spatial heterogeneity of PM_2.5 concentration in cities of the Yellow River Basin was obvious, and there was a significant positive spatial correlation agglomeration in the upstream and downstream. Collaborative prevention and control in cities will help to further improve air quality in the Yellow River Basin.