黄河流域城市PM2.5时空分异特征及污染物解析
Spatial-Temporal Variation Characteristics and Pollutants Analysisof Urban PM2.5 in the Yellow River Basin
-
摘要: 分析揭示黄河流域城市PM2.5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PM2.5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PM2.5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:①PM2.5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PM2.5浓度整体趋降,且表现为下游>中游>上游的空间分异性。②PM2.5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。③PM2.5空间分布格局总体呈“西北—东南”的地理空间走向,其浓度的分布在地理空间上分散化,但分布范围趋于缩减。④上游城市PM2.5污染源复杂多样,主要有PM10、NO2、CO和SO2,中游城市PM2.5污染主要来源于PM10、NO2和CO,而下游城市为PM10、CO。研究显示,黄河流域城市PM2.5浓度的时空分异性明显,上下游存在显著的空间正相关聚集,城市污染联防联控对流域空气质量的改善发挥重要作用。Abstract: Analyzing and revealing the spatial and temporal characteristics of PM2.5 in cities of the Yellow River Basin is of great significance for winning the battle of air pollution, promoting the establishment and improvement of the cross-regional cooperative control mechanism of air pollution, and the green high-quality development as well. Based on the PM2.5 concentration monitoring data of 456 monitoring stations, this paper analyzed the spatial and temporal variation characteristics and evolution pattern of PM2.5 in 70 cities in the Yellow River Basin from 2015 to 2021 by using the Moran index and standard deviation ellipse method, identified the pollution sources by Pearson correlation coefficient analysis method. The results showed that: (1) PM2.5 concentration reflected monthly and seasonal changes. The monthly average concentration shows a ‘U-shaped’ distribution with a wide and slow bottom, reaching the maximum in December or January; in terms of seasonal, the average concentration in winter was the highest (1.9~2.6 times that in summer), followed by spring and autumn, and the lowest in summer, the annual average concentration decreased overall, showed a spatial differentiation of downstream > midstream > upstream (2) The spatial aggregation of PM2.5 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 is relatively small. (3) The spatial distribution pattern of PM2.5 generally followed a ‘northwest-southeast’ geographical spatial trend,, the concentration distribution showed the characteristics of geographical spatial decentralization and gradual reduction of distribution range. (4) PM2.5 pollution in upstream cities were complex and diverse, mainly including PM10, NO2, CO and SO2, PM10, NO2 and CO were the main sources in midstream cities, while PM10 and CO in downstream cities. The spatial heterogeneity of PM2.5 concentration in cities of the Yellow River Basin is obvious, and there is a significant positive spatial correlation agglomeration in the upstream and downstream. Collaborative prevention and control in cities will help further improve air quality in the Yellow River Basin.
点击查看大图
计量
- 文章访问数: 146
- HTML全文浏览量: 37
- PDF下载量: 44
- 被引次数: 0