基于PM2.5浓度研究京津冀环境一体化

Investigating Environmental Integration in the Beijing-Tianjin-Hebei Region Based on PM2.5 Concentration

摘要: 本文旨在探究京津冀地区的PM2.5浓度分布及其相互之间的影响，通过分析京津冀地区供暖季与非供暖季的差异，探索京津冀地区PM2.5浓度的空间相关性。首先，使用十三个市的勘测点数据计算供暖季PM2.5浓度的全局莫兰指数和局部莫兰指数，以分析各地区间的相关性，结论为京津冀地区PM2.5浓度具有较高的相关性。接着，在京津冀地区对供暖季和非供暖季的PM2.5浓度进行克里金插值，识别浓度变化显著的区域，并进行PM2.5浓度差异原因分析以及高污染区域成因分析，探究京津冀PM2.5浓度相互之间的影响，有针对性地提出了相应环境保护建议。最后，由于勘测点的不均衡性可能导致结果偏差，本文依据克里金插值结果在京津冀地区扩大勘测点位重新计算各市供暖季的PM2.5平均值，并对比前后的莫兰指数，得出的结论为京津冀13个市PM2.5浓度相关性较强，污染呈现明显聚类，北部地区呈现“低–低”相聚，南部地区呈现“高–高”相聚，并且供暖季污染面以城市为中心规律扩散。

Abstract: This study aims to investigate the spatial distribution of PM2.5 concentrations and their inter-regional influences in the Beijing-Tianjin-Hebei (BTH) region, with a focus on analyzing seasonal variations between heating and non-heating periods. The spatial correlation characteristics of PM2.5 concentrations were systematically examined through spatial statistical approaches. Initially, Global Moran’s I and Local Moran’s I indices were calculated using monitoring data from 13 municipal-level cities during heating seasons, revealing significant spatial autocorrelation of PM2.5 concentrations across the BTH region. Subsequently, Kriging interpolation was implemented to map PM2.5 concentration patterns for both seasons, identifying regions with substantial variation and conducting causal analyses for concentration discrepancies and pollution hotspots. Based on these findings, targeted environmental protection recommendations were proposed. Finally, due to the uneven distribution of monitoring points potentially leading to biased results, this study recalculated the average PM2.5 levels during the heating season for each city in the Beijing-Tianjin-Hebei region by expanding the monitoring points based on Kriging interpolation results. A comparison of Moran’s Index before and after the adjustment revealed that the PM2.5 concentrations in the 13 cities of the region exhibited strong spatial correlations, with pollution showing significant clustering. The northern areas displayed a “low-low” aggregation pattern, while the southern areas exhibited a “high-high” aggregation. Additionally, during the heating season, pollution spread outward from urban centers in a regular pattern.