基于多尺度地理加权回归模型探求生态用地景观格局与季节性PM_2.5之间的联系——以长江经济带下游地区为例

A Study to Explore the Association between Ecological Land Landscape Pattern and Seasonal PM_2.5 Based on Multi-Scale Geographically Weighted Regression Models—Taking the Lower Reaches of the Yangtze River Economic Belt as an Example

摘要: 近年来，随着城市化进程的加快，长江经济带下游地区面临着严重的PM_2.5污染问题。PM_2.5作为主要的空气污染物，对公众健康构成了巨大威胁。因此，探讨生态用地景观格局对PM_2.5浓度的影响，寻找有效的污染防治策略，具有重要的现实意义。本研究以长江经济带下游地区为研究区域，旨在通过多尺度地理加权回归(MGWR)模型，分析2021年不同季节生态空间格局与PM_2.5浓度之间的关系。研究区域涵盖江苏、浙江、安徽和上海，面积约35.03万平方公里，具有显著的季节性气候特征和复杂的景观格局。研究利用遥感数据和地理信息系统(GIS)技术，表征该地区不同季节的PM_2.5浓度空间分布及生态景观格局特征。通过MGWR模型，探讨生态景观格局对PM_2.5污染的多尺度和季节性影响。研究结果显示，生态用地景观格局与PM_2.5浓度之间存在显著相关性，且这种相关性在不同季节表现出明显的差异。春季和秋季，生态用地面积和斑块边界复杂度对PM_2.5浓度的削减作用较强；夏季，生态用地空间的破碎化程度对PM_2.5浓度的影响更为显著；冬季，生态用地的聚集度和连接性对PM_2.5浓度的削减作用较为明显。此外，研究还发现生态景观的多样性和连通性在减少PM_2.5污染方面具有重要作用。本研究深化了对生态景观格局与PM_2.5污染关系的理解，揭示了不同季节生态景观特征对PM_2.5浓度的不同影响机制。研究结果为长江经济带下游地区的城市规划和环境治理提供了科学依据，有助于制定更有效的生态用地管理策略，改善空气质量，保障公众健康。

Abstract: In recent years, with the acceleration of urbanization, the lower reaches of the Yangtze River Economic Belt are facing serious pollution problems from PM_2.5. As a major air pollutant, PM_2.5 poses a great threat to public health. Therefore, it is of great practical significance to explore the influence of ecological land landscape pattern on PM_2.5 concentration and to find effective pollution prevention strategies. Taking the lower reaches of the Yangtze River Economic Belt as the study area, this study aims to analyses the relationship between ecological spatial patterns and PM_2.5 concentrations in different seasons in 2021 through a multi-scale geographically weighted regression (MGWR) model. The study area covers Jiangsu, Zhejiang, Anhui and Shanghai, with an area of about 350,300 km², with significant seasonal climatic characteristics and complex landscape patterns. Remote sensing data and geographic information system (GIS) technology were used to characterize the spatial distribution of PM_2.5 concentration and the ecological landscape pattern in different seasons in the region. Through the MGWR model, the multi-scale and seasonal effects of ecological landscape pattern on PM_2.5 pollution were explored. The results show that there is a significant correlation between ecological land landscape pattern and PM_2.5 concentration, and this correlation shows significant differences in different seasons. In spring and autumn, the area of ecological landscapes and the complexity of patch boundaries had a stronger effect on the reduction of PM_2.5 concentrations; in summer, the degree of spatial fragmentation of ecological landscapes had a more significant effect on PM_2.5 concentrations; and in winter, the degree of aggregation and connectivity of ecological landscapes had a more significant effect on the reduction of PM_2.5 concentrations. In addition, the study found that the diversity and connectivity of ecological landscapes play an important role in reducing PM_2.5 pollution. This study deepens the understanding of the relationship between ecological landscape patterns and PM_2.5 pollution, and reveals the different mechanisms by which ecological landscape features affect PM_2.5 concentrations in different seasons. The results of the study provide a scientific basis for urban planning and environmental management in the lower reaches of the Yangtze River Economic Belt, and help to develop more effective ecological land management strategies to improve air quality and protect public health.