## 1 Introduction
In recent years, the fusion of remote sensing technology with machine learning has enabled the continuous spatial estimation of near-surface PM\({}_{2.5}\) concentrations [33; 34; 35], effectively addressing numerous limitations associated with site-specific data. Satellite remote sensing observation technology provides uninterrupted spatial distribution data for PM\({}_{2.5}\) monitoring, mitigating the constraints imposed by the uneven deployment of ground-based monitoring stations. This approach facilitates comprehensive, expansive, and intricate observations across extensive spatial domains, offering a nuanced portrayal of PM\({}_{2.5}\) pollution source distribution [36; 37]. In the realm of research methodologies, techniques such as multiple linear regression, principal component regression models [38], geographical detectors [39], geographical weighted regression models [40], spatial econometric models [41], and gray correlation models are predominantly utilized to investigate the influencing factors of PM\({}_{2.5}\) concentration [42; 43]. Past studies have shown that China's PM\({}_{2.5}\) pollution has obvious regional and seasonal differences. Areas with high PM\({}_{2.5}\) concentrations are mainly concentrated in the North China Plain, the Yangtze River Delta, Central China, the Sichuan Basin, and the Pearl River Delta. The PM\({}_{2.5}\) concentration in winter is significantly higher than other seasons [44; 45]. Research points out that the decline in PM\({}_{2.5}\) levels is mainly due to effective emission reduction measures targeting anthropogenic emission sources in recent years [46; 47]. Although the concentration of PM\({}_{2.5}\) has gradually decreased, there is still room for improvement compared with international standards. In addition, meteorological conditions such as wind speed, rainfall, humidity and atmospheric boundary layer height also have a significant impact on PM\({}_{2.5}\) concentration changes. Therefore, in the future, China needs to continue to implement and strengthen emission reduction policies to further improve air quality. The significance of studying PM\({}_{2.5}\) pollution in Chenzhou lies in that, as a representative city in southern China, its PM\({}_{2.5}\) pollution characteristics can reflect the environmental problems of southern Chinese cities in the process of industrialization and urbanization. Owing to its distinctive geographical positioning, Chenzhou City has emerged as the southern conduit to Hunan and the pivotal interlink connecting Guangdong, Hong Kong, and Macao. It serves as an essential node within the atmospheric pollution transmission matrix of the Lingnan region, grappling with notably severe air pollution mandaries. In recent times, the Chenzhou Municipal Government, collaborating harmoniously with a spectrum of stakeholders, has embarked on concerted efforts to implement holistic strategies for the preservation and regulation of the atmospheric milieu. The objective has been to enhance the atmospheric conditions in southern Hunan, with a particular emphasis on Chenzhou City, yielding tangible progress. However, research delineating the spatial and temporal regional distribution of indigenous PM\({}_{2.5}\) within Chenzhou City, seasonal variations therein, and the prevailing atmospheric conditions subsequent to pollution abatement remains relatively sparse. In the context of this framework, the present study zeroes in on Chenzhou City, an emblematic urban hub in the southern Hunan Province. Utilizing ArcGIS software, we meticulously examine the spatial and temporal distribution patterns of PM\({}_{2.5}\) concentrations within the city. Concurrently, the Trajstat module within MeteInfo software is harnessed to delve into the dynamics of PM\({}_{2.5}\) pollution in Chenzhou City.