## 1 Introduction  
The concept of a geographical microenvironment pertains to local environmental conditions and characteristics within a relatively small spatial scale, encompassing descriptions and analyses of factors such as terrain, vegetation, soil, water bodies, and buildings. It emphasizes the localized and detailed nature of spatial scales. The complex layout and structure of microenvironments are closely linked to the distribution of air quality in residential areas. Different layouts and structures of geographical microenvironments significantly influence air flow patterns, determining the methods and extent of air pollution dispersion. In densely built-up areas, human activities and traffic emissions often lead to higher pollutant concentrations and a pronounced urban heat island effect [1]. Areas with abundant vegetation can notably reduce concentrations of PM\({}_{2.5}\) and ozone [2], while vegetation also affects air temperature and humidity, providing a cooling effect that makes densely vegetated areas relatively cooler [3]. Moreover, open impervious surfaces like parking lots and asphalt roads absorb more solar radiation, altering the radiative heat balance and impacting air quality [4; 5]. In terms of microenvironment air quality monitoring, traditional ground monitoring stations and on-site manual inspections are limited in number and coverage, making it challenging to reflect the dynamic changes in air quality comprehensively and in real-time. Additionally, they cannot precisely locate pollutant sources, hindering targeted mitigation efforts [6]. Air pollutants are distributed across three-dimensional space, influenced not only by horizontal wind fields but also by vertical atmospheric boundary layer fluctuations [7]. Traditional atmospheric detection methods, such as radiosondes, wind towers, and radar systems, though established, present significant challenges [8]. They suffer from high construction costs, low reuse rates, and complex maintenance requirements. Additionally, these methods provide limited spatial coverage and lack the resolution needed to track air pollutants in three-dimensional space effectively. In dynamic urban environments, where air pollutants are influenced by both horizontal wind patterns and vertical atmospheric boundary fluctuations, these methods often fall short of meeting the needs of environmental protection agencies [9]. As such, innovative solutions, such as UAV-based air quality monitoring systems, offer promising alternatives by providing high-resolution data and enhanced spatial coverage [10]. UAVs can flexibly change flight paths, enabling continuous detection at different vertical heights and horizontal distances. They can accurately measure pollutant concentrations at specific altitudes. Although UAV applications in air quality monitoring started relatively late in China, many studies have successfully applied them in practice. For instance, researchers such as [PERSON], [PERSON], and [PERSON] used UAVs equipped with air quality detection devices to monitor atmospheric pollutants in parts of Lin'an City and Shanghai Fengxian District, revealing the vertical distribution and diffusion patterns of PM\({}_{2.5}\) and other pollutants [11]. The team of [PERSON], [PERSON], and [PERSON] conducted an in-depth study using UAVs during a severe pollution event in Lin'an City, analyzing the three-dimensional spatial distribution data of PM\({}_{2.5}\) concentrations and concurrent meteorological conditions [12]. Internationally, researchers like [PERSON] used UAV technology for detailed aerial photogrammetry of the Fukushima nuclear waste storage site, successfully creating a 3D radiation map by integrating it with ground-based radiation mapping data [13]. [PERSON] et al. monitored various air pollutants in a small highway area using UAVs, displaying and analyzing the data in the form of three-dimensional scatter plots overlaid with orthophotos [14].