## 1 Introduction
However, the study of the impact of geographical microenvironments on the distribution of air pollutants, especially using UAVs, remains to be further explored. To investigate the impact of geographical microenvironments on air quality, this study presents several significant contributions:  
* Development of a UAV Air Quality Data Collection System: This study innovatively designed and implemented a UAV-based system capable of collecting air quality data (including PM\({}_{2.5}\), CO\({}_{2}\), temperature, and humidity) across multiple vertical levels in various microenvironments. Unlike previous research that primarily relied on fixed meteorological stations for two-dimensional studies, our work fills the gap in data collection platforms on a three-dimensional scale, providing a new perspective for microenvironmental research [15; 16]. In addition, unlike most existing studies that utilize off-the-shelf consumer drones, such as the DJI Matrice 300 or DJI Phantom RTK, where sensors are simply mounted, these commercial non-open-source drones often lack the capability to export accurate coordinate data with precise timestamps, as well as real-time motion status and attitude information. These data are crucial for subsequent data correction and three-dimensional analysis. In contrast, our custom-designed UAV system leverages an open-source drone platform that allows us direct access to spatial position, temporal information, motion status, and flight attitude data. This operational flexibility enhances the practicality and scientific applicability of our research compared to typical consumer-grade drones. * Air Quality Data Correction Model: We employed a BP neural network to correct airflow disturbances caused by UAV motion, ensuring that the collected air quality data are more accurate and reliable. This approach is particularly advantageous over traditional statistical methods, such as multiple linear regression, as it better captures complex non-linear relationships. It effectively models the intricate interactions between UAV motion states and their impact on air quality data, resulting in improved precision in our data analysis compared to conventional statistical methods [17]. * Construction of a Three-Dimensional Air Quality Spatial Interpolation Model: We applied the Kriging interpolation method to three-dimensional scatter data, generating a voxel-based 3D air quality model. While some existing studies in the three-dimensionalrealm perform two-dimensional interpolation of air quality data at various heights and visualize the results as three-dimensional isosurfaces or with colored scatter plots overlaid on two-dimensional orthophotos, these approaches often fail to accurately capture the vertical variations of air quality indicators. In contrast, our use of three-dimensional Kriging interpolation effectively addresses these limitations, allowing for a more precise representation of vertical changes in air quality metrics [18,19]. Moreover, Kriging excels in three-dimensional applications because it can effectively model the spatial relationships between data points across multiple dimensions. This is particularly beneficial for air quality studies, where pollutant concentrations may vary not only horizontally but also vertically. By generating a voxel-based 3D model, Kriging captures vertical gradients in air quality that are often missed by traditional methods, which typically flatten data into two-dimensional surfaces. High-precision oblique photogrammetry was utilized to create a detailed 3D model of the study area, which was then overlaid with the 3D air quality interpolation model. This combined approach not only enhances our understanding of the dynamic changes in air quality but also facilitates the exploration of potential relationships between geographical layouts and air quality in microenvironments, providing critical scientific support for urban planning and environmental management.