## 4 Discussion  
This study successfully demonstrates the capabilities of a novel UAV air quality monitoring platform, effectively correcting sensor data through controlled experiments and a BP neural network algorithm. By eliminating the influence of drone motion on sensor readings, we have established a reliable methodology for air quality data collection. Our results indicate that drone flight significantly impacts sensor data; however, this effect can be accurately predicted and corrected, showcasing the robustness of our correction model across various scenarios. Unlike existing UAV platforms, which often utilize commercially available drones with limited data accessibility, our custom-designed system enables direct access to critical internal flight data, including precise spatial coordinates and real-time motion parameters. This capability allows for more accurate corrections of sensor data, thereby enhancing the overall reliability of air quality measurements. By addressing the limitations of standard UAVs, our platform sets a new benchmark for precision in drone-based environmental monitoring. Our comprehensive analyses of air quality indicators, particularly CO\({}_{2}\) concentration, relative humidity, temperature, and PM\({}_{2.5}\) density, reveal complex variations with height, diverging significantly from traditional macro-scale studies. For instance, we observed that regions with dense vegetation exhibit higher CO\({}_{2}\) levels, suggesting that prolonged overcast conditions favor plant respiration over photosynthesis [29; 30]. The phenomenon of elevated CO\({}_{2}\) levels in densely vegetated areas highlights the necessity of integrating local climatic characteristics and building layouts when designing urban vegetation strategies. In regions characterized by extended cloudy or rainy conditions, or where buildings are densely clustered and tall, excessive planting of vegetation could lead to the accumulation  
Figure 16: Three-dimensional PM\({}_{2.5}\) spatial distribution: (**a**) side view and (**b**) top view. of CO\({}_{2}\) and other pollutants [31]. This is particularly concerning when sunlight exposure is limited, as reduced photosynthesis can hinder the natural cleansing effects of vegetation, resulting in stagnant air and increased humidity levels. Such conditions may impair air circulation and foster the proliferation of soil microorganisms, posing health risks to local populations. In the results of statistical analyses, significant variations in PM\({}_{2.5}\) concentrations were observed across different geographic locations, even within this small-scale area. However, PM\({}_{2.5}\) levels showed no overall significant difference with height, except in regions characterized by clusters of tall, dense buildings and vegetation. At these specific sites, PM\({}_{2.5}\) concentrations exhibited significant differences above and below certain fixed heights, likely due to the impact of structural features on air circulation patterns [32]. This suggests that while altitude alone may not consistently influence PM\({}_{2.5}\) distribution, the presence of concentrated vertical structures can create distinct particulate variations at certain height thresholds, underscoring the nuanced role of geographic layout in air quality within microenvironments. Moreover, the three-dimensional distribution models generated through our UAV platform indicate that air quality dynamics at the microenvironment scale are intricately linked to geographic and structural features. For example, the correlation between high humidity and elevated PM\({}_{2.5}\) concentrations in areas with extensive electronic equipment underscores the necessity for targeted monitoring strategies in urban planning and public health initiatives [33; 34]. Maintaining a safe distance between these pollution sources and residential or workplace environments is essential to mitigate air quality degradation and its associated health impacts.