## 5 Conclusions  
This study conducted comprehensive air quality measurements across various monitoring points on campus using drones equipped with advanced sensor systems. The application of a BP neural network algorithm effectively corrected the collected data, significantly mitigating the influences of drone movements--an improvement over traditional monitoring methods that often suffer from static positioning and limited data collection capabilities. By employing drones, this research enabled real-time, high-resolution data collection across a microenvironment, addressing the limitations of conventional ground-based monitoring stations, which can be hampered by their fixed locations and inability to capture spatial variations in pollutant concentrations. Additionally, the flexibility of drone deployment allows for targeted monitoring in areas that may be challenging to access otherwise. The analysis of air quality indicators--such as CO\({}_{2}\) concentration, humidity, temperature, and PM\({}_{2.5}\) density--revealed diverse patterns of variation. Notably, areas characterized by dense buildings, ongoing construction, and large electronic equipment demonstrated significantly elevated PM\({}_{2.5}\) levels. Conversely, high humidity and relatively enclosed spaces impeded air circulation, resulting in poor air quality, which poses potential health risks upon prolonged exposure. Geographic layout significantly impacts PM\({}_{2.5}\) distribution in microenvironments, with notable height-based variations occurring particularly in areas with dense, tall structures. This study highlights the potential of utilizing drones in future air quality assessments, as they offer a novel approach to understanding the dynamics of microenvironments. Furthermore, these findings provide crucial insights for urban planning and environmental management, emphasizing the need for adaptive strategies to improve air quality in densely populated areas. Overall, this innovative use of drone technology combined with advanced data processing techniques represents a significant advancement in the field of environmental monitoring. Furthermore, the integration of detailed 3D air quality information into urban layout and building design could significantly enhance sustainable development efforts. For instance, understanding air pollutant distribution in relation to urban structures may help optimize building orientation and ventilation systems to reduce pollution exposure. In addition, such 3D insights can guide the strategic placement of green infrastructure (e.g., parks, trees) and blue infrastructure (e.g., water bodies) to mitigate pollution and manage urban heat islands. This data-driven approach would support the design of healthier, more resilient cities by leveraging natural elements for air quality improvement and better urban climate regulation. However, despite the advantages of UAV techniques, certain limitations should be acknowledged. The limited battery life and payload capacity of drones can restrict the duration of monitoring and the range of sensors that can be carried. Additionally, drones are sensitive to environmental conditions such as wind speed and turbulence, which may influence data accuracy, particularly in regions with complex topography. Furthermore, the cost of high-quality sensor-equipped drones and the need for skilled operators could limit the widespread application of this technology. Addressing these limitations in future research could enhance the reliability and accessibility of UAV-based air quality monitoring. Conceptualization, Z.L. and R.L. ; data curation, Z.L. ; formal analysis, Z.L. ; funding acquisition, R.L. ; investigation, Z.L., [PERSON] ([PERSON]), [PERSON] ([PERSON]) and [PERSON]; methodology, Z.L. ; project administration, [PERSON] and R.L. ; resources, Z.L. and R.L. ; software, [PERSON] and [PERSON]. ([PERSON]); supervision, Z.L. and R.L. ; validation, [PERSON], [PERSON] ([PERSON]) and R.L. ; visualization, Z.L. ; writing--original draft, [PERSON] and J.H.