Complex air pollution, including particulate matter and ozone, is a significant environmental issue in China, with volatile organic compounds (VOCs) as key precursors. Traditional ground-based monitoring methods struggle to capture the vertical distribution and changes of pollutants in the troposphere. To address this, we developed a vertical monitoring and sampling platform using a quadcopter unmanned aerial vehicle (UAV). The platform, equipped with lightweight quartz sampling canisters and miniaturized sensors, collects air samples for VOC analysis and vertical data on meteorological parameters and particulate matter. Performance tests showed the quartz canisters had less than 15% adsorption loss, with sample storage stability exceeding 80% over three days. Sensor data showed strong correlations with standard instruments (R² > 0.80). Computational fluid dynamics simulations optimized the sampler’s inlet position and ascertained that ascending flight mitigates rotor-induced air recirculation. Field campaigns were conducted at six sites along the Chengdu Metropolitan Circle Ring Expressway. Vertical data from 0~300 m revealed particulate matter concentrations peaked at 50~70 m. Near-surface VOCs were dominated by alkanes, while aromatics were found concentrated at 150~250 m, indicating significant regional transport influences. The results confirmed the platform’s effectiveness for pollutant distribution analysis.

The accurate quantification of urban anthropogenic CO₂ emissions is of paramount importance for comprehending regional carbon fluxes and supporting climate change mitigation strategies. This study explores the applicability of a cost-effective unmanned aerial vehicle (UAV)-based mass balance method for independent urban-scale emission assessments. An integrated air–ground–satellite observation framework was established by combining UAV-based vertical CO₂ profiles, ground-based observations, and ERA5 reanalysis data, and applied to quantify CO₂ emissions in Chengdu, a major city in southwestern China. The UAV-derived CO₂ concentration profiles were coupled with meteorological parameters to compute cross-sectional fluxes, yielding an annual emission estimate of 48.4 MtCO₂, which aligns well with census-based estimations. The primary uncertainty, approximately 23.61%, stems from meteorological parameter variations, highlighting the need for improved data resolution and extended observation periods. This study demonstrates that UAV-based mass balance observations can serve as an independent and verifiable approach for urban emission estimation. Beyond supplementing existing inventories, it provides a robust reference for cross-validation, contributing to the development of more accurate and adaptive emission monitoring systems for urban climate governance.