Most cities in China are experiencing severe air pollution due to rapid economic development and accelerated urbanization. Long-term air pollution data with high temporal and spatial resolutions are needed to support research into physical and chemical processes that affect air quality, and the corresponding health risks. For the first time, data on PM₁₀, PM_2.5, SO₂, NO₂, O₃ and CO concentrations in 23 ambient air quality automatic monitoring stations and routine meteorological were collected between January 2014 and December 2016 to determine the spatial and temporal variation in these pollutants and influencing factors in Chengdu. The annual mean concentrations of PM_2.5 and PM₁₀ exceeded the standard of Chinese Ambient Air Quality and World Health Organization guidelines standards at all of the stations. The concentrations of PM₁₀, PM_2.5, SO₂ and CO decreased from 2014 to 2016, and the NO₂ level was stable, whereas the O₃ level increased markedly during this period. The air pollution characteristics in Chengdu showed simultaneously high PM concentrations and O₃. High PM concentrations were mainly observed in the middle region of Chengdu and may have been due to the joint effects of industrial and vehicle emissions. Ozone pollution was mainly due to vehicle emissions in the downtown area, and industry had a more important effect on O₃ in the northern area with fewer vehicles. The concentrations of PM₁₀, PM_2.5, NO₂ and CO were highest in winter and lowest in summer; the highest SO₂ concentration was also observed in winter and was lowest in autumn, whereas the O₃ concentration peaked in summer. Haze pollution can easily form under the weather conditions of static wind, low temperature and relative humidity, and high surface pressure inside Chengdu. In contrast, severe ozone pollution is often associated with high temperature.

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.