Reducing PM2.5 pollution remains a great challenge during wintertime in Shanghai. Here, we investigated the long-term trends of aerosol composition and formation mechanism in the cold seasons of Shanghai from November 2014 to February 2020 using in-situ continuous hourly data. With the reduction of SO2 by 96% relative to 2014, the annual declines for SO2 and sulfate concentrations were 26.1% and 13.2%, respectively. In contrast to the great achievement in reducing SO2 emissions, the control of NOx was less effective because the effect of industrial denitrification on reducing NOx emissions was partially offset by the increase in the number of automobiles. As tracers of biomass burning and soil dust, the concentration of K+ and Ca2+ also declined quickly, indicating that open biomass burning and construction dust were effectively controlled. In contrast to a significant reduction of heavy and moderate pollution events, lightly polluted haze events remained high frequency in December and January, shedding light on pollution control in the future. The NO3–/SO42– ratio during the lightly polluted period was below1.3 in 2014/2015 while it climbed above 2.5 in 2019/2020, indicating a distinct transition from the equal contribution of coal combustion and automobile exhaust to haze formation in 2014/2015 to nitrate-dominated in 2019/2020. The impact of inter-regional transport on Shanghai's air quality has significantly weakened from 2014 to 2020. The contributions of local emissions and regional transport from the Channel of Suzhou-Wuxi-Changzhou to PM2.5 episodes have increased, suggesting that strengthening joint prevention and control in the Yangtze River Delta is the key to continuous improvement in air quality. NO2 was rapidly converted to nitrate under high relative humidity and an ozone-rich atmosphere, indicating that nighttime N2O5 hydrolysis dominated the nitrate explosive growth mechanisms. To coordinate the control of PM2.5 and O3 pollution, it is recommended to implement a renewable energy strategy for non-road mobile equipment in addition to promoting new energy vehicles.

Cities are major sources of anthropogenic carbon dioxide (CO₂) emissions, making the study of intra-urban CO₂ concentration patterns an emerging research priority. However, limited data availability and the complexity of urban environments have impeded detailed spatiotemporal analyses at the city scale. To address these challenges, an analysis supported by multi-source data and GeoAI methods is carried out to examine the spatial distribution, vertical variation, temporal dynamics, and driving factors of CO₂ concentrations in urban areas. We combined OCO-2 satellite-derived XCO₂ data (2014–2024) with ground-based measurements from the Shanghai Tower (August 2024 to March 2025), alongside meteorological and socioeconomic variables. The analysis employed spatial interpolation (inverse distance weighting), nonparametric testing (Mann–Whitney U test), time series decomposition, ordinary least squares (OLS) regression, and machine learning techniques including random forest and SHAP (SHapley Additive exPlanations) analysis. Results reveal that CO₂ concentrations are significantly higher in central urban districts compared to suburban areas, with notable spatial heterogeneity. Elevated levels were detected near ports and ferry routes, with airports and industrial emissions identified as principal contributors. Vertically, CO₂ concentrations decline with increasing altitude but exhibit a peak at mid-level heights. Temporally, a pronounced seasonal pattern was observed, characterized by higher concentrations in winter and lower levels in summer. Both OLS regression and machine learning models highlight proximity to emission sources, wind speed, and temperature as key determinants of spatial CO₂ variability, with these factors collectively explaining 67% of the variance in OLS models. This study demonstrates how multi-source data and advanced methods can capture the spatial, vertical, and seasonal dynamics and driving factors of urban CO₂ concentrations, offering insights for policy, planning, and mitigation.