With the intensification of global warming and economic development in China, the near-surface ozone (O₃) concentration has been increasing recently, especially in the Beijing-Tianjin-Hebei (BTH) region, which is the political and economic center of China. However, O₃ has been measured in real time only over the past few years, and the observational records are discontinuous. Therefore, we propose a new method (WRFC-XGB) to establish a near-surface O₃ concentration dataset in the BTH region by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) algorithm. Based on this method, the 8-h maximum daily average (MDA8) O₃ concentrations are obtained with full spatiotemporal coverage at a spatial resolution of 0.1° × 0.1° across the BTH region in 2018. Two evaluation methods, sample- and station-based 10-fold cross-validation (10-CV), are used to assess our method. The sample-based (station-based) 10-CV evaluation results indicate that WRFC-XGB can achieve excellent accuracy with a high coefficient of determination (R²) of 0.95 (0.91), low root mean square error (RMSE) of 13.50 (17.70) µg m⁻³, and mean absolute error (MAE) of 9.60 (12.89) µg m⁻³. In addition, superb spatiotemporal consistencies are confirmed for this model, including the estimation of high O₃ concentrations, and our WRFC-XGB model outperforms traditional models and previous studies in data mining. In addition, the proposed model can be applied to estimate the O₃ concentration when it has not been measured. Furthermore, the spatial distribution analysis of the MDA8 O₃ in 2018 reveals that O₃ pollution in the BTH region exhibits significant seasonality. Heavy O₃ pollution episodes mainly occur in summer, and the high O₃ loading is distributed mainly in the southern BTH areas, which will pose challenges to atmospheric environmental governance for local governments.

Much of the population is exposed to PM_2.5 (particulate matter) pollution in China, and establishing a high-precision PM_2.5 grid dataset will be very valuable for air pollution and related studies. However, limited by the traditional models themselves and input data sources, PM_2.5 estimations are of low accuracy with narrow spatial coverage. Therefore, we develop a new spatiotemporally weighted random forest (SWRF) model to improve the estimation accuracy and expand the spatial coverage of PM_2.5 concentrations using the latest release of the Visible infrared Imaging Radiometer (VIIRS) Deep Blue (DB) aerosol product, along with meteorological variables, and socioeconomic data. Compared with traditional methods and the results of previous similar studies, our satellite-derived PM_2.5 distribution shows better consistency with surface-measured records, having a high out-of-sample (out-of-station) cross-validation (CV) coefficient of determination (CV-R²), root mean squared error (RMSE), and mean absolute error (MAE) of 0.87 (0.85), 11.23 (11.53) μg m⁻³ and 8.25 (8.78) μg m⁻³, respectively. The monthly, seasonal, and annual mean PM_2.5 were also successfully captured (CV-R² = 0.91–0.92, RMSE = 4.35–6.72 μg m⁻³). Then, the spatial characteristics of PM_2.5 pollution in 2018 were investigated, showing that although air pollution has diminished in recent years, China still faces a high PM_2.5 pollution risk overall, especially in winter (average = 50.43 + 16.81 μg m⁻³). In addition, 19 provinces or administrative regions have annual PM_2.5 concentrations >35 μg m⁻³, particularly the Xinjiang Uygur Autonomous Region (~55.25 μg m⁻³), Tianjin (~49.65 μg m⁻³), and Henan Province (~48.60 μg m⁻³). Our estimated surface PM_2.5 concentrations are accurate, which could benefit further research on air pollution in China.

Using in-situ aircraft observations from six flights over Zhejiang on Sep. 1 and Sep. 4, 2016, this study investigates differences in aerosol and cloud properties between daytime and nighttime. The samples were divided into marine type and continental type based on the backward air mass trajectories and aerosol characteristics. The results show that the aerosol number concentration (Na) near the ground during daytime is higher than that at nighttime. During daytime, Na has a significant decreasing trend near the top of the planetary boundary layer (PBL), which is not obvious during nighttime. There may be still a relative high concentration of aerosols remaining in the transition zone between the PBL and the free troposphere. Under similar liquid water content (LWC) conditions, the cloud droplet number concentration (Nc) at night is lower, and the cloud droplet effective diameter (cloud ED) is larger. The total Na of marine type aerosols is generally lower than that of continental type aerosols, but for aerosols with particle diameters greater than 1 μm, the marine type aerosols are higher. The study shows a strong negative Na-cloud ED relationship for marine type aerosols, but no obvious Na-cloud ED relationship for continental type aerosols. The number of cloud condensation nuclei (CCN) is higher under high-Na conditions; the ratio of CCN to Na reveals that the activation efficiency of marine type aerosols is higher than that of continental type aerosols. There is no obvious difference in activation efficiency between day and night.

Aerosols exert significant impacts on Earth’s radiation balance through direct and indirect effects, with the latter representing the largest uncertainty in current climate models. To clarify aerosol–cloud interactions over China, this study synergized MODIS satellite retrievals (2015–2020) with in-situ MA60 aircraft observations across six representative regions. Satellite data provided aerosol optical depth (AOD), cloud optical depth (COD), cloud effective radius (CER), and cloud phase, while aircraft measurements delivered vertical profiles and microphysical properties of aerosols and cloud droplets. Results show that AOD exhibits a “high east, low west” pattern, with hotspots in the North China Plain and Sichuan Basin, and a significant decreasing trend over polluted regions. Cloud phase is spatially heterogeneous, with water clouds dominating the southeast and ice clouds prevailing in the northwest (>85% over the Tibetan Plateau). Water cloud COD shows a southeast-high–northwest-low distribution, with a clear inverse CER-COD correlation. Aircraft data reveal that polluted northern sites have high near-surface aerosol concentrations with small effective diameters (~0.3 μm), while cloud droplet number concentration and size spectra vary markedly across regions. These findings provide a robust observational foundation for improving aerosol–cloud interaction parameterizations in climate models.