Fine particulate matter with aerodynamic diameters ≤2.5 µm (PM_2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM_2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM_2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM_2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM_2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R²) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) µg m⁻³, a small mean absolute error of 6.69 (7.15) µg m⁻³ and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PM_2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM_2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R²=0.80), which can be used to estimate historical PM_2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM_2.5 dataset across mainland China (i.e., ChinaHighPM_2.5), important for air pollution studies focused on urban areas.

Atmospheric pollutants have both beneficial and detrimental effects on carbon uptake by land ecosystems. Surface ozone (O₃) damages leaf photosynthesis by oxidizing plant cells, while aerosols promote carbon uptake by increasing diffuse radiation and exert additional influences through concomitant perturbations to meteorology and hydrology. China is currently the world's largest emitter of both carbon dioxide and short-lived air pollutants. The land ecosystems of China are estimated to provide a carbon sink, but it remains unclear whether air pollution acts to inhibit or promote carbon uptake. Here, we employ Earth system modeling and multiple measurement datasets to assess the separate and combined effects of anthropogenic O₃ and aerosol pollution on net primary productivity (NPP) in China. In the present day, O₃ reduces annual NPP by 0.6 Pg C (14 %) with a range from 0.4 Pg C (low O₃ sensitivity) to 0.8 Pg C (high O₃ sensitivity). In contrast, aerosol direct effects increase NPP by 0.2 Pg C (5 %) through the combination of diffuse radiation fertilization, reduced canopy temperatures, and reduced evaporation leading to higher soil moisture. Consequently, the net effects of O₃ and aerosols decrease NPP by 0.4 Pg C (9 %) with a range from 0.2 Pg C (low O₃ sensitivity) to 0.6 Pg C (high O₃ sensitivity). However, precipitation inhibition from combined aerosol direct and indirect effects reduces annual NPP by 0.2 Pg C (4 %), leading to a net air pollution suppression of 0.8 Pg C (16 %) with a range from 0.6 Pg C (low O₃ sensitivity) to 1.0 Pg C (high O₃ sensitivity). Our results reveal strong dampening effects of air pollution on the land carbon uptake in China today. Following the current legislation emission scenario, this suppression will be further increased by the year 2030, mainly due to a continuing increase in surface O₃. However, the maximum technically feasible reduction scenario could drastically relieve the current level of NPP damage by 70 % in 2030, offering protection of this critical ecosystem service and the mitigation of long-term global warming.

The Aerosol Robotic Network (AERONET) has been providing high-quality retrievals of aerosol optical properties from the surface at worldwide locations for more than a decade. Many sites have continuous and consistent records for more than 10 years, which enables the investigation of long-term trends in aerosol properties at these locations. In this study, we present the results of a trend analysis at selected stations with long data records. In addition to commonly studied parameters such as aerosol optical depth (AOD) and Ångström exponent (AE), we also focus on inversion products including absorption aerosol optical depth (ABS), single-scattering albedo (SSA) and the absorption Ångström exponent (AAE). Level 2.0 quality assured data are the primary source. However, due to the scarcity of level 2.0 inversion products resulting from the strict AOD quality control threshold, we have also analyzed level 1.5 data, with some quality control screening to provide a reference for global results. Two statistical methods are used to detect and estimate the trend: the Mann–Kendall test associated with Sen's slope and linear least-squares fitting. The results of these statistical tests agree well in terms of the significance of the trend for the majority of the cases. The results indicate that Europe and North America experienced a uniform decrease in AOD, while significant (>90%) increases in these two parameters are found for North India and the Arabian Peninsula. The AE trends turn out to be different for North America and Europe, with increases for the former and decreases for the latter, suggesting opposite changes in fine/coarse-mode fraction. For level 2.0 inversion parameters, Beijing and Kanpur both experienced an increase in SSA. Beijing also shows a reduction in ABS, while the SSA increase for Kanpur is mainly due the increase in scattering aerosols. Increased absorption and reduced SSA are found at Solar_Village. At level 1.5, most European and North American sites also show positive SSA and negative ABS trends, although the data are more uncertain. The AAE trends are less spatially coherent due to large uncertainties, except for a robust increase at three sites in West Africa, which suggests a possible reduction in black carbon. Overall, the trends do not exhibit obvious seasonality for the majority of parameters and stations.

Summer precipitation plays critical roles in the energy balance and the availability of fresh water over eastern China. However, little is known regarding the trend in local‐scale precipitation (LSP). Here we developed a novel method to determine LSP events in the summer afternoon throughout eastern China from 1970 to 2010 based on hourly gauge measurements. The LSP occurrence hours decrease at an annual rate of 0.25%, which varies considerably by region, ranging from 0.14% over the Yangtze River Delta to 0.56% over the Pearl River Delta. This declining frequency of LSP is generally accompanied by an increase in rain rate of LSP but a decrease in visibility, whose linkage to LSP events was investigated. In particular, more LSP events tended to form when the atmosphere was slightly polluted. Afterward, LSP was suppressed. These findings have important implications for improving our understanding of the climatology of daytime precipitation at local scales.