Based on the exposure data sets from the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/), we characterized the spatiotemporal variations in PM_2.5 and O₃ exposures and quantified the long- and short-term exposure related premature deaths during 2013–2020 with respect to the two-stage clean air actions (2013–2017 and 2018–2020). We find a 48% decrease in national PM_2.5 exposure during 2013–2020, although the decrease rate has slowed after 2017. At the same time, O₃ pollution worsened, with the average April–September O₃ exposure increased by 17%. The improved air quality led to 308 thousand and 16 thousand avoided long- and short-term exposure related deaths, respectively, in 2020 compared to the 2013 level, which was majorly attributed to the reduction in ambient PM_2.5 concentration. It is also noticed that with smaller PM_2.5 reduction, the avoided long-term exposure associated deaths in 2017–2020 (13%) was greater than that in 2013–2017 (9%), because the exposure–response curve is nonlinear. As a result of the efforts in reducing PM_2.5-polluted days with the daily average PM_2.5 higher than 75 μg/m³ and the considerable increase in O₃-polluted days with the daily maximum 8 h average O₃ higher than 160 μg/m³, deaths attributable to the short-term O₃ exposure were greater than those due to PM_2.5 exposure since 2018. Future air quality improvement strategies for the coordinated control of PM_2.5 and O₃ are urgently needed.

PM_2.5 chemical components play significant roles in the climate, air quality, and public health, and the roles vary due to their different physicochemical properties. Obtaining accurate and timely updated information on China's PM_2.5 chemical composition is the basis for research and environmental management. Here, we developed a full-coverage near-real-time PM_2.5 chemical composition data set at 10 km spatial resolution since 2000, combining the Weather Research and Forecasting-Community Multiscale Air Quality modeling system, ground observations, a machine learning algorithm, and multisource-fusion PM_2.5 data. PM_2.5 chemical components in our data set are in good agreement with the available observations (correlation coefficients range from 0.64 to 0.75 at a monthly scale from 2000 to 2020 and from 0.67 to 0.80 at a daily scale from 2013 to 2020; most normalized mean biases within ±20%). Our data set reveals the long-term trends in PM_2.5 chemical composition in China, especially the rapid decreases after 2013 for sulfate, nitrate, ammonium, organic matter, and black carbon, at the rate of -9.0, -7.2, -8.1, -8.4, and -9.2% per year, respectively. The day-to-day variability is also well captured, including evolutions in spatial distribution and shares of PM_2.5 components. As part of Tracking Air Pollution in China (http://tapdata.org.cn), this daily-updated data set provides large opportunities for health and climate research as well as policy-making in China.

The contribution of meteorology and emissions to long-term PM_2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used the combination of a machine learning model, statistical method, and chemical transport model to quantify the meteorological impacts on PM_2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM_2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM_2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods to examine the meteorological contribution to PM_2.5: a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM_2.5 retrievals and the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. We found good agreements between GAM estimations and the CMAQ model estimations of the meteorological contribution to PM_2.5 on a monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM_2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PM_2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and when haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM_2.5 pollution across the North China Plain and central China but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM_2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PM_2.5 and gas precursors in these years. Although emissions dominated the long-term PM_2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM_2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., −6.3 % to 4.9 % of the annual mean PM_2.5 concentrations in the Beijing-Tianjin-Hebei region, −5.1 % to 4.3 % in the Fenwei Plain, −4.8 % to 4.3 % in the Yangtze River Delta, and −25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM_2.5 and the possible worsening trend of meteorological conditions in the northern part of China where air pollution is severe and population is clustered, stricter clean air actions are needed to avoid haze events in the future.

In response to the severe air pollution issue, the Chinese government implemented two phases (Phase I, 2013-2017; Phase II, 2018-2020) of clean air actions since 2013, resulting in a significant decline in fine particles (PM_2.5) during 2013-2020, while the warm-season (April-September) mean maximum daily 8 h average ozone (MDA8 O₃) increased by 2.6 μg m^-3 yr^-1 in China during the same period. Here, we derived the drivers behind the rising O₃ concentrations during the two phases of clean air actions by using a bottom-up emission inventory, a regional chemical transport model, and a multiple linear regression model. We found that both meteorological variations (3.6 μg m^-3) and anthropogenic emissions (6.7 μg m^-3) contributed to the growth of MDA8 O₃ from 2013 to 2020, with the changes in anthropogenic emissions playing a more important role. The anthropogenic contributions to the O₃ rise during 2017-2020 (1.2 μg m^-3) were much lower than that in 2013-2017 (5.2 μg m^-3). The lack of volatile organic compound (VOC) control and the decline in nitrogen oxides (NO_x) emissions were responsible for the O₃ increase in 2013-2017 due to VOC-limited regimes in most urban areas, while the synergistic control of VOC and NO_x in Phase II initially worked to mitigate O₃ pollution during 2018-2020, although its effectiveness was offset by the penalty of PM_2.5 decline. Future mitigation efforts should pay more attention to the simultaneous control of VOC and NO_x to improve O₃ air quality.

High spatial resolution PM_2.5 data covering a long time period are urgently needed to support population exposure assessment and refined air quality management. In this study, we provided complete-coverage PM_2.5 predictions with a 1 km spatial resolution from 2000 to the present under the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/, last access: 3 October 2022) framework. To support high spatial resolution modeling, we collected PM_2.5 measurements from both national and local monitoring stations. To correctly reflect the temporal variations in land cover characteristics that affected the local variations in PM_2.5, we constructed continuous annual geoinformation datasets, including the road maps and ensemble gridded population maps, in China from 2000 to 2021. We also examined various model structures and predictor combinations to balance the computational cost and model performance. The final model fused 10 km TAP PM_2.5 predictions from our previous work, 1 km satellite aerosol optical depth retrievals, and land use parameters with a random forest model. Our annual model had an out-of-bag R² ranging between 0.80 and 0.84, and our hindcast model had a by-year cross-validation R² of 0.76. This open-access, 1 km resolution PM_2.5 data product, with complete coverage, successfully revealed the local-scale spatial variations in PM_2.5 and could benefit environmental studies and policymaking.