From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM _2.5 ) concentrations occurred nationwide. Here we estimate the drivers of the improved PM _2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population–weighted annual mean PM _2.5 concentrations decreased from 61.8 (95%CI: 53.3–70.0) to 42.0 µg/m ³ (95% CI: 35.7–48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM _2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM _2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9–7.1), 4.4- (95% CI: 3.8–4.9), 2.8- (95% CI: 2.5–3.0), and 2.2- (95% CI: 2.0–2.5) µg/m ³ declines in the national PM _2.5 concentration in 2017, respectively, and further reduced PM _2.5 -attributable excess deaths by 0.37 million (95% CI: 0.35–0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China’s recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

Although much attention has been paid to investigating and controlling air pollution in China, the trends of air-pollutant concentrations on a national scale have remained unclear. Here, we quantitatively investigated the variation of air pollutants in China using long-term comprehensive data sets from 2013 to 2017, during which Chinese government made major efforts to reduce anthropogenic emission in polluted regions. Our results show a significant decreasing trend in the PM_2.5 concentration in heavily polluted regions of eastern China, with an annual decrease of ∼7% compared with measurements in 2013. The measured decreased concentrations of SO₂, NO₂ and CO (a proxy for anthropogenic volatile organic compounds) could explain a large fraction of the decreased PM_2.5 concentrations in different regions. As a consequence, the heavily polluted days decreased significantly in corresponding regions. Concentrations of organic aerosol, nitrate, sulfate, ammonium and chloride measured in urban Beijing revealed a remarkable reduction from 2013 to 2017, connecting the decreases in aerosol precursors with corresponding chemical components closely. However, surface-ozone concentrations showed increasing trends in most urban stations from 2013 to 2017, which indicates stronger photochemical pollution. The boundary-layer height in capital cities of eastern China showed no significant trends over the Beijing–Tianjin–Hebei, Yangtze River Delta and Pearl River Delta regions from 2013 to 2017, which confirmed the reduction in anthropogenic emissions. Our results demonstrated that the Chinese government was successful in the reduction of particulate matter in urban areas from 2013 to 2017, although the ozone concentration has increased significantly, suggesting a more complex mechanism of improving Chinese air quality in the future.

The mixing layer is an important meteorological factor that affects air pollution. In this study, the atmospheric mixing layer height (MLH) was observed in Beijing from July 2009 to December 2012 using a ceilometer. By comparison with radiosonde data, we found that the ceilometer underestimates the MLH under conditions of neutral stratification caused by strong winds, whereas it overestimates the MLH when sand-dust is crossing. Using meteorological, PM_2.5, and PM₁₀ observational data, we screened the observed MLH automatically; the ceilometer observations were fairly consistent with the radiosondes, with a correlation coefficient greater than 0.9. Further analysis indicated that the MLH is low in autumn and winter and high in spring and summer in Beijing. There is a significant correlation between the sensible heat flux and MLH, and the diurnal cycle of the MLH in summer is also affected by the circulation of mountainous plain winds. Using visibility as an index to classify the degree of air pollution, we found that the variation in the sensible heat and buoyancy term in turbulent kinetic energy (TKE) is insignificant when visibility decreases from 10 to 5 km, but the reduction of shear term in TKE is near 70 %. When visibility decreases from 5 to 1 km, the variation of the shear term in TKE is insignificant, but the decrease in the sensible heat and buoyancy term in TKE is approximately 60 %. Although the correlation between the daily variation of the MLH and visibility is very poor, the correlation between them is significantly enhanced when the relative humidity increases beyond 80 %. This indicates that humidity-related physicochemical processes is the primary source of atmospheric particles under heavy pollution and that the dissipation of atmospheric particles mainly depends on the MLH. The presented results of the atmospheric mixing layer provide useful empirical information for improving meteorological and atmospheric chemistry models and the forecasting and warning of air pollution.

In January 2013, Beijing experienced several serious haze events. To achieve a better understanding of the characteristics, sources and processes of aerosols during this month, an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) was deployed at an urban site between 1 January and 1 February 2013 to obtain the size-resolved chemical composition of non-refractory submicron particles (NR-PM₁). During this period, the mean measured NR-PM₁ mass concentration was 89.3 ± 85.6 μg m⁻³, and it peaked at 423 μg m⁻³. Positive matrix factorization (PMF) differentiated the organic aerosol into five components, including a highly oxidized, low-volatility oxygenated organic aerosol (LV-OOA), a less oxidized, semi-volatile oxygenated OA (SV-OOA), a coal combustion OA (CCOA), a cooking-related OA (COA), and a hydrocarbon-like OA (HOA), which on average accounted for 28%, 26%, 15%, 20% and 11% of the total organic mass, respectively. A detailed comparison between the polluted days and unpolluted days found many interesting results. First, the organic fraction was the most important NR-PM₁ species during the unpolluted days (58%), while inorganic species were dominant on polluted days (59%). The OA composition also experienced a significant change; it was dominated by primary OA (POA), including COA, HOA and CCOA, on unpolluted days. The contribution of secondary OA (SOA) increased from 35% to 63% between unpolluted and polluted days. Second, meteorological effects played an important role in the heavy pollution in this month and differed significantly between the two types of days. The temperature and relative humidity (RH) were all increased on polluted days and the wind speed and air pressure were decreased. Third, the diurnal variation trend in NR-PM₁ species and OA components showed some differences between the two types of days, and the OA was more highly oxidized on polluted days. Fourth, the effects of air masses were significantly different between the two types of days; air was mainly transported from contaminated areas on the polluted days. The comparison also found that the aerosol was more acidic on polluted days. Additionally, the variation trends of the mass concentration and mass fractions of NR-PM₁ species and OA components were more dramatic when the NR-PM₁ mass loading was at a higher level. The serious pollution observed in this month can be attributed to the synergy of unfavorable meteorological factors, the transport of air masses from high-pollution areas, emission by local sources, and other factors.