China's rapid industrialisation and urbanisation has led to poor air quality. The Chinese government have responded by introducing policies to reduce emissions and setting ambitious targets for ambient PM 2.5 , SO 2 , NO 2 and O 3 concentrations. Previous satellite and modelling studies indicate that concentrations of these pollutants have begun to decline within the last decade. However, prior to 2012, air quality data from ground-based monitoring stations were difficult to obtain, limited to a few locations in major cities, and often unreliable. Since then, a comprehensive monitoring network, with over 1000 stations across China has been established by the Ministry of Ecology and Environment (MEE). We use a three-year (2015-2017) dataset consisting of hourly PM 2.5 , O 3 , NO 2 and SO 2 concentrations obtained from the MEE, combined with similar data from Taiwan and Hong Kong. We find that at 53% and 59% of stations, PM 2.5 and SO 2 concentrations have decreased significantly, with median rates across all stations of −3.4 and −1.9 μg m −3 year −1 respectively. At 50% of stations, O 3 maximum daily 8 h mean (MDA8) concentrations have increased significantly, with median rates across all stations of 4.6 μg m −3 year −1 . It will be important to understand the relative contribution of changing anthropogenic emissions and meteorology to the changes in air pollution reported here.

The outbreak of Coronavirus Disease 2019 (COVID-19) in China in January 2020 prompted substantial control measures including social distancing measures, suspension of public transport and industry, and widespread cordon sanitaires (‘lockdowns’), that have led to a decrease in industrial activity and air pollution emissions over a prolonged period. We use a 5 year dataset from China’s air quality monitoring network to assess the impact of control measures on air pollution. Pollutant concentration time series are decomposed to account for the inter-annual trend, seasonal cycles and the effect of Lunar New Year, which coincided with the COVID-19 outbreak. Over 2015–2019, there were significant negative trends in particulate matter (PM2.5, −6% yr−1) and sulphur dioxide (SO2, −12% yr−1) and nitrogen dioxide (NO2, −2.2% yr−1) whereas there were positive trends in ozone (O3, + 2.8% yr−1). We quantify the change in air quality during the LNY holiday week, during which pollutant concentrations increase on LNY’s day, followed by reduced concentrations in the rest of the week. After accounting for interannual trends and LNY we find NO2 and PM concentrations were significantly lower during the lockdown period than would be expected, but there were no significant impacts on O3. Largest reductions occurred in NO2, with concentrations 27.0% lower on average across China, during the lockdown. Average concentrations of PM2.5 and PM10 across China were respectively 10.5% and 21.4% lower during the lockdown period. The largest reductions were in Hubei province, where NO2 concentrations were 50.5% lower than expected during the lockdown. Concentrations of affected pollutants returned to expected levels during April, after control measures were relaxed.

Abstract. To improve poor air quality in Asia and inform effective emission-reduction strategies, it is vital to understand the contributions of different pollution sources and their associated human health burdens. In this study, we use the WRF-Chem regional atmospheric model to explore the air quality and human health benefits of eliminating emissions from six different anthropogenic sectors (transport, industry, shipping, electricity generation, residential combustion, and open biomass burning) over South and East Asia in 2014. We evaluate WRF-Chem against measurements from air quality monitoring stations across the region and find the model captures the spatial distribution and magnitude of PM2.5 (particulate matter with an aerodynamic diameter of no greater than 2.5 µm). We find that eliminating emissions from residential energy use, industry, or open biomass burning yields the largest reductions in population-weighted PM2.5 concentrations across the region. The largest human health benefit is achieved by eliminating either residential or industrial emissions, averting 467 000 (95 % uncertainty interval (95UI): 409 000–542 000) or 283 000 (95UI: 226 000–358 000) annual premature mortalities, respectively, in India, China, and South-east Asia, with fire prevention averting 28 000 (95UI: 24 000–32 000) annual premature mortalities across the region. We compare our results to previous sector-specific emission studies. Across these studies, residential emissions are the dominant cause of particulate pollution in India, with a multi-model mean contribution of 42 % to population-weighted annual mean PM2.5. Residential and industrial emissions cause the dominant contributions in China, with multi-model mean contributions of 29 % for both sectors to population-weighted annual mean PM2.5. Future work should focus on identifying the most effective options within the residential, industrial, and open biomass-burning emission sectors to improve air quality across South and East Asia.

Abstract. Air pollution is a serious environmental issue and leading contributor to disease burden in China. Rapid reductions in fine particulate matter (PM2.5) concentrations and increased ozone concentrations occurred across China during 2015 to 2017. We used measurements of particulate matter with a diameter <2.5 µm (PM2.5) and ozone (O3) from more than 1000 stations across China along with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) regional air quality simulations, to explore the drivers and impacts of observed trends. The measured nationwide median PM2.5 trend of -3.4µgm-3yr-1 was well simulated by the model (-3.5µgm-3yr-1). With anthropogenic emissions fixed at 2015 levels, the simulated trend was much weaker (-0.6µgm-3yr-1), demonstrating that interannual variability in meteorology played a minor role in the observed PM2.5 trend. The model simulated increased ozone concentrations in line with the measurements but underestimated the magnitude of the observed absolute trend by a factor of 2. We combined simulated trends in PM2.5 concentrations with an exposure–response function to estimate that reductions in PM2.5 concentrations over this period have reduced PM2.5-attributable premature mortality across China by 150 000 deaths yr−1.

&lt;p&gt;Air pollution is a serious environmental issue and leading contributor to the disease burden in China. Following severe air pollution episodes during the 2012-2013 winter, the Chinese government has prioritised efforts to reduce PM&lt;sub&gt;2.5&lt;/sub&gt; emissions, and established a national monitoring network to record air quality trends. Rapid reductions in fine particulate matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) concentrations and increased ozone concentrations have occurred across China, during 2015 to 2017. We used measurements of particulate matter with a diameter &lt; 2.5 &amp;#181;m (PM&lt;sub&gt;2.5&lt;/sub&gt;) and Ozone (O&lt;sub&gt;3&lt;/sub&gt;) from &gt;1000 stations across China combined with similar datasets from Hong Kong and Taiwan to calculate trends in PM&lt;sub&gt;2.5&lt;/sub&gt;, Nitrogen Dioxide, Sulphur Dioxide and O&lt;sub&gt;3&lt;/sub&gt; across the greater China region during 2015-2019. We then use the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) regional air quality simulations, to explore the drivers and impacts of observed trends. Using annually varying emissions from the Multi-resolution Emission Inventory for China, we simulate air quality across China during 2015-2017, and calculate a median PM&lt;sub&gt;2.5&lt;/sub&gt; trends of -3.9 &amp;#181;g m&lt;sup&gt;-3&lt;/sup&gt; year&lt;sup&gt;-1&lt;/sup&gt;. The measured nationwide median PM&lt;sub&gt;2.5&lt;/sub&gt; trend of -3.4 &amp;#181;g m&lt;sup&gt;-3&lt;/sup&gt; year&lt;sup&gt;-&lt;/sup&gt;. With anthropogenic emissions fixed at 2015-levels, the simulated trend was much weaker (-0.6 &amp;#181;g m&lt;sup&gt;-3&lt;/sup&gt; year&lt;sup&gt;-1&lt;/sup&gt;), demonstrating interannual variability in meteorology played a minor role in the observed PM&lt;sub&gt;2.5&lt;/sub&gt; trend. The model simulated increased ozone concentrations in line with the measurements, but underestimated the magnitude of the observed absolute trend by a factor of 2. We combined simulated trends in PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations with an exposure-response function to estimate that reductions in PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations over this period have reduced PM&lt;sub&gt;2.5&lt;/sub&gt;-attribrutable premature morality across China by 150 000 deaths year&lt;sup&gt;-1&lt;/sup&gt;.&lt;/p&gt;

Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM2.5) concentrations across China.The emulators were optimised based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM2.5 concentrations for a given input configuration of emission changes. PM2.5 concentrations are primarily sensitive to residential (51−94% of first−order sensitivity index), industrial (7−31%), and agricultural emissions (0−24%). Sensitivities of PM2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM2.5 exposure for changes in the five emission sectors is by 68−81% down to 15.3−25.9 g m −3 , remaining above the World Health Organization annual guideline of 10 g m −3 . The greatest reductions in PM2.5 exposure are driven by reducing residential and industrial emissions, emphasising the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 g m −3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors.

Air pollution exposure is a key public health problem in China (GBD, 2019 Risk Factors Collaborators, 2020Yin et al., 2020). In recent years, particulate air quality has improved, primarily attributed to reductions in anthropogenic emissions (

Air pollution exposure is a leading public health problem in China. Despite recent air quality improvements, fine particulate matter (PM2.5) exposure remains large, the associated disease burden is substantial, and population ageing is projected to increase the susceptibility to disease. Here, we used emulators of a regional chemical transport model to quantify the impacts of future emission scenarios on air pollution exposure in China. We estimated how key emission sectors contribute to these future health impacts from air pollution exposure. We found that PM2.5 exposure declines in all scenarios across China over 2020–2050, with reductions of 15% under current air quality legislation, 36% when exploiting the full potential of air pollutant emission reduction technologies, and 39% when that technical mitigation potential is combined with emission controls for climate mitigation. However, population ageing means that the PM2.5 disease burden under current legislation increases by 17% in 2050 relative to 2020. In comparison to current legislation in 2050, the application of the best air pollution technologies provides substantial health benefits, reducing the PM2.5 disease burden by 16%, avoiding 536,600 (95% uncertainty interval, 95UI: 497,800–573,300) premature deaths per year. These public health benefits are mainly due to reductions in industrial (43%) and residential (30%) emissions. Climate mitigation efforts combined with the best air pollution technologies leads to an additional 2% reduction in the PM2.5 disease burden, avoiding 57,000 (95UI: 52,800–61,100) premature deaths per year. Up to 90% of the 2020–2050 reductions in PM2.5 exposure are already achieved by 2030, assuming efficient implementation and enforcement of currently committed air quality policies in key sectors. Achieving reductions in PM2.5 exposure and the associated disease burden after 2030 will require further tightening of emission limits for regulated sectors, addressing other sources including agriculture and waste management, and international coordinated action to mitigate air pollution across Asia.