Based on the exposure data sets from the Tracking Air Pollution in China (TAP, http://tapdata.org.cn/), we characterized the spatiotemporal variations in PM2.5 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 PM2.5 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 PM2.5 concentration. It is also noticed that with smaller PM2.5 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 PM2.5-polluted days with the daily average PM2.5 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 PM2.5 exposure since 2018. Future air quality improvement strategies for the coordinated control of PM2.5 and O3 are urgently needed.
As the key for haze control, atmospheric fine particulate matter with aerodynamic diameter <2.5μm (or PM2.5) is of great concern lately in China. It is closely linked to fast pace of urbanization, ...industrialization and economic development, especially in eastern China. A good understanding of its sources is required for effective pollution abatement. Receptor models are one of the major methods for source apportionment used in China. The major objective of this study is to understand sources that contribute to fine particulate matter in China and key challenges in this area. Spatial distribution of fine particulate matter concentration, chemical composition and dominant sources in North and South China are summarized. Based on chemical speciation results from 31 cities and source apportionment results from 21 cities, it is found that secondary sources and traffic emission have higher contribution in South China while the percentage of coal combustion, dust and biomass burning to total PM2.5 are higher in North China. Source profiles established in China from 44 cities and areas are also summarized as references for future source apportionment studies. Suggestions for future research are also provided including methods for evaluating source apportionment results, ways for integrating multiple source apportionment methods, the need for standardizing protocols and developing tools for high-time resolution source apportionment.
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•Spatial distribution of chemical speciation, sources and source profiles in China are summarized.•In the south, contributions of secondary and traffic sources are higher while coal and dust are lower.•Future research needs for source apportionment in China are proposed.
Abstract
To control the spread of the 2019 novel coronavirus (COVID-19), China imposed nationwide restrictions on the movement of its population (lockdown) after the Chinese New Year of 2020, leading ...to large reductions in economic activities and associated emissions. Despite such large decreases in primary pollution, there were nonetheless several periods of heavy haze pollution in eastern China, raising questions about the well-established relationship between human activities and air quality. Here, using comprehensive measurements and modeling, we show that the haze during the COVID lockdown was driven by enhancements of secondary pollution. In particular, large decreases in NOx emissions from transportation increased ozone and nighttime NO3 radical formation, and these increases in atmospheric oxidizing capacity in turn facilitated the formation of secondary particulate matter. Our results, afforded by the tragic natural experiment of the COVID-19 pandemic, indicate that haze mitigation depends upon a coordinated and balanced strategy for controlling multiple pollutants.
Numerous previous studies have revealed that statistical models which combine satellite-derived aerosol optical depth (AOD) and PM2.5 measurements acquired at scattered monitoring sites provide an ...effective method for deriving continuous spatial distributions of ground-level PM2.5 concentrations. Using the national monitoring networks that have recently been established by central and local governments in China, we developed linear mixed-effects (LMEs) models that integrate Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements, meteorological parameters, and satellite-derived tropospheric NO2 column density measurements as predictors to estimate PM2.5 concentrations over three major industrialized regions in China, namely, the Beijing–Tianjin–Hebei region (BTH), the Yangtze River Delta region (YRD), and the Pearl River Delta region (PRD). The models developed for these three regions exploited different predictors to account for their varying topographies and meteorological conditions. Considering the importance of unbiased PM2.5 predictions for epidemiological studies, the correction factors calculated from the surface PM2.5 measurements were applied to correct biases in the predicted annual average PM2.5 concentrations introduced by non-stochastic missing AOD measurements. Leave-one-out cross-validation (LOOCV) was used to quantify the accuracy of our models. Cross-validation of the daily predictions yielded R2 values of 0.77, 0.8 and 0.8 and normalized mean error (NME) values of 22.4%, 17.8% and 15.2% for BTH, YRD and PRD, respectively. For the annual average PM2.5 concentrations, the LOOCV R2 values were 0.85, 0.76 and 0.71 for the three regions, respectively, whereas the LOOCV NME values were 8.0%, 6.9% and 8.4%, respectively. We found that the incorporation of satellite-based NO2 column density into the LMEs model contribute to considerable improvements in annual prediction accuracy for both BTH and YRD. The satisfactory performance of our models indicates that constructing LMEs models using various combinations of predictors for different regions would be helpful for predicting PM2.5 concentrations with high accuracy.
•We assessed the ground-level PM2.5 concentrations over BTH, YRD, and PRD in China.•Feasibility of LMEs models in different regions was confirmed.•AOD sampling biases were accounted by PM2.5 measurement-derived correction factor.•Satellite-based NO2 column improved annual prediction accuracy in BTH and YRD.
Direct emissions of air pollutants from the cement industry in China were estimated by developing a technology-based methodology using information on the proportion of cement produced from different ...types of kilns and the emission standards for the Chinese cement industry. Historical emissions of sulfur dioxide (SO₂), nitrogen oxides (NOX), carbon monoxide (CO), particulate matter (PM) and carbon dioxide (CO₂) were estimated for the years 1990–2008, and future emissions were projected up to 2020 based on current energy-related and emission control policies. Compared with the historical high (4.36Tg of PM₂.₅, 7.16Tg of PM₁₀ and 10.44Tg of TSP in 1997), PM emissions are predicted to drop substantially by 2020, despite the expected tripling of cement production. Certain other air pollutant emissions, such as CO and SO₂, are also predicted to decrease with the progressive closure of shaft kilns. NOX emissions, however, could increase because of the promotion of precalciner kilns and the rapid increase of cement production. CO₂ emissions from the cement industry account for approximately one eighth of China’s national CO₂ emissions. Our analysis indicates that it is possible to reduce CO₂ emissions from this industry by approximately 12.8% if advanced energy-related technologies are implemented. These technologies will bring co-benefits in reducing other air pollutants as well.
Aggressive emission control measures were taken by the Chinese government after the promulgation of the 'Air Pollution Prevention and Control Action Plan' in 2013. Here we evaluated the air quality ...and health benefits associated with this stringent policy during 2013-2015 by using surface PM2.5 concentrations estimated from a three-stage data fusion model and cause-specific integrated exposure-response functions. The population-weighted annual mean PM2.5 concentrations decreased by 21.5% over China during 2013-2015, reducing from 60.5 in 2013 to 47.5 μg m−3 in 2015. Subsequently, the national PM2.5-attributable mortality decreased from 1.22 million (95% CI: 1.05, 1.37) in 2013 to 1.10 million (95% CI: 0.95, 1.25) in 2015, which is a 9.1% reduction. The limited health benefits compared to air quality improvements are mainly due to the supralinear responses of mortality to PM2.5 over the high concentration end of the concentration-response functions. Our study affirms the effectiveness of China's recent air quality policy; however, due to the nonlinear responses of mortality to PM2.5 variations, current policies should remain in place and more stringent measures should be implemented to protect public health.
Air pollution has altered the Earth’s radiation balance, disturbed the ecosystem, and increased human morbidity and mortality. Accordingly, a full-coverage high-resolution air pollutant data set with ...timely updates and historical long-term records is essential to support both research and environmental management. Here, for the first time, we develop a near real-time air pollutant database known as Tracking Air Pollution in China (TAP, http://tapdata.org.cn/) that combines information from multiple data sources, including ground observations, satellite aerosol optical depth (AOD), operational chemical transport model simulations, and other ancillary data such as meteorological fields, land use data, population, and elevation. Daily full-coverage PM2.5 data at a spatial resolution of 10 km is our first near real-time product. The TAP PM2.5 is estimated based on a two-stage machine learning model coupled with the synthetic minority oversampling technique and a tree-based gap-filling method. Our model has an averaged out-of-bag cross-validation R 2 of 0.83 for different years, which is comparable to those of other studies, but improves its performance at high pollution levels and fills the gaps in missing AOD on daily scale. The full coverage and near real-time updates of the daily PM2.5 data allow us to track the day-to-day variations in PM2.5 concentrations over China in a timely manner. The long-term records of PM2.5 data since 2000 will also support policy assessments and health impact studies. The TAP PM2.5 data are publicly available through our website for sharing with the research and policy communities.
Air pollution is a worldwide environmental and health issue, especially in major developing countries. A recent World Health Organization report shows about 3 million deaths in the world in 2012 are ...due to ambient air pollution and China and India are the countries with the most severe challenge. Air pollution influences people's thought and experience of their lives directly by visual perceptions. This reduces people's subjective well-being (SWB) to a significant degree. Empirical researchers have made efforts to examine how self-reported well-being varies with air quality typically by survey method - matching SWB data with monitored air pollution data. Their findings show NO2, particles, lead, SO2 and O3 have significant negative impact on SWB. However, it is very hard to match air pollution characteristics from monitor stations with each respondent's state of SWB at the moment a survey is conducted. Also it is very hard to find the detailed trend impact from only air pollution factor on SWB. This review illustrates the features and limitations of previous survey studies on quantifying the effects of air pollution on subjective well-being. This review further displays the progress of psychophysics and its application in landscape and air quality research. We propose using psychophysics application to quantify air pollution impact on SWB.
•Satellite-based PM2.5 predictions at 1-km resolution during 2000–2018 were employed.•Clean air policies modified PM2.5 spatial patterns and pollution-economic relation.•Urban areas showed higher ...PM2.5 levels and greater PM2.5 reduction than rural areas.•Stricter emission control led to greater decline in urban-rural PM2.5 gap after 2013.
To improve air quality, China has been implementing strict clean air policies since 2013. These policies not only substantially improved air quality but may also modify the spatial distribution of air pollution, since urban emission sources were under stricter control and some were moved to rural regions with lower air quality improvement targets and lacking of monitoring. Here, we predicted satellite-based monthly PM2.5 concentrations during 2000–2018 at a 1-km resolution with complete spatial-temporal coverage to analyze changes in the spatial pattern of PM2.5 pollution in China. We found that the PM2.5 concentration in urban regions was higher than that in rural regions of the same city by an average of 3.3 μg/m3 during 2000–2018. This urban-rural disparity in PM2.5 concentration significantly increased from 2.5 μg/m3 in 2000 and peaked in 2007 of 3.8 μg/m3, then it sharply declined by 49% during 2013–2018 with the implementation of clean air policies. This shrinkage in the urban-rural PM2.5 gap was partly due to the 1.3 μg/m3 greater average decrease in the PM2.5 level in the urban region than in the rural region of the same town during 2013–2018 on average. We also observed that cities that started monitoring earlier experienced greater decreases in the urban-rural PM2.5 difference, and regions surrounding monitor showed significantly greater PM2.5 decrease than regions far away from monitor during 2013–2018. Additionally, clean air policies modified the relationship between PM2.5 concentrations and per capita gross domestic product (GDP), leading to a lower PM2.5 level with the same per capita GDP after 2013. Emissions in rural and suburban regions should be considered to further improve air quality in China.
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