Premature Mortality Attributable to Particulate Matter in China: Source Contributions and Responses to Reductions

Hu, Jianlin; Huang, Lin; Chen, Mindong; Liao, Hong; Zhang, Hongliang; Wang, Yafei; Zhang, Qiang; Ying, Qi

doi:10.1021/acs.est.7b03193

Cited by 164 publications

(95 citation statements)

References 55 publications

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“…The first such analysis used annual PM 2.5 ensemble predictions to assess the spatial distribution of excess mortality due to adult (> 30 years old) ischemic heart disease (IHD), cerebrovascular disease (CEV), chronic obstructive pulmonary disease (COPD), and lung cancer (LC) in China caused by PM 2.5 exposure (Hu et al, 2017a). Any health studies requiring human exposure infor-mation to different pollutants would benefit from this study.…”

Section: Ensemble Predictionsmentioning

confidence: 99%

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Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

Huang

et al. 2017

Atmos. Chem. Phys.

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Abstract. Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O 3 and PM 2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM 2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16) and MFE (0.26-0.31) of individual simulations. The ensemble annual daily maximum 1 h O 3 (O 3 -1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06-0.19 and MNE of 0.16-0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM 2.5 and O 3 -1h. The study demonstrates that ensemble predictions from combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories, and the results are publicly available for future health effect studies.

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Section: Ensemble Predictionsmentioning

confidence: 99%

“…Long-term exposure to such high levels of PM 2.5 greatly threatens public health in China. Recent studies have suggested that approximately more than 1 million premature deaths can be attributed to outdoor air pollution each year in China (Lelieveld et al, 2015;Liu et al, 2016;Hu et al, 2017a).…”

Section: Introductionmentioning

confidence: 99%

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

Huang

et al. 2017

Atmos. Chem. Phys.

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“…The impact of emissions from CPPs on air quality and health in China has been investigated in previous work [1,2,[15][16][17][18][19][20][21][22][23]. Early studies (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The following studies compared emission scenario simulations based on a real-world situation and an assumption that emission levels from CPPs are comparable with those from natural gas-fired plants, which enabled prediction of the potential air quality benefits of the ultra-low emissions standard [19,20]. Few studies have extended their analyses to health impacts [21][22][23]. To the best of our knowledge, premature mortality because of the power sector has only been quantified for the years prior to implementation of the ultra-low emission standard.…”

Section: Introductionmentioning

confidence: 99%

Air quality and health benefits of China’s emission control policies on coal-fired power plants during 2005–2020

Liu

Tong

et al. 2019

Environ. Res. Lett.

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Coal-fired power plants (CPPs) dominate China's energy supply systems. Over the past two decades, the explosive growth of CPPs has led to negative air quality and health impacts in China, and a series of control policies have been implemented to alleviate those impacts. In this work, by combining a CPPs emission database over China (CPED), a regional chemical transport model (WRF-CMAQ), and the integrated exposure-response model, we summarized historical and ongoing emission control policies on CPPs over China, investigated the air quality and health impacts of China's CPPs during 2005-2020, and quantified the benefits of each policy. We found that despite the 97.4% growth of coal-fired power generation during 2005-2015, PM 2.5 exposures caused by emissions from China's CPPs decreased from 9.0 μg m −3 in 2005 to 3.6 μg m −3 in 2015. The active emission control policies have decreased CPPs-induced PM 2.5 exposures by 10.0 μg m −3 during 2005-2015. We estimated that upgrading end-of-pipe control facilities and early retirement of small and low-efficiency units could respectively reduce PM 2.5 exposures by 7.9 and 2.1 μg m −3 during 2005-2015 and avoid 111 900 and 31 400 annual premature deaths. Since 2015, China's government has further required all CPPs to comply with the so-called 'ultra-low emission standards' before 2020 as a major component of China's clean air actions. If the policy is fully deployed, CPPs-induced PM 2.5 exposures could further decrease by 2.5 μg m −3 and avoid 43 500 premature deaths annually. Our study confirms the effectiveness of tailored control policies for China's CPPs and reveals that those policies have played important roles in air quality improvement in China.

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“…Due to the surge in air pollutant emissions, China frequently suffers serious haze events, causing millions of premature mortalities every year (Tao et al 2012, Hu et al 2017. These haze episodes are characterized by high concentrations of fine particulate matter (PM 2.5 ), which are primarily derived from emissions of primary PM 2.5 (Sun et al 2006) and chemical reactions of multiple precursors (such as sulfur dioxide (SO 2 ) and nitrogen oxide (NO x )) (Huang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

The roles of the metallurgy, nonmetal products and chemical industry sectors in air pollutant emissions in China

Yang

Liu

Wang

et al. 2018

Environ. Res. Lett.

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The metallurgy, nonmetal products and chemical industry sectors (MN&C) are the three major industrial sources of air pollutants in China. Previous studies have focused on calculating emissions from MN&C via production-based accounting and identifying the drivers of MN&C using consumption-based accounting. However, these previous studies did not discuss how the total outputs from MN&C upstream and downstream industries influence air pollutant emissions. In this study, we use a pure backward linkage (PBL) method to quantify emissions from industries upstream of MN&C and to evaluate how the downstream industries drive the outputs of MN&C in China. We find that the emissions from industries upstream of MN&C are generally higher than the production-based emissions of MN&C. In particular, the chemical industry largely increased its contribution to air pollution according to the PBL method. Furthermore, much of upstream emissions of MN&C are embodied in the interregional trade, transferring substantial emissions from the eastern region to inland provinces. Regarding the drivers of MN&C, our results reveal that consumption-based accounting underestimates the importance of many sectors. The outputs of these previously underestimated sectors could maximally cover 80% of metallurgy, 85% of nonmetal products and 25% of chemical industry outputs. We suggest that improving the structure of MN&C would considerably help decrease the environmental burden on the entire supply chain. Additionally, improving the productivities of MN&C downstream industries could also address the air pollution caused by MN&C.

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Premature Mortality Attributable to Particulate Matter in China: Source Contributions and Responses to Reductions

Cited by 164 publications

References 55 publications

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

Air quality and health benefits of China’s emission control policies on coal-fired power plants during 2005–2020

The roles of the metallurgy, nonmetal products and chemical industry sectors in air pollutant emissions in China

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