ENVIRONMENTAL STUDIES Copyright © 2020 Residential …and outdoor). The term “clean energy” is defined as the natural gas, LPG (liquefied petroleum gas), biogas, and electricity

Yun et al., Sci. Adv. 2020; 6 : eaba7621 28 October 2020

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Residential solid fuel emissions contribute significantly to air pollution and associated health impacts in ChinaXiao Yun, Guofeng Shen, Huizhong Shen, Wenjun Meng, Yilin Chen, Haoran Xu, Yuang Ren, Qirui Zhong, Wei Du, Jianmin Ma, Hefa Cheng, Xilong Wang, Junfeng Liu, Xuejun Wang, Bengang Li, Jianying Hu, Yi Wan, Shu Tao*

Residential contribution to air pollution–associated health impacts is critical, but inadequately addressed be-cause of data gaps. Here, we fully model the effects of residential energy use on emissions, outdoor and indoor PM2.5 concentrations, exposure, and premature deaths using updated energy data. We show that the residential sector contributed only 7.5% of total energy consumption but contributed 27% of primary PM2.5 emissions; 23 and 71% of the outdoor and indoor PM2.5 concentrations, respectively; 68% of PM2.5 exposure; and 67% of PM2.5-induced premature deaths in 2014 in China, with a progressive order of magnitude increase from sources to receptors. Biomass fuels and coal provided similar contributions to health impacts. These findings are particularly true for rural populations, which contribute more to emissions and face higher premature death risks than urban populations. The impacts of both residential and nonresidential emissions are interconnected, and efforts are necessary to simultaneously mitigate both emission types.

INTRODUCTIONThere is growing evidence suggesting that indoor combustion emis-sions have a notable impact on air quality and human health. A previous estimate indicates that in China, ~30% of ambient air pollution–associated premature deaths in 2010 were attributable to residential emissions (1). A recent study found that in 2012, rural residential emissions contributed to nearly 20% of ambient PM2.5 (e.g., particulate matter with aerodynamic sizes less than 2.5 m) exposure in China (2). While the importance of residential emis-sions has been recognized, results from these studies vary greatly, largely because of a lack of detailed and reliable data for residential energy use (2).

Moreover, many results are based on ambient PM2.5 concen-trations (1, 2), thus preventing a full understanding of this issue when indoor air exposures are excluded because people spend most of their time indoors (3–5). According to the latest Global Burden of Disease (GBD) report, 24% of PM2.5-associated premature deaths in China were attributed to household sources/exposures in 2017 (6). Recently, Chen et al. (7) included household heating emissions in their model and obtained a higher value. Recently, detailed first-hand data for energy types and activities, including stove locations, which were obtained from a nationwide residential energy survey (8, 9), have provided a unique opportunity to fully address this issue.

Here, we present the results of sequential modeling to track the contributions of residential energy use to emissions, indoor and outdoor PM2.5 concentrations, PM2.5 exposures, and PM2.5-associated premature deaths. The residential effects are categorized by energy type (e.g., coal, biomass, and clean energy), activity (e.g., cooking and heating), area (e.g., urban and rural), and location (e.g., indoor and outdoor). The term “clean energy” is defined as the natural gas, LPG (liquefied petroleum gas), biogas, and electricity used in resi-

dences, which emit relatively lower pollutant levels in households compared to solid fuels. All nonresidential sources are grouped together for comparison. A number of emission reduction scenarios are modeled to evaluate the effectiveness of the mitigation options. The detailed methods are presented in Materials and Methods.

RESULTSAmplified residential contributionsThe contributions of nonresidential and residential activities to energy use, primary PM2.5 emissions, ambient and indoor PM2.5 concentrations, exposures, and premature deaths (in order) are summarized in Fig. 1. For all factors, the total/average values (num-bers on the left), detailed residential energy types, activities, and indoor and outdoor PM2.5 concentrations are presented separately. SO2 (sulfur dioxide) emissions are also shown as a representative secondary aerosol precursor in addition to primary PM2.5. Urban and rural populations are addressed individually, and fluxes along the path from energy usage to premature deaths are marked. As shown, the contributions of residential solid fuels are amplified from energy consumption to premature deaths. In 2014, direct res-idential energy accounted for only 7.5% of total energy consump-tion in China in terms of joules. Solid fuels accounted for 88% of the total residential energy use. In terms of the percentage of use, solid fuels accounted for 41% of cooking and 82% of heating energy usage in rural China in 2014 (9).

Because the emission factors (EFs; e.g., quantities of pollutants emitted per unit of energy consumed) of primary PM2.5 for residen-tial stoves are orders of magnitude higher than those for other activ-ities (10), a 7.5% residential contribution to energy consumption led to a 27% contribution to primary PM2.5 emissions. Despite the fact that the residential sector contributed relatively low emissions of secondary aerosol precursors such as SO2 (7.5%), the contribu-tions from this sector to PM2.5 concentrations in the air were high. In terms of annual mean values, 23 and 71% of PM2.5 in ambient and indoor air were from this sector, respectively. These high con-tributions to indoor air pollution are due to emissions coming

College of Urban and Environmental Sciences, Laboratory for Earth Surface Pro-cesses, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China.*Corresponding author. Email: [email protected]

Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

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directly from stoves (11). The results of many field surveys have confirmed this finding and have revealed that PM2.5 concentrations in households using solid fuels can be very high (7, 12). For house-holds using clean energy, indoor PM2.5 is mainly originated from

the ambient air and thus leads to positive correlations between indoor and ambient PM2.5 concentrations (13, 14). By classify-ing the households into two categories (e.g., using solid or clean energy) and incorporating detailed information on stove locations

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Fig. 1. Relative contributions of residential and nonresidential energy consumption to the emissions of primary PM2.5 and SO2 (as a representative secondary aerosol precursor), indoor/outdoor PM2.5 concentrations, population exposure to PM2.5, and premature deaths associated with PM2.5 exposure. The emissions of both primary PM2.5 and SO2 are shown. Residential energy sources were further categorized into coal, biomass fuels, and clean energy for cooking and heating. Urban and rural usages are separated as both sources and receptors. The quantified contributions from one step to the next are shown as numbered arrows. Total quantities are shown in the centers of the pie charts. The relative contributions by the residential sector are presented for the individual steps on the right side.


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(Materials and Methods), the average indoor PM2.5 concentration in Chinese households (73 ± 34 g/m3) was determined to be three times that for outdoor air (22 ± 16 g/m3).

With both the quantification of ambient and indoor air PM2.5 concentrations, the average exposure of the entire population to PM2.5 was calculated as 69 ± 30 g/m3, which led to 1,150,000 (630,000 to 1,770,000 by using an uncertainty of 95%) premature deaths in 2014. Residential emissions accounted for 68% of the ex-posure and 67% of premature deaths. This value is much higher than the 24% (270,000 of 1,120,000) estimated by the latest GBD study for household contributions (6) mainly because updated resi-dential energy data, including heating data, were used. Chen et al. (7) used a similar approach but without stove locations or outdoor- to-indoor penetration and obtained a higher estimate of 670,000 to 930,000 (by using an uncertainty of 95%) premature deaths in rural China in 2010. This result is significantly higher than the 43% value for total premature deaths derived by Zhao et al. (15), which is likely due to the different methods (e.g., perturbation-normalization approach versus elimination method) used to calculate premature deaths.

Indoor exposure dominationBecause people spend the most time indoors, indoor exposure dominates the total PM2.5-associated premature deaths (91%). This trend was particularly true for rural areas (94%) where PM2.5 levels in the indoor air (95 ± 34 g/m3) were significantly higher than those in urban indoor air (58 ± 23 g/m3) because of the strong dependence of rural residents on solid fuels. For total indoor PM2.5 levels, 85 and 55% were from residential emissions in rural and ur-ban areas, respectively. In contrast, ambient PM2.5, which is mainly from nonresidential sources (77%), was higher in urban (45 ± 19 g/m3) than in rural areas (22 ± 15 g/m3). The differences between rural and urban areas are shown in Fig. 2 as cumulative frequency distributions of the exposures associated with residential (solid lines) and all (dashed lines) emissions. The percentage of the popu-lation with annual mean exposure originating from only residential sources exceeded the Chinese national ambient air standard (16) of 35 g/m3 by 35 and 84% in urban and rural areas, respectively, which is also the first World Health Organization (WHO) interim

target (17). Although these percentages are significantly higher (81 and 95%) if all sources are considered, residential sources con-tributed to a notable fraction of the total.

All-source PM2.5-associated premature deaths totaled 640,000 (370,000 to 980,000 by using an uncertainty of 95%) and 510,000 (260,000 to 790,000 by using an uncertainty of 95%) in rural and urban areas, respectively. Because the urban population (0.736 billion) is larger than the rural population (0.618 billion), the overall risk, which is defined as the probability of premature death, for the rural population (1.0 × 10−3) was 45% higher than that of the urban pop-ulation (6.9 × 10−4). If only residential emissions are considered, the risks in the rural and urban areas are 8.5 × 10−4 and 3.4 × 10−4, re-spectively, and thus show a 60% difference. Although residential emissions are localized, cross-influences between rural and urban areas do occur because of atmospheric transport. The contributions of rural (5.2 ± 3.9 g/m3) and urban (5.0 ± 6.5 g/m3) residential emissions to urban ambient PM2.5 levels were nearly the same, which can be explained by the fact that the majority of cities are sur-rounded by rural villages with high population densities. Therefore, any effort to reduce emissions from rural residential sources would improve air quality in both rural and urban areas.

Premature deaths associated with residential emissions vary extensively in space (Fig. 3) for both urban (the symbols have areas proportional to the population) and rural (shaded background) areas. The most-influenced areas were the Sichuan Basin and the Northern Plain; by contrast, the risks associated with residential emissions in the southeastern coastal provinces were much lower.

High contributions of biomass fuels and cookingAmong the 770,000 (430,000 to 1,170,000 by using an uncertainty of 95%) total premature deaths associated with residential solid fuels in China, coal (52%) and biomass fuels (48%) contributed equally if

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Fig. 2. Cumulative distributions of the annual mean population exposures to PM2.5 originating from residential sources and all sources in urban and rural China in 2014. The WHO air quality guideline (AQG) and interim targets 1 to 3 (IT-1, IT-2, and IT-3) (17), which are also applicable to indoor air, are shown as vertical lines. IT-1 (35 g/m3) is the same as the Chinese national ambient air standard (16).

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Fig. 3. Spatial distributions of premature deaths per square kilometer associ-ated with residential emissions of both primary PM2.5 and secondary PM2.5 precursors at the municipality level. The number of premature deaths per square kilometer in rural areas is shown with shaded blue backgrounds, and that in urban areas is shown as shaded circles; the areas of the symbols are proportional to the urban populations.


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model uncertainties are considered. Notable spatial variations in the relative contributions of the two fuel categories are shown in Fig. 4 (A and B). The patterns for coal were similar between urban and rural areas and showed significant correlations (P



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emissions (0.24:1). A detailed analysis of residential energy types, activities, and areas was conducted, and the results are shown in fig. S2 (D to F, respectively). Because coal and biomass fuels contributed nearly equally to PM2.5 exposure, the best way is to co-reduce these emissions at similar rates (e.g., 1.1:1). Compared to cooking, heat-ing should be emphasized (e.g., 1.5:1). Last, more efforts for reduc-ing emissions in rural areas (e.g., 2.4:1) can lead to better results. Note that this is only a technical approach and a realistic policy strategy needs to include socioeconomic aspects, which are often more important. Therefore, a full strategy should also be based on cost-benefit and social feasibility analyses. In addition, the calcula-tions are based on exposures instead of health consequences, and the nonlinear relationship between exposure and health impacts can lead to additional uncertainty.

DISCUSSIONResidential energy usage can cause air pollutant emissions, indoor and ambient air pollution, population exposure, and adverse health impacts associated with PM2.5. Although some individual steps of the process leading from energy use to health consequences have been investigated previously, in this study, a systematic approach connecting energy, emissions, air pollution, and health outcomes with quantification of the contributions of various residential energy types provides consistency in methodology and allows uncertainty analysis. We report that the magnification of the relative contributions of residential solid fuels was quantified and show that 7.5% of energy use in residential sector can lead to 67% of premature deaths. The methodology used has the potential to be applied to assess environ-mental, health, and climate influences of energy use in other sectors.

Although biomass fuels are extensively used in the rural residen-tial sector, adverse impacts on air quality and health associated with biomass use have been overlooked in comparison with coal (18). One reason is that the use of biomass fuels is relatively evenly dis-tributed in space and across seasons, whereas coal heating is often associated with severe pollution episodes in the heating season in northern China (19). We also determined the relative contributions of indoor and outdoor exposure, cooking and heating, and coal and biomass fuels. Our results indicate that biomass fuels contributed 32% of the overall premature deaths. Similarly, we also revealed the similar importance of cooking and heating in terms of health im-pacts, whereas the impacts of cooking are not covered by existing mitigation plans (20, 21).

By including both indoor and outdoor exposures, we also distin-guished the relative contributions of the two different paths on health impacts and found that indoor exposure is more important than outdoor exposure. By using a systematic approach with an indoor-outdoor exchange incorporated in the modeling, we demon-strated that the relative contribution of indoor exposure (91%) to total premature deaths was previously underestimated. We also found that compared with urban populations, rural populations contributed more to the emissions but were also affected more by the pollution generated.

MATERIALS AND METHODSEnergy and emissionsThe study year was 2014, which happened to be the turning point when a series of air pollution mitigation actions were launched and

the ambient PM2.5 concentration began to decline in China. Rural residential energy data from 2014 were previously derived (9) by extrapolating the results of a nationwide survey conducted in 2012 (8). Detailed energy types, including coal (e.g., bituminous coal and honeycomb briquette), charcoal, wood (e.g., fuel wood and brush wood), corncobs, crop residues (excluding corncobs), LPG, biogas, and electricity (for heaters, kettles, rice cookers, and induction stoves), were available (9). Urban residential energy data compiled by Shen et al. (22) were adopted. Detailed residential energy types for various areas (e.g., rural and urban) and activities (e.g., cooking and heating) are presented in table S1.

The emissions of primary PM2.5 and PM10 (e.g., particulate mat-ter with an aerodynamic size less than 10 m), black carbon, organic carbon, CO (carbon monoxide), NH3 (ammonia), SO2, and NOx (nitrogen oxides) from the residential sector in 2014 were calculated by multiplying the quantities of energy consumed by EFs. Values of EFs were from a database used to compile the Peking University (PKU) emission database (23). Emissions from all other sectors were directly extracted from the PKU emission database and distinguished between urban and rural areas based on an urban mask developed by Shen et al. (22). The emissions of non-combustion NH3 and non-methane volatile organic compounds were from EDGAR (Emission Database for Global Atmospheric Research) (24) and HTAP (Hemispheric Transport of Air Pollution) (25).

Ambient and indoor PM2.5The weather research and forecasting model coupled with chemis-try (WRF/Chem) version 3.5 (26) was used with a horizontal reso-lution of 50 km by 50 km and with 5-min time steps to model PM2.5 in the ambient air. The model domain covered mainland China and surrounding areas (e.g., 13°N to 56°N, 67°E to 143°E). The meteo-rological field data were obtained from the National Centers for Environmental Prediction Final Operational Global Analysis data (27). WRF-modeled meteorological fields were evaluated using ob-servations from the China Earth International Exchange Stations (28) (fig. S3). The modeled PM2.5 concentrations were downscaled using a Gaussian downscaling method (22) based on the PM2.5 emission inventory and wind fields. The downscaled modeled PM2.5 concentrations were evaluated using PM2.5 observations collected at 708 sites in 126 Chinese municipalities (figs. S4 and S5) (29) and PM2.5 observations from sites at the American embassy and consul-ates (fig. S6) (30). To distinguish the contributions of various sources, a perturbation method was applied by running the model repeatedly with a 20% emission reduction for an individual source in each run as well as a base run using the total emissions from all sources (31, 32). The outputs of the individual runs were normalized to al-low the sums of these runs to be equal to those from the base run (31, 32).

The PM2.5 concentrations in indoor air were calculated by cate-gorizing households into those with solid fuel use and those without such use based on the time-sharing fractions of solid fuel consump-tion and stove locations (8). For the former, the annual mean in-door PM2.5 concentrations were derived from the literature for coal, biomass fuels, and clean energy users in kitchens, living rooms, and other indoor microenvironments for the heating and nonheating seasons (7). For households without solid fuel use, the indoor PM2.5 concentrations were obtained based on the ambient air concentra-tions and infiltration factors derived on the basis of the method of Xiang et al. (33).


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Exposure and premature deathsPopulation exposures to PM2.5 were calculated for each grid cell based on the modeled ambient and indoor PM2.5 concentrations, population densities, and average time people spent indoors and outdoors in the heating and nonheating seasons in six regions of China, namely, northern, eastern, southern, northwestern, north-eastern, and southwestern China (3–5, 34); in addition, stove loca-tions were obtained from the same database that was used for residential energy use in 2012 (8). The entire population was classi-fied into four age groups, namely, 0 to 4, 5 to 14, 15 to 64, and >64 years for males and females. The premature deaths associated with PM2.5 exposure were derived for five diseases, namely, acute lower respiratory infections for children, lung cancer, ischemic heart disease, cerebrovascular disease (stroke), and chronic obstruc-tive pulmonary disease, which were based on the latest hazard ratios and the integrated exposure-response (IER) functions established by Cohen et al. (35). Background disease burdens at the provincial level were based on a GBD study (36) and were scaled to the latest GBD background disease burdens in China (37); these were catego-rized into urban and rural populations (38). A detailed calculation is presented in Supplementary Materials and Methods. To distin-guish the contributions of various energy-activity combinations, which are not linearly additive, both the exposures and health con-sequences, as represented by premature deaths, were quantified separately; the results were then normalized to allow the sum of all individual impacts to equal the total impact. This “normalized mar-ginal” approach can appropriately isolate contributions of the stud-ied source/sector to the others (31, 32, 39); however, its results may be different from those by using other methods (e.g., zeroed-out, source tracking), and the uncertainties in the following analysis should be addressed in the future. Emissions from household elec-tricity use do not occur onsite and were not covered in this study.

To evaluate the benefits of emission reductions associated with residential or nonresidential sources and their interactions, a set of 36 simulations was conducted and the two sources were reduced by 0, 20, 40, 60, 80, and 100% individually. The changes in population exposure, outdoor and indoor PM2.5 concentrations, and penetra-tion of PM2.5 from the ambient air to indoor environment were compared among the scenarios. Similarly, the consequences of res-idential emission reductions were compared for coal and biomass fuels, cooking and heating, and urban and rural areas. For each comparison, 36 scenarios were simulated with the emissions from the two sources set to 0, 20, 40, 60, 80, and 100% individually. The changes in population exposures were compared to identify the optimal reduction pathways.

Uncertainty analysisMonte Carlo simulations were performed 1000 times to address model uncertainties from emission calculations to premature deaths. For emissions, the coefficients of variation (CVs) for activity inten-sities and EFs (log-transformed) were from the PKU inventories (23). For the time people spent indoors, CVs of 5% were used on the basis of the method of Chen et al. (7). For the infiltration factors in indoor/outdoor air exchange and the amount of time windows are opened or closed, the CVs were from the Ministry of Environmen-tal Protection (3). For indoor PM2.5 concentrations in households using solid fuels, the CVs of log-transformed PM2.5 concentrations were derived by Chen et al. (7) on the basis of 1821 observations collected from the literature, and the mean and SD of the CVs for

various fuels-microenvironment-season combinations were 14 ± 16%, suggesting that the overall uncertainty of the calculated indoor PM2.5 concentrations and, consequently, exposure and premature deaths was likely from these data sources. More measurements are recommended in the future to improve the estimation. Because Monte Carlo simulations could not be conducted for atmospheric chemical transport modeling because of the high computational load, CVs of 10% were assumed for gridded ambient air PM2.5 (40). The distributions of parameters in the dose-response curves of pre-mature death models were from the IER (35).

SUPPLEMENTARY MATERIALSSupplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/44/eaba7621/DC1

REFERENCES AND NOTES 1. J. Lelieveld, J. S. Evans, M. Fnais, D. Giannadaki, A. Pozzer, The contribution of outdoor air

pollution sources to premature mortality on a global scale. Nature 525, 367–371 (2015). 2. G. F. Shen, M. Y. Ru, W. Du, X. Zhu, Q. R. Zhong, Y. L. Chen, H. Z. Shen, X. Yun, W. J. Meng,

J. F. Liu, H. F. Cheng, J. Y. Hu, D. B. Guan, S. Tao, Impacts of air pollutants from rural Chinese households under the rapid residential energy transition. Nat. Commun. 10, 3405 (2019).

3. Ministry of Environmental Protection, Exposure Factors Handbook of Chinese Population (Adult volume) (China Environmental Science Press, 2013).

4. Ministry of Environmental Protection, Exposure Factors Handbook of Chinese Population (Children 0–5 years old volume) (China Environmental Science Press, 2016).

5. Ministry of Environmental Protection, Exposure Factors Handbook of Chinese Population (Children 6–17 years old volume) (China Environmental Science Press, 2016).

6. Institute for Health Metrics and Evaluation, Global Burden of Disease Compare and Data Visualizations; vizhub.healthdata.org/gbd-compare (2017).

7. Y. Chen, H. Shen, K. R. Smith, D. Guan, Y. Chen, G. Shen, J. Liu, H. Cheng, E. Y. Zeng, S. Tao, Estimating household air pollution exposures and health impacts from space heating in rural China. Environ. Int. 119, 117–124 (2018).

8. S. Tao, M. Y. Ru, W. Du, X. Zhu, Q. R. Zhong, B. G. Li, G. F. Shen, X. L. Pan, W. J. Meng, Y. L. Chen, H. Z. Shen, N. Lin, S. Su, S. J. Zhuo, T. B. Huang, Y. Xu, X. Yun, J. F. Liu, X. L. Wang, W. X. Liu, H. F. Cheng, D. Q. Zhu, Quantifying the rural residential energy transition in China from 1992 to 2012 through a representative national survey. Nat. Energy 3, 567–573 (2018).

9. X. Zhu, X. Yun, W. Meng, H. Xu, W. Du, G. Shen, H. Cheng, J. Ma, S. Tao, Stacked use and transition trends of rural household energy in mainland China. Environ. Sci. Technol. 53, 521–529 (2019).

10. Y. Huang, H. Shen, H. Chen, R. Wang, Y. Zhang, S. Su, Y. Chen, N. Lin, S. Zhuo, Q. Zhong, X. Wang, J. Liu, B. Li, W. Liu, S. Tao, Quantification of global primary emissions of PM2.5, PM10, and TSP from combustion and industrial process sources. Environ. Sci. Technol. 48, 13834–13843 (2014).

11. J. J. Zhang, K. R. Smith, Household air pollution from coal and biomass fuels in China: Measurements, health impacts, and interventions. Environ. Health Perspect. 115, 848–855 (2007).

12. W. Du, X. Y. Li, Y. C. Chen, G. F. Shen, Household air pollution and personal exposure to air pollutants in rural China—A review. Environ. Pollut. 237, 625–638 (2018).

13. Y. Han, M. Qi, Y. Chen, H. Shen, J. Liu, Y. Huang, H. Chen, W. Liu, X. Wang, J. Liu, B. Xing, S. Tao, Influences of ambient air PM2.5 concentration and meteorological condition on the indoor PM2.5 concentrations in a residential apartment in Beijing using a new approach. Environ. Pollut. 205, 307–314 (2015).

14. M. Qi, X. Zhu, W. Du, Y. Chen, Y. Chen, T. Huang, X. Pan, Q. Zhong, X. Sun, E. Y. Zeng, B. Xing, S. Tao, Exposure and health impact evaluation based on simultaneous measurement of indoor and ambient PM2.5 in Haidian, Beijing. Environ. Pollut. 220, 704–712 (2017).

15. B. Zhao, H. Zheng, S. Wang, K. R. Smith, X. Lu, K. Aunan, Y. Gu, Y. Wang, D. Ding, J. Xing, X. Fu, X. Yang, K.-N. Liou, J. Hao, Change in household fuels dominates the decrease in PM2.5 exposure and premature mortality in China in 2005-2015. Proc. Natl. Acad. Sci. U.S.A. 115, 12401–12406 (2018).

16. Ministry of Environmental Protection, Ambient Air Quality Standard GB3095–2012 (China Environmental Science Press, 2012).

17. World Health Organization, WHO Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide: Global Update 2005, Summary of Risk Assessment (WHO Press, 2006).

18. Beijing Environmental Protection Bureau, Air pollution prevention and control plan of the Thirteen Five-Year plan of Beijing (2017).


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http://advances.sciencemag.org/cgi/content/full/6/44/eaba7621/DC1http://advances.sciencemag.org/cgi/content/full/6/44/eaba7621/DC1http://vizhub.healthdata.org/gbd-comparehttp://advances.sciencemag.org/



7 of 7

19. P. Liu, C. Zhang, C. Xue, Y. Mu, J. Liu, Y. Zhang, D. Tian, C. Ye, H. Zhang, J. Guan, The contribution of residential coal combustion to atmospheric PM2.5 in northern China during winter. Atmos. Chem. Phys. 17, 11503–11520 (2017).

20. The State Council of the People’s Republic of China, Action plan for ambient air pollution prevention and control (2013).

21. National Development and Reform Commission, Winter cleaning and heating plan for the northern region (2017–2021) (2017).

22. H. Z. Shen, S. Tao, Y. Chen, P. Ciais, B. Güneralp, M. Ru, Q. Zhong, X. Yun, X. Zhu, T. Huang, W. Tao, Y. Chen, B. Li, X. Wang, W. Liu, J. Liu, S. Zhao, Urbanization-induced population migration has reduced ambient PM2.5 concentrations in China. Sci. Adv. 3, e1700300 (2017).

23. Peking University, Emission Inventories of Major Air Pollutants; inventory.pku.edu.cn (2017). 24. European Commission, Joint Research Centre (JRC)/Netherlands Environmental

Assessment Agency (PBL), Emission Database for Global Atmospheric Research, release version 4.2 (EDGAR_v4.2); edgar.jrc.ec.europa.eu (2017).

25. G. Janssens-Maenhout, M. Crippa, D. Guizzardi, F. Dentener, M. Muntean, G. Pouliot, T. Keating, Q. Zhang, J. Kurokawa, R. Wankmüller, H. D. van der Gon, J. J. P. Kuenen, Z. Klimont, G. Frost, S. Darras, B. Koffi, M. Li, HTAP_v2.2: A mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution. Atmos. Chem. Phys. 15, 11411–11432 (2015).

26. G. A. Grell, S. E. Peckham, R. Schmitz, S. A. McKeen, G. Frost, W. C. Skamarock, B. Eder, Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 39, 6957–6975 (2005).

27. The National Center for Atmospheric Research, NCEP FNL Operational Model Global Tropospheric Analyses; rda.ucar.edu/datasets/ds083.2 (2020).

28. National Meteorological Information Center, China Earth International Exchange Station Climate Data Daily Value Data Set (v3.0); data.cma.cn (2013).

29. Historical Chinese Air Quality Observations; beijingair.sinaapp.com (2017). 30. U. S. Embassy and Consulates Air Quality Monitor; www.stateair.net/web/post/1/1.html

(2008). 31. C. Trudinger, I. Enting, Comparison of formalisms for attributing responsibility for climate

change: Non-linearities in the Brazilian proposal approach. Clim. Change 68, 67–99 (2005). 32. United Nations Framework Convention on Climate Change, Methodological Issues:

Scientific and Methodological Assessment of Contributions to Climate Change, Report of the Expert Meeting, Note by the Secretariat, New Delhi, India (23 to 29 October 2002).

33. J. Xiang, C. J. Weschler, Q. Wang, L. Zhang, J. Mo, R. Ma, J. Zhang, Y. Zhang, Reducing indoor levels of “outdoor PM2.5” in urban China: Impact on mortalities. Environ. Sci. Technol. 53, 3119–3127 (2019).

34. H. E. S. Mestl, K. Aunan, H. M. Seip, S. Wang, Y. Zhao, D. Zhang, Urban and rural exposure to indoor air pollution from domestic biomass and coal burning across China. Sci. Total Environ. 377, 12–26 (2007).

35. A. J. Cohen, M. Brauer, R. Burnett, H. R. Anderson, J. Frostad, K. Estep, K. Balakrishnan, B. Brunekreef, L. Dandona, R. Dandona, V. Feigin, G. Freedman, B. Hubbell, A. Jobling, H. Kan, L. Knibbs, Y. Liu, R. Martin, L. Morawska, C. Arden Pope III, H. Shin, K. Straif, G. Shaddick, M. Thomas, R. van Dingenen, A. van Donkelaar, T. Vos, C. J. L. Murray, M. H. Forouzanfar, Estimates and 25-year trends of the global burden of disease

attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. Lancet 389, 1907–1918 (2017).

36. M. Zhou, H. Wang, J. Zhu, W. Chen, L. Wang, S. Liu, Y. Li, L. Wang, Y. Liu, P. Yin, J. Liu, S. Yu, F. Tan, R. M. Barber, M. M. Coates, D. Dicker, M. Fraser, D. González-Medina, H. Hamavid, Y. Hao, G. Hu, G. Jiang, H. Kan, A. D. Lopez, M. R. Phillips, J. She, T. Vos, X. Wan, G. Xu, L. L. Yan, C. Yu, Y. Zhao, Y. Zheng, X. Zou, M. Naghavi, Y. Wang, C. J. L. Murray, G. Yang, X. Liang, Cause-specific mortality for 240 causes in China during 1990-2013: A systematic subnational analysis for the Global Burden of Disease Study 2013. Lancet 387, 251–272 (2016).

37. Institute for Health Metrics and Evaluation, Global Burden of Disease Results Tool; ghdx.healthdata.org/gbd-results-tool (2020).

38. National Health and Family Planning Commission of China, China Health and Family Planning Statistical Yearbook 2015 (Peking Union Medical College Press, 2015).

39. B. Li, T. Gasser, P. Ciais, S. Piao, S. Tao, Y. Balkanski, D. Hauglustaine, J.-P. Boisier, Z. Chen, M. Huang, L. Z. Li, Y. Li, H. Liu, J. Liu, S. Peng, Z. Shen, Z. Sun, R. Wang, T. Wang, G. Yin, Y. Yin, H. Zeng, Z. Zeng, F. Zhou, The contribution of China’s emissions to global climate forcing. Nature 531, 357–361 (2016).

40. Q. Zhong, J. Ma, G. Shen, H. Shen, X. Zhu, X. Yun, W. Meng, H. Cheng, J. Liu, B. Li, X. Wang, E. Y. Zeng, D. Guan, S. Tao, Distinguishing emission-associated ambient air PM2.5 concentrations and meteorological factor-induced fluctuations. Environ. Sci. Technol. 52, 10416–10425 (2018).

Acknowledgments Funding: This work is funded by the National Natural Science Foundation of China (grants 41830641, 41821005, 41629101, and 41922057), the Chinese Academy of Science (XDA23010100), and the 111 program (B14001). Author contributions: S.T. designed the study. X.Y. performed the simulations with help from H.S., J.M., and J.L. in model evaluation and validation and Y.C. in the health assessment. X.Y., S.T., G.S., H.S., Y.C., and W.M. performed data analysis with help from Xuejun Wang, Xilong Wang, J.H., Y.W., Q.Z., Y.R., H.X., and W.D. Writing was led by S.T. and X.Y. with substantial inputs from H.C., H.S., G.S., Y.C., and B.L. All authors participated in the interpretation of results and improvement of the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. The basic emissions data used in the study can be downloaded freely from inventory.pku.edu.cn.

Submitted 3 January 2020Accepted 14 September 2020Published 28 October 202010.1126/sciadv.aba7621

Citation: X. Yun, G. Shen, H. Shen, W. Meng, Y. Chen, H. Xu, Y. Ren, Q. Zhong, W. Du, J. Ma, H. Cheng, X. Wang, J. Liu, X. Wang, B. Li, J. Hu, Y. Wan, S. Tao, Residential solid fuel emissions contribute significantly to air pollution and associated health impacts in China. Sci. Adv. 6, eaba7621 (2020).


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impacts in ChinaResidential solid fuel emissions contribute significantly to air pollution and associated health

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ENVIRONMENTAL STUDIES Copyright © 2020 Residential …and outdoor). The term “clean energy” is defined as the natural gas, LPG (liquefied petroleum gas), biogas, and electricity