The Regional Impacts of Cooking and Heating Emissions on Ambient

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The Regional Impacts of Cooking and Heating Emissions on Ambient Air Quality and disease Burden in China Scott Archer-Nicholls, Ellison M Carter, Rajesh Kumar, Qingyang Xiao, Yang Liu, Joseph Frostad, Mohammad H Forouzanfar, Aaron Cohen, Michael Brauer, Jill Baumgartner, and Christine Wiedinmyer Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b02533 • Publication Date (Web): 01 Aug 2016 Downloaded from http://pubs.acs.org on August 4, 2016

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Environmental Science & Technology

The Regional Impacts of Cooking and Heating Emissions on Ambient Air Quality and Disease Burden in China Scott Archer-Nicholls,† Ellison Carter,‡ Rajesh Kumar,† Qingyang Xiao,¶ Yang Liu,¶ Joseph Frostad,§ Mohammad H. Forouzanfar,§ Aaron Cohen,k Michael Brauer,⊥ Jill Baumgartner,‡,# and Christine Wiedinmyer∗,† †National Center for Atmospheric Research (NCAR), Boulder, CO, 80301, USA ‡Institute on the Environment, University of Minnesota, St. Paul, MN, USA ¶Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA §Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA kHealth Effects Institute, Suite 500, 101 Federal Street Boston, MA, 02110, USA ⊥School of Population and Public Health, The University of British Columbia, 2206 East Mall, Vancouver, British Columbia, V6T1Z3, Canada #Institute for Health and Social Policy and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Vancouver, British Columbia, V6T1Z3, Canada, 1130 Pine Avenue West, Montreal, Quebec, H3A1A3, Canada E-mail: [email protected] Phone: +1 303-497-1414. Fax: +1 303-497-1400 Abstract 1

Exposure to air pollution is a major risk factor globally and particularly in Asia. A

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ACS Paragon Plus Environment


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large portion of air pollutants result from residential combustion of solid biomass and

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coal fuel for cooking and heating. This study presents a regional modeling sensitivity

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analysis to estimate the impact of residential emissions from cooking and heating ac-

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tivities on the burden of disease at a provincial level in China. Model surface PM2.5

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fields are shown to compare well when evaluated against surface air quality measure-

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ments. Scenarios run without residential sector and residential heating emissions are

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used in conjunction with the Global Burden of Disease 2013 framework to calculate the

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proportion of deaths and disability adjusted life years attributable to PM2.5 exposure

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from residential emissions. Overall, we estimate that 341,000 (306, 000–370, 000; 95%

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confidence interval) premature deaths in China are attributable to residential combus-

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tion emissions, approximately a third of the deaths attributable to all ambient PM2.5

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pollution, with 159,000 (142,000–172,000) and 182,000 (163,000–197,000) premature

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deaths from heating and cooking emissions respectively. Our findings emphasize the

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need to mitigate emissions from both residential heating and cooking sources to reduce

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the health impacts of ambient air pollution in China.

Introduction 17

Globally, almost three billion people primarily burn solid fuels (biomass and coal) for cooking

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and heating in their homes. 1,2 Traditional biomass and coal fuel stoves emit high concen-

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trations of health-damaging pollutants, including particulate matter less than 2.5 µm in

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aerodynamic diameter (PM2.5 ), into homes, communities, and the ambient environment. 3–5

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Exposure to PM2.5 is associated with negative health outcomes in adults and children, and

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contributed to an estimated 5.5 million premature deaths worldwide in 2013 from both in-

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door and ambient exposure. 6 In China, the population-weighted annual mean ambient PM2.5

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in 2013 was estimated to be 54.3 µg m−3 , well above the global average of 31.8 µg m−3 . 7

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Overall, 39% of primary PM2.5 emissions are estimated to come from the residential sec-

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tor. 8 An estimated 32% and 13% of households burning biomass for cooking and heating,

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respectively, while another 12% and 17% burn coal for cooking and heating, respectively. 9

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Global modeling studies estimate that between 308,000 and 1,022,000 premature deaths

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are attributable to ambient air pollution from residential solid fuel emissions. 10,11 Global

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models have also also been used to estimate the climate impacts of residential aerosol emis-

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sions, although global climate impacts are marred by large uncertainty, with indeterminate

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sign of direct radiative forcing due to the positive forcing of the absorbing black carbon

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component competing with the scattering co-emitted organic matter. 11–14 While providing

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a global perspective, it is difficult to resolve urban areas and mesoscale circulation in these

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models due to their coarser resolution (≈ 1◦ × 1◦ ). This limits our ability to simulate

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processes important for the transport and loss of pollutants, and the strong gradients in

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pollutant concentrations near emission sources that are important for resolving the high

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pollutant concentrations in urban areas. The Global Burden of Disease (GBD) Compara-

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tive Risk Assessment (CRA) improves estimation by assimilating higher-resolution remote

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sensing and surface measurement data with global chemical transport model simulations

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to generate global maps of annual average surface ozone (O3 ) and PM2.5 concentrations at

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0.1◦ × 0.1◦ resolution. 5,15

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This study estimates the impacts of residential cooking and heating emissions on ambient

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air pollution and disease burden in China using relatively high resolution (27 km horizontal

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grid spacing) simulations for the entirety of 2014 from a regional chemical transport model,

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the Weather Research and Forecast model with Chemistry 16 (WRF-Chem). This model has

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been extensively used for air pollution simulations in East Asia

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the WRF-Chem model output against surface air-quality measurements across all of China,

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by region and by season. Several emission scenarios are developed to represent extreme cases

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of residential combustion mitigation, allowing the calculation of the fractional contribution

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to ambient PM2.5 from residential cooking and heating emission sources. These fractions

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are applied to the exposure estimates, concentration response functions, and disease bur-

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den information from the GBD Comparative Risk Assessment 2013, 6,15,23 thus evaluating

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e.g. 17–22

. Here, we evaluate


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health impacts attributable to ambient exposure to PM2.5 from cooking and heating emis-

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sions in China. Using a high-resolution regional simulation helps reduce uncertainties in

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assessing the contributions of different sources to exposure at provincial or city scales. To

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our knowledge, this is the first study of its kind using a regional model in East Asia. By

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separately estimating the contribution of residential cooking and heating emissions to am-

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bient air-pollution-related health burden in China, we demonstrate the maximum potential

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health benefits from residential emission mitigation strategies, identify which provinces in

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China stand to benefit the most, and highlight sectors that should be of greater priority for

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future emission mitigation policy.

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Methods

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Model description

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This study uses WRF-Chem version 3.6.1; a fully-compressible, non-hydrostatic “online” re-

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gional coupled model (http://ruc.noaa.gov/wrf/WG11/). 16 We use the Model for OZone

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and Related chemical Tracers, version 4 (MOZART-4) chemical mechanism 24 for gas-phase

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chemistry, and the Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and

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Transport (GOCART) aerosol mechanism, 25 which simulates bulk sulfate, primary particu-

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late organic carbon (OC), black carbon (BC), and other unspeciated PM2.5 . The GOCART

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mechanism does not represent ammonium nitrate or secondary organic aerosol (SOA). Dust

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and sea-spray are respectively described with five and four sectional bins. The diagnostic

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PM2.5 variable is the sum of the dry mass of all bulk aerosol species plus dust and sea-spray

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less than 2.5 µm in diameter. Simulations are for the whole of 2014, with the last 7 days of

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2013 run as spin-up.

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The model domain has a horizontal grid spacing of 27 km along a 200×155 grid, covering

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all of China and neighboring regions in Asia (SI Figure S1). The domain has 51 vertical levels,

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up to a minimum pressure of 10 hPa, with 7 levels in the lowest 1 km of the atmosphere. 4


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The physical parameterizations used for the study are listed in the SI (Table S1).

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Initial and boundary conditions are driven by meteorological fields from the European

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Center for Medium Weather Forecasts (ECMWF) atmospheric operational model analyses

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and products, at 6 hourly intervals and horizontal grid spacing of 0.141◦ (http://www.ecmwf.

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int/). Model wind, water vapor, and temperature fields are analysis-nudged above the

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boundary layer using four-dimensional data assimilation (FDDA), 26 and aerosol–radiative

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feedbacks are turned off, to constrain meteorology in all scenarios. Chemical boundary

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conditions are interpolated from MOZART-4 global model output. 24 As the MOZART-4

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output uses an average climatology for its dust fields, the dust boundary conditions are

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overwritten by output from a Community Atmosphere Model (CAM) simulation, which

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simulates dust online based on the meteorology, and, in this case, used the same meteorology

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as MOZART-4 (GEOS-5). 27

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Emissions inventory description

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Anthropogenic emission for this study are derived from The Emissions Database for Global

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Atmospheric Research (EDGAR) Hemispheric Transport of Air Pollution (HTAP) version

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2.2 emissions inventory 28,29 for the year 2010 (see SI Section S1 for more details). Other,

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non-anthropogenic emission sources include dust (GOCART dust emissions scheme based on

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Ginoux et al. 30 ), open biomass burning (Fire INventory from NCAR [FINN] version 1.5 31 ),

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biogenic emissions (Model of Emissions of Gases and Aerosols from Nature [MEGAN] version

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2.04), 32 and sea-spray emissions. 33

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The Multi-resolution Emissions Inventory for China version 1.0 (MEIC; http://www.

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meicmodel.org) is used over China as part of EDGAR-HTAP. Diurnal emission cycles are

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applied based on functions developed by Olivier et al. 34 for European emissions, as used by

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Wang et al. 17 over East Asia. Power sector emissions are distributed vertically following

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Wang et al. 17 In the MEIC inventory, the residential sector is responsible for 39% of emitted

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anthropogenic primary PM2.5 in China, the second largest source after industrial emissions. 8 5



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The residential sector is inclusive of commercial emissions, as well other household emissions

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such as from lighting. 29 However, in China the residential sector is dominated by coal,

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biomass and agricultural waste burning for heating and cooking (Lei et al., 2011, Li et al.,

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2015). For the purposes of this study we assume the sector’s emissions are entirely from

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in-house cooking and heating emissions.

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Seasonal variation of the residential sector is estimated using a parameterization described

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by Streets et al. 35 Annual emissions are scaled by a factor based on assumed stove usage,

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applied to the EDGAR_HTAP inventory prior to our acquiring the data, varying with the

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provincial mean monthly temperature: 36

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• < 0◦ C: 16 hrs/day

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• 0 − 5◦ C: 12 hrs/day

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• 5 − 10◦ C: 6 hrs/day

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• > 10◦ C: 3 hrs/day

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The minimum stove-usage of 3 hrs/day implicitly assumes only cooking usage, based on

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Ravindranath and Ramakrishna. 37 The parameterization results in the residential emissions

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being substantially higher during winter in northern and mountainous regions, but show

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little seasonal variation in warmer climates. Whilst there is some variation in emissions from

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other sectors, the greatest seasonal change is in the residential sector, which increases by a

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factor of two or more across China in winter compared with summer. 8

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The sensitivity of ambient pollutant concentrations to the residential sector is evaluated

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with the simulation of three emission scenarios: a base scenario with all emissions (BASE); no

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residential emissions (NORES); and no heating emissions (NOHEAT). We approximate the

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removal of the heating component by setting the residential sector to July emission values

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for the entire year. We assume that the remaining emissions are entirely due to cooking

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activities, consistent with the assumptions of the Streets et al. 35 parameterization.

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Observational network description

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The WRF-Chem model output is evaluated with data from China’s national air quality

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monitoring station network. Daily average concentrations of PM2.5 and PM10 , O3 , nitrogen

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dioxide (NO2 ), carbon monoxide (CO), and sulphur dioxide (SO2 ) for 2014, from over 900

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sites in 187 cities across China, were collected and presented by the China National En-

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vironmental Monitoring Center (CNEMC, http://www.cnemc.cn/) and downloaded from

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http://pm25.in/, a direct mirror of data from CNEMC. Data from this network have been

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used in studies to statistically evaluate the distribution of air pollution across China. 7,38

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Daily averages are calculated between 00:00 and 23:00 local time (UTC+8hr). Data from

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sites located within the same model grid cell are averaged together prior to any comparison

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with model output. The monitoring sites tend to be operated from cities in more densely

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populated regions of China (see Stations_CNMEC.csv, included with the SI). A number of

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statistical metrics are used to evaluate the model against observations 39 (SI Section S1).

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Estimating premature deaths and disability attributed to residential

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PM2.5 pollution

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Premature deaths and disability adjusted life years (DALYs; a measure combining years spent

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living with disability and the years of life lost due to premature mortality 40 ) attributable to

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ambient PM2.5 exposure are estimated utilizing the methods of the Global Burden of Disease

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(GBD) 2013 framework. 5,6,15 The GBD2013 also estimates disease burden from O3 exposure.

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However, as premature mortality attributable to PM2.5 exposure greatly exceeds O3 expo-

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sure (2,926,000 compared with 217,000 globally in 2013 6 ), and O3 is largely insensitive to

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residential emissions (SI Figures S9 and S10), we focus here on the health burden due to

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ambient PM2.5 exposure.

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Integrated Exposure Response (IER) functions were developed by the GBD to estimate

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the increased health risk from PM2.5 exposure across a range of exposure levels. 41 The lack

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of large-scale cohort studies in countries with high ambient PM2.5 exposure levels, such as

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China, necessitated the inclusion of risk estimates from studies of other exposure types,

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including secondhand and active smoking. The IER functions assume that risk is a function

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of PM2.5 inhaled dose regardless of the chemical composition, an assumption consistent with

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recent reviews and assessments of particulate matter health effects. 42,43 The derived IER

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functions (SI Section S1), are nonlinear, showing a steep increase in risk at small increases

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in PM2.5 exposure above a minimum threshold, but have shallower gradient at high PM2.5

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levels. The IER functions are used to estimate increased incidence of premature deaths and

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DALYs due to ischemic heart disease, stroke, lung cancer, chronic obstructive pulmonary

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disease and lower respiratory infections using methods described for GBD2013, 6,44 utilizing

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provincial level information of baseline health and population data over China. 45

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Average annual surface PM2.5 concentrations from the three emissions scenarios (SI Fig-

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ure S9) are used to calculate the fraction of PM2.5 exposure attributable to residential and

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heating sources at each grid point:

FRES =

|PM2.5 |BASE − |PM2.5 |NORES |PM2.5 |BASE

(1)

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where |PM2.5 |BASE and |PM2.5 |NORES are the annual average surface PM2.5 concentrations

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from the BASE and NORES scenarios respectively. The residential heating and cooking

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fractions are similarly estimated:

FHEAT =

|PM2.5 |BASE − |PM2.5 |NOHEAT |PM2.5 |BASE

(2)

FCOOK =

|PM2.5 |NOHEAT − |PM2.5 |NORES |PM2.5 |BASE

(3)

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These fractions are multiplied by the GBD2013 ambient concentration estimates, which

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are based on global GEOS-Chem simulations assimilated with satellite-based observations of

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AOD and calibrated to ground measurements to generate a 0.1◦ × 0.1◦ resolution map of an-

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nual average PM2.5 exposure, 15 to estimate the fraction of disease burden attributable to each 8


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source. The total health burden attributable to PM2.5 exposure from all sources are based

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on those calculated using the non-linear IER functions and 0.1◦ × 0.1◦ ambient PM2.5 fields

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developed for the GBD2013. 15 We present 95% uncertainty intervals (2.5th −97.5th percentile

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range) of the disease burden, based on repeated calculations of health burden (N = 1000) at

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each grid cell, varying in the uncertainty range of PM2.5 exposure, population and the IER

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functions. 6,15 The proportion of total deaths and DALYs to residential sources are estimated

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by applying the above fractional contributions to the total disease burden attributable to all

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sources, assuming a linear apportionment between PM2.5 sources and relative health burden.

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Despite the nonlinearity of known IER functions, a linear apportionment approximation

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consistent with other studies is applied. 46–48

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Results

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Evaluation of surface PM2.5 in China

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The WRF-Chem model output from the BASE simulation is evaluated with observations

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of surface PM2.5 . Additional chemical and meteorological evaluations are presented in the

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SI (Sections S2 and S3). Summary statistics across China are presented in Table 1, using

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methods defined by Yu et al. 39 (described in SI Section S1). The WRF-Chem model is

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able to represent average PM2.5 concentrations well, with only a small negative normalized

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mean bias factor (NMBF) of −0.093. The correlation coefficient of 0.6 is comparable to

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other studies evaluating modeled PM2.5 concentrations against surface measurements,e.g. 49,50

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demonstrating that the model can sufficiently represent spatial and temporal variation. How-

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ever, while the model compares well with observations for PM2.5 , it does not perform as well

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for some other species, including CO and NO2 (SI Section S3).

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The model performs well when averaged across all sites over the entire country, but there

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are regional differences in performance. Generally, the model is able to simulate regional

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spatial and temporal variations (Figure 1). However, in less populated Northwestern China, 9



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the model typically underestimates surface PM2.5 (NMBF = −0.48) and shows a poorer

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correlation (R = 0.44), likely due to underestimation of dust emissions. In South-central

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and Southwest China, where residential emissions are the dominant source of PM2.5 , the

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model tends to overestimate PM2.5 (NMBF=+0.008 and +0.19 respectively).

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The small bias of modeled PM2.5 is not necessarily an endorsement of good model per-

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formance. Given that some aerosol formation processes are not represented in the GOCART

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mechanism (e.g. formation of SOA), one would expect the model to under-predict PM2.5

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loadings if primary aerosol emissions, secondary aerosol precursor emissions, and aerosol loss

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processes were accurate. The results of the model-measurement comparison may therefore

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be an indication that primary PM2.5 emissions in the EDGAR_HTAP inventory are too high

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for the year 2014. Further, the model over-estimates ambient SO2 (SI Section S3), which

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likely leads to too much sulfate aerosol formation. The model results are also biased low for

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precipitation (SI Section S2), implying that wet deposition rates are likely underestimated.

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A final consideration is the lack of ammonium-nitrate (NH4 NO3 ) formation in the GOCART

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mechanism. Including NH4 NO3 formation would increase the aerosol mass, and may also

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increase the disease burden attributable to the agricultural sector. 10 Without sufficient sec-

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ondary aerosol formation processes, the residential contribution to ambient PM2.5 presented

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here may be interpreted as an upper limit.

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Sensitivity to residential combustion emissions

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Average surface PM2.5 concentrations in China are compared with the different emission

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scenarios in Figure 2. PM2.5 loadings are typically higher in winter due to a combination of

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higher heating emissions and meteorological conditions that are conducive to high-pollution

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episodes. 51,52 Compared to observations, the BASE scenario is capable of effectively simu-

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lating the magnitude and day-to-day variation of ambient PM2.5 over China in both January

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and July.

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The difference between the BASE and NOHEAT scenarios in Figure 2 represents the con10


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tribution from heating emissions, while the difference between the NOHEAT and NORES

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scenarios represents that due to cooking emissions. In July, the NOHEAT and BASE sce-

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narios are identical, whereas in January surface PM2.5 in the BASE scenario is considerably

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higher than in the NOHEAT or NORES scenarios, showing heating emissions to contribute

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approximately 40% to ambient PM2.5 . Winter meteorological conditions (such as reduced

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precipitation and a lower boundary layer) contribute to higher ambient pollution, as demon-

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strated by higher PM loadings in winter than summer in the NORES and NOHEAT scenar-

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ios. However, meteorological variation alone is not sufficient to explain the high wintertime

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PM2.5 loadings in the BASE scenario.

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Contribution of residential emissions to premature deaths and disability-

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adjusted life years in China

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Estimates of premature deaths and DALYs attributable to ambient PM2.5 exposure over

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China are presented in Table 2. The uncertainty range presented is calculated based on the

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IER function fit uncertainty, and does not account for other errors such as those associated

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with model setup or emissions. Overall, of the 916,000 (95% uncertainty interval: 821–

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933,000) premature deaths (UI: 16.2–20.1 million DALYs) in China due to ambient exposure

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to PM2.5 from all emission sources, approximately 37% are attributable to emissions from

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the residential sector.

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An estimated 159,000 (UI: 142–172,000) premature deaths (3.18 million DALYs; UI: 2.78–

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3.50 million) are a result of exposure to PM2.5 from heating emissions, with the remaining

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182,000 (UI: 163–197,000) deaths (3.61 million DALYs) attributable to cooking emissions.

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Despite heating emissions being negligible for 6 to 9 months of the year, we find that heat-

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ing emissions contribute 17.6% of the total annual premature deaths attributable to air

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pollution. Estimates of premature deaths by province in China (Figure 3) are moderately

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correlated with population, but also with regions of high ambient PM2.5 , particularly North-

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ern provinces around Beijing and Southwestern Sichuan. The disease burden attributable 11



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to heating emissions is higher in the colder Northern and mountainous regions, while the

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impacts of cooking are more prevalent in the warmer South.

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Assessing the premature deaths or DALYs per unit population removes the population

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dependence, highlighting which provinces are most vulnerable to different emission sources

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(Figure 3). However, differences in demographics such age distribution and baseline inci-

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dence of diseases between provinces, described in detail by Zhou et al., 45 also influence the

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incidence of premature deaths and DALYs attributable to ambient PM2.5 differently between

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provinces. Sichuan province in Southwest China has the greatest disease burden attributable

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to residential emissions, with an estimated 38.3 premature deaths per 100k population

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(685 DALYs/100k); compared with the national average of 26.5/100k (471 DALYs/100k).

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Other provinces with high premature death rates include Henan (38.0 deaths/100k; 686 DALYs/100k),

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Guizhou (35.9 deaths/100k; 706 DALYs/100k) and Hebei (35.4 deaths/100k; 661 DALYs/100k).

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Deaths attributable to heating are more prevalent in the northern provinces, especially Hei-

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longjiang (23.9 deaths/100k; 438 DALYs/100k), Jilin (22.1 deaths/100k; 394 DALYs/100k),

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and Liaoning (19.7 deaths/100k; 354 DALYs/100k). In contrast, deaths attributable to

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cooking are more prevalent in the South; e.g. Sichuan (23.9 deaths/100k, 427 DALYs/100k),

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Guangxi (23.0 deaths/100k, 419 DALYs/100k) and Guizhou (23.0 deaths/100k, 452 DALYs/100k).

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A full detail of disease burden by province is provided in the SI file res_burden_china2014.csv.

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Discussion

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By evaluating WRF-Chem output with an in situ air quality observation network of stations

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across China, we show that the WRF-chem model does a satisfactory job of simulating surface

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PM2.5 concentrations. However, uncertainties associated with the aerosol representation in

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the model (such as limited size distribution representation and lack of SOA formation), lead

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to potential errors in the model output. SOA formation processes could be particularly

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important in severe haze conditions, whereby meteorological conditions enhance secondary

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aerosol formation, reinforcing an inversion and creating a further positive feedback of aerosol

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formation. 20,52

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The the magnitude residential combustion emissions is another source of uncertainty.

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As most bottom-up emission inventories are derived from government energy use statistics,

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residential sector emissions are often attributed with greater uncertainty than, say, power

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emissions, because the activity data are more difficult to derive. 35,53,54 Further uncertainties

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relate to the assumptions used to drive the seasonal variation of residential emissions, as

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defined by the Streets et al. 35 parameterization, also used to estimate the fraction of heating

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and cooking emissions. This parameterization is somewhat crude, as it is based on monthly

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mean provincial temperatures. A better approach may be to modulate emissions based on

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heating degree days, 55 as well as incorporating in situ measurements of seasonal patterns of

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energy usage. 56

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To estimate the health burden attributable to residential emissions, we use the GBD2013

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data as a baseline for PM2.5 exposure, which compares well with the WRF-Chem output (SI

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Figure S13). Strengths of using the GBD2013 inputs include the spatially resolved health and

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population data, which facilitates the generation of high-resolution, provincial-level estimates

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of attributable health burden. The study also benefits from the 27 km grid-spacing of the

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WRF-Chem model, as the fraction of ambient PM2.5 attributable to residential, cooking

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or heating sources is calculated at the resolution of the regional WRF-Chem model. This

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approach uses a linear approximation for the source apportionment, meaning the fraction of

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deaths attributable to a source may not be the same as the reduction that would occur if

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that source were removed, all else being the same. In a region such as China, with relatively

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high ambient PM2.5 loadings, this approach likely gives an upper-limit to the risk reduction,

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as the IER functions having a relatively shallow gradient at high PM2.5 concentrations for

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some health outcomes.

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The estimated premature deaths in China due to ambient PM2.5 attributable to cook-

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ing emissions presented in this study are higher than the 130,000 deaths (10.8% of those

13



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attributable to ambient air pollution from all sources) in 2010 in East Asia estimated by

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Chafe et al., 47 or the 121,075 premature deaths (8% of total) in East Asia from residential

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emissions estimated by Butt et al. 11 However, the PM2.5 loadings estimated by Butt et al. 11

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only calculate disease burden for those above 30 years of age, and their estimated ambient

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PM2.5 concentrations were lower than measurements. Premature death estimates increased

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considerably in sensitivity studies with doubled residential emissions (which also improved

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model comparison with observations). In contrast, our results are lower than those reported

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by Lelieveld et al., 10 who estimated 32% of the 1,357,000 total premature deaths in China

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attributable to PM2.5 exposure were due to residential energy use emissions. The relative

314

fraction of deaths attributable to residential emissions from our study is similar but slightly

315

higher than that from Lelieveld et al. 10 (37% vs. 32%). The difference may partly be due

316

to the lack of ammonium nitrate aerosol in GOCART, which means a reduced contribution

317

from the agricultural sector to aerosol mass in the current study. There are many differences

318

between the models, emission inventories, model setups and IER functions in these stud-

319

ies that may account for the differences between estimated premature deaths. This study

320

differs from others it its use of a regional rather than global model. The higher resolution

321

of a regional model facilitates better source apportionment of ambient PM 2.5 near emission

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sources. Residential emissions often contribute to pollution in otherwise more remote and

323

less-polluted rural regions, and may therefore have larger consequences to rural population

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health impacts compared with urban regions, where other sectors, such as industry or traffic,

325

are larger contributors to ambient air pollution. 22

326

The results of this study presented here show that emissions from the residential sector

327

are an important contributor to ambient PM2.5 pollution and disease burden in China. We

328

estimate that 37% of all premature deaths due ambient PM2.5 exposure across China are at-

329

tributable to emissions from the residential sector. Both space heating and cooking emissions

330

are important, with premature deaths associated with each of similar magnitude (182,000

331

from cooking, 159,000 from heating). Cooking emissions contribute to ambient PM2.5 across

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332

the country and throughout the year. In contrast, heating emissions are important during

333

winter, particularly in Northern provinces, contributing almost half of the ambient PM2.5

334

across China in January. However, mitigation strategies, new technologies and research into

335

impacts have almost exclusively focused on clean cooking technologies

336

sults therefore support the proposition that residential sector emission mitigation strategies

337

in China should consider whole household energy packages, addressing both cooking and

338

heating activities. 59

15


e.g. 1,2,47,57,58

. Our re-


339

Figures January 2014

July 2014

PM2.5 (µg m-3)

Figure 1: Map of monthly average surface PM2.5 concentrations from BASE scenario January (left) and July 2014 (right). Overlaid dots show observed monthly average values from CNEMC measurement sites across china.

Figure 2: Time-series of mean PM2.5 across all sites in China for January 2014 (left) and July 2014 (right). Comparing daily average of all sites (black circles) with average of coincident points from model domain for each model scenario.

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Attributable to Heating Emissions

Attributable to Cooking Emissions

DALYs/100k population

Premature Deaths

Attributable to Residential Emissions

Figure 3: Maps of provincial-level estimates of premature deaths (top) and disability adjusted life years per 100, 000 population (DALYs/100k, bottom) in 2014 due to ambient exposure to PM2.5 . Disease burden attributable to air pollution from all residential combustion sources (left), residential heating sources only (center) and residential cooking sources only (right).

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340

Page 18 of 28

Tables Table 1: Summary statisticsa of surface PM2.5 concentrations (µg m−3 ) comparing model output from BASE scenario with observations from all stations across China for the year 2014 across six regions of Chinab . The location of measurement sites in each region is listed in the SI file Stations_CNMEC.csv. Statistic Mean Obs Mean BASE Std. Obs Std. BASE No. stations R MB NMB MAE NME NMBF NMAEF

All China NW NE N E 58.34 58.14 55.56 75.68 59.28 53.36 39.29 47.49 68.69 52.65 45.36 47.78 49.21 64.68 36.75 42.18 26.15 31.66 43.46 32.47 921 74 104 133 309 0.596 0.438 0.520 0.644 0.618 -4.98 −18.84 -8.07 -6.99 -6.63 -0.085 -0.324 -0.145 -0.092 -0.112 24.39 27.62 24.45 31.75 22.04 0.418 0.475 0.440 0.420 0.372 -0.093 -0.480 -0.170 -0.102 -0.126 0.457 0.703 0.515 0.462 0.419

a

SC 49.80 50.22 37.38 41.0 210 0.635 0.42 0.008 22.01 0.442 0.008 0.442

SW 49.54 58.97 34.71 43.0 91 0.697 9.43 0.190 23.43 0.473 0.190 0.473

Statistical tools defined in SI Section S1: standard deviation (Std.) Pearson’s correlation coefficient (R), mean bias (MB), normalized bias (NMB), mean absolute error (MAE), normalized mean error (NME), Normalized Mean Bias Factor (NMBF), and Normalized Mean Absolute Error Factor (NMAEF). Formulations used based on Yu et al., (2006). b Regions of China: North-West (NW: Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang provinces); Northeast (NE: Liaoning, Jilin and Heilongjiang); North (N: Beijing, Tianjin, Hebei, Shanxi and Inner Mongolia); East (E: Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi and Shandong); South-Central (SC: Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan, Hong Kongc and Macauc ); Southwest (SW: Chongqing, Sichuan, Guizhou, Yunnan and Tibet). c Note no data processed for Hong Kong and Macau.

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Table 2: Estimated premature deaths and disability adjusted life years (DALYs) over China in 2014 due to different emission sources (95% uncertainty intervals based on uncertainty related to IER function fit and annual population exposure to PM2.5 ). Emission Source All sources Residential Heating Cooking

Deaths (thousands) 916 (821 − 933) 341 (306 − 370) 159 (142 − 172) 182 (163 − 197)

Deaths/100k 71.3 (64.6 − 76.7) 26.5 (24.0 − 28.5) 12.3 (11.1 − 13.3) 14.2 (12.9 − 15.3)

DALYs (millions) 18.2 (16.0 − 20.1) 6.78 (5.95 − 7.48) 3.18 (2.78 − 3.50) 3.61 (3.12 − 3.97)

DALYs/100k 1 264 (1 118 − 1 383) 471 (416 − 515) 220 (194 − 241) 251 (222 − 274)

341

Acknowledgement

342

This research was supported by U.S. Environmental Protection Agency’s (EPA) Science

343

to Achieve Results (STAR) program grant number R835422. NCAR is sponsored by the

344

National Science Foundation (NSF). The authors acknowledge high-performance computing

345

support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR’s Computational and

346

Information Systems Laboratory, sponsored by the NSF. We thank P. Saide and L. Emmons

347

for providing helpful comments in internal review.

348

Supporting Information Available

349

350

351

352

353

354

355

• I2_SAN_2016_ES&T_SI.docx: main supporting document with figures, text and tables to compliment those presented in the main paper. • Stations_CNMEC.csv: Table detailing information on observation station network used to evaluate surface air quality. • res_burden_china2014.csv: Provincial estimates of premature deaths and DALYs from all PM2.5 sources, residential emissions, and residential heating sources across China. This material is available free of charge via the Internet at http://pubs.acs.org/.

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Graphical TOC Entry Ambient PM2.5 [!g m-3] due to heating (left) and cooking (right) emissions.

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The Regional Impacts of Cooking and Heating Emissions on Ambient

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