Toxicological risk by inhalation exposure of air pollution emitted from

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Toxicological risk by inhalation exposure of air pollution emitted from China’s Municipal Solid Waste Incineration Qi Zhou, Jianxun Yang, Miaomiao Liu, Yang Liu, Stefanie Ebelt Sarnat, and Jun Bi Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b03352 • Publication Date (Web): 20 Sep 2018 Downloaded from http://pubs.acs.org on September 22, 2018

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Environmental Science & Technology

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Toxicological risk by inhalation exposure of air pollution emitted from China’s Municipal Solid

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Waste Incineration

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Qi Zhoua, Jianxun Yanga, Miaomiao Liua,*, Yang Liub, Stefanie Sarnatb, Jun Bia

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a

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University, Nanjing 210023, China

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b

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30322, United States

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* Corresponding author.

State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing

Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA

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Phone: (86)2589681529; fax: (86)2589680586; e-mail: [email protected] (M. Liu); mailing address:

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Xianlin Avenue 163#, Nanjing 210023, P. R. China

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ACS Paragon Plus Environment


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Abstract

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Municipal solid waste (MSW) incineration has developed rapidly in China. However, the air

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pollution-related health risks attributable to MSW incinerators are still far from known. In this context,

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an MSW incineration emission inventory was compiled using plant-level activity data and localized

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emission factors. Subsequently, Gaussian plume model and Risk Quotients Model were utilized to

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calculate the spatialized hazard index (HI) and carcinogenic risk (CR). Altogether, 76449 tons (t) of NOX,

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25725 t of SO2, 16937 t of CO, 9279 t of HCl, 5629 t of particulate matter, 33 t of Cr, 33 t of Pb, 20 t of

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Hg, 6 t of Cd, 4 t of Ni, 0.4 t of As, and 94 g-toxic equivalent quantity of polychlorinated

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dibenzo-p-dioxins and polychlorinated dibenzofurans were emitted in 2015. The national average HI

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was 1.88×10-2, which was far lower than the acceptable level (HI ≤ 1). However, the national average

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CR was 5.71×10-6, which was approximately five times higher than the acceptable level (CR ≤ 1×10-6).

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The spatial heterogeneity of health risks was observed. The results enrich spatial dimensions of prior

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estimates and provide policy implications from the aspects of accelerating technology upgrades,

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strengthening emission standards, optimizing site selection and enhancing risk communication.

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Key words: Municipal solid waste incineration; Emissions; Health risks; Noncarcinogenic;

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Carcinogenic; Spatial distribution


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Abstract graphic:

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INTRODUCTION In recent years, the production of municipal solid waste (MSW) in China has increased

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dramatically along with economic growth, accelerated urbanization and improved living standards.

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Among the three mainstream MSW disposal methods (landfill, incineration and composting)1,

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incineration has received support from the government for its excellent performance in land saving,

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waste reduction and energy regeneration. The number of MSW incinerators increased from 104 to 220

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during 2010-2015, accounting for 38% of the MSW harmless treatment equipment in 20152. However,

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epidemiological studies have proven that air pollutants, especially heavy metals and polychlorinated

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dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs), emitted from MSW incineration were

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associated with adverse health effects3-7, and the construction of MSW incinerators consequently raised

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not-in-my-backyard (NIMBY) issues in China8. In this context, it is urgent to know whether or not the

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health risks of China’s MSW incineration exceed safe levels.

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There is a large body of international literature concerning the estimation of the air pollutant

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emissions from MSW incineration, which is the first step in characterizing related health risks.

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Specifically, the United States Environmental Protection Agency (US EPA)9, European Environment

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Agency (EEA)10,11 and other agencies have published several air pollutant emission inventory

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guidebooks with default emission factors. These guides have provided a method for other countries to

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coarsely calculate the emissions but can hardly reflect local actual technical levels, especially in

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developing countries such as China. To address this issue, researchers have carried out field tests to

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obtain localized emission factors for MSW incineration in China12-13. Based on the literature

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investigations and statistical data of MSW incineration, Tian et al. offered important sights for the

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temporal trends and spatial variations in several hazardous air pollutant emissions for the period of

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2003-201014. However, prior studies cannot capture the impacts of the latest accelerated MSW


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incineration capacities, transitions of spatial distributions, and emission limits set by the new Standard

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for Pollution Control on the Municipal Solid Waste Incineration (GB18485-2014) (short form,

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Standard-2014) after 2014 (see Table S1 in the Supporting Information)15. Moreover, based on

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aggregated activity data at the provincial level, prior studies cannot reflect the exact locations of

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emission sources, which may further bias the results from atmospheric dispersion models and the

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estimates of health risk.

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Health risk assessments are one of the most common environmental assessment tools currently in

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use16. Generally, field tests or atmospheric dispersion models (i.e., ISC317, AERMOD18, and

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CALPUFF19) combined with exposure and individual risk models are common approaches to assessing

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the health risk of air pollutants. Using this approach, several studies have assessed the health risk of

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MSW incineration17-20. However, by considering the limitations of complicated data inputs, such as

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meteorological data, terrain data, population data or operation data, for the whole MSW incinerator

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process, prior studies have usually been targeted at a single plant or conducted at a small scale17-19, 21,

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and few have expanded to the regional or national scale20. In this context, assessing the health risk of

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MSW incineration across the whole of China at the plant level is of great importance for rapid

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identification of risky areas or pollutants.

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In this study, we first compiled a bottom-up multiple-pollutant inventory at the plant level based on

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localized emission factors from literature review and detailed operation information collected from each

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individual MSW incinerator. Second, a Gaussian plume dispersion model was constructed based on the

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plant-level emission inventory and meteorological information collected from the weather station that

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was geographically closest to each MSW incinerator. Finally, two health risk indices, the

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population-weighted hazard index (HI) and carcinogenic risk (CR), were applied as the basis for ranking

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the air pollution-related health risks of MSW incineration in China. The findings provide important



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implications for near-term MSW treatment policies in China.

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MATERIALS AND METHODS

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Localized emission inventory

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The basic information of MSW incineration plants in mainland China was obtained from the

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“China Association of Circular Economy”22, including locations, incinerator types, and treatment

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capacities. After gathering this information, we further examined the reliability of the treatment capacity

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data aggregated from individual MSW incinerators using national-level statistical data from the China

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Statistical Yearbook and China Circular Economy Yearbook2, 23. By March 2016, a total of 224 MSW

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incineration plants operated normally in mainland China (MSW incinerators in Hong Kong, Macao and

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Taiwan were not included due to a lack of data), with MSW incineration treatment capacities of 214785

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tons/day (t/d). This value was very close to the national-level statistical data of 219080 t/d in 2015. In

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addition, 2 of the 224 MSW incinerators with capacities of 2200 t/d were excluded from further analysis

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due to the lack of information about their geographic coordinates. Figure 1 shows the spatial locations of

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the remaining 222 MSW incinerators in the study area (the names of 31 provinces in China are marked

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in Figure S1). Most of the incinerators were concentrated in the eastern coastal areas of China. Jiangsu,

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Zhejiang and Guangdong provinces, marked by red in Figure 1, were the top three provinces with the

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most MSW treatment capacity. In terms of incineration technology, grate firing incinerators (GFIs) and

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fluidized bed incinerators (FBIs) are the two typical types of MSW incinerators used in China, altogether

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accounting for 99% of the total capacity (72% are GFIs and 28% are FBIs). GFIs are a subclass of mass

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burning, in which the MSW is placed on a grate that moves through the combustor, and air is supplied

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both below and above the grate9. FBIs are a subclass of refuse-derived fuel combustors, for which the

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MSW needs to be shredded first and then heated to graininess9. The emission factors of these two

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incinerator types are quite different from each other. More detailed information on MSW incinerator


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types at the provincial level is summarized in Figure S2.

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Figure 1. Spatial distribution of MSW incineration plants across China. MSW incinerators in Hong

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Kong, Macao and Taiwan were not included due to lack of data. (Map sources: Resource and

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Environment Data Cloud Platform, Institute of Geographic Sciences and Natural Resources Research;

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http://www.resdc.cn/, accessed March 20, 2016)

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The localized emission factors are derived from literature review. The systematic screening process

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for literature search and data extraction are summarized in Text S1. After the screening process, a total

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of 28 literatures were included to generate the distributions of emission factors. Table S2 and Table S3 in

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the Supporting Information summarizes the main analytical methods used in the individual studies. It

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turns out that though the analytical methods varied across individual literatures, most studies used

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standard testing methods regulated by the Chinese or American government that ensured the reliability

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of the results. As summarized in Eq. (1), the air pollutant emissions from each MSW incinerator were

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calculated by multiplying the daily waste treatment (DWT) of each incinerator and the comprehensive

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emission factors that were sub-grouped by incinerator types and equipped air pollution control devices

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(APCD).



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= 365 × ∑ , = 365 × ∑ × , = 365 × ∑ × , × 1 −

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(,) ! (1)

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where Ej represents the total emissions of pollutant j in the year of 2015 in mg; En,j represents the total

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emissions of pollutant j from incinerator n; DWTn represents the daily waste treatment of incinerator n in

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tons (t)/day; CEFi,j represents the comprehensive emission factor of pollutant j for incinerator type i in

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mg/ton, which have considered the impacts of APCD applications on emission reductions; Fluei,j

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represents the amounts of pollutant j in flue gas released from incinerator type i in mg/ton; PAPCD(i,j)

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represents the fraction of pollutant j removed by the installed APCD from incinerator type i in %; n

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represents the code of the incinerators; i represents the incinerator type, including GFIs and FBIs; and j

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represents the pollutant type, including particulate matter (PM), PCDD/Fs, nitrogen oxide (NOx), sulfur

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dioxide (SO2), carbon monoxide (CO), hydrogen chloride (HCl), lead (Pb), chromium (Cr), mercury

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(Hg), cadmium (Cd), nickel (Ni), and arsenic (As).

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At the plant level, data are only available for the treatment capacity rather than the daily waste

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treatment quantities. Therefore, we estimated the daily waste treatment of each MSW incinerator by

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allocating the national aggregated daily waste treatment quantities according to their corresponding

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capacities. The emission factors summarized from local experiments in the literature are presented in

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Table S2 and Figure S3. It should be noted that MSW can be classified into several types: food and

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kitchen waste, paper, plastics, fabrics, and electrical and electronic waste (WEEE). Different types of

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incinerated solid waste result in various emission characteristics, which should be separately treated in

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the processes of emission accounting. However, because no strict waste sorting process are performed

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before incineration in China, MSW incinerators combust the mixtures of different solid wastes. This

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situation does not allow us to quantify the impacts of solid waste types separately in the present work. In


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spite of this, we do not think it would not significantly challenge our findings for two reasons. First, the

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emission factors in the present work are from previous localized field tests in China that were also based

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on the mixtures of solid waste. Second, the composition of MSW in China does not vary greatly by

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municipality24.

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In addition to the incineration technologies and equipped APCD that have been considered in the

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compilation of the emission inventory of MSW incineration, operational management of the incineration

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plants (i.e., the quality of the flue gas absorption materials, the operating state of the equipment, and the

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temperature maintained in the incinerator) also influence pollutants emissions significantly. However,

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the relevant data are truly difficult to collect, as the on-line monitoring of MSW incineration is imperfect

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in China. It is also difficult to know the complex relationships between operational management and

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pollutant emissions. This may be one potential research area in the future.

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To quantify the uncertainty of the emission factors using Crystal Ball (Oracle Crystal Ball, Version

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11.1.2.4.400), the Monte Carlo stochastic simulation approach was employed to model the probability

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distributions of key input parameters. Daily waste treatment (DWT) values were assumed to satisfy a

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normal distribution with a coefficient of variation (CV) of 10%14. Emission factors were assumed to

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satisfy a normal distribution by data fitting. Table S4 in the Supporting Information summarizes the key

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characteristics of the distribution function curves for the emission factors. After 10,000 Monte Carlo

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sampling processes, the uncertainties of the emission inventory at the national level and the air

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pollution-related health risk at the national level, provincial level and county level were generated. The

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methods used for risk assessment are introduced in the following section.



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Atmospheric dispersion model The Gaussian plume model, a commonly used model for simulating the diffusion of pollutants from

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an elevated continuous point source, was used to estimate the ambient concentrations of multiple

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pollutants emitted from each MSW incinerator within predefined domains. Data inputs for the Gaussian

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plume models included the plant-level emission data from MSW incineration, meteorological data and

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elevation data. Meteorological data were collected from the China Meteorological Administration, which

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provided detailed data from 2160 weather stations in China25. Using simple mathematical calculations,

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each single MSW incinerator was matched with meteorological condition data derived from the closest

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weather station. The altitudes of both MSW incinerators and weather stations were derived from the

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SRTM 90 m DEM version 4 data set of the Consultative Group for International Agricultural Research

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Consortium for Spatial Information (CGIAR-CSI)26. After obtaining the pollutant concentrations around

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each incinerator from the Gaussian plume model, we overlaid and aggregated the pollutant

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concentrations from all incinerators to a 1 km-resolution grid over China. The detailed processes of

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Gaussian plume modeling are described in Text S2 in the Supporting Information. It should be noted that

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we did not consider the atmospheric transmission processes of dry and wet deposition in this model due

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to lack of data. This may have yielded some uncertainties or overestimation of the ambient pollutant

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concentrations.

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Health risk assessment and ranking

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The Risk Quotients Model was used to evaluate the contaminants’ risk based on their chemical

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toxicity27. Further description of the model is summarized in Text S3 in the Supporting Information. By

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superimposing the gridded population density spatial distribution data on the exposure data,

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population-weighted HI and CR values were calculated using Eq. (2) and Eq. (3) to indicate the


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non-carcinogenic risk and carcinogenic risk for 31 provinces and approximately 3000 counties,

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respectively. The aggregated risk levels at both the provincial and county level were further applied to

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identify the riskiest areas and the riskiest pollutant in a certain area. ∑&(∑%(%,&,' /)*% )×&,' )

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"#$ =

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+$ =

∑& &,' ∑&(∑%%,&,' ×,-./0,% )×&,' ! ∑& &,'

(2) (3)

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Where U represents the region code (provinces or counties); k represents the grid code in region U,

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k∈U; Cj,k,U represents the concentration of pollutant j in grid k of region U aggregated from the outputs

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of the Gaussian plume model; HIU represents the population-weighted hazard index in region U; CRU

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represents the population-weighted carcinogenic risk in region U; Pk,U represents the population in grid k

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of region U; RfCj represents the reference concentration of pollutant j for non-carcinogenic risk in mg/m3;

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and SFinh,j represents the inhalation slope factor of pollutant j for carcinogenic risk in (mg/m3)-1.

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The data for the reference concentrations and the inhalation slope factors for different pollutants

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were obtained from the Toxicity Database in Risk-Screening Environmental Indicators (RSEI)28. This

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database combines toxicity data from several authoritative sources, such as the EPA’s Integrated Risk

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Information System (IRIS), the Agency for Toxic Substances and Disease Registry (ATSDR), and the

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California Environmental Protection Agency (CalEPA). RSEI uses surrogate values for the various

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toxicity parameters in each category that contain similar chemicals29. In other words, surrogate toxicity

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values of chemicals are modeled based on the estimated emission fraction and toxicity of each type of

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valence and existence in the category that contain similar chemicals. See Text S3 and Table S5 in the

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supporting information for more details.

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Here, we considered only inhalation intake as the specific exposure pathway to describe the risk

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characteristics of MSW incineration, which was mainly due to the availability of data and the reliability



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of the method (see Text S3 for more explanations). Taking into account of the applicability of the Risk

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Quotients Model, the compliance with the Standard-2014 and the stigma effects of heavy metals and

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PCDD/Fs, this study included Cd, As, Cr, Ni, Pb, Hg and PCDD/Fs in the HI calculation, while only Cd,

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As, Cr, Ni and PCDD/Fs were included in the CR calculation, as Pb and Hg have not been proven to

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have carcinogenic risks through respiratory exposure according to the RSEI database. It should be noted

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that as the RSEI toxicity database only reported the carcinogenic and non-carcinogenic risk of PCDD/Fs

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through oral intake rather than respiratory exposure28. So we conducted the risk assessment of PCDD/Fs

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by converting the risk parameter of oral intake (RfD and SForl) to that of inhalation exposure (RfC and

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SFinh), by Eq. (S8) and Eq. (S9). See Text S3 and Table S6 in the supporting information for more details

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about the selection of targeted pollutants and conversion process. The gridded population density data at

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the resolution of 1 km adopted in this study were from the Institute of Geographic Sciences and Natural

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Resources Research, Chinese Academy of Science30, which has been widely used in high-quality studies

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published in top international journals31-32. All analyses were conducted using RStudio (Version 1.0.153)

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and ArcGIS 10.3 for Desktop (Version 10.3.0.4322).

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RESULTS AND DISCUSSION

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Multiple pollutant emission characteristics

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The results of the localized emission factors of MSW incineration in this study were compared with

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various existing datasets (see Table S7 and Table S8 for more information), such as that of the Tian et al.

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(2013)33, the Compilation of Air Pollutant Emission Factors (AP42)9, the EMEP/EEA Air Pollutant

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Emission Inventory Guidebook-2016 (EEA-2016)10, and the Toolkit for Identification and Quantification

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of Releases of Dioxins, Furans, and Other Unintentional POPs (UNEP-POPS-TOOLKIT)11. First, as

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shown in Table S7, except for Cd and Cr, most emission factors used in this study were smaller than (or

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almost equal to) those in the study of Tian et al. 14. This was mainly attributable to the fact that we


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included more results from the latest field tests conducted after the release of stricter new emission

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standards for MSW incineration. Additionally, by comparing our data with those of AP42, EEA-2016

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and UNEP-POPS-TOOLKIT, we found that the localized emission factors for China’s MSW incinerators

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decreased continuously to approximate the levels in the United States with the implementation of new

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emission limits. Specifically, the mean emission factor of PCDD/Fs entering into the air in the present

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study is 1.2 µg TEQ/t MSW incinerated, which is calculated based on Chinese localized field tests. In

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the UNEP-POPS-TOOLKIT, MSW incinerators are classified into four groups with unique PCDD/Fs

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emission factors by considering the technology of the MSW incinerator and the APCD (See Table S8). It

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could be found that the mean emission factor of PCDD/Fs entering into the air in the present study was

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within the range of 0.5-30 µg TEQ/t MSW incinerated (Class 3 to Class 4) in the

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UNEP-POPS-TOOLKIT (See Table S8). This trend is consistent with the development of emission

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standards in China. As early as the release of the Standard for Pollution Control on the Municipal Solid

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with Waste Incineration (GB18485-2001) (short form, Standard-2001), APCD was required to be

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equipped in China. So far, most MSW incinerators in China have been equipped with multiple APCD,

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which meets the requirement of class 3 in the UNEP-POPS-TOOLKIT. What’s more, as the release of

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Standard-2014, the emission standard of PCDD/Fs has been improved from 1 to 0.1 ng-TEQ/m3, which

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meets the requirement of class 4. Given the fact that the Standard-2014 does not come into force until

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2016, the overall technologic level of MSW incineration in China in 2015 should be the mix of class 3

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and class 4. On this basis, the emission factors used in the present work are thought to be comparable

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with the corresponding levels in UNEP-POPS-TOOLKIT and also reflect the realistic situations in

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China.

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Altogether, 76449 t of NOX, 25725 t of SO2, 16937 t of CO, 9279 t of HCl, 5629 t of PM, 33 t of Cr,

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33 t of Pb, 20 t of Hg, 6 t of Cd, 4 t of Ni, 0.4 t of As, and 94 g-TEQ of PCDD/Fs were emitted from 222



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MSW incinerators in China in 2015 (Figure 2). The uncertainties of the total emissions were quantified

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using Monte Carlo simulation, producing a 95% confidence interval (CI) ±0.27% for Cr, ±0.79% for As,

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±0.86% for PM, ±0.88% for NOx, ±1.09% for CO, ±1.18% for Pb, ±1.25% for HCl, ±1.43% for Cd,

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±1.46% for SO2, ±1.76% for Ni, ±2.62% for PCDD/Fs, and ±3.01% for Hg. The largest source of

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uncertainty was the emission factor, especially those for Hg, PCDD/Fs and Ni. Compared with the

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emissions in 2010 reported by Tian et al. (2013), air pollutant emissions increased dramatically in 2015,

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ranging from 21% (PM), 51% (As), 59% (Ni), 113% (SO2), 157% (HCl), 161% (CO), 169% (NOx), 253%

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(Pb), 298%(PCDD/Fs), 595% (Cd), to 650% (Cr) (Figure 2). Among 12 pollutants, only Hg emissions

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decreased by approximately 46% from 2010 to 2015. This may have been attributable to the impressive

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improvement in the emission standards for Hg in the new Standard-2014 by 75%, considering its high

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transport ability15, 34. For PCDD/Fs emissions, total amount from MSW incinerators in 2015 in the

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present study is comparable with the first official emission inventory of China35, wherein a total of 125.8

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g TEQ of PCDD/Fs was released into the air in 2004 by MSW incineration (see Table S9 for more

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details). Given that the MSW incineration treatment capacity in 2015 was over 10 times (214785 t/d) as

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high as that in 2004 (19958 t/d), and the emission standard of PCDD/Fs became 10 times more stringent

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(1 ng TEQ/m3 to 0.1 ng TEQ/m3) from 2004 to 2015, the results in the present study are also reasonable

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compared to those in former studies. Certainly, it should be noted that due to lack of historical

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time-series data after Standard-2014, temporal trends based on studies with discrepancies in the

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approaches and data sources can only provide a quick diagnosis for policy makers. Refined studies on

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the temporal variations in MSW incineration emissions are still needed in the future.


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Figure 2.. Total air pollutant emissions from MSW incinerators in China and the 95% confidence

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intervals (CI) obtained using the Monte Carlo simulation. The black error bars indicate the 95% CI of

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the emissions.

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A strong heterogeneity of air pollutant emissions was observed among regions in China (Figure 3).

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As shown in Figure 3A, Jiangsu, Zhejiang, Guangdong and Shandong, with the highest MSW treatment

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capacities, were also the top four provinces that contributed the most to the total air pollutant emissions

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from MSW incinerators in China. They emitted 12480 t SO2, 37948 t NOx, 7879 t CO, 4586 t HCl, 2682

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t PM, 17 t Cr, 16 t Pb, 10 t Hg, 3 t Cd, 2 t Ni, 0.2 t As, and 47 g-TEQ PCDD/Fs, which accounted for 47%

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- 51% of the national emissions in 2015. Certainly, this does not indicate that MSW treatment capacity

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or quantity was the only determinant for air pollutant emissions. We further calculated the per capita

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emissions by dividing the total emissions by the urban population that produced the incinerated waste to

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better understand the determinants of MSW incineration emissions (Figure 3B). After excluding the

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effects of population scales, the distribution of per capita emissions became greater among provinces



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compared with the total emissions but still had observable heterogeneity. The remaining heterogeneity

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was determined by the interactions among per capita MSW production, the proportion of incineration,

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and the application rates of FBI and GFI technology in MSW incinerators. Specifically, coastal

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developed provinces, such as Jiangsu, Zhejiang, Hainan, Fujian, and Shanghai, often have higher per

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capita MSW production accompanied with provinces with improved living standards (Table S10)36. On

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the other hand, these provinces are faced with more strict regulations by the central government on the

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proportion of incineration37. Altogether, this resulted in per capita emissions in these five provinces that

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were 126%, 113%, 90%, 124%, 103%, 130%, 124%, 139%, 118%, 128%, 128%, and 130% higher than

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the national levels for NOX, SO2, CO, HCl, PM, Hg, Pb, Cr, Cd, Ni, As, and PCDD/Fs, respectively.

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More importantly, these provinces with high per capita emissions also had high population densities,

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which would amplify the health risk associated with MSW incineration, as discussed in the following

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subsections. In addition, the application rates of FBI and GFI technology in MSW incinerators also

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determined the emission performance of certain provinces. Unlike per capita MSW production and the

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proportion of incineration, the effects of the application rates of FBI and GFI technology on emissions

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varied across different pollutant types, which resulted in the rank of per capita emissions in the top five

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provinces also being varied across different pollutant types (Figure 3B).


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Figure 3. Air pollutant emissions from MSW in 28 provinces of mainland China. (A) Total emissions

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and (B) Per capita emissions. Tibet, Qinghai and Gansu were not included because there were no MSW

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incinerators by March 2016. Hong Kong, Macao and Taiwan were not included due to a lack of data.



307 308

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National average health risk levels from MSW Before assessing the risk, we compared the results of Gaussian plume model with field tests and

309

modeling results from literatures that conducted around MSW incineration plants in China. Overall, our

310

results are convincing comparing with former field tests results and modeling results. The detailed

311

explanations are summarized in Text S2 and Figure S4 in the supporting information.

312

Table 1 presents the national average non-carcinogenic risk levels (indicated by HI) and

313

carcinogenic risk levels (indicated by CR) and a comparison with existing studies. Overall, the national

314

average HI in China was 1.88×10-2 with a 95% confidence interval (CI) of 1.87×10-2 - 1.90×10-2, which

315

was far lower than the maximum acceptable level (HI ≤ 1). However, the national average CR was

316

5.71×10-6 with a 95% confidence interval (CI) of 5.70×10-6 - 5.72×10-6, which was approximately

317

five-fold higher than the acceptable level defined by the US EPA for carcinogenic effects (CR ≤ 1×10-6).

318

The basic trends of the magnitude of the risk contributors were Cd > Cr > Pb > Hg > Ni > PCDD/Fs >

319

As for non-carcinogenic risk and Cr > Cd > PCDD/Fs > As > Ni for carcinogenic risk (Table S11). Cd,

320

Cr and Pb were the dominant components that contributed most to the non-carcinogenic health risk,

321

accounting for 47%, 24% and 16%, respectively. Regarding the carcinogenic risk, Cr held the majority

322

of the risk contribution (more than 96%), mainly because of its strong toxicity and relatively high

323

emissions.

324

There were several important differences between our analysis and previous studies (Table 1),

325

including the scope of sources, the method of compiling emission inventory, the choice of exposure

326

pathways and the method of risk assessment. First, existing studies have usually focused on a single

327

MSW incineration site or a single pollutant as the sources of risk, while this research studied the risk of

328

multiple pollutants emitted from all MSW incineration sites across China. Second, more detailed

329

information including incinerator types, technology levels, the application of APCD and emission


Page 19 of 32


330

regulations were used for the compilation of localized emission inventories, which increased the

331

reliability of the data input for risk assessment. Third, whereas some existing studies based their

332

estimates on multiple exposure pathways including inhalation, dermal absorption and ingestion, here, we

333

only considered the inhalation pathway, which may lead to lower estimates in this study than in previous

334

studies17. Last, compared with traditional methods using average pollutant concentrations as the proxy of

335

exposure, this study used the population-weighted method to describe the risk burden38-41. In spite of the

336

discrepancies mentioned above, our results generally confirmed that the air pollution-related health risks

337

of MSW incineration in China stayed at a relatively high level and provided additional spatial richness.



338

Page 20 of 32

Table 1. National average health risk attributable to MSW incineration in China and comparisons with other studies. Index

Sourcea

Country

Pathwayb

Pollutant

Risk

Reference

HI

222 MSWIP

China

Inh

Pb + Hg + Cd + As + Cr + Ni

1.88×10-2

This study

15 MSWIP

China’

Inh + IngS + D

Hg

2×10-2 ~ 7.5×10-1

(Zhou et al., 2015)42

8 MSWIP

China

Inh

Pb

< 1.83×10-3

(Liao et al., 2014)38

1 MSWIP

China

Inh + IngS + D

PCDD/Fs

1.67×10-4 ~ 2.99×10-4

(Hsieh et al., 2018)41

1 MSWIP

Spain

IngS

Pb + Hg + Cd + As + Cr + Ni + Be + 1.15×10-1 Mn + V

1 MSWIP

Italy

Inh + IngS + IngF + D Pb + Hg + Cd + As + Cr + Ni + Mn + 1.67×10-3 ~ 3.02×10-3 Zn + B(a)P + PCDD/Fs

1 MSWGP

Italy

Inh + IngS + IngF + D Hg

6.53×10-6 ~ 3.77×10-4

(Lonati et al., 2013)40

222 MSWIP

China

Inh

Cd + As + Cr + Ni

5.71×10-6

This study

1 MSWIP

China

Inh + IngS + D

PCDD/Fs

5.65×10-9 ~ 36.7

(Hsieh et al., 2018)41

1 MSWIP

Italy

Inh + IngS + IngF + D PCDD/Fs

6.70×10-10


1 MSWGP

Italy

Inh + IngS + IngF + D PCDD/Fs + Cd

1.60×10-10 ~ 2.7×10-8


1 MSWIP

Italy

Inh + IngS + IngF + D Cd + As + Cr + Ni + Pb + B(a)P + 1.88×10-7 ~ 4.54×10-7 PCDD/Fs

CR

339

a

MSWIP = Municipal solid waste incineration plant; MSWGP = Municipal solid waste gasification plant

340

b

Inh = Inhalation; IngS = Ingestion of soil; IngF = Ingestion of food; D = Dermal absorption from soil


(Schuhmacher et al., 1997)43 (Morselli et al., 2011)39

(Morselli et al., 2011)39

Page 21 of 32

341 342


Spatial patterns of health risks from MSW Figure 4A and Figure 4B present the spatial distributions of HI and CR attributable to MSW

343

incineration at the province level and county level, respectively. All provinces suffered

344

non-carcinogenic effects that were far lower than the maximum acceptable level (HI ≤ 1). The

345

highest non-carcinogenic risk was observed in coastal provinces with HIs higher than 10-2, including

346

a majority of east China (1.73×10-2 - 9.61×10-2), Jing-Jin-Ji (1.66×10-2 - 7.45×10-2), Guangdong

347

(3.14×10-2), Hubei (2.12×10-2), Chongqing (1.87×10-2), and Sichuan (1.12×10-2). Northeastern China

348

(2.04×10-3 - 7.19×10-3), Henan (6.07×10-3), Yunnan (5.22×10-3), Hainan (4.33×10-3), Jiangxi

349

(4.07×10-3), Hunan (3.60×10-3), Shaanxi (2.04×10-3), Guangxi (1.92×10-3), and Shanxi (1.03×10-3)

350

experienced moderately high non-carcinogenic risks, with HIs of 10-3 - 10-2. For carcinogenic effects,

351

17 provinces suffered a carcinogenic risk that exceeded the acceptable level (CR ≤ 1×10-6), 7 of

352

which were even higher than 10-5. Specifically, the highest CR was found in Shanghai (3.30×10-5),

353

followed by Beijing (2.70×10-5), Jiangsu (1.84×10-5), Zhejiang (1.51×10-5), Tianjin (1.48×10-5),

354

Guangdong (1.09×10-5), and Fujian (1.07×10-2). From the perspective of health protection, we

355

suggest that China’s Ministry of Environmental Protection (CMEP) adopt spatial–differentiation

356

strategies that impose stricter controls in provinces where the carcinogenic risk exceeds the

357

acceptable level.

358

At the county level, maps of health risks attributable to MSW incineration exhibited strong

359

spatial gradients, even within a given province (Figure 4). For the most part, the emergence of risky

360

hot spots was highly correlated with the sites of MSW incinerators. However, some mismatches were

361

observed in Beijing, Shandong, Jiangsu, Zhejiang and Guangdong, where there were risky hot spots

362

but no MSW incinerator sites, and in Guizhou, Ningxia, and Shanxi, with the opposite situation

363

(Figure 4). The former could be explained by the transboundary transport of air pollutants among

364

adjacent regions45. The latter may be attributable to the sparse population densities around the MSW

365

incinerator sites. As mentioned above, the population-weighted method used in this study tended to



366

produce relatively higher estimates of risk levels due to the amplified effects of the spatially

367

overlapped distribution of population and MSW incinerators in most cases32. Specifically, more than

368

70% of the provinces had a significant decline in risk, ranging from 2% to 80%, if we assumed

369

uniform population distributions, as traditional methods did (Table S12). In other words, most MSW

370

incinerator sites in China were located in areas where the population density was higher than the

371

average. However, in cases such as Xia county in Shanxi, Huade county in Inner Mongolia, Yuqing

372

county in Guizhou, Tuodexun county in Xinjiang, and Haiyuan county in Ningxia, the risk

373

attributable to MSW incineration increased by 0.70% - 225% if we assumed uniform population

374

distributions, as traditional methods did (Table S12). This indicated that MSW incinerator sites in

375

these counties were located in areas where the population density was sparse and lower than the

376

average.

377 378

Figure 4. Spatial distributions of health risks attributable to MSW incineration at the provincial level

379

and county level: (A) Carcinogenic risk, CR; (B) Non-carcinogenic risk, HI. Hong Kong, Macao and

380

Taiwan were not included due to a lack of data. (Map sources: Resource and Environment Data

381

Cloud Platform, Institute of Geographic Sciences and Natural Resources Research;

382

http://www.resdc.cn/, accessed March 20, 2016)

383


Page 22 of 32

Page 23 of 32

384 385


Implications Based on a bottom-up multiple-pollutant inventory of MSW incineration at the plant level and

386

the atmospheric dispersion model, we observed high-level and spatially heterogeneous air

387

pollution-related health risks attributable to MSW incineration across China. These new results for

388

the whole nation add useful spatial dimensions to prior estimates and provide implications for coping

389

with risk issues raised by MSW incineration from the aspects of accelerating technology upgrades,

390

strengthening emission standards, optimizing site selection and enhancing risk communication.

391

Currently, the main technologies used for MSW incinerators in China are FBIs and GFIs, which

392

have quite different treatment procedures accompanied with varied performances in pollution

393

emissions, as mentioned above. We assumed two scenarios with 100% FBIs and 100% GFIs to

394

compare the technologies from the perspective of risk management. The results showed that FBIs

395

were a comparatively risk-decreasing technology that could further reduce the CR and HI by

396

5.23×10-6 and 5.60×10-3, respectively, under a 100% FBIs scenario, while GFIs were a

397

risk-increasing technology that would increase the CR and HI by 2.10×10-6 and 2.30×10-3 under a

398

100% GFIs scenario, respectively (Table 2). In other words, even with stagnant technology

399

innovation, the risk attributable to MSW incineration could be limited to within acceptable levels,

400

except for in Beijing, Tianjin, Shanghai, Jiangsu and Zhejiang, as long as 100% replacement of FBI

401

technology is achieved in China (Figure S5). It should be noted that the application of FBI

402

technology requires stricter conditions in that MSW burned in FBIs need to be classified and

403

shredded. Therefore, we recommend that the Chinese government further promote classified

404

collection and shredding of MSW to facilitate the wide application of FBI technology.

405

Certainly, it is not wise to expect that one effort could address all issues. In this case, it will take

406

a relatively long time to achieve 100% replacement with FBI technology. During this process, it is

407

uncertain whether the risk attributable to MSW incineration could be limited to within acceptable

408

levels. In this context, we recommend strengthening emission standards as a synergetic solution.



Page 24 of 32

409

Though China already updated emission standards for MSW incineration in 2014, there are still

410

significant gaps compared with standards set by developed countries, especially for the restriction of

411

heavy metal emissions. It is not surprising that strict emission standards are incentives for technology

412

innovation, which would be reflected by a decrease in localized emission factors. To estimate

413

potential risk reductions from strengthening emission standards, we re-performed the risk assessment

414

by replacing emission factors in the main analysis with those from AP42 and EEA-2016. Under the

415

AP42 and EEA-2016 replacement scenarios, the national average CR was reduced to under the

416

acceptable level by amounts of 86% and 96%, respectively, and the national average HI was further

417

reduced by 54% and 90%, respectively (Table 2). At the provincial level, Beijing, Tianjin, Shanghai,

418

Jiangsu, Zhejiang, Fujian and Guangdong had CR values higher than 10-6 under the AP42

419

replacement scenario, while no provinces had CR values higher than 10-6 under the EEA-2016

420

replacement scenario (Figure S5). On this basis, we recommend that the Chinese government provide

421

legal and/or monetary incentives for new technology innovation by MSW incinerator producers by

422

setting emission standards as strict as those in Europe.

423 424

Table 2. National average health risks attributable to MSW incineration in China under different

425

scenarios. Scenarios

CR

HI

Main analysis

-6

5.71×10

1.88×10-2

EEA-2016 replacement

2.54×10-7

1.89×10-3

AP42 replacement

8.18×10-7

8.62×10-3

100% GFIs

7.81×10-6

2.11×10-2

100% FBIs

4.78×10-7

1.32×10-2

426 427

Moreover, NIMBY is a disputed and inevitable theme in site selection for new MSW

428

incineration projects. Though the Standard-2014 for MSW incineration set general limits for site

429

selection15, 46, we still observed amplified risk effects from the spatially overlapped distribution of

430

population and MSW incinerators for most provinces in this study. For example, 80.6 million people


Page 25 of 32


431

live within 5 km of MSW incinerators, accounting for approximately 11% of the total population of

432

cities where these MSW incinerators are located. If we assume that people living within 5 km, 10 km,

433

20 km, 30 km, 40 km and 50 km of MSW incinerators moved away, the CR would be reduced by

434

10%, 29%, 60%, 75%, 83%, and 88%, respectively (Figure 5). This analysis provides important

435

information for the Chinese government to set specific distance limits for site selection in new MSW

436

incineration projects.

437 438

Figure 5. Changes in the national average health risk attributable to people living within the buffer

439

moving away: (A) Changes in carcinogenic risk, CR; (B) Changes in non-carcinogenic risk, HI.

440 441

In addition, it was found that emissions from MSW incineration are far lower than those from

442

open burning. Specifically, 314 million tons of waste were burned openly with no treatment in 201336,

443

while only 80 million tons of waste were burned by MSW incinerators in 2015. Consequently, MSW

444

open burning emitted far more pollutants (not only gas pollutants but also heavy metals) than MSW

445

incinerators (Table S13)36, 47. However, people are more resistant to the establishment of MSW

446

incineration plants than open burning, even though the latter produces less pollution. Possible

447

reasons for this tricky social phenomenon are the stigma effects of MSW incineration plants in China.

448

Therefore, we recommend that the government enhance the communication of risks on the basis of

449

solid and convincing evidence in the risk assessment of MSW incineration plants.



450 451

ACKNOWLEDGEMENTS

452

We acknowledged the supports from the National Key R&D Program of China

453

(2016YFC0207603), Natural Sciences Foundation of China (71433007) and Jiangsu Natural

454

Sciences Foundation of China. The work of Y. Liu and S. Sarnat was partially supported by the

455

Office of Global Strategy and Initiatives and the Claus M. Halle Institute for Global Learning at

456

Emory led by Dr. Philip Wainwright through the Emory-NJU Global Partnership Initiative. The

457

contents of this paper are solely the responsibility of the authors and do not necessarily represent

458

official views of the sponsors.

459 460 461

DISCLOSURES The authors declare no competing financial interest.

462 463

ASSOCIATED CONTENT

464

Supporting Information.

465

Additional and detailed description of the atmospheric dispersion model; tables detailing MSW

466

related standards, coefficients used in the risk assessment and uncertainty calculations, comparisons

467

of emission factors, emission inventory and risk levels; and figures with location, productions,

468

emissions, and risk level information for MSW in China are shown here.

469


Page 26 of 32

Page 27 of 32


470

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Toxicological risk by inhalation exposure of air pollution emitted from

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