Estimating premature mortality attributable to PM2.5 exposure and benefit of air pollution control policies in China for 2020.

Science of the Total Environment 612 (2018) 683–693

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Estimating premature mortality attributable to PM2.5 exposure and benefit of air pollution control policies in China for 2020 Kamal Jyoti Maji a,⁎, Anil Kumar Dikshit a,b, Mohit Arora c, Ashok Deshpande d a

Center for Environmental Science and Engineering (CESE), Indian Institute of Technology Bombay, Mumbai 400076, India Urban Environmental Management, School of Environment Resources and Development, Asian Institute of Technology, Pathumthani 12120, Thailand Engineering Product Development Pillar, Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore d Berkeley Initiative in Soft Computing (BISC)–Special Interest Group (SIG)–Environment Management Systems (EMS), Berkeley, CA, USA b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• PM2.5-related mortalities were estimated in major cities in 9 regions in China. • Total premature mortality in 2015 was 652 thousand, which is 6.92% of total death. • Potential health benefits of ongoing air pollution control plans are 4.4% in 2020. • Higher health benefits could be possible if China strongly achieve WHO guidelines.

a r t i c l e

i n f o

Article history: Received 4 May 2017 Received in revised form 24 August 2017 Accepted 26 August 2017 Available online xxxx Editor: D. Barcelo Keywords: PM2.5 Premature mortality 13th five-year plan Target China

a b s t r a c t In past decade of rapid industrial development and urbanization, China has witnessed increasingly persistent severe haze and smog episodes, posing serious health hazards to the Chinese population, especially in densely populated cities. Quantification of health impacts attributable to PM2.5 (particulates with aerodynamic diameter ≤ 2.5 μm) has important policy implications to tackle air pollution. The Chinese national monitoring network has recently included direct measurements of ground level PM2.5, providing a potentially more reliable source for exposure assessment. This study reports PM2.5-related long-term mortality of year 2015 in 161 cities of nine regions across China using integrated exposure risk (IER) model for PM2.5 exposure-response functions (ERF). It further provides an estimate of the potential health benefits by year 2020 with a realization of the goals of Air Pollution Prevention and Control Action Plan (APPCAP) and the three interim targets (ITs) and Air Quality Guidelines (AQG) for PM2.5 by the World Health Organization (WHO). PM2.5-related premature mortality in 161 cities was 652 thousand, about 6.92% of total deaths in China during year 2015. Among all premature deaths, contributions of cerebrovascular disease (stroke), ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), lung cancer (LC) and acute lower respiratory infections (ALRIs) were 51.70, 26.26, 11.77, 9.45 and 0.82%, respectively. The premature mortality in densely populated cities is very high, such as Tianjin (12,533/year), Beijing (18,817/year), Baoding (10,932/year), Shanghai (18,679/year), Chongqing (23,561/ year), Chengdu (11,809/year), Harbin (9037/year) and Linyi (9141/year). The potential health benefits will be 4.4, 16.2, 34.5, 63.6 and 81.5% of the total present premature mortality when PM2.5 concentrations in China meet the APPCAP, WHO IT-1, IT-2, IT-3 and AQG respectively, by the year 2020. In the current situation, by the end of year 2030, even if Chines government fulfills its own target to meet national ambient air quality standard

⁎ Corresponding author at: Center for Environmental Science and Engineering (CESE), Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India. E-mail address: [email protected] (K.J. Maji).

http://dx.doi.org/10.1016/j.scitotenv.2017.08.254 0048-9697/© 2017 Elsevier B.V. All rights reserved.

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K.J. Maji et al. / Science of the Total Environment 612 (2018) 683–693

of PM2.5 (35 μg/m3), total premature mortality attributable to PM2.5 will be 574 thousand across 161 cities. The present methodology will greatly help policy makers and pollution control authorities to further analyze cost and benefits of air pollution management programs in China. © 2017 Elsevier B.V. All rights reserved.

1. Introduction The acute and chronic health impacts of short and long-term exposures to particulate matter (PM) are well-established in the literature (Zanobetti et al., 2008; Pope et al., 1995, 2004, 2011; Anenberg et al., 2011; Cesaroni et al., 2014; Beelen et al., 2014; Hamra et al., 2014; Korek et al., 2015; Brauer et al., 2012; Brunekreef and Holgate, 2002; Shi et al., 2016). Epidemiological cohort studies show that these health impacts rely on long-term ambient (both indoor and outdoor) PM concentrations and the associated risk factors vary from country to country (Pope and Dockery, 2006; Kan and Gu, 2011; Zhou et al., 2014; Zhang et al., 2011; Shang et al., 2013). Among the particulate matter, PM2.5 has more consistent and stronger associations with mortality (Lai et al., 2013a; WHO, 2013; USEPA, 2012; Lu et al., 2015; Kim et al., 2015; Pope et al., 2002; Pope and Dockery, 2006). Under the Global Burden of Disease (GBD) project of Institute for Health Metrics and Evaluation (IHME) and Health Effects Institute (HEI), air pollution has been considered as the high priority area. Air pollution was responsible for 7.6% of all global deaths in year 2015 (IHME and HEI, 2017; Forouzanfar et al., 2016). From 1990 to 2015, global population-weighted PM2.5 concentrations increased by 11.2% (from 39.7 to 44.2 μg/m3) and the increase was somewhat more rapid since 2010. In 2015, 92% of the world's population lived in areas that exceeded the WHO air quality guideline (AQG: 10 μg/m3). Nearly 86% populations in China, India, Pakistan and Bangladesh experienced the most extreme concentrations of PM2.5 (above 75 μg/m3) (IHME and HEI, 2017). Due to the highly exposed population, air pollution poses the fifth highest risk factor for premature mortality in the world (HEI, 2017). Rapid industrial development and urbanization have greatly changed the economic landscape of China in last 30 years, making it one of the fastest growing economy in the world (IMF, 2016; Ellis and Roberts, 2016). However, this growth also has a real cost to its environment and public health, especially in large cities. In the last 20 years, the urban population in China has increased from 30% to 54% and that will reach close to 1 billion by 2030 (WB, 2016; NCE, 2013). In 2010, China's urban residents were responsible for about 7 tons of CO2 emissions per capita (tCO2/capita) energy-related CO2 emissions (Wang et al., 2012; Ohshita et al., 2015). In 2015, the population-weighted average PM2.5 concentration in China was 58 μg/m3, with substantial variation in concentrations among provinces (19–79 μg/m3). Severe air pollution and associated negative health outcomes have added a significant burden to the Chinese economy (WB and SEPA, 2007; Matus et al., 2012; Li et al., 2016; Chen et al., 2017). Previous studies have shown the large differences in estimation of PM2.5-related mortality in China (WB and SEPA, 2007; Lelieveld et al., 2015; Lim et al., 2012; Rohde and Muller, 2015; Wang et al., 2013; Apte et al., 2015; Ford and Heald, 2016; Wu et al., 2017). The GBD assessments study showed 1.14 million premature deaths in 2005 and 1.1 million in 2010 due to PM2.5 and making air pollution 4th leading cause of deaths in China (HEI, 2013; Brauer et al., 2015). The GBD study for 2013 reported that about 5.5 million premature death occurs globally due to PM2.5 and 55% of those deaths occurred in China and India combined. The total premature deaths in China were 0.91 million (IHME, 2016; Salomon et al., 2015). WHO estimated that there were 3 million deaths globally for the year 2012 and China alone contribute 1.03 million (WHO, 2016). While the recent GBD study for 2015 estimated that ambient PM2.5 contributed to 4.2 million deaths globally and China (1.11 million) and India (1.1 million) each had the highest absolute numbers of deaths (IHME and HEI, 2017). Other studies

observed that about 1.22 million and 1.28 million premature death cases occurred due to outdoor air pollution in 2005 and 2010 (OECD, 2014). Lelieveld et al. (2013, 2015) reported that the premature mortality due to PM2.5 increased from 1 million in 2005 to 1.36 million in 2010 in China. Apte et al. (2015) estimated that the PM2.5-related deaths in 2010 were 1.7 million and due to air pollution control policy failure, the deaths will reach 1.59 million in 2030. Other studies estimated 1.26 million (Xie et al., 2016) and 1.2 million (G. Yang et al., 2013a, 2013b) premature deaths due to PM2.5 in China in 2010. Liu et al. (2017) showed that the premature mortality in China increased from 0.80 million in 2005 to 1.25 million in 2012. Most of these studies exploited chemical transport models and/or satellite measurements for ground level PM2.5 concentration. PM2.5 was added into National Ambient Air Quality Standard (NAAQS) in 2012 (GB3095-2012). The Ministry of Environmental Protection of China (MEPC) started to open the access of PM2.5 data at each national air quality monitoring site (NAQMS) of some major cities through the official website since 2013 (Zhang and Cao, 2015; Zhao et al., 2016). By the end of 2014, the Chinese government had established N 1436 air-monitoring stations in 367 cities for real-time and publicly available data on six pollutants: PM10, PM2.5, SO2, NO2, O3 and CO (Koleski, 2017). Such intensive ground-based monitoring networks made it possible to obtain insights into the real data-driven spatial variation of PM2.5 mass concentrations across China. In recent years, the premature mortality due to PM2.5 is estimated based on groundbased air quality monitoring PM2.5 data. The estimated PM2.5mortalities in 2013 were 1.37 million (Liu et al., 2017) and 1.52 million in 2015 (Song et al., 2017). In national studies based on city based estimations, total PM2.5-related premature deaths were 1.03 million in 74 cities in 2013 (Fang et al., 2016) and 0.72 million in 190 cities in 2014–15 (Maji et al., 2017). Rohde and Muller (2015) observed that PM2.5 is responsible for 1.6 million deaths in 2014 (17% of all reported deaths). Very diverse estimation of PM2.5-attributed premature deaths has been reported in the past studies for China. Chen et al. (2013) suggested that premature mortalities estimations for China were significantly overestimated. The difference in estimated PM2.5-related deaths are mainly due to - the use of different approaches for population exposure assessment (Brauer et al., 2012; Fang et al., 2016) and very diverse selection of exposure-response functions (ERF) (or relative risk) (Zhang et al., 2010; Burnett et al., 2014; Fang et al., 2016). In GBD studies, the relative risk of premature mortality due to PM2.5 is calculated based on non-linear integrated exposure risk function (IER) which estimates slightly higher value than other methods like non-linear power law (NLP) function (Chowdhury and Dey, 2016). The use of direct relative risk in a log-linear function that gives a much higher value of PM2.5related death (Madaniyazi et al., 2015), like Fang et al. (2016) reported that the 32% of total health in 74 cities in China due to PM2.5. The use of different baseline incidence rate (Liu et al., 2017; Xie et al., 2016) and age distribution are likely to be responsible for the difference in the results. The other uncertainty is the estimation of ground level PM2.5. Early estimation of mortality attributed to PM2.5 mainly relied on satellite data or chemical transport models (Saikawa et al., 2009; Evans et al., 2013; van Donkelaar et al., 2010; Apte et al., 2015; Barrett et al., 2010; Fang et al., 2013; Giannadaki et al., 2014; Lelieveld et al., 2015; Liu et al., 2016) to include huge rural areas in China with large rural populations, where measurements were unavailable, and there was often lack of validation with ground measurements (Chen et al., 2013; Ford and Heald, 2016). Ford and Heald (2016) reported that the premature


mortality in China from PM2.5 exposure during 2004–11 was 1.42 million per year when estimated using satellite data and 2.38 million per year by chemical transport model based estimations, highlighting how different approaches could significantly change the mortality estimations. To curve PM2.5 pollution, an action plan of Air Pollution Prevention and Control was initiated in 2013 (State Council, 2013; BMEMC, 2014). According to the plan, the targeted PM2.5 concentration in different cities should be achieved by year 2017. Because of the intensive planning to reduce coal consumption, removal of old vehicles, use of clean fuel, green energy and advance air pollution removal technology, PM2.5 concentration in many Chinese cities have been improving (Clean air Asia, 2016). Based on 13th five-year plan (FYP) and other city level plan in 2016, China further wants to reduce PM2.5 in most of the cities by 2020 (Koleski, 2017). The detailed concentration goals of the Air Pollution Prevention and Control Action Plan (APPCAP) in 2020 are listed in Table 1 (NPC, 2016; Lin, 2016; Hao, 2016; Taguiam, 2016; Jing, 2016; Tang et al., 2017). However, there is a huge gap between the goals and the NAAQS of China and the WHO-AQG (WHO, 2006). This study aims to estimate the health effects of PM2.5 pollution in China during year 2015 based on IER model and the potential health benefits of meeting the APPCAP, three WHO interim targets (ITs) and WHO-AQG in 2020 across nine regions of China. Presented research is based on newly available air quality monitoring data of PM2.5 concentrations in 161 cities during 2015. The results will provide the impetus and scientific basis for relevant policy-making by pollution control authorities for air pollution management programs in China and enhance the public participation in the fight against air pollution. 2. Methodology and data 2.1. Mortality estimation equation An integrated exposure risk (IER) model (Burnett et al., 2014) for PM2.5 exposure-response functions (ERF) was used to estimate premature deaths attributable to ambient PM2.5 in 161 cities across nine regions of China during the year 2015. The IER functions incorporate data from cohort studies of ambient air pollution and tobacco smoke to describe the exposure-response relationship throughout the full distribution of ambient PM2.5 concentrations, including the extremely high levels that may appear in China. It has already been employed by the GBD study to estimate global mortality of ambient and household air pollution in 2010 (Burnett et al., 2014; Lelieveld et al., 2015). Following the GBD approach, the estimated mortality for five leading causes of deaths attributable to PM2.5: cerebrovascular disease (stroke, CEV), ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD) and lung cancer (LC) among adults (≥25 years) and in addition, the acute lower respiratory infection (ALRI) was calculated for infants (b5 years). The relative risk (RR) was calculated through Eq. (1); ( RRIER ðC a Þ ¼

δ 1 þ α 1−exp−γðC a −C 0 Þ ; 1;

if C a NC 0

else

) ð1Þ

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where Ca is the annual average ambient PM2.5 concentration; C0 is the threshold concentration; and α, γ and δ are parameters used to describe the different shapes of the exposure-response curve among various diseases (Burnett et al., 2014; Jiang et al., 2015). The RR can be converted to the attributable proportion (AP), as defined by Eq. (2) AP ¼

RR−1 RR

ð2Þ

ΔEmort ¼ AP BIR EP

ð3Þ

The excess number of mortality (health impacts) (ΔMmort) attributable to PM2.5 were then estimated using Eq. (3) (L. Wang et al., 2015; Lelieveld et al., 2015; Liu et al., 2017; Maji et al., 2016), in which BIR is the baseline incidence rate of a given health impact and EP is the exposed population. BIR is cause-specific mortality rate per unit population for a particular age group. For example, in 2015 the acute lower respiratory infection (ALRI) related infants (b 5 years) mortality was 0.0003 per unit population (i.e. 30 per 100,000 population). The estimate of RR is basically impacted by: (1) Ca, the exposure PM2.5 concentration, ranging from 10 to 107 μg/m3 in this study; (2) in the past study (Zhang et al., 2015; Lim et al., 2012; Evans et al., 2013; Burnett et al., 2014), the theoretical-minimum-risk exposure concentration (threshold) ranged from 5.8 to 8.8 μg/m3 was reported but in this study, C0 is assumed to be 5.8 μg/m3, as suggested by Apte et al. (2015); (3) α, γ, and δ, parameters estimated with a nonlinear regression method and IHME (2013) provides 1000 sets of (α, γ, δ) parameters). The premature mortalities are presented as means with 95% confidential intervals (CI). The uncertainties were estimated by 1000 Monte Carlo simulations using 1000 sets of the joint parameter distributions (α, γ, δ, C0) in the IER functions for each disease and for specific age group, which is publicly available at IHME (2013). 2.2. Data The analysis of China's premature deaths attributable to ambient PM2.5 is based on three different categories of data sources. (1) Annual ambient PM2.5 concentrations in 161 cities across nine regions (BTH: Beijing-Tianjin-Hebei, YRD: Yangtze River Delta (YRD), PRD: Pearl River Delta (PRD), SC: South China, SWC: Southwest China, NWC: Northwest China, NEC: Northeast China, NC: North China and EC: East China) of China were downloaded from the website of the China National Environmental Monitoring Center (http://113.108.142.147: 20035/emcpublish/). The quality assurance and controls of statecontrolled monitoring data were reported in previous studies (Zhang and Cao, 2015; Zhao et al., 2016). (2) City wise population and age distribution data have been sourced from census data of National Bureau of Statistics of China (Zhang and Cao, 2015; NBSC, 2015) and projected for the year 2015 and 2020 (WB, 2017) (Table S1). (3) The disease-specific mortality rate is very different from province to province and city to city, for example- the lung cancer mortality value (per 100,000 population) in Beijing, Shanghai, Wuhan and Shenzhen city was 45, 76, 27 and 8 respectively (NBSC, 2015). The disease-specific baseline incidence rates

Table 1 Scenario settings of the PM2.5 concentration and population. Scenarios

Cities considered

Target concentration of each city

Average population and age distribution in each city

Base year – 2015 APPCAP – target in 2020 WHO – target in 2020

161 cities in 9 regions 13 cities in BTH; 25 cities in YRD; 9 cities in PRD; 7 cities in EC; 107 other cities 161 cities in 9 regions

Ground level observed data (Table S1) Reduction ratio from 2015 to 2020: for cities in BTH, 32%; YRD, 20%; PRD, 10%; (Tai'an, Binzhou, Jining, Laiwu, Liaocheng, Dezhou, Zibo), 35% and other 107 cities 18% IT-1 (35 μg/m3) IT-2 (25 μg/m3) IT-3 (15 μg/m3) AQG (10 μg/m3)

Observed data in 2015 Estimated in 2020 Estimated in 2020

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(BIR) (Eq. (3)) for stroke, IHD, COPD, LC and ALRI were derived based on the national disease specific mortality in the dataset of GBD study for 2015 (http://vizhub.healthdata.org/gbd-compare/). Disease-specific mortality rate has been converted to single base line mortality value for normalization across the country with reduced uncertainty in results. Single base line mortality value has been fixed for all cities in China at 137, 105.6, 65.9, 42.9 and 30 per 100,000 population for stroke, IHD, COPD, LC and ALRI, respectively. 2.3. Setting of the concentration scenarios Five reduction scenarios have been developed to assess the potentially avoidable premature mortality in 2020 by cutting down PM2.5 concentration across China, the APPCAP-target scenario, three WHO Interim Targets (ITs) (35, 25 and 15 μg/m3 for IT-1, IT-2, and IT-3 respectively) and WHO AQG (10 μg/m3) (WHO, 2006) as listed in Table 1. The first interim target (IT-1) is also the NAAQS of China (MEPC, 2012). In the base year scenario, the observed data of the monitored PM2.5 concentrations and the population of the leading cities in 2015 were used. The key assumptions include: (1) the BIR kept constant for different mortality and (2) If PM2.5 concentration for a city is less than ITs, that concentration is considered as such for a future year. 3. Results and discussions The present study analyzes PM2.5 data, collected from recently established air quality monitoring network by the MEPC in 161 major cities during 2015. These cities are located across the nine regions in China (Table S1). The annual average PM2.5 concentration is 55 ± 19 μg/m3 (ranging from 17 to 107 μg/m3), which has severely exceeded the new NAAQS (35 μg/m3) of China and WHO AQG (10 μg/m3). According to the new NAAQS, as many as 132 cities cannot meet the standard, accounting for 82% of the total number of cities investigated in this study. Top five cities having high PM2.5 concentrations are—Baoding (107 μg/m3), Xingtai (101 μg/m3), Liaocheng (101 μg/m3), Dezhou (101 μg/m3) and Hengshui (100 μg/m3). The five cities having low PM2.5 concentrations are—Sanya (17 μg/m3), Haikou (22 μg/m3), Lhasa (26 μg/m3), Huizhou (27 μg/m3) and Ordos (27 μg/m3). The annual average PM2.5 concentration in BTH, YRD, NC and EC region is 77, 53, 63 and 67 μg/m3 respectively. Analyzed 161 cities are home to 853 million people, about 62% of Chinese population. 3.1. Premature mortality due to PM2.5 Long-term exposure to PM2.5 is associated with increased mortality in adult (≥25 years) from stroke, IHD, COPD and LC, and it is also associated with increased incidence of acute ALRI in infants (b 5 years). Based on the health impact function, (Eq. (3)), premature mortality by stroke, IHD, COPD, LC and ALRI has been calculated along with the corresponding uncertainties (95% Confidence Interval) for all 161 cities. A summary of cause specific mortality attribute to PM2.5 in 161 cities in China is shown in Fig. 1. The total premature mortality attributable to PM2.5 in 161 cities was 652 thousand (95% CI: 298–902 thousand). The stroke and IHD related to PM2.5 cause 337 thousand (95% CI: 118–432) and 171 thousand (95% CI: 119–266) premature death which was 17.8% and 11.7% of cause-specific death in China. COPD, LC and ALRI contribute 76.5 thousand (95% CI: 37.5–110.8), 61.6 thousand (95% CI: 20.4–86.3) and 5.3 thousand (95% CI: 3.7–6.7) premature death and those are about 8.4, 10.6 and 21.4% of cause specific death in China in 2015. PM2.5-related stroke and IHD causes about 78% of total PM2.5related death. The total PM2.5-related death in BTH, PRD, YRD, SC and EC region was 95.2 thousand (95% CI: 45.4–127.7), 35 thousand (95% CI: 15.4–50.5), 122 thousand (95% CI: 54.3–196), 80.9 thousand (95% CI: 36.2–113.6) and 111.9 thousand (95% CI: 52.2–152.9) respectively. In total premature mortality, BTH, YRD, PRD, SC, SWC, NWC, NEC, NC and EC region contribute 14.60, 18.71, 5.37, 12.41, 10.63, 4.75, 8.18,

8.20 and 17.16% respectively. Cities with a large population with high PM2.5 concentration in developed metropolitan regions are facing large number of premature deaths. In BTH region, cities with N 10,000 deaths were Tianjin [12,533 (95% CI: 5784–17,018)], Beijing [18,817 (95% CI: 8937–25,233) and Baoding [10,932 (95% CI: 5540–14,315)]. In YRD region, premature death in Shanghai was very high, about 18,679 (95% CI: 8374–26,051). The city Chongqing and Chengdu in SWC region had premature death 23,561 (95% CI: 10,613–32,458) and 11,809 (95% CI: 5370–16,188). In NEC and EC region, Harbin [9037 (95% CI: 4170–12,271) and Linyi [9141 (95% CI: 4385–12,209)] had the highest number of death due to PM2.5. Cities with premature deaths N5 thousand have been shown in Fig. 2. As the same baseline incidence rate (BIR) value has been assigned to all cities for minimum uncertainty, the attributed number of premature mortality may be viewed as a function of PM2.5 concentration and exposed population. For example, though Wenzhou and Ningbo have similar populations (10.7 million) but the total PM2.5-attributed mortality in Wenzhou (9307/year) is much higher than in Ningbo (5851/year) because Wenzhou has a higher PM2.5 concentration (70 μg/m3) than Ningbo (30 μg/m3). Likewise, Suzhou (93 μg/m3), Wuxi (73 μg/m3) and Xuzhou (44 μg/m3) city have very different PM2.5-related deaths—8494, 7970 and 6506 in 2015 but they have close population values (9.3 million). On the other hand, despite having the same PM2.5 concentrations (70 μg/m3), mortality in Wuxi (12,533/year), Shenzhen (8696/year) and Dongguan (5627/year) are very different because of the different exposed population in these cities (14.8, 10.3 and 6.7 million). Similarly, the exposed population in Changchun and Zhengzhou was 30 and 8.9 million, and the PM2.5-related mortality was 23,561 and 6981 but they have same PM2.5 concentration (57 μg/m3). Wuhan (65 μg/m3) has much lower PM2.5-related mortality at 7160 compared to Chongqing (64 μg/m3) at 11,809, due to differences in city population (8.7 and 14.4 million) although they have similar PM2.5 concentration. 3.2. Previous city level studies in China and need for synergistic efforts In the recent years, city level health risk assessment studies have received significant attention for regional air quality management (Table S2). It is imperative to discuss various premature mortality estimates carried out by different studies in the past years. Health risk study in Beijing estimated premature deaths due to PM2.5 were 11,432 in 2005 (Lelieveld et al., 2013), 13,700 (Lelieveld et al., 2015) and 17,266 (Xie et al., 2016) in 2010, 22,000–30,000 in 2012 (Zheng et al., 2015), 18,100 in 2013 (Liu et al., 2017), 19,702 in year 2014–15 (Maji et al., 2017) and 20,900 in 2015 (Song et al., 2017). The PM2.5-related premature mortality in Tianjin city was 4774 in 2005 (Lelieveld et al., 2013), 4900 (Lelieveld et al., 2015) and 12,622 (Xie et al., 2016) in 2010, 18,600 in 2013 (Liu et al., 2017), 13,726 in year 2014–15 (Maji et al., 2017) and 13,600 in 2015 (Sing et al., 2017). In Shanghai city, premature deaths were 11,001 in 2005 (Lelieveld et al., 2013), 14,900 and

Fig. 1. PM2.5-related cause-specific deaths in the 161 cities of China.


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Fig. 2. PM2.5-related cause-specific mortality (≥5000) in the cities of China.

17,339 in 2010 (Lelieveld et al., 2015; Xie et al., 2016), 21,800 in 2013 (Liu et al., 2017), 19,617 in 2014–15 (Maji et al., 2017) and 15,000 in 2015 (Song et al., 2017) attributable to PM2.5. In Chengdu, city PM2.5related death was 7400 in 2010 (Lelieveld et al., 2015). Considering population increase and lack of pollution control in Beijing, Tianjin, Shanghai and Chengdu, projection studies have estimated that PM2.5related death will reach 11,300, 3900, 14,300 and 6400 in year the 2025, and 17,700, 6300, 19,400 and 9700 in year the 2050 respectively (Lelieveld et al., 2015). PM2.5-related death were 34,849 in 2010 (Xie et al., 2016), 33,600 in 2013 (Liu et al., 2017), 25,162 in 2014–15 (Maji et al., 2017) and 32,300 in 2015 (Song et al., 2017) in Chongqing city. In PRD region PM2.5related deaths were 32,712 in 2005, 49,200 in 2010 and will reach 65,200 in 2025 and 67,400 in 2050 (Lelieveld et al., 2015). City level estimations have a distinct advantage in providing city authorities to have specific goals to achieve in reducing the overall mortality, making a tailor-made pollution control strategy for each city. There is a need for local push on managing air quality along with national ambitions. Closer collaborative and synergistic action from city functionary- state and national machinery could only achieve optimum outcomes. To remove biases due to exposed population and to know the effect of PM2.5, the per capita mortality (per 10,000 person-years) is estimated in the present study. According to current study average per capita mortality, for all ages in 161 cities in China for year 2015 was 7.85 (95% CI: 3.50–10.57). In previous studies, the per capita mortality among all ages due to PM2.5 in China was 9.2 and 9.0 for 2013 (Apte et al., 2015; Ottery, 2015) and 8.3 in 2014–15 (Maji et al., 2017). According to the GBD study, the per capita mortality was about 6.6 in 2013 and 8 in 2015 (IHME, 2016; HEI, 2017). The average per capita mortality was 9.05 in 2010 and it will reach 13.74 in 2030 (OECD, 2016) but according to Apte et al. (2015) it will be 11.4 in 2030.

base year 2015) with the most possibility of increased occurrence in near future. The potential mortalities benefits in scenarios where the PM2.5 concentrations in China meet the WHO IT-1, IT-2, IT-3 and AQG are 105.8 (95% CI: 58.5–119.7) thousand, 224.9 (95% CI: 104.4–241.3) thousand, 414.7 (95% CI: 173.1–544) thousand and 532.3 (95% CI: 268.7–657.9) thousand, which represent 16.2, 34.5, 63.6 and 81.5% of the base year (year 2015) premature mortalities (Fig. 3b). Human health benefits would not increase proportionally to the reduction of PM2.5 concentrations, as the total population and aged population (≥25 years) will increase in 2020. For comparison, the regional features for the mortality benefit under different scenarios into nine regions in China and the regional averages of the reduced cause-specific mortality were calculated based on the results of the cities within each region (Fig. 4a). Under APPCAP scenario, the region BTH, SC and EC will get 7.7, 5.5 and 5.9% health benefit compared to the base year. The potential mortality benefits in WHO IT-1 scenario (which is also Chinese ambient

3.3. Health benefit under different target scenarios in 2020 Based on the setting of the different scenarios (Table 1) for the year 2020, premature mortality related to PM2.5 by different causes was estimated, as shown in Fig. 3(a). Total premature mortality attributable to PM2.5 in 161 cities in China, under the city specific policies scenario APPCAP, WHO IT-1, IT-2, IT-3 and AQG in year 2020 is estimated to be 623.5 (95% CI: 278.9–880.3) thousand, 546.4 (95% CI: 239.7–781.9) thousand, 427.3 (95% CI: 193.9–660.3) thousand, 237.5 (95% CI: 125.1–357.6) thousand and 120.9 (95% CI: 29.5–243.7) thousand respectively. The potential health benefits are significantly different for all the scenarios. The least potential health benefit was observed for APPCAP, only 28.7 thousand deaths would be avoided (4.4% compared with the

Fig. 3. (a) PM2.5-related mortality and (b) potentially avoidable premature mortalities with relative percentage from reducing PM2.5 concentration under various pollution scenarios in 2020.

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air quality standard) in BTH (25.4%), YRD (15.5%), SWC (13.8%), NWC (16.8%), NEC (15.9%), NC (20.6%) and EC (20.1%) region are comparatively moderate. In IT-2 and IT-3 scenarios, the potentially avoidable premature deaths attributable to PM2.5 in BTH (42.2 and 67.9%), YRD (34.4 and 63.6%), PRD (17.4 and 54.1%), SC (27.9 and 59.7%), SWC (32.4 and 62.5%), NWC (35.5 and 64.2), NEC (35 and 63.9%), NC (38.3 and 65.7%) and EC (37.1 and 65%) are high. The maximum potentially avoidable premature death can be achieved in WHO AQG scenario where BTH, YRD, PRD, SC, SWC, NWC, NEC, NC and EC will have 83.7, 81.5, 76.6, 79.5, 80.9, 81.8, 81.6, 82.5 and 82.2% avoidable deaths compared to the base year. Fig. 4(b-g) present the PM2.5-related stroke, IHD, COPD, LC and ALRI mortality under different scenarios for the different regions. Potentially avoidable premature mortality, for stroke associated with PM2.5 under different scenario, is highest in BTH region and lowest in PRD region. The average avoidable deaths by stroke in the nine regions are 2.8% (range: − 1 to 5.7%), 14.1% (range: −2 to 22.5%), 36% (range: 21.5 to 43%), 72.4% (range: 66.1 to 75.4%) and 90.1% (range: 87.8 to 91.2%) under APPCAP, WHO IT-1, IT-2, IT-3 and AQG scenario, respectively. For IHD, average potentially avoidable premature deaths are low in five scenarios (1.9, 10.5, 21.9, 43.2 and 61.3%). The average avoidable deaths, in 2020, for COPD are 7.7, 20.6, 40.6, 63.7 and 83.9%, and for LC these are 8.1, 22.1, 39.4, 63.7 and 83.1% under five scenarios. The total PM2.5-related ALRI mortality is very low in 2015 and with reduction of

PM2.5 under APPCAP, WHO IT-1, IT-2, IT-3, and AQG scenario in 2020, ALRI mortality will further go down by 20.1, 36.9, 56.3, 81.3 and 94.4% respectively, on average in all nine regions. By 2030, Chines government has the target to meet NAAQS of PM2.5 (35 μg/m3) (Tang et al., 2017). In 2030, the Chinese population will be 1.4 billion and demographics of such a population would be 4.1% infants (b5 years), 72.9% adults of N 25 years respectively (EI, 2016). The total premature mortality attributed to PM2.5 in 2030 will be 574 thousand (95% CI: 251–821 thousand) (stroke: 300,699; IHD: 159,336; COPD: 62,252; LC: 48,926 and ALRI: 2455) in 161 cities. The highest mortality will be observed in Pearl River Delta [108,521 (95% CI: 47,544–154,773)] and East China [93,814 (95% CI: 41,205–134,835)] region. Xie et al. (2016) estimated that with PM2.5 pollution control policy in 2030, China would face 0.56 million premature deaths related to PM2.5. Madaniyazi et al. (2015) estimates that under policy control scenario, PM2.5 will be responsible for 124 thousand cases of total mortality in 2030. Ambient PM2.5 concentrations may decline in China with the implementation of air pollution control strategies, especially those declared in recent years (Clean Air Asia, 2016) but they might not necessarily mitigate the related health impacts when considering the current trends of demographic and epidemiological change in future, driven by urbanization. China is still undergoing rapid urbanization, another 310 million rural population is expected to migrate into cities by 2020 (UNDP, 2013).

Fig. 4. (a) Total PM2.5-related mortality and mortalities (b: cerebrovascular disease (stroke); c: ischemic heart disease (IHD); d: chronic obstructive pulmonary disease (COPD); e: lungcancer (LC); f: acute lower respiratory infections (ALRI)) within nine regions under various pollution scenarios. Note: Region: BTH – Beijing-Tianjin-Hebei region, YRD – Yangtze River Delta (YRD), PRD – Pearl River Delta (PRD), SC – South China, SWC – Southwest China, NWC – Northwest China, NEC – Northeast China, NC – North China and EC – East China. Scenario: Base year (2015), APPCAP – Air Pollution Prevention and Control Action Plan in 2020, WHO: IT-1 – first interim target scenario (or Chinese Ambient Air Quality Standard (35 μg/m3), WHO: IT -2 – second interim target scenario (25 μg/m3), WHO: IT -3 – third second interim target scenario (15 μg/m3), WHO: AQG – Air Quality Guideline (10 μg/m3).


3.4. Policy progress towards tackling PM2.5 emission sources and need for additional priorities For proper air quality management to reduce PM2.5 pollution urgently and effectively, the sources of PM2.5 must be controlled. There is a scientific consensus on ambient PM2.5 pollution caused by prime sources like power generation (i.e. biomass burning), transportation, agriculture, road/soil dust, industry open burning in China yet source specific quantification remains a bigger scientific challenge (Zhang et al., 2015; Guo et al., 2014; Huang et al., 2014). Zhang et al. (2013) reported that soil dust, coal combustion, biomass burning, traffic and waste incineration emission, industrial pollution and secondary inorganic aerosol are major sources of PM2.5 in 2009– 10 in Beijing city and these sources contribute 15, 18, 12, 4, 25, and 26%, respectively (R. Zhang et al., 2013). The contributions from vehicles, road dust and industry sources to PM2.5 in Beijing, Tianjin, Shijiazhuang, Nanjing, Shanghai, Hangzhou, Ningbo, Guangzhou and Shenzhen cities were in the range of 20–41%, 10–30% and 12–47%, respectively in 2014. However, vehicular emission contributed more in Shenzhen (41%) than that Beijing, Tianjin, and Shijiazhuang city (around 20%) (Zheng et al., 2016). Coal combustion accounted for 13–29% of PM2.5 in these cities (Zheng et al., 2016). In BTH, the contributions of PM2.5 are mainly industry (44%), residential (30%), power plants (8%), agriculture (8%) and transportation (7%) (Li et al., 2015). In North China region, secondary sulphate and nitrate, biomass burning, industry, crustal matter, vehicles and copper smelting were important sources of PM2.5 and they contribute 54.3, 15.8, 10.7, 8.3, 5.2 and 4.9% respectively (Yao et al., 2016). And Zong et al. (2016) reported that vehicle emission (15.9%), traffic dust (4.24%), ship emission (8.95%), industrial process (2.63%), biomass burning (19.3%), mineral dust (12.8%), coal combustion (29.6%) and sea salt (6.59%) are main contributor of PM2.5 in North China. Chen et al. (2015) estimated the contribution of straw open burning which is about 28.21% of total PM2.5 emissions in Tianjin during 2001 to 2010. In Jinan city, coal combustion, biomass burning, industry, secondary sulphate and nitrate, soil dust, motor vehicle contributes 20.98, 4.45, 2.87, 55.15, 9.30 and 6.06% respectively, to total PM2.5 mass concentration (L. Yang et al., 2013a, 2013b). Various other studies attempted to examine common causes in other cities of China (Yu et al., 2013; Wang et al., 2013; Tao et al., 2014; Rohde and Muller, 2015; J. Wang et al., 2015; Wu et al., 2016). From these studies, it was found that coal and coal-related industrial processes account for 50–60% of PM2.5, as China is the world's largest consumer of coal for its energy and is responsible for around half the world's coal consumption. China's coal consumption rose from 1055 billion metric tons in 1990 to 4244 billion metric tons in 2013. Coal burning by industry, power plants and domestic heating combined accounted for 40% of population-weighted ambient PM2.5 concentration and was responsible for 60% of the air pollution related health impacts in China (Greenpeace, 2015). In 2013, particulate matter emissions from coal burning was responsible for 0.37 million premature deaths and without control strategies, it will reach 0.99 to 1.3 million in 2030 but with continued reduction in of coal consumption targets, PM2.5 shall decline substantially leading to reduced premature deaths in year 2030 at 0.28 million (HEI, 2016). To reduce coal consumption, MEPC has announced the Action Plan for Clean and Efficient Utilization of coal 2015–2020. Based on the Action Plan in 2015, the national total energy consumption was 4.3 billion tons of standard coal, up by 0.9% compared with the previous year, but the percentage of coal consumption was 64.0%, down by 3.7% as against 2014, and the percentage of clean energy consumption was 17.9%, up 1% compared to 2014. By the end of 2015, four cities (Langfang, Baoding, Tangshan and Guangzhou) in Hebei Province reduced coal consumption by 1.85 million tons, Tianjin reduced 5 million tons and Beijing's coal consumption was down to 12 million tons. Based on these numbers, China accomplished its target of total coal consumption control ahead of schedule. The overall coal consumption went down by 3.7%, from 2014 to 2015 (GESY, 2016;

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Finamore, 2016). In 2015, BTH, YRD and PRD were quite effective in eliminating and retrofitting coal-fired boilers. In 2015, BTH eliminated 2500 coal-fired boilers under 10 t/h, YRD eliminated and retrofitted 34,052 coal-fired boilers and furnaces, exceeding the target of 11,000 for 2015. Jiangsu eliminated 3100 small coal-fired boilers and Zhejiang achieved 221% of its target. PRD eradicated coal-fired boilers under 4 t/h and eliminated 81% of coal-fired boilers under 10 t/h. In Guangdong, the number of coal-fired boilers reduced from 5981 in 2010 to 2597 in 2015 (Clean Air Asia, 2016). China bans the approval of new conventional coal-fired power plants and wants to increase the share of non-fossil fuels in its energy mix to 20% in the year 2030 (Climate Nexus, 2017). By 2015, BTH had reduced the iron production capacity by 10.69 million tons, steel by 17.27 million tons, coke by 3.90 million tons, cement by 23.35 million tons, flat glass by 15.37 million weight boxes and electric power generation capacity of 3.09 GW (Giga Watt). In March 2015, Beijing shut down 1.28 GW coal-fired units at Guohua and Shijingshan power plants and eliminated 326 polluting enterprises. Hebei Province planned to reduce crude steel production capacity by 5 million tons, pig iron by 5 million tons, cement by 6 million tons, flat glass by 3 million weight boxes and to eliminate outdated production capacity by 0.7 GW in the electric power industry. Shandong Province planned to reduce the iron and steel production capacity by N 1 million tons, capping the total to be b 50 million tons. Other major air pollution cause is the number of old vehicles, which is about 7.8% of vehicles on China's roads that do not meet the minimum NAAQS (Duggan, 2014). Guo et al. (2010) shows that the total economic cost of health impacts due to air pollution contributed from transport in Beijing in 2008 was 298 million US$. To reduce air pollution MEPC assigned specific tasks to eliminate 5 million yellow-label vehicles that registered before 2005 in BTH, YRD, and PRD region. At the end of 2015, 1.786, 0.84 and 0.567 million yellow-label and outdated vehicles in BTH region (including 0.34 million vehicles in Beijing), YRD and PRD region had been eliminated. The government provides 50 to 60% subsidy to encourage people to purchase of alternative fuel vehicles. Government is also encouraging people to use public transport and alternative fuel vehicles. By the end of 2020, China has the target to develop 5 million alternative fuel vehicles, 0.2 million alternative fuel buses, 12,000 centralized charging and battery swapping stations and 4.8 million distributed charging poles. By the end of 2022, China will reduce 20 to 30% of the city's pollutant emissions caused by vehicles using China VI vehicular emission standard (Xiaoying, 2016). Under the integrated scenarios, considering all the control measures, the maximum reduction potential of PM emissions for year 2020 has been estimated up to 50% (Guo et al., 2016; Lang et al., 2012; Saikawa et al., 2011). In 2015, Beijing, Tianjin and Jiangsu Province started to levy emissions fee for construction-site dust emissions, the average charge is RMB 3/kg. If the management level falls short of the attainment level, the rate should be RMB 6/kg. Emissions from port and vessels are one of the major sources of air pollution in coastal cities in China as most of the Chinese ports run on bunker fuel, also known as residual fuel and almost all port vehicles and equipment are powered by diesel fuel that exhaust high levels of diesel PM (Fung et al., 2014; Ng et al., 2013) which is adversely affecting human health (Lai et al., 2013b). In 2015, several government documents explicitly set policies and targets for the prevention and control of vessel and port pollutants for 2015–2020, such policies include setting up emission control areas, developing more stringent standards for vessel fuel, improving vessel emission standards and conducting oil-vapor recovery. In 2019, vessels entering emission control areas will have to use fuel with no N 0.5% of sulfur content. To achieve stringent standards for vessel fuel, government issued the Regular Diesel Standard, requiring that China IV and China V regular diesel should be supplied nationwide and that the sulfur content of distillate fuel oil and residual fuel oil should be controlled within 0.1 to 1.5% and 0.1 to 3.5% respectively. Starting from 2015, oil-vapor recovery experiments

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at ports would be conducted for three years in key regions, such as BTH, YRD, PRD, southeast coastal region and regions alongside the Yangtze River (Simon, 2013). Agricultural straw burning is also an important source of air pollution and has many adverse health, environmental and ecological impacts (L. Zhang et al., 2016; Lyu et al., 2015; Guan et al., 2014; Li et al., 2016; Chen et al., 2016). H. Zhang et al. (2016) reported that the total PM2.5 emissions from open straw burning in China were 1.036 billion kg/year. At present, the open burning of crop straw is prohibited in China and 0.75 billion RMB has been invested to support the crop straw overall utilization and recycling projects (Chinadaily.com.cn, 2017; UNEP, 2015). In the next few years, air pollution is expected to reduce due to the recent air pollution control policies in China but to ensure long-term improvement of air quality, the government should implement all policies with a strong hand. For most cities in China, the PM2.5 reduction target is still far behind the national standard and WHO guidelines. Moreover, current policies are aimed at reducing PM level yet not dealing with other pollutants effectively- such as the increasing O3 concentration in recent years. To protect public health, the ministry of environmental protection should provide clear timelines and roadmaps for air-quality attainment in regions and cities. After the national government's release of the policies, governments at all levels have introduced a series of policy measures, however, there are questions if the measures will work under local conditions. The local administration often lacks understanding of the actual impacts of the measures that create lack of confidence in achieving the relevant targets on time. Setting up a science-based system for the pre-evaluation, tracking and post-implementation evaluation of policies will help local governments choose the most effective measures, adjust their control plans during the implementation process and improve the precision of control measures. In conjunction with measures like city ranking, public admonitory talks and liability statements, the national government should put significant pressure on local governments to implement the Action Plan. However, cities still need technical capacity and resources to achieve air quality improvement. Appropriate punitive measures such as monetary penalties, property seizures, facility closures, other injunctive relief and imprisonment are critical for establishing the necessarily credible threat to assure regulated enterprises comply with the rules.

of traffic accidents because it causes physical distractions, perhaps an itching nose or limiting visibility during haze in Beijing like city (Sager, 2016). (7) Short-term PM2.5-mortality due to haze events has also not been considered in the present study (Gao et al., 2015; L. Wang et al., 2015). 4. Conclusions Based on the integrated exposure risk model and recently available ground measurements of PM2.5 in 161 cities in nine regions across China, this study provides an overall assessment of the PM2.5-related mortalities in 2015 and the potential health benefits of different air quality scenarios in year 2020. It is estimated that total premature mortality attributable to PM2.5 in 161 cities was 652 thousand (95% CI: 298–902 thousand), which is about 6.92% of total deaths in China during year 2015. In total PM2.5-attributed premature deaths, contributions of stroke, IHD, COPD and LC (adults ≥ 25 years) and ALRIs (infants b 5 years) were 51.70, 26.26, 11.77, 9.45 and 0.82%, respectively. The BTH, YRD, PRD, SC, SWC, NWC, NEC, NC and EC region contribute 14.60, 18.71, 5.37, 12.41, 10.63, 4.75, 8.18, 8.20 and 17.16%, respectively, in total PM2.5-related deaths. The potential health benefits associated with different PM2.5 concentration scenarios in 2020 are significant, although there are large differences in outcomes for achieving each one of them. For the APPCAP, which has the least benefit, only 28.7 thousand deaths would be avoided (a reduction of 4.4% compared to 2015). If the PM2.5 concentrations in China were to meet the WHO IT-1, IT-2, IT-3 and AQG target in 2020, it would be possible to achieve mortality benefits of 16.2, 34.5, 63.6 and 81.5% of the current premature deaths (652 thousand) related to PM2.5, respectively. In a realistic scenario, by the end 2030, Chinese government has the target to meet NAAQS of PM2.5 (35 μg/m3) in all cities, then total premature mortality attributable to PM2.5 will be 574 thousand across 161 cities. These results will provide a great impetus for the policy makers to acknowledge premature mortality associated with air pollution and design efficient policies to improve the air quality for a better public health environment. Efficient implementation and effectiveness of policy measures at the grassroots remains the key for successful transitions towards clean air with limited adverse health effects. Appendix A. Supplementary data

3.5. Limitations and assumptions There are number of methodological uncertainties that limit the applicability of this approach. In particular: (1) the risk estimates are based on IER model, which integrates secondhand smoking, active smoking and household air pollution cohort. Thus, there is inherent uncertainty involved in assessing health risks for other countries (Chen et al., 2013) as there are significant differences within Chines cohort studies too. (2) Populations are generally exposed to multiple pollutants, possibly causing synergistic effects (Johns et al., 2012; Konishi et al., 2014) and this may lead to underestimation of values in absolute terms. (3) The health risk assessment study focuses on the mass concentration of PM2.5 and assumes that the toxicity of PM2.5 depends on mass concentration only, not chemical composition or sources but there is considerable evidence that the chemical composition, size distribution and sources of PM may influence its health effects (Ostro et al., 2015). (4) The present study considered only PM2.5 whereas past studies show serious health impacts of ultrafine particles matter (e.g., PM1), O3, SO2, NO2 and poly aromatic hydrocarbon (PAH), which were not considered in this study. (5) The simple assumption in this study is that cause-specific baseline mortality rates remain constant in 2020 but in the past two decades, mortality from non-communicable diseases has increased rapidly, due to changed lifestyle, urban environments, and aging demographics (Forouzanfar et al., 2016). (6) The external causes of mortality related to air pollution are not considered in the present study, for example, air pollution enhances the probability

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