Health effects of PM2.5 emissions from on-road vehicles during weekdays and weekends in Beijing, China

Journal Pre-proof Health effects of PM2.5 emissions from on-road vehicles during weekdays and weekends in Beijing, China Ruipeng Tong, Jiefeng Liu, Wei Wang, Yingqian Fang PII:

S1352-2310(19)30896-9

DOI:

https://doi.org/10.1016/j.atmosenv.2019.117258

Reference:

AEA 117258

To appear in:

Atmospheric Environment

Received Date: 25 July 2019 Revised Date:

27 December 2019

Accepted Date: 30 December 2019

Please cite this article as: Tong, R., Liu, J., Wang, W., Fang, Y., Health effects of PM2.5 emissions from on-road vehicles during weekdays and weekends in Beijing, China, Atmospheric Environment (2020), doi: https://doi.org/10.1016/j.atmosenv.2019.117258. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

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Health effects of PM2.5 emissions from on-road vehicles during weekdays and

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weekends in Beijing, China

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Ruipeng Tonga*, Jiefeng Liub, Wei Wangc, Yingqian Fangd

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a

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Mining and Technology-Beijing, Beijing 100083, China. Email:

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[email protected]

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b

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Mining and Technology-Beijing, Beijing 100083,China. Email:

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[email protected]

School of Emergency Management & Safety Engineering, China University of

School of Emergency Management & Safety Engineering, China University of

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c

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Mining and Technology-Beijing, Beijing 100083, China. Email: [email protected]

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d

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Mining and Technology-Beijing, Beijing 100083, China. Email:

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[email protected]

School of Emergency Management & Safety Engineering, China University of

School of Emergency Management & Safety Engineering, China University of

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* Corresponding author. Tel.: +86 137 1843 1777; Fax: +86 10 62339060. E-mail:

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[email protected]; Address: D11, Xueyuan Road, Haidian District, Beijing

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100083, China.

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Abstract

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Mobile source emissions have significantly contributed to fine particulate matter (PM2.5) pollution

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in urban atmospheric environments. Few studies have explored on-road vehicular PM2.5 emissions

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and the health effects attributed to these emissions under different traffic conditions. Based on

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driving data obtained from 20000 taxi receipts, a motor vehicle emission simulator (MOVES)

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model was used to estimate the PM2.5 emission levels of motor vehicles in the urban area within

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the Sixth Ring Road of Beijing (SRRB) on weekdays and weekends, respectively. Two different

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PM2.5 exposure scenarios were further simulated using an atmospheric dispersion model.

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Subsequently, the health effects attributable to traffic-related PM2.5 exposure were quantified by

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using exposure-response function to calculate the population acute morbidity and premature

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mortality during different time periods. We found that PM2.5 emission levels of motor vehicles on

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normal weekdays were overall higher than those on weekends. The median vehicular PM2.5

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dispersion concentration in the study area was 2.68 µg/m3 on weekdays and 1.82µg/m3 on

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weekends. Annually, there were 4435 premature deaths attributed to vehicle emissions (95%

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confidence interval (CI): 3655, 4904) under weekday exposure conditions, and this number

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sharply decreased to 3462 (95% CI: 3052, 4011) on weekends. Considering the hourly

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measurements, road traffic emissions have the greatest impact on public health during morning

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rush hour (8:00 am). Total PM2.5 emissions were closely associated with road traffic conditions

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and largely determined the magnitude of the health impacts caused by traffic-related PM2.5

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exposure. These findings provide information to aid in formulating reasonable public health

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policies to address vehicular PM2.5 emission-induced health implications. 2

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Key words: Health effects; Vehicular emission; Air pollution; PM2.5; Urban traffic

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1. Introduction

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With the acceleration of urbanization and growth in residents’ purchasing power, motor vehicles

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have become an important mode of transportation. While greatly improving convenience, this

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surge in vehicular traffic has created a number of issues, among which the most concerning is

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atmospheric pollution from vehicle emissions. In China, total motor vehicle emissions containing

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particulate matter (PM), total hydrocarbon (THC) and other pollutants exceed approximately 44

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million tons per year, significantly contributing to urban air pollution (Ministry of Ecology and

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Environment of China, 2018; Wu et al., 2017). Europe and America are also faced with

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considerable exhaust pollution despite rigorous vehicle emission standards (Amato et al., 2013;

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Patel et al., 2009). Traffic congestion, an issue common to the entire worldwide urban road

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network, has exacerbated this problem by increasing travel times and corresponding economic

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costs while increasing automobile emission related atmospheric pollution (Yu et al., 2017; Schrank

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et al., 2012; Chin, 1996). In the case of traffic congestion, microcosmic unstable driving

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conditions, such as idling, low speeds and speed fluctuations, can further increase exhaust

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emissions (Ahn et al., 2002; Smit et al., 2008), exacerbating the environmental burden.

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As a representative harmful substance present in both automotive exhaust and non-exhaust, fine

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particulate matter (aerodynamic diameter ≤ 2.5 µm; PM2.5) has been a public concern, with

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numerous studies showing a strong association between ambient PM2.5 and health effects for the

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population (Chen et al., 2018a; Zhang et al., 2017). According to the toxicological and

3

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epidemiological literature, ambient PM2.5 is closely linked to increases in cardiovascular and

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respiratory morbidity and premature mortality with long-term exposure (Liao et al., 2017; Xu et al.,

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2016; Vodonos et al., 2018; Liu et al., 2016). The Global Burden of Disease (GBD) study

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estimated that PM2.5 has become the fifth leading risk factor for death worldwide, resulting in 4.2

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million premature deaths per year (Cohen et al., 2017). Moreover, considering the World Health

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Organization (WHO) recommended standards for annual PM2.5 concentrations of lower than 10

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µg/m3, approximately 90% of the urban population continue to live in environments with

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excessive PM2.5 (WHO, 2016). With respect to traffic-related PM2.5, previous studies have

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reported that vehicular PM2.5 emissions are an important component of ambient PM2.5 pollution,

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accounting for 16%, 26%, and 39.8% of local environmental PM2.5 concentration in New York,

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Beijing and Shanghai, respectively (Masiol et al., 2016; Gao, 2016; Wang et al., 2016). Compared

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with unobstructed roads, the excess PM2.5 emissions from motor vehicles during periods of traffic

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congestion may result in more severe health issues. Li et al., (2016) have quantified the increase in

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PM2.5 concentrations attributable to automotive urban congestion, and this annual 10.95 µg/m3

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increase may pose a serious health threat to residents. For health effects in particular, studies have

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analyzed vehicle emission-related pollution and consequent health impacts (Levy et al., 2010;

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Zhang et al., 2013). In the USA, Kheirbek et al., (2016) have estimated the PM2.5-related health

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burden from on-road mobile sources in New York City, which account for 320 deaths and 5850

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years of lost life expectancy. In light of the significant differences in road traffic and exposure

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status between various regions (Panis et al., 2010), it is necessary to quantify the PM2.5-related

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health impacts attributable to vehicular emissions in China.

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In recent years, China has undergone vigorous development of road transportation, with motor 4

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vehicle numbers surging since the 1990s from 5 million to 310 million (Ministry of Ecology and

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Environment of China, 2018; National Bureau of Statistics of China, 2018). Faced with increases

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in the number of vehicles on the roads, limited road capacity has gradually become unable to

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accommodate the huge traffic pressure due to the rapidly increasing population, in spite of

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substantial investments in infrastructure. Especially in megacities with dense urban populations, a

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tremendous number of privately owned vehicles and concentrated commuter traffic during work

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day rush hours have contributed to frequent traffic congestion. In 2017, approximately 81% of

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domestic cities have experienced varying degrees of heavy traffic congestion during commuting

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hours (AutoNavi, 2018). A sharp drop in commuting motor vehicles on the roads tends to reverse

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these conditions on non-working days; however, increases in traffic on urban expressways and

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trunk roads owing to recreational driving is a complicating factor. PM2.5 emissions from on-road

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vehicles under changing traffic conditions and their consequent health impacts are unclear,

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although a few papers have considered similar problems in other countries and cities (Levy et al.,

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2010; Requia et al., 2017).

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The objective of this paper was to investigate the PM2.5 emission characteristics of motor vehicles

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on typical urban roads and the health effects attributable to on-road vehicular PM2.5 exposure

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under the different scenarios of weekdays and weekends.

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2. Materials and Methods

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2.1 Study area

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This study was conducted within the Sixth Ring Road of Beijing (SRRB), which is located in the

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core urban area of Beijing, China (39°54′N, 116°23′E) (Fig. 1). As a megacity with over 21.72 5

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million permanent residents, PM2.5 pollution has always been a headache in Beijing (Beijing

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Municipal Bureau of Statistics, 2018). Faced with substantial emissions from multiple sources,

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annual ambient PM2.5 concentrations still exceed 50 µg/m3 although Beijing municipal has

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adopted a large number of control measures in recent years, such as the “Five-year Clean Air

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Action Plan (2013-2017)” (Beijing Municipal Ecological Environment Bureau, 2013). Rapid

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increases in both vehicle numbers and travel demand have significantly contributed to heavy

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traffic on the urban road network and to the resulting increases in atmospheric PM2.5.

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Disproportionate urban road grade configurations due to the emphasis the Beijing municipal

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government has placed on construction of main roads, coupled with slow progress in the

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construction of minor roads has resulted in a low road network density in Beijing, making it

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difficult to ensure that roads are unobstructed during peak periods (China Academy of Urban

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Planning and Design, 2018). Consequently, serious traffic congestion frequently occurs due to the

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presence of 5.7 million motor vehicles and increasing diversified trip demand (Beijing Municipal

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Bureau of Statistics, 2018; Beijing Transport Institute, 2018). It is estimated that average commute

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time in Beijing has reached 50 minutes and corresponding per capita economic losses owing to

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congestion exceed 8700 yuan, ranking first among all Chinese cities (Global Times, 2016; DiDi

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Research Institute and CBNData, 2018).

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The SRRB is the core zone of Beijing, accounting for 75% of the total population. The most

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congested roads in Beijing (e.g., West Second-Ring Road and East Second-Ring Road) are located

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in this area (Beijing Transport Institute, 2018). Although on-road mobile sources have been

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considered the major PM2.5 emission sources (Beijing Municipal Ecological Environment Bureau,

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2018), little information has been gathered about population health effects from exposure to 6

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traffic-related PM2.5 emissions. Yin et al., (2017) have quantified the external economic costs and

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health effects of ambient PM2.5 in Beijing without detailed calculation of the proportion due to

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traffic emission. Recently, the municipality of Beijing has released the “Three-Year Plan on

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Defending the Blue Sky”, which sets a goal of reducing traffic pollutants by 30% before 2020

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(Beijing Municipal Government, 2018). In this context, it is urgent to conduct research on the

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PM2.5 emission characteristics of vehicular traffic and the consequent health impacts.

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[Insert Figure. 1]

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2.2 Approach

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As shown in Fig. 2, we constructed a PM2.5 emissions and health impact estimation framework for

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motor vehicles on the urban road network during different time periods. A large number of taxi

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receipts containing information about dates, travel times, and travel distances were collected to

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calculate vehicle driving data. Consequent vehicular PM2.5 emissions can be estimated by the

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application of an emissions simulation model developed by USEPA. Combined with the

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atmospheric dispersion model AERMOD, PM2.5 dispersion concentrations were estimated. Finally,

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the health impacts on the urban population attributable to PM2.5 were quantitatively characterized

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using a dose-response function.

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[Insert Figure. 2]

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2.3 Road traffic data

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Traffic emissions on both expressways and arterial roads within the SRRB were studied, as both

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types of roads are principal urban roads with the highest road classification in China according to

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the national standard code for design of urban road engineering (CJJ37-2012). Compared with

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feeder roads with a lower traffic capacity, these roads support the majority of urban traffic, 7

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approximately 60% of peak volumes (DiDi Research Institute and CBNData, 2018). Besides,

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because of the similar road design standards and traffic capacities for urban expressways and

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thruways, certain sections of thruway in the SRRB were combined with expressways and included

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in this study.

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The detailed road network information that forms the essential data for this paper was generated

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from electronic data produced by the local survey department (Fig.1). Considering the lack of

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specific traffic volumes, vehicle speeds and other vehicle information, despite the Beijing traffic

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management bureau's real-time monitoring of the traffic conditions of each road, we collected a

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large sample of taxi receipts as primary sources to obtain vehicle travel data under different traffic

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conditions (Beijing Traffic Management Bureau, 2019; Xiao et al., 2017a). Taxis are a common

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mode of transportation, and to a large extent, their driving data represent the overall driving

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conditions. Based on the relevant information displayed on receipts, we can estimate traffic

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condition on the various roads under study. Specifically, depend on the Beijing taxi service

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management system, we retrieved the specific driving route of each taxi within the time period

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shown on receipts. Then, key data from the receipts of taxis driving on the same road section

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within the same hour were summarized, including the time spent driving, waiting time and travel

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distance, to calculate hourly average speed of all taxis. And the traffic flow velocities of each road

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can be represented by the corresponding taxis average speed. To obtain information about hourly

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road traffic flow, we adopted online road condition monitoring data with 5-minute update

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frequency from Beijing traffic management bureau to estimate corresponding vehicle volume, in

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which road sections of different colors indicate various traffic condition (Beijing Traffic

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Management Bureau, 2019). According to the traffic classification standard and previous 8

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researches (Liao et al., 2012; Dong et al., 2010; Fan et al., 2015), road sections marked with the

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color “crimson”, “red”, “yellow” and “green” represent the traffic flow speed of less than 15,

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15-30, 30-50 and more than 50 km/h, respectively. A total of 20 thousand receipts were collected,

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among which ten thousand receipts were generated on weekdays (Monday to Friday), and the

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other half were generated on weekends (Saturday and Sunday).

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[Insert Table. 1]

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2.4 Emission modelling

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PM2.5 emission factors of motor vehicles under different traffic conditions were estimated by

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application of the motor vehicle emission simulator (MOVES, Version 2014b) model (USEPA,

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2015a). MOVES is a vehicle emission estimation model developed by USEPA, which was

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constructed using accumulated data from a large number of onboard emission tests and bench tests.

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Because of its adaptability to different regions and its high precision of measurement, MOVES has

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become a standard model for on-road motor sources emission estimation throughout the United

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States (USEPA, 2015; Huang et al., 2010). PM2.5 emissions was calculated using Eq. (1).

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,



= (∑

×

,

)×

（1）

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where TE is total PM2.5 emissions from on-road mobile sources; ER is the emission rate; Ac is the

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automobiles' working driving conditions such as start, acceleration and idling; and Aj is the

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adjustment factor. The default databases for this model originated from local environment and

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vehicle-related information from the United States, which cannot be directly applied to domestic

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motor vehicle emissions measurement. Recently, there have been attempts to apply this model to

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domestic vehicular emissions estimation with improvements in model openness and localization.

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Yue et al., (2013) have proposed a method for localized input parameter acquisition at the micro 9

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level, confirming the feasibility of applying MOVES in China. Qiu et al., (2013) constructed an

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integrated framework for evaluating traffic-related PM2.5 pollution based on localized parameters,

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such as vehicle type, fleet composition and fuel category. Drawing on previous work, model

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parameters have been revised and defined according to actual conditions of vehicle and roads in

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the SRRB area.

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The running year of the model is 2017, and the specific time is from 0:00 to 24:00 on working

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days and non-working days in August. Built-in geographic parameters from the United States in

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this model forced us to select New York County in New York City as the modeling location, for its

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latitudinal and topographic similarity to our study area. For the on-road vehicle setting, between

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6:00 and 23:00, only passenger cars were considered because of the low number of freight trucks

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in local ownership as well as the vehicles restriction measures (Fan et al., 2015). The

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implementation of “Clean Air Action Plan” has forced local traffic management bureau to

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introduce a series of vehicle restriction policies which aimed at cutting emissions from heavy duty

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vehicles. All trucks are strictly prohibited from driving on the roads within the Fifth Ring Road

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from 6:00 to 23:00. Hence, PM2.5 emissions from passenger cars and other type of on-road

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vehicles were both took into account from 23:00 to 6:00 next day. Vehicle registration and age

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distribution data are derived from statistical information (Beijing Municipal Bureau of Statistics,

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2018; Beijing Transport Institute, 2018). The local models are matched with the types of vehicles

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as defined by MOVES. The matching results are shown in Table 2. Regarding vehicle fuel

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parameters, the MOVES model divides vehicle fuels into different subtypes, gasoline, diesel and

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natural gas, and provides the ability to customize fuel components. In 2017, Beijing took the lead

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in implementing a new stage of vehicle fuel standards, the “Beijing VI standard” with specific fuel 10

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parameters including sulfur content, olefin content and boiling range. The MOVES model was set

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up using the prescribed values in these standards (Beijing Municipal Ecological Environment

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Bureau, 2016).

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[Insert Table. 2]

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2.5 Dispersion simulating and exposure estimation

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The atmospheric PM2.5 dispersion simulation was conducted using the AERMOD model. As a

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Gaussian plume dispersion model, the AERMOD model is applicable to estimating the dispersion

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of stationary or mobile pollution sources on the scale of urban roads or blocks (US EPA, 2015b).

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Previous studies have uncovered health risks caused by pollutant dispersion using the AERMOD

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model (Chen et al., 2018b; Balter et al., 2017). Verified by near-road monitoring results, the

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dispersion concentration levels of contaminants from various on-road motor vehicles have also

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been simulated using this model (Yan et al., 2014; Qiu et al., 2014). However, these studies have

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mostly focused on pollutant dispersion on small scales, such as single road sections, while lacking

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analysis on regional road networks. The line pollution pattern in the AERMOD model was use to

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simulated the PM2.5 dispersion to estimate hourly dispersion concentrations on multiple urban

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roads.

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Surface meteorological parameters, including hourly wind speed, wind direction, dry bulb

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temperature, dew point temperature, surface roughness, site pressure, cloud cover, and upper air

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meteorological data, including elevation, atmospheric pressure and upper air wind speed, were

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indispensable for PM2.5 dispersion modeling. The related data were mainly based on

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measurements from the National Meteorological Information Center, and the original data were

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preprocessed using the AERMET module (National Meteorological Information Center, 2017). An 11

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orthogonal plane coordinate system was established for the whole SRRB area, divided into square

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grids of 500 m × 500 m with the acceptor point as the grid center. Urban roads per unit length

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were designated as line polluting sources with corresponding coordinates and the altitude of each

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start-point.

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2.6 Calculations of the health impacts attributable to emissions

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Numerous papers have used an exposure-response (ER) function to examine the health impact of

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fine particles, quantifying the number of adverse health outcomes or mortality variations resulting

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from changes in particle concentrations (Lepeule et al., 2012; Brook et al., 2010). Nevertheless,

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limited by insufficient retrospective cohort studies of long-term exposure to PM2.5 and the

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difficulty of accessing specialized health data, some studies have only considered health effects in

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terms of acute morbidity or mortality due to short-term exposure (Huang et al., 2016; Fang et al.,

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2016); in addition, the estimation of chronic health damage was only obtained by extrapolating ER

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functions from developed areas with lower PM2.5 concentrations in spite of probable deviations in

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assessment results (Kan et al., 2005; Liu et al., 2015). Thus, in this paper, we take into account

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both the acute morbidity from short-term exposure and the long-term health effects of premature

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death. Based on population grid data generated from demographic information from the SRRB

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area (Beijing Municipal Bureau of Statistics, 2018), the population-weighted PM2.5 exposure level

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(CP) was first calculated using Eq. (2) (Aunan et al., 2018).

258



!

"

= ! × ∑ (# ×

)

（2）

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where P is the total size of the exposed population; Pi is the population in a grid cell i; and Ci is

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the estimated PM2.5 concentration of grid i obtained from simulated dispersion results (µg/m3).

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For the calculation of PM2.5-induced morbidity, a Poisson regression (log-linear regression) model 12

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was applied as in Eq. (3). $ = # × {1 − ()*+, × (

!

−

- ).} × 0-

（3）

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where Y is the estimation of various acute health endpoints, and in this study, we consider

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outpatient visits and hospital admissions caused by PM2.5-induced diseases (respiratory diseases

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and circulatory diseases); β is the ER coefficient indicating changes in morbidity of various

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diseases due to PM2.5 concentration variation; and C0 is the baseline concentration (µg/m3), we

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here set to 35 µg/m3 according to national ambient air quality standards (GB 3095-2012); and I0 is

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the baseline morbidity of each endpoint, collected from the national health statistical yearbook due

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to a lack of urban-scale incidence statistics (National Health Commission of China, 2018). By

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using a similar formula, reliable health burden assessment results have been obtained for major

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Chinese cities, indicating good applicability (Fang et al., 2016).

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For the estimation of chronic health impacts, the integrated exposure-response (IER) model was

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used to calculate premature deaths attributable to traffic-related PM2.5 exposure. A nonlinear IER

275

function was fitted for cause-specific adult premature mortality for representative adverse health

276

outcomes associated with ambient PM2.5 exposure: ischemic heart disease (IHD), cerebrovascular

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disease (CVD), chronic obstructive pulmonary disease (COPD) and lung cancer (LC) (Cohen et al.,

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2017; Burnett et al., 2014). Based on the integration of numerous cohort studies of diverse

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exposure sources, such as ambient air pollution and household PM2.5 exposure, the IER

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methodology provides good performance in predicting the health impacts of air pollution. Due to

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its validity and rationality, the IER model has been employed as a powerful mathematic tool to

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assess the population health burden from global air pollution in the Global Burden of Diseases

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Study (GBD 2010 project) (Cohen et al., 2017).The attributable mortality was calculated by 13

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following Eq. (4): 123 ( ) = 4

285

1 + 6 71 − ()* 8−9:

−

;<

=

>? ,

1 ,

# B = # ×

286

!

33CDE (F)G" × 033CDE (F)

≥

<

;

;

287

where RRIER(C) means the relative risk under the estimated PM2.5 concentration CP; Ccf is the

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counterfactual concentration indicating that there is no additional health risk if the exposure

289

concentration is lower than this value; α, γ, and δ are relevant parameters representing the relative

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risk curve shape, conforming to a specific data distribution (Song et al., 2017); and PAM is the

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population attributable mortality. The specific parameter values are listed in Table 3.

292

[Insert Table. 3]

293 294

3. Results

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3.1 Temporal and spatial variations of vehicular PM2.5 emissions

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As shown in Fig. 3, we estimated that the daily average PM2.5 dispersion concentration from motor

297

vehicles was 2.68 µg/m3 on normal weekdays. Compared with vehicular emissions on weekdays,

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the primary PM2.5 concentration on weekends was reduced by approximately 32% with 1.82 µg/m3

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atmospheric PM2.5. On weekends, traffic volume decreased dramatically due to light commuting,

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and, therefore, road congestion is partly alleviated, reducing exhaust emissions.

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In terms of hourly simulated dispersion concentration, the highest PM2.5 emissions mainly

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occurred during normal commuting hours from 7:00 to 9:00 and from 17:00 to 20:00 on both

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weekdays and weekends, especially at approximately 7:00 on weekdays, when traffic-related the

304

average PM2.5 dispersion concentrations were estimated to peak at approximately 4.4 µg/m3 and

14

（4）

（5）

305

3.18 µg/m3 on expressways and arterial roads, respectively. The statistical hourly on-road vehicle

306

speed information also estimates the slowest traffic velocities during morning rush hour, when

307

average travel speed was 36.8 km/h on expressways and 22 km/h on arterial roads. By contrast, we

308

observed that PM2.5 dispersion concentrations dropped considerably during non-commuting times,

309

especially from 9:00 to 16:00, during which times the average PM2.5 dispersion concentration

310

from the road network was less than 1 µg/m3. Except for morning or evening traffic peak hours,

311

PM2.5 densities in the daytime were well below those at night. Because of a lower specific heat

312

capacity, the temperatures of land surfaces tend to increase more quickly after daybreak, leading

313

to near surface fine particulate matter being rapidly transported to the upper atmosphere with

314

rising warm air, while weak atmospheric convection after nightfall makes near-ground PM2.5

315

dispersion difficult, thus maintaining relatively high PM2.5 levels at night (Turner D.B., 1994). The

316

concentration curves peaked again to 2.78 µg/m3 between 2:00 and 3:00. The main reason for this

317

phenomenon is vehicle restrictions that mandate a series of requirements aimed at high-emission

318

and heavy-polluting vehicles, including motorcycles, special vehicles and heavy duty trucks,

319

which are strictly prohibited to drive within the SRRB area between 6:00 and 23:00. (Beijing

320

Traffic Management Bureau, 2017). The limited time available for permissible driving inevitably

321

contributes to concentrated traffic volumes of high-emission vehicles during certain periods of

322

time and to the high levels of intensive emissions that exacerbate ambient PM2.5 pollution.

323

Moreover, great diurnal temperature differences in autumn lead to obvious fluctuations in PM2.5

324

concentrations at different times.

325

[Insert Figure. 3]

326

To further investigate the spatial distribution characteristics of PM2.5 emissions from on-road 15

327

motor vehicles, daily mean dispersion concentrations were simulated as illustrated in Fig. 3.

328

Spatial PM2.5 contamination within the SRRB area has an evident skewed distribution, with

329

approximately 68% of the PM2.5 concentrated in 35% of the grid area. Due to the scattered roads

330

and the sparse traffic volumes, PM2.5 concentration in some grid cells area outside fifth ring road

331

is relatively low compared with the densely populated center. The median value of daily average

332

PM2.5 concentration was 2.68 µg/m3 and 1.89 µg/m3 on weekdays and weekends, respectively. For

333

most grid cells adjacent to expressways, PM2.5 released from transportation emissions contributes

334

more than 10 µg/m3, especially within the Fifth Ring. Not surprisingly, since area within the Fifth

335

Ring Road is considered as a hot spot area with frequent road congestion, the traffic performance

336

index (TPI) of roads in this area is greater than 8 year round, a road congestion level defined as

337

“serious” (Beijing Transport Institute, 2018). Overall, with regard to the spatial pattern of PM2.5

338

dispersion concentrations, concentrations tend to be higher in the north and lower in the south,

339

consistent with previous research (Wang et al., 2018). This result is in agreement with the fact that

340

higher densities of road network and higher traffic counts are centered in the northern SRRB area.

341

Comparatively speaking, there are more grid cells with high concentrations (> 4 µg/m3) in the

342

central SRRB area on weekends, particularly in the vicinity of the busy traffic arterial roads

343

between the Second and Fifth Ring, while ambient PM2.5 concentrations near expressways are

344

relatively low due to sharp decreases in commuting vehicles. In light of the differences in PM2.5

345

dispersion conditions between weekdays and weekends, well-designed traffic dispersion and

346

management measures should be implemented.

347 348

[Insert Figure. 4] 3.2 Health effects attributable to vehicle emissions 16

349

The health effects attributed to traffic-related PM2.5 exposure can be calculated using ER functions

350

based on simulated PM2.5 concentrations. For PM2.5 exposure at different times, the PM2.5-induced

351

premature mortality per one million population and the number of cases suffering from

352

PM2.5-caused diseases are listed in Table 4.

353

[Insert Table. 4]

354

Based on the IER function, the total estimated annual premature mortality was 4435 (95% CI:

355

3655, 4904) on weekdays and 3462 (95% CI: 3052, 4011) on weekends. Among the total early

356

deaths, IHD accounts for 49.2% while CVD, COPD and LC account for 30.2%, 10.6% and 10%,

357

respectively. For health effects from vehicular PM2.5 exposure during weekends, there is a

358

significant decrease in annual premature mortality, with low levels of PM2.5 emissions due to a

359

sharp decline in commuting traffic volume. In terms of the risk of morbidity from PM2.5-related

360

circulatory and respiratory diseases, the incidence on weekdays, as expected, is projected to be

361

higher than that during weekends, leading to a more severe public health burden.

362

With regard to adverse health outcomes due to early death or morbidity effects, potentially health

363

benefits can be achieved by reducing vehicular emissions and limiting traffic volumes. Over the

364

past decade, there has been an increase in traffic flow on the urban roads of Beijing, especially on

365

the ring expressways due to demand for quick travel (Beijing Transport Institute, 2018). At the

366

peak of rush hour, approximately 40% of the traffic volume passes along expressways, although

367

6.1% of expressway mileage in the whole road network.

368

Fig. 5 shows the hourly levels of health impacts (annual mortality and acute diseases) attributable

369

to PM2.5 at different times. We predicted a peak PM2.5-related mortality risk in the SRRB area at

370

8:00 on working days, with the highest number of premature deaths reaching approximately 540 17

371

people per hour, and 41,020 cases of acute morbidity, while an obvious improvement in health

372

effects occurred at the same time on weekends. The number of premature deaths dropped to 86

373

and incidence decreased by 38%. Similarly, since large number of people commuting home,

374

extreme health impairment occurred during the evening rush hour. However, due to the lower

375

dispersion concentrations of PM2.5 compared with the morning, the number of premature deaths

376

and morbidity decreased to 440 and 32520, respectively. In contrast, the most severe health

377

impacts on weekends appeared at approximately 18:00, probably as a result of a substantial

378

reduction in traffic volume in the morning with increased travel demand in the afternoon. It should

379

be further mentioned that the suspension of vehicle plate number traffic restriction measures on

380

weekends increased traffic volume. Traffic statistics from the Beijing Transport Annual report

381

confirm this phenomenon. Traffic guidance is essential in this area, with substantial travel on main

382

roads near a bustling commercial center and tourist attractions, during weekends.

383

[Insert Figure. 5]

384 385

3.3 Sensitivity analysis

386

To explore the impact of variables on health risk and the robustness of the estimated results, a

387

Spearman rank correlation, commonly applied in risk assessment, was used to analyze the

388

sensitivity of key parameters (Tong et al., 2018). The closer the correlation coefficient is to 1, the

389

greater is the impact of the parameters on health outcomes. Since the heaviest traffic and highest

390

health impact occurred at approximately 8:00 in SRRB area, we conducted a sensitivity analysis

391

based specifically on the traffic conditions during this period.

392

[Insert Figure. 6] 18

393

As shown in Fig. 6 (a), the relative risk (RR) is the most influential parameter. The average

394

correlation coefficient is 0.99, meaning that the RR value virtually determines the severity of the

395

health outcomes of premature death. Following RR, the sensitivity of PM2.5 concentrations and

396

baseline mortality are 0.77 and 0.29, respectively, which exert effects on the health impacts level

397

that cannot be ignored. Some previous studies have suggested that there is a nonlinear relationship

398

between PM2.5 concentrations and human health effects (Martenies et al., 2015; Burnett et al.,

399

2018). In addition, Zhao et al., (2019) have investigated the influence of concentrations on PM2.5

400

associated health effects, demonstrating that premature mortality resulting from PM2.5 exposure is

401

highly sensitive to the variation in primary PM2.5 emissions, consistent with our sensitivity

402

analysis. Overall, the parameter sensitivity differences among four types of diseases is minor, with

403

maximum of less than 0.05, which is almost negligible.

404

For the health effect of morbidity, the most sensitive parameter is concentration-response

405

coefficient, with an average correlation of 0.47 (Fig. 6(b)). While the correlation of PM2.5

406

concentration is only 0.23, which is less than its sensitive to premature death. Similarly, there was

407

a minimal difference in parameter sensitivity between outpatient visits and hospital admission.

408 409

4. Discussion

410

4.1 Relationship between the PM2.5 emission rate and vehicle speed

411

Real-time on-road vehicular emissions tend to vary with vehicle speed and other driving

412

conditions (Shen et al., 2014; Jaiprakash, 2017). Since urban road traffic flow constantly fluctuates,

413

the hourly PM2.5 emission rate was simulated. As shown in Fig. 7, the relationship between

414

average driving speed and PM2.5 emission rates of on-road vehicles in the road network is 19

415

explored.

416

[Insert Figure. 7]

417

It is common knowledge that road congestion depends on traffic volume, characterized by

418

real-time average driving speeds. An important feature reflecting driving conditions, vehicular

419

running speed significantly affects emissions (Franco et al., 2013). Yu et al. (2016) have simulated

420

vehicle exhaust emissions under different traffic congestion levels within Beijing's Fifth Ring

421

Road, indicating that emission rates at lower vehicle speeds are several times higher than those at

422

higher velocities. Therefore, the relationship between traffic flow speed and PM2.5 emission is a

423

key point. Overall, driving speed is approximately inversely proportional to the emission rate per

424

hour. Specifically, in the morning and evening peak periods when vehicular speeds are relatively

425

slow, the PM2.5 emission rate of on-road vehicles reaches a maximum, especially at 8:00 when

426

emissions increase to 3.9 g/h (95% CI: 3.46, 4.57). However, a peak afternoon emission rate of

427

2.92 g/h (95% CI: 2.11, 3.82) occurred during the heaviest commuting period. Lower emissions

428

along with increasing running speeds and a reduction in traffic volume after rush hour illustrates

429

the influence of driving speed on PM2.5 emissions. The corresponding release rate during this

430

period is the lowest for the whole day.

431

Furthermore, we associate PM2.5 emission levels with traffic performance levels, expressed by

432

velocity of traffic flow, using the local standard urban road traffic performance index (DB11/T

433

785-2011). As defined, the congestion degrees of “severe”, “moderate”, “mild” and “unobstructed”

434

corresponds to speed range of 0~10, 10~20, 20~30 and beyond 30 km/h, respectively. Thus, the

435

average PM2.5 release rate for vehicles is greater than 3 g/h under moderate congestion, while

436

during mild congestion and unobstructed times, the release rate of road network vehicles decreases 20

437

significantly, with a minimum of 1.2 g/h.

438

4.2 Comparison of attributable premature mortality in different cities

439

Huge health inequalities induced by ambient PM2.5 exposure have been found across China,

440

resulting from differences in baseline health conditions and contamination level among regions

441

(Song et al., 2017). Based on environmental monitoring data, Li et al. (2018b) have estimated the

442

public health burden associated with ambient PM2.5 and specific premature deaths caused by PM2.5

443

pollution for leading Chinese cities. Similarly, faced with significant spatial variation in motor

444

vehicle ownership and emission standards for automobiles, the PM2.5-related health impacts from

445

on-road motor sources can vary; however, it seems difficult to directly calculate health outcomes

446

from traffic emissions due to the inaccessibility of local road traffic information and accurate

447

vehicle emission data. Fortunately, Global Road Safety Facility et al. (2014) have proposed a

448

method called “direct proportion of burden”, which quantitatively estimates the traffic-related

449

PM2.5 health burden in accordance with the proportionate contribution of vehicle sources to total

450

PM2.5 emissions without detailed emission data, providing a method to support the following

451

discussion.

452

According to related data gathered from published PM2.5 source apportionment studies and the

453

population distribution data of each city, we selected Shanghai, Tianjin, Chongqing and Shenzhen

454

as comparison cities with similar population sizes (Wang et al., 2016; Chen et al., 2017; Gao et al.,

455

2017). To calculate health effects, the proportion of PM2.5 concentration attributable to traffic

456

sources Pj was first determined from the above cited papers. Combined with total premature death

457

data, we proportionately predict the premature mortality attributable to vehicle emissions as shown

458

in Fig. 8. 21

459

[Insert Figure. 8]

460

Significant differences in PM2.5 related health burdens caused by traffic emissions among cities

461

are illustrated. Concretely, attributable premature mortality is highest in Beijing owing to that

462

city's most serious vehicular PM2.5 emissions, accounting for 29.7% of total ambient PM2.5, with

463

21.3 (95% CI: 11.4, 26.8) premature deaths per 100,000 people, followed by Shanghai and Tianjin.

464

Although Beijing has vigorously promoted the popularization of new energy vehicles, it is

465

unrealistic to greatly reduce the proportion of primary PM2.5 originated from traffic in the short

466

term, due to the large number of fossil fuel vehicles. In addition to massive traffic emissions, the

467

aging urban population in these cities increases premature mortality, since older people are more

468

likely to die from PM2.5-related diseases due to their poor tolerance of PM2.5 (Wang et al., 2015;

469

Wang et al., 2018). Despite possessing the most exposed population, by contrast, the health

470

impacts in Chongqing are relatively low, with 4.3 (95% CI: 0.9, 9.7) premature deaths from

471

on-road mobile sources, largely due to a vehicular PM2.5 contribution of less than 10%. The

472

number of premature deaths is only 0.9 at the 95% confidence interval, less than one tenth those of

473

Shanghai and Tianjin, for Shenzhen, the first megacity that has met the national air quality

474

secondary standard, with excellent local climate conditions and stringent vehicle emission controls

475

playing an important roles in reducing PM2.5 pollution. Hence, the formulation of diversified PM2.5

476

emissions-cutting measures for megacities should be in line with actual pollution status.

477

4.3 Limitations and research prospects

478

To some extent, these results provide a data reference for health impact estimation and PM2.5

479

emissions from on-road mobile sources. However, limited by the insufficiency of basic research

480

data and by the imperfection of applied models, this study inevitably possesses limitations. In light 22

481

of the vehicle restriction policy on trucks in the SRRB area and the high proportion of passenger

482

vehicles among total motor vehicles (91.7%), only passenger car were considered, ignoring PM2.5

483

emissions from trucks, especially large-displacement diesel trucks, such as garbage trucks and

484

engineering vehicles. Consequently, the PM2.5 pollution concentrations and health burden results

485

may be smaller than the actual levels, since the evidence suggests that heavy diesel trucks may

486

emit higher PM2.5 concentrations than other types of vehicles (Song et al., 2018). To reduce the

487

complexity of simulation, the unadjusted default parameters in MOVES model such as I/M

488

(inspection and maintenance) programs and vehicle settings can increase the uncertainty of our

489

evaluation results.

490

Furthermore, the exposed population was not classified into groups according to age and sex. Due

491

to limitations in obtaining baseline health data and the lack of accurate statistics by age group,

492

differences in health effects between groups were not analyzed. The health consequences of

493

exposure to PM2.5 may vary between sexes and age groups, especially among children and the

494

elderly whose poor tolerance to environmental PM2.5 can make them more susceptible to adverse

495

health effects (Long et al., 2018). Another limitation was that only premature death, emergency

496

and hospitalization with relatively adequate epidemiological data were estimated, while we were

497

unable to quantify other health endpoints related to PM2.5 exposure, such as decreased lung

498

function, limited activity and adverse reproductive outcomes. In the wake of continuously

499

improving relevant epidemiological data, it is necessary to conduct research comparing different

500

population groups.

501

In this paper, we assumed that the health effects of ambient PM2.5 released from all types of

502

on-road motor vehicles are equivalent, although the chemical composition of PM2.5 from different 23

503

sources, and consequently their health effects, may vary greatly. In the future, with a deepening of

504

research on the components and toxicity of PM2.5 from various sources, the complex effects of

505

mixed PM2.5 on human health can be characterized more precisely, and scientific suggestions for

506

the formulation of urban public health policies and the implementation of PM2.5 pollution

507

prevention and control measures can be offered.

508 509

5. Conclusion

510

To explore the PM2.5 emission characteristics of on-road motor vehicles and consequent health

511

impacts, this paper collected detailed traffic data and vehicle information within the Sixth Ring

512

Road, Beijing, China. A MOVES model from USEPA was applied to simulate vehicular PM2.5

513

emission concentrations on different days. The simulation results show that PM2.5 emissions from

514

on-road mobile sources during normal weekdays were greater overall than those on weekends due

515

to higher traffic volume, especially on expressways. In terms of hourly emission levels, the most

516

severe PM2.5 emissions usually occurred during morning and evening rush hours (8:00 and 19:00),

517

while there was significant mitigation at other times. It should be noted that vehicular PM2.5

518

emission intensity was greatly influenced by driving speed. Temporal and spatial PM2.5 dispersion

519

was analyzed using the AERMOD dispersion model. Comparatively, high PM2.5 hot spots were

520

concentrated in the north and central areas of the SRRB. The median values of daily average PM2.5

521

concentration were 2.63 µg/m3 and 1.79 µg/m3 on weekdays and weekends, respectively. To

522

estimate health effects attributable to vehicular PM2.5 dispersion, a Poisson model and an IER

523

model were separately used to quantitatively calculate acute morbidity and premature mortality.

524

With regard to the health burden attributable to on-road mobile sources emission, the total number 24

525

of annual premature deaths was 4435 (95% CI: 3655, 4904) on weekdays, and this number

526

decreased to 3462 (95% CI: 3052, 4011) on weekends. Divided into various health endpoints, IHD

527

accounted for approximately 50% of premature deaths. For the adverse health outcomes of

528

morbidity, the number of outpatient visits and hospital admission were consistent with the

529

premature death distribution. Compared with other megacities, Beijing was the most severely

530

affected by PM2.5 emitted from urban road vehicles, with a premature mortality of 21.3 per

531

100,000 people, followed by Shanghai and Tianjin (12.2), while the consequent health effects in

532

Shenzhen are negligible, with less than 1 early death.

533

Admittedly, although capturing the contribution to ambient PM2.5 pollution of motor vehicular

534

emissions was challenging given the complexity of modeling, it was necessary for estimating

535

PM2.5 related health effects caused by urban road mobile sources. Despite some limitations, these

536

results can help support policymaking for the alleviation of vehicular PM2.5 emissions and

537

subsequent evaluations of control measures. At the national level, it is necessary to further support

538

the new energy automobile industry (such as subsidies to relevant enterprises or give policy

539

support). Besides, the government also should encourage people to actively purchase and use

540

clean energy vehicles or reduce the use of private vehicles. As for the measures to cut off fuel

541

powered vehicles emissions, many provinces have already begun to implement a new stage of

542

vehicle emission standards to reduce harmful substances in automobile exhaust. Recently, the

543

“Vehicle-Fuel-Road” integrated framework for controlling urban vehicle emissions has been

544

established in Beijing and has made some progress in PM2.5 pollution reduction. In the future, we

545

can investigate variations in vehicular PM2.5 pollution and estimate the refined health benefits

546

following the implementation of specific control measures such as odd-and-even restrictions, 25

547

promotion of new energy vehicles or economic incentives (UNEP, 2016). With the utilization of

548

geographic information systems (GIS) and national vehicle emission inventories we can conduct

549

research on the health impacts of nationwide on-road mobile sources to provide an accurate

550

reference for the formulation of public health policy.

551 552

6. Conflict of Interest

553

None declared

554 555

7. Acknowledgment

556

This work was supported by the National Natural Science Foundation of China (grant number

557

51674268).

558 559

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38

Table 1(a). Sample distribution of vehicle speed on different types of road during weekdays Average speed ID

1

2

3

4

5

6

7

8

9

10

11

12

13

Average speed (km/h)

4

8

16

24

32

40

48

56

64

72

80

88

96

Speed interval (km/h)

0-4

4-12

12-20

20-28

28-36

36-44

44-52

52-60

60-68

68-76

76-84

84-92

92-100

Expressway (N)

18

285

588

943

1773

3448

1921

633

160

23

5

2

1

Arterial roads (N)

88

426

910

1840

2975

1738

929

201

63

26

1

0

0

Table 1(b). Sample distribution of vehicle speed on different types of road during weekends Average speed ID

1

2

3

4

5

6

7

8

9

10

11

12

13

Average speed (km/h)

4

8

16

24

32

40

48

56

64

72

80

88

96

Speed interval (km/h)

0-4

4-12

12-20

20-28

28-36

36-44

44-52

52-60

60-68

68-76

76-84

84-92

92-100

Expressway (N)

85

525

1084

1388

1947

2548

1296

475

89

12

3

1

0

Arterial roads (N)

146

847

1210

1789

2648

1944

810

217

44

16

2

0

0

Table 2. Local vehicle statistical information and input data in the MOVES Vehicle name

Vehicle type

Fuel type

Vehicle population

Passenger car Passenger truck Intercity bus

LDV LDV HDV

Gasoline Diesel Diesel

Transit bus Transit bus

Urban bus

Diesel

Urban bus

CNG

3550000 90000 550 6900 8000

Table 3. Baseline incidence of disease endpoints and exposure-response coefficient Baseline incidence (‰)

Health endpoints

Premature mortality

Exposure-response coefficient (95% CI) α

γ

δ

Ccf

IHD

1.1293

0.843

0.0724

0.544

6.96

CVD

1.2641

1.01

0.0164

1.14

8.38

COPD

0.5983

18.3

0.000932

0.682

7.17

LC

0.4784

159

0.000119

0.735

7.24

β Outpatient visits

Respiratory diseases

547.3

0.00050

Circulatory diseases

1190.9

0.00047

Hospital admission

Respiratory diseases

8.8

0.00178

Circulatory diseases

20

0.00056

Table 4. Annual number of population affected by different health endpoints attributed to PM2.5 under different scenario Health endpoints

Premature mortality

IHD CVD COPD LC

Outpatient visits

Respiratory diseases Circulatory diseases

Hospital admission

Respiratory diseases Circulatory diseases

Number of people with health impairment (95%CI) Weekday

Weekend

2194 (1879, 2367) 1338 (1201, 1484) 464 (378, 556) 439 (342, 527)

1721 (1610, 1893) 1024 (903, 1198) 375 (285, 472) 342 (254, 448)

103424 (92078, 115166)

92446 (86541, 101214)

211310 (201062, 224920)

192658 (183762, 201827)

6008 (4519, 7732)

5017 (3925, 6211)

4235 (3102, 5396)

3289 (2182, 4157)

Study Area

Fig.1. Study Area

Fig. 2. Evaluation approach for the health effects attributed to PM2.5 from on-road mobile sources

Fig. 3. Average hourly dispersion concentration of PM2.5 emitted from motor vehicles

Fig. 4. The spatial distribution of daily mean concentration of PM2.5 from vehicles on weekday (a) and weekend (b)

Fig. 5. The vehicular PM2.5-attributable health effects on weekdays and weekends

Fig. 6. The parameter sensitivity analysis for health effect of premature death (a) and acute morbidity (b).

Fig. 7. PM2.5 emission rate and average speed of vehicles in SRRB area. Note: The results presented in this picture are based on the data analysis during weekdays

Fig. 8. Premature mortality caused by urban vehicular PM2.5 emission and contribution proportion among megacities

PM2.5 emissions of on-road urban vehicles in different time periods were estimated. Health effects attributed to vehicular PM2.5 were quantified with E-R functions. People could suffer worse health effects exposed to vehicular PM2.5 on weekdays. Traffic situation greatly affects the PM2.5 pollution and public health burden.

Author contributions statement Ruipeng Tong: Conceptualization, Methodology, Supervision, Resources. Jiefeng Liu: Investigation, Data curation, Writing-Original draft preparation, Writing-Reviewing and Editing. Wei Wang: Visualization, Investigation, Software, Writing-Original draft preparation. Yingqian Fang: Data curation, Investigation, Software, Validation, Writing-Reviewing and Editing.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: