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Assessing the future vehicle fleet electrification: the impacts on regional and urban air quality Wenwei Ke, Shaojun Zhang, Ye Wu, Bin Zhao, Shuxiao Wang, and Jiming Hao Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b04253 • Publication Date (Web): 13 Dec 2016 Downloaded from http://pubs.acs.org on December 22, 2016

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Environmental Science & Technology

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Assessing the future vehicle fleet electrification: the

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impacts on regional and urban air quality

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Wenwei Ke 1, Shaojun Zhang 2, Ye Wu 1, 3, *, Bin Zhao 4, Shuxiao Wang 1, 3, Jiming Hao 1, 3

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1. School of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, P. R. China.

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2. Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan 48109,

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U.S.A.

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3. State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, P. R. China.

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4. Joint Institute for Regional Earth System Science and Engineering and Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, U.S.A.

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*Corresponding author

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Phone: +86-10-62794947; fax: +86-10-62773597; e-mail: [email protected]

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TOC Art

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ACS Paragon Plus Environment


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ABSTRACT

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There have been significant advancements in electric vehicles (EVs) in recent years. However,

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the different changing patterns in emissions at upstream and on-road stages and complex

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atmospheric chemistry of pollutants lead to uncertainty in the air quality benefits from fleet

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electrification. This study considers the Yangtze River Delta (YRD) region in China to investigate

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whether EVs can improve future air quality. The Community Multi-scale Air Quality model

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enhanced by the two-dimensional volatility basis set module is applied to simulate the temporally,

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spatially and chemically resolved changes in PM2.5 concentrations and the changes of other

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pollutants from fleet electrification. A probable scenario (Scenario EV1) with 20% of private

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light-duty passenger vehicles and 80% of commercial passenger vehicles (e.g., taxis and buses)

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electrified can reduce average PM2.5 concentrations by 0.4 to 1.1 µg m-3 during four representative

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months for all urban areas of YRD in 2030. The seasonal distinctions of the air quality impacts with

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respect to concentration reductions in key aerosol components are also identified. For example, the

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PM2.5 reduction in January is mainly attributed to the nitrate reduction, whereas the secondary

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organic aerosol reduction is another essential contributor in August. EVs can also effectively assist in

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mitigating NO2 concentrations, which would gain greater reductions for traffic-dense urban areas

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(e.g., Shanghai). This paper reveals that the fleet electrification in the YRD region could generally

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play a positive role in improving regional and urban air quality.

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Word count: 5936 words for texts, plus 1 table and 4 figures.

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INTRODUCTION

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There have been significant advancements in commercializing electric vehicles (EVs),

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including plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs), in recent

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years. From a global perspective, fleet electrification is an essential gateway for the on-road

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transportation sector to utilize non-fossil energy and alleviate climate change

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and cities have proposed fiscal policies, primarily including subsidies and tax exemptions [4], leading

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to a surge in global EV sales [5, 6]. Total global EV sales jumped from 321,000 in 2014 to 550,000 in

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2015, representing an annual increase of 72% [6]. Among all major economies, China achieved one of

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the most impressive sales record in 2015 by overtaking the U.S. and becoming a global leader in the

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EV market. Annual EV sales in China accounted for 1.3% of the total vehicle sales in the country in

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2015, and represented a growth rate of approximately 300% during 2014 and 2015 [7, 8]. This trend in

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the EV market in China is expected to continue, likely approaching the target of total EV sales of 5

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million vehicles by 2020 proposed by the central government [8].

[1-3]

. Many countries

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Addressing global climate issues is an important role in this process. Furthermore, many

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policy-makers are aware of the potential environmental benefits of EVs in mitigating urban

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atmospheric pollution

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environmental benefits over a regional scale have been ongoing for a number of years [11, 12]. A major

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area of research has applied life-cycle assessment (LCA) methods to determine the well-to-wheels

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(WTW) reduction benefits of energy consumption and emissions of greenhouse gases [18-20]

[9, 10]

. Discussions regarding whether fleet electrification can deliver actual

[13-17]

and air

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pollutants

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the power generation mix and vehicle technology. For example, for light-duty passenger vehicles

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(LDPVs), BEVs can significantly reduce the WTW emissions of VOCs but cause increased

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emissions of sulfur dioxide (SO2) and fine particulate matter (PM2.5) in regions with a considerable

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share of coal-based electricity (e.g., North China)

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deployment of EVs to replace gasoline cars may not reduce WTW emissions in the present year [19].

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However, BEVs could significantly reduce WTW NOX emissions for the heavy-duty urban bus fleet

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[20]

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low-speed urban cycles for conventional diesel buses [21].

for e-mobility, which are in a complex pattern and vary considerably depending on

[18, 19]

. In terms of nitrogen oxides (NOX), the

, as the selective catalytic reduction (SCR) system is likely to perform unsatisfactorily under

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In addition to the complex effects on WTW emissions varying by air pollutant species and

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vehicle technology, the spatial heterogeneity of emission patterns between power plants and on-road

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vehicles and the atmospheric chemistry of secondary species (e.g., secondary aerosols) are also

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major barriers to evaluate the regional air quality benefits of fleet electrification. In 2016, Reuters 3



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expressed the concern that developing EVs in China might be conflict with the original policy

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intention to mitigate smog pollution

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electric cars in China would increase WTW emissions of NOX, SO2 and PM2.5 in many provinces due

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to the high share of coal-based power

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not sufficient to affect the motivation to promote EVs in China, because spatial difference of

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emissions shift from on-road vehicles to upstream sectors and complex atmospheric chemistry are

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not considered. To overcome the technical challenge, air quality models including long-distance

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transport and atmospheric chemistry modules can be applied to study the regional and urban air

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quality impacts of EVs. In China, few studies have used sophisticated air quality models to assess the

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air quality impacts from fleet electrification so far. Some researchers have recently run air quality

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simulations for fleet electrification scenarios in other regions of the world [23-26]. These international

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studies could provide useful references in terms of scenario design, modeling methodology, result

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comparison and sensitivity analysis. For example, Soret et al.

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Barcelona and Madrid could lead to substantial improvements in nitrogen dioxide (NO2)

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concentrations and lower the benefits related to PM2.5 (3-7%). Tessum et al.

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difference of PM2.5 concentrations from fleet electrification in various regions of the U.S.

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[22]

, where Huo et al.’s findings were mentioned that current

[19]

. We argue that the changes of air pollutant emissions are

[24]

found that fleet electrification in [25]

discussed the spatial

Considering the strong momentum of EV growth, the high share of coal power [28, 29]

[27]

, and

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substantial air pollution problems

in China, the regional air quality impacts of ongoing fleet

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electrification should be adequately evaluated. The Yangtze River Delta (YRD, including Jiangsu,

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Shanghai and Zhejiang) Region, one of the most densely populated metropolitan areas in the world,

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is selected as a case to evaluate the air quality impacts from vehicle fleet electrification in the

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medium-term future (2030). In addition to the populous landscape, the YRD region is selected for

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three additional reasons. First, recent source apportionment results have indicated that mobile

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sources were important local contributors of PM2.5 concentrations in some large cities in the YRD

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region (e.g., Hangzhou and Shanghai)

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municipal governments to support EV development, providing more favorable opportunities and

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incentives to promote e-mobility in the YRD region than in other interior regions

[30, 31]

. Second, many policies have been formulated by local [32]

. Third, the

[17-19]

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electricity generation mix in the YRD region is comparable to the national average

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would yield modest results among various regions in China. The Community Multi-scale Air Quality

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(CMAQ) model enhanced by the two-dimensional volatility basis set (2D-VBS) is employed to

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simulate the spatial, temporal and species-resolved air quality impacts. The state-of-the-art 2D-VBS

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module can improve the simulation performance of the secondary organic aerosol (SOA) over the

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conventional CMAQ model [33, 34]. This paper aims to determine whether the fleet electrification can

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, which

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improve regional air quality for a populous and coal power rich region.

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2

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2.1 Research scope and fleet electrification scenarios

METHOD AND DATA

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In this study, 2030 is selected as the model year representing the medium-term future. Future

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fleet electrification is considered by the designed scenarios to occur in Shanghai and fifteen other

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prefecture-level cities in the Jiangsu and Zhejiang provinces (i.e., the core area of the YRD region)

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[31]

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cities (e.g., see PM2.5 in Table S1) are reported because of their high population density and vehicle

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use intensity. Table S1 also lists the simulation results of PM2.5 in the rural areas of these cities. We

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suggest further studies compare the benefits from fleet electrification between urban and rural areas

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based on assessments of health impacts (e.g., exposure, intake fraction)

. In later sections, the concentrations of major air pollutants in the urban areas of these sixteen

[25, 35]

or environmental

[36]

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justice issues

, which can help investigate the role of demographic characteristics. However, we

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limit our focus on simulated concentrations in urban areas and the spatial pattern over the entire

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region in this study.

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PM2.5 is the air pollutant of prioritized concern, as the limit exceedance of ambient PM2.5

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concentration is currently the most significant air pollution issue in China. Other related criteria, such

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as NO2, SO2, and ozone (O3), are also included in this assessment. The air quality simulations are

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conducted in January, May, August and November under a probable fleet electrification scenario

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(Scenario EV1, see in the next paragraph) to represent the seasonal distinctions of meteorological

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conditions and chemical transport, for example, which may affect key relevant aerosol components,

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including nitrate and SOA [37, 38].

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Three fleet electrification scenarios are designed for 2030: (1) no EV penetration (Scenario w/o

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EVs), which assumes that no EVs are deployed in the fleet of the research area; (2) moderate EV

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penetration (Scenario EV1, see Table S2), which assumes that EVs comprise 20% of LDPVs (e.g.,

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private light-duty passenger cars) and 80% of other commercial passenger vehicle fleets (e.g., buses,

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taxis) (Scenario EV1 is seen as a most plausible plan for the mid-term future); and (3) full EV

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penetration (Scenario EV2, see Table S2), which assumes that all passenger vehicles are electrified.

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Scenario EV2 is intended to be used to examine the maximum air quality benefit; however, this

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scenario may not become reality within the next fifteen years and. In this study, Scenario EV2 is only

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applied for the air quality simulations in January and August 2030 for comparing with Scenario EV1.

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Both electrification scenarios consider only BEVs for simplicity because BEVs are responsible for 5



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[7]

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75% of total EV sales in China

, and PHEVs can be methodologically seen as partial BEVs with

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adequately electrified mileage splits.

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2.2 Emission inventory development with spatial, temporal, and fuel-cycle considerations

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The historical and future emission trends (2005-2030) of major air pollutants (e.g., NOX, SO2,

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primary PM2.5, NH3, non-methane volatile organic compounds (NMVOC)) have been previously

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evaluated at the provincial level, including for power plants, industrial sectors, residential sectors,

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transportation areas and agriculture activities

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YRD region, the emission inventory is further improved at the city level [41]. For all emission sectors

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except for on-road transportation, the emission inventory reported by Fu et al.

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baseline year in 2010 and the PC[1] emission scenario reported by Wang et al.

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inventory in 2030. For on-road transportation, the emission factors are updated using the

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EMBEV-China model [42-44], which is developed based on local laboratory and on-road measurement

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data of tailpipe and evaporative emissions and considers numerous local corrections (e.g., fuel

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quality, weather conditions, driving conditions, vehicle size, high-emitters). In 2015, The Ministry of

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Environmental Protection (MEP) of China released its first draft version of The National Emission

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Inventory Guidebook for On-road Vehicles, with the EMBEV-China model as an archetype [45].

[39, 40]

. When the spatial resolution is restricted to the [41]

[40]

is used as the

is used for future

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Figure S1 presents the total primary air pollutant emissions in the YRD region under Scenario

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w/o EVs. Special interest is given to the power and on-road transportation sectors that will be most

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influenced by the fleet electrification trend. For power plants, the share of coal-fired electricity

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generation in the YRD region is estimated to decrease from over 80% in 2010 to 49% in 2030 [40] as

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the penetration of non-fossil electricity (e.g., hydro, nuclear, wind power) significantly increases.

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Furthermore, the generation efficiency of thermal power units will be improved, and

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high-performance end-of-pipe emission control devices will be nearly fully adopted

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significant reductions in air pollutant emissions are expected from the power sector in the future. For

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on-road transportation, ultra-low sulfur gasoline and diesel have been delivered recently, and

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estimated China 6 emission standards will be implemented before 2020 in the YRD region [44].

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Furthermore, the substantial scrapping of older vehicles is estimated to decrease total on-road vehicle

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emissions in the YRD region by 66% for NMVOC, 73% for NOX and 90% for PM2.5. Figure S2

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presents the regional emissions from the on-road transportation sector by vehicle category under

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Scenario w/o EVs.

[17, 40]

. Thus,

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The emission reductions under Scenarios EV1 and EV2 compared with Scenario w/o EVs are

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calculated based on the BEV penetration rates for passenger vehicle fleets considering that BEVs 6


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have no on-road emissions. Figure S3 presents the hourly emissions allocations from the on-road

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transportation sector under the three vehicle fleet electrification scenarios, which are estimated based

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on the diurnal fluctuations in traffic activity and average speeds for typical cities in the YRD region.

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From a fuel-cycle perspective, increased emissions from power plants are in turn estimated based on

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the generation increments associated with EV charging. The marginal generation mix required by EV

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charging is assumed to be identical to the regional average generation mix. The generation

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increments should comprise the electricity consumption of BEV operation and the electricity losses

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in the transmission and charging processes. The total electricity consumption of BEV operations is

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estimated based on vehicle population, annual mileage

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consumption (see Table S3). The overall charging efficiency of BEVs and the transmission efficiency

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of the power grid are estimated to be 90%

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24-hour profile of EV charging load is presented to allocate the daily incremental emissions from

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power plants into an hourly resolution (see Figure S4) [46].

[17]

and 94%

[43]

, and distance-specified energy

[20]

, respectively, by 2030. A hypothetic

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Coupled with the spatial requirement by the grid-based air quality modeling, emissions from

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industrial and residential activities are distributed to the county-level based on economic quantity

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(i.e., gross domestic production, GDP) and further allocated to grid cells based on the spatial

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distribution of the resident population

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to the spatial density of traffic networks and the activity split within the urban areas for

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high-polluting freight trucks

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sources according to power unit technology and location information (i.e., longitude and latitude).

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2.3 Air quality modeling

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[37]

. For on-road vehicles, emissions are distributed according

[47]

. Power plant emissions are explicitly calculated as large point

The CMAQ model (version 5.0.1)

[26, 48]

is applied to simulate the air pollutant concentrations

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under various fleet electrification scenarios. The default SOA mechanisms applied by the CMAQ

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model have been criticized due to the large underestimation of SOA concentrations

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would significantly affect the evaluation results of air quality impacts from fleet electrification. In

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previous studies, an enhanced module based on the 2D-VBS simulation technology [33, 34, 37] was

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incorporated into the CMAQ v5.0.1 model to improve the SOA simulation. The 2D-VBS parameters

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were constrained to simulate the aging of SOA derived from anthropogenic and biogenic NMVOC,

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aging of primary organic aerosol (POA), and photo-oxidation of intermediate-volatility organic

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compounds (IVOCs) against a series of smog-chamber experiments. A case study in Eastern China

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indicated that the underestimation in OA concentrations could be reduced from 45% to 19% using an

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enhanced 2D-VBS model instead of the default SOA simulation processes of CMAQv5.0.1. The

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simulated fraction of OA consisting of SOA would correspond well with in-situ observation data 7


[34, 37]

, which

[34,


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200

37]

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conditions, and meteorological data from 2010 [49] are applied as input for future scenarios. The

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reason for using the meteorological data in 2010 is to maintain the consistency of time framework

. The Weather Research and Forecasting (WRF) model is used to generate the meteorological

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with previous data regarding emission inventories, air quality simulations and ambient measurements

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[37, 49, 50]

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difference of meteorological conditions in various years may result in potential bias of simulated

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results, however, which can be re-visited in the future. One-way nesting is performed from a coarser

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domain to the finest one to retrieve the meteorological and chemical conditions to finer domains. The

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modeling domain includes a coarse grid setup covering most of China and part of East Asia with a 36

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km × 36 km resolution, a medium grid setup covering East China with a 12 km × 12 km resolution,

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and a fine grid setup over the YRD region with a 4 km × 4 km resolution (see Figure S5). Fourteen

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vertical layers are defined in the modeling system for all grid setups. Detailed specifications and

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parameters of the CMAQ and WRF models are summarized in Table S4, and the modeling system

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has been validated by multi-year satellite and in situ observation data [50, 51].

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2.4 Sensitivity analysis

, which were applied in the development and verification of the 2D-VBS module

[34]

. The

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Various scenarios are considered for the differential fleet electrification levels; furthermore,

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other issues exist that may affect the air quality impacts of EV deployment. First, previous results

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noted that the electricity mix is a major factor leading to changes in the WTW emissions of air

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pollutants for EVs [18-20]. For example, China’s national average share of coal-fired power generation

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in 2030 is projected to range from 38% to 58% under various energy roadmaps[52, 53], compared with

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49% [40] used in this study for the YRD region. To examine the changes in air quality impacts due to

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the electricity mix, two marginal electricity generation mix scenarios are generated that assume the

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marginal electricity would be fully powered by regional non-fossil energy (Scenario Non-fossil) and

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coal (Scenario Coal), respectively. Thus, the role of cleaner electricity in mitigating urban air

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pollution using EVs as the media can be explored. The changes in emissions and air quality under

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these two additional scenarios relative to Scenario w/o EV are simulated and compared with the

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results under Scenario EV1 using the regional average mix. For the air quality impacts, the

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sensitivity tests are conducted in January and August 2030.

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EV deployment will increase the demand for vehicle batteries and shrink the surge of petroleum

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fuels. To evaluate the environmental impacts of the industrial shift due to fleet electrification, a

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localized GREET model is applied to calculate the energy input profiles (e.g., fuel type, energy

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technology, and sector) concerning the production of vehicle batteries and petroleum fuels

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the footnote of Table S5). Furthermore, the emission inventory tools are applied to roughly estimate 8


[54]

(see

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the air pollutant emission changes according to the electrification level under Scenario EV1 based on

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the energy input profiles. Nevertheless, it is impossible to adequately simulate the air quality results

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using the present modeling framework without high-resolution profiles of petroleum fuels and

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battery production activities (e.g., location, annual production, and usage of pollution control devices)

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throughout entire supply chains.

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3

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3.1 Emission changes

RESULTS AND DISCUSSION

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Table 1 presents the estimated emissions of the major primary pollutants (NOX, SO2, PM2.5, and

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NMVOC) under various fleet electrification scenarios in the YRD region during 2030. After fleet

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electrification in the YRD region, certain trends in reduced NMVOC and NOX emissions and

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increased SO2 emissions will occur, but slight changes will be realized for PM2.5 emissions due to the

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significant emission distinctions between on-road vehicles and power plants. For example, Scenarios

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EV1 and EV2 could lead to reductions of 2.2% and 7.8% for total NMVOC emissions in the YRD

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region, respectively, accompanied with reductions of 8.1% and 10% in total NOX emissions,

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respectively, even though the power sector is also an important source of NOX (29%). Unlike

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previous estimates indicating that BEVs would have higher WTW NOX emissions than gasoline cars

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around 2010

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controls and real-world driving data [55] suggest that BEVs can reduce WTW NOX emissions by 52%

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relative to gasoline cars in the YRD region by 2030. In addition, the electrification of bus fleets could

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also significantly reduce WTW emissions of NOX and NMVOC in 2030 [20].

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3.2 Air quality impacts

[19]

, the updated results of this study using the up-to-date outlook for future emission

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PM2.5 and major aerosol components. Figure 1 (January and August) and Figure S6 (May and

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November) present the spatial results of simulated monthly average PM2.5 concentrations for the

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YRD region. The results indicate that the mean urban PM2.5 concentration in the YRD region during

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January, May, August and November 2030 under Scenario w/o EVs is simulated as 50, 26, 20 and 43

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µg m-3 (note: all results stated in this section are intended for simulated grids covering the urban

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areas of 16 core cities in the YRD region

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annual-average limit of the National Ambient Air Quality Standard (NAAQS) by small margin (34.8

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µg m-3 vs. 35 µg m-3) [56]. Relative to Scenario w/o EVs, Scenario EV1 in 2030 is estimated to reduce

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PM2.5 concentrations by 0.8±0.6 µg m-3 in January, 0.8±0.8 µg m-3 in May, 0.4±0.5 µg m-3 in August,

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and 1.1±0.8 µg m-3 in November, presenting reductions of 2~3% in four months (see Table S6).

[31]

, hereinafter), of which the mean value meets the

9



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Different spatial patterns of PM2.5 reductions are observed between various periods. Taking January

265

(winter) and August (summer) for example (see Figure 1), the greatest PM2.5 reductions in January

266

are simulated to occur in cells within the urban areas of Hangzhou and Wuxi (0.9±0.6 µg m-3) under

267

Scenario EV1 compared with Scenario w/o EVs, whereas in August, the area with the greatest PM2.5

268

concentration reduction would occur in Shanghai (1.0±1.1 µg m-3). The detailed simulated air quality

269

benefits of reducing PM2.5 concentrations for the 16 case cities are presented in Table S1.

270

Nevertheless, the spatial patterns of PM2.5 concentration changes are not identical to those of

271

emission changes (e.g., hotspots of emission reductions within traffic-populated urban areas), which

272

could be attributed to two aspects. First, the contribution from primary vehicular PM2.5 emissions to

273

ambient PM2.5 concentration would be minor due to stringent tailpipe emission controls in the future.

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For example, the on-road emissions of primary PM2.5 exhausted from LDPVs could only account for

275

0.8% of total anthropogenic PM2.5 emissions in 2030 under Scenario w/o EV. Thus, the benefits in

276

reducing ambient PM2.5 concentrations from EV penetration in the LDPV fleet under Scenarios EV1

277

and EV2 should be primarily attributed to lowered emissions of gaseous precursors (e.g., NOX and

278

VOCs) that would contribute to secondary aerosol formation, which will be discussed in next

279

paragraph. Second, the reaction and transport time framework of secondary aerosol formation from

280

gaseous precursors would weaken the spatial relationship between emission mitigation and

281

concentration reductions. The aggressive Scenario EV2 can achieve greater reductions in PM2.5

282

concentration by 1.7±1.2 and 1.0±1.0 µg m-3, respectively, in January and August, according to the

283

simulation results compared with those under Scenario w/o EVs. Furthermore, the areas with the

284

greatest air quality benefits from the fleet electrification spatially resembles those under Scenario

285

EV1, where the PM2.5 concentration reductions under Scenario EV2 would be enhanced to over 2.0

286

µg m-3 in January (cells in Hangzhou and Wuxi) and 2.3 µg m-3 in August (north Shanghai) (see

287

Figure 1).

288

When examining key aerosol components (see Figure 2), nitrate and SOA are the two most

289

important contributors to the reductions in PM2.5 concentrations, which could be attributed to the

290

emission reductions of NOX and NMVOC from EV deployment (see Table 1). The seasonal

291

distinctions of air quality impacts from the vehicle fleet electrification are also identified. During

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winter, under Scenario EV1, nitrate reduction (e.g., 0.7±0.4 µg m-3 in winter) is estimated to play a

293

major role in mitigating PM2.5 than under Scenario w/o EVs. The large nitrate reduction could also

294

contribute to lower concentrations of ammonium aerosol in the particle phase. However, the

295

concentrations of SOA in January will be increased by 0.1±0.2 µg m-3, which could be attributed to

296

oxidant increases (e.g., OH and O3, see in a later section). In summer, using August for example, the

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reduction of SOA (0.2±0.2 µg m-3) is responsible for 48% of the total PM2.5 reduction on average

298

under Scenario EV1. Meanwhile, nitrate reduction decreases to 0.2±0.3 µg m-3 in August. Such

299

seasonal difference is primarily attributed to the meteorological conditions and atmospheric

300

chemistry mechanisms that differ between the two periods and is consistent with seasonal patterns

301

observed in situ in the YRD region [57, 58]. In winter, low temperatures and poor dispersion conditions

302

favor the formation of nitrate

303

concentration reductions during the nighttime than those during the daytime (see Figure 3). By

304

contrast, high temperatures and high ambient oxidant concentrations would favorably lead to SOA

305

formation

306

has greater reduction benefits to nitrate concentrations in January than those in August, but reducing

307

NMVOC emissions has the opposite effects on SOA concentrations. The net reduction of PM2.5

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concentration is estimated greatest in November among all the four months (see Table S6). A

309

considerable of the reduction is attributed to the reduced formation of nitrate (see Figure S7), which

310

is comparable to the circumstances in January. On the other hand, several atmospheric and

311

meteorological conditions in November (e.g., increased O3 concentration, cool temperature, and low

312

boundary layer) would also favor SOA formation as well as condensation and accumulation in the

313

atmosphere. Similar trends were also reported by the study regarding the development and

314

verification of the 2D-VBS module

315

components as Scenario EV1, except for the positive effect of reducing SOA levels in January

316

(0.5±0.6 µg m-3) (see Figure 2). These results occur because the greater reduction in NMVOC

317

emissions under Scenario EV2 would play a more significant role in lowering SOA levels than the

318

increased effects of oxidants. Furthermore in August, the SOA reduction would be more important

319

than nitrate reduction and become a major contributor of total PM2.5 reduction under Scenario EV2.

320

The concentration changes of element carbon (EC) aerosol are estimated to be minor because

321

high-efficiency particle filters and collectors will be widely used to reduce EC emissions for both

322

vehicles and power plants. The results also indicate that elevated sulfate concentrations are

323

insignificant compared to the reduction benefits of other aerosol components (e.g., nitrate), although

324

fleet electrification would increase SO2 emissions from power plants (see Table 1).

[38]

; these conditions are also the probable causes of higher PM2.5

[34, 37]

, which will significantly favor nitrate evaporation. Thus, reducing NOX emissions

[34, 37]

. Scenario EV2 shows similar impact patterns for aerosol

325

NO2 and SO2. The simulations suggest that the mean NO2 concentrations (22 µg m-3) for the

326

four months are able to attain the annual limits required by the NAAQS (i.e., 40 µg m-3) in the YRD

327

region in 2030. Compared with Scenario w/o EVs, Scenario EV1 can reduce NO2 concentrations by

328

1.7 to 2.6 µg m-3 during the four months, presenting an average reduction of approximately 10% over

329

the urban areas of the core cities in the YRD region (see Table S6). Furthermore, Scenario EV2 has a

11



330

slightly greater reducing effect on NO2 concentrations (with a total reduction of approximately 13%)

331

than Scenario w/o EVs. Thus, combined with previous conclusions, the most significant marginal

332

effect of Scenario EV2 compared to Scenario EV1 is the mitigation of SOA concentrations rather

333

than nitrate aerosol or NO2 concentrations.

334

Nevertheless, the simulation results under Scenario w/o EVs show that NO2 exceedance is

335

highly likely to occur in certain hotspots in traffic-populated metropolitan areas, such as the urban

336

areas of Shanghai, Nanjing and Hangzhou (see Figure S8), which has been seen in many

337

traffic-populated cities in Europe

338

average NO2 concentration was 46 µg m-3 during 2015, exceeding the limit of NAAQS and posing

339

less reductions than other pollutants (e.g., SO2, PM10) [60]. This clearly indicated that mitigating NO2

340

concentrations is also a great challenge

341

concentrations from EV penetration will occur in traffic-populated urban areas (e.g., Shanghai,

342

Hangzhou, Suzhou, and Wuxi), which suggests that vehicle fleet electrification would result in

343

adequate air quality benefits where the NO2 exceedance risk is high. For example, the largest

344

reductions in NO2 concentrations under Scenario EV1 would be up to 4.8 µg m-3 in January and 4.2

345

µg m-3 in August, which are simulated to both occur in the urban area of Shanghai, where the

346

baseline NO2 concentrations under Scenario w/o EVs are estimated to be 45±18 µg m-3 in January

347

and 27±13 µg m-3 in August. The NO2 concentration reductions in the urban area of Shanghai are

348

shown to be 10% in January and 16% in August, which are close to the air quality benefits from the

349

fleet electrification estimated for Spanish cities by Soret et al.

350

NO2 concentration of the urban area of Shanghai during January (45 µg m-3) exceeds the annual

351

NAAQS limit (40 µg m-3), and Scenario EV1 is critical to greatly reduce the exceedance risk. Thus,

352

vehicle fleet electrification can effectively help in mitigating NO2 in traffic-populated cities.

[59]

. In the YRD region, taking Shanghai for example, the annual

[40, 61]

. Figure 4 illustrates that a greater reduction of NO2

[24]

The simulated monthly average

353

Figure 3 further indicates that the highest NO2 concentration reduction in the urban areas of the

354

YRD region would occur during the peak periods of ambient NO2 concentrations (e.g., reductions of

355

up to 4 µg m-3 during 7 p.m. to 9 p.m. in January). The NO2 reduction benefits from vehicle

356

electrification will be greater for traffic-populated megacities. For example, in the urban area of

357

Shanghai, the largest hourly air quality benefits regarding NO2 concentrations are simulated to be up

358

to 7 µg m-3 during the two traffic rush hour periods under Scenario EV1 (see Figure S9). However,

359

considering the spatial resolution limit of the CMAQ model applied in this study (i.e., 4 km × 4 km),

360

it is impossible to explicitly capture the underlying gradients in NO2 concentrations around traffic

361

hotspots or cross busy roads. Thus, finer-scale air quality models can be further incorporated with

362

regional air quality models based on a higher-resolution vehicle emission inventory (e.g., link level) 12


Page 12 of 25

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363

[62]

364

traffic hotspots).

to improve mapping the air quality and health benefits for some areas of great concerns (e.g.,

365

Vehicle fleet electrification would increase the ambient SO2 concentration by 0.1 µg m-3 in the

366

four study months under Scenario EV1 compared with those under Scenario w/o EVs (see Figure 4

367

and Table S6). Because the present urban SO2 concentrations of the YRD region (annual average of

368

20 µg m-3 in 2015) and simulated results under the Scenario w/o EVs for 2030 are both far lower than

369

the NAAQS limit, the adversely increased SO2 concentration from fleet electrification is acceptable.

370

O3. More complex patterns exist in the air quality benefit regarding reducing O3 concentration

371

than for other air pollutants, such as PM2.5 and NO2. The simulations indicate that the changes in O3

372

concentrations under fleet electrification scenarios are spatially, temporally and seasonally sensitive

373

to the emission changes of precursor pollutants (e.g., NOX and NMVOCs). Under Scenario w/o EVs,

374

the distribution of O3 concentration is spatially opposite to that of NO2. Low-O3 areas would occur in

375

urban areas (e.g., Shanghai, Wuxi, and Hangzhou), where high fresh NO emissions from on-road

376

vehicles would accelerate the removal of O3 through the process of NOX titration

377

high-O3 areas would be observed in hilly and marine areas in January (see Figure S8). Compared to

378

Scenario w/o EVs, the general trends according to the simulations indicate that Scenarios EV1 and

379

EV2 would increase daily peak 8-h O3 concentrations by 1-2 ppb for low-O3 areas (i.e., the urban

380

area of Shanghai) due to weakened NOX titration effects, which is also characteristic of

381

VOC-sensitive areas [64] (see Figure 4). Similar results were found in previous studies. For example,

382

Brinkman et al.

383

peak 8-h average O3 concentrations by approximately 2-3 ppb; however, the central urban area of

384

Denver may experience increased O3 concentrations. Soret et al.

385

maximum concentrations would increase in downtown areas of Madrid and Barcelona, Spain under

386

their vehicle electrification scenarios.

[23]

[63]

, whereas

indicated that a 100% penetration of PHEVs in Denver, U.S. could reduce the [24]

showed that the O3 hourly

387

For the temporal patterns, the results indicate different effects on the daily maximum 8-h

388

average O3 levels in January and August (see Figure 3). In January, vehicle fleet electrification under

389

Scenario EV1 would elevate the O3 concentrations almost all day except for in the mid-afternoon in a

390

few cells, leading to an increase in the daily maximum 8-h average O3 level of 0.5 ppb. Higher

391

elevations would occur along with traffic rush hours (e.g., increased by 1.5 ppb around 8 a.m. under

392

Scenario EV1) under electrification scenarios. By contrast, fleet electrification would lower O3

393

concentrations during noon and afternoon hours in August because NOX emissions, the precursor of

394

short-time O3 under this circumstance, would be reduced. The average daily maximum 8-h average 13



395

O3 concentration under Scenario EV1 can be lowered by 0.7 ppb (see Figure 3 and Table S6). When

396

focusing on the urban area of Shanghai, the most populous area in the YRD region, one-year

397

monitoring data during 2015 show that the peak hourly O3 concentrations also occur in the afternoon

398

in August (see Figure S9). Scenarios EV1 can reduce the hourly O3 concentrations for the urban area

399

of Shanghai by 1.6 ppb in afternoons during August. In other two months, we can also observed

400

different effects on the daily maximum 8-h O3 concentrations from Scenario EV1 with an overall

401

increase in November while reduction in May (see Table S6).

402

3.3 Discussion

403

The emission changes (see Table S5) and pollutant concentrations (see Table S7) are simulated

404

under two scenarios regarding the source of marginal electricity generation (i.e., Scenario Non-fossil

405

and Coal) for January and August with vehicle electrification assumptions under Scenario EV1.

406

Scenario Non-fossil implies an ideal power landscape to pursue in the long-term future, which can

407

avoid increased SO2 emissions and enhance the benefits in reducing emissions of NOX, NMVOCs

408

and PM2.5. In terms of ambient concentrations of air pollutants, a full penetration of non-fossil

409

marginal electricity can result in greater PM2.5 reductions by approximately 0.1 µg m-3 in both

410

January and August relative to Scenario EV1 using the regional average generation mix (see Table

411

S7). Thus, the uncertainty in the marginal electricity generation cleanness is approximately ±10% for

412

the concentration reduction of PM2.5. In addition, the regional average concentration changes of NO2

413

are less than ±3% from the variations in marginal electricity generation relative to Scenario EV1

414

using the regional average generation mix. For future studies, unit commitment and dispatch models

415

[23, 65]

416

and temporal accuracy of the emission profiles. These small bias imply the importance of

417

end-of-pipe emissions control for coal-fired plants, which might guarantee that coal-fired plants will

418

not decrease the air quality benefits from fleet electrification.

are recommended to estimate the emission changes from generation units to enhance the spatial

419

As for the demand changes of vehicle battery and petroleum fuels due to fleet electrification, the

420

total emission changes of major air pollutants are estimated in Table S5. Overall, compared with the

421

estimated emissions under Scenario EV1, the trade-off between increased battery production and

422

reduced petroleum fuel production would result in increases in NOX and SO2 emissions as well as

423

reductions in PM2.5 and NMVOCs emissions. Those emissions changes are all slight, within ±0.2%

424

relative to total emissions in the YRD region. Emission changes due to battery and fuel production

425

activities are estimated under the presumption that all life-cycle production activities would occur

426

within the YRD region. In the future, an improved simulation including air quality impacts can be

427

performed when detailed supply-chain profiles of vehicle battery and petroleum fuels become 14


Page 14 of 25

Page 15 of 25

428


available [25].

429

Nevertheless, anthropogenic emissions from other sectors (e.g., industrial and residential) in

430

China also considerably contribute to regional PM2.5 concentration [40, 66], posing a more complex air

431

pollution pattern than those in Europe and the U.S. As a result, substantial emission reductions from

432

the power and on-road transportation sectors under EV scenarios will not necessarily represent a

433

large change of regional PM2.5 concentrations by 2030, because anthropogenic emissions from other

434

sectors (e.g., industrial, residential) would remain comparable to the present circumstances according

435

to a most plausive plan for future years

436

and residential sectors in China are considered less progressive than those implemented for on-road

437

vehicles and power plants

438

residential sectors is beyond the scope of this study, which could only be improved when future

439

policies and action plans become more certain. We acknowledge the importance of the full

440

involvement of all sectors in air pollution control is vital in China. The role of fleet electrification in

441

improving regional air quality will become increasingly significant in the long-term future as

442

emission controls have been further implemented in other sectors.

[40]

. The processes of controlling emissions from industrial

[28]

. However, construction of emission inventories for industrial and

443

Despite the uncertainties noted above, a number of policy suggestions are presented for on-road

444

vehicles. First, fleet electrification can considerably reduce ambient NO2 concentrations in urban

445

areas, which will be valuable for some metropolitan downtown areas where the exceedance risks

446

would exist in certain months (e.g., January). Second, fleet electrification is capable of reducing

447

regional PM2.5 concentrations contributed by road transportation activities. Greater EV penetration

448

levels can yield higher air quality benefits in terms of NO2 and PM2.5. End-of-pipe emission controls

449

for thermal power plants and penetration of non-fossil electricity should be assured to guarantee the

450

potential air quality benefits. Increasingly stringent vehicle fuel consumption standards required to

451

alleviate global climate change, notably the corporate-average fuel consumption ceilings adopted by

452

many nations, will be a strong, mandatory driver for manufacturers to deploy more EV models.

453

Finally, supportive policies (e.g., fiscal policies, traffic management) and infrastructure (e.g.,

454

construction of charging facilities) are fundamental to expand both the purchase and use (i.e.,

455

electrified mileage) of EVs by consumers in China [67].

456

ASSOCIATED CONTENT

457

Supporting Information Available: The Supporting Information include supplementary

458

figures and tables noted in the manuscript. This material is available free of charge via the Internet at 15



459

http://pubs.acs.org.

460

ACKNOWLEDGMENTS

461

This study was sponsored by the National Natural Science Foundation of China (91544222,

462

51322804) and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ). The contents of

463

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

464

of the sponsors.

465

References

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653

Tables

654

Table 1. Estimated emissions of NOX, SO2, PM2.5, and NMVOCs in the YRD region under Scenario

655

w/o EVs and the emissions changes under Scenarios EV1 and EV2, 2030

Scenario Sector

656

Air pollutant emissions (kt y-1) NOX

SO2

PM2.5

NMVOC

w/o EVs

Power plants 320 On-road vehicles 223 Total 1113

375 0.9 840

37 5.2 334

10 274 2729

EV1

Power plants +8.0 (+2.5% a) +9.1 (+2.4%) +0.9 (+2.4%) +0.2 (+2.4%) On-road vehicles -98 (-44%) -0.2 (-17%) -1.5 (-29%) -60 (-22%) Total -90 (-8.1%) +9.0 (+1.1%) -0.6 (-0.2%) -59 (-2.2%)

EV2

Power plants +25 (+7.7%) On-road vehicles -136 (-61%) Total -112 (-10%)

a

+30 (+8.1%) -0.6 (-67%) +30 (+3.5%)

+2.9 (+7.9%) +0.8 (+8.1%) -3.5 (-67%) -214 (-78%) -0.6 (-0.2%) - 213 (-7.8%)

The relative changes of annual emissions are given in parenthesis.

21



1

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Figures

2 3

Figure 1. Monthly average PM2.5 concentrations under Scenario w/o EVs (left) and the changes under Scenarios EV1 (middle) and EV2

4

(right), respectively, during January (top) and August (bottom) 2030.

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Figure 2. Monthly-average concentration changes of PM2.5 and major aerosol components in the urban areas of the YRD region under Scenarios EV1 (left) and EV2 (right) relative to Scenario w/o EVs, during January (top) and August (bottom) 2030.

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9 10

Figure 3. The averages (lines) and standard deviations (color belts) of hourly concentration changes for PM2.5 (left), NO2 (middle), and

11

O3 (right) in urban areas of YRD region under scenario EV1 relative to Scenario w/o EVs, during January (top) and August (bottom)

12

2030.

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Figure 4. The changes of monthly-average concentrations of NO2 (left), SO2 (middle), and daily peak 8-h average O3 (right) under Scenario EV1 relative to Scenario w/o EVs, during January (top) and August (bottom) 2030.

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Assessing the Future Vehicle Fleet Electrification ... - ACS Publications

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