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Resources, Conservation and Recycling 121 (2017) 115–127

Contents lists available at ScienceDirect

Resources, Conservation and Recycling

jo ur nal home p age: www.elsev ier .com/ locate / resconrec

ull length article

mpacts of power generation on air quality in China—Part II: Futurecenarios

ianlin Hua, Lin Huanga, Mindong Chena, Gang Heb,∗, Hongliang Zhanga,c,∗∗

Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Engineering Technology Research Center of Environmentalleaning Materials, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science andngineering, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, ChinaDepartment of Technology and Society, College of Engineering and Applied Sciences, Stony Brook University, Stony Brook, NY 11794, USADepartment of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA

r t i c l e i n f o

rticle history:eceived 6 January 2016eceived in revised form 29 March 2016ccepted 17 April 2016vailable online 6 May 2016

eywords:ower generation scenariosir qualityMAQarticulate matterzonehina

a b s t r a c t

Power generation is an important source of air pollution in China since it is mostly from coal-fired powerplants. Future power generation plans are needed to meet both increasing power needs and air qualityimprovement. In this study, five future power development scenarios in 2030 were considered. The REFscenario is the base case in which the growth was assumed to follow the existing projection (business asusual). The CAP scenario represents power sector in the trajectory to achieve 80% reduction by 2050 asproposed by IPCC, the LOW scenario reflects low cost of renewable to foster wind and solar development,the PEAK scenario allows China to peak its carbon emission by 2030, while the WEST scenario assumesthat the coal power bases build all planned capacity by 2030 and no coal power in Beijing, Tianjin andShanghai by 2030. Then, impacts of the scenarios on air quality were simulated with the CommunityMultiscale Air Quality (CMAQ) model in January and August 2030 with unchanged emissions from othersectors and the same meteorology in 2013. The results indicate that air quality gets worse in the REFscenario in both months compared to 2013. The CAP and WEST scenarios generally have larger impacts onpollutant concentrations than the LOW and PEAK scenarios. The four scenarios improve PM2.5 total massand SO4

2− in North China, with maximum decreases of over 100 �g m−3 in January and over 10 �g m−3

in August in the Hohhot area. However, PM2.5 total mass and SO42− pollution are worsened in Shandong

for CAP and WEST scenarios and in Chongqing for LOW and PEAK scenarios. NO3− and O3 get worsened

in the four scenarios in large areas of the North China Plain (NCP), East and South China due to moreNH3 available for NO3

− formation associated with reduction in SO42− and aerosol radiative effects on UV

radiation for O3 formation. Power development plans greatly affect air quality in Beijing, with decreasein PM2.5 and PM10, but increase in O3. Reducing NOx and SO2 combined with NH3 should be considered

pow

to reduce contribution of

. Introduction

China has been the fastest developing country in the worlduring the last a few decades. Associated with fast developmentf industrialization, urbanization, and motorization, fossil fuelonsumption has been dramatically increased. Liu et al. (2015)

eported that a 479% growth in coal consumption from 1990 and010. Large amount of fossil fuel combustion significantly con-ributes to emissions of air pollutants such as sulphur dioxide (SO2),

∗ Corresponding author.∗∗ Corresponding author at: Department of Civil and Environmental Engineering,ouisiana State University, Baton Rouge, LA 70803, USA

E-mail addresses: gang.he@stonybrook.edu (G. He), hlzhang@lsu.edu (H. Zhang).

ttp://dx.doi.org/10.1016/j.resconrec.2016.04.011921-3449/© 2016 Elsevier B.V. All rights reserved.

er generation to future air pollution in China.© 2016 Elsevier B.V. All rights reserved.

nitrogen oxides (NOx), and particulate matter (PM), causing severeair pollution problems regionally in China (Chan and Yao, 2008;Cheng et al., 2013; Fu et al., 2008; He et al., 2001; Hu et al., 2014;Huang et al., 2014; Liu et al., 2013; Sun et al., 2014; Tie and Cao,2009; Wang et al., 2014b) and globally through long distance trans-port (Lin et al., 2012; Wang et al., 2009). The severe air pollutionhas been linked to health risks (Huang et al., 2009), visibility degra-dation (Deng et al., 2008), and climate change (Ding et al., 2013).

The power generation in China has been increasing signifi-cantly to support the rapid economic and social development (Yue,2012). Coal-fired plants contribute about 75% of power generation

and become an important source contributor to air pollutions. Forexample, in 2012 the power sector contributes to 33% of NOx, 23% ofSO2, and 8% of PM (Huang et al., 2016). SO2, NOx, and PM are amongthe six criteria pollutants regulated by the National Ambient Air

116 J. Hu et al. / Resources, Conservation and Recycling 121 (2017) 115–127

Table 1The general assumptions associated with each of the power generation development scenario.

Scenario Description Reference

REF Reference scenario Hu et al. (2011)CAP Trajectory to reduce 80% on 1990 level by 2050 IPCC (2015)LOW Low cost of renewable energy, aggressive learn curve of wind and solar DOE (2012)PEAK Peak carbon emission by 2030 NDRC (2015)WEST Assuming the 14 coal-power bases in 7 provinces build half of planned capacity by 2020 and all

planned capacity by 2030, No coal in Beijing, Tianjin and Shanghai by 2030NDRC (2012)

Note: The electricity generation in the REF scenario is from the Scenario 2 by the Intelligent Laboratory for Economy-Energy-Electricity-Environment (ILE4) of the State GridEnergy Research Institute (Hu et al., 2011). In the PEAK scenario as China has nationally determined its actions by 2030 to achieve the peaking of carbon dioxide emissionsaround 2030 and making best efforts to peak early; to lower carbon dioxide emissions per unit of GDP by 60%–65% from the 2005 level; and to increase the share of non-fossilfuels in primary energy consumption to around 20% (NDRC, 2015). In the CAP scenario, China enhances its efforts to achieve 80% reduction of carbon emission on 1990 levelby 2050 as recommended by IPCC (2015). In the LOW scenario, the overnight cost of wind and solar technology is assumed to follow the projections by DOE SunShot andL ner MW and ShN y by 2

Qc(sSaitPff

cisosSaaebbbfbic

rCiTu(vda

2

2

dbtee

BNL wind study (DOE, 2012). The coal power base in China locates in Xinjiang, InEST scenario, we assume coal power is completely phased out in Beijing, Tianjin,

ingxia, Heilongjiang, Gansu, Shaanxi, and Shanxi by 2020. And all planned capacit

uality Standards of China (MEP, 2012). In addition, SO2 and NOx

an form secondary PM components of sulfate (SO42−) and nitrate

NO3−), and NOx is also precursor of ozone (O3). Previous studies

howed that power sector was a major contributor to particulateO4

2− and NO3− in China (Wang et al., 2014a; Zhang et al., 2012a)

nd inter-regional transport was significant (Ying et al., 2014). Its necessary to consider controlling emissions from power genera-ion to improve air quality. In 2011, the Ministry of Environmentalrotection of China revised the Air Pollutants Emission Standardsor Coal-Fired Power Plants (MEP, 2011) and lowered the standardsor dust, SO2, NOx, and etc.

Controlling emissions from power generation faces manyhallenges. The coal consumption for power generation keepsncreasing. Tremendous efforts have been made to reduce emis-ions from power generation by installing emission control systemsf desulphurization, selective catalytic reduction (SCR) or electro-tatic precipitators. It was estimated that these techniques reducedO2 emissions by 17 million tons in 2010 (Zhang et al., 2012b). As

result, declining trends have been found in the emissions of SO2nd PM from power generation (Huang et al., 2016). However, themissions of NOx, CO, CO2, and VOC from power generation haveeen identified to be still growing. NOx emission has increasedy about 10%, and CO, CO2, and VOC emissions have increasedy about 30% from 2008 to 2012 (Huang et al., 2016). Therefore,uture development plans of power generation are needed to meetoth the increasing power needs and the requirement on air qual-

ty improvement, in addition to developing and applying emissionontrol techniques.

In a companion paper, Huang et al. (2016) reviewed the cur-ent status of power generation’s contribution to air quality inhina. In this study, four scenarios for future power generation

n 2030 in China were tested against the normal growth case.he future air quality associated with each scenario was predictedsing a regional chemical transport model (CTM) in a winter monthJanuary) and a summer month (August) in 2030. The results pro-ide valuable information for designing future power generationevelopment plans to meet the growing power need and improveir quality in China.

. Method

.1. Future power emission scenarios

The Multi-resolution Emission Inventory for China (MEIC)eveloped by Tsinghua University (http://www.meicmodel.org) for

ase year of 2012 was used in this study. The power emission inven-ory is a unit-based emission inventory for power plants (Wangt al., 2012). Emission rates of major pollutants from power gen-ration in each province are shown in Table S1. This inventory has

ongolia, Ningxia, Heilongjiang, Gansu, Shanxi, Shaanxi, Guizhou, and Anhui. In theanghai by 2020; and half of planned capacity is online in Xinjiang, Inner Mongolia,030. Other provinces keep the same capacity (NDRC, 2012; Song et al., 2012).

been widely used for studies of air pollution in China and its effectsglobally (Guan et al., 2014; Hu et al., 2015; Lin et al., 2014; Zhenget al., 2015).

Five future emission scenarios of power plants in 2030 weredeveloped based on certain assumptions, as listed in Table 1. TheREF scenario is the base case in which the growth was assumedto follow the existing projection (business as usual) (Hu et al.,2011). The CAP scenario represents power sector in the trajectoryto achieve 80% reduction on 1990 level by 2050 as proposed byIPCC, the LOW scenario reflects low cost of renewable to foster windand solar development, the PEAK scenario allows China to peak itscarbon emission by 2030, and the WEST scenario assumes the coalpower bases build all planned capacity by 2030 and no coal power inBeijing, Tianjin and Shanghai by 2030 (He, 2015; He and Kammen,2014, 2016). Provincial growth factors for power plants were gener-ated for each scenario (shown in Table 2), and the future emissionin were obtained by multiplying the 2013 emissions in Table S1with the scaling factors. It should be noted that the scenarios usedare based on different assumptions from previous studies. Due thetransition of China to a more consumption and service orientedeconomy (China’s New Normal), policymakers and power systemplanners in China should re-evaluate power demand projectionsand the need for new generation capacity to avoid over-investmentthat could lead to stranded generation assets.

Other emission inputs to the model include biogenic emissionsgenerated by the Model for Emissions of Gases and Aerosols fromNature (MEGAN) v2.1, open biomass burning generated using an in-house program based on the Fire INventory from NCAR (FINN) fromsatellite observations (Wiedinmyer et al., 2011), as well as dust andsea salt emissions generated in line during the CMAQ simulations.Details of the models and their settings can be found in previouspapers (Hu et al., 2015; Qiao et al., 2015; Zhang et al., 2014). Theseemissions are kept the same for future scenarios since the purposeof this paper is to see the impacts of power generation.

2.2. Air quality modeling

The Community Multiscale Air Quality (CMAQ) model v5.01 wasapplied to investigate the impacts of future power generation sce-narios on air quality in China. Concentrations of air pollutants inthe five scenarios were simulated in January to represent winterepisode and August to represent summer episode using a 36-km horizontal resolution domain that covers the entire China.Meteorological conditions in winter generally lead to the high-est particulate matter but the least ozone pollution in China; and

meteorological conditions in summer lead to the lowest particulatematter but the highest ozone pollution. Therefore by simulatingJanuary and August, the highest and lowest impact of changes inpower generation on particulate matter and ozone can be assessed.

J. Hu et al. / Resources, Conservation an

Table 2Power emission scaling factors from 2012 to 2030 in each of the power generationdevelopment scenario for different provinces.

Province REF CAP LOW PEAK WEST

Anhui 1.29 1.36 1.31 1.31 1Beijing 0 2.20 0 0 0Chongqing 1.16 1.22 4.28 3.96 1Fujian 0.26 1.31 0 0.23 1Gansu 1.25 1.31 1.24 1.25 4.78Guangdong 0.02 1.21 0.11 0.07 1Guangxi 0 1.17 0 0 1Guizhou 0 0.93 0 0 1Hainan 1.6 1.29 1.16 0.01 1Hebei 0 1.27 0 0 1Heilongjiang 0.77 1.21 0.63 0.63 3.09Henan 0.08 1.33 0.13 0.48 1Hubei 1.19 1.24 1.19 1.17 1Hunan 1.36 1.41 1.36 1.35 1Inner Mongolia 17.49 2.57 13.34 13.64 2.78Jiangsu 0 1.36 0 0.10 1Jiangxi 1.36 1.41 1.36 1.35 1Jilin 2.17 1.33 2.25 2.09 1Liaoning 1.38 1.31 1.23 1.24 1Ningxia 2.58 1.61 1.81 1.85 3.36Qinghai 17.10 1.29 5.22 1.99 1Shaanxi 12.95 1.36 9.51 7.60 3.46Shandong 0.06 1.26 0.07 0.10 1Shanghai 0.14 1.44 0 0.35 0Shanxi 0.37 1.25 0.41 0.37 2.90Sichuan 2.47 1.29 1.23 1.24 1Tianjin 0.23 1.32 0.23 0.23 0Tibet 22.85 1.27 1.24 1.22 1Xinjiang 2.51 1.21 1.64 1.11 1.68Yunnan 0 0.01 0 0 1Zhejiang 0.06 1.35 0 0.32 1

Note: Inner Mongolia, Shanxi, and Shaanxi are coal rich provinces, and it is whythose provinces have very high scaling factors in the REF scenario. Qinghai and Tibetstarted at a lower level and the request for development brings a high scaling factor.Inner Mongolia is a center both for coal, wind and solar, and close to the demandcC

Twsbo

enecMsbfedre2

shp2asaev

The first way is influence on emissions of O precursors. O is

enter (Jing-Jin-Ji metropolitan area), therefore it has high scaling factor even in theAP, LOW, and PEAK scenarios.

he Weather Research and Forecasting (WRF) model version 3.6as used to generate the meteorological inputs to drive the CMAQ

imulations. Detailed model configurations of WRF model haveeen described by Zhang et al. (2012a), and model configurationsf CMAQ model have been described by Hu et al. (2016, 2015).

Except the emissions from power plants, all other anthropogenicmissions and biogenic emissions were kept the same for all sce-arios as in 2012. The other anthropogenic emissions includemissions from cement plants (Lei et al., 2011), high-resolutionounty-level vehicle emission inventory (Zheng et al., 2014) inEIC. Biogenic emissions were generated using the Model for Emis-

ions of Gases and Aerosols from Nature (MEGAN) v2.1. Openiomass burning emissions were generated from the Fire INventoryrom NCAR (FINN) based on satellite observations (Wiedinmyert al., 2011). Dust and sea salt emissions were generated in lineuring the CMAQ simulations. Emissions from other countries andegions rather than China in the domain were based on the Regionalmission inventory in ASia version 2 (REAS2) (Kurokawa et al.,013).

Model performance with the same settings, base-case emis-ion inputs, and meteorological conditions for the whole year 2013as been evaluated. PM2.5, PM10, O3, SO2, NO2, and CO were com-ared with available ambient observations in 60 cities (Hu et al.,016), and elemental carbon (EC) have been compared againstmbient observations available in literature (Hu et al., 2015). Inummary, the model reproduces the O and PM concentrations

3 2.5t most cities for most days and source contributions are in gen-ral agreement with the estimations using the receptor models. Thealidation results indicate that the model successfully represents

d Recycling 121 (2017) 115–127 117

the major processes of air pollution in China and can be used foranalysis of future development scenarios.

3. Results

Fig. 1 shows the monthly average concentrations of PM2.5 totalmass, SO4

2−, NO3−, and 8 h peak O3 over the entire month of

January 2013. In January 2013, extremely severe haze pollutionoccurred in China (Sun et al., 2014; Tao et al., 2014; Wang et al.,2014a; Zheng et al., 2015). Model predicted PM2.5 total mass con-centrations are more than 100 �g m−3 over 1/3 of China, mainlyin the North China Plain (NCP), the Northeast China, the SichuanBasin, and the Yangtze River Delta region (YRD). Model predictsextreme PM2.5 concentrations over 300 �g m−3 in the populouscities in these regions. The concentrations are several times of the24-h average PM2.5 China National Ambient Air Quality Standards(CNAAQS) of 35 �g m−3 (MEP, 2012). SO4

2− and NO3− are major

components of PM2.5 total mass in January 2013. Concentrationsover 160 �g m−3 of SO4

2− are predicted in the area of Hohhot, alsoconcentrations over 100 �g m−3 of SO4

2− are predicted in the NCP,the Sichuan basin, and Central China such as Wuhan, the provin-cial capital of Hubei. High NO3

− concentrations are predicted overthe NCP, YRD, the Sichuan Basin, the Northeast China, and theHohhot area. Different to SO4

2−, NO3− displays a smoother and

more widespread spatial distribution, indicating that SO42− is more

influenced by point sources while non-point sources such as mobilevehicles also have important contribution to NO3

−. In the wintermonth, 8 h peak O3 is generally low. Especially in the NorthernChina, 8 h peak O3 is mostly less than 35 ppb.

Fig. 1 also presents the concentration changes in the REF sce-nario relative to the base-case for PM2.5 total mass, SO4

2−, NO3−,

and 8 h peak O3. The model simulations for the future REF scenario(also same for CAP, LOW, PEAK, and WEST future scenarios) usedthe same meteorology conditions of January 2013 and the sameemission inputs for other source sectors as in the base-case, there-fore the predicted difference in concentrations reflects the impactsof different power development scenarios on air quality in January.Following the power development plans in the REF scenario, PM2.5total mass is predicted to increase in China, except small decreasein Southwest Shandong and boundary of Yunnan and Guizhouprovinces. PM2.5 increase is more profound in Inner Mongolia, withover 100 �g m−3 increase predicted around the Hohhot area. SO4

2−

and NO3− are the main cause of the PM2.5 total mass increase in

Inner Mongolia, consistent with the SO2 and NO2 increase (Fig. S1in the Supplemental Materials). NO3

− also is predicted to increasesignificantly in the Northeast China relative to the base-case, indi-cating that the PM2.5 chemical properties could likely change inthe future in this area with power development. NO3

− is predictedto decrease in the YRD region, due to the decrease of its precur-sor NOx (indicated by NO2 change shown in Fig. S1). However, Inthe Guanzhong Plain region, centered with the city of Xi’an, NO3

−

is predicted to decrease while NO2 is predicted to increase andmeanwhile SO4

2− is predicted to increase. NO3− decrease is likely

caused by the particle chemistry of ammonia (NH3). NH3 neutral-izes sulfuric and nitric acid and form particulate SO4

2− and NO3−.

Less NH3 becomes available for nitric acid when sulfuric acid isincreased with SO2 emission increase, and consequently forms lessparticulate NO3

−.The winter 8 h peak O3 is predicted to decrease significantly

(>10 ppb) over the central to the Northeast China. The emissionchanges in power plants influence the O3 in two different ways.

3 3formed through photochemical processes from precursors of NOx

and VOCs. NOx emissions from power plants in the four scenariosare mostly decreased in the Northern China (Fig. S1). O3 response to

118 J. Hu et al. / Resources, Conservation and Recycling 121 (2017) 115–127

F ion ch( pb fo

NaT

ig. 1. Average concentrations in the base-case (1) in 2013 January, and concentratb), NO3

− (c), and 8 h O3 (d). Unit is �g m−3 for PM2.5 total mass, SO42− , NO3

− , and p

Ox deduction is nonlinear, depending on the ratio of NOx/VOCs. Inreas with plenty NOx, decrease in NOx can lead to increase in O3.he second way is influence on ultraviolet (UV) radiation. Stronger

ange relative to the base-case in the REF scenario (2) for PM2.5 total mass (a), SO42−

r 8 h O3.

UV radiation leads to more intensive photochemistry in the atmo-sphere and thus more O3 formation. Aerosols weaken UV radiationby absorbing and scattering incoming solar radiation (Martin et al.,

ion an

2tri

ttTesttrStnCpDahi8Hitc

ltnmtPdtitPC(bt

csatiscoTtPsoWtmF

tsos

J. Hu et al. / Resources, Conservat

003). Therefore, the increase of PM2.5 total mass in the central tohe Northeast China (Fig. 1) in the REF scenario decreases the UVadiation and O3 formation. Similar change in 1 h peak O3 is shownn Fig. S1.

Even though emissions from power plants are mostly constanthroughout a year, air pollution in China has distinct seasonal varia-ions driven by the meteorological conditions (Wang et al., 2014b).herefore, it is necessary to investigate the impacts of power gen-ration development scenarios on air quality in both winter andummer. Fig. 2 shows the concentrations in the base-case for PM2.5otal mass, SO4

2−, NO3−, and 8 h peak O3 and their changes in

he REF scenario relative to the base-case in August 2013 (and theesults for SO2, NO2, and 1 h peak O3 in August are shown in Fig. S1).imilarly, PM2.5 total mass is predicted to increase in large areas ofhe North, Northeast, and East China, but with a much smaller mag-itude. SO4

2− is predicted to increase in the North and Northeasthina, but to decrease in the East and South China, while NO3

− isredicted to increase in all these areas except in the Pearl Riverelta region. The difference in the concentrations between Januarynd August is mainly driven by the meteorological conditions, andighlights the importance to analyze the seasonal variation of the

mpacts of future power development scenarios on air quality. The h peak O3 in August is predicted to decrease in the areas aroundohhot of Inner Mongolia, Xi’an of Shaanxi, and Beijing-Tianjin but

ncrease in the other regions of China. O3 decrease in the abovehree regions is due to significant increase of NOx emissions (indi-ated by NO2 shown in Fig. S1).

Results of the REF scenario indicate that air quality would veryikely become worse in the future, especially in North China, dueo power development. Therefore, additional considerations areeeded to meet the increasing demand on the power supply andeanwhile to minimize its impacts on air quality. Fig. 3 presents

he impacts of power development assumptions in the CAP, LOW,EAK, and WEST scenarios on PM2.5 total mass concentrations asifference relative to the REF scenario. The CAP scenario influenceshe PM2.5 total mass concentrations mainly in Northern China. Thempacts exhibit large PM2.5 total mass decrease over 100 �g m−3 inhe surrounding area of Hohhot (the capital city of Inner Mongolia).M2.5 total mass also decreases about 30–50 �g m−3 in Northeasthina, about 20 �g m−3 in the east part of Sichuan basin and Urumqithe capital city of Xinjiang). However, PM2.5 total mass increasesy up to 20 �g m−3 in Shandong and Henan and up to 10 �g m−3 inhe YRD. PM2.5 total mass also increases by 2–4 �g m−3 in the PRD.

The LOW scenario shows much less impacts on PM2.5 total massoncentrations, compared to the CAP scenario. Relative to the REFcenario, PM2.5 total mass decreases in the Hohhot surroundingrea, but the influenced area and magnitude are much smaller thanhe CAP scenario. PM2.5 total mass increases by up to 10 �g m−3

n Chongqing in the LOW scenario, because aggressive wind andolar development in nearby provinces that shifts moderate coalapacity to Chongqing. The PEAK scenario shows similar impactsn PM2.5 total mass to the LOW scenario, with even less extent.he influenced area and magnitude are similar, decreasing PM2.5otal mass mainly in the Hohhot surrounding area and increasingM2.5 total mass in Chongqing. The WEST scenario displays veryimilar impacts on PM2.5 total mass to the CAP scenario. The extentf PM2.5 increase in Shandong, Henan, and the YRD region in theEST scenario is about half of that in the CAP scenario, because

hose provinces are the focal points of coal power bases develop-ent in China’s Twelfth (2011–2015) and Thirteenth (2016–2020)

ive-year Plan.Fig. 4 illustrates the impacts on the monthly averaged concen-

rations of SO42− in January for the CAP, LOW, PEAK, and WEST

cenarios relative to the REF scenario. Impacts of the four scenariosn SO4

2− are similar to PM2.5 shown in Fig. 3. The CAP and WESTcenarios show large decrease up to over 50 �g m−3 of SO4

2− in

d Recycling 121 (2017) 115–127 119

Hohhot and adjacent areas in Shaanxi, Shanxi, Hebei, and Beijing.SO4

2− increases by up to 10 �g m−3 in the CAP scenario and by upto 5 �g m−3 in the WEST scenario in Shandong and the boundaryarea of Yunnan and Guizhou. The LOW and PEAK scenarios yieldsmaller SO4

2− decrease in the Hohhot area, but increase SO42− in

Chongqing. The SO42− results are directly related to SO2 changes in

the four scenarios, which are shown in Fig. S2.Fig. 5 displays the impacts on the monthly averaged concen-

trations of NO3− in January. The impacts of four scenarios on NO3

−

are significantly different from the SO42− changes. NO3

− is found todecrease in relatively small areas around Hohhot and the NortheastChina, but increase in large areas in Central and East China, with themost significant increase around Xi’an. However, NO2 concentra-tions (Fig. S2) decrease generally, consistent with the changes inSO2 concentration (see Fig. S1). This opposite changes in NO3

− andSO4

2− are caused by the NH3 particle chemistry that has been dis-cussed previously. More NH3 becomes available for nitric acid whensulfuric acid is reduced with SO2 reduction, and consequently formsmore particulate NO3

−. This “offset” effect by NO3− to SO4

2− reduc-tion demonstrates the complexity and also difficulty in controllingone source category for improving air quality in China.

Fig. 6 exhibits the impacts on the monthly averaged 8 h O3 inJanuary. The CAP and WEST scenarios significantly increase 8 h peakO3 by over 10 ppb in the Northern China. The LOW and PEAK sce-narios also increase 8 h O3 by a few ppb. The generally oppositecorrelation in the spatial pattern between PM2.5 (Fig. 3) and O3demonstrates the aerosol’s UV effects on O3 formation as discussedpreviously. For example, in the CAP scenario, the PM2.5 decreasesin Inner Mongolia and increases in Shandong, while correspond-ingly the O3 increases in Inner Mongolia and decreases in Shandong.Similar results in 1 h O3 are presented in Fig. S4.

Figs. 7–10 show the impacts on the monthly averaged Augustconcentrations of PM2.5 total mass, SO4

2−, NO3− and 8 h O3, respec-

tively, and Fig. S5–S7 show the results for SO2, NO2, and 1 h O3,respectively. The impacts of the four scenarios on PM2.5 totalmass and SO4

2− have generally similar spatial patterns in Augustand January. The impacts on NO3

− are much smaller in August,with 2–10 �g m−3 increase mostly in Shandong and Jiangsu and2–5 �g m−3 decreases in Shaanxi and Northeast China in the CAPand WEST scenarios. Changes in other regions are marginal, andthe impacts on NO3

− of the LOW and PEAK scenarios in the entireChina are insignificant. As explained previously, additional ammo-nium nitrate forms with the decrease of SO4

2−. However, hightemperature in August makes ammonium nitrate tends to stay inthe gas phase. The impacts on summer O3 exhibits more complexspatial pattern. Generally the CAP and WEST scenarios increaseO3 in North China, especially in Inner Mongolia (∼10 ppb), theNCP (3–5 ppb), and South China, (2–4 ppb) but decrease O3 inWest China (2–6 ppb), Heilongjiang (1–3 ppb), and the YRD regionaround Shanghai (4–6 ppb), and the PRD region around Guangzhou(2–4 ppb). The spatial patterns of the impacts of the LOW and PEAKscenarios are generally similar with less extent, but little change ofO3 in South China, the YRD and the PRD regions, compared to theCAP and WEST scenarios.

Fig. 11 shows changes of PM10, PM2.5, NH4+, NO3

−, SO42−, 1 h

O3, 8 h O3, NO2, and SO2 in Beijing in the four scenarios relative tothe REF scenario in January and August, respectively. All the fourscenarios decrease the concentrations of PM10, PM2.5, NH4

+, SO42−,

and SO2 and increase the 1 h and 8 h O3 in both the winter and thesummer month. The CAP and WEST scenarios have greater impactson the concentrations than the LOW and PEAK scenarios. NO3

− con-centrations increase in Beijing in the CAP and WEST scenarios in

both January and August, while in the LOW and PEAK scenariosNO3

− concentrations increase in January, but decrease in August.NO2 concentrations in Beijing decrease in the LOW, PEAK, and WESTscenarios, but increase in the CAP scenario in both January and

120 J. Hu et al. / Resources, Conservation and Recycling 121 (2017) 115–127

F on cha( pb fo

Ai1

ig. 2. Average concentrations in the base-case (1) in 2013 August, and concentratib), NO3

− (c), and 8 h O3 (d). Unit is �g m−3 for PM2.5 total mass, SO42− , NO3

− , and p

ugust. The WEST scenario generally yields the greatest impactsn January in Beijing, with a reduction of 13% in PM10 and PM2.5,0% in NH4

+, 38% in SO42−, and 19% in SO2, and an increase of 29%

nge relative to the base-case in the REF scenario (2) for PM2.5 total mass (a), SO42−

r 8 h O3.

of NO3−, 49% of 1 h O3 and 45% of 8 h O3. In August, the CAP sce-

nario yields the greatest impacts in Beijing for PM10 (−7%), PM2.5(−8%), NH4

+ (−16%), NO3− (7%), SO4

2− (-23%), but the WEST sce-

J. Hu et al. / Resources, Conservation and Recycling 121 (2017) 115–127 121

Fig. 3. PM2.5 mass concentration changes in 2013 January in the four future power generation development scenarios relative to the REF scenario. Unit is �g m−3.

Fig. 4. PM2.5 sulfate concentration changes in 2013 January in the four future power generation development scenarios relative to the REF scenario. Unit is �g m−3.

122 J. Hu et al. / Resources, Conservation and Recycling 121 (2017) 115–127

Fig. 5. PM2.5 nitrate concentration changes in 2013 January in the four future power generation development scenarios relative to the REF scenario. Unit is �g m−3.

Fig. 6. 8 h O3 mixing ratio changes in 2013 January in the four future power generation development scenarios relative to the REF scenario. Unit is ppb.

J. Hu et al. / Resources, Conservation and Recycling 121 (2017) 115–127 123

Fig. 7. PM2.5 mass concentration changes in 2013 August in the four future power generation development scenarios relative to the REF scenario. Unit is �g m−3.

Fig. 8. PM2.5 sulfate concentration changes in 2013 August in the four future power generation development scenarios relative to the REF scenario. Unit is �g m−3.

124 J. Hu et al. / Resources, Conservation and Recycling 121 (2017) 115–127

Fig. 9. PM2.5 nitrate concentration changes in 2013 August in the four future power generation development scenarios relative to the REF scenario. Unit is �g m−3.

Fig. 10. 8 h O3 mixing ratio changes in 2013 August in the four future power generation development scenarios relative to the REF scenario. Unit is ppb.

J. Hu et al. / Resources, Conservation and Recycling 121 (2017) 115–127 125

elopm

nra

Fig. 11. Relative changes of concentrations in the power generation dev

ario leads to the largest increase in 1 h O3 (6%) and 8 h O3 (8%). Theesults indicate the emissions from power generation can greatly

ffect air quality in Beijing.

ent scenarios to the REF scenario in Beijing in 2013 January and August.

4. Discussion

It should be reiterated that the model results in the current studyare not to forecast future air quality in China, but rather a com-parison of possible scenarios that are based on several importantdevelopment assumptions for power generation. The difference

1 ion an

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26 J. Hu et al. / Resources, Conservat

etween each scenario and the base-case quantifies the effectsf those assumptions and provide valuable insights in designinguture development plans. The results indicate that the REF sce-ario very likely worsens air quality in China, especially in the Northhina, and the PEAK, LOW, and CAP scenarios have significant envi-onmental benefits. The WEST scenario shows the regional shiftsf pollutants should be managed when planning large coal powerases in West China. The regional disparity of pollution should alsoe included in the power capacity expansion plan.

The “offset” effect of NO3− to SO4

2− reduction has importantmplications for air quality control policies in China. It indicateshat emission control programs targeting on any single pollutantikely would not be successful. In case of the secondary inorganicM, joint multi-pollutant control programs on reducing NOx andO2 should be considered. In addition, controls on NH3 emissionseed to be considered for reducing PM2.5 SO4

2− and NO3− level in

hina.More importantly, the current study results indicate that reduc-

ng SO2 and NOx from power plants increases NO3− and O3 in

opulous regions in China. Recent epidemiological studies haveound different PM components have different human healthffects (Laurent et al., 2014; Ostro et al., 2007, 2015, 2010). O3 alsoauses adverse health effects. Therefore, even though SO4

2− andM2.5 total mass concentrations decrease, certain sensitive groupsf people may be exposed to more air pollution risks. Impacts ofuture development plans on emissions, air quality, and humanealth should be studied to obtain more comprehensive under-tanding.

. Conclusions

Impacts of five power generation development scenarios onuture air quality in China were evaluated using the CMAQ air qual-ty model. The results reveal that the air quality becomes worse inhe REF scenario, especially in the North China. The CAP and WESTcenarios generally have larger impacts on the pollutant concen-rations than the LOW and PEAK scenarios. PM2.5 total mass andO4

2− improve in North China, with maximum decrease in areasurrounding Hohhot. However, PM2.5 total mass and SO4

2− increasen Shandong in the CAP and WEST scenarios and in Chongqing in theOW and PEAK scenarios. NO3

− and O3 pollution are worsened inhe four scenarios in large areas of the NCP region, East and Southhina due to more NH3 available for NO3

− formation associatedith reduction in SO4

2− and aerosol radiative effects on UV radia-ion for O3 formation. Power development plans greatly affect airuality in Beijing, with decrease in PM2.5 and PM10, but increase in3. The results imply that joint multi-pollutant control programsn reducing NOx, SO2 and NH3 should be considered to improve airuality in China. Nonetheless, comprehensive studies on impactsf development plans on emissions, air quality, and human healthhould be conducted for designing future power generation plans.

cknowledgements

This project is partly funded by the Startup Fund for Talentt NUIST under Grant No. 2243141501008 and 2243141501009nd the Priority Academic Program Development of Jiangsu Higherducation Institutions (PAPD), the Natural Science Foundation ofiangsu Province under Grant No. BK20150904, Jiangsu Key Lab-ratory of Atmospheric Environment Monitoring and Pollutionontrol of Nanjing University of Information Science and Tech-

ology, and Jiangsu Province Innovation Platform for Superiorityubject of Environmental Science and Engineering (No. KHK1201).he authors also thank Dr. Qi Ying from Texas A&M University foris help to conduct this research.

d Recycling 121 (2017) 115–127

Appendix A. Supplementary data

Supplementary data associated with this article can be found,in the online version, at http://dx.doi.org/10.1016/j.resconrec.2016.04.011.

References

Chan, C.K., Yao, X., 2008. Air pollution in mega cities in China. Atmos. Environ. 42(1), 1–42.

Cheng, Z., et al., 2013. Long-term trend of haze pollution and impact of particulatematter in the Yangtze River Delta, China. Environ. Pollut. 182, 101–110.

DOE, 2012. SunShot Vision Study. Department of Energy, Washington D.C.Deng, X.J., et al., 2008. Long-term trend of visibility and its characterizations in the

Pearl River Delta (PRD) region, China. Atmos. Environ. 42 (7), 1424–1435.Ding, A.J., et al., 2013. Intense atmospheric pollution modifies weather: a case of

mixed biomass burning with fossil fuel combustion pollution in eastern China.Atmos. Chem. Phys. 13 (20), 10545–10554.

Fu, Q.Y., et al., 2008. Mechanism of formation of the heaviest pollution episode everrecorded in the Yangtze River Delta, China. Atmos. Environ. 42 (9), 2023–2036.

Guan, D., et al., 2014. Determinants of stagnating carbon intensity in China. Nat.Clim. Change 4 (11), 1017–1023.

He, G., Kammen, D.M., 2014. Where, when and how much wind is available? Aprovincial-scale wind resource assessment for China. Energy Policy 74,116–122.

He, G., Kammen, D.M., 2016. Where, when and how much solar is available? Aprovincial-scale solar resource assessment for China. Renew. Energy 85, 74–82.

He, K.B., et al., 2001. The characteristics of PM2.5 in Beijing, China. Atmos. Environ.35 (29), 4959–4970.

He, G., 2015. Decarbonizing China’s Power Sector: Potential, Prospects and Policy.Ph.D. Thesis. University of California, Berkeley, 99 pp.

Hu, Z., Tan, X., Xu, Z., 2011. 2050 China Economic Development and ElectricityDemand Study. China Electric Power Press, Beijing, 154 pp.

Hu, J., Wang, Y., Ying, Q., Zhang, H., 2014. Spatial and temporal variability of PM2.5and PM10 over the North China Plain and the Yangtze River Delta, China.Atmos. Environ. 95, 598–609.

Hu, J., et al., 2015. Source contributions and regional transport of primaryparticulate matter in China. Environ. Pollut. 207, 31–42.

Hu, J., Chen, J., Ying, Q., Zhang, H., 2016. One-Year Simulation of Ozone andParticulate Matter in China Using WRF/CMAQ Modeling System. Atoms. Chem.Phys. Discuss., http://dx.doi.org/10.5194/acp-2016-148, in review.

Huang, W., et al., 2009. Visibility, air quality and daily mortality in Shanghai, China.Sci. Total Environ. 407 (10), 3295–3300.

Huang, R.J., et al., 2014. High secondary aerosol contribution to particulatepollution during haze events in China. Nature 514 (7521), 218–222.

Huang, L., Hu, J., Chen, M., Zhang, H., 2016. Impacts of Power Generation on AirQuality in China—Part I: An Overview. Resources, Conservation and Recycling,Accepted for publication.

IPCC, 2015. Climate Change 2014: Mitigation of Climate Change: Working Group IIIContribution to the IPCC Fifth Assessment Report. Cambridge University Press,1458 pp.

Kurokawa, J., et al., 2013. Emissions of air pollutants and greenhouse gases overAsian regions during 2000–2008: regional emission inventory in ASia (REAS)version 2. Atmos. Chem. Phys. 13 (21), 11019–11058.

Laurent, O., et al., 2014. Sources and contents of air pollution affecting term lowbirth weight in Los Angeles County, California, 2001–2008. Environ. Res. 134,488–495.

Lei, Y., Zhang, Q., Nielsen, C., He, K., 2011. An inventory of primary air pollutantsand CO2 emissions from cement production in China, 1990–2020. Atmos.Environ. 45 (1), 147–154.

Lin, M., et al., 2012. Springtime high surface ozone events over the western UnitedStates: quantifying the role of stratospheric intrusions. J. Geophys. Res. Atmos.117 (D21).

Lin, J., et al., 2014. China’s international trade and air pollution in the United States.Proc. Natl. Acad. Sci. U. S. A. 111 (5), 1736–1741.

Liu, X.G., et al., 2013. Formation and evolution mechanism of regional haze: a casestudy in the megacity Beijing, China. Atmos. Chem. Phys. 13 (9), 4501–4514.

Liu, F., et al., 2015. High-resolution inventory of technologies, activities, andemissions of coal-fired power plants in China from 1990 to 2010. Atmos.Chem. Phys. 15 (23), 13299–13317.

MEP, 2011. Air Pollutants Emission Standards for Coal-Fired Power PlantsGB13223-2011, http://kjs.mep.gov.cn/hjbhbz/bzwb/dqhjbh/dqgdwrywrwpfbz/201109/t20110921 217534.htm, (accessed on 10.10.15).

MEP, 2012. China National Ambient Air Quality Standards. MEP, Beijing, China.Martin, R.V., Jacob, D.J., Yantosca, R.M., Chin, M., Ginoux, P., 2003. Global and

regional decreases in tropospheric oxidants from photochemical effects ofaerosols. J. Geophys. Res. Atmos. 108 (D3).

NDRC, 2012. Coal Industry 12th Five-year Plan. National Development and ReformCommission, Beijing.

NDRC, 2015. Enhanced Actions on Climate Change: China’s Intended NationallyDetermined Contributions. National Development and Reform Commission,Beijing.

ion an

O

O

O

Q

S

S

T

T

W

W

Zheng, B., et al., 2015. Heterogeneous chemistry: a mechanism missing in currentmodels to explain secondary inorganic aerosol formation during the January2013 haze episode in North China. Atmos. Chem. Phys. 15 (4), 2031–2049.

Zheng, B., et al., 2014. High-resolution mapping of vehicle emissions in China in2008. Atmos. Chem. Phys. 14 (18), 9787–9805.

J. Hu et al. / Resources, Conservat

stro, B., Feng, W.Y., Broadwin, R., Green, S., Lipsett, M., 2007. The effects ofcomponents of fine particulate air pollution on mortality in California: resultsfrom CALFINE. Environ. Health Perspect. 115 (1), 13–19.

stro, B., et al., 2010. Long-term exposure to constituents of fine particulate airpollution and mortality: results from the California Teachers Study. Environ.Health Perspect. 118 (3), 363–369.

stro, B., et al., 2015. Associations of mortality with long-term exposures to fineand ultrafine particles, species and sources: results from the CaliforniaTeachers Study Cohort. Environ. Health Perspect., http://dx.doi.org/10.1289/ehp.1408565.

iao, X., et al., 2015. Modeling dry and wet deposition of sulfate, nitrate, andammonium ions in Jiuzhaigou National Nature Reserve, China using asource-oriented CMAQ model: part I. Base case model results. Sci. TotalEnviron. 532, 831–839.

ong, X., Bo, H., Ma, Y., 2012. Study on Water Resources and Coal Power BasesDevelopment. China Environment Press, Beijing, 106 pp.

un, Y., et al., 2014. Investigation of the sources and evolution processes of severehaze pollution in Beijing in January 2013. J. Geophys. Res. Atmos. 119 (7),http://dx.doi.org/10.1002/2014JD021641.

ao, M., et al., 2014. Formation process of the widespread extreme haze pollutionover northern China in January 2013: implications for regional air quality andclimate. Atmos. Environ. 98, 417–425.

ie, X., Cao, J., 2009. Aerosol pollution in China: present and future impact onenvironment. Particuology 7 (6), 426–431.

ang, K., Zhang, Y., Jang, C.J., Phillips, S., Wang, B.Y., 2009. Modeling study ofintercontinental air pollution transport over the trans-Pacific region in 2001using the Community Multiscale Air Quality (CMAQ) modeling system. J.

Geophys. Res., 114.

ang, S.W., Zhang, Q., Streets, D.G., He, K.B., Martin, R.V., Lamsal, L.N., Chen, D., Lei,Y., Lu, Z., 2012. Growth in NOx emissions from power plants in China:bottom-up estimates and satellite observations. Atmos. Chem. Phys. 12 (10),4429e4447.

d Recycling 121 (2017) 115–127 127

Wang, D., et al., 2014a. Source contributions to primary and secondary inorganicparticulate matter during a severe wintertime PM2.5 pollution episode inXi’an, China. Atmos. Environ. 97 (0), 182–194.

Wang, Y., Ying, Q., Hu, J., Zhang, H., 2014b. Spatial and temporal variations of sixcriteria air pollutants in 31 provincial capital cities in China during 2013–2014.Environ. Int. 73, 413–422.

Wiedinmyer, C., et al., 2011. The Fire INventory from NCAR (FINN): a highresolution global model to estimate the emissions from open burning. Geosci.Model Dev. 4, 625–641.

Ying, Q., Wu, L., Zhang, H., 2014. Local and Inter-regional Contributions to PM2. 5Nitrate and Sulfate in China. Atmos. Environ. 94, 582–592.

Yue, L., 2012. Dynamics of clean coal-fired power generation development inChina. Energy Policy 51, 138–142.

Zhang, H., et al., 2012a. Source apportionment of PM2.5 nitrate and sulfate in Chinausing a source-oriented chemical transport model. Atmos. Environ. 62 (0),228–242.

Zhang, Q., He, K., Huo, H., 2012b. Policy: cleaning China’s air. Nature 484 (7393),161–162.

Zhang, H., et al., 2014. Evaluation of a seven-year air quality simulation using theWeather Research and Forecasting (WRF)/Community Multiscale Air Quality(CMAQ) models in the eastern United States. Sci. Total Environ. 473–474,275–285.