Supplementary materials

1.1 Emission inventory and scenarios

1.1.1 Energy and control measures

Table S1 shows key parameters of the energy scenario (NBS, 2013a; NBS, 2013b;NBS, 2013c;NBS, 2013e). We assume the annual average GDP growth rate to decrease gradually from 8.0% during 2011-2015 to 7.5% during 2015-2020, respectively. The national population is projected to increase from 1.35 billion in 2012 to 1.42 billion in 2017 and 1.44 billion in 2020, and urbanization rate (proportion of people in urban areas) is assumed to increase from 52.6% in 2012 to 56% and 58% in 2017 and 2020, respectively. These assumptions are similar with PC scenario in our previous studies (Wang et al., 2014d; Zhao et al., 2013c).

Table S1. Summary of the major assumptions of the energy scenario (Wang et al., 2014; Zhao et

al., 2013c).

　 2010 2012 2017 2020

GDP (2005 price)/billion CHY a 31165 36663 52919 65741

Population/billion 1.34 1.35 1.42 1.44

Urbanization rate/% 49.7 52.6 56 58

Power generation/TWh 4205 4987 5598 5997

Share of coal-fired power generation/% 75.3 67.5 65.8 63.7

Crude steel yield/Mt 627 702 700 710

Cement yield/Mt 1880 2184 2000 2000

Urban residential building area per capita/m2 23 23 27 27

Rural residential building area per capita/m2 34.1 37.1 38 38

Vehicle population per 1000 persons 58.2 80.7 143.3 178.5

Share of new and renewable energy/% b 7.5 8.3 11.6 13.1

a CHY, Chinese Yuan.

b Including hydro power, solar energy, wind energy, ocean energy, and nuclear energy; excluding biomass.

As shown in Fig.S1, total energy consumption in China will increase from 4,867 Mtce in 2012 to 5,498 Mtce in 2020. Coal continues to dominate the energy consumption, but the proportion of coal will reduce from 64% in 2012 to 56% and 54% in 2017 and 2020 respectively, which agrees with the regulations in the Action Plan (The State Council of the People's Republic of China, 2013). The proportion of crude oil will increase slightly, resulting from the continuous increase in vehicle

population. Due to the implementation of sustainable energy strategy, proportions of natural gas, clean utilization of biomass, nuclear power and other renewable energy resources in 2017 and 2020 are 3% and 5% higher than that in 2012, respectively.

(a) Energy consumption by fuel (b) Energy consumption by sector

Fig.S1. Total energy consumption in China.

(a) Power plant sector (b) Heat supply sector

Fig. S2. Energy consumption structure in power sectorand heat supply sector.

Fig. S2shows energy consumption structure in power and heat supply sector in China (NBS, 2013d; NBS, 2013f; NBS, 2013h). For power plants, the proportion of

energy consumption from coal-fired power plants is expected to decrease 10% from 2012 to 2020. The share of natural gas and “other renewable energy and nuclear energy” are assumed to increase from 8% and 21% in 2012 to 10% and 29% in 2020, respectively. Heat supply is also estimated based on the demand of final consumption sectors. The total heat supply is projected at 269 Mtce and 295 Mtce in 2017 and 2020, respectively. The share of coal-fired grate boiler is assumed to decrease while the share of combined heat and power generation (CHP) and gas-fired boiler are assumed to increase to 61% and 13% in 2020, respectively.

Adjustment and optimization of the industrial structure is also an important measure in the Action Plan, which includes strict control of new production capacity of industries with "high consumption and high pollution" ("two highs"), accelerated elimination of outdated production capacity, and reduction of surplus production capacity(The State Council of the People's Republic of China, 2013). For example, in 2017, the Jing-Jin-Ji region plans to eliminate outdated production capacity of cement of 70 million tons and the steel production capacity in Hebei Province is to be cut down by 60 million tons(The State Council of the People's Republic of China, 2013). In 2020, besides the above measures, more facilities with outdated capacity will be compressed facilities with excess capacity and strive to develop energy-saving and environment-friendly industries.Yield of steel and cement production in China are shown in Table S1(NBS, 2013b;NBS, 2013c).

Vehicle ownership will be strict limited in China, especially in key regions. Table S1 shows the vehicle population per 1000 persons in China (NBS, 2013c;NBS, 2013g). Fig. S3 shows the share of vehicle stock in on-road transportation sector. The share of energy-saving and new energy vehicles (like hybrid vehicles, plug-in hybrid vehicles, battery electric vehicles and natural gas vehicles) will increase gradually in the future.

For end-of-pipe control measures, we develop the technical ways that are consistent withthe targets in the Action Plan (The State Council of the People's Republic of China, 2013; Wang et al.,2015), which put forwards a series of end-of-pipe control measures for accelerating the application of desulfurization, denitration, and dedusting facilities. The penetration of selected control measures assumed for key sources are summarized in Table S2 and Table S3.

Fig. S3. Share of vehicle stock in transportation sector: (a) truck; (b) bus; (c) car.

Notes: HDT, heavy duty truck; LDT,light duty truck; HDB,heavy duty bus;LDB, light duty bus;

HEV, hybrid electric;PHEV,plug-in hybrid electric;EV, electric; CNG, compressed natural

gas;LPG, liquefied petroleum gas; D, diesel; G, gasoline.

Power sector: As shown in Table S2, most power plants have already been equipped with flue gas desulfurization (FGD) (MEP, 2013; MEP, 2014). We assume that newly-built power plants will be equipped with low NOX combustion technology (LNB) and selective catalytic reduction (SCR)/selective non-catalytic reduction (SNCR). The existing generator sets with capacity more than 300 MW were equipped with SCR before 2015, and generator sets with capacity less than 300 MW will be equipped with SCR gradually after 2015. High efficiency deduster (HED; i.e. bag filter, electric-bag filter) will be promoted gradually and will be used universally in key regions before 2020 (The State Council of the People's Republic of China, 2013).

Industrial and solvent use sector: All sintering machines and pelletizing production equipment of steel plants, catalytic cracking devices of oil refinery enterprises, and non-ferrous smelting enterprises will be installed with desulfurization facilities before 2020. Existing dedusting facilities of industrial kilns will be upgraded and transformed(The State Council of the People's Republic of China, 2013). Table S3 shows penetrations of major control technologies for selected industrial processes in China. As to SO2, FGD will be put into use on a large scale, especially in Jing-Jin-Ji region. As to NOX, the newly-built industrial furnaces will be equipped with LNB and the existing furnaces will be updated from 2012 to 2017, and most of the furnaces will be equipped with LNB by 2020. As to PM, electrostatic precipitator (ESP) and HED will gradually replace the inefficient WET. As to NMVOC, new emission standards of NMVOC will be issued and implemented in key sectors like petrochemical, organic chemicals, surface coating, packaging and printing. Refinery will be equiped with leak detection and repair facilities gradually(The State Council of the People's Republic of China, 2013).

Residential sector and open-burning of biomass: Adjustments of energy structure will play a key role in reducing emission in residential sector, but the effect of end-of-pipe control is also important. As shown in Table S2,we assume cyclone dust removal and wet scrubbers as primary ways to remove dust for residential boilers, HED and coal with low sulfur will be utilized gradually. In addition, we consider thatadvanced boilers and biomass stoves (such as restructuring combustion mode and using catalytic stoves) will be used in 2017 and 2020.

Transportation sector: The control of motor vehicles emissions will be continuously tightened. In 2015, the Jing-Jin-Ji region implemented the national V emission standard; and Beijing will implement a more stringent national VI emission standard in 2016. By the end of 2017, gasoline and diesel fuel for vehicles that meet national V standards will be provided nationwide(The State Council of the People's

Republic of China, 2013). In 2020, the gasoline and diesel fuel for new vehicles that meet national VI standards will be provided nationwide, and Jing-Jin-Ji region will achieve this goal in advance.Fig. S4shows the implementation time of the vehicle emission standards in China.

Agricultural sector: There are no end-of-pipe control measures for NH3 emissions in agricultural sector in 2012. We assume that no more control measures will be applied until 2017. In 2020, we assume that low-nitrogen feed will applied in Jing-Jin-Ji and other key regions, but there will be still no control measures for other regions of China.

Table S2.Penetration of selected control measures assumed for key sources in China(%).

Year 2012 2017 2020

Sector Control JJJOther

sJJJ

Other

sJJJ

Other

s

Pulverized coal

combustion

WET (PM) 0 0 0 0 0 0

ESP (PM) 80 89 57 77 46 71

HED (PM) 20 11 43 23 54 29

FGD (SO2) 99 94 100 100 100 100

LNB+SCR (NOX) 52 32 90 81 93 86

LNB+SNCR (NOX) 0 1 0 0 0 0

LNB (NOX) 42 59 10 19 8 14

Industrial grate

boilers

CYC (PM) 0 0 0 0 0 0

WET (PM) 80 93 57 75 43 68

ESP (PM) 0 0 0 8 0 10

HED (PM) 20 7 43 17 58 23

FGD (SO2) 40 31 80 45 85 53

LNB (NOX) 20 18 80 71 60 64

LNB+SCR (NOX) 10 6 20 11 40 23

Residential

boilers

CYC (PM) 10 14 0 0 0 0

WET (PM) 80 85 75 88 56 77

HED (PM) 25 1 25 12 44 23

DC (SO2) 15 15 30 21 35 26

Coal stoves STV_ADV_C 0 0 15 10 20 12

Biomass stovesSTV_ADV_B 0 0 15 10 20 12

STV_PELL 0 0 0 0 0 0

Notes: JJJ, Jing-Jin-Ji region; CYC, cyclone dust collector; WET, wet scrubber; ESP, electrostatic

precipitator; HED, high efficiency deduster; FGD, flue gas desulfurization; CFB-FGD, flue gas

desulfurization for circulated fluidized bed; LNB, low NOX combustion technology; SCR,

selective catalytic reduction; SNCR, selective non-catalytic reduction; DC, application of (low-

sulfur) derived coal; STV_ADV_C, replacement of advanced coal stove; STV_ADV_B,

replacement of advanced biomass stove (e.g. better combustion condition, catalytic stove);

STV_PELL, biomass pellet stove.

Table S3.Penetrations of major control technologies for selected industrial process in China (%).

(1) SO2

Industrial

processControl technology

2012 2017 2020

JJJ Others JJJ Others JJJ Others

Sintering FGD 54 21 100 83 100 86

Coke oven

FGD for coal filling

process

5 5 15 15 11 16

FGD for coke oven gas 5 5 15 24 11 24

Combination of the

technologies above

0 0 30 9 48 21

Glass production

(float process)FGD

8 8 70 20 78 27

Sulfuric acid

production

Ammonia acid

desulfurization method

10 10 60 22 70 27

(2) NOX

Industrial processControl

technology

2012 2017 2020

JJJ Others JJJ Others JJJ Others

Sintering SCR 20 7 50 25 63 42

SNCR 0 0 30 37 23 30

Precalcined cement

kilnLNB+SCR

0 0 70 21 78 39

LNB+SNCR 0 0 30 31 23 26

LNB 50 46 0 30 0 23

Glass production

(float process)OXFL

16 13 30 30 38 38

SCR 8 6 20 20 25 25

Nitric acid (dual

pressure process)ABSP

16 16 5 18 4 14

SCR 30 30 15 72 11 56

ABSP+SCR 0 0 80 0 85 23

Nitric acid (other

process)ABSP

65 65 5 5 4 4

SCR 33 33 15 15 11 13

ABSP+SCR 5 5 80 80 85 83

Notes: ABSP, absorption method; OXFL, oxy-fuel combustion technology.

(3) PM

Industrial processControl

technology

2012 2017 2020

JJJ Others JJJ Others JJJ Others

Sintering (flue gas)

CYC 0 0 0 0 0 0

WET 0 0 0 0 0 0

ESP 80 80 57 68 43 65

HED 20 20 43 32 58 35

Blast furnace (flue gas)WET 100 100 100 100 100 100

ESP 100 100 100 100 100 100

Basic oxygen furnaceESP 25 25 8 18 6 16

HED 75 75 92 82 94 84

Electric arc furnace

WET 20 20 0 0 0 0

ESP 50 50 37 48 28 46

HED 30 30 63 52 73 55

Coke ovenWET 100 100 67 78 50 75

HED 0 0 33 22 50 25

Precalcined cement kiln

WET 0 0 0 0 0 0

ESP 40 40 17 28 13 26

HED 60 60 83 72 88 74

Glass production

CYC 0 0 0 0 0 0

WET 20 20 0 4 0 3

ESP 75 75 87 85 65 83

HED 5 5 13 10 35 14

Brick production

CYC 30 30 27 30 20 27

WET 20 20 30 31 30 32

ESP 20 20 33 30 25 31

HED 0 0 10 0 25 4

(4) NMVOC

Sector Control2012 2017 2020

JJJ Others JJJ Others JJJ Others

Plant oil

extraction

No control 87 90 0 10 0 0

Activated carbon adsorption 12 10 50 50 20 40

Schumacher type DTDC and

activated carbon adsorption1 0 35 30 30 30

Schumacher type DTDC and

new recovery section0 0 15 10 50 30

Pharmacy

No control 80 100 0 15 0 0

Primary measures and low-

level end-of-pipe measures20 0 30 50 25 40

Primary measures and high-

level end-of-pipe measures0 0 70 35 75 60

Refinery

No control 70 85 0 10 0 0

Leak detection and repair

program20 10 20 25 15 20

Covers on oil and water

separators10 5 10 5 10 10

Combination of the above

options0 0 70 60 75 70

Paint use in

vehicle

manufacturing

No control (water-based

primer, solvent-based paint

for other parts)

90 95 10 20 0 0

Substitution with water-based

paint5 5 10 20 10 30

Adsorption, incineration 5 0 30 30 30 30

Substitution + adsorption,

incineration0 0 50 30 60 40

Paint use in

wood coating

No control (solvent-based

paint)90 95 30 45 20 33

Incineration 5 0 15 20 10 20

Substitution with high solids

paint5 5 15 20 10 20

Substitution with water-based

or UV paint0 0 40 15 60 28

Adhesive use in

wood

processing

No control 95 100 50 70 40 60

Add-on control technology 5 0 50 30 60 40

Adhesive use in

manufacturing

of shoes

No control (solvent-based

adhesive)90 95 50 60 40 50

Substitution with low solvent

adhesive10 5 50 40 60 50

Type 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20

Light duty vehicle 1 1 1 1 1 2 2 2 3 3 3 4 4 4 4 4 5 5 5 5 6

Heavy duty diesel vehicle 1 1 1 2 2 2 3 3 3 3 3 3 4 4 4 5 5 5 6 6

Heavy duty gasoline vehicle 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Motorcycle (2&4 strokes) 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3

Rural Vehicle 1 2 2 2 2 2 2 3 3 3 3 4 4 4 5

Tractors, machines 1 1 2 2 2 2 3A 3A 3A 3A 3B 3B 3B

Train, inland water 3A 3A 3A 3A 3B 3B 3B

Fig. S4. The implementation time of the vehicle emission standards in China.

The Arabic numbers 1-6 represent Euro I to Euro VI vehicle emission standards. Numbers in black represent standards released by the end of 2012, and that in red represent those to be released in the future.

1.1.2 Emissions

In 2012, the anthropogenic emissions of SO2, NOx, PM10, PM2.5, EC, OC, NMVOC and NH3 in China were estimated to be 23.8 Mt, 26.3 Mt, 16.2 Mt, 11.9Mt, 1.94 Mt, 3.43 Mt, 23.2Mt, and 9.62 Mt, respectively. The emissions by province are given in Table S4, and emissions by sector are given in Table S5.

In this study, the “emission ratio” is defined as the ratio of pollutants emissions relative to emissions in 2012. The emission ratios for the year 2017 and 2020 are shown in Table S6. By 2017, the emissions of PM2.5, SO2, NOX and NMVOC in China reduce 22%, 30%, 25% and 6% from the 2012 level, respectively. The emissions of NH3increase slightly.

Table S4. Emissions by province in 2012 (kt)

　 SO2 NOx PM10 PM2.5 EC OC NMVOC NH3

Beijing 184.2 398.4 89.9 62.7 15.8 13.9 358.8 52.2 Tianjin 287.6 387.8 151.6 113.4 17.2 26.5 297.4 45.4 Hebei 1085.6 1589.9 1155.7 861.9 141.7 217.9 1349.5 627.5 Shanxi 1013.2 1013.9 674.8 500.5 115.7 137.0 704.7 197.3

Neimeng 1187.1 1308.2 640.2 480.8 108.7 135.7 603.2 366.4 Liaoning 974.6 1133.1 625.9 469.0 70.0 132.4 1008.6 382.3

Jilin 358.9 648.9 478.4 360.0 54.8 105.7 483.7 271.9 Heilongjiang 316.3 826.9 584.0 459.3 81.9 172.5 644.8 345.7

Shanghai 603.2 420.0 155.7 106.3 11.2 9.7 622.2 41.0 Jiangsu 1086.5 1490.4 928.2 665.3 77.1 163.7 1889.8 461.6

Zhejiang 1386.7 1212.8 464.7 310.7 34.1 50.3 1534.5 173.2

Anhui 567.6 1030.0 796.5 608.2 90.3 219.2 1027.1 417.1 Fujian 472.0 724.8 312.2 220.3 27.8 49.5 658.2 166.7 Jiangxi 449.2 537.0 400.1 268.9 36.0 66.8 469.4 238.5

Shandong 2317.2 2628.1 1355.6 982.3 165.3 258.1 2291.4 789.5 Henan 1080.9 1703.4 1128.3 815.8 124.3 227.0 1386.6 954.4 Hubei 1210.2 1115.8 846.7 610.4 113.3 171.4 928.4 435.5 Hunan 796.8 855.1 738.3 532.1 89.5 149.3 788.9 483.4

Guangdong 1079.5 1610.1 660.8 462.6 58.5 121.6 1563.5 365.5 Guangxi 742.3 635.8 563.3 423.0 50.5 144.8 731.1 352.3 Hainan 88.9 108.8 58.8 44.2 4.6 13.4 142.0 58.7

Chongqing 1108.0 488.3 324.6 236.3 39.9 71.9 401.0 175.4 Sichuan 1812.9 1044.7 825.0 641.2 96.5 251.0 1231.0 785.7 Guizhou 1175.0 682.4 497.2 383.7 89.0 137.2 364.4 248.8 Yunnan 501.9 579.3 449.1 336.0 63.5 102.8 430.4 337.1 Xizang 5.9 24.9 10.6 8.5 1.4 2.7 16.2 97.8 Shaanxi 805.7 722.1 451.8 343.5 62.5 115.6 495.7 210.6 Gansu 299.7 433.1 284.3 221.8 34.8 67.1 262.3 165.2

Qinghai 55.3 115.3 86.3 67.2 9.3 12.5 55.5 86.3 Ningxia 222.2 245.4 130.7 94.6 12.0 14.5 82.7 40.2 Xinjiang 494.6 601.6 338.5 255.1 42.4 73.2 349.0 248.1 China 23769.9 26316.4 16207.9 11945.6 1939.7 3434.9 23172.1 9621.3

Table S5. Emissions by sector in 2012 (kt)

　 SO2 NOx PM10 PM2.5 EC OC NMVOC NH3

Power plants 6850.8 8026.4 1129.0 664.9 9.6 13.1 　 　Industrial combustion 7498.9 4380.5 1600.6 1084.6 159.1 42.2 126.2 　Industrial process 5487.7 5446.2 6980.3 4472.2 559.7 473.0 6106.9 215.0 Cement 1540.3 2676.4 2979.9 1881.3 11.4 34.2 263.1 　 Steel 2062.4 535.4 1322.4 970.9 34.6 43.9 223.5 　Domestic sources 2964.6 1096.7 4369.4 3922.5 964.6 2367.2 4703.8 918.6 Biofuel 74.1 489.2 3041.2 2946.1 515.6 2018.1 4345.4 　Transportation 877.4 6837.1 373.1 353.4 188.1 76.8 2860.8 　 On-road 582.9 4701.4 116.0 109.9 49.3 33.0 1975.5 　 Off-road 294.5 2135.7 257.1 243.5 138.8 43.8 885.3 　Solvent use 　 　 　 　 　 8155.3 　Others 90.6 529.5 1755.6 1448.0 58.5 462.6 1219.2 8487.6 Biomass open burning 90.6 529.5 1755.6 1448.0 58.5 462.6 1219.2 　 Livestock farming 　 　 　 　 　 　 　 5489.8 Mineral fertilizer 　 　 　 　 　 　 　 2997.9

application

National total emissions 23769.9 26316.4 16207.9 11945.6 1939.7 3434.9 23172.1 9621.3

Table S6. Emission ratios for the year 2017 and 2020. (2012=1.0)

SO2 NOX PM2.5 NMVOC NH3

2017 2020 2017 2020 2017 2020 2017 2020 2017 2020

China 0.70 0.66 0.76 0.64 0.78 0.71 0.94 0.86 1.10 1.07

Beijing 0.56 0.49 0.59 0.38 0.64 0.57 0.95 0.81 1.10 0.96

Tianjin 0.58 0.53 0.60 0.48 0.70 0.58 0.94 0.87 1.10 1.04

Hebei 0.67 0.64 0.73 0.62 0.71 0.60 0.85 0.75 1.10 1.04

Shanxi 0.75 0.72 0.77 0.67 0.82 0.78 0.83 0.73 1.10 1.08

Neimeng 0.64 0.62 0.62 0.53 0.76 0.71 0.88 0.75 1.10 1.08

Liaoning 0.67 0.63 0.80 0.67 0.81 0.75 1.04 0.99 1.10 1.08

Jilin 0.68 0.65 0.66 0.57 0.75 0.71 0.90 0.86 1.10 1.08

Heilongjian

g0.66 0.66 0.70 0.60 0.75 0.70 0.87 0.81 1.10 1.08

Shanghai 0.70 0.65 0.74 0.63 0.73 0.65 1.10 1.02 1.10 1.05

Jiangsu 0.69 0.64 0.78 0.65 0.74 0.64 0.94 0.86 1.10 1.05

Zhejiang 0.70 0.61 0.70 0.57 0.70 0.61 0.94 0.84 1.10 1.05

Anhui 0.73 0.72 0.75 0.65 0.78 0.72 0.87 0.82 1.10 1.08

Fujian 0.82 0.79 0.98 0.87 0.83 0.78 1.04 1.02 1.10 1.08

Jiangxi 0.65 0.63 0.85 0.74 0.80 0.74 0.97 0.93 1.10 1.08

Shandong 0.74 0.69 0.79 0.66 0.83 0.77 1.02 0.95 1.10 1.08

Henan 0.79 0.77 0.81 0.69 0.87 0.80 0.92 0.86 1.10 1.08

Hubei 0.60 0.58 0.69 0.60 0.75 0.70 0.93 0.87 1.10 1.08

Hunan 0.75 0.73 0.81 0.71 0.81 0.76 0.92 0.88 1.10 1.08

Guangdong 0.68 0.64 0.86 0.72 0.75 0.66 1.02 0.93 1.10 1.05

Guangxi 0.90 0.88 0.81 0.70 0.90 0.84 0.89 0.86 1.10 1.08

Hainan 0.68 0.68 0.82 0.69 0.79 0.73 1.07 1.07 1.10 1.08

Chongqing 0.65 0.62 0.76 0.65 0.79 0.74 0.90 0.83 1.10 1.08

Sichuan 0.57 0.50 0.80 0.66 0.74 0.67 0.87 0.78 1.10 1.08

Guizhou 0.66 0.66 0.62 0.54 0.82 0.76 0.88 0.80 1.10 1.08

Yunnan 0.80 0.81 0.77 0.65 0.79 0.71 0.86 0.79 1.10 1.08

xizang 0.89 0.88 0.88 0.67 0.74 0.68 0.86 0.78 1.10 1.08

Shaanxi 0.69 0.67 0.70 0.59 0.74 0.69 0.87 0.79 1.10 1.08

Gansu 0.78 0.76 0.77 0.67 0.74 0.69 0.87 0.80 1.10 1.08

Qinghai 0.68 0.65 0.63 0.51 0.61 0.54 0.88 0.81 1.10 1.08

Ningxia 0.75 0.73 0.71 0.62 0.74 0.70 0.85 0.75 1.10 1.08

Xinjiang 0.83 0.77 0.68 0.58 0.78 0.73 0.90 0.83 1.10 1.08

(a) PM2.5 (b) PM10

(c) SO2(d) NOX

(e) NH3 (f) NMVOC

Fig.S5.Spatial distributions of anthropogenic pollutants emissions in China in 2012.

Fig.S6.Spatial distribution of power plants in China and their PM2.5 emissions in 2012.

1.2 Evaluation of model performance

Model performance evaluation is very important to air quality modeling.Temporal variations of the modeled and observed PM2.5 concentrations in Beijing, Shijiazhuang, Xianghe, Tianjin in the baseline 2012 are shown in Fig. S7. Here, Shijiazhuang is the capital city of Hebei province and Xianghe is one observation site of Langfang city in Hebei province. Fig.S7 shows that most ofthe observed peak can be captured by the simulated concentrations, especially in Beijing, Shijiazuang, and Xianghe, whose indexes of agreement (IOA) are more than 0.76 (See Table S7).The evaluation is based on RMSE (Root-mean-square Error), Gross ERROR, BIAS, FBIAS (Fraction of BIAS), FERROR, IOA (Index of Agreement) as the statistical parameters(Emery et al., 2001), as shown in Table S7. For Shijiazhuang site, the simulations agree well with the observations in 2012 although there are lack some observations in April.The simulations underestimate in summer in Beijing site. For Tianjin city, the simulations agree better with the observations in autumn than those in other seasons.Overall, the model performance in reproducing the concentrations of PM2.5 in those sites varied from average (FBIAS ≤±60% and FERROR ≤75%) to good (FBIAS ≤±30% and FERROR ≤50%) (Morris et al., 2005).Consequently this modeling system can be used

to analyze the impact of emission control on PM2.5 concentrations in Jing-Jin-Ji region.

0

100

200

300

400

1/1 1/11 1/21 1/31 4/10 4/20 4/30 7/10 7/20 7/30 10/9 10/19 10/29

Conc

entr

ation

(µg·

m-3

)

Date

Beijingsim obs

0

200

400

600

1/1 1/11 1/21 1/31 4/10 4/20 4/30 7/10 7/20 7/30 10/9 10/19 10/29

Conc

entr

ation

(µg·

m-3

)

Date

Shijiazhuangsim obs

-200

0

200

400

600

1/1 1/11 1/21 1/31 4/10 4/20 4/30 7/10 7/20 7/30 10/9 10/19 10/29Conc

entr

ation

(µg·

m-3

)

Date

Xianghesim obs

0

200

400

600

1/1 1/11 1/21 1/31 4/10 4/20 4/30 7/10 7/20 7/30 10/9 10/19

Conc

entr

ation

(µg·

m-3

)

Date

Tianjinsim obs

Fig.S7.Comparison of modeled and observed PM2.5 daily concentrations in 2012.

Table S7. Quantitative evaluation of simulated PM2.5 concentrations with daily observations

during Jan., Apr., Jul. and Oct.,2012.

City Simulated

average

(μg/m3)

Measured

average

(μg/m3)

Number

of data

pairs

BIAS

(μg/m3)

ERROR

(μg/m3)

RMSE

(μg/m3) FBIAS FERROR IOA

Beijing 75.2 79.0 118 -3.8 37.8 49.6 -4.2% 53.9% 0.81

Shijiazhuang 111.7 102.1 118 9.5 55.4 77.7 15.0% 55.0% 0.78

Tianjin 68.9 70.4 104 1.5 37.6 59.0 -13.8% 49.7% 0.60

Xianghe 67.2 91.6 118 24.4 41.2 54.2 -25.9% 55.9% 0.83

(a) O3 (b) Nitrate

Fig.S8. Spatial distributions of concentration increase of O3 and nitrate in January with NOx

emission reduction by 20% from baseline 2012.

1.3 Discussion of nitrate formation in July

Compared Fig.S9 with Fig.10, there are obvious difference for nitrate formation between in

July and in January. Nitrate concentrations are much lower in daytime in July than those in

January, mainly due to much higher temperature in July. By comparison, nitrate concentrations are

much higher in night (Fig.S9), indicating the heterogeneous reaction at nighttime which greatly

promoted by higher accumulated O3 concentration during daytime, and nitrate formation pathway

during nighttime dominated the nitrate formation in July. Fig.S9 also shows that nitrate

concentrations in July for both future scenarios are higher in daytime and lower at night than those

for 2012 baseline, while O3 concentrations for both in July for both future scenarios are lower

than those for 2012 baseline. Fig.S10 shows the relative small change of average O3

concentrations in July for future scenarios from baseline, by compared with those in January.

These changes of O3 and nitrate concentrations for future scenarios from baseline are the

integrated effect of emission changes of multiple pollutants.

Fig.S9. Monthly average diurnal profiles of O3 and nitrate concentrations in July over the whole

simulated domain under the 2012 baseline, and 2017 and 2020 scenarios, respectively.

(a) 2017 scenario (b) 2020 scenario

Fig.S10. Spatial distribution of the change of O3 concentration in July under the future scenarios of 2017 and 2020 from baseline 2012 over the whole simulation domain.

References

Emery, C. A., Tai, E., and Yarwood, G.,2001. Enhanced meteorological modeling and

performanceevaluation for two Texas ozoneepisodes, Project Report prepared for the Texas Natural

ResourceConservation Commissions, ENVIRON International Corporation, Novato, CA.

MEP (Ministry of Environmental Protection of China), 2013. Bulletin of Urban Sewage Treatment

Facilities, and Flue Gas Desulfurization/Denitrification Facilities of Coal-fired Power Plants,

Beijing (in Chinese).

MEP (Ministry of Environmental Protection of China), 2014.The announcements of desulfurization

anddenitrification facilities and other key atmospheric pollution reduction facilities in coal-fired

units in China (in Chinese).

Morris, R. E. and Koo, B., 2005. Application of Multiple Models to Simulation Fine Particulate in the

Southeastern US, National RPOModeling Meeting, Denver, CO.

National Bureau of Statistics of China (NBS), 2013a. China Energy Statistical Yearbook 2013. China

Statistics Press, Beijing. (in Chinese).

National Bureau of Statistics of China (NBS), 2013b. China Industrial Economy Statistical Yearbook

2013. China Statistics Press, Beijing. (in Chinese).

National Bureau of Statistics of China (NBS), 2013c. China Statistical Yearbook 2013. China Statistics

Press, Beijing. (in Chinese).

National Bureau of Statistics of China (NBS), 2013d. China Rural Statistical Yearbook 2013. China

Statistics Press, Beijing. (in Chinese).

National Bureau of Statistics of China (NBS), 2013e. China Environmental Statistical Yearbook 2013.

China Statistics Press, Beijing. (in Chinese).

National Bureau of Statistics of China (NBS), 2013f. China Electric Power Yearbook 2013. China

Statistics Press, Beijing. (in Chinese).

National Bureau of Statistics of China (NBS), 2013g. China Transportation Statistical Yearbook 2013.

China Statistics Press, Beijing. (in Chinese).

National Bureau of Statistics of China (NBS), 2013h. China Urban Construction Statistical Yearbook

2013. China Statistics Press, Beijing. (in Chinese).

The State Council of the People's Republic of China, 2013. Air Pollution Prevention and Control

Action Plan: http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm, access: 10 Sep 2013 (in

Chinese).

Wang, S. X.,Zhao, B.,Wu, Y., Hao, J. M., 2015.Target and Measures to Prevent and Control Ambient

Fine Particle Pollution in China,Chinese Journal of Environmental Management,7(2): 37-43.