Environmental effects of the evolution of air pollutants ...iopscience.iop.org/1748-9326/8/2/024031/media/erl467736suppdata.pdf8 a CFB – Circulated Fluidized Bed; NOC – No Control;

1

Supplementary data for the paper entitled: 1

Environmental effects of the evolution of air pollutants 2

emissions in China during 2005-2010: implications for 3

particulate matter pollution and soil acidification 4

5

Bin Zhao 1, Shuxiao Wang 1,2, Xinyi Dong 3, Jiandong Wang 1, Lei Duan 1, Xiao Fu 1, Jiming Hao 1,2, 6

Joshua Fu 3 7 1 School of Environment, and State Key Joint Laboratory of Environment Simulation and Pollution 8

Control, Tsinghua University, Beijing 100084, China 9 2 State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, 10

Beijing 100084, China 11 3 Department of Civil and Environmental Engineering, University of Tennessee, TN 37996, USA 12

13

E-mail: [email protected] 14

15

1 Air pollutants emissions 16

We developed an emission inventory of SO2, NOx, PM10, PM2.5, BC, OC, NMVOC, and NH3 for 17

China for the years 2005-2010. An “emission factor method” was used to calculate air pollutant 18

emissions, as described in detail in Wang et al (2011). The emissions from each sector in each 19

province were calculated from the activity data (energy consumption, industrial products, solvent use, 20

etc.), technology-based emission factors, and penetrations of control technologies. The activity data, 21

and technology distribution for each sector were derived based on the Chinese Statistics (NBS 2011a, 22

b, c, d, China Association of Automobile Manufacturers 2011), a wide variety of Chinese technology 23

reports (China Electricity Council 2011, ERI 2009, 2010, THUBERC 2009, Wang 2011), and an 24

energy demand modeling approach. The emission factors were reanalyzed to incorporate the latest 25

field measurements. The references of the updated emission factors based on Wang et al (2011) are 26

summarized in table S1. The adoption of removal technologies has been changing rapidly due to the 27

control policies during 2005-2010, and they were updated until 2010 according to the governmental 28

bulletin (MEP 2011), the evolution of emission standards, and a variety of technical reports. The 29

penetration of major NOX, SO2, and particulate matter control technologies is summarized in table S2-30

S5. A unit based method was applied to estimate the emissions from large point sources including 31

coal-fired power plants, iron and steel plants, and cement plants (Lei et al 2011, Wang et al 2011, 32

2

Zhao et al 2008). While the emissions of large point sources were located based on their geographical 1

coordinates, the emissions of other sectors were distributed into 36 km × 36 km grid cells using 2

various spatial proxies at a grid resolution of 1 km × 1 km using the methodology described in Streets 3

et al (2003) and Woo et al (2003). The temporal profiles, i.e., monthly, weekly and hourly variation of 4

emissions, are consistent with our previous work (Wang et al 2011). The biogenic NMVOC emissions 5

were calculated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN, ver.2.04) 6

(Guenther et al 2006). NMVOC emissions were further disaggregated into 16 chemical species based 7

on the CB05 chemical mechanism that is used in the CMAQ simulations. 8

9

Table S1 Summary of the sources of updated emission factors. 10

Sector Pollutant Data reference

Bio-fuel stoves SO2, NOX, NMVOC Wang et al (2009), Zhang (2008) PM Li et al (2007b), Li et al (2009b)

Biomass open burning SO2, NOX, PM Li et al (2007a) NMVOC Li et al (2007a), Li et al (2009a)

On-road transportation SO2, NOX, PM, NMVOC Huo et al (2012a), Huo et al (2012b), Zhang (2008), Wei et al (2008)

Off-road transportation SO2, NOX, PM, NMVOC Yao et al (2011), Zhang (2008), Wei et al (2008)

11

Table S2 Penetrations of major NOX removal equipments assumed in this study (%)a. 12

Sector Technology Removal equipment 2005 2010

Power plants Coal-fired power plants ＜100MW

(exc. CFB)

NOC 46 11 LNB 54 89 LNB+SNCR 0 0 LNB+SCR 0 0

Coal-fired power plants ≥100MW

(exc. CFB)


CFB NOC 100 100 LNB 0 0 LNB+SNCR 0 0 LNB+SCR 0 0

NGCC NOC 70 21 LNB 30 74 LNB+SNCR 0 1 LNB+SCR 0 5

Industry sector Precalcined cement kiln ＜2000 t

d-1


Precalcined cement kiln 2000-4000 NOC 70 65

3

t d-1 LNB 30 35 LNB+SNCR 0 0 LNB+SCR 0 0

Precalcined cement kiln ≥4000 t

d-1


Nitric acid – dual pressure process NOC 75 70 ABSP 10 12 SCR 15 18 ABSP+SCR 0 0

Nitric acid – other process NOC 10 5 ABSP 60 63 SCR 30 32 ABSP+SCR 0 0

a CFB – Circulated Fluidized Bed; NOC – No Control; LNB – Low NOX Burner; SCR – Selective Catalytic 1

Reduction; SNCR – Selective Non-catalytic Reduction; LNB+SCR – combination of LNB and SCR; 2

LNB+SNCR – combination of LNB and SNCR; ABSP – Absorption Method; OXFL – Oxy-fuel Combustion 3

Technology. The table gives the national average penetration of NOX removal equipments. However, the 4

penetrations vary with provinces. The penetration of the “key region” is usually larger than that of other regions. 5

6

Table S3 Penetrations of major SO2 removal equipments assumed in this study (%)a. 7


Power plants Coal-fired power plants 100~300 MW (exc. CFB)

NOC 90 21 FGD 10 79

Coal-fired power plants ≥

300MW (exc. CFB)

NOC 83 7 FGD 17 93

CFB NOC 83 47 CFB-FGD 17 53

Industry sector

Coal-fired industrial grate boiler

NOC 27 4

WET 73 95 FGD 0 1

Sintering NOC 100 90 FGD 0 10

a CFB – Circulated Fluidized Bed; NOC – No Control; FGD – Flue Gas Desulfurization; CFB-FGD – Flue Gas 8

Desulfurization for Circulated Fluidized Bed; WET – Wet Scrubber. The table gives the national average 9

penetration of SO2 removal equipments. However, the penetrations vary with provinces. The penetration of the 10

“key region” is usually larger than that of other regions. 11

12

Table S4 Penetrations of major particulate matter removal equipments assumed in this study (%)a. 13


Power plants Coal-fired grate boiler NOC 0 0 CYC 12 12

4

WET 88 88 Pulverized coal combustion boilers NOC 0 0

CYC 0 0 WET 8 0 ESP 92 93 FF 0 7

CFB NOC 0 0 CYC 0 0 WET 8 0 ESP 92 100 FF 0 0

Industry sector Coal-fired industrial grate boiler NOC 4 0 CYC 23 0 WET 73 95 ESP 0 0 FF 0 5

Coal-fired industrial fluidized bed boiler

NOC 0 0 WET 100 100

Sintering - flue gas NOC 0 0 CYC 5 0 WET 20 5 ESP 65 75 FF 10 20

Sintering - fugitive NOC 0 0 CMN 80 60 HIEF 20 40

Blast furnace - flue gas b NOC 0 0 CYC 0 0 WET 100 100 ESP 100 100 FF 0 0

Blast furnace - fugitive NOC 0 0 CMN 0 0 HIEF 100 100

Basic oxygen furnace NOC 0 0 CYC 0 0 WET 0 0 ESP 40 30 FF 60 70

Electric arc furnace NOC 0 0 CYC 0 0 WET 60 30 ESP 30 50 FF 10 20

Casting - flue gas NOC 0 0 CYC 40 40 WET 40 40

5

ESP 20 20 FF 0 0 NOC 20 0

Casting - fugitive NOC 0 0 CMN 70 70 HIEF 10 30

Alumina production NOC 0 0 CYC 0 0 WET 0 0 ESP 35 30 FF 65 70

Electrolytic aluminium production NOC 20 0 CYC 40 30 WET 0 0 ESP 40 60 FF 0 10

Copper production NOC 0 0 CYC 0 0 WET 5 0 ESP 35 30 FF 60 70

Shaft cement kiln NOC 0 0 CYC 13 0 WET 41 5 ESP 40 60 FF 6 35

Precalcined cement kiln NOC 0 0 CYC 0 0 WET 1 0 ESP 52 40 FF 47 60

Other rotary cement kiln NOC 0 0 CYC 0 0 WET 13 0 ESP 77 50 FF 10 50

Glass production NOC 0 0 CYC 5 0 WET 25 20 ESP 68 75 FF 3 5 Brick production NOC 53 30 CYC 40 30 WET 8 20 ESP 0 20 FF 0 0 Lime production NOC 13 0

6

CYC 50 40 WET 28 30 ESP 8 20 FF 3 10 Coke oven NOC 0 0 WET 100 100

Domestic sector Coal-fired domestic boiler NOC 14 8 CYC 23 14 WET 63 78

a CFB – Circulated Fluidized Bed; NOC – No Control; CYC – Cyclone dust collect; WET – Wet Scrubber; ESP 1

- Electrostatic precipitator; FF - Fiber filter; CMN – Common control of fugitive emissions; HIEF – High-2

efficiency control of fugitive emissions. The table gives the national average penetration of SO2 removal 3

equipments. However, the penetrations vary with provinces. The penetration of the “key region” is usually 4

larger than that of other regions. 5 b The blast furnaces in China are usually equipped with washing tower and double venturi, which have 6

approximately the same removal efficiency as the combination of wet scrubber and electrostatic 7

precipitator. 8

9

Table S5 Penetrations of on-road vehicle emission standards assumed in this study (%)a. 10

Vehicle type

Emission standards

2005 2010 Vehicle type

Emission standards

2005 2010

HDT-D NOC 19 1 LDT-G NOC 27 0 HDT-D HDEUI 42 8 LDT-G LFEUI 56 13 HDT-D HDEUII 39 22 LDT-G LFEUII 16 29 HDT-D HDEUIII 0 70 LDT-G LFEUIII 0 58 HDB-D NOC 28 8 LDB-G NOC 31 6 HDB-D HDEUI 40 18 LDB-G LFEUI 54 22 HDB-D HDEUII 32 24 LDB-G LFEUII 15 23 HDB-D HDEUIII 0 51 LDB-G LFEUIII 0 48 LDT-D NOC 11 0 CAR-G NOC 23 3 LDT-D MDEUI 65 13 CAR-G LFEUI 55 16 LDT-D MDEUII 23 30 CAR-G LFEUII 23 28 LDT-D MDEUIII 0 58 CAR-G LFEUIII 0 53 a HDT-D, Heavy Duty Diesel Truck; HDB-D, Heavy Duty Diesel Bus; LDT-D, Light Duty Diesel Truck; LDT-11

G, Light Duty Gasoline Truck; LDB-G, Light Duty Gasoline Bus; CAR-G, Gasoline Car; HDEUI~ HDEUIII, 12

EURO I~III standards on heavy duty diesel road vehicles; MDEUI~ MDEUIII, EURO I~III standards on light 13

duty diesel road vehicles; LFEUI~ LFEUIII, EURO I~III standards on light duty spark ignition road vehicles (4-14

stroke engines). 15

16

7

(a) PM10 (b) BC

(c) OC (d) NMVOC

Figure S1 The emissions of PM10, BC, OC and NMVOC by sector from 2005 to 2010. 1

2

2 Configuration and evaluation of the modeling system 3

2.1 Model configuration 4

The Models-3 Community Multi-scale Air Quality (CMAQ) modeling system version 4.7.1 was used 5

in this study. The modeling domain, simulation periods, vertical resolution, scientific options, initial 6

conditions, and boundary conditions for CMAQ have been described in the main text. The Weather 7

Research and Forecasting Model (WRF, version 3.3) was used to generate the meteorological fields. 8

In the WRF simulations, 23 sigma levels are selected for the vertical grid structure with the model top 9

pressure of 100 mb at approximately 15 km. The National Center for Environmental Prediction 10

(NCEP)’s Final Operational Global Analysis data were used to generate the first guess field with a 11

horizontal resolution of 1×1at every 6 h. The NCEP’s Automated Data Processing (ADP) data was 12

used in the objective analysis scheme. The physics options selected in the WRF model were Grell-13

Devenyi cumulus schemes, the NCEP/Oregon State University/Air Force/Hydrologic Research Lab 14

(NOAH) land surface model, the Mellor-Yamada-Janjic PBL scheme, WRF Single-Moment (WSM) 15

3-class scheme for cloud microphysics, the Rapid Radiative Transfer Model (RRTM) longwave and 16

8

shortwave radiation scheme. The Meteorology-Chemistry Interface Processor (MCIP) version 3.6 was 1

applied to process the meteorological data in a format required by CMAQ. 2

2.2 Evaluation of meteorological predictions 3

The meteorological prediction lays the foundation for the air quality simulation. In this study, the 4

meteorological parameters simulated by WRF were compared with the observational data obtained 5

from the National Climatic Data Center (NCDC), where hourly or every third hour observations are 6

available for 383 sites scattering within the domain. Due to the limited observational data available, 7

the statistical evaluation was restricted to the temperature at 2 m (T2), wind speed at 10 m (WS10), 8

and humidity at 2 m (H2). The statistical indices used include the mean observation (Mean OBS), the 9

mean simulation (Mean SIM), the bias, and gross error (GE). A detailed explanation of these indices 10

can be found in Baker (2004). 11

Table S6 lists the model performance statistics and the benchmarks suggested by Emery et al (2001). 12

These benchmark values were derived based on performance statistics of the Fifth-Generation 13

NCAR/Penn State Mesoscale Model (MM5) from a number of studies over the U.S. domain (mostly at 14

grid resolution of 12km or 4km), and have been widely accepted in many regional air quality 15

modeling studies. We expect these standards should also be applicable in our simulation domain. For 16

WS10, the GEs of all the months are within the benchmark range. The biases for most months are 17

below the benchmark value of 0.5 m s-1, and the values of May 2005, Dec 2005, Feb-May 2010, and 18

Dec 2010 are slightly above this benchmark (0.52-0.64 m s-1). Given that a grid resolution of 12-km or 19

finer generally gives more accurate meteorological predictions than that at 36-km, the benchmark 20

values may be stringent when applying to this study, therefore the slight exceedance is believed to be 21

acceptable. The observed temperature and humidity are reproduced quite well, with all the statistical 22

indices within the benchmark values. In summary, these statistics indicate an overall good 23

performance of meteorological predictions. 24

25

Table S6 Performance statistics for meteorological variables. 26

Wind speed Temperature Humidity

Mean OBS

Mean SIM

Bias GE Mean OBS

Mean SIM

Bias GE Mean OBS

Mean SIM

Bias GE

Unit m s-1 m s-1 m s-1 m s-1 K K K K g kg-1 g kg-1 g kg-1 g kg-1

Benchmark

≤ ±

0.5 ≤2

≤ ±

0.5 ≤2

≤ ±

1 ≤2

Jan-05 2.57 2.98 0.41 1.22 275.0 275.0 0.08 1.39 3.87 4.19 0.32 0.63 Feb-05 2.91 3.39 0.48 1.32 275.9 276.2 0.31 1.27 4.62 5.02 0.40 0.64 Mar-05 3.03 3.51 0.48 1.39 282.0 282.3 0.28 1.37 5.33 5.71 0.38 0.81 Apr-05 3.09 3.53 0.45 1.37 286.6 286.9 0.28 1.35 7.54 8.02 0.48 0.99 May-05 2.95 3.47 0.52 1.37 294.3 294.4 0.10 1.34 12.23 12.43 0.20 1.34 Jun-05 2.72 3.17 0.45 1.32 298.7 298.8 0.09 1.40 15.33 15.08 -0.25 1.76

9

Jul-05 2.72 3.17 0.45 1.31 300.5 300.5 -0.03 1.38 17.70 17.66 -0.03 1.79 Aug-05 2.67 3.08 0.41 1.22 298.8 298.7 -0.05 1.23 17.22 17.35 0.13 1.65 Sep-05 2.63 3.08 0.44 1.20 296.4 296.3 -0.12 1.22 14.51 14.71 0.19 1.60 Oct-05 2.67 3.13 0.46 1.21 290.3 290.4 0.02 1.18 9.33 9.81 0.48 1.23 Nov-05 2.55 2.97 0.42 1.18 285.5 285.6 0.07 1.17 7.46 7.79 0.33 0.94 Dec-05 2.90 3.43 0.53 1.35 276.2 276.4 0.13 1.37 3.88 4.05 0.17 0.75 Jan-10 2.79 3.27 0.48 1.33 276.3 276.5 0.15 1.30 4.65 4.95 0.30 0.66 Feb-10 2.93 3.54 0.61 1.41 279.0 279.1 0.13 1.32 5.79 6.10 0.31 0.71 Mar-10 3.28 3.92 0.64 1.52 282.5 282.7 0.21 1.35 6.17 6.48 0.31 0.82 Apr-10 3.16 3.71 0.55 1.43 286.6 286.8 0.16 1.33 7.73 8.07 0.34 0.95 May-10 2.85 3.42 0.57 1.36 294.0 293.9 -0.07 1.32 11.78 11.77 -0.01 1.25 Jun-10 2.53 3.00 0.47 1.24 297.4 297.4 -0.03 1.28 14.69 14.36 -0.33 1.57 Jul-10 2.63 3.11 0.48 1.26 300.7 300.6 -0.09 1.31 18.27 18.10 -0.17 1.86 Aug-10 2.43 2.76 0.34 1.13 299.8 299.7 -0.04 1.31 17.26 17.22 -0.04 1.65 Sep-10 2.42 2.80 0.38 1.12 296.1 296.1 0.01 1.18 15.11 15.07 -0.04 1.46 Oct-10 2.20 2.52 0.32 1.06 285.7 285.8 0.09 1.38 5.81 6.33 0.52 1.25 Nov-10 2.57 3.01 0.44 1.20 284.4 284.4 0.06 1.29 6.23 6.36 0.13 0.95 Dec-10 3.07 3.63 0.57 1.39 278.4 278.5 0.12 1.41 4.37 4.72 0.35 0.80

1

2.3 Evaluation of air quality predictions against satellite observations 2

NO2 vertical column density (VCD) and Aerosol Optical Depth (AOD) simulated by the CMAQ 3

model were compared with those measured by the remote sensors of OMI and MODIS/Terra 4

(Boersma et al 2007, Chu et al 2002). We use the DOMINO (Dutch OMI NO2) product by KNMI 5

(Boersma et al 2007) to compare with the simulated NO2 VCD. The AOD data were prepared as 6

described in Chu et al (2002). Considering each satellite’s descending nodes, we choose the CMAQ 7

outputs at 14:00 and 11:00 Beijing time (BT) to compare with the OMI NO2 and MODIS-AOD 8

respectively. 9

The modeled concentrations were integrated from the surface to the model top to obtain the vertical 10

column density of NO2 and AOD in each grid cell. To make comparisons, the satellite dataset is 11

interpolated to the 36 km × 36 km grid system compatible with the CMAQ simulation. The 12

conversion of aerosol mass profiles to aerosol optical thickness follows the same method as Zhang et 13

al (2009). An empirical equation developed by Chameides et al (2002) was used to calculate the 14

AODs from the PM2.5 concentrations predicted by CMAQ and the vertically resolved temperature and 15

pressure given by WRF. 16

The evaluation was conducted for the whole domain. Given the fact that the eastern part of China is 17

the most polluted area, and that AOD data are not available for a vast part of the western China, the 18

statistical indices were calculated for the Eastern China, as shown in figure 1. The statistical indices 19

employed in this study include, mean observation (mean OBS), mean simulation (mean SIM), 20

correlation coefficient (Corr.Coeff.), normalized mean bias (NMB) and normalized mean error (NME). 21

10

Figure S2 and S3 compare the NO2 VCD and AOD between CMAQ predictions and OMI/MODIS 1

observations, respectively. Table S7 summarizes the performance statistics of monthly mean NO2 2

VCD and AOD. For NO2 VCD, the spatial distribution of the CMAQ simulations agrees very well 3

with the satellite retrievals for all seasons. High concentrations are found in the North China Plain, the 4

Yangtze River Delta, the Pearl River Delta, and the Sichuan Basin. The correlation coefficients for 5

the Eastern China are 0.89, 0.89, 0.79, 0.89, 0.88, 0.81, 0.64, and 0.83 in winter 2005, spring 2005, 6

summer 2005, autumn 2005, winter 2010, spring 2010, summer 2010, and autumn 2010, respectively, 7

showing good agreement of CMAQ simulations with OMI observations. CMAQ underpredicts the 8

NO2 VCD, with NMBs ranging from -9% to -43% in the Eastern China. The factors contributing to 9

the underestimation include the exclusion of NOX emissions from soil and lighting, the uncertainty of 10

anthropogenic NOX emissions, etc. The differences between the simulated NO2 VCD over east China 11

with and without NOx emissions from soil and lighting could be over 10%, especially in summer, 12

according to our previous study (Wang et al 2011). 13

For AOD, CMAQ-simulated AOD values are lower than MODIS AOD values over most of the 14

domain in spring and summer, especially in northwest and south China. This underestimation may be 15

attributed to the exclusion of radiative feedback of aerosols, underestimation of SOA formation, the 16

uncertainty of hygroscopic growth of hydrophilic aerosols due to enhanced relative humidity, and the 17

exclusion of wind-blown dust in the northwestern China. The uncertainty of the MODIS derived AOD 18

column, especially the significant overestimation in the northern arid and semiarid regions as 19

illustrated in Wang et al (2007), also contributes to the discrepancy. In winter and autumn, CMAQ 20

overestimates MODIS derived AOD values over most of the domain, except for the southernmost part 21

of mainland China in winter. As described in the previous studies (Wang et al 2007, Wang et al 2010), 22

this overestimation may be due to the difficulties in measuring AODs from satellites when snow/ice 23

exist on the land in the northern and central China in cold seasons, or the systematic underprediction 24

of the MODIS AOD retrievals for southern forest areas. The uncertainties in emission inventories (e.g. 25

biomass open burning) and meteorological predictions may also result in the overestimation. The 26

underlying reasons for the discrepancy are still to be explored in depth. 27

The comparison of simulated AOD changes during the studied period with the observation of MODIS 28

could verify the simulated changes of fine particulate matter pollution in the whole domain. As shown 29

in figure S8(a) and figure S8(b), the modeling results can reproduce the spatial pattern of AOD 30

changes fairly well, especially the notable increase in the Sichuan Basin and the southern part of the 31

North China Plain, and the decline along the southeastern coast. The largest discrepancy occurred in 32

the central southern China, where CMAQ had some difficulty reproducing the MODIS observations, 33

due to both observation and simulation errors, as described in the paragraphs above. 34

35

Winter 2005

11

Spring 2005

Summer 2005

Fall 2005

Winter 2010

Spring 2010

12

Summer 2010

Fall 2010

1

Figure S2 Comparison of seasonal average NO2 VCD between CMAQ (14:00, Beijing Time, left 2

column) and OMI (right column). 3

4

Winter 2005

Spring 2005

13

Summer 2005

Fall 2005

Winter 2010

Spring 2010

Summer 2010

14

Fall 2010

1

Figure S3 Comparison of seasonal average AOD between CMAQ (11:00, Beijing Time, left column) 2

and MODIS (right column). 3

4

Table S7 Comparison of CMAQ simulations with satellite retrievals in the Eastern China (1 × 1015 5

molecules cm-2 for NO2 VCD; Dobson unit for AOD). 6

Variables 2005 2010

Winter Spring Summer Fall Winter Spring Summer Fall

NO2 VCD Mean SIM 7.01 3.09 1.63 3.35 9.92 4.92 2.41 4.62 Mean OBS 7.67 3.93 2.84 3.68 10.57 4.76 3.26 4.68 Corr.Coeff. 0.89 0.89 0.79 0.89 0.88 0.81 0.64 0.83 NMB -9% -21% -43% -9% -6% 3% -26% -1% NME 31% 36% 51% 35% 31% 44% 55% 41% AOD Mean SIM 0.49 0.33 0.34 0.44 0.47 0.27 0.38 0.44 Mean OBS 0.39 0.56 0.51 0.40 0.36 0.55 0.52 0.34 Corr.Coeff. 0.68 0.72 0.83 0.78 0.67 0.45 0.88 0.84 NMB 26% -41% -34% 10% 30% -51% -28% 28% NME 43% 41% 37% 29% 49% 52% 33% 44%

7

2.4 Evaluation of air quality predictions against surface observations 8

The Ministry of Environmental Protection of China (MEP) reported daily primary pollutant and its air 9

pollution index (API) for 84 and 86 major cities over the country on its official website 10

(http://datacenter.mep.gov.cn) in 2005 and 2010 respectively. Using each city’s API and primary 11

pollutant, it is possible to back-calculate the daily average concentration for the primary pollutant 12

15

(Cheng et al 2013, Qu et al 2010). PM10 is the primary air pollutant on most of the days. The simulated 1

and API-derived PM10 concentrations are therefore compared. The simulated values are extracted from 2

the grid cell where the city center is located, since a 36 × 36 km grid is large enough to cover the 3

urban area of most cities in China. The observation of a specific city was adopted if the API-derived 4

PM10 concentrations were available for more than 200 days during the simulation period (365 days in 5

total). 6

A number of statistical indices including Mean SIM, Mean OBS, NMB, NME, the mean fractional 7

bias (MFB), and the mean fractional error (MFE), were calculated for the cities to give a quantitative 8

assessment of the model performance, as shown in Table S8. These indices were also specifically 9

calculated for the cities in the Eastern China (shown in Table S8), given the importance of this region. 10

The benchmarks proposed by Boylan (2005) and Morris et al (2005) are also listed in Table S8. It can 11

be seen that the PM10 concentrations are underestimated for all the seasons. This underestimation may 12

be mainly attributable to the exclusion of fugitive dust emissions, and the underestimation of 13

secondary organic aerosols (SOA). The statistics for all seasons in the Eastern China, as well as for the 14

whole China in winter, summer, and autumn, meet the criteria. However, the MFBs of the spring 15

predictions for cities in the whole China exceed the statistical criteria. The relatively larger 16

underestimation in spring is due primarily to the greater impact of wind-blown dust during spring, 17

especially in the northwestern China. For example, the wind-blown dust storm happened fifteen times 18

in spring, once in autumn, twice in winter and did not happen in summer in 2010 according to the 19

statistics of China Meteorological Administration (CMA 2012). 20

21

Table S8 Model performance for daily PM10 concentrations at major cities in China. 22

Variables a 2005 2010

BenchmarkWin Spr Sum Fal Avg Win Spr Sum Fal Avg

China

Mean SIM, μg m-3 77.7 53.6 47.5 71.3 62.5 75.4 46.5 47.1 69.9 59.7

Mean OBS, μg m-3 109.9 101.3 74.5 97.3 95.8 103.7 89.9 68.9 90.7 88.3

NMB, % -29 -47 -36 -27 -35 -27 -48 -32 -23 -32 NME, % 39 49 43 35 41 35 51 42 33 40

MFB, % -38 -66b -55 -37 -49 -41 -64b -49 -32 -47 ±50-60%

MFE, % 55 73 67 55 63 56 73 65 53 62 ±75%

the Eastern China

Mean SIM, μg m-3 82.7 58.7 51.0 77.7 67.5 78.2 48.9 49.8 73.5 62.6

Mean OBS, μg m-3 109.8 103.3 75.8 99.6 97.1 104.9 91.6 70.1 92.2 89.7

NMB, % -25 -43 -33 -22 -30 -25 -47 -29 -20 -30 NME, % 36 46 41 32 38 35 50 40 32 39

MFB, % -31 -58 -49 -30 -42 -37 -60 -44 -28 -42 ±50-60%

MFE, % 51 67 63 50 58 53 70 61 50 59 ±75% a Win – winter, Spr – spring, Sum – summer, Fal – fall, Avg – Annual Average. 23

16

b The values beyond the benchmark range are considered to indicate a relatively poor performance in this study 1

and are highlighted in bold. 2

3

The observation data of fine particulates were very spare and not publicly available during the 4

simulation period. In this study, the simulated PM2.5 concentrations were compared with the 5

observations in Miyun site and Chongming site during some months of 2010 (see figure 1). The 6

Miyun site (40.48°N, 116.78°E, at an altitude of 152m, see figure 1), about 100 km away from the 7

Beijing urban center, is a rural site in the northeast of Beijing downtown area (Xing et al., 2011). The 8

Chongming site (31.52°N, 121.91°E, at an altitude of 2m, see figure 1), located in the easternmost 9

of the Chongming Island, Shanghai City, is a background site facing the East China Sea, and is far 10

from the urban areas. The modelling system can capture the temporal variation fairly well in both sites. 11

The simulated monthly average concentrations are comparable with observations in Miyun site, with 12

NMBs ranging from -15% to 1%. The simulation of Chongming site also agrees well with the 13

observations, with NMBs ranging from -23% to 4%. 14

15

16

Figure S4 Comparison of the simulated PM2.5 concentrations with the observations in Miyun site (top) 17

and Chongming site (middle and bottom) during some months in 2010. 18

19

3 Environmental effects of the changes of air pollutants emissions 20

Winter

17

Spring

Summer

Autumn

Figure S5 Spatial distribution of the seasonal mean sulfate concentrations in 2010 (left column), the 1

differences between 2010 and 2005 (2010 minus 2005, middle column), and the differences between 2

2010 and the 2005SENS case (2010 minus 2005SENS, right column). 3

4

Winter

Spring

Summer

18

Autumn

Figure S6 Spatial distribution of the seasonal mean nitrate concentrations in 2010 (left column), the 1



4

Winter

Spring

Summer

Autumn

19

Figure S7 Spatial distribution of the seasonal mean SIA concentrations in 2010 (left column), the 1



4

AOD is also a measure of absorption and scattering of light. Therefore, it is frequently used as a 5

comprehensive index of fine particulate matter pollution. The effects of emission changes on 6

extinction coefficient have been described in detail in section 3.2 of the main text. Similar to 7

extinction coefficient, AOD also presented an upward tendency in most parts of China, especially in 8

the Sichuan Basin and the Eastern Hubei, and a slight downward tendency in the Yangtze River Delta, 9

as an effect solely of emission changes (see figure S8). While emission changes dominated the 10

temporal trends, the meteorological conditions enhanced the increase of AOD in the Sichuan Basin, 11

and shifted the increasing region in the Eastern Hubei northeasterly towards the southern part of the 12

North China Plain. 13

(a) (b)

(c)

Figure S8. Spatial distribution of the simulated and observed changes of annual mean AOD during 14

2005-2010: (a) the simulated changes (2010 minus 2005); (b) the observed changes (2010 minus 15

2005); (c) the simulated changes as an effect solely of emission changes (2010 minus 2005SENS). 16

17

Table S9 Summary of the critical load exceedance in 2005, 2010, and the hypothetic 2005SENS case 18

in typical regions. 19

Exceedance of CLmax(S) Exceedance of CLnut(N) Exceedance of CL(S) 2005 2010 2005SENS 2005 2010 2005SENS 2005 2010 2005SENS

20

Area percentage of the region (%) China 28.2 27.4 29.0 10.0 12.3 10.1 28.6 28.3 29.5 the Eastern China 57.8 54.3 59.2 20.2 25.6 20.7 58.7 56.5 60.3 the North China Plain 67.9 48.5 66.4 18.3 25.7 18.5 69.1 53.0 67.2 the Yangtze River Delta 63.1 64.6 72.3 13.8 18.5 16.9 64.6 64.6 72.3 the Pearl River Delta 44.4 46.3 44.4 20.4 25.9 16.7 46.3 50.0 46.3 the Sichuan Basin 58.3 70.1 61.8 28.4 31.4 27.9 59.8 72.1 66.2 the Eastern Hubei 61.3 62.5 65.0 11.3 25.0 16.3 62.5 68.8 66.3 Amount of exceedance (Mt S/N) China 3.575 3.173 3.681 1.190 1.665 1.142 3.940 3.633 4.049 the Eastern China 2.600 2.285 2.804 0.755 1.192 0.824 2.869 2.630 3.087 the North China Plain 0.649 0.436 0.635 0.139 0.207 0.135 0.687 0.474 0.664 the Yangtze River Delta 0.136 0.142 0.187 0.010 0.029 0.022 0.141 0.154 0.197 the Pearl River Delta 0.059 0.066 0.056 0.015 0.020 0.007 0.063 0.072 0.058 the Sichuan Basin 0.353 0.360 0.272 0.197 0.183 0.122 0.391 0.408 0.300 the Eastern Hubei 0.110 0.144 0.144 0.024 0.050 0.034 0.119 0.162 0.158

1

4 Environmental effects of the changes of meteorological conditions 2

As documented in the main text, the annual average SIA concentrations increased by 1.58, 3

0.97, 1.03, 1.67, 6.36, and 5.43 g m-3 in the Eastern China, the North China Plain, the 4

Yangtze River Delta, the Pearl River Delta, the Sichuan Basin, and the Eastern Hubei 5

respectively, as an effect solely of emission changes. When considering the impact of 6

meteorological conditions, the total changes of SIA are 1.38, 1.65, 1.26, 0.77, 9.29, and 7

5.90g m-3 respectively in the six regions above. The emission changes usually dominate the 8

temporal trends of SIA concentrations. But in some regions the meteorological conditions 9

also have considerable effects. For example, in the Sichuan Basin, the seasonal mean wind 10

speeds decreased by 0.29-0.83 m s-1 from 2005 to 2010 (see table S10), thereby enhancing the 11

SIA concentrations during this period. For other regions, the effects of meteorological 12

condition are positive in some seasons and negative in others. On an annual average basis, the 13

meteorological condition enhanced the SIA concentrations in the North China Plain, and 14

mitigated the SIA concentration in the Pearl River Delta, mainly attributed to the decline of 15

wind speed in summer, and the increase of wind speed in winter, respectively (see table S10). 16

17

Table S10 Seasonal mean wind speed, temperature, and humidity in the North China Plain, the Pearl 18

River Delta, and the Sichuan Basin in 2005 and 2010. 19

Item Region Unit 2005 2010

Spring Summer Fall Winter Annual Spring Summer Fall Winter Annual

Wind speed the North China Plain m s-1 3.66 3.02 2.65 2.79 3.03 3.69 2.81 2.71 3.00 3.05

21

Temperature the North China Plain K 285.6 298.7 286.8 269.6 285.2 285.6 298.6 286.3 271.2 285.4

Humidity the North China Plain g kg-1 5.44 14.01 7.12 1.95 7.13 5.27 13.61 6.74 2.15 6.94

Wind speed the Pearl River Delta m s-1 3.83 3.69 3.62 3.88 3.75 3.95 3.41 3.60 4.22 3.79

Temperature the Pearl River Delta K 295.4 301.3 298.7 288.9 296.1 296.2 301.5 297.7 290.3 296.4

Humidity the Pearl River Delta g kg-1 15.15 21.07 16.33 9.46 15.50 15.51 21.08 15.85 11.26 15.92

Wind speed the Sichuan Basin m s-1 3.75 2.75 2.92 3.50 3.23 2.92 2.46 2.24 2.77 2.60

Temperature the Sichuan Basin K 291.0 299.2 291.4 280.0 290.4 290.5 298.9 291.5 281.9 290.7

Humidity the Sichuan Basin g kg-1 9.15 14.99 9.96 4.40 9.63 8.22 14.95 9.25 4.27 9.17

1

5 Comparison with other studies 2

Zhao et al (2013) developed a multiple pollutant emission inventory for the period 2005-2010, and 3

analyzed the effects of recently implemented control measures on the inter-annual trends, sector and 4

spatial distributions of China’s anthropogenic emissions. Table S11 compares the annual emissions of 5

SO2, NOX, PM10, PM2.5, BC, and OC in our estimates with Zhao et al (2013). It can be seen that the 6

differences between the two studies are generally within 10% for NOX, PM10, and PM2.5 emissions. 7

The differences for SO2, BC, and OC often exceed 10%, but are generally within 20%. The differences 8

in SO2 emissions are mainly attributed to different assumptions on the average removal rate of flue gas 9

desulfurization in power plants, and on the sulfur content and the release ratio of sulfur in the industry 10

sector. The uncertainty of emission factors accounts for the relatively large differences in BC and OC 11

emissions. 12

Since this study focuses on the emission trends and their environmental effects, the uncertainty of the 13

growth rate of air pollutant emissions may have larger impact on the modeling results than that of the 14

absolute emissions. Therefore, we compare the growth rates of emissions during the studied period 15

between the two studies, which are also shown in table S11. It can be seen that the largest discrepancy 16

is associated with the NOX emission trends. During 2005-2010, the growth rate of NOX emissions is 17

estimated at 33.8% and 46.7% in our study and Zhao et al (2013) respectively. Different assumptions 18

on the penetrations of NOX control technologies in power plants are major contributors to the different 19

growth rates. SO2 emissions are estimated to decrease by 14.9% and 10.8% respectively in the two 20

studies, due largely to different assumptions on the average removal rate of flue gas desulfurization in 21

power plants. The change rates of PM2.5 emissions are estimated at -11.7% and -5.9% respectively, 22

and the difference between the two studies is attributable to the unclear changes in the penetration 23

levels of dust collectors at industrial sources. The change rates of BC and OC emissions are similar in 24

these two studies. 25

We also compare the temporal trends of air pollutant emission at provincial levels. We focus on the 26

studied regions of this paper, including the North China Plain (roughly Beijing, Tianjin, Hebei, and 27

Shandong provinces), the Yangtze River Delta (roughly Shanghai, Jiangsu, and Zhejiang provinces), 28

the Pearl River Delta (roughly Guangdong province), the Sichuan Basin (roughly Sichuan and 29

22

Chongqing provinces), and the Eastern Hubei (roughly Hubei provinces). Generally speaking, the 1

temporal trends in the two studies agree well with each other at provincial levels. SO2 emissions 2

decreased in most provinces of the Eastern China. In both studies, the declining rates are estimated at 3

over 20% in the North China Plain and the Yangtze River Delta; the emissions are estimated to grow 4

by less than 10% in Sichuan province and over 10% in Chongqing and Hubei provinces. The declining 5

rates of Guangdong are estimated at over 10% and less than 10% in our study and Zhao et al (2013), 6

respectively. NOX emissions increased across China because of limited control measures. In both 7

studies, the emissions are estimated to increase by 20%-50% in the North China Plain and Guangdong 8

province, and by over 50% in Sichuan, Chongqing, and Hubei provinces. In the Yangtze River Delta, 9

the growth rates are estimated at 10%-20% and over 20% in our study and Zhao et al (2013), 10

respectively. In both studies, the declining rates of PM10 emissions are estimated to be over 20% in 11

Guangdong provinces, 10%-20% in Sichuan and Chongqing provinces, and less than 10% in Hubei 12

provinces. In Yangtze River Delta, we estimated the declining rate to be 10% larger than Zhao et al 13

(2013). 14

The emission variations between different studies may affect the modeled environmental effects to 15

some extent. For example, Zhao et al (2013) estimated the SO2 and primary PM2.5 emissions to 16

decrease slower, and NOX emissions to increase faster than our estimates. If the estimates of Zhao et al 17

(2013) were used to quantify the environmental effects of emission changes, the reduction of primary 18

PM2.5 concentrations would be smaller, while the increase of SIA concentrations would be larger. 19

Consequently, it would accelerate the increase of PM2.5 concentrations in some regions, and slow 20

down the decrease elsewhere. In addition, the decline of S deposition would be smaller, and the 21

increase of N deposition would be larger, thereby worsening the soil acidification conditions. 22

23

Table S11 Comparison of the emission estimates in this study with Zhao et al (2013). 24

2005 2006 2007 2008 2009 2010 2010/2005

SO2 This study 28.7 27.9 26.5 24.9 23.8 24.4 0.851 Zhao et al (2013) 31.1 32.1 31.4 29.0 27.7 27.7 0.892 Difference 8.3% 14.9% 18.6% 16.4% 16.5% 13.5%

NOX This study 19.5 21.1 22.6 23.5 24.4 26.1 1.338 Zhao et al (2013) 19.6 21.6 23.6 24.1 26.0 28.8 1.467 Difference 0.9% 2.2% 4.5% 2.4% 6.4% 10.6%

PM10 This study 18.6 18.5 17.7 16.7 16.3 15.8 0.849 Zhao et al (2013) 18.9 18.8 18.9 17.7 17.8 17.0 0.899 Difference 1.6% 2.0% 6.5% 5.8% 9.1% 7.5%

PM2.5 This study 13.3 13.4 12.9 12.3 12.1 11.8 0.883 Zhao et al (2013) 13.0 12.9 13.0 12.3 12.5 12.2 0.941 Difference -2.7% -3.3% 0.2% -0.1% 3.4% 3.6%

BC This study 1.94 1.94 1.91 1.97 2.02 1.93 0.991 Zhao et al (2013) 1.64 1.61 1.59 1.60 1.65 1.67 1.013

23

Difference -15.4% -17.1% -16.9% -18.8% -18.7% -13.5%

OC This study 3.70 3.77 3.64 3.57 3.61 3.51 0.947 Zhao et al (2013) 3.15 2.91 2.79 2.78 2.83 2.85 0.903 Difference -14.8% -22.8% -23.3% -22.1% -21.7% -18.8%

1

2

References 3

Baker K 2004 Meteorological Modeling Protocol for Application to PM2.5/haze/ ozone Modeling 4 Projects Lake Michigan Air Directors Consortium Des Plaines, IL 5

Boersma K F, Eskes H J, Veefkind J P, Brinksma E J, van der A R J, Sneep M, van den Oord G H J, 6 Levelt P F, Stammes P, Gleason J F and Bucsela E J 2007 Near-real time retrieval of tropospheric 7 NO2 from OMI Atmos. Chem. Phys. 7 2103-2118 8

Boylan J 2005 PM Model Performance Goal and Criteria National Regional Planning Organizations 9 Modeling Meeting Denver, CO 10

Chameides W L, Luo C, Saylor R, Streets D, Huang Y, Bergin M and Giorgi F 2002 Correlation 11 between model-calculated anthropogenic aerosols and satellite-derived cloud optical depths: 12 Indication of indirect effect? J. Geophys. Res-Atmos. 107 13

Cheng Z, Jiang J K, Fajardo O, Wang S X and Hao J M 2013 Characteristics and health impacts of 14 particulate matter pollution in China (2001-2011) Atmos. Environ. 65 1-9 15

China Association of Automobile Manufacturers 2011 China Automotive Industry Yearbook 2011 16 (Beijing: China Commercial Publishing House) 17

China Electricity Council 2011 Annual Development Report of China's Power Industry (Beijing: 18 China Electric Power Press) (in Chinese) 19

China Meteorological Administration (CMA) 2012 Sand-dust weather almanac 2010 (Beijing: China 20 Meteorological Press) (in Chinese) 21

Chu D A, Kaufman Y J, Ichoku C, Remer L A, Tanre D and Holben B N 2002 Validation of MODIS 22 aerosol optical depth retrieval over land Geophys. Res. Lett. 29 23

Emery C, Tai E and Yarwood G 2001 Enhanced meteorological modeling and performance evaluation 24 for two texas episodes. Report to the Texas Natural Resources Conservation Commission 25 ENVIRON, International Corp Novato, CA 26

Energy Research Institute in China (ERI) 2009 China's Low Carbon Development Pathways by 2050: 27 Scenario Analysis of Energy Demand and Carbon Emissions (Beijing: Science Press) (in Chinese) 28

Energy Research Institute in China (ERI) 2010 Guidebook for the Financing of Energy Efficiency and 29 Renewable Energy Projects (Beijing: China Environmental Science Press) (in Chinese) 30

Guenther A, Karl T, Harley P, Wiedinmyer C, Palmer P I and Geron C 2006 Estimates of global 31 terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from 32 Nature) Atmos. Chem. Phys. 6 3181-3210 33

Huo H, Yao Z L, Zhang Y Z, Shen X B, Zhang Q, Ding Y and He K B 2012a On-board measurements 34 of emissions from light-duty gasoline vehicles in three mega-cities of China Atmos. Environ. 49 35 371-377 36

Huo H, Yao Z L, Zhang Y Z, Shen X B, Zhang Q and He K B 2012b On-board measurements of 37 emissions from diesel trucks in five cities in China Atmos. Environ. 54 159-167 38

Li X G, Wang S X, Duan L, Hao J, Li C, Chen Y S and Yang L 2007a Particulate and trace gas 39 emissions from open burning of wheat straw and corn stover in China Environ. Sci. Technol. 41 40 6052-6058 41

Li X H, Duan L, Wang S X, Duan J C, Guo X M, Yi H H, Hu J N, Li C and Hao J M 2007b Emission 42 characteristics of particulate matter from rural household biofuel combustion in China Energ. Fuel. 43 21 845-851 44

Li X H, Wang S X, Duan L and Hao J M 2009a Characterization of non-methane hydrocarbons 45 emitted from open burning of wheat straw and corn stover in China Environ. Res. Lett. 4 46

24

Li X H, Wang S X, Duan L, Hao J M and Nie Y F 2009b Carbonaceous Aerosol Emissions from 1 Household Biofuel Combustion in China Environ. Sci. Technol. 43 6076-6081 2

Ministry of Environmental Protection of China (MEP) 2011 Bulletin of urban sewage treatment 3 facilities, and flue gas desulfurization/denitrification facilities of coal-fired power plants, available 4 at http://www.mep.gov.cn/gkml/hbb/bgg/201104/t20110420_209449.htm (in Chinese) 5

Morris R, Koo B and McNally D 2005 Application of Multiple Models to Simulation Fine Particulate 6 in the Southeastern U.S. National Regional Planning Organizations Modeling Meeting Denver, CO 7

National Bureau of Statistics (NBS) 2011a China Energy Statistical Yearbook 2010 (Beijing: China 8 Statistics Press) 9

National Bureau of Statistics (NBS) 2011b China Industrial Economy Statistical Yearbook 2011 10 (Beijing: China Statistics Press) 11

National Bureau of Statistics (NBS) 2011c China Statistical Yearbook 2011 (Beijing: China Statistics 12 Press) 13

National Bureau of Statistics (NBS) 2011d China Statistical Yearbook for Regional Economy 2011 14 (Beijing: China Statistics Press) 15

Qu W J, Arimoto R, Zhang X Y, Zhao C H, Wang Y Q, Sheng L F and Fu G 2010 Spatial distribution 16 and interannual variation of surface PM10 concentrations over eighty-six Chinese cities Atmos. 17 Chem. Phys. 10 5641-5662 18

Streets D G, Bond T C, Carmichael G R, Fernandes S D, Fu Q, He D, Klimont Z, Nelson S M, Tsai N 19 Y, Wang M Q, Woo J H and Yarber K F 2003 An inventory of gaseous and primary aerosol 20 emissions in Asia in the year 2000 J. Geophys. Res-Atmos. 108 21

Tsinghua University Building Energy Research Center (THUBERC) 2009 Annual Report on China 22 Building Energy Efficiency (Beijing: China Architecture & Building Press) (in Chinese) 23

Wang L L, Xin J Y, Wang Y S, Li Z Q, Liu G R and Li J 2007 Evaluation of the MODIS aerosol 24 optical depth retrieval over different ecosystems in China during EAST-AIRE Atmos. Environ. 41 25 7138-7149 26

Wang L T, Jang C, Zhang Y, Wang K, Zhang Q A, Streets D, Fu J, Lei Y, Schreifels J, He K B, Hao J 27 M, Lam Y F, Lin J, Meskhidze N, Voorhees S, Evarts D and Phillips S 2010 Assessment of air 28 quality benefits from national air pollution control policies in China. Part II: Evaluation of air 29 quality predictions and air quality benefits assessment Atmos. Environ. 44 3449-3457 30

Wang Q Y 2011 Energy Data 2010 for the China Sustainable Energy Program The Energy 31 Foundation Beijing(in Chinese) 32

Wang S X, Wei W, Du L, Li G H and Hao J M 2009 Characteristics of gaseous pollutants from 33 biofuel-stoves in rural China Atmos. Environ. 43 4148-4154 34

Wang S X, Xing J, Chatani S, Hao J M, Klimont Z, Cofala J and Amann M 2011 Verification of 35 anthropogenic emissions of China by satellite and ground observations Atmos. Environ. 45 6347-36 6358 37

Wei W, Wang S X, Chatani S, Klimont Z, Cofala J and Hao J M 2008 Emission and speciation of non-38 methane volatile organic compounds from anthropogenic sources in China Atmos. Environ. 42 39 4976–4988 40

Woo J H, Baek J M, Kim J W, Carmichael G R, Thongboonchoo N, Kim S T and An J H 2003 41 Development of a multi-resolution emission inventory and its impact on sulfur distribution for 42 Northeast Asia Water. Air. Soil. Poll. 148 259-278 43

Xing J, Zhang Y, Wang S X, Liu X H, Cheng S H, Zhang Q A, Chen Y S, Streets D G, Jang C, Hao J 44 M and Wang W X 2011 Modeling study on the air quality impacts from emission reductions and 45 atypical meteorological conditions during the 2008 Beijing Olympics Atmos. Environ. 45 1786-46 1798 47

Yao Z L, Huo H, Zhang Q, Streets D G and He K B 2011 Gaseous and particulate emissions from 48 rural vehicles in China Atmos. Environ. 45 3055-3061 49

Zhang C Y 2008 Studies on Present and Future Emissions of PM, SO2, NOx Department of 50 Environmental Science and Engineering Tsinghua University Beijing p. 147 (in Chinese) 51

Zhang Y, Vijayaraghavan K, Wen X Y, Snell H E and Jacobson M Z 2009 Probing into regional 52 ozone and particulate matter pollution in the United States: 1. A 1 year CMAQ simulation and 53 evaluation using surface and satellite data J. Geophys. Res-Atmos. 114 54

25

Zhao Y, Zhang J and Nielsen C P 2013 The effects of recent control policies on trends in emissions of 1 anthropogenic atmospheric pollutants and CO2 in China Atmos. Chem. Phys. 13 487–508 2

Documents

Environmental effects of the evolution of air pollutants ...iopscience.iop.org/1748-9326/8/2/024031/media/erl467736suppdata.pdf8 a CFB – Circulated Fluidized Bed; NOC – No Control;