1

Supporting Information

Source forensics of black carbon aerosols from

China

Bing Chen,†,‡,╞

August Andersson,§ Meehye Lee,

║ Elena N. Kirillova,

§ Qianfen Xiao,

Martin

Kruså,§ Meinan Shi,

# Ke Hu,

# Zifeng Lu,

├ David G. Street,

├ Ke Du,

*, †, Örjan Gustafsson,

*,§

†Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China;

‡Associate Unit CSIC-University of Huelva “Atmospheric Pollution”, University of Huelva,

E21071 Huelva, Spain;

§Department of Applied Environmental Science and the Bert Bolin Centre for Climate Research,

Stockholm University, 10691 Stockholm, Sweden;

║Department of Earth and Environmental Sciences, Korea University, Seoul 136-701, South

Korea;

School of Environmental Science and Technology, Tongji University, Shanghai 200092, China;

School of Marine Science, China University of Geosciences, Beijing 100038, China;

├Decision and Information Sciences Division, Argonne National Laboratory, Argonne, Illinois

60439, USA

╞Current address: Environmental Research Institute, Shandong University, Jinan 250100, China.

*To whom correspondence may be addressed. Email: orjan.gustafsson@itm.su.se; Tel. +46-70-

347317 or Email: kdu@iue.ac.cn; Tel. +86-592-6190767

Supporting information includes:

18 pages, 4 figures, and 5table

2

SI Materials and Methods

Sensitivity analysis of EC isolation

During isolation of EC using the thermal-optical transmission (TOT) program a potential

artifact is incomplete removal, and thus inadvertent inclusion, of some of the pyrolyzed carbon

(PyrC) that may form from the OC during the initial He atmosphere phase. The formation, and

subsequent oxidative removal, of PyrC is a well-recognized process that is inherent to the design

of the method and has been investigated extensively (1-3). Commercially available TOT

instruments, such as the herein employed Sunset Instrument, account for the PyrC by on-line

monitoring of the laser transmission signal through the filter. The creation of PyrC blackens the

filter, reducing the laser transmission. When entering subsequent program stages, with a higher

oxygen mixing ratio, the method is designed to burn off the PyrC.

The split between PyrC and EC is defined as the point where the laser transmission returns to

the value it had at start before any PyrC was formed. This correction is thus introduced to provide

correct estimates of the amounts of EC. However, it is conceivable that this process may also

introduce a certain amount of mixing of carbon from different sources, such that some ‘true’ EC

is burned off as PyrC, and that some PyrC is included in the observed EC fraction. For

concentration estimates this potential mixing is not expected to have a large interference, since

the mass absorption cross section for PyrC and EC are assumed to be similar. However, for

isotopic measurements the observed isotopic signature of EC may be influenced by the isotopic

signature of PyrC, which is expected to be similar to the OC from which it is formed.

To explore any putative effects of such exchange between PyrC and ‘true’ EC, a sensitivity

analysis was carried out on the samples collected during the GoPoEx11 campaign at KCOG. To

this end, the following system of mass balance equations is examined:

3

[S1]

[S2]

observed fraction fossil of EC

observed fraction fossil of OC

‘true’ fraction fossil of EC

‘true’ fraction fossil of OC

x fraction PyrC contributing to EC

[EC] concentration EC

[OC] concentration OC

In this model the fraction fossil for PyrC is assumed to be equal to the fraction fossil of true

OC. For the GoPoEx11 the mean = 0.55. The effect on the fraction fossil of “true” EC of

including 0 - 25% of the PyrC in place of the “true” EC was calculated from an isotopic mass

balance criterion (Table S5). This sensitivity analysis clearly demonstrate that the effects of

including even a large portion of PyrC, up to 25%, in the EC only affects the computed ffossil

within the uncertainty range of EC (Table S5). Furthermore, the direction of any such effect is

such that any “correction” would further elevate the estimated ffossil of BC in the air from China

above the current estimate of 80±6%, yielding an even larger offset between 14

C-BC top-down

source apportionment and the bottom-up EI estimates and thus act to strengthen the conclusion of

this study. This result shows that the current estimates of ffossil for EC and associated conclusions

are likely to be robust with respect to potential interference from inadvertent inclusion of

instrument-induced PyrC.

4

Monthly-resolved emission inventory of BC for China from 2008 to 2010

Annual and monthly BC emissions from China by fuel type during 2008-2010 are derived from

our previous work (4). We used a technology-based methodology to estimate the annual BC

emissions from China. The emission sources are categorized into five major sectors (i.e., power

generation, industry, residential, transport, and open biomass burning) and more than 120

sector/fuel (or product)/technology combinations, including both fuel combustion and non-

combustion sources. Time-dependent trends in activity rates and emission factors are

incorporated in the calculation (4). For the seasonal variations of BC emissions, year-specific

monthly temporal distributions for each major sector during 2008-2010 were developed. We

followed the same methodology used in the TRACE-P (Transport and Chemical Evolution over

the Pacific) inventory (5), assuming a dependence of stove operation on provincial monthly mean

temperatures, to generate monthly emissions for the residential sector. For the other sectors,

monthly temporal distributions were determined from official biweekly or monthly statistics of

power generation, industrial GDP, sulfuric acid and coke production, volume of passenger and

freight transported by ship, railway, and aviation, etc. The monthly emissions of open biomass

burning from forest and savanna were obtained directly from Global Fire Emissions Database

(GFED) version 3.1 (6), and those from agricultural waste burning were determined based on the

work of Wang and Zhang (7). Figure S4 shows the fraction of fossil BC predicted by the

monthly-resolved EI in winter period is consistently lower than that predicted for the annual

average.

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7

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8

29. Ito A, Penner JE (2005) Historical emissions of carbonaceous aerosols from biomass and

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Figure S1. Topography (shaded) and Asian winter monsoon outflow (average of December,

January, February and March during 1948-2011 of meteorological model projection (8)).

9

Figure S2. Backward air trajectories (9) for sampling campaign at Beijing (A), Shanghai (B),

South China Climate Observatory (SCCO), Shinglin Bay (C), and Korea Climate Observatory -

Gosan (KCOG), Jeju Island (D). The backward trajectories were projected for 72 hours ending at

500 m AGL for Chinese sites and at 850 m AGL for the KCOG site.

10

Figure S3. Relationships between 13

C and 14

C for black carbon aerosols from Beijing and

Shanghai. The end members for 13

C are shown: coal combustion (~-25 to ~-22 ‰) (10-12),

vehicle exhaust (~-28 to ~-25 ‰) (13-15), C4 plant burning (grasses, ~-16 to ~-19 ‰) (16), and

C3 plant burning (trees, ~-28 to ~-34 ‰) (16).

11

Figure S4. Temporal information for both “bottom-up” Emission Inventory (EI) model and “top-

down” 14

C-based atmospheric measurements for fraction of fossil BC from China. (A) Monthly

variation of fraction-fossil BC from emission inventory for 2008-2010. Winter mean is the

average of Dec, Jan, Feb and Mar, which is the main season for BC emissions (45±1% of annual

BC emissions) and corresponds to the period of the East Asia 14

C-BC field campaigns. (B)

Comparison between EI and 14

C-based sourcing of fraction-fossil BC for the winter months. The

horizontal bars for the three KCOG samples taken during Dec – late Jan indicate the 7-day

sampling durations, while other data points have sampling durations of about one day.

12

Table S1. Mass and isotope results for black carbon aerosols at the five sampling sites

Sample Date Hours Volume (m3) EC*

(g m-3

)

δ13

C

(‰)

Δ14

C

(‰)

Shanghai 1 2010/1/19-20 23 137 1.8 -25.5 -859.0±-9.7

Shanghai 2 2010/1/20-21 23 137 4.0 -25.8 -802.5±-7.7

Shanghai 3 2010/1/21-22 23 137 1.6 -24.7 -842.5±-8.5

Shanghai 4 2010/1/22-23 23 137 2.4 -25.8 -758.3±-6.5

Shanghai 5 2010/1/23-24 23 137 2.9 -25.2 -810.7±-10.6

Shanghai 6 2010/1/24-25 23 137 2.0 -25.4 -752.6±-6.6

Shanghai 7 2010/1/25-26 23 137 1.8 -25.0 -825.7±-13.2

Beijing 1 2010/2/6-7 23 137 1.5 -25.0 -743.1±-14.1

Beijing 2 2010/2/8-9 23 137 3.0 -24.9 -787.2±-9.9

Beijing 3 2010/2/9-10 23 137 2.5 -25.1 -810.0±-11.0

Beijing 4 2010/2/10-11 23 137 1.9 -24.2 -842.3±-11.1

Beijing 5 2010/2/11-12 23 137 2.0 -24.5 -877.4±-10.7

Beijing 6 2010/2/12-13 23 137 2.0 -25.0 -815.9±-7.5

Xiamen 1 2009/12/11-12 23 137 2.2 -25.3 -891.8±-12.3

Xiamen 2 2009/12/12-13 23 137 2.0 -25.3 -882.5±-11.9

Xiamen 3 2009/12/13-14 23 137 1.8 -25.0 -821.5±-12.8

Xiamen 4 2009/12/14-15 23 137 1.9 -24.6 -875.2±-16.0

Xiamen 5 2009/12/15-16 23 137 1.6 -24.9 -822.9±-14.8

SCCO 1 2009/12/24-25 23 137 2.3 -25.6 -749.0±-6.8

SCCO 2 2009/12/25-26 23 137 1.1 NA† -704.8±-5.2

SCCO 3 2009/12/26-27 23 137 1.4 -24.6 -691.6±-6.7

13

SCCO 4 2009/12/27-29 47 281 0.8 -25.0 -800.1±-10.7

SCCO 5 2009/12/29-31 71 402 1.3 -25.8 -791.8±-8.3

SCCO 6 2010/1/3-4 23 137 1.7 -25.3 -756.5±-6.8

SCCO 7 2010/1/4-5 23 137 1.6 -25.2 -783.3±-8.3

KCOG 1 2011/3/8-9 18 530 0.5 -25.2 -683.8±-5.4

KCOG 2 2011/3/10-11 11 337 0.8 -25.8 -709.9±-9.2

KCOG 3 2011/3/11-11 9 275 1.0 -25.3 -734.8±-5.8

KCOG 4 2011/3/11-12 11 327 0.6 -27.7 -822.0±-8.7

KCOG 5 2011/3/14-14 14 410 0.5 -27.6 -761.7±-7.3

KCOG 6 2011-3/14-15 8 243 0.5 -25.7 -704.1±-9.4

KCOG 7 2011-3/16-16 9 267 0.5 -27.2 -821.0±-11.8

KCOG 8 2011-3/19-19 12 365 0.4 -27.6 -802.9±-7.7

KCOG 9 2011-3/21-22 9 267 0.6 -26.4 -757.0±-7.4

KCOG w1 2010/12/15-22 7 days 164 1.6 -24.8 -627.3±-3.5

KCOG w2 2010/12/23-30 7 days 158 1.1 -24.8 -653.3±-4.5

KCOG w3 2011/1/20-27 7 days 158 1.1 -23.7 -640.9±-4.3

*The reported 14

C error is the larger of the internal error (statistical error calculated using the

number of counts measured from each AMS sample) and external error (error calculated from the

reproducibility of individual analyses of a single sample) (17). Error for the 13

C data is less than

0.2‰. The uncertainty for EC concentrations based on triplicate analyses of seven samples is

3.7%.

†Not available. Sample lost during processing.

14

Table S2. Overview of fraction fossil black carbon and total black carbon estimations from

bottom-up emission inventories for China, India, and the whole World. Except noted, median

value of each inventory model output is shown

Literature Fossil BC (%) Fossil BC (Gg) Total BC (Gg) Year

China

(18) 56 751 1342 1995

(19) 71 1057 1489 1996

(4) 66 1004 1524 1996

(20) 72 1079 1499 2000

(21) 57 628 1093 2000

(22) 45 603 1345 2000

(4) 59 741 1263 2000

(23) 61 720 1180 2000

(24) 64 1090 1710 2001

(22) 52 707 1366 2005

(23) 61 920 1510 2005

(4) 59 1057 1787 2008

(25) 64 1066 1719 2008

(25) 63 1166 1881 2009

(4) 61 1122 1850 2010

Average 61 914 1504

1 SD 7 199 244

India

(19) 30 180 597 1996

(4) 41 293 718 1996

(26, 27) 29 100 350 1996-1997

15

(28) 12 59 509 1999*

(28) 45 370 820 1999†

(21) 23 179 795 2000

(4) 39 297 753 2000

(4) 44 429 979 2008

Average 33 238 690

1 SD 12 130 198

Global

(19) 38 3021 7951 1996

(29) 41 1968 4800 2000

Average 40 2495 6376

1 SD 2 745 2228

* Low value of fossil fuel combustion.

†High value of fossil fuel combustion.

16

Table S3. Consumption of wood (residential fuel and forest fire), and freshly-produced biomass

(residential fuel, agricultural open burning and grassland fire) in China

Fuel type Tg burned Literature

Wood consumption

Residential fuel

171 (24, 30)

136 (20)

Forest fire 25 (5)

Wood minimum 161

Wood maximum 196

Wood average 179 (27%)

Freshly-produced biomass (grass)

Residential fuel

291 (20)

305 (24, 30)

Open burning

110 (5)

158 (20)

Grassland fire 52 (5)

Freshly-produced biomass minimum 453

Freshly-produced biomass maximum 515

Freshly-produced biomass average 484 (73%)

17

Table S4. Interannual stability of the EC/TC ratio of the TOT-Sunset OCEC analyses

Time EC/TC ratio*

Klouda et al.,

2005(31)

0.28±0.02

Nov-06 0.29±0.05

May-09 0.29±0.07

Aug-09 0.31±0.04

Aug-12 0.28±0.06

*average ± 2 x standard deviation

18

Table S5. Sensitivity analysis of putative inadvertent inclusion of instrument-pyrolyzed carbon

(PyrC) in the EC isolate for the computed fraction fossil (ffossil) of the “true” EC for the

GoPoEx11 campaign samples

Fraction of PyrC in

the EC isolate (%)

“true” EC

ffossil (%)

1 SD (%)

0 77 6

5 79 5

10 81 5

15 84 5

20 86 5

25 89 5