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Supporting information for “Chemical composition, sources and aging process of submicron aerosols in Beijing: contrast between summer and winter”
3
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Weiwei Hu1*, Min Hu1, Wei Hu1, Jose L. Jimenez2,3, Bin Yuan1*,**, Wentai Chen1, Ming
Wang1, Yusheng Wu1, Chen Chen1, Zhibin Wang1***, Jianfei Peng1, Limin Zeng1, Min Shao1
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1 State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of
Environmental Sciences and Engineering, Peking University, Beijing 100871, China
2 Cooperative Institute for Research in the Environmental Sciences (CIRES)
3 Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO, USA
*now at: Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder,
CO 80309, USA
**now at: Earth System Research Laboratory, Chemical Sciences Division, Boulder, Colorado 80305,
USA
***now at: Multiphase Chemistry Department, Max Planck Institute for Chemistry, 55128,
Mainz, Germany
Correspondence to: minhu@pku.edu.cn
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1. Parameters of AMS instruments
Table S1 Detection limits of main components of aerosol detected by AMS in this study. Units
are ng m-3. Detection limit of each species was determined by three times the standard deviations
of detected signal of this species under particle-free condition.
Campaign
Summer
Winter
24
a:
Mode
SO42-
NO3-
NH4+
Cl-
OAa
V
28
13
110
18
84
W
23
15
92
27
69
V
56
33
58
15
90
W
24
15
52
100
110
OA=organic aerosol
25
26
27
28
29
Figure S1 Ion efficiency (IE) and IE/air beam (AB) of AMS in this study. The dashed line is the
15% uncertainty of the average IE/AB.
30
31
32
33
34
35
36
Figure S2 Time series of chemical composition based CE in the (a) winter and (b) summer
studies. These CE values were calculated based on the method reported in Middlebrook et al.
[2012]
37
2. Comparisons between AMS and other instruments
38
39
40
41
42
43
44
Figure S3 Time series and scatter plots of inorganic aerosols (nitrate, sulfate, ammonium and
chloride) between AMS and custom-built gas aerosol collector (GAC) instrument in summer.
The inorganic aerosol in GAC system can be detected by ion chromatography. The detail
description of GAC can be obtained in Dong et al. [2012]. The orthogonal distance regression
method is used in the scatter plots. R represents Pearson’s R.
45
46
47
48
49
Figure S4 Time series and scatter plots of aerosol mass concentrations detected by AMS plus
BC vs SMPS. The orthogonal distance regression method (2-sided fit) was used here. The
aerosol density based on chemical composition of aerosols was used to convert SMPS volume
concentrations to be mass concentrations [DeCarlo et al., 2008].
50
51
52
53
54
55
56
57
58
Figure S5 Time series and scatter plot of aerosol mass concentrations detected by AMS plus BC
vs TEOM. The size cut of TEOM is PM2.5, which should be the main reason that lower
concentration in AMS+BC observed. The size cut off for BC is PM 2.5.
59
60
61
62
63
64
65
66
67
68
69
70
3. Determination of the PMF solution in Beijing summer and winter
Factor number from 1 to 10 and the different seeds (0-50) were selected to run in the model. A 4factor solution was selected for the final results in Beijing summer, 6-factor solution for Beijing
winter. In the winter study, six PMF OA factors are more-oxidized oxygenated OA (MO-OOA;
O/C =0.58), less-oxidized oxygenated OA (LO-OOA; O/C =0.47), cooking OA (COA; O/C
=0.14), biomass burning OA (BBOA; O/C =0.22), coal combustion OA (CCOA; O/C =0.16) and
hydrocarbon-like OA (HOA, O/C =0.15). In summer study, four PMF OA factors are MO-OOA
(O/C =0.82), LO-OOA (O/C =0.62), COA (O/C =0.17) and HOA (O/C =0.22). The
performances of spectra and time series of the four factors in summer and six factors in winter at
different Fpeak were also investigated. The detailed information on how to select PMF factors
can be found in Table S2- S5 and Figure S6-S10.
71
3.1 The 4-factor PMF solution determined in Beijing summer.
72
73
74
75
76
Figure S6 Diagnostics plots of PMF selection in Beijing summer (PartI). Very stable PMF
solution among different seed numbers (0-50) in summer study was also found, which suggests
the PMF solution is robust here.
77
78
79
80
81
Figure S7 Diagnostics plots of PMF selection in Beijing summer (PartII). The spectra and time
series of 4-factor solution at different Fpeak values.
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83
Table S2 Descriptions of PMF solutions obtained at Beijing summer
Factor
number
Fpeak
Seed
Q/Qexp
Solution Description
1
0
0
4.60
Too few factors, large residuals at time periods and
key m/z’s
2
0
0
3.03
Too few factors, large residuals at time periods and
key m/z’s
2.63
Too few factors (LV-OOA-, SV-OOA- and COAlike). The Q/Qexp at different seeds (0-50) are very
unstable. Factors are mixed to some extend based on
the time series and spectra.
2.39
Optimum choices for PMF factors (LV-OOA, SVOOA, HOA and COA). Time series and diurnal
variations of PMF factors are consistent with the
external tracers. The spectra of four factors are
consistent with the source spectra in AMS spectra
database.
3
4
84
85
0
0
0
0
5-10
0
0
2.29-2.07
4
-3 to 3
0
2.39-2.43
Factor split. Take 5 factor number solution as an
example, SV-OOA was split into two factors with
similar spectra, however, different time series. When
factor num. = 6, there is extra split factor from COA.
In FPEAK range from −1.0 to1.0, factor MS and time
series are nearly identical, with variations less than
1%. However, when the fpeak=-1.6, factor 3 and
factor 2 were exchanged in sequence. For extreme
low negative fpeaks, the mass in LV-OOA and HOA
spectra tends to be transferred to the other two
factors. For the high positive fpeaks, factors are quite
stable and show little differences.
86
87
88
89
90
91
92
Table S3 Correlation coefficients (Pearson’s R) of four OA factors in the summer of Beijing
with other gas and aerosol species and meteorology parameters. The fond with correlation
coefficient>0.6 was bold.
a
Species name
MO-OOAa
LO-OOAb
COAc
HOAd
RH
0.18
-0.15
0.10
0.43
Pressure
-0.18
-0.11
0.11
0.05
Temp
0.08
0.37
-0.18
-0.27
SO2
0.49
0.10
0.03
0.11
BC
0.54
0.54
0.25
0.85
NOx
0.10
0.13
0.48
0.73
NO2
0.20
0.30
-0.09
0.24
NO
0.03
-0.08
0.34
0.59
Ox
0.23
0.44
-0.22
-0.26
O3
0.15
0.32
-0.20
-0.43
CO2
0.21
0.09
0.10
0.59
CO
0.55
0.10
0.24
0.46
NO3-
0.82
0.49
0.06
0.68
SO42-
0.90
0.34
0.08
0.37
NH4+
0.93
0.42
0.06
0.54
Cl-
0.51
0.21
0.16
0.73
Toluene
0.43
0.47
0.42
0.79
Benzene
0.52
0.49
0.36
0.80
Acetone
0.52
0.73
0.12
0.43
Acetaldehyde
0.45
0.76
0.27
0.60
Isoprene
0.26
0.53
0.13
0.40
MVKe+MACRf
0.13
0.61
0.15
0.09
Monoterpene
-0.02
0.16
0.13
0.47
b
: MO-OOA= More-oxidized oxygenated OA; : LO-OOA=Less-oxidized oxygenated OA; c:
COA=Cooking OA; d: HOA=hydrocarbon-like OA. e: MVK=methyl vinyl ketone; f: MACR=
methacrolein; Both MACR and MVK are oxidation products from isoprene [Claeys et al., 2004].
93
3.2 The 6-factor PMF solution determined in Beijing winter.
94
95
96
97
98
99
100
Figure S8 Diagnostics plots of PMF selection in Beijing winter (PartI). The PMF solution in the
winter study show 2 solutions at seed 7 and 27 that cannot be resolved and three solutions at seed
20, 33 and 49 which are identical to each other however different with the rest solutions. We
found the time series and spectra of 5 PMF factors (LO-OOA-, MO-OOA-, CCOA-, BBOA- and
COA-like) in these three solutions at seed 20, 33 and 49 are similar to the PMF factors being
101
102
103
used in the manuscript, except the time series of HOA is similar to LO-OOA, indicating factor
mixing happened, as shown in Fig. S9.
104
105
106
Figure S9 Diagnostics plots of PMF selection in Beijing winter (PartII).
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109
110
111
Figure S10 Time series of different PMF factors at seed number of 20, 33 and 49 in the
winter study.
112
Table S4 Descriptions of PMF solutions obtained in the winter study of Beijing
Factor
Fpeak Seed
number
Q/Qexp
Solution Description
1
0
0
6.53
Too few factors, large residuals at time periods and key
m/z’s
2
0
0
4.13
Too few factors, large residuals at time periods and key
m/z’s. Q/Qexp decreases very fast (>15%).
3
0
0
3.14
Too few factors (MO-OOA-, CCOA- and COA-like).
Factors are mixed at the time series and spectra, e.g., the
CCOA looked like a factor may contain BBOA in the
spectrum.
4
0
0
2.76
Except LO-OOA, COA and CCOA, the characteristic of
the fourth factor is not clear. The fourth factor may be
mixed by CCOA and BBOA.
5
0
0
2.57
Except MO-OOA, COA and CCOA, the characteristics of
other two factors are not clear. It seems that HOA mixed
with BBOA, and BBOA mixed with LO-OOA.
6
0
0
2.292.07
Optimum choices for PMF factors (MO-OOA, LOOOA, HOA, COA, CCOA and BBOA). Time series
and diurnal variations of PMF factors are consistent
with the external tracers. The spectra of six factors are
consistent with the source spectra in AMS spectra
database.
7-10
0
0
2.372.20
Factors split, e.g., COA and CCOA
2.392.43
All the factors are identical when fpeak change from -1 to
0.6. When fpeak=-1, the mass of COA was added to LOOOA in the time series from November 30 to December 1,
however, the spectra of two factors do not change very
much.
6
113
-3 to
3
0
114
115
116
117
Table S5 Correlation coefficients (Pearson’s R) of six OA factors in the winter study with gas
and aerosol species, as well as metrology parameters. Note that 5 VOCs species (in bold text)
only contain the data from 16th to 22nd, December. The fond with correlation coefficients>0.7
was bold.
Species Name
122
HOAd
BBOAe
CCOAf
0.61
0.69
0.79
0.65
0.66
SO2
0.51
0.71
0.57
0.78
0.65
0.54
NOx
0.70
0.68
0.67
0.72
0.60
0.63
NO
0.64
0.55
0.69
0.64
0.59
0.64
RH
0.79
0.25
0.40
0.14
0.17
0.35
Temp
0.00
0.28
0.09
0.09
0.01
-0.18
CO
0.65
0.58
0.69
0.69
0.72
0.71
CO2
0.55
0.62
0.66
0.59
0.66
0.57
PM2.5
0.83
0.67
0.65
0.61
0.61
0.57
O3
-0.42
-0.43
-0.44
-0.43
-0.25
-0.33
NO2
0.69
0.81
0.63
0.73
0.55
0.48
Acetonitrile
0.85
0.55
0.63
0.85
0.91
0.70
Acetaldehyde 0.86
0.76
0.60
0.91
0.77
0.50
Acetone
0.86
0.68
0.59
0.89
0.87
0.63
Benzene
0.81
0.52
0.65
0.82
0.88
0.69
Toluene
0.81
0.61
0.69
0.87
0.83
0.62
2-
0.93
0.57
0.53
0.34
0.52
0.46
-
0.78
0.86
0.62
0.66
0.64
0.47
-
0.61
0.67
0.67
0.83
0.84
0.68
NH4+
0.83
0.79
0.65
0.65
0.68
0.53
Cl
121
COAc
0.72
NO3
a
MO-OOAb
BC
SO4
118
119
120
LO-OOAa
: MO-OOA= More-oxidized oxygenated OA; b: LO-OOA=Less-oxidized oxygenated OA; c:
COA=Cooking OA; d: HOA=hydrocarbon-like OA; e: BBOA=Biomass burning OA;
f
:CCOA=coal combustion OA.
123
124
125
126
127
Figure S11. Diurnal patterns of average mass concentrations of (a) OA, (b) sulfate, (c) chloride
(left) and Temperature (right), (d) NOx, (e) nitrate, (f) ammonium, (g) black carbon (left) and
wind speed (right) and (h) O3 and Ox (=NO2+O3).
128
4 The chemical structure of chloride in aerosol phase
129
For biomass burning aerosols, field and emission studies have shown that a large fraction of
130
KCl can exist in the fresh biomass burning plumes. As biomass burning plumes get aged, more S
131
and N containing species (e.g., KNO3 and K2SO4) in aerosol phase have been found [Li et al.,
132
2003; Yokelson et al., 2009]. NaCl and NH4Cl have also been reported to be important
133
components in the aerosols from direct biomass burning emissions [Lewis et al., 2009; Levin et
134
al., 2010]. Chloride existing in the form of KCl and NaCl in aerosol phase for coal combustion
135
sources has been reported [McNallan et al., 1981; Doshi et al., 2009].
136
Here, the chloride chemical structures from biomass burning and coal combustion in the
137
winter and summer studies were investigated based on the results from other field and chamber
138
studies discussed above. Time series of fitted signals of K+, Na+, and Cl+ ion from W-mode in
139
both studies are shown in Figure S12-S13. Cl+ showes good correlation with HCl+ (R=1.00 and
140
0.98 for the winter and summer studies, respectively), and accounts for 21% and 24% of HCl+
141
mass in winter and summer, respectively (Fig. S15). Cl+ and HCl+ are the two major ions for
142
chloride masses. Good correlation between Cl+ and HCl+ ions indicates the sources of these two
143
ions should be similar. Both K+ and Na+ show similar variations with Cl+ ion in the winter study.
144
The scatterplot between Na+ and Cl+ exhibits a good linear correlation (R=0.92, Fig S14). The
145
K+/Cl+ ratios varied significantly from the measured periods, which may be caused by instrument
146
break down after Dec. 12 that changed the effective ionization field or surface ionization after re-
147
pumping the instrument down. Na/Cl ratios changed slightly but less than K/Cl ratios. During the
148
Dec. 14-22 with the highest aerosol pollution periods in the winter study, strong correlation
149
between K+ and Cl+ (R=0.92) were seen. Clear enhancement of KCl+ and NaCl+ ions (R=0.51
150
between KCl+ and Cl+; R=0.82 between NaCl+ and Cl+) were also observed in this period,
151
although associated with very low signal intensities. Contrast to winter, no obvious correlation
152
between Na+ and Cl+, as well as K+ and Cl+ in summer were observed. NaCl+ and KCl+ signals
153
were below the detection limits in the summer study.
154
The good correlation of K+, Na+, KCl+, NaCl+ with Cl+ indicates the partial chloride
155
detected in AMS in the winter study exist as KCl and NaCl in the aerosol phase, consistent with
156
other chamber and field studies from biomass burning and coal combustion plumes[Li et al.,
157
2003; Doshi et al., 2009; Lewis et al., 2009; Yokelson et al., 2009]. In summer study, Cl was
158
supposed to exist as NH4Cl detected by AMS, which is consistent with its diurnal pattern does
159
appear to follow an inverse temperature trend (Fig. S11).
160
In addition to KCl and NaCl, the possibility that if chloride can exist as NH4Cl in aerosol
161
phase in Beijing winter was also investigated. The scatter plot between measured NH4 vs
162
predicted NH4 from SO4 and NO3, under the assumption that SO4 and NO3 species are fully
163
neutralized by NH4, was shown in Fig. S16. The measured NH4 exceeded the predicted NH4
164
from NO3 and SO4 by an average factor of 1.2. It has been reported that adjustment ratio change
165
of measured NH4 vs predicted NH4 with accounting for organosulfate (12% of measured SO4),
166
organic nitrate (10% of NO3) and amine (0.7% of OA mass) is around 10% [Doshi et al., 2009;
167
Docherty et al., 2011]. The excess NH4 here (>20%) cannot fully explained by the organic
168
influences to the measured inorganic species in AMS. The most likely explanation is that part of
169
the chloride exists as NH4Cl in the aerosol of the winter study. The time series of excess NH4
170
show similar variations to that of chloride and predicted NH4 from chloride only (NH4Cl
171
assumed), as displayed in Fig. S17.
172
To quantify the fraction of NH4Cl in total chloride, two scenarios were assumed. One is
173
assuming sulfate is fully neutralized in forms of (NH4)2SO4, the other one is assuming sulfate
174
exist as NH4HSO4 in aerosol phase. In the former (NH4)2SO4 scenario, excess NH4 can account
175
for around 50% of the predicted NH4 from chloride (Fig. S18a; Slope=0.52). If excess NH4 all
176
exist with chloride in aerosol phase, then around 50% of the chloride are calculated to be in form
177
of NH4Cl. The other half exists as other salts, e.g. NaCl and KCl. In the latter NH4HSO4
178
scenario, the excess NH4- can fully explained by predicted NH4 from chloride (Fig. S18b;
179
slope=0.98). Hence, the main chemical structure of chloride is NH4Cl under this scenarios. In
180
summary, a large fraction of chloride (~50-100%) can exist as NH4Cl in aerosol depending on
181
the chemical structure of SO4 ((NH4)2SO4 vs. NH4HSO4).
182
Chamber studies show that NH4Cl can be directly emitted from some types of biomass
183
burning, e.g., rice straw and palmetto burning [Levin et al., 2010]. There is also possibility that
184
sulfuric acid (H2SO4) or nitric acid (HNO3) can react with co-emitted KCl and NaCl aerosols in
185
biomass burning and coal combustion plumes, then release gas phase HCl. HCl can react with
186
NH3 and be re-condensed as NH4Cl, which is detected by AMS [Li et al., 2003; Yokelson et al.,
187
2009].
188
189
190
191
Figure S12 Time series of Na+, K+, NaCl+, KCl+ and Cl+ ion signals detected in AMS in the
winter study. No relative ion efficiency (RIE) was applied to all the ions shown here.
192
193
194
Figure S13 Time series of Na+, K+, and Cl+ ion signals detected in AMS in summer study. No
relative ion efficiency (RIE) was applied to all the ions shown here.
195
196
197
198
Figure S14 Scatter plots between Na+ and Cl+ ions (a) and K+ and Cl+ ions (b); ODR linear
curve fitting has been done in both plots. Scatter points are color-coded by date time of the
winter campaign.
199
200
201
202
203
204
205
Figure 15 Scatter plots between Cl+ and HCl+ in the (a) winter and (b) summer studies. IE and
RIE (1.3) were both applied.
206
207
208
Figure 16 Scatter plots between measured NH4 and predicted NH4. The predicted NH4 was
calculated by assuming NO3 and SO4 are fully neutralized by NH4.
209
210
211
Figure 17 Time series of measured chloride, predicted NH4 from chloride by assuming NH4Cl
existing in the aerosol phase, and differences between measured NH4 and predicated NH4
212
213
214
215
216
217
Figure S18 Scatter plots of excessed NH4 vs predicted NH4 from chloride only. The excessed
NH4 on the Y-axis is defined as the differences between measured NH4 and predicted NH4 from
NO3 and SO4 (a) by assuming full neutralization of NO3 and SO4 as NH4NO3 and (NH4)2SO4, or
(b) by assuming NO3 and SO4 exists as NH4NO3 and NH4HSO4.
218
219
220
Figure S19 Particle number size distributions measured by the SMPS in the winter campaign.
221
222
223
224
225
226
227
228
229
Figure S20 Ammonium balances of sub-micron aerosols in summer (a) and winter (b). The
scatter in (b) is color-coded by sulfate. Aerosols tend to be more acidic with higher sulfate
concentrations. The predicted NH4 was calculated assuming full neutralization of particulate
anions of NO3, SO4, and Cl in both studies. The linear orthogonal distance fit (2_sided fit) is
used here. R represents Pearson’s R.
230
231
232
Figure S21 Size distributions of main species in PM1. For comparison, the heights of inorganic
species were normalized to be the same as that of OA.
233
234
235
236
Figure S22 Size distributions of main species in PM1 and selected m/z in polluted, high-RH and
clean periods of the winter study.
237
238
239
240
Figure S23 comparison of (a-b) HOA and (c-d) COA Spectra between the summer and winter
studies in Beijing.
241
242
243
244
245
Figure S24 Scatter plots of abundant of C4H7+ vs C4H9+ in OA that is fC4H7+ vs fC4H9+ (Left), and
abundant of fC3H3O+ vs fC3H5O+ that is fC4H7+ vs fC4H9+ (Right).
246
247
248
Figure S25 Time series of C6H10O+ (at m/z 98), which has been proposed as a tracer of COA,
and its contributions from different PMF factors in summer
249
250
251
Figure S26 Time series of C6H10O+ (at m/z 98), which has been proposed as a tracer of COA,
and its contributions from different PMF factors in winter
252
253
254
255
256
257
258
259
260
Figure S27 Scatter plots of f44 vs f60 in the ambient OA of summer (a) and winter (b) of Beijing.
The brown triangle and the background level are adopted from Cubison et al. [2011]. BBOA
emissions and BBOA PMF factors are from He et al. [2010], Hu et al. [2013] and Aiken et al.
[2009]. The other primary POA factors from the winter study have also been added to Fig. S27b.
261
262
Table S6 Mass concentration of main species in sub-micron aerosol detected by AMS at
different sites. All units are µg m-3.
Observation sites
Sulfate
Nitrate
Ammonium
Chloride
OA
Citations
19
16.8
8.7
21.7
9
9.3
14.3
9.7
10.9
7.34
2.5
3.2
1.8
7
3.6
3.1
3.3
4.9
3.9
2.4
0.86
0.52
3.4
0.76
3.0
1.2
15
10.0
7.7
16.5
12.4
10.9
12.5
4.8
4.4
0.765
2.7
1.5
3.9
0.87
3.5
3.7
4.3
0.38
0.68
2.6
0.55
0.28
3.7
0.81
3.3
0.6
12
10.0
6.8
13.5
8
8.6
9.2
3.9
4.5
2.3
1
1.8
2.7
2.4
2
2.1
2.4
1.5
1.7
1.7
0.68
0.3
2.3
0.59
1.7
0.51
1
0.6
5.8
1
0.5
3.5
2.6
0.5
0.7
0
3.92
0.15
0.78
0.06
0.4
0.31
0.09
0.02
0.03
0.18
0.04
0
0
0
0.19
0.04
25
24.0
34.5
26.1
20
34.4
38.3
8.8
17.7
4.74
11.5
6.5
7
4.4
17.3
22
8.9
6
5.9
4.8
4.9
1.9
4.9
3
8.0
1.9
[Sun et al., 2010]
[Huang et al., 2010]
This study
This study
[Sun et al., 2012]
[Sun et al., 2013a]
[Zhang et al., 2014]
[Huang et al., 2012]
[He et al., 2011]
(Li et al., 2015)
(Xu et al., 2014)
[Zhang et al., 2007]
[Zhang et al., 2007]
[Zhang et al., 2007]
[Aiken et al., 2010]
[Zhang et al., 2007]
[Zhang et al., 2007]
[Zhang et al., 2007]
[Zhang et al., 2007]
[Zhang et al., 2007]
[Zhang et al., 2007]
[Zhang et al., 2007]
[Zhang et al., 2007]
[Zhang et al., 2007]
[Mohr et al., 2012]
[Zhang et al., 2007]
8.3
11.2
12.8
10.0
2
2.1
1.7
2
12.2
3.5
1.8
6.2
0.35
0.3
0.51
2.1
6.5
4.6
4.9
4.6
0.76
0.65
0.68
1.3
1.3
0.4
0.9
1.4
0.01
0.01
0.04
0.09
13.4
11.2
17.0
17.4
5.8
5.0
2.5
2.1
[Hu et al., 2013]
[Huang et al., 2011]
[Xiao et al., 2011]
[Gong et al., 2012]
[Zhang et al., 2007]
[Zhang et al., 2007]
[Zhang et al., 2007]
[Zhang et al., 2007]
Urban Sites
Beijing06 Sum,China
Beijing08 Sum,China
Beijing10 Win,China
Beijing11 Sum,China
Beijing11 Sum, China
Beijing11 Win,China
Beijing13 Win,China
Shanghai,China
Shenzhen,China
Hongkong, China
Lanzhou, China
Tokyo Sum,Japan
Tokyo Win,Japan
Pittsburgh,PA, USA
Mexico City06,Mexico
Mexico City03,Mexico
Riverside,CA, USA
Houston,TX, USA
NYCSum,NY, USA
NYCWin,NY, USA
Vancouver,Canada
Edinburgh,UK
Manchester Sum,UK
Manchester Win,UK
Barcelona,Spain
Mainz,DE,German
Downwind Sites
Changdao,China
Kaiping PRD,China
BackGarden,China
Heshan,China
NEAQSL1
NEAQSL2
Torch1,UK
Torch2,UK
263
264
265
266
267
268
Table S7 Comparison of elemental ratios calculated by previous Aiken method [Aiken et al.,
2007] and updated Canagaratna method [Canagaratna et al., 2015] in ambient OA and PMF
factors in this study.
Species
O/Cold
O/Cnew
Ambient OA
PMF factor:
HOA
COA
LO-OOA
MO-OOA
0.45
0.56
0.18
0.15
0.53
0.76
0.22
0.17
0.62
0.82
Ambient OA
PMF factor:
HOA
COA
BBOA
CCOA
LO-OOA
MO-OOA
0.26
0.12
0.11
0.18
0.13
0.39
0.50
Change (%)
H/Cold
Beijing summer
24
1.53
H/Cnew
Change (%)
1.61
5
1.62
1.67
1.38
1.24
1.78
1.80
1.45
1.24
10
8
5
0
0.32
22
13
17
8
Beijing winter
23
1.50
1.65
10
0.15
0.14
0.22
0.16
0.47
0.58
25
27
22
23
21
16
1.64
1.70
1.45
1.46
1.56
1.37
1.75
1.75
1.55
1.56
1.65
1.47
7
3
7
7
6
7
269
270
Table S8 Comparisons of source apportionment results of OA (OA factor fractions in total OA, %)
in the summer of Beijing in recent years.
Sources
apportionm
ent/Year
Filter
CMB
Filter CMB
EC tracer
method
AMSPMF
AMS-PMF
AMSPMF
ACSMPMF
2006
2006
2008
2011
2011
55a
41a
18
13
24
21.3
24+34
28.4c+3
7.3d
2000
2005
2006
Vehicle
emission
19.0
20.2
12.8
Biomass
burning
-
11.7
9.9
Coal
combustion
5.8
2.1
1.8
Cooking
-
24.5
23.8
SOA/Other
s
45.9 b
41.1b
51.4 b
Vegetative
detritus
1.5
0.3
0.3
-
Road dust
2.2
-
-
-
Cigarette
25.6
-
-
-
References
(1)
(2)
(2)
45
(3)
44 c
+15d
(4)
(5)
36a
64
(6)
271
a
272
References: (1) [Zheng et al., 2005]; (2) [Wang et al., 2009]; (3) [Lin et al., 2009]; (4) [Sun et al.,
2010]; (5) [Huang et al., 2010]; (6) This study; (7) [Sun et al., 2012]
273
274
275
: refer to total primary OA or HOA; b: Others; c: MO-OOA; d: LO-OOA;
(7)
276
277
Table S9 Comparisons of source apportionment results of OA (OA factor fractions in total OA, %) in
the winter of Beijing in recent years.
Sources
apportion
ment/Year
Filter
CMB
EC tracer
method
Filter
CMB
AMSPMF
ACSMPMF
AMSPMF
ACSMPMF
2000
2005
2007
2010
2011
2013
2013
17.1
17b
17 b
11 b
14 b
26.1
12.0
N/A
N/A
N/A
Vehicle
emission
10
Biomass
burning
15
Coal
combustio
n
14
17.2
24
33
15
19
Cooking
-
17.3
19
19
20
12
SOA/Othe
rs
52c
21.9c
18d+13e
31
18d+26e
55
Vegetative
detritus
-
0.5
-
Road dust
2
-
-
Cigarette
7
-
-
Reference
s
(1)
a
81
19
(2)
(3)
(4)
(5)
(6)
(7)
278
a
279
References: (1) [Zheng et al., 2005]; (2) [Lin et al., 2009]; (3) [Wang et al., 2009]; (4) This study;
(5) [Sun et al., 2013b]; (6) [Zhang et al., 2014]; (7) [Sun et al., 2014]
280
281
: refer to total primary OA; b: HOA; c: Others; d: MO-OOA; e: LO-OOA;
282
283
284
285
286
287
Figure S28 Probability density distribution of CO in the summer and winter study (a) and
zoomed in plot (b). The CO background (100 ppb in summer and 140 ppb in winter) was
determined by the lowest CO concentration observed in the summer and winter study,
respectively as shown in Fig. S28b.
288
289
290
291
Figure S29 Scatter plots of primary organic aerosols versus ΔCO. CO background =100 ppb in
summer and 140 ppb in winter were subtracted here. The linear orthogonal distance fit is used
here.
292
293
294
295
296
297
298
299
Figure S30 Times series of liquid water contents (LWC) in the aerosol phase during the (a)
winter and (b) summer study; LO-OOA from the winter study was also shown on the right axis
of upper figure. LWC was calculated based on ISORROPIAII model. In the winter study, four
species (sulfate, nitrate, ammonium and chloride) measured by AMS were input in the model.
Reverse and metastable mode were selected. In the summer study, eight species (sulfate, nitrate,
ammonium, chloride, sodium, potassium, calcium and magnesium) are used.
300
301
302
303
304
305
Figure S31 Average ratios of sulfur from sulfate vs total sulfur (sulfur from sulfate and SO2) as a
function of relative humidity in the summer and winter studies. The ratio on the Y axis is also
known as sulfate oxidation ratio (SOR).
306
307
308
309
310
Figure S32 Scatter plots of OOA vs Ox (a), sulfate vs Ox (b) and LO-OOA vs Ox (c) and MOOOA vs Ox (d) in the winter study. All the scatter points are color-coded by the ratio between
measured NH4 and predicted NH4. Predicted NH4 is calculated by assuming SO4, NO3 and
chloride are fully neutralized by NH4.
311
312
313
Figure S33 Scatter plots of MO-OOA or LO-OOA spectra in the winter study vs (a) MO-OOA
and (b) LO-OOA spectra in summer study.
314
315
316
317
Figure S34 Comparison between calculated kOH of OA aging in Beijing summer and Changdao
island.
318
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