Supplemental Information
Characterization of single particles in urban atmospheric aerosols
over Nagoya based on mass spectral analysis
Toshiyuki Mihara and Michihiro Mochida*
Department of Earth and Environmental Sciences, Graduate School of Environmental Studies, Nagoya
University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
*Corresponding author phone/fax: +81-52-788-6157; e-mail: mochida.michihiro@g.mbox.nagoyau.ac.jp
Number of pages: 30
Number of figures: 16
1
S1. Size cutoff of aerosol particles in the PM1 cyclone
A portion of the ~1 m particles may have been lost in the PM1 cyclone. The cyclone was placed
in the laboratory, and the cutoff profiles on a dry-diameter basis could have been affected by possible
hydroscopic growth depending on the relative humidity of the sampled air and the degree of heat
exchange between the sampled air and the tubing. This may have resulted in the loss of particles
detectable by the HR-ToF-AMS, although the effect can be reduced if the particle density is greater than
unity. We did not account for this effect because the cutoff profile is not critical for the analyses of this
study.
S2. Evaluation of the collection efficiency of HR-ToF-AMS
To assess the collection efficiency (CE) of HR-ToF-AMS, the mass concentrations of nonrefractory species measured in the MS mode of the HR-ToF-AMS during the ambient aerosol
measurement were compared to those estimated from the scanning mobility particle sizer (SMPS) data.
The number concentrations from the SMPS were converted to mass concentrations based on the particle
densities calculated based on the assumption that the particles were composed of organics, ammonium
sulfate, and ammonium nitrate. The density of organics (org) was estimated based on the following
relationship:
ρorg = 1000 [(12 + 1(H:C) + 16(O:C)]/[7.0 + 5.0(H:C) + 4.15(O:C)]
2
(1)
as described by Kuwata et al. (2011). The mass concentrations of organics, sulfate, and nitrate from the
HR-ToF-AMS data were used to calculate the particle densities. For comparison, we neglected the
possible disagreement between the size range of the SMPS measurement of the dry mobility diameter
(72.3–649.4 nm) and that of the AMS measurement, the latter of which may be primarily determined by
the separation of particles using the PM1 cyclone prior to the dehumidification of the aerosol and by the
transmission of particles in the aerodynamic lenses of the HR-ToF-AMS after the dehumidification. The
scatter plots of AMS- and SMPS-derived particle mass/volume concentrations are presented in Figure
S1. Although the CE values based on the slopes of the regression lines for the mass and volume
concentrations were low (0.34 and 0.24, respectively), the correlation coefficients between the AMSand SMPS-derived concentrations were very high both on mass and volume bases (0.97 and 0.98,
respectively). These high correlations strongly suggest the stability of the CE throughout the
observations.
After the observations of the atmospheric aerosols, the ammonium-sulfate particles generated by
atomization were introduced into the HR-ToF-AMS and the SMPS (the RH monitored downstream of
the prehumidifier: 85.0 ± 0.2% (mean ± std)). The CE of ammonium sulfate in this study (0.40) was
lower than that at approximately 85% RH in the work of Matthew et al. (0.6). A decrease in RH at the
inlet in the HR-ToF-AMS is a possible cause of the low CE. The temperature of the HR-ToF-AMS inlet
shell (mean ± std: 24.9 ± 0.2 °C) was higher than that of the RH sensor and its housing (mean ± std: 23.1
± 0.2 and 21.8 ± 0.2 °C, respectively), which may have resulted in the decrease in RH (decrease of 8%
3
and 13% depending on the temperature applied to the air of which the RH was measured) and may have
lowered the CE. This phenomenon is supported by the report by Matthew et al. (2008), in which the CE
of ammonium sulfate at approximately 70% RH was approximately 0.4. Similarly, if the RH of the
atmospheric aerosol samples was approximately 70%–80%, the CE of atmospheric particles may have
been lower than that at 85% RH. Moreover, an imperfect alignment of the aerodynamic lens of the HRToF-AMS, resulting from an incomplete alignment procedure, is not completely ruled out. Other
possible factors affecting the CE are the presence of refractory species (e.g., elemental carbon),
differences in the particle size range of HR-ToF-AMS and SMPS measurements, and particle
asphericity. The low CE is not discussed further in this study because the absolute mass concentrations
are not the main point of discussion.
S3. Efficiency of the detection of aerosol mass based on single-particle analysis
Figures S2 and S3 present the averages of the mass concentrations and the size distributions
measured in mass spectral (MS) mode, PToF mode, brute-force single particle (BFSP) mode without a
threshold, and BFSP mode with a threshold (mean + 11 std) for the entire study period. Here, the
concentration from the BFSP mode with no threshold was calculated from the summation of all BFSP
spectra in each diameter bin regardless of the particle detection, whereas that from the BFSP mode with
the threshold (mean + 11 std) was calculated using the method described in section 2.2. Note that the
threshold was corrected using the air beam signal and the sample flow rate (see section 2.2). As shown
4
in Figure S3a, the size distribution from the BFSP mode with no threshold is comparable to that from
the PToF mode, as expected. The mean concentration from the BFSP mode with the threshold (mean +
11 std) is one-third of that from the PToF mode (Figure S2). Comparison of the size distribution from
the BFSP mode with the threshold (mean + 11 std) with that from the PToF mode (Figure S3) indicates
a low detection efficiency of small particles; the ratios of the organic masses from the BFSP mode with
the threshold (mean + 11 std) to those from the PToF mode for size ranges of 100–200, 200–400, 400–
600, 600–800, and 800–1,000 nm were, respectively, 0.14, 0.39, 0.61, 0.60, and 0.32.
S4. Determination of the volume equivalent diameter of single particles
The volume equivalent diameter dve of a single particle was calculated by dividing dva by the
single-particle density estimated with the following assumptions: (1) sulfate, nitrate, and chloride were
fully neutralized with ammonium; (2) the particles were composed of ammonium sulfate, ammonium
nitrate, ammonium chloride and organics; and (3) the particles were spheres. The assumed densities of
ammonium sulfate, ammonium nitrate, and ammonium chloride were 1.770, 1.720, and 1.519 g cm−3,
respectively. The densities of the organics in the single particles were estimated from O/C and H/C
based on the relationship in Kuwata et al. (2012) (section S2). Here, O/C was calculated from f44 as
described in section 3.2, and H/C was calculated based on the relationship between O/C and H/C from
the MS mode data in this study (H/C = −0.823 × O/C + 1.852, r: 0.96). The negative values of O/C were
substituted by zero.
5
S5. Number-based detection efficiency of single particles
Figure S4 presents the scatter plots of the particle number concentrations corresponding to the
detected single particles versus the SMPS-derived particle number concentrations. To calculate the
particle number concentrations corresponding to the detected single particles, the duty cycles of the
chopper (0.02) and data transfer and saving (0.018) were applied. The slope of the regression line
constrained to the origin of the particle number concentrations based on the detected single particles
versus the SMPS-derived particle number concentrations were defined as the single-particle detection
efficiency. The detection efficiency for the size range of the SMPS measurements (72.3 nm≤dve≤694
nm) was 0.053. The efficiency for particles larger and smaller than the median diameter (257 nm) of the
single particles in the range of 72.3 nm≤dve≤694 nm were, respectively, 0.51 and 0.026, indicating a
strong size-dependence of the detection efficiency. The correlation coefficients between the particle
number concentrations based on the detected single particles and the SMPS-derived particle number
concentrations were high (>0.85) for sizes of 72.3 nm≤dve≤694 nm, 72.3 nm≤dve<257 nm and 257
nm<dve≤694 nm.
S6. Evaluation of the single-particle analysis using reference particles
To evaluate the method for analyzing single particles in this study, reference particles generated
by the atomization of an aqueous solution of a mixture of ammonium sulfate and adipic acid (1:1, w/w,
6
hereafter referred to as AS/AD) were introduced into the HR-ToF-AMS. Figure S5 presents the
relationships between the measured f1,3-5 of AS/AD and (a) their single-particle diameter and (c) Forg.
Different from the results for ambient aerosols (Figure 5c and Figure 8a), neither a bimodal distribution
of particle counts in the f1,3-5 axis, nor a broad distribution of low f1,3-5 and high Forg to high f1,3-5 and
low Forg were observed (Figure S5c). If AS/AD particles were assumed to be fully internally mixed, the
distributions in Figure S5 provide guides for the uncertainties of Forg and f1,3-5.
S7. Determination of the number of factors in the positive matrix factorization analysis
A three-factor solution of the positive matrix factorization (PMF) for the mass spectra of organics
from the MS mode was used for the analysis of single particles. Figure S6a presents the mass spectra of
the three factors, which are named semi-volatile oxygenated organic aerosol (SV-OOA), low-volatility
oxygenated organic aerosol (LV-OOA), and hydrocarbon-like organic aerosol (HOA). A four-factor
solution was not adopted because its fourth factor, whose mass spectrum was dominated by the signal at
m/z 29, could not be reasonably assigned to a specific type of organic aerosol. A downweighting of m/z
29, as reported by Mohr et al. (2011), was not performed. Regarding three-factor solutions, the values of
Q/Qexpected and the mass fractions of PMF factors do not change substantially by changing the fPeaks
and seed values, indicating the appropriate representativeness of the solution used (fPeak: 0, seed: 0)
(Figure S7).
7
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Trajectory)
Model
access
via
NOAA
ARL
READY
Website
(http://ready.arl.noaa.gov/HYSPLIT.php). NOAA Air Resources Laboratory, Silver Spring, MD.
Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., Prevot, A. S. H., Zhang, Q., Kroll, J. H., DeCarlo,
P. F., Allan, J. D., Coe, H., Ng, N. L., Aiken, A. C., Docherty, K. S., Ulbrich, I. M., Grieshop, A. P.,
Robinson, A. L., Duplissy, J., Smith, J. D., Wilson, K. R., Lanz, V. A., Hueglin, C., Sun, Y. L.,
Tian, J., Laaksonen, A., Raatikainen, T., Rautiainen, J., Vaattovaara, P., Ehn, M., Kulmala, M.,
Tomlinson, J. M., Collins, D. R., Cubison, M. J., Dunlea, E. J., Huffman, J. A., Onasch, T. B.,
Alfarra, M. R., Williams, P. I., Bower, K., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S.,
Weimer, S., Demerjian, K., Salcedo, D., Cottrell, L., Griffin, R., Takami, A., Miyoshi, T.,
Hatakeyama, S., Shimono, A., Sun, J. Y., Zhang, Y. M., Dzepina, K., Kimmel, J. R., Sueper, D.,
Jayne, J. T., Herndon, S. C., Trimborn, A. M., Williams, L. R., Wood, E. C., Middlebrook, A. M.,
Kolb, C. E., Baltensperger, U., Worsnop, D. (2009). R. Evolution of Organic Aerosols in the
Atmosphere. Science (New York, N.Y.) 326, 1525–9.
Huffman, J. A., Docherty, K. S., Aiken, A. C., Cubison, M. J., Ulbrich, I. M., DeCarlo, P. F., Super, D.,
Jayne, J. T., Worsnop, D. R., Ziemann, P. J., Jimenez, J. L. (2009). Chemically-resolved aerosol
volatility measurements from two megacity field studies. Atmos. Chem. Phys. 9, 7161–7182.
8
Kuwata, M., Zorn, S. R., Martin, S. T. (2011). Using elemental ratios to predict density of organic
material composed of carbon, hydrogen, and oxygen. Environ. Sci. Technol., 46 (22), 787–794.
Matthew, B. M., Middlebrook, A. M., Onasch, T. B. (2008). Aerosol Science and Technology 42, 884–
898.
Ministry of the Environment, Government of Japan (2012) Report on the State of Air Pollution Data,
Japan, 2010; Gyousei Co., Ltd, Japan.
Mohr, C., Richter, R., DeCarlo, P. F., Prevot, A. S. H., Baltensperger, U. (2011). Spatial variation of
chemical composition and sources of submicron aerosol in Zurich during wintertime using mobile
aerosol mass spectrometer data. Atmos. Chem. Phys. 11, 7465–7482.
Rolph, G. D. (2010). Real–time Environmental Applications and Display sYstem (READY) Website
(http://ready.arl.noaa.gov). NOAA Air Resources Laboratory, Silver Spring, MD.
Takegawa, N., Miyakawa, T., Kondo, Y., Jimenez, J. L., Zhang, Q., Worsnop, D. R., Fukuda, M. (2006).
Seasonal and diurnal variations of submicron organic aerosol in Tokyo observed using the Aerodyne
aerosol mass spectrometer. J. Geophys. Res., 111, D11206, doi: 10.1029/2005JD006515.
9
5
4
3
2
Slope: 0.34
r: 0.97
0
5
10
15
3.0
(b)
2.5
2.0
1.5
1.0
0.5
Slope: 0.24
r: 0.98
0.0
0
20
-3
2
4
6
8
10
12
14
3
Mass concentration (SMPS) (g cm )
FIG. S1.
-3
6
0
3
(a)
1
3.5
Volume concentration (AMS) (m cm )
7
-3
Mass concentration (AMS) (g cm )
Figures
-3
Volume concentration (SMPS) (m cm )
Scatter plots of the (a) mass and (b) volume concentrations derived from the AMS versus
those from the SMPS with a time resolution of 10 min.
10
Mass fraction Mass concentration
-3
(g m )
FIG. S2.
2.0
1.5
Organics
Ammonium
Sulfate
Chloride
Nitrate
(a)
1.0
0.5
0.0
1.0
0.8
0.6
0.4
0.2
0.0
(b)
MS
PToF
BFSP
BFSP
Threshold
No
threshold Mean
+ 11 std
(a) The mass concentrations and (b) mass fractions measured in MS mode, PToF mode,
BFSP mode without a threshold, and BFSP mode with the threshold of mean + 11 std.
11
FIG. S3.
The mass-size distributions of organics, sulfate, nitrate, ammonium, and chloride from (a)
the PToF mode (solid lines), the BFSP mode without a threshold (dashed lines), and (b) the
BFSP mode with a threshold of mean + 11 std.
12
(a)
300
250
200
150
100
50
Slope: 0.053
r: 0.91
0
2000
4000
6000
-3
Count of single particles per volume (cm )
0
(b)
200
150
100
50
Slope: 0.51
r: 0.89
0
0
50
100
150
200
250
120
(c)
100
80
60
40
20
Slope: 0.026
r: 0.85
0
0
1000
2000
3000
4000
-3
Number concentration (cm )
13
5000
FIG. S4.
Scatter plots of counts of single particles per volume for (a) 72.3 nm≤dve≤694 nm, (b) 257
nm<dve≤694 nm, and (c) 72.3 nm≤dve<257 nm versus SMPS-derived number concentrations.
A diameter of 257 nm is the median diameter of the detected single particles in the range of
72.3 nm≤dve≤694 nm.
14
f1,3-5
0
1.0
0.8
0.6
0.4
0.2
0.0
Particle count
10
20
30
0
40
(a)
(c)
0.8 (b)
0.6
0.4
0.2
0.0
(d)
100
250
500
750 1000
dva (nm)
FIG. S5.
0.0
5
Particle count
10 15 20
0.4
0.8
25
1.2
Forg
(a) Size-resolved histograms of f1,3-5 and (c) the 2D histogram of single particles with axes
of Forg and f1,3-5 generated from an aqueous solution of adipic acid and ammonium sulfate;
(b) the contour plots generated from (a); (d) the contour plots generated from (c). The bars
represent the range of Forg and f1,3-5 within their standard deviation.
15
(a)
-3
Mass concentration (g m )
factor 1
0.10
Relative intensity
0.05
0.00
0.2
factor 2
0.1
0.0
0.10
factor 3
0.05
0.00
20
40
60
m/z
80
100
factor 1
-3
Mass concentration (g m )
Relative intensity
0.00
0.2
factor 2
0.1
0.0
0.10
factor 3
0.05
0.00
0.2
factor 4
0.1
0.0
FIG. S6.
factor 1
factor 2
factor 3
2010/11/02
0.05
60
m/z
(b)
2010/11/04
Date
0.10
40
1.2
0.8
0.4
0.0
1.5
1.0
0.5
0.0
120
(c)
20
1.5
1.0
0.5
0.0
80
100
120
1.6
1.2
0.8
0.4
0.0
1.2
0.8
0.4
0.0
1.5
1.0
0.5
0.0
0.8
0.6
0.4
0.2
0.0
(d)
factor 1
factor 2
factor 3
factor 4
2010/11/02
2010/11/04
Date
The mass spectra of the organic factors derived from the PMF and the time series of the
mass concentrations of the factors. The fPeak values used in the analysis with three and four
factors were 0. The seed values used in the analysis with three and four factors were 0 and 3,
respectively.
16
3.404
3.403
3.401
3.400
3.399
3.398
3.397
Factor1
Factor2
0.8
0.6
0.4
0.2
(d)
(c) 3.3980
Q for P of 3
Solution used
1
0.8
0.6
0.4
0
0.2
1.0
-0.2
0.5
-0.4
0.0
fPeak
-0.6
-0.5
-1
0.0
-1.0
fPeak
Residual
Factor3
Factor1
Factor2
1.0
Mass fraction
3.3975
Q/Qexpected
Residual
Factor3
1.0
3.402
Mass fraction
Q/Qexpected
(b)
Q for P of 3
Solution used
-0.8
(a)
3.3970
3.3965
0.8
0.6
0.4
0.2
3.3960
0.0
0
2
4
6
8
0
10
1
2
3
4
Seed
(e)
3.066
Residual
Factor3
7
8
Factor1
Factor4
9 10
Factor2
1.0
3.064
Mass fraction
Q/Qexpected
(f)
Q for P of 4
Solution used
5 6
Seed
3.062
3.060
3.058
0.8
0.6
0.4
0.2
Q for P of 4
Solution used
Residual
Factor3
1
0.8
0.6
0.4
0
0.2
fPeak
Factor1
Factor4
Factor2
1.0
Mass fraction
3.0570
-0.2
1.0
-0.4
0.5
-0.6
0.0
fPeak
(h)
(g) 3.0572
Q/Qexpected
-0.5
-0.8
-1.0
-1
0.0
3.0568
3.0566
3.0564
0.8
0.6
0.4
0.2
3.0562
0.0
0
2
4
6
8
0
10
Seed
17
1
2
3
4
5 6
Seed
7
8
9 10
FIG. S7.
Evaluation of (a-d) three-factor solutions and (e-h) four-factor solutions from the PMF
analysis. The fPeak values used in the analysis with three and four factors were 0. The seed
values used in the analysis with three and four factors were 0 and 3, respectively.
18
dva (nm)
0
100 250 500
1000
2000
4000
Organics
Baseline region
Baseline value
-3
Mass concentration (g m )
5000
4500
4000
3500
3000
2500
0
FIG. S8.
1000
2000
3000
4000
5000
Particle time-of-flight (s)
6000
The averaged time-resolved mass concentrations of organics in BFSP mode without a
threshold for the entire study period. The baseline signals were not subtracted. The baseline
region and the baseline level are shown by the shaded area in the gray and a red line,
respectively. The detection of VOCs by the AMS is evident from the peak of signals from
organics prior to the detection of aerosol components. Signals from VOCs and other gaseous
species in the studied aerosols may have contributed to a substantial baseline level that
corresponds, on average, to 90% of the signals from organics at dva of 500 nm.
19
3500
Signal intensity (bit*ns)
3000
2500
2000
1500
1000
500
0
100
2
3
4
5
6
7
8
9
1000
2
3
4
5
dva (nm)
FIG. S9.
Example of a single-particle event measured in BFSP mode. The signal intensity is the sum
of the particle-related signals. The range of the particle diameter shaded in grey is the
baseline region used to calculate the threshold.
20
Relative signal intensity
normarized by the value of maximum signal
1.0
0.8
0.6
0.4
0.2
0.0
0
500
1000
1500
2000
Particle time-of-flight (s) from the maximum peak position
FIG. S10. The average of the relative signal intensity of the single-particle events. The signal intensity
was normalized before averaging. The time bin range for the quantification of the chemical
components is shaded in gray (6 time bins including the bin at the peak position and
additional 1 and 4 bins before and after the peak bin).
21
FIG. S11. Five-day backward air mass trajectories with start times with three-hour intervals from 1200
JST October 31 to 1500 JST November 5 calculated using the HYSPLIT model (Draxler
and Rolph, 2010; Rolph, 2010). The trajectories were calculated from (a) 1,000 m and (b)
2,000 m above mean ground level over the measurement site. The colors indicate the date of
the start times: Black, October 31; grey, November 1; blue, November 2; light green,
November 3; green, November 4; and red, November 5.
22
FIG. S12. Time series of (a) the mass concentrations and (b) the mass fractions of organics, sulfate,
nitrate, ammonium, and chloride measured in MS mode; time series of (c) the
concentrations of gaseous components (SO2, NO, NO2, NOx, Ox, and CO) and (d) the
meteorological conditions (precipitation, solar radiation, wind speed, humidity, and
23
temperature). The gaseous components were observed at the air quality monitoring station in
Nagoya (located 2.6 km north of our building; Ministry of the Environment, Government of
Japan) (Ministry of the Environment, Government of Japan, 2012). The meteorological
conditions were observed at the Local Meteorological Observatory (located 2.0 km northnorthwest
of
our
building;
http://www.data.jma.go.jp/obd/stats/etm/).
24
Japan
Meteorological
Agency;
20
15
f44
10
5
0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
f1,3-5
FIG. S13. Scatter plots of f44 versus f1,3-5 from the MS mode data with a time resolution of 10 min.
25
Particle count
4
8
12
0
(b)
(c)
0.4
C
0.2
B
0.0
A
0.4
28, 2100-0000
0.6
C
0.2
B
0.0
A
29, 0000-0300
0.6
f1,3-5
0.4
C
0.2
0.0
A
B
30, 0300-0600
0.6
0.4
C
0.2
B
0.0
A
31, 0600-0900
0.6
0.4
C
0.2
B
0.0
A
32, 0900-1200
0.6
0.4
C
0.2
0.0
A
0.2 0.4 0.6 0.8 1.0 0 20 40 60 80 100
Forg
Particle count
B
200
27, 1800-2100
(a)
0.6
16
500 1000
dva (nm)
FIG. S14. (a) Three-hour snapshots of the 2D distributions of single particles with axes of Forg and
f1,3-5 during the time periods 27–32. (b) Three-hour snapshots of the single-particle counts
as a function of f1,3-5. (c) Three-hour snapshots of the 2D distributions of the single-particle
counts with axes of dva and f1,3-5. The contours and line colors in column (a) were generated
26
from the 2D plots with resolutions of 0.1 and 0.1 for Forg and f1,3-5, respectively. The
contours and line colors in column (c) were generated from the 2D plots with resolutions of
100.1 and 0.1 for dva and f1,3-5, respectively. The periods are from 1800 JST November 3 to
1200 JST November 4 of 2010. The red circles and red squares in column (a) show the
positions with maximum counts for periods 27 and 32, respectively. The black crosses in
column (a) show the positions with the maximum counts in each period. The markers A, B
and C show the peak positions with low f1,3-5 at <200 nm, with low f1,3-5 at 300 nm, and
with high f1,3-5 at 300 nm, respectively, at period 27.
27
FIG. S15. (a) The 2D distributions of the mass concentrations of organics with axes of Forg and f44. (b)
The mass distributions of organics (green triangles), sulfate (red circles), and nitrate (blue
squares). The results of the single Gaussian fitting for sulfate and nitrate and the two-mode
Gaussian fitting for organics are shown by solid lines. The dashed lines show the individual
modes from the two-mode fitting of the mass distribution of organics. (c) The correlation
coefficients of the mass concentration of organics at each f1,3-5 bin versus the mass
concentrations of the PMF factors named HOA (green), SV-OOA (yellow), and
LV-OOA (pink). The estimated values of f44, O/C, and org, calculated from f44 as explained
in the main text, are shown in the right axes.
28
0
Particle count
50
100 150
200
40 (a)
20
f44
0
-20
40 (b)
20
0
-20
0.0
0.4
0.8
1.2
Forg
FIG. S16. Size-resolved distributions of single particles on the f44 axis presented as (a) a 2D histogram
and (b) a contour plot generated from (a).
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