ccn spectra, hygroscopicity, and droplet activation kinetics...
TRANSCRIPT
CCN Spectra, Hygroscopicity, and Droplet Activation
Kinetics of Secondary Organic Aerosol Resulting
from the 2010 Deepwater Horizon Oil Spill
Richard H. Moore,† Tomi Raatikainen,‡ Justin M. Langridge,¶,§ Roya Bahreini,¶,§
Charles A. Brock,¶ John S. Holloway,¶,§ Daniel A. Lack,¶,§ Ann M. Middlebrook,¶
Anne E. Perring,¶,§ Joshua P. Schwarz,¶,§ J. Ryan Spackman,¶,‖ and Athanasios
Nenes∗,†,‡
School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta,
Georgia, USA., School of Earth & Atmospheric Sciences, Georgia Institute of Technology,
Atlanta, Georgia, USA., Earth System Research Laboratory, National Oceanic and Atmospheric
Administration, Boulder, Colorado, USA., Cooperative Institute for Research in Environmental
Sciences, University of Colorado, Boulder, Colorado, USA., and Science and Technology
Corporation, Boulder, Colorado, USA.
E-mail: [email protected]
1
Abstract2
Secondary organic aerosol (SOA) resulting from the oxidation of organic species emitted3
by the Deepwater Horizon oil spill were sampled during two survey flights conducted by a Na-4
∗To whom correspondence should be addressed†School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.‡School of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.¶Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, Colorado, USA.§Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA.‖Science and Technology Corporation, Boulder, Colorado, USA.
1
tional Oceanic and Atmospheric Administration (NOAA) WP-3D aircraft in June 2010. A new5
technique for fast measurements of cloud condensation nuclei (CCN) supersaturation spectra6
called Scanning Flow CCN Analysis (SFCA) was deployed for the first time on an airborne7
platform. Retrieved CCN spectra show that most particles act as CCN above (0.3±0.05)% su-8
persaturation, which increased to (0.4±0.1)% supersaturation for the most organic-rich aerosol9
sampled. The aerosol hygroscopicity parameter, κ , was inferred from both measurements of10
CCN activity and from humidified-particle light extinction, and varied from 0.05-0.10 within11
the emissions plumes. However, κ values were lower than expected from chemical composi-12
tion measurements, indicating a degree of external mixing or size-dependent chemistry, which13
was reconciled assuming bimodal, size-dependent composition. The CCN droplet effective14
water uptake coefficient, γcond , was inferred from the data using a comprehensive instrument15
model, and no significant delay in droplet activation kinetics from the presence of organics was16
observed, despite a large fraction of hydrocarbon-like SOA present in the aerosol.17
Introduction18
The explosion and loss of the Deepwater Horizon (DWH) oil platform on 20 April 2010 resulted19
in the release of millions of barrels of oil into the waters of the Gulf of Mexico during April-July,20
2010 (1). While a large portion of the oil-gas mixture remained dissolved or dispersed in the water21
column, a substantial portion reached the water surface and evaporated into the atmosphere over a22
period of hours to days (2, 3). Volatile organic carbon (VOC) and intermediate volatility organic23
carbon (IVOC) species are oxidized in the atmosphere, which lowers their volatility causing them24
to nucleate new particles or condense onto existing aerosol particles. Termed secondary organic25
aerosol (SOA), these particles are an important but uncertain contributor to adverse air quality and26
climate change (4, 5).27
In this paper, we present a detailed characterization of the hygroscopic and droplet-forming28
properties of SOA formed in the vicinity of the DWH site during two survey flights conducted29
by the National Oceanic and Atmospheric Administration (NOAA) WP-3D aircraft on 8 and 1030
2
June 2010. These survey flights provide a unique case study of hydrocarbon-derived SOA that has31
experienced relatively little atmospheric oxidative processing and whose concentrations exceed32
those of the more oxidatively-aged organic aerosol background. Both fresh and aged SOA coexist33
in urban environments, although the latter species are usually much more abundant (6), and, hence,34
may obscure the influence of the former on measured cloud condensation nuclei (CCN) activation35
and droplet growth.36
Methodology37
The observational data were obtained on two survey flights conducted near the site of the DWH38
oil platform (28◦44’12”N, 88◦23’13”W) on 8 and 10 June 2010. Data were filtered to include39
only those sampled within the lower portion of the marine boundary layer at between 50 and 15040
meters altitude above sea level. Intermittent periods with elevated CO mixing ratios (>150 ppbv),41
indicative of combustion sources, were rarely observed but were also excluded from the dataset in42
order to focus solely on the SOA signature. This filtering process also excludes the interception43
of the plume from a surface oil burn southwest of the DWH site on 8 June (7). Flight tracks near44
the DWH site are shown in Figure 1, while the entire, unfiltered flight tracks are provided in the45
supporting information.46
CCN and Aerosol Measurements47
CCN concentration measurements were obtained using a Droplet Measurement Technologies (DMT)48
stream-wise thermal-gradient cloud condensation nuclei counter (CCNC) (9, 10), which exposes49
an aerosol to a specified water vapor supersaturation and counts and sizes the droplets that form.50
Since the supersaturation in the instrument is sensitive to pressure fluctuations, the pressure inside51
the growth chamber was kept constant at 500 hPa using a flow orifice and active control system.52
On 8 June, the CCNC was operated at a constant flow rate (0.5 L min−1) and a single supersat-53
uration of 0.33% for the duration of the flight, while on 10 June, the CCNC supersaturation was54
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an
ic V
olu
me
Fra
ctio
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CC
N-D
eriv
ed
Hygro
sco
pic
ity (
κ)
DWH Site
Spill Extent
10 June 2010
Win
d D
irectio
n
P1
P2
Figure 1: Aircraft trajectories for the survey flights on 8 June (left) and 10 June (right) when theaircraft was sampling near the DWH spill site. Markers are colored by the CCN-derived hygro-scopicity and sized by the aerosol organic volume fraction. The gray shaded area represents thesatellite-derived extent of surface oil (both fresh and aged) (8). Winds on June 8th were light andvariable, but were more sustained from the southeast on 10 June. P1 and P2 denote the separateplume interceptions on 10 June described by (3). The ordinate and abscissa denote degrees latitudeand longitude, respectively.
dynamically scanned over a range of supersaturations (0.2-0.7%) every 15 seconds using Scanning55
Flow CCN Analysis (SFCA) (11). Supersaturations were calibrated in terms of the CCNC inter-56
nal temperature gradient and flow rate using size-classified ammonium sulfate aerosol and Köhler57
theory (12–14). The supersaturation absolute uncertainty is estimated to be ±0.04%.58
Subsaturated hygroscopicity measurements were obtained from a cavity ringdown (CRD) spec-59
trometer measuring aerosol extinction at 532 nm wavelength under both dry (10%RH) and humid-60
ified (70-95%RH) conditions (15). Gas phase absorption at 532 nm wavelength was measured61
using a designated filtered CRD channel and subtracted from the aerosol measurements but was62
observed to be low during both flights.63
Fine mode dry particle size distribution measurements (0.004 to 1 µm diameter) were obtained64
every second from an ultra-high sensitivity aerosol size spectrometer (UHSAS) and a nucleation65
mode aerosol size spectrometer (NMASS). The NMASS consists of five condensation particle66
4
counters (0.004, 0.008, 0.015, 0.030, and 0.055 µm cutoff diameters) that are coupled to the67
UHSAS distribution using a nonlinear inversion algorithm to obtain the complete size distribu-68
tion (16, 17).69
Non-refractory, sub-micron aerosol chemical composition was measured using a compact time-70
of-flight aerosol mass spectrometer (C-ToF-AMS) with a pressure-controlled inlet (18, 19). The71
instrument was operated in “mass spectrum” mode to obtain bulk (i.e., size-averaged) mass spec-72
tra with a 0.1 Hz resolution and in “time-of-flight” mode to obtain size-resolved mass spectra,73
which were then averaged over 5 minute periods to improve signal-to-noise. The mass spectra74
were integrated to calculate the total mass loadings for sulfate, nitrate, ammonium, and organic75
aerosol components. The C-ToF-AMS collection efficiency (CE) parameterization of Middlebrook76
et al.(20) was used in the data inversion, resulting in an average CE for the flights of ∼0.46 and77
0.48. Mass loadings of elemental carbon were obtained from a single particle soot photometer78
(SP2) and were found to be much smaller than the non-refractory mass measured by the C-ToF-79
AMS (7, 21). Comparison of the total aerosol mass measured by the C-ToF-AMS and SP2 with80
that derived from the measured size distribution, shows reasonable agreement (slope = 1.11-1.18,81
R2=0.91-0.93), verifying the applicability of the chosen C-ToF-AMS CE.82
Coupled CCNC Instrument Model83
An important feature of the DMT CCNC is the ability to measure the size distribution of activated84
droplets leaving the instrument flow chamber; this makes it possible to retrieve information about85
CCN activation kinetics. To first order, this can be done by qualitatively comparing the measured86
droplet size distribution to that obtained for calibration aerosol (e.g., (NH4)2SO4 or NaCl) at the87
same instrument operating conditions (flow rate, pressure, and applied temperature gradient). If88
the measured mean droplet size exceeds that for calibration aerosol, slow activation kinetics can be89
ruled out, while a lower measured mean droplet size may suggest slow kinetics. Termed Threshold90
Droplet Growth Analysis, this procedure has been applied in a number of past studies with suc-91
cess (22). However, in addition to the basic instrument operating parameters, droplet sizes are also92
5
dependent on the aerosol size distribution, and to a lesser extent, the aerosol number concentration93
in the growth chamber (22). We use a detailed numerical model to deconvolve these dependencies,94
allowing the quantification of composition impacts on droplet activation kinetics in terms of an95
empirical water uptake coefficient, γcond , that accounts for gas- and particle-phase mass transfer96
resistances, solute dissolution kinetics, and for the sticking probability of a water vapor molecule97
colliding with a growing water droplet.98
The coupled CCNC instrument and droplet growth model (10, 22), together with recent im-99
provements by Raatikainen et al.(23) is used here to numerically solve the coupled momentum,100
mass, and energy balance equations for an aerosol population traversing the CCNC flow field.101
Model predictions of both the droplet number concentration and size distribution leaving the102
CCNC growth chamber are compared to the measurements in order to determine the value of103
γcond that gives the best agreement between predicted and measured droplet sizes.104
Analysis105
The ability of a particle to act as a CCN depends on its size, chemical composition, and on the106
ambient water vapor supersaturation (12). This compositional dependence is commonly parame-107
terized in terms of a hygroscopicity parameter, κ , in Köhler theory (24)108
κ =4
s2D3p,c
(4σMw
3RT ρw
)3
(1)
where s is the water vapor supersaturation, Dp,c is the particle critical dry diameter (above which all109
particles act as CCN), σ is the surface tension of the solution droplet, R is the gas constant, T is the110
absolute temperature, and Mw and ρw are the molar mass and density of water, respectively. In this111
study, the surface tension of pure water (71.4 mJ m−2) is assumed. Following Moore et al. (25) and112
as described in the supporting material, κ is calculated by integrating the aerosol size distribution113
above some Dp,c so that the integrated concentration matches the measured CCN concentration at114
a specified supersaturation. This value of Dp,c is then used in Eq. (1) to find κ . Given the range of115
6
supersaturation and observed Dp,c, a more complex form of Eq. (1) is not necessary.116
While the hygroscopicity parameter is unable to unambiguously account for complex aerosol117
mixing state or surface tension impacts, using the above approach offers a simple way to param-118
eterize composition for global models and is applicable to other measures of water uptake, such119
as in subsaturated conditions. While κ is expected to be similar for both water vapor saturation120
ratios less than and greater than unity, solution non-ideality or surface tension effects in the former121
may yield a derived subsaturated κ that is somewhat lower than for supersaturated cloud droplets,122
which are more dilute (24, 26, 27).123
The subsaturated humidity dependence of the CRD aerosol extinction was used to derive the124
aerosol humidification factor, γext , as125
σRH
σRHre f
=
[1−RH
1−RHre f
]γext
(2)
where σRH is the measured aerosol extinction at relative humidity, RH. In this work, the reference126
RH is 10%, while the elevated RH is 85%. The humidification factor represents the dependence127
of aerosol extinction on RH, which results from changes in the particle size and refractive index128
upon humidification. Thus, γext and κ are related quantities and one can employ Mie theory with129
a number of assumptions to independently derive κ from γext . These calculations were performed130
using the CRD data together with the measured dry particle size distribution and a prescribed131
refractive index (RI) of 1.45 - 0i. Further details regarding the calculation approach and sensitivity132
to the RI assumption and σ uncertainty are presented in the supporting information.133
Results and Discussion134
CCN Activity and Hygroscopicity135
Figure 1 shows the collocated spatial distribution of the CCN-derived κ and the organic aerosol136
volume fraction for the low-level flight legs near the DWH site. Greater variability in κ for ad-137
7
jacent points far from the DWH site on 10 June exceeds that seen on 8 June, which may reflect138
size-dependent aerosol composition because the changing instrument supersaturation during SFCA139
operation on 10 June also changes the size range over which CCN measurements are most sensi-140
tive (i.e., diameters near Dp,c). Winds on 8 June were light with variable direction, while a more141
sustained southeasterly flow was present on 10 June. This gives rise to a distinct plume of low-142
hygroscopicity, organic aerosol to the northwest of the DWH site on the 10th. de Gouw et al. (3)143
examined the gas- and aerosol-phase composition on this day for a plume transect near the DWH144
site (P1) and a transect farther downwind (P2). They found a narrow plume of VOCs surrounded145
by a much broader plume of hydrocarbon-like SOA, with the organic aerosol concentration and146
size distribution both increasing from P1 to P2 (3). Transport calculations based on wind speed147
and direction suggest that the enhancement in SOA results from less-volatile IVOC precursors148
(likely C14 to C16 compounds), which evaporate over a period of hours to days after surfacing and149
are chemically transformed to SOA within a few hours in the atmosphere (3). The importance of a150
sustained wind direction in dispersing the oil emissions is apparent from the lack of an appreciable151
organic fraction south of the DWH site on the 10th, but a wider impacted area on the 8th. Satellite152
imagery of the spill extent (gray shaded region in Figure 1) on both days does not necessary coin-153
cide with enhancements in gas- or aerosol-phase species, suggesting that the “highly-aged” portion154
of the oil slick consists of low volatility compounds, which do not contribute appreciably to SOA.155
The changes in the total particle size distribution (dNCN /dlogDp) and CCN supersaturation dis-156
tribution (dNCCN /ds) across different organic aerosol fractions are shown in Figure 2. The solid157
trace denotes the geometric means and the circles denote the mean values for P1 and P2. A small158
accumulation mode of a few thousand particles per cm3 is present at all organic fractions with a159
significant Aitken mode appearing at organic fractions above 60%. The mean size of the Aitken160
mode increases by roughly three-fold over the observed range of organic fractions, consistent with161
condensational growth from semi-volatile organic vapors. A large Aitken mode is also present162
in Figure 2 between 30%-40% organics, which reflects the sampling of freshly nucleated parti-163
cles in the absence of organic condensation on existing large particles just upwind of the DWH164
8
site (28). For organic volume fractions less than 75-80%, the peak of the CCN distribution is165
around (0.30±0.05)% supersaturation, which broadens considerably and shifts to approximately166
(0.4±0.1)% supersaturation at the highest organic fractions.167
The increase in critical supersaturation coincides with an increase in the mean particle diameter,168
which implies a significant decrease in the particle hygroscopicity since increasing particle size169
tends to strongly decrease the critical supersaturation. This is shown by the inverse correlation170
between κ , γext , and organic volume fraction in Figure 3a,b. The overall κ for an aerosol containing171
n components is calculated as172
κ =n
∑i
εiκi (3)
where εi and κi are the volume fraction and hygroscopicity of the i-th aerosol component, respec-173
tively. Two-component mixing lines were calculated from Eq. (3) assuming a constant inorganic174
hygroscopicity, κinorg, of 0.6 and a constant organic hygroscopicity, κorg, of either 0 or 0.1 (dashed175
traces in Figure 3a). It can be seen from Figure 3a that the observed aerosol hygroscopicity lies176
below even the κorg=0 mixing line, which likely reflects size-dependent composition or a partially177
externally-mixed aerosol population not captured by these simple, but commonly-employed mix-178
ing rules. Extrapolating the piecewise linear fit of the median κ data in Figure 3a to εorg=1 yields179
an effective κorg=0.05. In modeling the condensational growth rate of SOA near the DWH site,180
Brock et al. (28) assumed an intermediate-volatility organic species with a molar mass of 0.292181
kg mol−1 and density of 1000 kg m−3. From Köhler theory, the hygroscopicity of a pure, soluble182
organic aerosol can be found as κorg = (Mw/ρw)(ρorg/Morg)νorg, where ρorg, Morg, and νorg are183
the density, molar mass, and van’t Hoff factor of the organic solute, respectively (36). Using the184
values of ρorg and Morg from Brock et al. (28) with an assumed unit van’t Hoff factor yields a value185
of 0.06, which is consistent with the CCN-derived estimate.186
A number of ambient and SOA chamber studies have shown that the pure organic hygroscopic-187
ity increases with increasing organic oxygenation (29–32). The C-ToF-AMS mass fraction of the188
m/z 44 peak to total organic mass, f44, is correlated with the organic O:C ratio (33, 34), and the rela-189
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nm
)
1000050000
dN / ds (2 x cm STP),
dN / dlogD (cm STP)CN
CCN
p
-3
-3
Organic Volume Fraction
Figure 2: Average particle size distributions (top) and CCN supersaturation distributions (bottom)plotted versus the C-ToF-AMS organic volume fraction for the 10 June flight. Solid traces denotethe geometric mean diameter and supersaturation in the top and bottom figures, respectively, cal-culated for a single mode. Circles show the mean values for the intercepted plume at P1 and P2 (±1 standard deviation).
10
tionship between κorg and f44 or O:C has been reported for some previous measurements (30–32).190
From Figure 3c, it can be observed that aerosol composed almost entirely of SOA are less-oxidized191
than aerosol composed only partially of SOA. This trend reflects the varying contribution of the192
low-O:C, fresh SOA and the higher-O:C, aged background organic aerosol to the total aerosol193
composition, where the former dominate at high εorg near the DWH site and the latter dominate194
at lower εorg outside of these SOA plumes. Using three parameterizations for κorg and a con-195
stant κinorg=0.6 yields the shaded regions shown in Figure 3a, which considerably overpredict the196
aerosol hygroscopicity parameter by a similar amount as the κorg=0.1 mixing line.197
As shown in Figure 2, a distinct accumulation size mode is present throughout the survey198
flights with a more prominent Aitken size mode associated with the organic-rich aerosol near the199
DWH site. Consequently, size-resolved C-ToF-AMS chemical composition was used to look for200
compositional differences between the two modes, which may explain the overprediction shown201
in Figure 3a. As discussed in the supporting information, a lower size-resolved, organic vol-202
ume fraction εSR,org was observed for the accumulation mode (∼0.4-0.8) versus the Aitken mode203
(∼0.85-1). While the larger-sized particles affect the bulk (i.e., size-averaged) C-ToF-AMS com-204
position more so than the smaller particles, the Aitken-mode-dominated number size distribution205
is a more important determinant of CCN activity. Using the average composition for each mode, κ206
was calculated for both the accumulation mode and the Aitken mode aerosol assuming κorg=0.05207
and κinorg=0.6. The overall κ is then obtained from a CCN number-weighted average of the two208
modes and is shown as the blue shaded region in Figure 3, where it can be seen that predictions of209
κ based on a two-mode, size-dependent composition are in much better agreement with observa-210
tions than those obtained from bulk (i.e., size-averaged) composition, both using the same simple211
mixing rule assumptions.212
A comparison between the supersaturated, CCN-derived κCCN and the subsaturated, CRD-213
derived κCRD is shown for the entire June 8th flight in Figure 4d. For most of the flight, the mean214
value of κCRD is approximately 50% less than κCCN (Figure S3), although the data are highly215
correlated assuming a constant 50% bias (R2 = 0.75) and the propagated uncertainty of κCCN is216
11
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232201 179 77
CC
N-D
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te
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tin
ctio
n
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atio
n F
ac
to
r (
γ )
ex
t
γ = a - a εext 0 1 org
a2
a = 0.56
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a = 2.44
0
1
2
A
B
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1.00.80.60.40.20.0
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419
268
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200 82
f 44
Organic Volume Fraction (ε )org
f = a - a ε44 0 1 org
a = 0.30
a = 0.270
1
C
κ = f(f ) from
Duplissy et al., 2011org 44
κ = f(O:C) fromChang et al., 2010Lambe et al., 2011
org
Constant κ Mixing Lines
org
Piecewise Linear Fit
1.00
0.90
0.80
0.70
0.60
0.50
0.40
O:C
2-ModeExternal Mixture
Figure 3: Distribution of (a) CCN-derived hygroscopicity, (b) extinction humidification factor, and(c) f44 ratio and O:C ratio (from the Aiken et al. (33) correlation) plotted versus C-ToF-AMSorganic volume fraction. Boxes denote the median and interquartile range for all observationsfrom both flights between 50 and 150 meters altitude, while the numbers beside each box denotethe number of 0.1 Hz points used in the calculation. Solid traces are fits to the median data.Circles show the mean values for the intercepted plume at P1 and P2 on June 10th (± 1 standarddeviation). Shaded areas and dashed traces in (a) are κ predictions from different compositionalassumptions. The organic volume fraction bin centered at 0.25 in (c) has been excluded due to lowsignal-to-noise.
12
relatively large. A similar discrepancy (∼30-50%) has been previously observed for comparision217
of CCN-derived hygroscopicities and those inferred from subsaturated measurements with an hu-218
midified tandem differential mobility analyzer (HTDMA) (26, 27, 35). This suggests that while219
the humidification of organic-rich aerosol appears to have a small effect on their size and light220
scattering (κCRD ∼ 0.01-0.05), these particles have a greater contribution to CCN activation (κCCN221
∼ 0.05-0.10).222
The derived organic hygroscopicity of ∼ 0.05 is on the low end of past studies looking at223
the CCN-derived κ of SOA produced from the oxidation of IVOCs, but is consistent with some224
past subsaturated hygroscopic growth results. For example, Jimenez et al. (29) report κorg ∼ 0.06225
from hygroscopic growth measurements of aerosol in Mexico City and from the smog chamber226
oxidation of α-pinene for aerosol with O:C ∼ 0.4. Meanwhile, CCN measurements have found227
κorg of 0.04 for β -caryophyllene SOA produced via ozonolysis (36), of 0.01-0.10 for SOA derived228
from the OH oxidation of n-heptadecane (O:C ∼ 0.1-0.2) (32), and of 0.07-0.10 for SOA derived229
from the OH oxidation of longifolene (O:C ∼ 0.2-0.3). This work is contrasted with many other230
studies of ambient SOA in the literature that find higher κ on the order of 0.1-0.3 (37–41), likely231
due to the more-aged nature of the sampled aerosol, which increases both κ and O:C. Thus, this232
work provides important observations of the CCN-derived hygroscopicity of relatively unoxidized233
SOA derived from IVOCs that likely require less oxidation in the atmosphere to form reaction234
products which partition into the aerosol phase (42, 43). Near local emissions sources such as235
in urban environments, the presence of these less hygroscopic SOA species may partially explain236
some large CCN overpredictions based on measured aerosol composition (44).237
Droplet Activation Kinetics238
Figure 4a,b presents the droplet size distribution of activated CCN in the CCNC (normalized by239
the total number of droplets), the number-averaged mean droplet size, and the modeled mean240
droplet size for aerosol with an effective water uptake coefficient, γcond , of 0.2. It can be seen that241
the temporal variability is strongly correlated for the measured and modeled mean droplet sizes242
13
when accounting for supersaturation depletion effects from moderate CCN concentrations in the243
CCNC growth chamber (∼1000-3000 cm−3 STP) (22). Neglecting the depletion effects leads to244
a larger mean droplet size and much less variability in the predictions, which is not in agreement245
with observations. This is because, even though supersaturation depletion has a limited effect246
on the measured CCN concentration, it can have an observable effect on the measured droplet247
size distribution. The simulated traces were corrected by a constant 2.3 µm bias, which gives248
the best agreement between the γcond=0.2 simulated and observed droplet sizes (see supporting249
information). Simulations of the activation and growth of ammonium sulfate calibration aerosol250
reveal a 2.1 µm model overprediction bias, which is in good agreement with the correction bias251
applied here. Regression analysis of the modeled versus measured mean droplet sizes indicates that252
a constant value of γcond between 0.1 and 0.2 best reproduces the observed droplet size variability253
for the entire dataset.254
While it is known that droplet formation is less sensitive to changes in γcond in the range of 0.1-255
1 versus lower values (45), the exact value of γcond , even for pure water droplets, remains uncertain256
with reported values in the range of 0.04 to 1 (46–49). The most realistic value is probably between257
0.06 and 0.3 (47, 48, 50). As the inferred coefficients in this study are similar to the reference val-258
ues for pure water, this suggests that the fresh SOA generated near the DWH site do not promote259
kinetic delays upon condensation on ambient CCN. Given the relatively low hygroscopicity and260
hydrocarbon-like nature of the organic species (O:C ratios ∼ 0.4-0.5 around the plume), this is a261
somewhat unexpected result. Previous work has shown that hydrophobic organic compounds may262
retard water uptake through slow dissolution, which reduces the amount of solute in the droplet263
and shifts the water vapor-liquid equilibrium more towards the gas-phase than if all of the solute264
had immediately dissolved (51). Alternatively, organics may form compressed films on the droplet265
surface, which increases the condensational mass transfer resistance (52). A number of past studies266
have found distinctly slower activation kinetics for smog-chamber SOA resulting from the photo-267
oxidation of β -caryophyllene (53), aerosol above the Pacific marine boundary layer (54), biogenic268
aerosol in rural Canada (55), and both urban and rural aerosol at ground-based locations around the269
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2.8
1.0
0.8
0.6
0.4
0.2
2
4
6
0.1
2
4
6
102
103
104
5.0
4.0
3.0
2.0
1.0
103
102
101
100
UTC Date and Time
Dro
ple
t
Siz
e (
μm
)
Me
an
Dro
ple
t S
ize
(μ
m)
Hyg
rosco
pic
ity
Pa
ram
ete
r (κ
)
CC
N
Co
nce
ntr
atio
n (
cm
)
-3
Org
an
ic V
olu
me
Fra
ctio
n
Dry
Extin
ctio
n (M
m )-1
Dry Extinction (532 nm)
CCN (0.33% supersaturation)
Transit Leg From
Tampa, FL
Transit Leg To
Tampa, FL
Box Pattern Near
DWH Site
Background
Gulf Air
Near Florida
Coastline
Near
DWH Site 0.5
0.4
0.3
0.2
0.1
0.0
No
rma
lize
d
Co
nce
ntra
tion
cond γ = 0.2 without Depletion Effects
Observed
cond γ = 0.2 with Depletion Effects
CCN-Derived κ
CRD-Derived κ Org. Volume Fraction
A
B
C
D
Figure 4: Timeseries of measured (a, b) and modeled (b) droplet sizes of activated CCN in theCCNC during the June 8th flight. Shown for comparison are the measured CCN concentrationsand dry extinction at 532 nm (c) and the aerosol organic volume fraction and hygroscopicitiesderived from the supersaturated CCN and subsaturated cavity ring down measurements (d). Theshaded regions in (d) denote the propagated uncertainty of each κ (± 1 standard deviation), whosecalculation is described in the supporting material.
15
United States (56). Lance et al. (57) examined airborne measurements of CCN in Houston, TX,270
during the 2006 GoMACCS campaign and found no evidence for slow activation kinetics (57),271
contrary to Ruehl et al. (54) and Asa-Awuku et al. (58), who did observe delayed CCN activation272
from concurrent ground-based and airborne platforms. Since some of these studies were carried273
out with size-selected aerosol (i.e., a small fraction of the overall CCN concentration was sampled274
at a time), supersaturation depletion effects may not have caused the apparent kinetic delays. A275
potential difference may be the phase state of the aerosol (e.g., glassy/amorphous versus partially-276
deliquesced) and its impact on water uptake kinetics. Given that most kinetic delays were reported277
for dry aerosol may lend some support to this hypothesis. A previously-deliquesced aerosol state278
is consistent with the current results as even a small amount of residual water could inhibit glassy279
transition and promote rapid activation kinetics. If true, this suggests that condensation of even280
the most hydrophobic SOA onto existing inorganic CCN may not impact activation kinetics. This281
finding is relevant for the growth of newly-formed particles in the atmosphere, which have been282
hypothesized to form as H2SO4 seeds and grow to CCN-relevant sizes primarily through the con-283
densation of SOA from IVOCs and VOCs (59–61).284
Acknowledgement285
RHM acknowledges support from DOE GCEP and NASA ESS Graduate Research Fellowships.286
TR acknowledges support from the Finnish Cultural Foundation. AN and RHM acknowledge287
support from NOAA and NSF CAREER. We acknowledge K. Cerully for CCN support during the288
California component of CalNex, and thank T. Ryerson and J. de Gouw for helpful comments on289
this manuscript.290
16
Supporting Information Available291
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24
Supporting information for: CCN Spectra,
Hygroscopicity, and Droplet Activation Kinetics of
Secondary Organic Aerosol Resulting from the 2010
Deepwater Horizon Oil Spill
Richard H. Moore,† Tomi Raatikainen,‡ Justin M. Langridge,¶,§ Roya Bahreini,¶,§
Charles A. Brock,¶ John S. Holloway,¶,§ Daniel A. Lack,¶,§ Ann M. Middlebrook,¶
Anne E. Perring,¶,§ Joshua P. Schwarz,¶,§ J. Ryan Spackman,¶,‖ and Athanasios
Nenes∗,†,‡
School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta,
Georgia, USA., School of Earth & Atmospheric Sciences, Georgia Institute of Technology,
Atlanta, Georgia, USA., Earth System Research Laboratory, National Oceanic and Atmospheric
Administration, Boulder, Colorado, USA., Cooperative Institute for Research in Environmental
Sciences, University of Colorado, Boulder, Colorado, USA., and Science and Technology
Corporation, Boulder, Colorado, USA.
E-mail: [email protected]
1
∗To whom correspondence should be addressed†School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.‡School of Earth & Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA.¶Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, Colorado, USA.§Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA.‖Science and Technology Corporation, Boulder, Colorado, USA.
S1
Flight Overview Maps2
S2
30 30
29 29
28 28
-90 -88 -86 -84 -82
DWH Site
Spill Extent
10 June 20100.6
0.5
0.4
0.3
0.2
0.1
0.0
CC
N-D
eriv
ed
Hygro
scopic
ity (
κ)
30 30
29 29
28 28
-90 -88 -86 -84 -82
8 June 2010
Figure 1: Complete aircraft trajectories for the survey flights on 8 June (top) and 10 June (bottom).Both flights originate and end in Tampa, Florida. Markers are colored by the CCN-derived hygro-scopicity. The gray shaded area represents the extent of surface oil (1). The ordinate and abscissadenote degrees latitude and longitude, respectively.
S3
CCN-Derived Hygroscopicity3
The CCN-derived hygroscopicity parameter, κCCN is obtained from the supersaturation-dependent4
CCN concentration and aerosol size distribution following Moore et al. (2):5
1. The normalized cumulative size distribution was calculated for each particle size bin as6
ncum(Dp) =1
NCN
∫ Dp
4nm
dNCN
d logDpd logDp (1)
where Dp is the particle diameter, NCN is the fine particle concentration larger than 4 nm,7
dNCN is the fine particle concentration in the bin, and d logDp is the bin width in logarithm8
space.9
2. The CCN-active aerosol fraction, Ra = NCCN/NCN was calculated, where NCCN is the mea-10
sured CCN concentration at a given supersaturation. For an internally-mixed aerosol whose11
composition does not vary with size, all particles larger than some critical supersaturation,12
Dp,c act as CCN. Thus, Dp,c can be found directly by interpolating the normalized cumula-13
tive size distribution as Ra = 1−ncum(Dp,c).14
3. The interpolated value of Dp,c is used in Köhler theory (Eq. 1 in the main text) to find κCCN .15
Since κ is strongly dependent on the critical diameter through its cubic dependence in Köhler16
theory, determining Dp,c is likely to be the largest source of uncertainty in deriving κCCN . Conse-17
quently, we also calculate the sensitivity of the derived κCCN to uncertainties associated with NCCN18
and NCN . The relative CN concentration uncertainty, εCN is reported as 11%, while εCCN depends19
on the concentration-dependent Poisson statistical uncertainty as well as that due to the CCNC20
flow rate uncertainty (εQCCN ∼ 7%). Thus, the overall uncertainty of Ra is21
εRa =
√ε2
CN +
(τCCN
NCCNQCCN
)+ ε2
QCCN(2)
S4
where τCCN is the OPC integration time (1 second), QCCN is the CCNC sample flow rate (45.4522
cm3 min−1). In addition to accounting for the uncertainty of the activated ratio, Ra, an additional23
10% uncertainty was applied to Dcrit after interpolation in Step 2 above to account for the UHSAS24
sizing uncertainty (3). The derived uncertainty in κCCN was found to vary significantly between25
50-200% during the flight on 8 June (see Figure 3 and Figure 4 in the main text).26
CRD-Derived Hygroscopicity27
The data from the cavity ringdown (CRD) spectrometer were used to infer the hygroscopicity28
parameter, κ , as follows:29
1. The extinction-derived humidification factor, γext , was used to calculate the change in ex-30
tinction between a dry particle at 10%RH and a humidified particle at 85%RH, f (85%RH),31
as32
f (85%RH) =σ85%RH
σ10%RH=
[1−0.851−0.10
]γext
(3)
where σRH is the is the aerosol extinction at relative humidity, RH.33
2. Mie theory equations (4) were iteratively solved to find the hygroscopic growth factor,34
g(85%RH)=Dp(85%RH)Dp(10%RH) , that reproduces the measured humidified light extinction at 85%RH,35
where Dp(RH) is the RH-dependent particle diameter. The Mie theory calculations were per-36
formed at the CRD laser wavelength of 532 nm and used the fine particle size distribution37
(Dp < 2 µm) with a prescribed complex refractive index (RI) of 1.45 - 0i that is characteristic38
of alkane and aromatic species (5). At each iteration step, the humidified particle refractive39
index is calculated by volume-weighting the dry particle refractive index with that of water40
(1.33 - 0i).41
3. The hygroscopicity parameter, κ , is then calculated for each aerosol size distribution bin as42
in Petters and Kreidenweis (6)43
S5
κ =(g3−1
)(exp( ADpg)
RH100
−1
)(4)
where A = (4Mwσw)/(RT ρw), R is the ideal gas constant, T is the absolute temperature of44
the measurement, and σw, Mw and ρw are the surface tension, molar mass, and density of45
water, respectively. The overall κ is calculated as a volume-weighted average of the κ for46
all size distribution bins.47
Table 1: Pure component densities and refractive indices used to compute κCRD.
Density RefractiveComponent (kg m−3) IndexAmmonium Sulfate 1769a 1.53−0.00ia,b
Ammonium Nitrate 1725a 1.61−0.00ia
Organic Carbon 1400c 1.45−0.00id
Elemental Carbon 1800e 1.95−0.79ie
Water 996a 1.33−0.00ia
aGreen and Perry, (7)bToon et al., (8)cAlfarra et al., (9)dRiazi and Al-Sahhaf, (5)eBond and Bergstrom, (10)
Implicit in the above method is the assumption that the aerosol are internally-mixed and can be48
described well by a single, complex refractive index. To test the sensitivity of the derived κCRD to49
the RI assumption, the measured aerosol composition from a compact time of flight aerosol mass50
spectrometer (C-ToF-AMS) and a single particle soot photometer (SP2) was used to compute an51
RI based on a volume-weighted average of the pure component refractive indices (Table 1). As52
shown in Figure 2a, the derived κCRD is insensitive to the assumed RI over the range of observed53
values (Figure 2b,c) with the uncertainty in κCRD from this assumption estimated to be less than54
0.02, absolute. While elemental carbon has the potential to significantly impact aerosol extinction55
because of its large RI, the low mass loadings observed during the the 8 June flight do not appear56
to have a large influence (Figure 2b,c). The observed insensitivity of κCRD to the refractive in-57
dex assumption may not be true for some heterogeneous environments with large local sources of58
S6
elemental carbon, however, the finding is consistent with at least one previous study (11). Uncer-59
tainties arising from the measurement of f (RH) were found to vary between 5-8%, on average,60
which were propagated in the calculation procedure above to find the κCRD uncertainty over the61
range of observed values (shown in Figure 3 and Figure 4 in the main text). This uncertainty62
analysis neglects uncertainties associated with the aerosol size distribution measurement, such as63
non-idealities arising from particle non-sphericity, inhomogeneity, or core-coating structure. Thus,64
the derived κCRD uncertainty should be considered a lower limit.65
Figure 3 shows a direct comparison between κCCN and κCRD for the June 8 flight, with the66
corresponding timeseries shown in Figure 4 of the main article. Overall, the CRD-derived hygro-67
scopicity is approximately two-fold lower than the CCN-derived hygrosopicity, with κCCN ranging68
from 0.05-0.6, while κCRD varies from 0.01-0.2. A linear fit between the two quantitities gives69
a slope of 0.47 and a coefficient of determination, R2 = 0.75. An interesting feature of Figure 370
is that a clear minimum value of κCCN is apparent between 0.05-0.10, despite wider variation in71
κCRD. Similarly, κCRD reaches a maximum value of 0.15-0.20, despite much wider variation in72
κCCN . These asymptotic limits coincide with high and low organic volume fractions, and seem to73
imply that organic species reduce subsaturated hygroscopic growth more so than CCN activation.74
Size-Dependent CCN Composition75
Figure 4 shows the size-resolved organic aerosol volume fraction (εSR,org) plotted versus the bulk76
(i.e., size-averaged) organic volume fraction (εorg). The Aitken mode aerosol contains mostly77
organics (εSR,org∼0.85-1), but its influence is eclipsed by the more-massive accumulation mode78
aerosol resulting in lower values of bulk εorg. A 100-nm cutsize was used to differentiate the79
two modes, and the average composition in each mode was used to determine the modal κ =80
εSR,orgκorg + εSR,inorgκinorg, assuming κorg=0.05 and κinorg=0.6. As shown the lower part of Fig-81
ure 4, the accumulation mode κ is roughly two-fold greater than the Aitken mode κ . The modal κ82
were linearly regressed against the bulk εorg, and were combined in an aerosol number-weighted83
S7
average to find the overall aerosol hygroscopicity, shown as filled circles in Figure 4. Using a84
number-weighted average versus a volume-weighted average is analogous to assuming the two85
modes are externally mixed rather than internally mixed.86
S8
0.01
2
3
4
5
6
789
0.1
2
0.012 3 4 5 6 7 8 9
0.12
500400300200100
0Fre
qu
en
cy
-0.05 0.00 0.05
1.0
0.9
0.8
0.7
0.6
0.5
0.4Org
anic
Volu
me
Fra
ction
CRD-Derived Hygroscopicity κ
(Composition-Dependent Refractive Index)
CR
D-D
erived H
ygro
scopic
ity κ
(Consta
nt R
efr
active Index)
300
200
100
0
Fre
quency
1.561.541.521.501.481.461.441.42
Real Refractive Index
500
400
300
200
100
0
0.100.080.060.040.020.00
Imaginary Refractive Index
Fre
quency
B
C
A
Absolute Error
Figure 2: (a) Comparison plot of the CRD-derived hygroscopicity for the 8 June flight calcu-lated assuming constant and composition-dependent refractive indices. Data from the high altitudetransit legs are excluded. (b,c) Frequency of occurrence of the real and imaginary parts of thecomposition-dependent refractive index during the 8 June flight.
S9
0.01
2
3
4
5
6
78
0.1
2
3
4
5
6
0.012 3 4 5 6 7 8 9
0.12 3 4 5 6
CCN-Derived Hygroscopicity (κ )CCN
1:1
1:2
1:4
1.00.90.80.70.60.50.4
Organic Volume Fraction
250
200
150
100
50
0
Fre
qu
en
cy
-1.0 -0.5 0.0 0.5
C
RD
-De
rive
d H
yg
rosco
pic
ity (κ )
CR
D
Relative Error
Figure 3: Comparison plot of the CRD-derived and CCN-derived hygroscopicity parameters ob-served during the 8 June flight.
S10
10
2
4
6
100
2
4
6
1000
0.900.800.700.600.500.400.30
6
8
2
4
Number Geometric Mean Diameter
Total Mass Geometric Mean Diameter1.0
0.1
Hygro
scopic
ity
Para
mete
r (κ
)
Part
icle
Dry
Dia
mete
r (n
m)
0.8
0.6
0.4
0.2
0.0
Siz
e-R
eso
lve
d O
rga
nic
Vo
lum
e F
ractio
n (
ε )S
R,o
rg
Bulk Organic Volume Fraction (ε )org
AccumulationMode κ
Aitken Mode κ
2-Mode κ
Linear Fit
κ = 0.45 - 0.35 εorg
κ = 0.21 - 0.15 εorg
Figure 4: Average size-resolved organic volume fraction (εSR,org) from the C-ToF-AMS (top)and calculated modal hygroscopicity parameters (κ) (bottom) plotted versus the bulk (i.e., size-averaged) C-ToF-AMS organic volume fraction εorg for both flights. The 2-Mode κ is an aerosolnumber-concentration-weighted average of the linear fits to the Aitken and accumulation mode κ .
S11
Coupled CCNC and Droplet Growth Model87
A detailed description of the coupled CCNC instrument and droplet growth model is given by Raatikainen88
et al. (12), so only a brief description is included here. A simplified form of the coupled model89
was employed here, which assumes parabolic velocity fields while solving the water vapor and en-90
ergy conservation equations. Neglecting explicit calculation of the velocity fields has a negligible91
effect on the simulation results, but substantially decreases the necessary computational time (12).92
The model has been successfully applied in the past to simulate the instrument behavior for both93
steady-state and transient operation (13–15). Recent work by Lathem and Nenes (16) has shown94
that moderately high CCN concentrations in the growth chamber can slightly decrease the cen-95
terline supersaturation profile. This effect appears to be unimportant for measurements of CCN96
concentrations below 104 cm−3 (16), which is the typical mode of operation. However, supersatu-97
ration depletion can have a detectable effect on the OPC-measured droplet size distribution when98
the CCN concentrations exceed several hundred per cm3. The model can be used to simulate the99
CCN droplet size distributions, both with and without such supersaturation depletion effects as a100
function of the effective water uptake coefficient, γcond . The optimal value of γcond is then found101
by iteratively matching the simulated and measured droplet size distributions.102
As described in detail by Raatikainen et al. (12), a number of uncertainties exist that challenge103
the simulations. For example, aerosol mixing state and size-dependent composition give rise to104
a polydisperse hygroscopicity distribution, which is poorly-constrained. Additionally, there are105
various instrument non-idealities (e.g., column thermal resistance, possible OPC sizing biases,106
incomplete wetting of the column wall and overall mass transfer coefficient changes, non-linearity107
of the wall temperature profile). To quantify the impact of these effects, CCN droplet distributions108
for ammonium sulfate calibration aerosol were compared to those predicted by the model, and it109
was found that the model simulations overpredict the measured mean droplet size by approximately110
2.1 µm, which agrees well with the correction bias of 2.3 µm uncovered for γcond=0.2 (Table 2)111
and applied to the June 8th simulation timeseries in Figure 4 of the main article.112
Figure 5 shows 1:1 comparison plots for the uncorrected simulated mean droplet size (D̄p,sim)113
S12
versus the OPC measured mean droplet size (D̄p), and the linear regression coefficients are listed114
in Table 2. It can be seen that forcing the regression slope to unity has a negligible effect on the115
coefficient of determination for all simulations except those where supersaturation depletion effects116
are neglected. Simulations assuming γcond=0.05-1 show the best correlation with observations117
(R2∼0.51), and the overprediction bias for the γcond=0.1-0.2 simulations is most similar to the118
2.1 µm bias identified from calibrations. The correlation worsens significantly for γcond <0.05 or119
when depletion effects are neglected. Thus, the regression analysis indicates that CCN near the120
Deepwater Horizon oil spill do not exhibit slow activation kinetics and derived values of γcond are121
consistent with those for ammonium sulfate calibration aerosol and previously reported values for122
pure water droplets (17, 18).123
Table 2: Linear regression coefficients between modeled and observed mean droplet sizes formodel simulations with varying water uptake coefficients and with supersaturation depletion ef-fects considered or turned off. Both 1-parameter (D̄p,sim = D̄p +Bias) and 2-parameter (D̄p,sim =Slope× D̄p +Bias) are listed.
Model Simulation 1-Parameter Fit 2-Parameter Fitγcond Depletion Effects? Bias R2 Slope Bias R2
1.00 Yes 2.46 0.49 0.76 3.20 0.530.20 Yes 2.29 0.50 0.76 3.03 0.530.10 Yes 2.09 0.49 0.76 2.83 0.540.05 Yes 1.70 0.49 0.76 2.45 0.530.04 Yes 1.53 0.47 0.76 2.28 0.520.03 Yes 1.25 0.44 0.75 2.03 0.500.02 Yes 0.78 0.39 0.73 1.63 0.460.01 Yes -0.21 0.26 0.65 0.88 0.370.008 Yes -0.53 0.22 0.62 0.67 0.350.005 Yes -1.11 0.04 0.52 0.41 0.291.00 No 2.69 0.15 0.56 4.06 0.360.20 No 2.50 0.18 0.58 3.82 0.360.10 No 2.27 0.20 0.60 3.53 0.360.05 No 1.85 0.24 0.63 3.02 0.360.04 No 1.66 0.25 0.64 2.79 0.360.03 No 1.36 0.25 0.65 2.46 0.350.02 No 0.85 0.26 0.66 1.91 0.350.01 No -0.19 0.22 0.63 0.97 0.340.008 No -0.51 0.19 0.61 0.72 0.330.005 No -1.10 0.04 0.52 0.42 0.29
S13
6.2
6.0
5.8
5.6
5.4
5.2
5.0
3.83.63.43.23.02.82.6
6.4
6.2
6.0
5.8
5.6
5.4
5.2
3.83.63.43.23.02.82.6
With Depletion Effects Without Depletion Effects
6.0
5.8
5.6
5.4
5.2
5.0
4.8
3.83.63.43.23.02.82.6
6.2
6.0
5.8
5.6
5.4
5.2
5.0
3.83.63.43.23.02.82.6
5.8
5.6
5.4
5.2
5.0
4.8
4.6
3.83.63.43.23.02.82.6
6.0
5.8
5.6
5.4
5.2
5.0
4.8
3.83.63.43.23.02.82.6
5.6
5.4
5.2
5.0
4.8
4.6
4.4
3.83.63.43.23.02.82.6
5.6
5.4
5.2
5.0
4.8
4.6
4.4
3.83.63.43.23.02.82.6
3.6
3.4
3.2
3.0
2.8
2.6
2.4
3.83.63.43.23.02.82.6
3.8
3.6
3.4
3.2
3.0
2.8
2.6
2.4
3.83.63.43.23.02.82.6
γ = 1.00cond
γ = 0.20cond
γ = 0.10cond
γ = 0.05cond
γ = 0.01cond
γ = 1.00cond
γ = 0.20cond
γ = 0.10cond
γ = 0.05cond
γ = 0.01cond
Sim
ula
ted M
ean D
rople
t S
ize (
µm
)
Measured Mean Droplet Size (µm)
Figure 5: Comparison plots of the simulated CCN droplet sizes obtained from the instrumentmodel versus the measured mean droplet sizes during the 8 June flight. Solid traces are a 1-parameter linear fit (constant bias), and dashed traces are a 2-parameter linear fit (slope and bias).Regression coefficients are listed in Table 2.
S14
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