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: athanasios.nenes@gatech.edu

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.

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Flight Overview Maps2

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-90 -88 -86 -84 -82

DWH Site

Spill Extent

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CC

N-D

eriv

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scopic

ity (

κ)

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-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.

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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

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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

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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

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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

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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.

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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.

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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 .

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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

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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

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6.2

6.0

5.8

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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

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3.83.63.43.23.02.82.6

5.6

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3.83.63.43.23.02.82.6

3.6

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3.8

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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.

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