Online Supplemental Material

Gas-particle partitioning of primary organic aerosol emissions: (1)

gasoline vehicle exhaust

Andrew A. May, Albert A. Presto, Christopher J. Hennigan, Ngoc T. Nguyen, Timothy D.

Gordon, Allen L. Robinson*

Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh PA

* Corresponding author alr@andrew.cmu.edu, Dept. of Mechanical Engineering, Carnegie Mellon University,

Pittsburgh, PA

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Vehicle Information

Here, we provide tabular information describing the vehicles tested as well as emissions data.

Table S1. List of gasoline vehicles tested providing vehicle ID, driving cycle, emission factors and fuel economy.

Vehicle ID Test ID Modelyear Mileage Engine

size (L)Bodystyle

Dilution ratio%

EFQ@

(mg kg-fuel-1)EFQBT

@

(mg kg-fuel-1)COA

&,@

(g m-3)EFEC

@

(mg kg-fuel-1)

Fuel economy(miles

gallon-1)Pre-LEV-1#,

$ 1032442 1987 197631 4.1 PC 24.5 61.7 13.4 174 39.0 14.6

Pre-LEV-2# 1032440 1988 224758 1.6 PC 22.0 111 27.6 489 30.5 23.8Pre-LEV-2#,

$ 1032444 1988 224758 1.6 PC 44.4 128 17.5 182 45.4 24.1

Pre-LEV-3#,

$ 1032303 1990 58617 5.0 LDT 9.8 52.4 5.7 460 3.7 12.9

Pre-LEV-3 1032389 1990 58617 5.0 LDT 9.9 61.4 5.9 390 2.8 13.1Pre-LEV-4 1032392 1989 123085 1.3 PC 21.2 47.9 7.9 159 77.9 27.2Pre-LEV-5 1032320 1990 118050 3.8 PC 14.3 4.0 3.2 4.6 3.6 19.4Pre-LEV-6 1028060 1990 179275 3.0 PC 42.3 89.1 26.6 122 13.6 18.5Pre-LEV-7 1027853 1990 228311 2.3 PC 15.1 36.9 6.5 180 120 18.1Pre-LEV-8 1027920 1991 123466 3.8 PC 42.6 25.5 13.2 23.3 35.0 18.5Pre-LEV-9 1032443 1991 144000 4.0 PC 25.8 14.7 6.5 25.9 21.4 16.4Pre-LEV-10 1028029 1992 263305 3.4 PC 12.0 7.4 4.9 17.4 8.8 15.7Pre-LEV-11 1027921 1992 n/a 3.8 PC 41.3 22.0 13.3 17.0 11.5 18.0Pre-LEV-12 1028026 1992 118042 3.8 PC 14.1 12.4 6.2 36.3 5.7 18.4Pre-LEV-13 1027922 1992 153780 5.7 LDT 24.8 98.0 20.9 257 15.2 10.5Pre-LEV-14 1027872 1993 165819 4.9 PC 10.6 21.1 4.9 131 13.6 13.1Pre-LEV-15 1032445 1993 161476 4.3 VAN 24.9 68.7 10.4 204 22.9 15.0

LEV-I-2 1032302 1996 130485 3.0 PC 14.5 3.8 3.6 0.6 1.8 19.3LEV-I-2 1032304 1996 130485 3.0 PC 14.4 3.9 3.10 4.5 3.2 19.2LEV-I-3 1032346 1997 90638 3.0 PC 13.7 2.1 1.9 2.0 1.2 18.3LEV-I-3 1032362 1997 90638 3.0 PC 14.1 2.7 2.4 2.4 3.5 18.8LEV-I-4 1032393 1999 118294 2.0 PC 17.8 7.5 5.2 10.3 4.2 23.6

LEV-I-5#,$ 1027904 2000 104446 2.2 PC 17.1 22.9 10.5 120 2.5 23.0

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Vehicle ID Test ID Modelyear Mileage Engine

size (L)Bodystyle

Dilution ratio%

EFQ@

(mg kg-fuel-1)EFQBT

@

(mg kg-fuel-1)COA

&,@

(g m-3)EFEC

@

(mg kg-fuel-1)

Fuel economy(miles

gallon-1)LEV-I-5 1027915 2000 104446 2.2 PC 17.0 37.2 11.4 57.8 4.0 23.0LEV-I-6# 1027881 2003 110445 3.5 PC 42.3 111 29.8 190 58.7 18.3LEV-I-6#,$ 1027917 2003 110445 3.5 PC 40.4 118 25.9 207 95.4 17.1LEV-I-6#,$ 1027918 2003 110445 3.5 PC 44.8 121 24.6 156 135 16.5LEV-I-7 1027909 1999 97854 3.0 PC 14.7 10.7 5.74 26.6 21.3 19.9LEV-I-8 1027976 1994 124697 1.9 PC 16.7 23.9 5.98 88.5 56.8 21.7LEV-I-10 1028025 1995 191672 4.0 mSUV 12.0 21.6 9.0 85.3 24.7 15.5LEV-I-12 1027870 1996 76175 5.4 LDT 8.6 4.5 1.9 23.9 5.6 11.5LEV-I-13 1027968 1997 126831 4.6 PC 12.3 18.8 3.8 98.9 29.8 16.2LEV-I-14 1027972 1997 103797 2.3 PC 15.1 20.4 9.7 56.2 22.3 20.3LEV-I-16 1028027 1998 111607 1.8 PC 21.0 8.6 6.0 9.7 6.4 27.3LEV-I-18 1027903 1999 132401 1.5 PC 19.7 39.5 11.6 117 73.4 25.6LEV-I-19 1028075 2000 92328 3.5 PC 15.1 8.6 3.3 27.9 14.9 20.1LEV-I-21 1032388 2001 53301 2.2 PC 16.2 9.3 4.8 22.2 8.3 21.8LEV-I-22 1028024 2001 122880 2.8 PC 12.7 10.6 3.2 48.1 26.2 16.3LEV-I-23 1028030 2002 98582 5.3 LDT 10.4 11.1 7.1 30.3 16.8 14.1LEV-I-25 1028023 2003 98546 3.0 PC 13.3 28.5 7.1 132 23.3 17.3LEV-I-26 1027970 2003 94467 1.8 PC 20.3 24.5 5.5 74.2 24.3 26.8LEV-II-1 1027879 2007 29433 3.9 MPV 12.2 5.1 2.9 39.9 14.6 16.4LEV-II-1 1027958 2007 29433 3.9 MPV 12.0 5.3 3.7 48.6 15.3 16.4LEV-II-1 1027961 2007 29433 3.9 MPV 12.3 11.8 4.5 10.0 21.7 16.0LEV-II-1 1027967 2007 29433 3.9 MPV 12.2 12.5 6.3 13.9 17.5 16.4LEV-II-2# 1028022 2008 43378 4.2 LDT 11.2 7.8 3.0 33.8 25.0 15.4LEV-II-3 1032283 2008 35786 3.5 PC 14.6 2.8 2.6 1.5 14.8 19.6

LEV-II-4#,$ 1027971 2010 18236 3.6 mSUV* 11.7 5.4 3.0 16.1 39.8 15.8LEV-II-5 1032342 2011 10911 2.0 PC 15.5 3.7 2.4 20.9 41.4 22.3LEV-II-5 1032351 2011 10911 2.0 PC 16.7 7.3 3.2 6.0 37.6 21.1LEV-II-6 1032321 2011 29249 3.6 PC 11.7 2.8 2.5 2.1 1.0 15.9LEV-II-8 1027908 2004 46126 2.2 PC 18.5 18.3 6.0 53.7 65.4 24.7LEV-II-10 1027978 2005 34771 2.7 mSUV 14.7 6.0 3.2 85.3 11.5 19.3LEV-II-11 1027973 2005 63308 1.8 PC 19.8 18.9 5.3 23.9 52.5 26.2LEV-II-13 1027863 2008 40938 1.6 PC 19.2 7.0 4.5 98.9 5.1 25.5LEV-II-15 1027977 2008 41638 2.7 PC 13.3 7.4 3.3 56.2 18.0 17.8

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Vehicle ID Test ID Modelyear Mileage Engine

size (L)Bodystyle

Dilution ratio%

EFQ@

(mg kg-fuel-1)EFQBT

@

(mg kg-fuel-1)COA

&,@

(g m-3)EFEC

@

(mg kg-fuel-1)

Fuel economy(miles

gallon-1)LEV-II-17 1027848 2009 14280 5.3 fSUV 10.5 6.3 3.5 9.7 5.2 14.0LEV-II-18 1028021 2009 n/a 5.7 fSUV 10.0 8.4 3.2 117 31.5 13.8LEV-II-20 1027907 2009 31313 2.4 PC 14.1 9.6 4.8 27.1 8.8 18.8LEV-II-23 1032310 2011 31006 n/a PC 15.2 4.7 3.2 7.9 5.9 20.4LEV-II-24 1032383 2012 14792 3.6 mSUV 11.3 5.3 1.9 23.5 62.3 15.2LEV-II-25 1032322 2012 9563 3.6 PC 12.7 5.0 2.0 18.3 39.0 17.4Dyn. blank n/a n/a n/a n/a n/a n/a 4.0 n/a n/a 3.3 n/aDyn. blank n/a n/a n/a n/a n/a n/a 2.9 n/a n/a 2.4 n/aDyn. blank n/a n/a n/a n/a n/a n/a < DL n/a n/a < DL n/aDyn. blank n/a n/a n/a n/a n/a n/a 2.8 n/a n/a 2.3 n/aDyn. blank n/a n/a n/a n/a n/a n/a 2.7 n/a n/a 2.2 n/aDyn. blank n/a n/a n/a n/a n/a n/a 4.2 n/a n/a 3.5 n/aDyn. blank n/a n/a n/a n/a n/a n/a 4.6 n/a n/a 3.9 n/aDyn. blank n/a n/a n/a n/a n/a n/a < DL n/a n/a < DL n/aDyn. blank n/a n/a n/a n/a n/a n/a 4.9 n/a n/a 4.1 n/aDyn. blank n/a n/a n/a n/a n/a n/a 4.5 n/a n/a 3.8 n/aDyn. blank n/a n/a n/a n/a n/a n/a 3.2 n/a n/a 2.7 n/aDyn. blank n/a n/a n/a n/a n/a n/a 3.6 2.3 7.2 < DL n/aDyn. blank n/a n/a n/a n/a n/a n/a 1.8 2.10 0.0 < DL n/aDyn. blank n/a n/a n/a n/a n/a n/a 2.3 2.2 0.7 < DL n/aDyn. blank n/a n/a n/a n/a n/a n/a 4.2 3.82 1.9 < DL n/aDyn. blank n/a n/a n/a n/a n/a n/a < DL 5.82 0.0 < DL n/aDyn. blank n/a n/a n/a n/a n/a n/a < DL < DL < DL < DL n/a

n/a: no record available# Chamber data; $ Thermodenuder data; @ Filter measurements collected at 45 ± 1 oC following CFR 1065 protocol

% Calculated using Equation S1for campaign-average CVS temperature of 28 ± 3 oC; & Derived as difference between quartz and quartz-behind-Teflon filter;

* Gasoline direct injectionPC Passenger car; LDT Light-duty truck; mSUV Mid-size sport utility vehicle;

fSUV Full-size sport utility vehicle; VAN Van (e.g., minibus); MPV Multi-purpose vehicle (e.g., minivan)< DL indicates lower than detection limit

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Dilution Ratios

The dilution ratios reported in Tables S1 and S2 were calculated following the approach of

Lipsky and Robinson (2006):

DR=[C O2 ]ex−[C O2 ]bkd

[C O2 ]tun− [C O2 ]bkd

(S1)

where subscripts ex, bkd, and tun represent the mixing ratio of CO2 in the exhaust (at the

tailpipe), background air, and CVS, respectively. Background and CVS concentrations of CO2

were directly measured during each test. Exhaust CO2 concentrations were calculated assuming

stoichiometric combustion:

Cn Hm+(n+ m4 ) (O2+3.76 N 2) → nC O2+

m2

H 2O+3.76(n+ m4 )N

2(S 2)

We obtain subscripts n and m from fuel analysis to be 7.08 and 15, respectively (fuel is 85%

carbon by mass). We then calculate the mixing ratio of CO2 in the exhaust as:

%C O2=n

n+ m2

+3.76(n+ m4 )

(S 3)

By substitution ofthe value of [CO2] calculated in Equation S3 into Equation S1, we are able to

calculate the CVS dilution ratios reported in Table S1.

Equilibrium Timescales

To assess whether aerosol has reached phase equilibrium, it is necessary to calculate the

equilibrium timescale , which can be approximated as the inverse of the condensation sink (CS)

[Seinfeld and Pandis, 2006]:

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τ=CS−1= (2 π d p N t DF )−1(S4 a)

F= 1+Kn

1+0.3773 Kn+1.33 Kn 1+ Knα

(S 4b)

where dp is the mass-median particle size (assumed monodisperse), Nt is the total aerosol number

concentration, D is the diffusion coefficient for the organic vapors in air (assumed to be 5 x 10-6

m2 s-1 [Riipinen et al., 2010]), and F is the Fuchs-Sutugin correction factor, which accounts for

non-continuum effects. Kn is the Knudsen number (= 2/dp, where = 65.2 nm, the mean free

path of organic molecules in air at 1 atm and 25 °C), and is the mass accommodation

coefficient. Saleh et al. [2011] recommend that a system should be treated as equilibrium only if

the ratio of the residence time in the system to is greater than 5. Assuming = 1 which appears

reasonable for our system (see Figure 5 in main text), the equilibrium timescale within the

chamber ranges from ~2-90 minutes.

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Experimental Data

Here, we provide tabular data that has been used to generate Figures 3, 4, and 5 in the main

text.

Table S2. Dilution data presented in Figures 3 and 4 in the main text.

Vehicle ID Test IDEFOA

(CVS)(mg kg-fuel-1)

Dilution ratio(CVS)

Xp

(CVS)

EFOA

(chamber)(mg kg-fuel-1)

Dilution ratio(chamber)

Xp

(chamber)

Pre-LEV-1 1032442 48.3 24.5 0.78 24.0 56.5 0.39Pre-LEV-2 1032440 110.2 22.0 0.86 45.4 30.2 0.41Pre-LEV-2 1032444 83.3 44.4 0.75 50.8 31.2 0.40Pre-LEV-3 1032303 55.5 9.9 0.90 56.1 30.4 0.91Pre-LEV-3 1032389 46.8 9.8 0.89 3.0 34.0 0.06LEV-I-5 1027904 25.9 17.1 0.69 13.9 31.3 0.37LEV-I-6 1027881 92.2 42.3 0.78 10.9 26.7 0.09LEV-I-6 1027917 96.6 40.5 0.80 26.5 29.6 0.21LEV-I-6 1027918 80.9 44.8 0.73 28.5 34.3 0.26LEV-II-2 1028022 4.8 11.2 0.61 1.2 31.3 0.15LEV-II-4* 1027971 2.4 11.7 0.45 4.5 34.0 0.84

*Gasoline direct injection vehicle

Table S3. Thermodenuder data presented in Figure 5 in the main text.

Vehicle ID Test ID COA

(g m-3)dp

(nm)MFR

(25 oC)MFR

(40 oC)MFR

(80 oC)MFR

(100 oC)MFR

(120 oC)Pre-LEV-

1 1032442 1.1 39, 214* 0.83 0.77 0.25 0.10 n/a

Pre-LEV-2 1032444 2.3 51, 171* 0.89 0.78 n/a 0.26 n/a

Pre-LEV-3 1032303 10.9 149 0.91 0.97 0.59 n/a n/a

LEV-I-5 1027904 1.5 208 n/a 1.00 0.15 n/a 0.01LEV-I-6 1027917 1.4 188 n/a 1.03 0.32 n/a 0.12LEV-I-6 1027918 1.3 185 n/a 0.73 0.26 n/a 0.21

LEV-II-4* 1027971 0.9 234 n/a 0.66 0.40 n/a n/a

n/a: no data point collected at given temperature; * Bimodal distribution

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Variability in volatility distributions

While all experimental data can be modeled reasonably well with a single volatility

distribution (Figures 3 and 5), desorption of organics from the CVS walls can bias the

measurements. Table S4 provides the volatility distributions derived for each vehicle. There is

some vehicle-to-vehicle variability, as we have already demonstrated in the main text, but

overall, the distributions are mostly consistent. Figure S1 plots the fractional amount of IVOC

mass as a function of EFQ for all the gasoline vehicles. Also shown are data for dynamic blanks

for both sets of tests at the Haagen-Smit Lab. Lower emitting vehicles, especially those tested

during Phase 1 (2010), appear to be highly influenced by organics present in the CVS

background.

Table S4. Volatility distributions from quartz filter analysis. Italicized rows were not used in deriving the

volatility distribution provided in Table 1 in the main text since their emission rate was less than 3x that of the

dynamic blank.

Vehicle ID Test IDTest EF Q

dyn. blank EFQ*

10-2 10-1 100 101 102 103 104 105 106

Pre-LEV-2 1032444 0.44 0.13 0.09 0.14 0.12 0.04 0.02 0.01 0.02

LEV-I-6 1027917 0.12 0.14 0.17 0.29 0.15 0.02 0.01 0.00 0.11

Pre-LEV-6 1028060 0.13 0.13 0.14 0.26 0.13 0.02 0.01 0.00 0.19

Pre-LEV-15 1032445 0.27 0.25 0.24 0.19 0.03 0.01 0.01 0.00 0.01

Pre-LEV-1 1032442 0.23 0.20 0.20 0.29 0.05 0.01 0.01 0.00 0.00

Pre-LEV-3 1032389 0.17 0.19 0.18 0.32 0.13 0.01 0.00 0.00 0.00

Pre-LEV-4 1032392 0.16 0.17 0.24 0.36 0.05 0.01 0.01 0.00 0.00

LEV-I-19 1028075 0.10 0.12 0.18 0.30 0.18 0.03 0.01 0.00 0.007

LEV-I-5 1027904 0.07 0.10 0.14 0.25 0.16 0.03 0.01 0.00 0.23

Pre-LEV-8 1027920 0.13 0.07 0.09 0.17 0.21 0.09 0.04 0.02 0.18

Pre-LEV-11 1027921 0.06 0.06 0.10 0.19 0.23 0.11 0.05 0.01 0.19

Pre-LEV-14 1027872 0.15 0.12 0.13 0.21 0.16 0.04 0.02 0.01 0.16

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Vehicle ID Test IDTest EF Q

dyn. blank EFQ*

10-2 10-1 100 101 102 103 104 105 106

LEV-I-14 1027972 0.15 0.14 0.20 0.29 0.10 0.04 0.03 0.00 0.05

LEV-I-13 1027968 0.44 0.13 0.12 0.11 0.09 0.02 0.01 0.00 0.08

LEV-II-8 1027908 0.10 0.07 0.13 0.14 0.18 0.11 0.07 0.02 0.18

Pre-LEV-9 1032443 0.13 0.15 0.18 0.32 0.14 0.03 0.02 0.01 0.02

LEV-II-1 1027958 0.05 0.13 0.23 0.30 0.15 0.03 0.01 0.00 0.10

LEV-I-7 1027909 0.03 0.04 0.09 0.17 0.18 0.08 0.04 0.01 0.36

LEV-II-20 1027907 0.08 0.06 0.08 0.09 0.10 0.08 0.03 0.01 0.48

LEV-I-21 1032388 0.22 0.16 0.25 0.25 0.08 0.01 0.01 0.00 0.01

LEV-II-18 1028021 0.05 0.05 0.07 0.11 0.14 0.05 0.03 0.01 0.49

LEV-II-2 1028022 0.04 0.04 0.07 0.09 0.16 0.10 0.04 0.01 0.46

Pre-LEV-10 1028029 0.03 0.05 0.10 0.19 0.18 0.11 0.09 0.02 0.24

LEV-II-13 1027863 0.01 0.02 0.05 0.11 0.20 0.13 0.04 0.01 0.43

LEV-II-17 1027848 0.01 0.02 0.04 0.06 0.08 0.07 0.04 0.01 0.67

LEV-I-2 1032304 0.14 0.12 0.11 0.15 0.24 0.12 0.06 0.03 0.04

LEV-II-4 1027971 0.02 0.02 0.04 0.12 0.25 0.05 0.02 0.01 0.46

LEV-II-5 1032342 0.15 0.10 0.13 0.21 0.23 0.07 0.05 0.02 0.03

LEV-II-24 1032383 0.13 0.06 0.09 0.15 0.26 0.17 0.07 0.03 0.04

LEV-II-25 1032322 0.05 0.05 0.11 0.19 0.28 0.17 0.08 0.03 0.05

LEV-II-23 1032310 0.02 0.04 0.09 0.19 0.35 0.17 0.08 0.03 0.04

LEV-I-12 1027870 0.02 0.04 0.09 0.17 0.17 0.05 0.02 0.01 0.42

LEV-I-3 1032346 0.16 0.10 0.15 0.25 0.21 0.06 0.04 0.02 0.02

Pre-LEV-5 1032320 0.09 0.10 0.12 0.26 0.26 0.07 0.05 0.02 0.03

LEV-II-3 1032283 0.18 0.10 0.10 0.15 0.22 0.10 0.06 0.03 0.06

* Ratio of the bare-quartz emission factor derived for the given vehicle test to the average bare-quartz emission factor derived from all dynamic blank tests. Tests with ratios < 3 (italicized) are not used in deriving the

recommended volatility distribution provided in the main text due to possible biases by desorption of organic gases from the CVS walls.

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1.0

0.8

0.6

0.4

0.2

0.0

IVO

C fr

actio

n

2 3 4 5 6 7 810

2 3 4 5 6 7 8100

EFQ (mg kg fuel-1

)

Dynamic blank EFQ

Phase 1 Phase 3POA emissions Dynamic blank

IVOCs

Figure S1. Ratio of IVOC mass to total organic mass from TD-GC-MS analysis as a function of EFQ. Lower

emitting vehicles have less measured LVOC mass, while higher emitting vehicles have more measured LVOC mass.

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Dynamic blank volatility distributions

Quartz filters were collected for dynamic blanks following the same approach as emissions

testing, analyzing for organic carbon collected on bare-quartz and QBT filters as well as TD-GC-

MS analysis for volatility distributions. Figure S2 presents a box-and-whisker plot for the

derived dynamic blank volatility distributions. Boxes represent 25th and 75th percentiles, while

whiskers represent 10th and 90th percentiles. The median volatility distribution from Table 1 in

the main text is also shown for comparison. The dynamic blank is comprised of ~50% IVOCs

(Ci* > 1000 g m-3), which is substantially more than the fraction found in the vehicle emissions.

Figure S2. Volatility distribution derived for dynamic blanks (box and whisker plots with 25th/75th and 10th/90th

percentiles) compared to the median value for POA emissions. There is significantly more IVOC mass (Ci* > 1000

g m-3) in the blank compared to the emissions. This implies that for lower emitting vehicles, gas-particle

partitioning may be affected by these adsorbed IVOC vapors.

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For low-emitting vehicles, the dynamic blank influences the overall gas-particle partitioning

inside the CVS due to higher relative amounts of IVOCs vapors. We have only used data from

the higher emitting vehicles to derive our best estimate volatility distribution. This is illustrated

in Figure S3 for different thresholds. Vehicles having EFQ ≥ 3xEFQ,blank (~50% of all vehicles

tested) do not appear to be influenced by the dynamic blank. However, the lower-emitting

vehicles do appear to be impacted by the IVOC vapors present in the CVS since predicted Xp

values are reduced. Consequently, we only use vehicles having EFQ ≥ 3xEFQ,blank to derive the

volatility distribution reported in Table 1 in the main text.

Figure S3. Predictions of gas-particle partitioning for various sets of vehicles: all vehicles, vehicles having EFQ

≥ 5xEFQ,blank, vehicles having EFQ ≥ 3xEFQ,blank, and vehicles having EFQ < 3x EFQ,blank, as well as the dynamic blank.

Gas-particle partitioning for lower-emitting vehicles (as well as the entire set of vehicles) appear to be impacted by

the dynamic blank, since there is a reduction in the predicted Xp compared to the higher-emitting vehicles.

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Comparisons to lubricating oil

Lubricating oil has been shown to be a major component of the POA emissions from motor

vehicles (Brandenberger et al., 2005; Caravaggio et al., 2007; Kleeman et al., 2008; Sonntag et

al., 2012). Figure S4 reproduces Figure 3 from the main text to compare model predictions made

using the median volatility distribution for POA emissions and the volatility distribution derived

for used lubricating oil. Both model curves do a reasonable job at reproducing the experimental

data (artifact-corrected filters and chamber AMS measurements). While the predictions for the

actual emission and lubricating oil do not agree perfectly, it is likely that there are higher

volatility organics emitted from the vehicles that are not present in the used lubricating oil

sample (e.g., from incomplete fuel combustion). Regardless, both volatility distributions do a

much better job in representing the data than the traditional, non-volatile POA assumption.

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Figure S4. Comparison of equilibrium gas-particle partitioning data to model predictions using volatility

distributions derived for POA emissions and used lubricating oil. Both predictions have equally good performance

as lubricating oil is a major component of POA emissions.

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Modeling thermodenuder data

For dynamic systems, a kinetic equation must be applied, tracking both particle- and gas-

phase concentrations of each volatility bin i:

dC p ,i

dt=−2 π d p N t DF ( Xm,i KeC i

¿−Cg ,i ) ( S 5a )

d Cg , i

dt=

−dC p ,i

dt( S 5b )

where Cp,i is the particle-phase mass concentration of i, dp is the particle diameter, D is the

diffusion coefficient of particles in air, and Cg,i is the gas-phase mass concentration. Xm,i is the

mass fraction of i in the particle phase. F is the Fuchs-Sutugin correction term and Ke represents

the Kelvin effect:

F= 1+Kn

1+0.3773 Kn+1.33 Kn(1+Knα )

(S 6)

X m,i=f i C tot

COA(1+

Ci¿ (T )

COA)−1

(S 7)

Ke=exp( 4 σ MW i

ρRT d p)(S 8)

where Kn is the Knudsen number (2/dp), is the mass accommodation coefficient, is the

surface tension of the bulk particle, MWi is the molecular weight of i, is the density of the bulk

particle, R is the ideal gas constant, and T is the temperature. We use the values of D, MWi, ,

and from Riipinen et al. (2010), while dp and COA (and thus Nt) were calculated from the

experimental measurements; these values are summarized in Table S5.

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Table S5. Summary of input parameters used to model TD data.

Parameter Value

Aerosol mass concentration, COA 1 g m-3

Mass-median particle diameter, dp 200 nm

Mass accommodation coefficient, 1

Gas-phase diffusivity, D 5 x 10-6 m2 s-1

Surface tension, 0.05 N m-1

Particle density, 1100 kg m-3

Molecular weight, MWi 454 – 45 log Ci* g mol-1

Enthalpy of vaporization, Hvap,i 85 – 11 log Ci* kJ mol-1

Ci* will vary with temperature following the Clausius-Clapeyron equation:

C i¿ (T )=Ci

¿ (298 K ) exp[−∆ H vap ,i

R ( 1T

− 1298 K )]298 K

T(S 9)

where Hvap,i is the enthalpy of vaporization of i. We use the Hvap parameterization from

Ranjan et al. (2012).

We can use this model to compare the thermodenuder data to model predictions using the

volatility distributions from Table 1 in the main text. Figure S5 reproduces Figure 5 in the main

text, adding experimental data for flash-vaporized lubricating oil sampled from vehicle LEV-I-6;

aerosol concentrations were similar to observed COA during vehicle testing. Figure S3 indicates

that the vehicle POA emissions and lubricating oil aerosol have similar behavior upon heating.

However, the two datasets diverge at high temperature (120 oC). The lubricating oil aerosol has

completely evaporated by ~100 oC, while there are ~10% of the POA emissions remaining in the

particle phase at 120 oC. This may be an indication of adsorptive partitioning, since the POA-to-

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EC ratio of the vehicle emissions decreases with heating. Alternatively, this could be attributed

to additional low-volatility compounds that are created during the combustion of fuel that are not

present in the lubricating oil. However, model predictions for both volatility distributions are

nearly indistinguishable (the curve based on motor oil is essentially superimposed upon the curve

based on the emissions), which is expected since unburned lubricating oil is a significant fraction

of vehicular POA emissions.

Figure S5. Reproduction of Figure 5 in the main text, adding experimental data for flash-vaporized lubricating

oil sampled from one of the tested vehicles and a model prediction using the volatility distribution derived for

lubricating oil. These results are expected since lubricating oil makes up a significant fraction of POA emissions

from gasoline vehicles.

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Thermodenuder Description

The TD used in the present study is described in detail in this section. A schematic of this

thermodenuder is provided as Figure S6. The heating stage is a stainless steel tube (2.7 cm ID x

65 cm L) that is wrapped in heating tape. There are three individually-controlled sections of heat

tape that are monitored simultaneously via thermocouple. This design is similar to the one

described in Huffman et al. (2008). The temperature in each of the three heating sections is set

and logged via serial connection using the iTools Engineering Studio software (Version 7.55;

Eurotherm USA, Ashburn, VA). No insulation is added outside the heating tape; the temperature

will remain relatively stable over time once the set point has been reached. The heating stage is

connected to the denuding stage using a sanitary fitting. The denuding stage is set up similarly to

previous designs such that it mimics a diffusion drier, with two concentric cylinders comprising

this stage. The outer cylinder is a solid stainless steel tube (7.8 cm ID x 51 cm L) while the inner

cylinder is a perforated stainless steel tube (2.3 cm ID x 51 cm L). The volume between the

cylinders is filled with activated carbon remove organic vapors. Residence time within the TD is

considered as the centerline residence time (tres) at 298 K and varies as a function of the sampling

flow rates of downstream aerosol instrumentation.

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Figure S6. Schematic of the thermodenuder used in the present study. The thermodenuder features a heating

zone with three individually-controlled heat tapes and a diffusion denuder filled with activated carbon. Experiments

can be performed using a bypass line to characterize aerosol at ambient temperatures while the TD is heating.

Excess flow can also be drawn through the TD to vary the CLRT in the TD.

Two optional configurations of the TD involve the addition of solenoid valves to modify

flow in the TD. The first configuration adds two electrically-actuated three-way valves (MS-

142ACX; Swagelok Co., Solon, OH) that allow the sampled aerosol to pass through the TD or an

external bypass line, depending on the valve state. During this operation, the bypass line is open

while the thermodenuder is heating to a set temperature. The valve state then changes, and

aerosol is sampled through the TD, allowing for the MFR to be calculated using Equation 3 in

the main text. The second configuration adds a single two-way solenoid valve (NEMA-1,

normal-closed; Noshok, Inc., Berea, OH) downstream of the outlet to the TD that allows for

additional (excess) flow to be drawn through the TD. The excess flow rate is controlled using a

critical orifice (O’Keefe Controls Company, Trumbull, CT). Operating in this configuration

allows for a reduction of the tres beyond what is achievable from the sampling flow rates of the

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downstream aerosol instrumentation. Both of these optional configurations are depicted in Figure

S6.

Temperature Profile

The longitudinal temperature profile in the TD was characterized to determine the centerline

temperature as a function of set point. This was measured using a thermocouple inserted to

different lengths along the center axis of the TD. As it can be seen in Figure S7, the TD does a

reasonable job of maintaining the centerline temperature to the set point.

100

80

60

40

TD te

mpe

ratu

re (o C

)

6050403020100

Axial distance from inlet (cm)

Figure S7. Longitudinal temperature profile for the thermodenuder. It can be seen that a reasonably flat internal

temperature profile is achieved. Vertical dashed lines represent position of the thermocouples connected to the three

heat tapes.

Particle Number Losses

Particle number losses within the TD were characterized as a function of temperature and

particle size. Ammonium sulfate (AS) particles were generated using a 0.1g L-1 solution and an

atomizer (TSI 3076; TSI, Inc., Shoreview, MA). Solution was supplied to the atomizer at a

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constant feed rate using a syringe pump (BS-300; Braintree Scientific, Inc., Braintree, MA). Air

feed rate to the atomizer was maintained at 25 psi. AS aerosol exited the atomizer and passed

through a diffusion drier and entered one of the Carnegie Mellon University 10 m3 Teflon

environmental chamber described in other work. Aerosol was sampled from the chamber into a

scanning mobility particle sizer (SMPS; TSI 3081/3772) to measure aerosol number size

distributions within the chamber and from the chamber through the TD into a second SMPS (TSI

3081/3010) to determined size-resolved particle number losses through the TD. Prior to TD

characterization, there was a collocation period to account for discrepancies between the two

SMPS and for differences in line losses between the two instruments. Particle transmission is

calculated using an equation identical to Equation 3 in the main text, substituting number

concentrations for mass concentrations to calculate the transmission.

Size-resolved particle number losses are shown in Figure S8a. The results at 25 oC can be

modeled reasonably well using predicted particle diffusion and sedimentation losses from Hinds

(1999). Additionally, it can be seen that particles having 50 nm < dp < 500 nm have similar

penetration through the TD as a function of temperature, which agrees with results from Romay

et al. (1998), who observed a weak size-dependence on thermophoretic losses. Since typical OA

studies have aerosol populations with the vast majority of their mass within 50-500 nm, the

particle number losses for a given temperature can be described as the integrated particle number

loss over the entire size range. These results are shown in Figure S8b. It can be seen that the

temperature can be modeled as linear with a correlation coefficient of -0.91. These losses are

similar to those reported by Huffman et al. (2008). Alternatively, number losses can be

accounted for by dividing MFR by TD transmission; this approach was used to correct for

particle number losses in this study.

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1.2

1.0

0.8

0.6

0.4

0.2

0.0

TD tr

ansm

issi

on

102 3 4 5 6 7 8 9

1002 3 4

dp (nm)

T = 25 oC

T = 40 oC

T = 60 oC

T = 80 oC

T = 100 oC

T = 120 oC

Modeled trans. (T = 25 oC)

a)1.05

1.00

0.95

0.90

0.85

0.80

0.75

TD tr

ansm

issi

on

12010080604020

Temperature (oC)

Observed integrated loss Best fit (transTD = 1.05 - 1.57e-3*T(

oC)

Huffman et al. (2007) data

b)

Figure S8. a) Size-resolved particle number transmission through the TD at different temperatures. It can be

seen that losses are relatively constant as a function of temperature between 50 and 500 nm, suggesting that size-

dependent corrections are unnecessary for the majority of aerosol systems. Results can be predicted reasonably well

at 25 oC. b) Temperature-dependent thermodenuder transmissions. There is an apparent linear correlation between

transmission and temperature. Results are comparable to Huffman et al. (2008).

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References

Brandenberger, S., Mohr, M., Grob, K., Neukomb, H.P., 2005. Contribution of unburned lubricating oil and diesel fuel to particulate emission from passenger cars. Atmos Environ 39, 6985-6994.

Caravaggio, G.A., Charland, J.P., MacDonald, P., Graham, L., 2007. n-Alkane profiles of engine lubricating oil and particulate matter by molecular sieve extraction. Environmental Science & Technology 41, 3697-3701.

Hinds, W.C., 1999. Aerosol technology : properties, behavior, and measurement of airborne particles, 2nd ed. Wiley, New York.

Huffman, J.A., Ziemann, P.J., Jayne, J.T., Worsnop, D.R., Jimenez, J.L., 2008. Development and characterization of a fast-stepping/scanning thermodenuder for chemically-resolved aerosol volatility measurements. Aerosol Sci Tech 42, 395-407.

Kleeman, M.J., Riddle, S.G., Robert, M.A., Jakober, C.A., 2008. Lubricating oil and fuel contributions to particulate matter emissions from light-duty gasoline and heavy-duty diesel vehicles. Environmental Science & Technology 42, 235-242.

Lipsky, E.M., Robinson, A.L., 2006. Effects of dilution on fine particle mass and partitioning of semivolatile organics in diesel exhaust and wood smoke. Environmental Science & Technology 40, 155-162.

Ranjan, M., Presto, A.A., May, A.A., Robinson, A.L., 2012. Temperature Dependence of Gas-Particle Partitioning of Primary Organic Aerosol Emissions from a Small Diesel Engine. Aerosol Sci Tech 46, 13-21.

Riipinen, I., Pierce, J.R., Donahue, N.M., Pandis, S.N., 2010. Equilibration time scales of organic aerosol inside thermodenuders: Evaporation kinetics versus thermodynamics. Atmos Environ 44, 597-607.

Romay, F.J., Takagaki, S.S., Pui, D.Y.H., Liu, B.Y.H., 1998. Thermophoretic deposition of aerosol particles in turbulent pipe flow. J Aerosol Sci 29, 943-959.

Sonntag, D.B., Bailey, C.R., Fulper, C.R., Baldauf, R.W., 2012. Contribution of Lubricating Oil to Particulate Matter Emissions from Light-Duty Gasoline Vehicles in Kansas City. Environmental Science & Technology 46, 4191-4199.

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