Supporting Information
Determining Source Strength of Semivolatile Organic Compounds Using Measured
Concentrations in Indoor Dust
Hyeong-Moo Shin1,*, Thomas E. McKone2,3, Marcia G. Nishioka4, M. Daniele Fallin5, Lisa A.
Croen6, Irva Hertz-Picciotto1, Craig J. Newschaffer7, Deborah H. Bennett1
1
Department of Public Health Sciences, University of California, Davis, CA, USA
2
School of Public Health, University of California, Berkeley, CA, USA
3
Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory,
Berkeley, CA, USA
4
College of Public Health, Ohio State University, Columbus, OH, USA
5
Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
6
Division of Research, Kaiser Permanente of Northern California, Oakland, CA, USA
7
Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA, USA
*Corresponding author:
Hyeong-Moo Shin, PhD
University of California, Davis
One Shields Avenue, MS1-C, Davis, CA 95616
E-mail: hmshin@ucdavis.edu
Phone: 949.648.1614
Fax: 530.752.5300
1
TABLE OF CONTENTS
Data S1. Summary of Fugacity and a Fugacity-Based Indoor Model ......................................4
Data S2. A Method to Estimate Surface Area of Couch .............................................................6
Figures .............................................................................................................................................3
Figure S1. Overview of this study ........................................................................................................ 3
Figure S2. Model framework for the indoor fugacity model ................................................................ 5
Figure S3. Dimension of couch used to estimate surface areas ............................................................ 6
Figure S4. Percent contribution of model inputs on the uncertainty of emission rates for 15 studied
compounds from the multilinear regression analysis. Input parameters with a p-value less than 0.05
from the multilinear regression with a contribution to output uncertainty less than 10% are included
together as “All others with a contribution <10%” ............................................................................... 7
Tables...............................................................................................................................................8
Table S1. Summary of Quality Control and Measured Dust Concentrations by Product Type (µg/g) . 8
Table S2. Distribution of Input Variables Used for Uncertainty Analysis (Mean and coefficient of
variation (CV) for lognormal distribution and v1 and v2 for uniform and beta distributions) (Bennett
and Furtaw, 2004; Shin et al., 2012, 2013) ........................................................................................... 9
Table S3. Properties of Particles in Three Size Fractions, Mean Value, and Coefficient of Variation in
Parentheses (Bennett and Furtaw, 2004; Shin et al., 2012) ................................................................. 10
Table S4. Fugacity Capacities for Each Phase and Compartment (Bennett and Furtaw, 2004; Shin et
al., 2012) ............................................................................................................................................. 11
Table S5. Equations Used to Compute Transfer Factors (1/d)a (Bennett and Furtaw, 2004; Shin et al.,
2012) ................................................................................................................................................... 12
Table S6. Equations Used to Compute the Fraction of the Compound in the Mobile Phase (unitless)
(Bennett and Furtaw, 2004; Shin et al., 2013)..................................................................................... 13
Table S7. Estimated Chemical Concentrations in Consumer Products ............................................... 14
Table S8. Daily Application Rate (mg/day) and Retention Factor of Consumer Products ................. 15
References .....................................................................................................................................16
2
FIGURES
Figure S1. Overview of this study
3
DATA S1. SUMMARY OF FUGACITY AND A FUGACITY-BASED INDOOR MODEL
Fugacity can be regarded as the partial pressure or the tendency of a chemical to leave or escape
from a given state or compartment (Bennett and Furtaw, 2004). Fugacity, f (Pa), is linearly
related to mass by fugacity capacity, Z (mol/m3·Pa), M = f·Z·V, where M is the mass of the
compound in the compartment (mol) and V is the volume of the compartment (m3). The fugacity
capacity defines the holding capacity of a material for a chemical substance based on the
properties of both the material and the chemical (Bennett and Furtaw, 2004).
The fugacity-based indoor mass-balance model is a dynamic mass-balance model with several
compartments simultaneously exchanging mass in time due to an imbalance of sources and
losses (Bennett and Furtaw, 2004). Figure S2 shows the model framework for the indoor fugacity
model. The mass in each compartment is the state variable, and mass transfer between
compartments is defined by mass-transfer rate coefficients accounting for both diffusive and
advective transfers. We use a boundary layer approach to quantify diffusive transfer rates
between the compartments. We define the fugacity capacity of each compartment including all
phases for advective transfer rates. The total fugacity capacity of each compartment is volume
weighted between phases. The surface-air partition coefficients are determined when the air and
surface are in equilibrium. The compartment mass is defined by a set of differential equations
accounting for all gain and loss processes. The sources of chemicals to the compartments are
direct emissions to room air and surface applications to both carpet and vinyl. The removals from
the compartments are ventilation, degradation, and surface cleaning. The model also considers
the transport/removal pathways of dust including resuspension, deposition, ventilation, and
surface cleaning.
4
Figure S2. Model framework for the indoor fugacity model
5
DATA S2. A METHOD TO ESTIMATE SURFACE AREA OF COUCH
In order to estimate the surface area of a couch, we used length, height, and depth of 66 couches.
Couch dimensions were recorded for the largest or most used couch within the main living area
of homes that were selected to participate in a sub-study, as part of the Study of Use of Products
and Exposure Related Behaviors (SUPERB) study (Hertz-Picciotto et al., 2010). California
residents from homes with young children (97% had a child 5 years or younger) and from homes
headed by older adults (generally aged 55 or above) were enrolled in the SUPERB study. All
SUPERB study homes were located in Northern or Central California. Homes for the sub-study
were selected from the main SUPERB study population based on willingness to participate in inhome environmental sample collection and blood sample collection. A sub-sampling of these
homes had couch dimension measurements collected. Figure S3 shows dimensions of a couch in
inches. Assuming that the depth (D) of the couch is large enough to ignore the thickness of back
cushion, we used the following equation to estimate the surface area of a couch in m2.
(2.54cm)2
m2
SA = 2 × {(L × D) + (L × H) + (D × H)} ×
×
(100cm)2
in2
Figure S3. Dimension of couch used to estimate surface areas
6
DEP
DiBP
DnBP
BBP
DEHP
DiNP
AHTN
HHCB
ODP
OMC
TBPH
TBB
TCEP
TPP
TDCPP
0
20
40
60
80
Percent
Area of house
Air exchange rate
Removal Rate from Cleaning - Carpet
Embedded Dust Ratio
Dust loading on carpet
Concentration on dust
All others with a contribution <10%
100
Figure S4. Percent contribution of model inputs on the uncertainty of emission rates for 15
studied compounds from the multilinear regression analysis. Input parameters with a pvalue less than 0.05 from the multilinear regression with a contribution to output
uncertainty less than 10% are included together as “All others with a contribution <10%”
7
TABLES
Table S1. Summary of Quality Control and Measured Dust Concentrations by Product Type (µg/g)
Product
type
Phthalates
Personal
care
product
ingredients
Flame
retardants
a.
b.
c.
d.
e.
f.
g.
h.
i.
Abbr
LOD a
SMB b
DEP
DIBP
DBP
BBP
DEHP
DINP
AHTN
HHCB
ODP
OMC
TBPH
TBB
TCEP
TPP
TDCPP
0.012
0.012
0.012
0.012
0.012
0.240
0.001
0.001
0.001
0.010
0.003
0.003
0.001
0.001
0.007
0.03
0.02
0.06
0.02
0.12
ND f
ND
ND
ND
ND
ND
ND
ND
ND
ND
Spikes c
(% recovery)
83 ± 6
87 ± 2
92 ± 2
80 ± 6
-- e
95 ± 9
59 ± 1
56 ± 2
68 ± 3
-- g
68 h
87 ± 25
60 ± 7
94 ± 26
128 i
Duplicates d
(rpd)
0±0
6±0
8±1
8±4
3±1
6±4
5±2
6±2
21 ± 20
11 ± 1
22 ± 19
16 ± 5
4±4
6±2
13 ± 0
% detection
(n=30)
100
100
100
100
100
100
66
100
100
100
100
100
100
100
100
Mean
0.8
6.3
10.1
24.1
154.0
170.0
0.4
0.7
0.03
13.1
0.5
2.1
1.1
3.1
8.9
Std Dev
0.7
5.2
12.0
31.4
100.0
173.0
0.4
0.4
0.03
14.9
0.5
4.1
1.4
2.9
14.6
Median
0.7
4.6
8.1
14.9
144.0
110.0
0.3
0.6
0.01
9.2
0.3
0.9
0.5
2.0
3.6
Max.
3.0
24.0
67.9
156.0
338.0
779.0
1.7
1.6
0.15
70.9
1.8
21.9
7.2
11.7
72.5
Limit of Detection; effective dust concentration for instrument signal giving quantitation ion peak area with 3:1 S:N
Solvent Method Blank, n=2
Fortification of one sample per set selected at random; average recovery ± deviation from mean, n=2
Replicate sample analysis of one sample per set selected at random; average relative percent difference (rpd) ± deviation from mean, n=2
Could not be determined as native levels in selected dusts were much higher than spike level; 92 ± 3% obtained from another program using
the method
Not Detected, <LOD
Could not be determined as native levels in selected dusts were much higher than spike level; 101% recovery for sample where native level
was 10X spike level, 194% recovery for sample where native level was 42X spike level
Anomalous 23% recovery in second spike sample
May be subjected to error as native level was 5X spike level; 227% recovery in second sample where native level was 100X spike level
8
Table S2. Distribution of Input Variables Used for Uncertainty Analysis (Mean and
coefficient of variation (CV) for lognormal distribution and v1 and v2 for uniform and beta
distributions) (Bennett and Furtaw, 2004; Shin et al., 2012, 2013)
property name (units)
symbo
l
T
mean
area of house (m2)
A
153
1.00
lognormal
height of ceiling (m)
h
2.44
0.10
lognormal
air exchange rate (1/d)
a
12.7
1.15
lognormal
thickness of organic film (m) a
δfilm
1.0 × 10-7
0.8
lognormal
fraction organic matter in films (--)
fom_f
1.0
point
fraction organic matter in dust (--)
fom_dust
0.2
point
ρfilm
1200
point
fraction of floor that is carpet (--)
ffc
0.84
carpet thickness (m)
δc
1.0 × 10-2
0.50
lognormal
vinyl thickness (m)
δv
5.0 × 10-4
0.50
lognormal
boundary layer thickness over surface (m)
δbl
3.3 × 10-2
0.77
lognormal
dust removal rate from carpet cleaning (1/d) b
kc
8.0 × 10-3
1.00
lognormal
dust removal rate from vinyl cleaning (1/d) b
kv
6.0 × 10-2
1.00
lognormal
COH
1.0 × 105
0.33
lognormal
R
8.314
embedded dust ratio c
Edr
11.3
1.00
lognormal
dust loading on carpet (kg/m2)
ρc
3.8E-04
0.75
lognormal
dust loading on vinyl (kg/m2)
ρv
8.5E-05
0.75
lognormal
airborne particle density (kg/m3)
ρd
1500
0.20
lognormal
ρdust
2000
0.20
lognormal
temperature (K)
film density (kg/m3)
OH radical concentration (molecules/cm3)
ideal gas constant (Pa·m3/mol·K)
surface dust density (kg/m3)
a
CV
298
v1
v2
dist
293
299
uniform
4.0
0.74
beta
point
From Diamond et al. (2000)
From Shin et al. (2013)
c
The ratio of the mass of total dust loading in carpet (dust removed by standard dust collection
method plus dust removed by vacuuming) to the mass removed by standard vacuuming
b
9
Table S3. Properties of Particles in Three Size Fractions, Mean Value, and Coefficient of Variation in Parentheses (Bennett
and Furtaw, 2004; Shin et al., 2012)
fraction in size fraction
carpet, fc,j
vinyl, fv,j
(unitless)
(unitless)
particle size
fraction
fraction of
organic carbon,
foc, j (unitless)
particulate
mass in air, ρp,j
(µg/m3)
vertical deposition
rate coefficient, vv,j
(1/d)
resuspension rate
coefficient, vr,j
(1/d)
0 ― 2.5
0.65 (0.20)
13 (0.85)
9 (0.30)
4.3 × 10-4 (1.00)
0.02 (1.00)
0.04 (1.00)
2.5 ― 10
0.28 (0.20)
12 (0.80)
34 (0.30)
2.7 × 10-3 (1.00)
0.06 (1.00)
0.09 (1.00)
10 ― 150
0.06 (0.25)
4.3 (0.50)
166 (0.30)
4.8 × 10-3 (1.00)
0.54 (1.00)
0.77 (1.00)
10
Table S4. Fugacity Capacities for Each Phase and Compartment (Bennett and Furtaw, 2004; Shin et al., 2012)
compartment
phase
equation
description
R is the ideal gas constant (Pa·m3/mol·K) and T is the ambient
1
pure air
temperature (K).
Zair =
R×T
air
air particles Zap,i =
Za =
carpet
Zcarpet
carpet dust
Zcp =
carpet
total carpet
vinyl
film
vinyl
vinyl dust
total vinyl
Zc =
K p,i × ρd × 109
R×T
∑3i=1 Zap,i × ρp,i
total air
Kp is the partition coefficient between particles and air (m3/µg)
and ρd is the particle density (kg/m3). log Kp,i = log Koa + log
(foc,i/0.74) -11.91, where Koa is the octanol-air partition
coefficient (unitless) and foc,i the fraction of organic carbon for
particles in a specific size fraction (unitless). Koa = (Kow
×R·T)/H, where Kow is the octanol-water partition coefficient
(unitless) and H is the Henry’s law constant (Pa·m3/mol).
ρd × 109
K ca
=
R×T
+ Zair
K pc × ρd × 109
R×T
Zcp ρc
Zcarpet δc × ( ρ ) (1 + Edr )
d
δc + ρc /ρd
ρp,i is the particle mass concentration in the air for a given size
fraction (µg/m3).
Kca is the carpet-air partition coefficient (unitless). Kca = 104.83 –
0.82 log (VP)
and VP is the vapor pressure (Pa).
Kpc is the partition coefficient between particles and air on
carpet dust (m3/µg). Kpc = 10logKoa+logfoc,c-11.91 and foc,c is the
fraction of organic carbon on carpet dust. foc,c = ∑3j=1 foc,j × fc,j
δc is the thickness of carpet (m), ρc is the particle mass
concentration on carpet (µg/m3), and Edr is the embedded dust
ratio(unitless).
Kva is the vinyl-air partition coefficient (unitless). Kva = 105.20–
K va
Zvinyl =
R×T
0.48 × K ow × fom,f × ρd
Zfilm =
H × 1000
0.68 log (VP)
fom,f is the fraction of organic matter in the film (unitless).
K pc × ρd × 109
Zcp =
R×T
Zfilm δfilm × Zvinyl δv × Zvp ρv /ρd
Zv =
δfilm + δv + ρv /ρd
11
Kpv is the partition coefficient between particles and air on vinyl
dust (m3/µg). Kpv = 10logKoa+logfoc,v-11.91 and foc,c is the fraction of
organic carbon on carpet dust. foc,c = ∑3j=1 foc,j × fv,j
δfilm is the thickness of film (m), δv is the thickness of vinyl (m),
and ρv is the particle mass concentration on vinyl (µg/m3).
Table S5. Equations Used to Compute Transfer Factors (1/d)a (Bennett and Furtaw, 2004; Shin et al., 2012)
variables symbol
advective
diffusive
description
air to
carpet
air to vinyl
carpet to
air
vinyl to air
a
vv is the vertical deposition rate coefficient (1/d), h is the height of the ceiling (m),
Zap is the fugacity capacity of air particles (mol/m3·Pa), ρp is the particle mass
concentration in the air for a given size fraction (µg/m3), ρd is the dust particle
density (kg/m3), Za is the fugacity capacity of the air compartment, ffc is the fraction
of floor that is carpet (unitless), and Yac is the fugacity-based transfer factor between
the air and carpet (mol/(m2Pa·d)). For VP>35.4, Yac = 10(-1.64-0.31·log (VP)) and for
VP≤35.4, Yac = Dair·Zair/δbl, where Dair is the diffusion coefficient in pure air (m2/d),
δbl is the boundary layer thickness (m).
Ta_c
vv Zap × ρp
(
) (ffc )
h ρd × Za
Yac
ff
Za h c
Ta_v
vv Zap × ρp
(
) (ffv )
h ρd × Za
Yav
ff
Za h v
ffv is the fraction of floor that is vinyl (unitless) and Yav is the fugacity-based transfer
factor between the air and vinyl (mol/(m2Pa·d)). For VP>0.16, Yav = 10(-2.38-0.33·log
(VP))
and for VP≤0.16, Yav = Dair·Zair/δbl
Tc_a
Zcp × ρc
vrc (
)
ρd δc Zc
Yac
Zc δc
vrc is the resuspension rate coefficient from carpet (1/d), Zcp is the fugacity capacity
of carpet dust, ρc is the particle mass concentration on carpet (kg/m3), δc is the
thickness of carpet (mol/m3·Pa), Zc is the fugacity capacity of the carpet
compartment
Tv_a
Zvp × ρc
vrv (
)
ρd δv Zv
Yav
Zv δv
vrv is the resuspension rate coefficient from vinyl (1/d), Zvp is the fugacity capacity of
vinyl particles, ρv is the particle mass concentration on vinyl (kg/m3), δv is the
thickness of vinyl (mol/m3·Pa), Zv is the fugacity capacity of the vinyl compartment
Total transfer factors are the sum of advective and diffusive mass transfer terms
12
Table S6. Equations Used to Compute the Fraction of the Compound in the Mobile Phase (unitless) (Bennett and Furtaw,
2004; Shin et al., 2013)
compartment symbol
equation
description
Mcp
carpet
θc
(Mcp + Mcarpet )
1
=
Zcarpet ∙ Vcarpet
(1 +
Zcp ∙ Vcp )
Vcarpet is the volume of carpet (m3) and Vcp is the volume of carpet
dust (m3). Vcarpet = A·ffc·δc. Vcp = A·ffc· (ρc· (1+Edr)/ρp), where A is
the area of house (m2)
Mvp + Mfilm
vinyl
θv
(Mvp + Mfilm + Mvinyl )
Vvinyl is the volume of vinyl (m3), Vcp is the volume of vinyl dust (m3),
1
Vfilm is the volume of film in the vinyl flooring (m3). Vvinyl = A· (1=
Zvinyl ∙ Vvinyl
(1 + Z ∙ V + Z
) ffc)·( δfilm+(ρv/ρp)+δv). Vvp = A·(1-ffc)· (ρv/ρp). Vfilm = A· (1-ffc) · δfilm.
vp
vp
film ∙ Vfilm
13
Table S7. Estimated Chemical Concentrations in Consumer Products
HHCB
AHTN
Octinoxate
(Dodson et al., 2012; (Dodson et al., 2012;
(Dodson et al., 2012)
Roosens et al., 2007) Roosens et al., 2007)
10
10
body lotion
316.2
10
shampoo
316.2
conditioner
10
shaving cream
face lotion
10
facial cleanser
10
10
deodorant
foundation
lipstick
10
10
hair spray/gel
3162
3162
fragrance
316.2
body wash
nail polish
3162
10
10
sunscreen
14
DEP
(Dodson et al., 2012;
Roosens et al., 2007)
10
316.2
10
10
10
316.2
316.2
10
10
3162
10
DIBP
(Dodson et al., 2012)
3162
10
10
Table S8. Daily Application Rate (mg/day) and Retention Factor of Consumer Products
Application rate
(Wormuth et
(Loretz et
(Loretz et al., (Neale et al.,
(Hall et al.,
Reference
al., 2005)
al., 2006)
2008)
2002)
2007)
body lotion
4,543
shampoo
5,203
12,800
6,034
conditioner
13,770
shaving cream
684
face lotion
906
facial cleanser
4,060
deodorant
559
790
foundation
670
lipstick
24.6
hair spray/gel
3,225
3,570
fragrance
203
530
body wash
1,450
nail polish
143
sunscreen
1,500
15
Average
4,543
8,012
13,770
684
906
4,060
675
670
25
3,398
367
1,450
143
1,500
Retention factor
(Wormuth et al.,
2005)
1
0.01
0.01
0.05
1
0.01
1
1
1
1
1
0.05
0.05
1
REFERENCES
Bennett, D.H. and Furtaw, E.J. (2004) Fugacity-based indoor residential pesticide fate model,
Environ. Sci. Technol., 38, 2142-2152.
Diamond, M.L., Gingrich, S.E., Fertuck, K., McCarry, B.E., Stern, G.A., Billeck, B., Grift, B.,
Brooker, D. and Yager, T.D. (2000) Evidence for organic film on an impervious urban surface:
Characterization and potential teratogenic effects, Environ. Sci. Tech., 34, 2900-2908.
Dodson, R.E., Nishioka, M., Standley, L.J., Perovich, L.J., Brody, J.G. and Rudel, R.A. (2012)
Endocrine disruptors and asthma-associated chemicals in consumer products, Environ. Health
Perspect., 120, 935-943.
Hall, B., Tozer, S., Safford, B., Coroama, M., Stelling, W., Leneveu-Duchemin, M.C.,
McNamara, C. and Gibney, M. (2007). European consumer exposure to cosmetic products, a
framework for conducting population exposure assessments, Food Chem. Toxicol., 45, 20972108.
Hertz-Picciotto, I., Cassady, D., Lee, K., Bennett, D.H., Ritz, B. and Vogt, R. (2010) Study of
Use of Products and Exposure-Related Behaviors (SUPERB): study design, methods, and
demographic characteristics of cohorts, Environ. Health, 9.
Loretz, L., Api, A.M., Barraj, L., Burdick, J., Davis, D.A., Dressler, W., Gilberti, E., Jarrett, G.,
Mann, S., Pan, Y.H.L., Re, T., Renskers, K., Scrafford, C. and Vater, S. (2006) Exposure data for
personal care products: Hairspray, spray perfume, liquid foundation, shampoo, body wash, and
solid antiperspirant, Food Chem. Toxicol., 44, 2008-2018.
Loretz, L.J., Api, A.M., Babcock, L., Barraj, L.M., Burdick, J., Cater, K.C., Jarrett, G., Mann, S.,
Pan, Y.H.L., Re, T.A., Renskers, K.J. and Scrafford, C.G. (2008) Exposure data for cosmetic
products: Facial cleanser, hair conditioner, and eye shadow, Food Chem. Toxicol., 46, 1516-1524.
Neale, R., Williams, G. and Green, A. (2002) Application patterns among participants
randomized to daily sunscreen use in a skin cancer prevention trial, Archives of Dermatology,
138, 1319-1325.
Roosens, L., Covaci, A. and Neels, H. (2007) Concentrations of synthetic musk compounds in
personal care and sanitation products and human exposure profiles through dermal application,
Chemosphere, 69, 1540-1547.
Shin, H.M., McKone, T.E. and Bennett, D.H. (2012) Intake fraction for the indoor environment:
a tool for prioritizing indoor chemical sources, Environ. Sci. Technol., 46, 10063-10072.
Shin, H.M., McKone, T.E., Tulve, N.S., Clifton, M.S. and Bennett, D.H. (2013) Indoor residence
times of semivolatile organic compounds: Model estimation and field evaluation, Environ. Sci.
Technol., 47, 859-867.
Wormuth, M., Scheringer, M. and Hungerbuhler, K. (2005) Linking the use of scented consumer
products to consumer exposure to polycyclic musk fragrances, J. Ind. Ecol., 9, 237-258.
16