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Risk Evaluation for
Perchloroethylene
(Ethene, l,l»2,2-Tetrachloro)
CASRN: 127-18-4
Supplemental File:
Perchloroethylene Exposure from
Consumer Products and Articles
AEPA
United States
Environmental Protection Agency
Office of Chemical Safety and
Pollution Prevention
CI CI
CI CI
April 2020, DRAFT
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1 Consumer Modeling Details
EPA evaluated perchloroethylene exposure resulting from the use of relevant consumer products
and consumer articles. A full systematic review of the literature was conducted.
Perchloroethylene concentrations measured in residential air or personal breathing zone samples
are reported in Section 2.4.2.1. Monitoring and/or controlled laboratory data were available for a
limited number of consumer use scenarios. Where necessary, EPA utilized a modeling approach
to estimate perchloroethylene exposure via use of consumer products and articles (Section
2.4.2.2.2).
1.1 Consumer Exposure
Consumer products containing perchloroethylene are readily available at retail stores and via the
internet for purchase and use. Use of these products can result in exposures of the consumer user
and bystanders to perchloroethylene during and after product use. Consumer exposure can occur
via inhalation, dermal, and oral routes.
Consumer products containing perchloroethylene were identified through review and searches of
a variety of sources, including the National Institutes of Health (NIH) Household Products
Database, various government and trade association sources for products containing
perchloroethylene, company websites for Safety Data Sheets (SDS), Kirk-Othmer Encyclopedia
of Chemical Technology, and the internet in general. Identified consumer products were then
categorized into sixteen consumer use groups considering (1) consumer use patterns, (2)
information reported in SDS, (3) product availability to the public, and (4) potential risk to
consumers. Table 2-1 summarizes the sixteen consumer use groups evaluated as well as the
routes of exposure for which they were evaluated.
Table 2-1: Consumer Uses and Routes of Exposure Assessed
Consumer Uses
CEM Scenario
Routes of
Exposure
1. Aerosol Cleaners for Motors; Coils;
Electrical Parts; Cables; Stainless
Steel; Marine Equipment; Wire and
Ignition Demoisturants
Degreasers I
Inhalation and
Dermal
2. Parts cleaner
Generic (Liquid Bath)
3. Brake Cleaner
Degreasers II
4. Vandalism mark & stain remover;
Mold cleaner; Weld splatter protectant
Ail-Purpose Spray Cleaner
5. Marble polish; Stone cleaner (liquid)
Ail-Purpose Liquid Cleaner
6. Cutting fluid
Non-Spray Lubricant
7. Spray lubricant; Penetrating oil
Spray Lubricant
8. Industrial adhesive; Adhesive; Arts
and crafts adhesive; Gun ammunition
sealant
Glues and Adhesives
9. Livestock grooming adhesive
Spray Fixative and Coatings
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10. Caulk; Sealant; Column Adhesives
Caulk
11. Coatings; Primers (aerosol)
Aerosol Spray Paint
12. Rust primer; Sealant
Solvent-Based Wall Paint I
13. Metallic overglaze (ceramics)
Lacquers and Stains
14. Sealant (outdoor water shield)
Solvent-Based Wall Paint II
15. Stone cleaner; Marble polish (wax)
All-Purpose Waxes and
Polishes
16. Dry Cleaning (vapor, articles)
Article Diffusion (dermal);
(MCCEM was use for
inhalation estimation)
The U.S. EPA evaluated acute inhalation and dermal exposure of the consumer to
perchloroethylene for this evaluation. Acute inhalation exposure is an expected route of exposure
for all sixteen consumer use groups. Acute dermal exposure is also a possible route of exposure
for sixteen consumer use groups. The U.S. EPA does not expect exposure under any of the
sixteen consumer use groups evaluated to be chronic in nature and therefore does not present
chronic exposure for consumers. The U.S. EPA does not expect oral exposure to occur under any
of the sixteen consumer use groups evaluated and therefore did not evaluate the oral route of
exposure.
The U.S. EPA evaluated inhalation and dermal exposure for the consumer user and evaluated
only inhalation exposure for a non-user (bystander) located within the residence during product
use. The consumer user consisted of three age groups (adult, greater than 21 years of age; Youth
A, 16-20 years of age; and Youth B, 11-15 years of age) which includes the susceptible
population woman of childbearing age. The bystander can include individuals of any age (infant
through elderly).
1.2 Consumer Modeling
The model used to evaluate consumer exposures was EPA's Consumer Exposure Model (CEM).
Table 2-2 summarizes the specific models used for each consumer use group and the associated
routes of exposure evaluated.
Table 2-2: Models Used for Routes of Exposure Evaluated
Consumer Uses
Routes of Exposure
Inhalation
Dermal
1. Cleaners for Motors; Coils;
CEM
CEM
Electrical Parts; Cables; Stainless
Steel; Marine Equipment; Wire and
Ignition Demoisturants (aerosol)
2. Parts cleaner (liquid)
CEM
CEM
3. Brake Cleaner (aerosol)
CEM
CEM
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4. Vandalism mark & stain remover;
CEM
CEM
Mold cleaner; Weld splatter
protectant (aerosol)
5. Marble and stone polish (liquid)
CEM
CEM
6. Cutting fluid (liquid)
CEM
CEM
7. Spray lubricant; Penetrating oil
CEM
CEM
(aerosol)
8. Industrial adhesive; Adhesive; Arts
CEM
CEM
and crafts adhesive; Gun
ammunition sealant (liquid)
9. Livestock grooming adhesive
CEM
CEM
(aerosol)
10. Caulk; Sealant; Column adhesive;
CEM
CEM
(gel/liquid)
11. Coatings; Primers (aerosol)
CEM
CEM
12. Rust primer; Sealant (liquid)
CEM
CEM
13. Metallic overglaze (liquid)
CEM
CEM
14. Sealant (outdoor water shield)
CEM
CEM
(liquid)
15. Stone cleaner; Marble polish
CEM
CEM
(gel/wax)
16. Dry Cleaning (vapor, articles)
MCCEM
CEM
Readers are referred to each model's user guide and associated user guide appendices for details
on each model, as well as information related to equations used within the models, default
values, and the basis for default values. Each model is peer reviewed. Default values within
CEM are a combination of high end and mean or central tendency values derived from U.S.
EPA's Exposure Factors Handbook, literature, and other studies.
1.3 CEM Approach
CEM is a deterministic model which utilizes user provided input parameters and various
assumptions (or defaults) to generate exposure estimates. In addition to pre-defined scenarios,
which align well with the sixteen consumer uses identified in Table 2-1, CEM is peer reviewed,
provides flexibility to the user allowing modification of certain default parameters when
chemical-specific information is available and does not require chemical-specific emissions data
(which may be required to run more complex indoor/consumer models).
CEM predicts indoor air concentrations from consumer product use through a deterministic,
mass-balance calculation derived from emission calculation profiles within the model. There are
six emission calculation profiles within CEM (E1-E6) which are summarized in the CEM users
guide and associated appendices (U.S. EPA 2019). If selected, CEM provides a time series air
concentration profile for each run. These are intermediate values produced prior to applying pre-
defined activity patterns.
CEM uses a two-zone representation of the building of use when predicting indoor air
concentrations. Zone 1 represents the room where the consumer product is used. Zone 2
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represents the remainder of the building. Each zone is considered well mixed. CEM allows
further division of Zone 1 into a near field and far field to accommodate situations where a
higher concentration of product is expected very near the product user when the product is used.
Zone 1-near field represents the breathing zone of the user at the location of the product use
while Zone 1 far field represents the remainder of the Zone 1 room.
Inhalation exposure is estimated in CEM based on zones and pre-defined activity patterns. The
simulation run by CEM places the product user within Zone 1 for the duration of product use
while the bystander is placed in Zone 2 for the duration of product use. Following the duration of
product use, the user and bystander follow one of three pre-defined activity patterns established
within CEM, based on modeler selection. The selected activity pattern takes the user and
bystander in and out of Zone 1 and Zone 2 for the period of the simulation. The user and
bystander inhale airborne concentrations within those zones, which will vary over time, resulting
in the overall estimated exposure to the user and bystander.
CEM contains two methodologies for estimating dermal exposure to chemicals in products, the
permeability method (P-DER1) and the fraction absorbed method (A-DER1). Each of these
methodologies further has two model types, one designed for dermal exposure from use of a
product (P-DERI a and A-DERla), the other designed for dermal exposure from use of an article
(P-DERlb and A-DERlb). Each methodology has associated assumptions, uncertainties anddata
input needs within the CEM model. Both methodologies factor in the dermal surface area to
body weight ratio and weight fraction of chemical in a consumer product.
The permeability model is based on the ability of a chemical to penetrate the skin layer once
contact occurs. The permeability model assumes a constant supply of chemical, directly in
contact with the skin, throughout the exposure duration. The ability to use the permeability
method can be beneficial when chemical-specific skin permeability coefficients are available in
the scientific literature. However, the permeability model within CEM does not consider
evaporative losses when it estimates dermal exposure and therefore may be more representative
of a dermal exposure resulting from a constant supply of chemical to the skin due to a barrier or
other factor that may restrict evaporation of the chemical of interest from the skin (a product
soaked rag against the hand while using a product), or immersion of a body part into a pool of
product. Either of these examples has the potential to cause an increased duration of dermal
contact and permeation of the chemical into the skin resulting in dermal exposure.
The fraction absorbed method is based on the absorbed dose of a chemical. This method
essentially measures two competing processes, evaporation of the chemical from the skin and
penetration of the chemical deeper into the skin. This methodology assumes the application of
the chemical of concern occurs once to an input thickness and then absorption occurs over an
estimated absorption time. The fraction absorbed method can be beneficial when chemical
specific fractional absorption measurements are available in the scientific literature. The
consideration of evaporative losses by the fraction absorbed method within CEM may make this
model more representative of a dermal exposure resulting from scenarios that allow for
continuous evaporation and typically would not involve a constant supply of product for dermal
permeation. Examples of such scenarios include spraying a product onto a mirror and a small
amount of mist falling onto an unprotected hand.
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All consumer use groups identified in Table 2-2 and evaluated with CEM used CEM's El, E2,
E3, or E5 emission model and profile for inhalation exposure. For the El emission model, the
model assumes a constant application rate over a user-specified duration of use. Each
instantaneously applied segment has an emission rate that declines exponentially over time, at a
rate that depends on the chemical's molecular weight and vapor pressure. For the E2 emission
model, the model assumes an initial fast release by evaporation followed by a slow release
dominated by diffusion. The E3 emission model assumes a percentage of a consumer product
used is aerosolized (e.g. overspray) and therefore immediately available for uptake by inhalation.
Finally, the E5 model is for products that are placed in the environment but not added to water.
The U.S. EPA also used the near-field and far-field option within CEM for all consumer use
groups evaluated with CEM. For dermal exposure within CEM, the permeability method model,
P-DER2b was used for the sixteen consumer scenarios.
In an effort to characterize a potential range of consumer inhalation exposures, the EPA varied
three key parameters within the CEM model while keeping all other input parameters constant.
The key parameters varied were duration of use per event (minutes/use), amount of chemical in
the product (weight fraction), and mass of product used per event (gram(s)/use). These key
parameters were varied because they provide representative consumer behavior patterns for
product use. Additionally, CEM is highly sensitive to two of these three parameters (duration of
use and weight fraction). A summary of a sensitivity analysis performed of CEM is provided in
Appendix E with details provided within the CEM users guide and associated CEM user guide
appendices. Finally, all three parameters had a range of documented values within literature
identified as part of Systematic Review allowing the EPA to evaluate inhalation exposures across
a spectrum of use conditions.
To characterize a potential range of consumer dermal exposures, the EPA varied two key
parameters within CEM while keeping all other input parameters constant. The key parameters
varied for dermal exposure evaluation were weight fraction and duration of use per event. The
mass of product used is not a factor in the dermal exposure equations within CEM and therefore
was not varied.
Once the data was gathered for the parameters varied, modeling was performed to cover all
possible combinations of these three parameters. This approach results in a maximum of 27
different iterations for each consumer use. Certain uses, however, only had a single value for one
or more of the parameters varied which reduces the total number of iterations.
Post-processing to determine personal concentration exposures for the user and bystander was
conducted by independently assigning the Zone 1, Zone 2, and outside (zero) concentration to
the user and bystander. These zone concentrations were assigned based on the pre-defined
activity patterns within CEM. Time-weighted average concentration exposures were then
calculated from the personal exposure time series for each base case and scaled to develop
estimates for all iterations within each consumer use category. Time weighted averages (TWA)
were determined for 1 hour, 3 hours, 8 hours, and 24 hours, although for this evaluation the 24-
hour TWA concentration was utilized based on health endpoints used to calculate risks.
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1.4 CEM Inputs
Numerous input parameters are required to generate exposure estimates within CEM. These
parameters include physical chemical properties of the chemical of concern, product information
(product density, water solubility, vapor pressure, etc.), model selection and scenario inputs
(pathways, CEM emission model(s), emission rate, activity pattern, product user, background
concentration, etc.), product or article property inputs (frequency of use, aerosol fraction, etc.),
environmental inputs (building volume, room of use, near-field volume in room of use, air
exchange rates, etc.), and receptor exposure factor inputs (body weight, averaging time, exposure
duration inhalation rate, etc.). Several of these input parameters have default values within CEM
based on the pre-defined use scenario selected. Default parameters within CEM are a
combination of high end and mean or median values found within the literature or based on data
taken from U.S. EPA's Exposure Factors Handbook (U.S. EPA 2011). Details on those
parameters can be found within the CEM Users Guide and associated Users Guide Appendices at
(U.S. EPA 2019). or can be cross referenced to U.S. EPA's Exposure Factors Handbook (U.S.
EPA 2011). As discussed earlier, while default values are initially set in pre-defined use
scenarios, CEM has flexibility which allows users to change certain pre-set default parameters
and input several other parameters.
Key input parameters for the sixteen consumer uses identified in Table 2-4 evaluated with CEM
are discussed below. Detailed tables of all input parameters used for each consumer use
evaluated with CEM are provided in Table 2-5.
Physical chemical properties of perchloroethylene were kept constant across all consumer uses
and iterations evaluated. The saturation concentration in air (one of the factors considered for
scaling purposes) was estimated by CEM as 1.65E+05 milligrams per cubic meter. A chemical-
specific skin permeability coefficient of 0.018 centimeters per hour was estimated within CEM
and utilized for all scenarios modeled for dermal exposure.
Model selection is discussed in the previous section (CEM modeling approaches). Scenario
inputs were also kept constant across all consumer uses and iterations. Emission rate was
estimated using CEM. The activity pattern selected within CEM was stay-at-home. The start
time for product use was 9:00 AM and the product user was adult (>21 years of age) and Youth
(16 through 20 years of age). The background concentration of perchloroethylene for this
evaluation was considered negligible and therefore set at zero milligrams per cubic meter.
Frequency of use for acute exposure calculations was held constant at one event per day. The
aerosol fraction (amount of overspray immediately available for uptake via inhalation) selected
within CEM for all consumer uses evaluated was six percent. Building volume used for all
consumer uses was the default value for a residence within CEM (492 cubic meters). The near-
field volume selected for all consumer uses was one cubic meter. Averaging time for acute
exposure was held constant at one day.
Certain model input parameters were varied across consumer use scenarios but kept constant for
all model iterations run for that particular consumer use. These input parameters include product
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density, room of use, and pre-defined product scenarios within CEM. Product densities were
extracted from product-specific SDS. Room of use was extracted from an EPA directed survey
of consumer behavior patterns in the United States titled Household Solvent Products: A
National Usage SurvevfWestat 1987) (Westat Survey), identified in the literature search as part
of systematic review. The Westat survey is a nationwide survey which provides information on
product usage habits for thirty-two different product categories. The information was collected
via questionnaire or telephone from 4,920 respondents across the United States.
The Westat Survey was rated as a high-quality study during data evaluation within the systematic
review process. The room of use selected for this evalution is based on the room in which the
Westat Survey results reported the highest percentage of respondents that last used a product
within the room. When the Westat Survey identified the room of use where the highest
percentage of respondents last used the product as "other inside room", the utility room was
selected within CEM for modeling. The pre-defined product scenarios within CEM were selected
based on a cross-walk to similar product categories within the Westat Survey. A crosswalk
between the perchloroethylene Consumer Use Scenarios and the corresponding Westat product
category selected to represent the exposure scenario is provided below. In instances where a pre-
defined product was not available within CEM, a generic model scenario was assigned in CEM
with would run the requisite inhalation, emission, and dermal models.
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Table 2-3: Crosswalk Between perchloroethvlene Consumer Use Scenarios and Westat Product Category
Perchloroethylene Consumer Use
Scenario
Representative Westat Product Category
1. Aerosol Cleaners for Motors;
Solvent-Type Cleaning Fluids Or Degreasers
Coils; Electrical Parts; Cables;
Stainless Steel; Marine
Equipment; Wire and Ignition
Demoisturants
2. Parts Cleaner
Spot Removers
3. Brake Cleaner
Brake Quieters/Cleaners
4. Vandalism Mark & Stain
Remover; Mold Cleaner; Weld
Solvent-Type Cleaning Fluids Or Degreasers
Splatter Protectant
5. Marble Polish; Stone Cleaner
(liquid)
Solvent-Type Cleaning Fluids Or Degreasers
6. Cutting Fluid
Other Lubricants (Excluding Automotive)
7. Spray Lubricant; Penetrating oil
Other Lubricants (Excluding Automotive)
8. Industrial Adhesive; Adhesive;
Contact Cement, Super Glues, And Spray Adhesives
Arts and Crafts Adhesive; Gun
Ammunition Sealant
9. Livestock Grooming Adhesive
Contact Cement, Super Glues, And Spray Adhesives
10. Caulk; Sealant; Column
Adhesive
Primers And Special Primers (Excluding Automotive)
11. Coatings; Primers (aerosol)
Aerosol Spray Paint
12. Rust Primer; Sealant
Primers And Special Primers (Excluding Automotive)
13. Metallic Overglaze
Contact Cement, Super Glues, And Spray Adhesives
14. Sealant (Outdoor Water Shield)
Outdoor Water Repellent
15. Stone Cleaner; Marble Polish
Solvent-Type Cleaning Fluids Or Degreasers
(wax)
16. Dry Cleaning (vapor, articles)
N/A
Additional key model input parameters were varied across both consumer use scenario and
model iterations. These key parameters were duration of use per event (minutes/use), amount of
chemical in the product (weight fraction), and mass of product used per event (gram(s)/use).
Duration of use and mass of product used per event values were both extracted from the Westat
Survey (Westat 1987). To allow evaluation across a spectrum of use conditions, the EPA chose
the Westat Survey results for these two parameters from the above cross-walked product
categories representing the tenth, fiftieth (median), and ninety-fifth percentile data, as presented
in the Westat Survey.
The amount of chemical in the product (weight fraction) was extracted from product specific
SDS. This value was varied across the given range of products within the same category to
obtain three values, when available. Unlike the Westat survey results which gave percentile data,
however, product specific SDS across products did not have percentile data so the values chosen
represented the lowest weight fraction, mean weight fraction (of the range available), and the
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highest weight fraction found. Even using this approach, some SDS were only available for a
single product with a single weight fraction or very small range, or multiple products which only
provided a single weight fraction or a very small range. For these product scenarios, only a single
weight fraction was used in CEM for modeling. The following table summarizes the input
parameter values used for these three parameters by consumer use.
Table 2-4: Model Input Parameters Varied by Consumer Use
Consumer Use
Duration of Use
Mass of Product Used
Amount of Chemical
In Product
(minutes/use)
(gram(s)/use)
(weight fraction)
10th
50th
95th
10th
50th
95th
Low
Mean
High
AEROSOL Cleaners
for Motors; Coils;
Electrical Parts;
Cables; Stainless Steel;
2.0
15.0
120.0
26.83
155.69
1532.91
0.1
0.8
1
Marine Equipment;
Wire and Ignition
Demoisturants
Parts cleaner
0.25
5.00
30.00
9.91
52.70
441.01
0.5
0.6
Brake Cleaner
1.0
15.0
120.0
39.03
156.13
624.52
0.4
0.91
1
Vandalism mark &
stain remover; Mold
cleaner; Weld splatter
2.0
15.0
120.0
26.83
155.69
1532.91
0.05
0.40
1
protectant
Marble polish
2.0
15.0
120.0
61.88
330.05
1608.99
0.10
0.85
1
Cutting fluid
0.08
2.00
30.00
26.83
155.69
1532.91
0.1 (sing
e)
Spray Lubricant;
Penetrating oil
0.08
2.00
30.00
4.79
26.35
239.52
0.05
0.54
1
Industrial adhesive;
Adhesive; Arts and
crafts adhesive; Gun
0.33
4.25
60.00
1.16
9.68
167.34
0.3
0.89
1
ammunition sealant
Livestock grooming
adhesive
0.33
4.25
60.00
1.29
10.72
185.23
0.15
Caulk; Sealant;
Column adhesive
5.0
30.0
360.0
45.39
387.07
8121.46
0.05
0.48
0.75
Coatings; Primers
5.0
20.0
120.0
61.88
330.05
1608.99
0.09
.010
0.14
Rust primer; Sealant
5.0
30.0
360.0
53.22
453.82
9521.90
0.09
0.1
0.11
Metallic overglaze
0.33
4.25
60.00
0.89
7.39
127.74
0.2
0.3
Sealant (water shield)
15.0
60.0
300.0
302.80
2422.37
24223.74
0.45
Stone cleaner; Marble
polish
2.0
15.0
120.0
23.18
134.54
1324.74
0.85
0.95
1
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1.5 MCCEM Approach
1.5.1 Basis for Modeling Analysis
The setup of the modeling analysis was based on papers by Tichenor (1990) and Sherlach (20111 which
were identified as high-quality and most relevant during systematic literature review. The Tichenor (1990)
authors were affiliated with EPA's Indoor Air Branch (Air and Energy Engineering Research Laboratory,
Research Triangle Park, NC). They measured perchloroethylene concentrations in a small chamber and in
a test house due to off-gassing from 12 freshly dry-cleaned fabrics - Arnel (triacetate), Acetate (diacetate),
Polypropylene, Spun Dacron 54, Spun Dacron 64, Polyester double knit, Nylon 66, Orion, Acrilan, Wool,
and Fiberglass - under different conditions (in a bag, out of a bag, and aired out). The authors fit a model
to the emissions and included a reversible sink in an attempt to explain the measured concentrations. The
objectives for this modeling exercise were (1) to determine whether the measurements in the EPA test
house could be reasonably matched and (2) if so, to extend the model to a generic house as a basis for
estimating exposures for the dry-cleaning scenario(s). The Sherlach (2011) study measured residual PCE
retained in wool, polyester, cotton and silk fabrics cleaned at five different commercial dry cleaners using
PCE as the cleaning solvent. Concentrations of PCE retained in fabrics were measured by GC/MS
immediately after single dry-cleaning cycles and after each of six repeat dry-Ocleaning cycles, for each
fabric type, at each dry-cleaning establishment.
The EPA test house layout, shown in Figure 1 from the Tichenor (1990) paper on the next page, was used
to develop volumes for the zone of use (closet), adjacent zone (bedroom), and the rest of house (ROH).
Tichenor et al. did not report include the house volume, individual zone volumes, or airflow rates
(although they indicated that air exchange rates were measured). In a separate paper (Chang et al. 1998) by
authors from the same EPA branch, the house volume was reported as 305 m3 and the whole-house air
exchange rate as 0.5 h-1. Individual room volumes and airflow rates were not reported; room volumes
were estimated from the house diagram (closet volume = 3 m3 and bedroom volume excluding closet = 24
m3) and the interzonal airflow rate (IAR) between the bedroom and ROH was estimated using the Koontz
and Rector algorithm3. For the initial modeling step, airflow rates between the closet (near-field zone) and
bedroom (far-field zone) were set to 10% of the airflow rate between the bedroom and ROH, as shown in
Table 2-1.
Table 2-1. MCCEM Airflow Rates (m3/hr) for the EPA Test House (Volume
= 305 m3) "
Zones
OA
Near
Field
(Closet)
Far Field
(Bedroom)
ROH
OA
==
0
12
139
Near Field (Closet)
0
==
7.1
0
Far Field
(Bedroom)
12
7.1
..
71
ROH
139
0
71
==
1 Koontz, MD, and Rector, HR. 1995, Estimation of Distributions for Residential Air Exchange Rates, final report
for USEPA Office of Pollution Prevention and Toxics, GEOMET Technologies, Inc. IAR = (0.078 + 0.31*A)*V,
where
IAR = Interzonal air exchange rate(m3/h), A = whole-house air exchange rate (l/li), and V = house volume (m3).
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-J Moslem
X Both
rr
Master
Bedroom
Bath
G/y
Cfeoninq
CI as
is [ CI pa
CI 0901
ft* turn
rEffl
Corner
Bed-oom
Ctos
^'
Utility
Middle
Bed-oom
© Den
L-.
Kitchen D|ning
i Room
Llvfng Room
;
ill
Clas
instruments
Garage
(U » Sampling Location
= Registers
Fig. 1. IAQ test hinise.
In the Tichenor study, the clothes (wool skirt, two polyester/rayon blouses and a two-piece suite)
were dry cleaned at a commercial facility where the clothing was bagged, immediately
transported to the house, and placed in the closet of the corner bedroom. The closet doors were
closed; all other interior doors were opened.
The house air was sampled at three locations (closet, bedroom, and den). Tichenor et al. fit the
measured air concentrations to a model that included the following three equations:
(1)
The souiee term used to model the perchloroethyl-
ene emission was based on the small chamber data
and is in the lorm:
E U)=R0c
(6)
A re-emitting sink was used in the perchloroethyl-
;nc modeling. The rate going to the sink was assumed
to be:
(2) R. = k,C,<*. (7)
(3)
Et=ktM,At (Cr - CJ, when Cr > Ct (8)
E,=0, when C, Ct {9)
The first equation is a standard first-order exponential emission model, the second is a first-order
sink model, and the third is a concentration-dependent re-emission model.
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The three figures below show the average daily concentrations extracted from the bar charts
presented by Tichenor for the four cases in the closet, bedroom, and den, respectively, as well the
concentrations predicted by Tichenor's model:
1 2 3 4 5 6 7
Time, days
Time, days
—©— Bedroom, Bag off (1)
—A—Bedroom, Bag on
—Bedroom, Aired Out
—•—Bedroom, Bag off (2)
- — -Tichenor Bedroom Model
The use of the aired-out case was not chosen because it deviated substantially from the others.
For example, at all three sampling locations the concentrations for the first two days for that case
were more than double those for all other cases, then dropped to values similar to the other cases
for days 3-6 and rose substantially on day 7.
MCCEM does not have the capability of representing the concentration-dependent re-emission
model shown above in Tichenor's Equations 8 and 9. Although MCCEM does have a reversible-
sink capability based on mass in the sink, that model does not include a concentration-feedback
term (Cr-Cc). For these reasons, and because only the concentrations for the aired-out case rose
toward the end of the experiments, the fit using only the emission term (i.e., without considering
sinks) was initially evaluated, reserving the possibility of using the MCCEM reversible-sink
model as a follow-up strategy.
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1.5.2 MCCEM Parameterization and Predictions for EPA Test House
The single-exponential emission model in MCCEM was parameterized with the values from
Tichenor's paper (Ro= 1.6 mg/m2, k = 0.03/h, A = 8.6 m2). The initial emission rate (13.76
mg/h) was determined by multiplying R0 by A and the theoretical perchloroethylene mass
available for release to the indoor air (459 mg), which can be obtained by integrating Tichenor's
Equation 6 from time = 0 to time = go, was determined by dividing this initial emission rate by k.
As shown in the figures below, the model with these values over predicted the concentrations on
day 1 and then declined more rapidly than the measured concentrations, resulting in substantial
under prediction from day 3 onward.
1.8
1.6
1.4
1.2
"e
00
E 1
Model Parameters:
Emission, Single Exponential:
k = 0.03h1;E0 = 13.76 mg/h, Total Mass = 459 mg
Whole House ACH = 0.5 h1
^—Closet (MCCEM, no Sinks)
^—Bedroom (MCCEM, no Sinks)
ROH (MCCEM, no Sinks)
• Closet, Bag off (1)
• Closet, Bag off (2)
~ Bedroom, Bag off (2)
¦ Bedroom, Bag off (1)
~ Den (ROH), Bag off (1)
~ Den (ROH), Bag off (2)
Concentration,
o o o o
) nj cn co h
•
• \
•
•
y t
•
•
•
¦
t
u
0
2 4
6 8 10 12
Time, days
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
To "slow down" the rapid decline, the value for k was lowered by a factor of three (from 0.03 to
0.01 h"1) while adjusting the initial emission rate to maintain the same theoretical
perchloroethylene mass available for release. As shown in the figure below, the model with
these values improved the fit somewhat but erred in the opposite direction; that is, with these
values the model tended to under predict on the first several days but did come closer to
matching the data toward the end of the time series.
1.6
1.4
1.2
£ 1
CUD
£
•2 0.8
Model Parameters:
Emission, Single Exponential:
k = 0.01 h E0 = 4.58 mg/h. Total Mass = 459 mg
Whole House ACH = 0.5 h 1
Closet (MCCEM, no Sinks)
—Bedroom (MCCEM, no Sinks)
ROH (MCCEM, no Sinks)
• Closet, Bag off (1)
• Closet, Bag off (2)
~ Bedroom, Bag off (2)
¦ Bedroom, Bag off (1)
~ Den (ROH), Bag off (1)
~ Den (ROH), Bag off (2)
Time, days
Close-up of Bedroom
and Den Concentration!
0.14
0.12
0.1
*£
m0.08
£
c
o
2 0.06
u0.04
0.02
Model Parameters:
Emission, Single Exponential:
~
~
k = 0.01 h"1; E0 = 4.58 mg/h. Total Mass = 459 mg
Whole House ACH = 0.5 h1
^—Bedroom (MCCEM, no Sinks)
ROH (MCCEM, no Sinks)
~ Bedroom, Bag off (2)
¦ Bedroom, Bag off (1)
~ Den (ROH), Bag off (1)
¦\l
~ \
~
¦
~
N. ¦
¦
~ Den (ROH), Bag off (2)
~
~
4 6
Time, days
10
12
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
As shown below, lowering the whole-house air exchange rate from 0.5 to 0.33 h"1 generally
resulted in an improved fit for the bedroom and the ROH, but there still was some under
prediction for both the closet and the bedroom concentrations on the first day.
Model Parameters:
Emission, Single Exponential:
k = 0.01 h1; E0 = 4.58 mg/h, Total Mass = 459 mg
Whole House ACH = 0.33 h1
E
m0.08
E
o
t?0.06
^—Bedroom (MCCEM, no Sinks)
ROH (MCCEM, no Sinks)
~ Bedroom, Bag off (2)
¦ Bedroom, Bag off (1)
~ Den (ROH), Bag off (1)
~ Den (ROH), Bag off (2)
4 6
Time, days
Close-up of Bedroom
and Den
Concentrations
•
Model Parameters:
Emission, Single Exponential:
k = 0.01 h"1; E0 = 4.58 mg/h, Total Mass = 459 mg
Whole House ACH = 0.33 h1
—Closet (MCCEM, no Sinks)
^—Bedroom (MCCEM, no Sinks)
ROH (MCCEM, no Sinks)
• Closet, Bag off (1)
• Closet, Bag off (2)
~ Bedroom, Bag off (2)
¦ Bedroom, Bag off (1)
~ Den (ROH), Bag off (1)
~ Den (ROH), Bag off (2)
•
• \
•
•
• V
~ n
V-
-» ¦
0 2 4 6 8 10 12
Time, days
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
To "fine tune" the model, several different combinations of k and the closet-bedroom airflow
rate were tried, judging the combination of k = 0.011"1 and an airflow rate of 5.5 m3/h to best
"split the difference" between the low and high values in both the closet and the bedroom on the
first day of the two tests.
Model Parameters.
Emission, Single Exponential:
k = O.Oll h-';Ea = 5.05 mg/h, Total Mass = 459 mg
Near Field to Far Field Flow = 5.5 m3/hr
Whole House ACH = 0.33 h'1
EPA Test House Closet (MCCEM, no Sinks)
EPA Test House Bedroom (MCCEM, no Sinks)
EPA Test House ROH (MCCEM, no Sinks)
0 2 4 6 8 10 12
Time, days
Close-up of Bedroom
and Den
Concentrations
~
~
Model Parameters:
Emission, Single Exponential:
k = 0.011 h1; Eq = 5.05 mg/h,Total Mass = 459 mg
Near Field to Far Field Flow =5.5 m3/hr
Whole House ACH = 0.33 h"1
¦ ¦
~
~
EPA Test House Bedroom (MCCEM, no Sinks)
EPA Test House ROH (MCCEM, no Sinks)
~ Bedroom, Bag off (2)
¦ Bedroom, Bag off (1)
~ Den (ROH), Bag off (1)
~ Den (ROH), Bag off (2)
¦
<~
fl
4^:
0 2 4 6 8 10 12
Time, days
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
1.5.3 Model Application to Generic House
Because the above modeling approach appeared to fit the data well, it was concluded that the
reversible sink could be ignored and the modeling could proceed with a more simplistic
representation. This model was then applied to a generic house with the volume (446 m3) in
CEM. The house and zone volumes for this house are shown, in comparison to those for the
EPA test house, in the table below.
Zone
Zone Volumes (m3)
EPA Test House
Generic House
Near Field (Closet)
3
5
Far Field (Bedroom)
24
31
Rest of House
278
410
TOTAL HOUSE
305
446
VOLUME
The airflow rates, summarized in the table below, were scaled up based on house volume (i.e.,
from the 305-m3 EPA test house to the 446-m3 generic house); an air exchange rate of 0.45 hr"1
was assumed for consistency with CEM runs.
M
CCEM Air Flows for the Generic House (Volume = 446 m3)
ZONES
OA
Near
Field
(Closet)
Far Field
(Bedroom)
ROH
OA
0
13.95 184.5
Near Field (Closet)
0
8.0
0
Far Field
(Bedroom)
13.95
8.0
97
ROH
184.5 0
97
The clothes included in the Tichenor test consisted of five items (wool skirt, two polyester/rayon
blouses and a two-piece suite) with a combined of 8.6 m2. As a first approximation, the clothing
area by the ratio of house volumes were scaled up (446/305) for a new quantity of 12.6 m2,
adding 4 m2 of fabric or approximately 2 additional pieces of clothing. As shown in the figure
below, predictions for the generic house with scaled-up values were nearly identical to those for
the EPA house. The peak predicted air concentration in the closet ( ~ 1 mg/m3) is well below the
saturation concentration (~ 1.65 E+05 mg/m3).
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Model Parameters:
EPA House Emission, Single Exponential:
k = 0.011 h»; E0 = 5.05 mg/h, Total Mass =459 mg
Whole House ACH = 0.33 h1
NFF=5.5 mVh
Generic House Emission, Single Exponential:
k = 0.011 h1; Eo = 7.38 mg/h,Total Mass = 670 mg
Whole House ACH = 0.45 h1
NFF S m3/h
Generic House Closet (MCCEM, nosinks)
— » EPA Test House Closet (MCCEM, noSinks)
EPA Test House Bedroom (MCCEM, no Sinks)
— — — Generic House Bedroom (MCCEM, nosinks)
Generic House ROH (MCCEM, no sinks)
V EPA Test House ROH (M CCEM, no Sinks)
0 2 4 6 8 10 12
Time, days
1.1.1.1 Modeling Recommendations and Issues
Based on the above analysis, the following are recommended parameters for the MCCEM base
case:
k = 0.011 hr"1 based on the above fit to the Tichenor data
Eo = 7.38 mg/h based on the fitted value to the Tichenor data, adjusted for an increased
amount of dry-cleaned clothing.
It is recommended that MCCEM be executed for the generic house base case, as described
above. Because the saturation concentration will not be exceeded, the modeling results can be
scaled to the desired quantity of clothing for different percentiles of an assumed distribution (to
be determined). The mass (i.e., area of clothing) will scale in direct proportion to the initial
emission rate, assuming that the rate constant for emissions decay, k, remains constant. Both
chronic and acute inhalation dose will scale proportionally to mass, and the chronic dose will
scale proportionally to frequency of use.
The CEM activity pattern for a stay-at-home adult for a product/article in the bedroom is
recommended as a basis for estimating inhalation exposure for the dry-cleaning scenario, with
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
one added perturbation, namely an assumed small amount of time (e.g., 5 minutes or less) in the
morning and in the evening spent in the closet (near field). Based on this adjustment (see
highlights), the recommended activity pattern is given in the table below.
Activity Patterns for the Dry-Cleaning Scenario
Location
Start Time
End Time
Stay at Home
Full Time
Part-Time
12:00 AM
1:00 AM
Residence - Bedroom
Residence - Bedroom
Residence - Bedroom
1:00 AM
2:00 AM
Residence - Bedroom
Residence - Bedroom
Residence - Bedroom
2:00 AM
3:00 AM
Residence - Bedroom
Residence - Bedroom
Residence - Bedroom
3:00 AM
4:00 AM
Residence - Bedroom
Residence - Bedroom
Residence - Bedroom
4:00 AM
5:00 AM
Residence - Bedroom
Residence - Bedroom
Residence - Bedroom
5:00 AM
6:00 AM
Residence - Bedroom
Residence - Bedroom
Residence - Bedroom
6:00 AM
7:00 AM
Residence - Bedroom
Residence - Bedroom
Residence - Bedroom
7:00 AM
7:55 AM
Residence - Bathroom
Residence - Bathroom
Residence - Bathroom
7:55 AM
8:00 AM
Residence - Closet
Residence - Closet
Residence - Closet
8:00 AM
9:00 AM
Automobile
Automobile
Automobile
9:00 AM
10:00 AM
Office/School
Office/School
Office/School
10:00 AM
11:00 AM
Residence - Living Room
Office/School
Office/School
11:00 AM
12:00 PM
Residence - Living Room
Office/School
Office/School
12:00 PM
1:00 PM
Residence - Kitchen
Office/School
Office/School
1:00 PM
2:00 PM
Outside
Office/School
Office/School
2:00 PM
3:00 PM
Residence - Living Room
Office/School
Residence - Living
Room
3:00 PM
4:00 PM
Residence - Living Room
Office/School
Residence - Living
Room
4:00 PM
5:00 PM
Residence - Utility Room
Office/School
Residence - Utility
Room
5:00 PM
6:00 PM
Outside
Outside
Outside
6:00 PM
7:00 PM
Residence - Kitchen
Residence - Kitchen
Residence - Kitchen
7:00 PM
8:00 PM
Residence - Living Room
Residence - Living
Room
Residence - Living
Room
8:00 PM
9:00 PM
Residence - Living Room
Residence - Living
Room
Residence - Living
Room
9:00 PM
9:55 PM
Residence - Bedroom
Residence - Bedroom
Residence - Bedroom
9:55 PM
10:00 PM
Residence - Closet
Residence - Closet
Residence - Bathroom
10:00 PM
11:00 PM
Residence - Bedroom
Residence - Bedroom
Residence - Bedroom
11:00 PM
12:00 AM
Residence - Bedroom
Residence - Bedroom
Residence - Bedroom
For youth and child inhalation exposures, one key decision is whether or not to assume that dry-
cleaned fabrics would be present in their respective bedrooms; if not, then their bedrooms should
be treated as part of the ROH. Alternatively, exposure estimates could be developed for both
possibilities. Other key modeling inputs are the perchloroethylene mass (related to assumed
number of clothing items) and the frequency with which dry-cleaned clothing is assumed to be
brought into the house
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
The dermal uptake would result primarily from vapor-phase contact, but direct contact (as well
as added inhalation exposure) also could occur if any clothing items are worn within the primary
off-gassing period of approximately 7 days. The vapor-phase model incorporated in CEM is
recommended for use.
1771
Where:
K?_g = Indoor air transdermal permeability coefficient that describes transport of a gas-phase chemical from air
in the core of a room ttirough the boundary layer adjacent to skin and then through the stratum
corneum/viable epidermis composite to dermal capillaries (m/h)
V& = Deposition velocity (m/h)
KP b = Permeability coefficient that describes the transport of a gas-phase chemical from the boundary layer s
the skin surface (b) through the stratum comeum/viable epidermis composite to dermal capillaries
(m/h)
DerFluxzl,c = (78)
, \ SA
I c X FracTime^ c 4- DerFlux^ c X FracTime^ c) X -=rrrr X ED,?r X CFi
= J ^ ^ —
X CF2
(jDffrfiu:rzl,fl x FracTimeTirC + DerFluxZ2,a X PmiTiBie«>.) X X X CFx
~ ATac X CF2
Where:
Cg,z2c = Average chronic gas phase concentration
CF = Conversion factor (10000 crn^m1)
£)erF!tixzl c. = Chronic dermal flux
—
SH'
FracTimezi^ = Fraction of time in Zone
= Surface area to body weight ratio (cmVkg)
EDcr = Exposure duration, chronic (years)
CFi - Conversion fa nor (24 hrs/d ay)
ATcr = Averaging time, chronic (years)
CF2 = Conversion factor (1000 iig/mg)
The calculation can be incorporated into the spreadsheet analysis using parameters extracted
from CEM, estimated using PARAMS, or from the literature search.
1.6 Consumer Exposure Results
All modeling results were exported into an Excel workbook for additional processing and
summarizing. Outputs from the models used for consumer exposure were in units of mg/m3.
Health endpoints were provided in parts per million (ppm), therefore the U.S. EPA converted
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
units from mg/m3 to ppm by multiplying the concentration output by the molar volume (24.45)
and dividing by the molecular weight of perchloroethylene (165.833 g/mol) using the following
equation.
Concentration (ppm) = 24.45 X concentration (mg/m3)/MW
All modeling inputs and outputs are summarized in Table 2-5.
Table 2-5: Consumer Exposure Inputs and Outputs by Consumer Use Group Evaluated
Consumer
Uses
Inputs
Out
DUtS
Inhalation
Dermal
Inhalation
Dermal
Aerosol
Cleaners for
Motors;
Coils;
Electrical
Parts; Cables;
Stainless
Steel; Marine
Equipment;
Wire and
Ignition
Demoisturant
s
i
1
i
1
Degreasers I
(aerosol) Inhalation.
Degreasers I
(aerosol) Dermal.xlsx
Degreasers I
(aero sol), xlsx
Degreasers I
(aero sol), xlsx
Parts cleaner
Continuous Action
Air Freshener (proxy
Continuous Action
Air Freshener (proxy
Continuous Action
Air Freshener (proxy
Continuous Action
Air Freshener (proxy
Brake Cleaner
Degreasers III
(aero sol ).xlsx
Degreasers III
(aero sol ).xlsx
Degreasers III
(aerosol) Inhalation.
Degreasers III
(aerosol) Dermal.xlsx
Vandalism
mark & stain
remover;
Mold cleaner;
Weld splatter
protectant
All-Purpose Spray
Cleaner (aerosol).xls
H,
m
All-Purpose Spray
Cleaner (aerosol).xls
All-Purpose Spray
Cleaner (aerosol) In
All-Purpose Spray
Cleaner (aerosol) De
Marble
polish; Stone
Cleaner
(liquid)
All-Purpose Liquid
Cleaner (liquid).xlsx
e
All-Purpose Liquid
Cleaner (liquid).xlsx
All-Purpose Liquid
Cleaner (liquid) Inha
All-Purpose Liquid
Cleaner (liquid) Der
Cutting fluid
Non-Spray
Lubricant (liquid).xls
£3
Non-Spray
Lubricant (liquid).xls
Non-Spray
Lubricant (liquid) In
Non-Spray
Lubricant (liquid) De
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
Spray
lubricant;
Penetrating
oil
Spray Lubricant
(aero sol ).xlsx
Spray Lubricant
(aero sol ).xlsx
Spray Lubricant
(aerosol) Inhalation.
Spray Lubricant
(aerosol) Dermal.xlsx
Industrial
adhesive;
Adhesive;
Arts and
crafts
adhesive; Gun
ammunition
sealant
Glues and
Adhesives (liquid).xl
Glues and
Adhesives (liquid).xl
Glues and
Adhesives (liquid) In
Glues and
Adhesives (liquid) D
Livestock
grooming
adhesive
Spray Fixative and
Finishing Spray Coa
e
Spray Fixative and
Finishing Spray Coa
Spray Fixative and
Finishing Spray Coa
Spray Fixative and
Finishing Spray Coa
Caulk;
Sealant;
Column
adhesive
Caulk (liquid).xlsx
Caulk (liquid).xlsx
Caulk (liquid)
Inhalation, xlsx
Caulk (liquid)
Dermal .xlsx
Coatings;
Primers
(aerosol)
Aerosol Spray Paint
(aero sol ).xlsx
Aerosol Spray Paint
(aero sol ).xlsx
Aerosol Spray Paint
(aerosol) Inhalation.
Aerosol Spray Paint
(aerosol) Dermal.xlsx
Rust primer;
Sealant
Solvent-Based Wall
Paint I (liquid).xlsx
e
Solvent-Based Wall
Paint I (liquid).xlsx
Solvent-Based Wall
Paint I (liquid) Inhal
Solvent-Based Wall
Paint I (liquid) Derm
Metallic
overglaze
Lacquers and Stains
(liquid).xlsx
Lacquers and Stains
(liquid).xlsx
Lacquers and Stains
(liquid) Inhalation.xl
Lacquers and Stains
(liquid) Dermal.xlsx
Sealant
(outdoor
water shield)
Solvent-Based Wall
Paint II (liquid).xlsx
Solvent-Based Wall
Paint II (liquid).xlsx
Solvent-Based Wall
Paint II (liquid) Inhal
Solvent-Based Wall
Paint II (liquid) Derm
Stone cleaner;
Marble polish
(wax)
All-Purpose Waxes
and Polishes (liquid
fcd
All-Purpose Waxes
and Polishes (liquid
Ail-Purpose Waxes
and Polishes (liquid
Ail-Purpose Waxes
and Polishes (liquid
Dry Cleaning
(vapor,
articles)
Dry Cleaning
Inhalation MCCEM I
Dry Clean
Article-Dermal input
Dry Cleaning
Inhalation MCCEM
Dry Clean
Article-Dermal outp
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
2 Model Sensitivity Analysis
Model sensitivity analyses conducted on the models used for this evaluation enable users to
identify what input parameters have a greater impact on the model results (either positive or
negative). This information was used for this evaluation to help justify the approaches used and
input parameters varied for the modeling.
2.1 CEM Sensitivity Analysis
The CEM developers conducted a detailed sensitivity analysis for CEM version 1.5, as described
in Appendix C of the CEM User Guide.
In brief, the analysis was conducted on non-linear, continuous variables and categorical variables
that were used in CEM models. A base run of different models using various product or article
categories along with CEM defaults was used. Individual variables were modified, one at a time,
and the resulting Chronic Average Daily Dose (CADD) and Acute Dose Rate (ADR) were then
compared to the corresponding results for the base run. Two chemicals were used in the
analysis: bis(2-ethylhexyl) phthalate was chosen for the SVOC Article model (emission model
E6) and benzyl alcohol for other models. These chemicals were selected because bis(2-
ethylhexyl) phthalate is a SVOC, better modeled by the Article model, and benzyl alcohol is a
VOC, better modeled by other equations.
All model parameters were increased by 10% except those in the SVOC Article model (increased
by 900% because a 10% change in model parameters resulted in very small differences). The
measure of sensitivity for continuous variables was elasticity, defined as the ratio of percent
change in each result to the corresponding percent change in model input. A positive elasticity
means that an increase in the model parameter resulted in an increase in the model output
whereas a negative elasticity had an associated decrease in the model output. For categorical
variables such as receptor and room type, the percent difference in model outputs for different
category pairs was used as the measure of sensitivity. The results are summarized below for
inhalation vs. dermal exposure models and for categorical vs. continuous user-defined variables.
2.1.1 Exposure Models
For the first five inhalation models (E1-E5) a negative elasticity was observed when increasing
the use environment, building size, air zone exchange rate, and interzone ventilation rate. All of
these factors decrease the chemical concentration, either by increasing the volume or by
replacing the indoor air with cleaner (outdoor) air. Increasing the weight fraction or amount of
product used had a positive elasticity because this change increases the amount of chemical
added to the air, resulting in higher exposure. Vapor pressure and molecular weight also tended
to have positive elasticities.
For most inhalation models, the saturation concentration did not have a notable effect on the
ADR or the CADD. Mass of product used and weight fraction both had a positive linear
relationship with dose. All negative parameters had elasticities less than 0. 4, indicating that
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PEER REVIEW DRAFT. DO NOT CITE OR QUOTE
some terms (e.g., air exchange rates, building volume) mitigated the full effect of dilution. That
is, even though the concentration is lowered, the effect of removal/dilution is not stronger than
that of the chemical emission rate. Most models had an increase in dose with increasing duration
of use. Increasing this parameter typically increases the peak concentration of the product, thus
giving a higher overall exposure.
The results for the dermal model were different from the inhalation models, in that the elasticities
for CADD and ADR were nearly the same. This outcome is consistent with the model structure,
in that the chemical is placed on the skin so there is no time factor for a peak concentration to
occur. The modeled exposure is based on the ability of a chemical to penetrate the skin layer
once contact occurs. Dermal permeability had a near linear elasticity whereas log Kow and
molecular weight had zero elasticities.
2.1.2 User-defined Variables
These variables were separated into categorical vs. continuous. For categorical variables there
were multiple parameters that affected other model inputs. For example, varying the room type
changed the ventilation rates, volume size and the amount of time per day that a person spent in
the room. Thus, each modeling result was calculated as the percent difference from the base run.
For continuous variables, each modeling result was calculated as elasticity.
Among the categorical variables, both inhalation and dermal model results had a positive change
when comparing an adult to a child and to a youth, with dermal having a smaller change between
receptors than inhalation and the largest difference occurring between an adult and a child for
both models. The time of day when the product was used and the duration of use occurred while
the person was at home; thus, there was no effect on the ADR because the acute exposure period
was too short to be affected by work schedule. Most rooms had a negative percent difference for
inhalation, with the single exception of the bedroom where the receptor spent a large amount of
time with a smaller volume than the living room. For dermal, the only room that resulted in a
large percent difference was office/school, due to the fact that the person spent only V2 hour at
that location when the stay-at-home activity pattern was selected. For inhalation, changing from
a far field to a near field base resulted in a higher ADR and CADD, likely because the near field
has a smaller volume than that of the total room.
There are three input parameters for the near-field, far-field option for CEM product inhalation
models. To determine the sensitivity of model results to these inputs, CEM first was run in base
scenario with the near-field option, after which separate runs were performed whereby the near-
field volume was increased by 10%, the far-field volume was increased by 10%, and the air
exchange rate was increased by 10%. For inhalation, both the air exchange rate and volume had
negative elasticities, but the air exchange rate had a much higher elasticity (near one) than the
volume (0.11).
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3 Supplemental Information for Consumer Exposure
3.1 Systematic Review for Perchloroethylene for Consumer Exposure Data Evaluation Tables
See supplemental file: Draft Perchloroethylene Risk Evaluation Systematic Review Supplemental File: Data Quality Evaluation for Data
Sources on Consumer and Environmental Exposure
3.2 Monitoring Data Extracted for Perchloroethylene for Indoor Air, Personal Breathing Zone, Surface Water, and
Wastewater
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
Indoor Air (jig/m-3)
US
Michigan (south-
east)
Commercial/Public
Office area of
commercial
buildings (n=4),
including two art
museums, a
university building
and a tire store/auto
service. Stationary
samples collected
2005-
2008
5(0.8)
0.002
ND to 39.7
8.02 (mean);
0.1 (median)
0.91
2214330
(Jia et al. 2010)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
from breathing
height.
US
Detroit, MI area
Residential
Homes (n=126)
with children with
asthma
2009-
2010
126
(0.91)
0.09
ND to 13.7
0.71 (mean);
0.26 (median)
2443355
(Chin et al. 2014)
High
US
California
(statewide)
Commercial/Public
F urniture/hardware
stores (n=8)
2011-
2013
58(0.48)
0.32
0.32 to 22.2
5.6 (mean);
NR (median)
2535652
(Chan et al.
High
2014)
us
California
(statewide)
Commercial/Public
Grocery stores
(n=8) '
2011-
2013
76 (0.32)
0.32
0.32 to 5.9
1 (mean);
NR (median)
2535652
(Chan et al.
High
2014)
us
California
(statewide)
Commercial/Public
Apparel stores
(n=2)
2011-
2013
20 (0.3)
0.32
0.32 to NR
0.2 (mean);
NR (median)
2535652
(Chan et al.
2014)
High
us
Baltimore, MD
Commercial/Public
(Near Source:
photocopy shop)
Personal samples
from breathing
zone. One from
each of the three
printing centers.
2000
4(1)
NR
0.678 to 3.39
2.04 (mean);
1.36 (median)
4.75
1953674
(Stefaniak et al.
2000)
High
us
Baltimore, MD
Commercial/Public
(Near Source:
photocopy shop)
Area samples from
different locations
within each of the
three printing
centers.
2000
17(0.94)
NR
ND to 21.7
2.04 (mean);
1.36 (median)
1953674
(Stefaniak et al.
2000)
High
us
Elizabeth, N.T;
Houston, TX; and
Los Angeles, CA
Residential
Non-smoking
households (n=310)
1999-
2001
539 (NR)
0.21
NR
1.85 (mean);
0.82 (median)
7.29
2128575
(Su et al. 2013)
Medium
us
CA (five regions)
Commercial/Public
Commercial
buildings (n= 37), 1
m from floor: Fleet
2011
40 (0.94)
0.22
ND to 118
NR (mean);
NR (median);
0.18 (GM)
1062239
(Wu et al. 2011)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
service / Gas
station
convenience
store, Dentist
office /
Healthcare
facility,
Grocery /
Restaurant,
Hair salon /
Gym, Office
Miscellaneous,
Retail
US
Southeast
Michigan
Residential Homes
(n = 15) sampled in
various locations in
the home (upstairs
downstairs)
2005
15(0.73)
0.07
NRto 4.4
0.6 (mean);
NR (median)
1065558
(Batterman et
al. 2007)
High
US
Southeast
Michigan
Residential
Garages of
residences (n = 15)
2005
15(0.33)
0.07
NRto 1.6
0.3 (mean);
NR (median)
1.7
1065558
(Batterman et
al. 2007)
High
us
Boston, MA
Residential
Garage of
residences
2004-
2005
16(0.81)
0.07
ND to NR
2.8 (mean);
0.3 (median)
3.4
1065844
(Dodson et al.
2008)
High
us
Boston, MA
Residential
Apartment hallway
of residences
2004-
2005
10(0.9)
0.07
ND to NR
1.9 (mean);
0.8 (median)
0.92
1065844
(Dodson et al.
2008)
High
us
Boston, MA
Residential
Basement of
residences
2004-
2005
52 (0.98)
0.07
ND to NR
1.7 (mean);
0.5 (median)
3.1
1065844
(Dodson et al.
2008)
High
us
Boston, MA
Residential
Interior room of
residences
2004-
2005
83 (0.92)
0.07
ND to NR
1.9 (mean);
0.6 (median)
0.2
1065844
(Dodson et al.
2008)
High
us
Los Angeles
Residential
Homes (n=35) in
inner-city
neighborhood,
sampled in the fall
2000
32(1)
0.15
0.6 to 6.8
1.8 (mean);
1.3 (median)
1.9
1066049
(Sax et al.
2004)
High
us
Los Angeles, CA
Residential
Homes (n=40) in
2000
40(1)
0.15
0.7 to 11
2.3 (mean);
1.9 (median)
8.7
1066049
(Sax et al.
2004)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
inner-city
neighborhood
sampled in the
winter
US
New York, NY
Residential
Homes (n=41) in
inner-city
neighborhood,
sampled in the
summer
1999
30 (0.78)
0.15
ND to 43
5.3 (mean);
2 (median)
13.1
1066049
(Sax et al.
2004)
High
US
New York, NY
Residential
Homes (n=38) in
inner-city
neighborhood,
sampled in the
winter
1999
36(1)
0.15
0.8 to 78
6.7 (mean);
3.5 (median)
1.2
1066049
(Sax et al.
2004)
High
us
Ami Arbor,
Ypsilanti, and
Dearborn
Michigan
Residential
Residences
(n=159) in
industrial, urban
and suburban cities
over two seasons
2004-
2005
252
(0.99)
0.02
ND to 27.8
0.93 (mean);
0.39 (median)
1488206
(Jia et al. 2008a)
Medium
us
CA
School
Early childhood
education facilities
(n=33) at sample
height of 1 ni.
2010-
2011
33 (0.52)
NR
0.07 to 7.8
0.4 (mean);
0.1 (median);
0.1 (GM)
5.31
3453092
(Hoana et al.
2016)
High
us
Southern
California
Commercial/Public
Gene Autry
Museum, sampled
in various areas (an
exhibit area,
hallway near truck
delivery door, and
conservation room)
1989
600 (NR)
NR
0.20 to 5.97
NR (mean);
NR (median)
235
28104
(Hisham and
Grosiean 1991)
Medium
us
Southeast Chicago
Residential
Urban homes
(n=10) sampled
over a 10-month
1994-
1995
48(1)
NR
0.54 to 13.1
2.61 (mean);
2.17 (median)
31210
(Van Winkle and
Scheff 2001)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
No. of
Samples
(Det.
Freq.)
Concentration
Reference (HERO ID)
Country
State/City/Region
Site
Year
Detection
Level
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
period. Stationary
samples were
collected from the
kitchen in the
breathing zone.
US
NR
Commercial/Public
(Near Source:
printmaking)
Printmaking art
studio at a
university (n =1).
Mechanically
vented second-floor
studio, with area
samples collected
near a cleaning
station and in the
middle of the studio
during a
printmaking
session.
2002
18(<1)
NR
ND to NR
0.4 (mean);
0.18 (median)
1.2
49414
(Rvan et al. 2002)
High
US
NR
Commercial/Public
Non-art related
floor at a
university, three
floors above a
printmaking floor
with separate
ventilation (n = 1).
Area samples
collected from
hallway.
2002
18(<1)
NR
ND to NR
0.4 (mean);
0.18 (median)
8.1
49414
(Rvan et al. 2002)
High
us
Washington, DC
area
Coin Operated
Laundry with Dry
Cleaning Machines
Laundry facility
(Site A), sampled at
6 to 7 ft above floor
1980
18(1)
NR
617 to 1357
882 (mean);
NR (median)
58127
(Howie 1981)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
No. of
Samples
(Det.
Freq.)
Concentration
Reference (HERO ID)
Country
State/City/Region
Site
Year
Detection
Level
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
at three locations.
Use of dry cleaning
machine low, but
dry-cleaned clothes
stored on site.
Large facility.
Good airflow.
US
Washington, DC
area
Coin Operated
Laundry with Dry
Cleaning Machines
Laundry facility
(Site C), sampled at
6 to 7 ft above floor
at three locations.
Eight attendant
operated dry
cleaning machines
on-site. Good air
circulation because
of floor plan, front
door open at all
times.
1980
18(1)
NR
1696 to
18318
8820 (mean);
NR (median)
58127
(Howie 1981)
High
US
Washington, DC
area
Coin Operated
Laundry with Dry
Cleaning Machines
Laundry facility
(Site B), sampled at
6 to 7 ft above floor
at three locations. 2
attendant operated
dry-cleaning
machines on-site.
Ventilation and
circulation good,
front door open
regularly.
1980
18(1)
NR
509 to 4749
2171 (mean);
NR (median)
58127
(Howie 1981).
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
No. of
Samples
(Det.
Freq.)
Concentration
Reference (HERO ID)
Country
State/City/Region
Site
Year
Detection
Level
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
US
Washington, DC
area
Coin Operated
Laundry with Dry
Cleaning Machines
Laundry facility
(Site D), sampled at
6 to 7 ft above floor
at three locations.
Four customer-
operated dry-
cleaning machines
on-site. Limited air
circulation, but
front door open at
all times.
1980
18(1)
NR
3148 to 4206
39351
(mean); NR
(median)
58127
(Howie 1981)
High
US
Washington, DC
area
Coin Operated
Laundry with Dry
Cleaning Machines
Laundry facility
(Site E), sampled at
6 to 7 ft above floor
at three locations.
Four attendant-
operated dry-
cleaning machines
on-site. Air-
conditioned site
with re-circulated
indoor air.
1980
18(1)
NR
12891 to
94985
58348
(mean); NR
(median)
58127
(Howie 1981)
High
us
Washington, DC
area
Coin Operated
Laundry with Dry
Cleaning Machines
Laundry facility
(Site F), sampled at
6 to 7 ft above floor
at three locations.
Eight attendant-
operated dry
cleaning machines
1980
18(1)
NR
2239 to
21032
8820 (mean);
NR (median)
58127
(Howie 1981)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
on-site. Limited air
circulation because
of floor plan; front
door open at all
times.
US
Denver, CO
Residential
Homes, occupied
(n=9)
1994
9(0.89)
0.14
ND to 1.99
0.66 (mean);
0.33 (median)
2.63
78782
(Lindstrom et
al. 1995)
Medium
US
Minneapolis, MN
School
Indoors in five
randomly selected
classrooms in each
school, during the
spring.
2000
113
(0.86)
NR
NR
NR (mean);
0.3 (median)
632310
(Adaate et al.
2004)
Medium
us
Minneapolis, MN
School
Indoors in five
randomly selected
classrooms in each
school, during the
winter.
2000
113
(0.96)
NR
NR
NR (mean);
0.3 (median)
632310
(Adaate et al.
2004)
Medium
us
Minneapolis, MN
Residential
Indoors in the
child's primary
residence, during
the spring.
2000
113
(0.95)
NR
NR
NR (mean);
0.4 (median)
632310
(Adaate et al.
2004)
Medium
us
Minneapolis, MN
Residential
Indoors in the
child's primary
residence, during
the winter.
2000
113
(0.98)
NR
NR
NR (mean);
0.5 (median)
632310
(Adaate et al.
2004)
Medium
MX
Mexico City
Metropolitan Area
Residential
Homes
1998-
1999
30(1)
NR
NRto 43.6
5.5 (mean);
3 (median);
3.6 (GM)
56224
(Serrano-
Trespalacios et al.
High
2004)
CA
NR
Residential
Homes (n=12),
main floor
1986
12(1)
NR
1 to 171
28.1 (mean);
NR (median)
27974
(Chan et al. 1990)
Medium
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
CA
NR
Residential
Homes (n=6), main
floor
1987
6(1)
NR
2 to 18
6.2 (mean);
NR (median)
27974
(Chan et al. 1990)
Medium
IT
NR
Residential
Control Homes - 25
private homes with
individuals not
occupationally
exposed, but within
the same district
near the dry-
cleaners' homes.
1994
25(1)
1
ND to 16
3 (mean);
2 (median);
2 (GM)
21778
(Aaaazzotti
et al. 1994a)
Medium
IT
Modena
Residential
Households (n=29)
with no association
with dry cleaning
establishments.
1992-
1993
58 (NR)
1
1 to 56
NR (mean);
6 (median);
0.006 (GM)
3
74875
(Aaaazzotti
et al. 1994b)
High
NT
Ede and
Rotterdam
Residential
Suburban homes
built post WWII,
Inner-city homes
built prior to
WWII, and newer
homes < 6 years
old. Samples
collected in living
room.
1981-
1982
319(0.3)
2
ND to 205
NR (mean);
1 (median)
22186
(Lebret et al.
1986)
Medium
FT
NR
Residential
Normal houses (not
"sick houses").
50 "Normal
houses" in this
study.
1995
50 (NR)
NR
ND to 5.65
0.46 (mean);
0.3 (median)
11
76241
(Kostiainen
1995)
Medium
FI
NR
Residential
"Sick houses" -
houses in which
people complained
1995
7 (NR)
NR
0.19 to 29.8
4.86 (mean);
0.73 (median)
0.66
76241
(Kostiainen
1995)
Medium
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
about the odor or
they had symptoms,
which resembled
WHO'S Sick
Building Syndrome
(headache, nausea,
irritation of the
eyes, mucous
membranes, and the
respiratory system,
drowsiness, fatigue,
and general
malaise.
38 "sick houses" in
this study.
SG
nation-wide
School
Child-care centers
(n=104), sampled
from middle of the
classroom near the
breathing zone of
children
(approximately
0.5-0.7 m)
2007
84 (0.72)
0.6
ND to 8.5
NR (mean);
0.3 (median)
632758
(Zuraimi and
Tham 2008)
High
DE
Hamburg area
Vehicle (Near
Source: dry-
cleaning)
Dry-cleaned down
jacket placed into a
car.
1990
3(1)
NR
9300 to
24800
NR (mean);
NR (median)
713690
(Gulvas and
Hemmerling
1990)
Medium
SA
Kuwait
Residential
Houses (n=20),
sampled from
living room
1998
226
(0.93)
0.26
ND to NR
NR (mean);
NR (median)
1744157
fBouhamra and
Elkilani 1999)
Medium
FR
nation-wide
Residential
Main
dwelling s(n=490),
2003-
2005
490
(0.84)
0.4
ND to 72.1
NR (mean);
1.3 (median)
733119
(Billionnet et
al. 2011)
Medium
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
samples collected
from bedroom.
FR
Paris area
Residential
Homes (n=196) of
the PARIS birth
cohort with
sampling in the
infant bedroom at
1,6, 9, and 12
months old..
Annual levels
averaged from hot
and cold seasonal
levels.
2003-
2007
177(1)
0.4
0.6 to 124.2
NR (mean);
2.3 (median);
2.8 (GM)
2128839
(Rodaetal. 2013)
Medium
FR
Paris area
Residential
Homes (n=196) of
the PARIS birth
cohort with
sampling in the
infant bedroom at
1,6, 9, and 12
months old. Hot
season levels.
2003-
2008
177 (NR)
0.4
0.4 to 245
NR (mean);
2.1 (median);
2.4 (GM)
2128839
(Rodaetal. 2013)
Medium
FR
Paris area
Residential
Homes (n=196) of
the PARIS birth
cohort with
sampling in the
infant bedroom at
1,6, 9, and 12
months old.. Cold
season levels.
2003-
2009
177(1)
0.4
0.6 to 59.2
NR (mean);
2.4 (median);
2.8 (GM)
15.8
2128839
(Rodaetal. 2013)
Medium
FR
nation-wide
Residential
Dwellings with
clothes that have
been dry cleaned in
the previous 4
weeks. (n=94)
2003-
2005
98 (NR)
NR
NR
5.3 (mean);
NR (median);
2.5 (GM)
10.6
2855333
(Brown et al.
Medium
2015)
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
FR
nation-wide
Residential
Dwellings without
clothes that have
been dry cleaned in
the previous 4
weeks. (n=447)
2003-
2005
456 (NR)
NR
NR
3.7 (mean);
NR (median);
1.1 (GM)
32.6
2855333
(Brown et al.
Medium
2015)
RS
Novi Sad
Commercial/Public
(Near Source:
photocopy shop)
Photocopy shop
(n=l) with a
desktop computer,
laptop computer, 2
copiers, and a
printer
2015
225
(0.64)
6.78
6.78 to 96342
4953 (mean);
6.78 (median)
3371701
(Kiurski et al.
2016)
Medium
SG
NR
Commercial/P
ublic Office
building
(n=l), 6
months old
with normal
occupancy
and steady
state
ventilation
system
sampled in the
middle
2004
8 (NR)
NR
NR
2321 (mean);
NR (median)
78.5
3393192
(Tham et al.
2004)
Low
DE
Essen and Borken
Residential
Residential homes,
collected in room
where inhabitants
spent the most
amount of time at a
height of 1.5 to 2
meters.
1996
229(1)
NR
0.03 to 7.33
2.21 (mean);
NR (median)
3561656
(Beaerow et al.
1996)
High
DE
Leipzig
Residential
Homes (n=85),
sampled from
bedroom of infants
for 4 weeks after
birth.
1997-
1999
85 (NR)
NR
NR
NR (mean);
1.8 (median)
34460
(Lelimann et
al. 2002)
Medium
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
No. of
Samples
(Det.
Freq.)
Concentration
Reference (HERO ID)
Country
State/City/Region
Site
Year
Detection
Level
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
EU
Sweden, Finland,
Estonia,
Lithuania,
Belgium, UK,
France, Austria,
Germany, Poland,
Slovakia, Czech
Republic,
Hungary,
Romania,
Bulgaria, Serbia,
Bosnia and
Herzegovina,
Italy, Portugal,
Malta, Greece,
Cyprus, and
Albania
School
Kindergartens
(n=25).
2014
25 (NR)
NR
ND to 6
1 (mean);
0.18 (median)
2
4440449
(Ec 2014)
High
EU
Sweden,
Finland,
Estonia,
Lithuania,
Belgium,
UK, France,
Austria,
Germany,
Poland,
Slovakia,
Czech
Republic,
Hungary,
Romania,
Bulgaria,
Serbia,
Bosnia and
Herzegovin
a,Italy,
Portugal,
Malta,
Greece
Cyprus, and
Albania
School
Primary schools
(n=300).
2014
300 (NR)
NR
ND to 81
1 (mean);
0.18 (median)
2
4440449
(Ec 2014)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
EU
Sweden, Finland,
School
2014
106 (NR)
NR
ND to 31
1 (mean);
-
4440449
(Ec 2014)
High
Estonia
Primary schools
0.18 (median)
Lithuania,
where teachers
Belgium, UK,
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
No. of
Samples
(Det.
Freq.)
Concentration
Reference (HERO ID)
Country
State/City/Region
Site
Year
Detection
Level
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
France, Austria,
Germany, Poland,
Slovakia, Czech
Republic,
Hungary,
Romania,
Bulgaria, Serbia,
Bosnia and
Herzegovina,
Italy, Portugal,
Malta, Greece,
Cyprus, and
Albania
participated
(n=106).
CN
NR
Commercial/Public
Non-office
premises (11= 10)
including one
library, one social
services center, two
customer services
centers, two
shopping malls,
two recreational
building units, one
reception area and
one training center
under renovation.
1.1111 above the
floor level.
1998-
2000
10(0.6)
0.3
ND to 10.9
3 (mean);
2.2 (median);
1.4 (GM)
9.2
824555
(Chao and Chan
2001)
Medium
CN
NR
Commercial/Public
1998-
10(0.6)
0.3
ND to 30.5
5.2 (mean);
—
824555
(Chao and Chan
Medium
Office buildings
(n=10), 1.1111
above the floor
2000
1.8 (median);
1.9 (GM)
2001)
CN
Shanghai
Residential
Eight residences
that had been
renovated within
the previous year.
2015
8 (NR)
NR
NR
2.38 (mean);
0.72 (median)
0.15
3453725
(Daietal. 2017)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
Three
sampling
sites were
used in each
participating
residence (the
living room
bedroom, and
study).
JP
Shimizu, Shizuoka
Prefecture
Residential
Single-family
houses (n=25) in
industrial harbor
area, sampled in the
main living area
2001
25(1)
NR
NR
NR (mean);
NR (median);
0.16 (GM)
632484
(Ohura et al.
2006)
High
JP
Shimizu, Shizuoka
Prefecture
Residential
Single-family
houses (n=21) in
industrial harbor
area, sampled in the
main living area
2001
21(1)
NR
NR
NR (mean);
NR (median);
0.16 (GM)
0.33
632484
(Ohura et al.
High
2006)
JP
Katsushika Ward,
Tokyo
Residential
30 houses'
bathrooms, sampled
for 4 consecutive
24 hour periods.
n=119
1995
119(1)
NR
0.363 to 22.5
2.56 (mean);
NR (median);
1.83 (GM)
3545469
(Amaaai et al.
1999)
Medium
JP
Katsusliika Ward,
Tokyo
Residential
13 houses' living
rooms, sampled for
4 consecutive 24
hour periods. n=52
1995
52(1)
NR
0.294 to 8.13
1.42 (mean);
NR (median);
0.986 (GM)
3545469
(Amaaai et al.
1999)
Medium
JP
Katsusliika Ward,
Tokyo
Residential
13 houses' kitchens
sampled for 4
consecutive 24 hour
periods. n=52
1995
52(1)
NR
0.295 to 8.25
1.17 (mean);
NR (median);
0.829 (GM)
3545469
(Amaaai et al.
1999)
Medium
JP
Katsusliika Ward,
Tokyo
Residential
13 houses'
bedrooms, sampled
1995
52(1)
NR
0.215 to 10.6
1.64 (mean);
NR (median);
0.998 (GM)
3545469
(Amaaai et al.
1999)
Medium
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City /Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
for 4 consecutive
24 hour periods.
n=52
JP
Katsushika Ward,
Tokyo
Residential
13 houses'
bathrooms, sampled
for 4 consecutive
24 hour periods.
n=52
1995
52(1)
NR
0.172 to 5.36
1.06 (mean);
NR (median);
0.774 (GM)
3545469
(Amaaai et al.
1999)
Medium
JP
Katsushika Ward,
Tokyo
Residential
30 houses' living
rooms, sampled for
4 consecutive 24
hour periods.
n=238
1995
238(1)
NR
0.292 to 57
3.69 (mean);
NR (median);
2.36 (GM)
3545469
(Amaaai et al.
1999)
Medium
JP
Katsushika Ward,
Tokyo
Residential
30 houses' kitchens
sampled for 4
consecutive 24 hour
periods. n=119
1995
119(1)
NR
0.339 to 30.8
3.03 (mean);
NR (median);
2.02 (GM)
3545469
(Amaaai et al.
1999)
Medium
JP
Katsushika Ward,
Tokyo
Residential
30 houses'
bedrooms, sampled
for 4 consecutive
24 hour periods.
n=238
1995
238(1)
NR
0.358 to 71
4.24 (mean);
NR (median);
2.42 (GM)
3545469
(Amaaai et al.
1999)
Medium
Personal Breathing Zone (jig/nr')
US
IL, IN, OH, MI
MN, WI (Great
Lakes Region)
Residential
Non-
institutionalized
persons residing in
households in six
states
1995-
1997
386
(0.61)
NR
ND to NR
31.9 (mean);
1.98 (median)
14003
(Clavton et al.
High
1999)
US
Columbus, OH
Residential
Non-smoking
women (n=24) with
non-smoking
husbands
1991
24 (NR)
NR
ND to 5.13
1.24 (mean);
0.7 (median)
1.46
22045
(Heavner et al.
1995)
Medium
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
No. of
Samples
(Det.
Freq.)
Concentration
Reference (HERO ID)
Country
State/City/Region
Site
Year
Detection
Level
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
US
Columbus, OH
Residential
Non-smoking
(n=25) women with
smoking husbands
1991
25 (NR)
NR
ND to 3.78
0.89 (mean);
0.68 (median)
0.96
22045
(Heavner et al.
1995)
Medium
US
NR
Commercial/Public
(Near Source:
printniaking)
12 students and 1
faculty member in
university art
(printmaking)
studio.
Mechanically
ventilated second-
floor.
2002
90 (NR)
NR
ND to NR
0.7 (mean);
0.5 (median)
2.3
49414
(Rvan et al. 2002)
High
us
NR
General
Personal VOC
exposures of 851
adults, who were
part of the
NHANES study (no
additional exclusion
criteria), sampled
via badge-type
passive exposure
monitors for 48-72
h. Additionally,
participants were
administered a
short questionnaire
regarding the length
of time they wore
their badge and 30
other questions on
factors potentially
related to VOC
exposures, e.g.,
contact with dry
1999-
2000
665
(0.69)
0.42
ND to 659
5.2 (mean);
0.7 (median);
1 (GM)
31.2
484177
(Jia et al. 2008b)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
cleaning, tobacco
smoke and gasoline
vapor over the past
several days.
US
Minneapolis, MN
Residential
In personal
breathing zones,
during the winter.
2000
113(1)
NR
NR
0.4 (median)
632310
(Adaate et al.
2004)
Medium
US
Minneapolis, MN
Residential
In personal
breathing zones,
during the spring.
2000
113
(0.97)
NR
NR
0.4 (median)
632310
(Adaate et al.
2004)
Medium
us
Minneapolis-St.
Paul, MN
General
Adults, non-
smoking (n=70)
living in three
neighborhoods:
(inner-
city/economically
disadvantaged,
blue-collar/near
manufacturing
plants, and affluent)
1999
333 (1)
NR
NR
27.8 (mean);
0.9 (median)
730121
(Sexton et al.
2007)
High
us
Elizabeth, N.T;
Houston, TX; and
Los Angeles, CA
General
Adults (n=309) and
children (n=118)
from 310 non-
smoking
households.
1999-
2001
544 (NR)
0.21
NR
7.17 (mean);
0.89 (median)
112.35
2128575
(Su et al. 2013)
Medium
us
Greater Boston
Metropolitan Area
Commercial/Public
Drug Stores (n=8)
2003
7(NR)
0.22
0.45 to 2.16
0.86 (GM)
—
2442846
(Loh et al. 2006)
High
us
Greater Boston
Metropolitan Area
Commercial/Public
Furniture Stores
(n=ll)
2003
6 (NR)
0.22
0.49 to 6.35
1.34 (GM)
2442846
(Loh et al. 2006)
High
us
Greater Boston
Metropolitan Area
Commercial/Public
Grocery Stores
(n=16)'
2003
12 (NR)
0.22
0.42 to 4.83
0.95 (GM)
2442846
(Loh et al. 2006)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
US
Greater Boston
Metropolitan Area
Commercial/Public
Hardware Stores
(n=32)
2003-
2004
23 (NR)
0.22
0.22 to 21.1
1.79 (GM)
2442846
(Loh et al. 2006)
High
US
Greater Boston
Metropolitan Area
Commercial/Public
Housewares Stores
(n=16)
2003
7(NR)
0.22
1.27 to 7.41
1.48 (GM)
2442846
(Loh et al. 2006)
High
us
Greater Boston
Metropolitan Area
Commercial/Public
Multipurpose
Stores (11= 24)
2003-
2005
43 (NR)
0.22
0.52 to 43.8
1.18 (GM)
2442846
(Loh et al. 2006)
High
us
Greater Boston
Metropolitan Area
Commercial/Public
Sporting Goods
Stores (n=14)
2003
7 (NR)
0.22
1.24 to 11.6
2.96 (GM)
2442846
(Loh et al. 2006)
High
us
Greater Boston
Metropolitan Area
Commercial/Public
Dining Stores
(n=20)
2004
20 (NR)
0.22
0.24 to 83.4
NR
2442846
(Loh et al. 2006)
High
us
Greater Boston
Metropolitan Area
Commercial/Public
Transportation
Stores (11= 5)
2003-
2004
21 (NR)
0.22
0.32 to 5.17
0.78 (GM)
2442846
(Loh et al. 2006)
High
us
Greater Boston
Metropolitan Area
Commercial/Public
Department Stores
(n=10)
2004
5 (NR)
0.22
1.27 to 4.89
2.04 (GM)
2442846
(Loh et al. 2006)
High
us
Greater Boston
Metropolitan Area
Commercial/Public
Electronics Stores
(n=9)
2004
7 (NR)
0.22
ND to 8.49
0.47 (GM)
2442846
(Loh et al. 2006)
High
us
C A and N.T
General
Adults conducting
normal daily
activities
1981-
1984
772 (NR)
0
NR
5.6 to 45
(mean)
23081
(Wallace 1986)
High
MX
Mexico City
Metropolitan Area
General
General - different
activity patterns:
Three individuals
from each family
were selected to
represent different
activity patterns: a
long commuter,
another engaged in
1998-
1999
90(1)
NR
NRto 84.4
5.9 (mean);
3.7 (median);
4.1 (GM)
9.9
56224
(Serrano-
Trespalacios
et al. 2004)
Low
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
some activities
outside the home
during the day but
with no routine
long commutes, and
one staying at or
near the home most
of the day
Indoor Air, Personal Breathing Zones, and Breath from Exposure Studies with Dry-Cleaned Textiles (jig/m3)
US
Bayomie and
Elizabeth, N.T
Residential
Indoor air of living
rooms and
bedrooms of nine
homes with two to
ten sets of dry-
cleaned clothes
were brought into
the homes.
NR
18
NR
NR to 297
NR
28307
(Thomas et al.
1991)
High
US
Bayomie and
Elizabeth, N.T
Residential
Personal air two to
ten sets of dry-
cleaned clothes
were brought into
the homes.
NR
7
1
NR to 303
NR
28307
(Thomas et al.
1991)
High
us
Bayonne and
Elizabeth, N.T
Residential
Exhaled breath, two
to ten sets of dry-
cleaned clothes
were brought into
the homes.
NR
7
1
NR to 303
NR
28307
(Thomas et al.
1991)
High
us
NR
Residential
Single story
residential house
with dry-cleaning
placed in closet.
Samples collected
from the closet.
NR
NR
1
NR
100-2,900
(daily avg)
27401
(Tichenor et al.
1990)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
US
NR
Residential
Single story
residential house
with dry-cleaning
placed in closet.
Samples collected
from the bedroom.
NR
NR
1
NR
20-195 (daily
avg)
27401
(Tichenor et al.
1990)
High
US
NR
Residential
Single story
residential house
with dry-cleaning
placed in closet.
Samples collected
from the den.
NR
NR
1
NR
10-80 (daily
avg)
27401
(Tichenor et al.
1990)
High
us
Washington, DC
Residential
In late summer,
Private home in
rural residential
area. Samples
collected over 7
days after placing
dry-cleaned
clothing in the
house.
1980
7(1)
NR
42.0 to 692
NR
58127
(Howie 1981)
High
us
NR
Automobile
Modeled air
concentration in
vehicle with dry-
cleanedjacket.
NR
NR
NR
NR to 2,300
NR
85812
(Park et al. 1998)
High
DE
NR
Automobile
Car with a diy-
cleaned down
jacket placed in the
car.
1990
3(1)
NR
9,300 to
24,800
NR
713690
(Gulvas and
Hemmerlina
1990)
Medium
CN
Hong Kong
Residential
Home (Site A) with
dry cleaned clothes
in closet of urban
1996
28(1)
NR
4.6 to 76
NR
3559311
(Chao et al. 1999)
Medium
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City /Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
5tli floor apartment
bedroom.
CN
Hong Kong
Residential
Home (Site B) with
dry cleaned clothes
in closet of
suburban 2nd floor
apartment bedroom.
1996
28(1)
NR
21 to 494
NR
3559311
(Chaoetal. 1999)
Medium
CN
Hong Kong
Residential
Home (Site C) with
dry cleaned clothes
in closet of urban
10th floor
apartment bedroom.
1996
28(1)
NR
0.93 to 100
NR
3559311
(Chaoetal. 1999)
Medium
JP
NR
Residential
Homes in Japan,
dry cleaned clothes
sampled in chest of
drawers.
NR
9(1)
NR
2.9 to 326.6
NR
3563210
(Kawauchi and
Nishivama
1989)
Medium
JP
NR
Residential
Homes in Japan,
dry cleaned clothes
sampled in same
room as chest of
drawers.
NR
6(1)
NR
1.3 to 7.4
NR
3563210
(Kawauchi and
Nishivama
1989)
Medium
Surface Water (ng/L)
US
Anchorage, AK
Background
Chester Creek (6
urban sampling sites)
1998-
2001
11(0)
0.2
A11ND
ND
NR
3975042
(USGS 2006)
Medium
US
Nation-wide
Background
Surface water for
drinking water
sources (rivers and
reservoirs)
1999-
2000
375
(0.008)
0.2
ND to 5.5
NR
NR
3975046
(USGS2003)
Medium
us
Nation-wide
Surface water for
drinking water
sources (rivers and
reservoirs)
1999-
2000
375
(0.0027)
0.2
ND to 2.6
NR
NR
3975046
(USGS 2003)
Medium
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
US to CL
NR
Background
Eastern Pacific
Ocean (California
US to Valparaiso,
Chile)
1979-
1981
30 (0.90)
0.0001
ND to 0.0028
0.7 (mean);
0.0004
(median)
0.0007
29192
(Singh et al. 1983)
Medium
US to CL
NR
Eastern Pacific
Ocean (California
US to Valparaiso,
Chile)
1979-
1981
30 (0.93)
0.0004
ND to 0.008
0.0031
(mean)
0.0032
29192
(Singh etal. 1983)
Medium
BR
NR
Background
Santo Antonio da
Patrulha, Tres
Coroas, and Parobe
in the Sinos River
Basin; River samples
collected from seven
points on the three
main rivers of the
Sinos River Basin
2012-
2013
60
(0.083)
NR
ND to 0.8
0.03 (mean)
NR
3489827
(Bianchi et al.
2017)
Medium
BR
NR
Santo Antonio da
Patrulha, Tres
Coroas, and Parobe
in the Sinos River
Basin; River samples
collected from seven
points on the three
main rivers of the
Sinos River Basin
2012-
2013
60 (0.72)
NR
ND to 0.0588
0.0019
(mean)
NR
3489827
(Bianchi et al.
2017)
Medium
CN
NR
Background
Yellow Sea and East
China Sea (53
stations)
2011
53(1.0)
NR
0.00022 to
0.0051
0.0019
(mean)
NR
2128010
(He etal. 2013a)
High
CN
NR
Background
Daliao River (n=20
sites), heavily
industrialized
2011
20 (0.1)
NR
NR to 0.11
0.016 (mean)
NR
3488897
(Ma etal. 2014)
High
CN
NR
Background
2010
41(1)
NR
0.000065 to
0.0015
0.0004
(mean)
NR
1940132
(He etal. 2013b)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
East China Sea;
Seawater (41
stations)
ES
North-Western area
Background
River Duero (11
stations)
2007
11 (NR)
NR
NR to 0.09
0.01 (mean)
NR
3501965
(Blanco and
Becares 2010)
Medium
GB
NR
Background
Irish Sea; Liverpool
Bay and River
Mersey (18 stations)
2006
18 (NR)
0.000025
ND to 0.0455
NR
NR
2277377
(Bravo-Linares
Medium
et al. 2007)
RU
NR
Background
Kalmykian Steppe;
Rivers, springs, lakes,
salt lakes (n=23);
polluted and remote
areas
1999-
2003
23 (0.83)
0.005
ND to 310
24.6 (mean)
81.8
104106
(Weissfloa et
al. 2004)
Medium
PT
Nation-wide
Background
sea, estuarine, river
water and industrial
effluents (46 water
sample locations)
1999-
2000
644
(0.20)
0.4
ND to 13
NR
NR
659075
(Martinez et al.
2002)
Medium
BE
NR
Background
Southern North Sea;
Southern Bight,
Belgian Continental
Shel, the mouth of
the Scheldt estuary
and the Channel (10
stations total)
1998-
2000
47 (NR)
NR
NR to 0.28
0.023 (mean);
0.0015
(median)
NR
660096
(Huvbrechts et
al. 2005)
High
EU
NR
Background
Estuaries of the
Scheldt (n=2),
Thames, Loire, Rhine
1997-
1999
73 (NR)
0.000099
ND to 1.2
NR
NR
3242836
(Christof et al.
2002)
High
EU
NR
Estuaries of the
Scheldt (n=2),
Thames, Loire, Rhine
1997-
1999
73(1)
NR
0.0003 to 4.98
NR
NR
3242836
(Christof et al.
2002)
High
GR
Northern Greece
Background
1996-
1998
104 (NR)
0.02
ND to 0.19
NR
NR
1024859
(Kostopoulou et
al. 2000)
High
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
No. of
Samples
(Det.
Freq.)
Concentration
Reference (HERO ID)
Country
State/City/Region
Site
Year
Detection
Level
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
Rivers (n=4) and
lakes (n=5). Rivers
sampled at the
estuary and near the
frontier.
Lakes - Vegoritida,
Volvi, Vistonida,
Large Prespa and
Small Prespa. Rivers
- Evros, Nestos,
Strimonas, and Axios
JP
Osaka
Background
Rivers in heavily
industrialized area
(n=10 stations).
Wastewater
treatments upstream
from the sampling
sites.
1995-
1997
106
(0.85)
NR
0.47 to 86.2
4.83 (mean);
2.44 (median)
9.32
2310570
(Yamamoto et
al. 2001)
Medium
FR
Paris
Background
River samples (raw)
collected from the
River Seine (n=14
stations), River
Mame(n=l station)
and River Oise (n=l
station). Wastewater
treatment plants are
located on the river.
1994-
1995
43(1)
NR
0.068 to 1.037
0.31 (mean);
0.196
(median)
0.248
3587944
(Duclos et al.
2000)
Medium
FR
Paris
River samples (raw)
collected from the
River Seine (n=14
stations), River
Marne (n=l station)
and River Oise (n=l
station). Wastewater
1994-
1995
43(1)
NR
0.016 to 4.92
1.004 (mean);
0.473
(median)
1.218
3587944
(Duclos et al.
2000)
Medium
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INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City/Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
treatment plants are
located on the river.
JP
Osaka
Rivers and estuaries
(30 sites) in
industrialized city
1993-
1995
136 (NR)
NR
NR to 134
1.7 (median)
NR
645789
(Yamamoto et
al. 1997)
High
BE
NR
Background
Southern North Sea
and Scheldt Estuary;
Seven sites in the
southern North Sea
and Scheldt Estuary.
1994-
1995
38 (NR)
NR
NR
0.00268
(median)
NR
644857
(Dewulf et al.
1998)
High
EU
NR
Background
Mersey Estuary;
Freshwater input
collected from the
Howley Weir.
1987-
1989
5 (NR)
NR
NR
0.6 (mean);
0.6 (median)
NR
2802879
(Roaers et al.
1992)
Medium
GR
Thermaikos and
Kavala, Northern
Greece
Background
Seawater collected
from Thermaikos
Gulf (6 stations; near
large city and
industrial area) and
Kavala Gulf stations
(4 stations; near small
city and off-shore oil-
wells).
1981-
1982
10(1)
NR
0.00027 to
0.003
0.00131
(mean);
0.00116
(median)
0.00099
4149731
(Fvtianos et al.
1985)
Low
CH
Background
Background
River Aare; River
samples collected at
River Aare.
1980-
1981
12 (NR)
NR
NR
0.24 (mean)
0.12
3797825
(Schwarzenbach
et al. 1983)
Medium
CH
Background
Background
River Glatt; River
samples collected at
River Glatt.
1979-
1980
16 (NR)
NR
NR
0.6 (mean)
0.70
3797825
(Schwarzenbach
et al. 1983)
Medium
GB
NR
Background
Estuaries, docks,
channels, bays, and
inshore (n=48)
1992
48 (0.44)
NR
0.01 to 0.274
0.04491
(mean);
0.0125
(median)
0.0645
2803418
(Dawes and
Waldock 1994)
Medium
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INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
No. of
Samples
(Det.
Freq.)
Concentration
Reference (HERO ID)
Country
State/City/Region
Site
Year
Detection
Level
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
SE
Stenungsund area
Background
Seawater (n=13
stations), sampled on
two occasions
(depths of 1-10 m) in
area of petrochemical
centre
1988
52 (NR)
NR
NR
0.0025
(mean)
NR
658636
(Abrahamsson
etal. 1989)
Medium
GB
NR
Background and
Near Facility (Ship
Tanker Cleaning
Operations)
North Sea; North
Sea: 32 sampling
stations in the
Thames, Humber,
Tees, Forth, and
Felixstowe (0 to 20
miles from shore) and
the Central North Sea
(distance from shore
not provided). Tank
cleaning operations at
North Sea ports.
1986
32 (0.47)
0.002
ND to 0.16
15 (mean);
0.002
(median)
0.037
4149734
(Hurford et al.
1989)
Medium
IT
Emilia-Romagna
region
Background
Canal (n=l) which
receives wastewater.
1984
6 (0.574)
NR
18 to 168
136 (mean)
NR
4149721
(Aggazzotti and
Predieri 1986)
Low
AQ
NR
Background
Northern Victoria
Land; Five lakes
(Carezza Lake,
Edmonson Point
Lakes, Tarn Flat
Lake, Inexpressible
Island Lake and
Gondwana Lake)
2011-
2012
6(1)
NR
0.0056 to
0.0166
0.0097
(mean)
0.0038
2800175
(Insogna et al.
2014)
High
AQ
NR
Background
Ross Sea
1997-
1998
48 (NR)
NR
0.0002 to 0.071
0.02 (mean);
0.0056
(median)
0.023
2189687
(Zoccolillo et
al. 2004)
Medium
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INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Country
State/City /Region
Site
Year
No. of
Samples
(Det.
Freq.)
Detection
Level
Concentration
Reference (HERO ID)
Range
Central
Tendency
Standard
Deviation
HERO
Citation
Data
Eval.
Score
AQ
NR
Background
Lakes at Tarn Flat
and Edmonson Point;
Two freshwater lakes
1998
4 (NR)
NR
0.0023 to
0.0041
0.0032
(mean);
0.0031
(median)
0.0007
2189687
(Zoccolillo et
al. 2004)
Medium
Wastewater (ng/L)
KR
Nation-wide
Near Facility
(industrial WWTPs)
Influent/Effluent
2012
81 (NR)
1
1 to 23
1 (median)
3580141
(Lee et al. 2015)
Medium
KR
Nation-wide
Near Facility
(industrial WWTPs)
Effluent
2012
81(0)
1
ND
3580141
(Lee et al. 2015)
Medium
Biota (ng/kg)
BE
Nation-wide
Background
Eel, skin
2003
20 (0.5)
0.1
0.1 to 89
13.4 (mean);
0.78 (median)
NR
1066543
(Roose et al.
Medium
2003)
Study Info: The information provided includes the HERO ID and citation; country and year samples collected; number of samples and detection frequency.
Abbreviations: If a value was applicable, it is shown in this table as "—"; ND = not detected at the reported detection limit; GM = geometric mean; NR = not reported.
The following abbreviations are for countries/continents: AQ = Antarctica, BE = Belgium, BR = Brazil, CA = Canada, CH = Switzerland, CL = Chile, CN = China, DE =
Germany, ES = Spain, EU = Europe, FI = Finland, FR = France, GB = Great Britain, GR = Greece, IT = Italy, .TP = Japan, KR = Korea, MX = Mexico, NT = Netherlands, PT =
Portugal, RS = Serbia, RU = Russia, SA = Saudi Arabia, SE = Sweden, SG = Singapore, US = United States.
Parameters: All statistics are shown as reported in the study. All minimum values determined to be less than the detection limit are shown in this table as "ND". If a maximum
value was not provided, the highest percentile available is shown (as indicated in parentheses); if a minimum value was not provided, the lowest percentile available is shown (as
indicated in parentheses).
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3.1 Biomonitoring Data
Systematic review identified blood biomonitoring measurements from multiple sources. The
most comprehensive source is the National Health and Nutrition Examination Survey
(NHANES) conducted by CDC's National Center for Health Statistics (NCHS). The survey is
"a complex, stratified, multistage, probability-cluster design survey" designed to collect data on
the health and nutrition of a representative sample of the US population. NHANES measured
perchloroethylene in whole blood of males and females ages 12+ years. In the Fourth Report on
Human Exposure to Environmental Chemicals (CDC, 2019), statistics were reported for the 50th,
75th, 90th, and 95th percentiles for 2-year cycles starting in 2001 through 2016. Sample sizes
ranged from 978 to 3,302. The concentrations in all samples were less than the limit of detection
(0.048 ng/mL) at the 50th percentile for all years. At the 95th percentile, concentrations ranged
from 0.075 in 2015-2016) to 0.190 ng/mL (in 2001-2002). Another source (Sexton et. al., 2005),
measured concentrations of perchloroethylene in whole blood from 150 children from two poor,
minority neighborhoods in Minneapolis, Minnesota in four periods during 2000-2001. These
samples were collected as part of the School Health Initiative: Environment, Learning, Disease
(SHIELD) study, perchloroethylene was detected in 37 to 63% of the samples, with
concentrations ranging from 0.02-0.03 ng/mL (10th percentile) to 0.19-0.82 ng/mL (99th
percentile). The limit of detection was 0.022 ng/mL. The SHIELD study also collected 2-day,
integrated personal air samples. Blood samples were also collected as part of the National
Human Exposure Assessment Survey (NHEXAS) Phase I conducted by EPA (Clayton et. al.,
1999). Samples were collected from 147 people in six states (IL, IN, OH, MI, MN, and WI) in
1995-1997. perchloroethylene was detected in 37% of the samples, with a mean of 0.21 ng/mL,
a 50th percentile of 0.05 ng/mL, and a 90th percentile of 0.16 ng/mL. NHEXAS Phase I also
collected indoor air and personal air samples, perchloroethylene concentrations in blood were
similar between the NHANES, SHIELD, and NHEXAS surveys conducted between 1995 and
2016.
In addition to blood, NHANES also collected urine spot samples. The perchloroethylene
metabolite N-Acetyl-S-(trichlorovinyl)-L-cysteine was measured in males and females ages 6+
years in survey years 2005-2006 (n=3,349), 2011-2012 (n=2,464-2,466), and 2013-2104
(n=2,618-2,619). The concentrations in all samples were less than the limit of detection (3.0
Hg/L).
Breath samples were also collected as part of the Total Exposure Assessment Methodology
(TEAM) Study, which also collected concurrent personal inhalation monitoring samples and
outdoor air samples. In Phase II and III of the study conducted between 1981 and 1984, samples
were collected from adults conducting normal daily activities in industrial/chemical
manufacturing and /or petroleum refining regions of the US, including Elizabeth and Bayonne,
NJ, Los Angeles, CA, and Contra Costa, CA (n= 660). Arithmetic means ranged from 8.3 to 13
|ig/m3, with detection in 58 to 100% of samples.
**Su looked at Nhanes III, so did not discuss since have the 2019 CDC study.
Reference for Updated Tables, 2019 (not in systematic review)
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Centers for Disease Control and Prevention. Fourth Report on Human Exposure to Environmental Chemicals,
Updated Tables, (January 2019). Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease
Control and Prevention, https ://www .cdc. eov/exposurereport/
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3.2 Consumer Products
Table 6. List ofperchloroethylene containing products available for consumer use, from EPA 's 2017 Preliminary Information on Manufacturing, Processing, Distribution,
Use, and Disposal: Tetrachloroethylene (Perchloroethylene) (2017J and Use and Market Profile fU.S. EPA 2017)
Product Name
Company Name
(Manufacturer)
% by Weight of
Chemical
Use
Summary Use
Consumer
Scenario
Consumer,
Commerical or Both
Uses
E6000 Industrial Adhesive
(Black and Clear)
Eclectic Products,
Inc.
60-100%
Industrial
adhesive
Adhesive -
Industrial
Glues and
Adhesives
(small scale)
Both
E6100 Non-Slump All
Colors
Eclectic Products,
Inc.
65%
Industrial
adhesive
Adhesive -
Industrial
Glues and
Adhesives
(small scale)
Both
E6800
Eclectic Products,
Inc.
60-100%
Industrial
adhesive
Adhesive -
Industrial
Glues and
Adhesives
(small scale)
Both
FRP Column Adhesive
Eclectic Products,
Inc.
30-60%
Industrial
adhesive
Adhesive -
Industrial
Caulk; maybe
also Glues and
Adhesives
Both
E6100 Black
Eclectic Products,
Inc.
60-100%
Industrial
adhesive
Adhesive -
Industrial
Glues and
Adhesives
(small scale)
Both
E6100 Clear
Eclectic Products,
Inc.
30-60%
Industrial
adhesive
Adhesive -
Industrial
Glues and
Adhesives
(small scale)
Both
E6100 Gray
Eclectic Products,
Inc.
60-100%
Industrial
adhesive
Adhesive -
Industrial
Glues and
Adhesives
(small scale)
Both
E6100 White
Eclectic Products,
Inc.
60-100%
Industrial
adhesive
Adhesive -
Industrial
Glues and
Adhesives
(small scale)
Both
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INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Amazing GOOPII MAX
Eclectic Products,
Inc.
60-100%
Adhesive
Adhesive -
Light repair
Glues and
Adhesives
(small scale)
Both
Amazing GOOP Trim
Repair
Eclectic Products,
Inc.
60-100%
Adhesive
Adhesive -
Light repair
Glues and
Adhesives
(small scale)
Both
Primetime Adhesive
Sullivan Supply,
Inc.
15%
Livestock
grooming
adhesive
Adhesive -
Livestock
grooming
Spray fixative
and finishing
spray coatings
Both
Cable Clean RD
CRC Industries, Inc.
90-100%
Cable cleaner
Aerosol
degreaser
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Cleaner - Degreaser, Non
Flammable (Aerosol)
Ashburn Chemical
Technologies
30-60%
Cleaner and
degreaser
Aerosol
degreaser
Degreaser
Both
18 Oz Terand Coil Clnr
Solvent-Based C
CPC
40-60%
Coil cleaner
Aerosol
degreaser
Degreaser
Both
AST Super Dry II
Anti-Seize
Technology
60-100%
Degreaser
Aerosol
degreaser
Degreaser
Both
Heavy Duty Degreaser
CRC Industries, Inc.
80-90%
Degreaser
Aerosol
degreaser
Degreaser
Both
Quick Clean Safety Solvent
and Degreaser
CRC Industries, Inc.
90-100%
Degreaser
Aerosol
degreaser
Degreaser
Both
Aerosol Degreasing Solvent
EF
Nu-Calgon
Wholesaler, Inc.
85-95%
Degreaser
Aerosol
degreaser
Degreaser
Both
142LA Ignition & Wire
Dryer
Pennatex, Inc.
80-90%
Demoistures
ignition and wire
Aerosol
degreaser
Degreaser
Both
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Mag 1 Electric Motor
Cleaner 445
Warren Distribution
Inc.
98%
Electric motor
cleaner
Aerosol
degreaser
Degreaser
Both
Tool Crib Electric Motor
and Contact Cleaner
Seymour of
Sycamore
97%
Electrical cleaner
Aerosol
degreaser
Degreaser
Both
B900, Berkebile Electric
Contact Cleaner
The Berkebile Oil
Company, Inc.
>95%
Electrical cleaner
Aerosol
degreaser
Degreaser
Both
GB Electrical Degreaser
Power Products
LLC (dba Gardner
Bender)
97.50%
Electrical
degreaser
Aerosol
degreaser
Degreaser
Both
Gunk Electric Motor Contact
Cleaner - Energized
Equipment
RSC Chemical
Solutions, a division
of Radiator
Specialty Company
90-100%
Energized
cleaner
Aerosol
degreaser
Degreaser
Both
Lectra-Motive Electric Parts
Cleaner
CRC Industries, Inc.
90-100%
Energized
electrical cleaner
Aerosol
degreaser
Degreaser
Both
Electrical Parts Cleaner
CRC Industries, Inc.
90-100%
Energized
electrical cleaner
Aerosol
degreaser
Degreaser
Both
Lectra Clean Heavy Duty
Energized Electrical Parts
Degreaser
CRC Industries, Inc.
90-100%
Energized
electrical cleaner
Aerosol
degreaser
Degreaser
Both
Lectra Clean Heavy Duty
Electrical Parts Degreaser
CRC Industries, Inc.
90-100%
Energized
electrical cleaner
Aerosol
degreaser
Degreaser
Both
Clean Up Aerosol
Jet-Lube, Inc.
NA
General purpose
degreaser
Aerosol
degreaser
Degreaser
Both
Marine Cleaner and
Degreaser
CRC Industries, Inc.
90-100%
Marine - Cleaner
and degreaser
Aerosol
degreaser
Degreaser
Both
Quicksilver Marine Parts
Degreaser and Cleaner
Mercury Marine
75-80%
Marine - Cleaner
and degreaser
Aerosol
degreaser
Degreaser
Both
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INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
LOCTITE SF 7611 PARTS
CLEANER known as
Loctite(R) Pro Strength Parts
Henkel Corporation
60-100%
Parts cleaner
Aerosol
degreaser
Degreaser
Both
Electro Kleen
Superior Chemical
Corp.
30-60%
Solvent cleaner
Aerosol
degreaser
Degreaser
Both
Cool-Cut
Anti-Seize
Technology
90-100%
Cutting tool
coolant
Aerosol
lubricant
Spray
lubricant
Both
Penetrating Lube
Ashburn Chemical
Technologies
60-100%
Lubricant
Aerosol
lubricant
Spray
lubricant
Both
Grease Gun in a Can
K-Chem, Inc.
5-10%
Lubricant
Aerosol
lubricant
Spray
lubricant
Both
Nut Buster
K-Chem, Inc.
NA
Lubricant
Aerosol
lubricant
Spray
lubricant
Both
80-695 Heavy Duty Silicone
Kimball Midwest
30-40%
Lubricant
Aerosol
lubricant
Spray
lubricant
Both
L2 Moisture Displacer/Deep
Penetrant
Sprayway, Inc.
40-60%
Lubricant
Aerosol
lubricant
Spray
lubricant
Both
Break Away
Superior Chemical
Corp.
60-100%
Penetrating
lubricant
Aerosol
lubricant
Spray
lubricant
Both
Moisture Guard
Mfg. for Excalibur
35-45%
Penetrating oil
and lubricant
Aerosol
lubricant
Spray
lubricant
Both
Talon White Lithium Grease
Fastenal
48%
White lithium
grease
Aerosol
lubricant
Spray
lubricant
Both
E6000 Craft (Clear, Black
and White)
Eclectic Products,
Inc.
60-100%
Arts and crafts;
Adhesive
Arts and
crafts;
Adhesive
Glues and
Adhesives
(small scale)
Both
Aleene's Platinum Bond
7800 Adhesive
Duncan Enterprises
70%
Arts and crafts;
Adhesive
Arts and
crafts;
Adhesive
Glues and
Adhesives
(small scale)
Consumer
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INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Aleene's Platinum Bond
Super Fabric Textile
Adhesive
Duncan Enterprises
Arts and crafts;
Adhesive
Arts and
crafts;
Adhesive
Glues and
Adhesives
(small scale)
Consumer
E6000 Shoe Dazzle
Eclectic Products,
Inc.
60-100%
Arts and crafts;
Adhesive
Arts and
crafts;
Adhesive
Glues and
Adhesives
(small scale)
Consumer
E6000 Jewelry & Bead Glue
Eclectic Products,
Inc.
60-100%
Arts and crafts;
Adhesive
Arts and
crafts;
Adhesive
Glues and
Adhesives
(small scale)
Consumer
Duncan OG 802 White Gold
Duncan Enterprises
20-30%
Solvent based
metallic
overglaze
Arts and
crafts;
Overglaze
Laquers and
Stains
Both
Parts Master Brake & Parts
Cleaner #1733
Aftennarket Auto
Parts Alliance, Inc.
NA
Automotive -
Brake cleaner
Brake Cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
AST Brake Cleaner
Anti-Seize
Technology
90-100%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
NAPA Mac's Brake and
Brake Parts Cleaner
Ashland, Inc. or
Niteo Products
>90-<100%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
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INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Pyroil Brake Parts Cleaner
Ashland, Inc. or
Niteo Products
91.78%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Carquest Brake Parts
Cleaner
CRC Industries, Inc.
90-100%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Brakleen Brake Parts
Cleaner
CRC Industries, Inc.
90-100%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Brake Cleaner. Cleaning
agent. Automotive Kit.
CRC Industries, Inc.
60-100%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
C32, Brake & Parts Clean
Cyclo Industries,
Inc.
85-100%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Pro-Strength Brake and Parts
Cleaner
ITW Pennatex
(Devcon)
40-70%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
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INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Gunk Brake Parts Cleaner -
Chlorinated
RSC Chemical
Solutions, a division
of Radiator
Specialty Company
40-<50%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Jolinsen's Brake Parts
Cleaner
Technical Chemical
Company
85-100%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Berkebile 2+2 Clean Brake
The Berkebile Oil
Company, Inc.
100%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Service Pro Chlorinated
Brake Cleaner, 4820
Chlorinated Brake Cleaner
The Penray
Companies, Inc.
60-100%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Chlorinated Brake Parts
Cleaner
Sprayway, Inc.
90-100%
Automotive -
Brake cleaner
Brake cleaner
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Rubber Roller Restorer
CleanTex
35%
Solvent
Cleaner
Degreaser
Both
E1009 Restore Black Battery
Reconditioner
The Noco Company
14%
Coating
Coating
Aerosol spray
paints
Both
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INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Black Zero Rust Primer
Amteco, Inc.
10.20%
Non flat coating
Coating
Aerosol spray
paints
(aerosol);
Solvent based
wall paint
(liquid)
Both
Blue Zero Rust Primer
Amteco, Inc.
10.63%
Non flat coating
Coating
Aerosol spray
paints
(aerosol);
Solvent based
wall paint
(liquid)
Both
Gray Zero Rust Primer
Amteco, Inc.
10.27%
Non flat coating
Coating
Aerosol spray
paints
(aerosol);
Solvent based
wall paint
(liquid)
Both
Green Zero Rust Primer
Amteco, Inc.
9.18%
Non flat coating
Coating
Aerosol spray
paints
(aerosol);
Solvent based
wall paint
(liquid)
Both
Red Zero Rust Primer
Amteco, Inc.
10.20%
Non flat coating
Coating
Aerosol spray
paints
(aerosol);
Solvent based
wall paint
(liquid)
Both
Safety Red Zero Rust Primer
Amteco, Inc.
10.00%
Non flat coating
Coating
Aerosol spray
paints
(aerosol);
Solvent based
wall paint
(liquid)
Both
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INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Safety Yellow Zero Rust
Primer
Amteco, Inc.
9.97%
Non flat coating
Coating
Aerosol spray
paints
(aerosol);
Solvent based
wall paint
(liquid)
Both
Tan Zero Rust Primer
Amteco, Inc.
8.79%
Non flat coating
Coating
Aerosol spray
paints
(aerosol);
Solvent based
wall paint
(liquid)
Both
Yellow Zero Rust Primer
Amteco, Inc.
8.80%
Non flat coating
Coating
Aerosol spray
paints
(aerosol);
Solvent based
wall paint
(liquid)
Both
Degreasing Solvent EF
Nu-Calgon
Wholesaler, Inc.
10-20%
Degreaser
Degreaser
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Tetrachloroethylene 500ml
Consolidated
Chemical &
Solvents LLC
100%
Solvent
Laboratories
N/A
Both
Original Formula Alumtap
Winfield Brooks
Company, Inc.
<10%
Cutting fluid
Lubricant
Non-spray
lubricant
Both
Heavy Duty Mold Cleaner
CRC Industries, Inc.
90-100%
Mold cleaner
Mold cleaner,
release,
protectant
All-purpose
spray cleaner;
or Aerosol
spray paint
Both
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Budget Silicone Mold
Release
Plastic Process
Equipment, Inc.
5-10%
Mold cleaner
Mold cleaner,
release,
protectant
All-purpose
spray cleaner;
or Aerosol
spray paint
Both
MR™351 Mold Cleaner
Aerosol
Sprayon Products
(part of Sherwin-
Williams®
Company)
25%
Mold cleaner
Mold cleaner,
release,
protectant
All-purpose
spray cleaner;
or Aerosol
spray paint
Both
WL™941 Dry Weld Spatter
Protectant Aerosol
Sprayon Products
(part of Sherwin-
Williams®
Company)
55.40%
Protectant
Mold cleaner,
release,
protectant
All-purpose
spray cleaner;
or Aerosol
spray paint
Both
Fire Ant Injector Spray
(Pesticide, TSCA??) '
K-Chem, Inc.
NA
Fire ant killer
Pesticide - Fire
ants
??
Both
Lexel White VOC
Sashco, Inc.
30-60%
Caulk
Sealant
Caulk
(Sealant)
Both
ACM™ Gutter/Narrow
Seam Sealant, Aluminum
Gray
ACM American
Construction Metals
50.60%
Sealant
Sealant
Caulk
(Sealant)
Both
ACM™ Gutter/Narrow
Seam Sealant, White
ACM American
Construction Metals
50%
Sealant
Sealant
Caulk
(Sealant)
Both
AMERIMAX®
SeamerMate® Professional
Grade Permanent Gutter
Seal Gray
Amerimax Home
Products, Inc.
>50-<75%
Sealant
Sealant
Caulk
(Sealant)
Both
AMERIMAX®
SeamerMate® Professional
Grade Permanent Gutter
Seal White
Amerimax Home
Products, Inc.
>25-<50%
Sealant
Sealant
Caulk
(Sealant)
Both
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Geocel® Instant Gutter Seal
Gutter & Narrow Seam
Sealant
Geocel Products
Group A Business
Unit of the Sherwin-
Williams Company
49.67%
Sealant
Sealant
Caulk
(Sealant)
Both
Geocel® Pro Flex® RV
Flexible Sealant
Geocel Products
Group A Business
Unit of the Sherwin-
Williams Company
42.70%
Sealant
Sealant
Caulk
(Sealant)
Both
Geocel® 2000®
Construction Caulking
Sealant
Geocel Products
Group A Business
Unit of the Sherwin-
Williams Company
45.51%
Sealant
Sealant
Caulk
(Sealant)
Both
Geocel® Pro Flex®
Tripolymer Sealant
Geocel Products
Group A Business
Unit of the Sherwin-
Williams Company
47.50%
Sealant
Sealant
Caulk
(Sealant)
Both
Geocel® 2300®
Construction Tripolymer
Sealant
Geocel Products
Group
A Business Unit of
The Sherwin-
Williams Company
47.50%
Sealant
Sealant
Caulk
(Sealant)
Both
Geocel® S2™ Solar Panel
Roof Installation Sealant
(white)
Geocel Products
Group
A Business Unit of
The Sherwin-
Williams Company
47%
Sealant
Sealant
Caulk
(Sealant)
Both
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Geocel® 2320®
Construction Tripolymer
Gutter and Narrow Seam
Sealant
Geocel Products
Group
A Business Unit of
The Sherwin-
Williams Company
49.70%
Sealant
Sealant
Caulk
(Sealant)
Both
Geocel® 2350 MHRV™
Sealant
Geocel Products
Group
A Business Unit of
The Sherwin-
Williams Company
41.96%
Sealant
Sealant
Caulk
(Sealant)
Both
Geocel® Water Shield®
Caulking Sealant
Geocel Products
Group
A Business Unit of
The Sherwin-
Williams Company
45.35%
Sealant
Sealant
Solvent based
wall paint (or
Varnish?)
Both
ProCOLOR SWD
Tripolymer Sealant
Geocel Products
Group
A Business Unit of
The Sherwin-
Williams Company
30.16%
Sealant
Sealant
Caulk
(Sealant)
Both
White Lightning® Storm
Blaster® All Season Sealant,
White
White Lightning
Products (part of
Sherwin-Williams®)
Company)
47%
Sealant
Sealant
Caulk
(Sealant)
Both
Hornady - One Shot Primer
Sealer (Lock-N-Load Primer
Sealer Kit)
Hornady
Manufacturing Co.
30-50%
Sealant - gun
ammunition
Sealant - gun
ammunition
Glues and
Adhesives
(small scale)
Both
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
Sheila Shine (Aerosol and
liquid)
Sheila Shine, Inc.
10-30%
Polishing
agent/burnishing
compound
Stainless steel
polish, aerosol
Degreaser (if
spray); All-
purpose liquid
cleaner (if
liquid)
Both
Hagerty Silversmiths' Spray
Polish
W. J. Hagerty &
Sons, Ltd., Inc.
NA
Compounded
cleaner
Stainless steel
polish, aerosol
Both [Update ML
3/1/18: changed to
Both, since is avaialble
on Amazone and Home
Depot]
Cera Fluida Classic
Tenax Spa
50-100%
Brightener wax
for natural stones
Stone/metal
cleaner
All-purpose
waxes and
polishes
Both
Special Preparation for
Polishing (paste)
Bellinzoni
[Update ML
2/28/18: MSDS lists
PCE content
between 70 and
85%)
Marble polish
Stone/metal
cleaner
All-purpose
waxes and
polishes
(solid); All-
purpose liquid
cleaner
(liquid)
Both
Solid Wax
AKEMI
50-100%
Wax, stone wax
stone/metal
cleaner
All-purpose
waxes and
polishes
Both
Husky 1229 Vandalism
Mark & Stain Remover
Canberra Corp.
NA
Cleaner
Vandal mark
remover
All-purpose
liquid cleaner
Both
Tyme-1 Cold Parts Cleaner
CRC Industries, Inc.
50-60%
Parts cleaner
Wipe cleaner
Continuous
action air
freshener (E5
model) +
Dermal
models
Both
-------
INTERAGENCY DRAFT. DO NOT CITE OR QUOTE
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