Consumer Product Chemicals in Indoor Dust: A Quantitative Meta-analysis of U.S. Studies
† Milken
Institute School of Public Health, George
Washington University, Washington,
D.C. 20052, United States
‡ Silent
Spring Institute, Newton, MA 02460, United States
§ Health
and Environment Program, Natural Resources
Defense Council, San Francisco, California 94104, United States
∥ Harvard T. H.
Chan School of Public Health, Boston, Massachusetts 02115, United States
⊥ Occupational
and Environmental Medicine Program, University
of California San Francisco, San
Francisco, California 94143, United States
Environ. Sci. Technol., Article ASAP
DOI: 10.1021/acs.est.6b02023
Publication Date (Web): September 14, 2016
Copyright © 2016 American Chemical Society
*Phone: (202) 994-9289; fax: 2052-994-0082; e-mail: azota@gwu.edu.
Abstract
Indoor
dust is a reservoir for commercial consumer product chemicals,
including many compounds with known or suspected health effects.
However, most dust exposure studies measure few chemicals in small
samples. We systematically searched the U.S. indoor dust literature on
phthalates, replacement flame retardants (RFRs), perfluoroalkyl
substances (PFASs), synthetic fragrances, and environmental phenols and
estimated pooled geometric means (GMs) and 95% confidence intervals for
45 chemicals measured in ≥3 data sets. In order to rank and
contextualize these results, we used the pooled GMs to calculate
residential intake from dust ingestion, inhalation, and dermal uptake
from air, and then identified hazard traits from the Safer Consumer
Products Candidate Chemical List. Our results indicate that U.S. indoor
dust consistently contains chemicals from multiple classes. Phthalates
occurred in the highest concentrations, followed by phenols, RFRs,
fragrance, and PFASs. Several phthalates and RFRs had the highest
residential intakes. We also found that many chemicals in dust share
hazard traits such as reproductive and endocrine toxicity. We offer
recommendations to maximize comparability of studies and advance indoor
exposure science. This information is critical in shaping future
exposure and health studies, especially related to cumulative exposures,
and in providing evidence for intervention development and public
policy.
Introduction
People in developed countries spend more than 90% of their time in indoor environments,(1)
creating an important link between indoor environmental quality and
public health. Consumer products and building materials including
furniture, electronics, personal care and cleaning products, and floor
and wall coverings contain chemicals that can leach, migrate, abrade, or
off-gas from products resulting in human exposure.(2, 3)
Consequently, chemicals such as phthalates, phenols, flame retardants,
and polyfluorinated alkyl substances (PFASs) are widely detected in the
U.S. general population, including vulnerable populations such as
pregnant women and children.(4-6)
Exposure to one or more of these chemical classes has been associated
with adverse health effects including reproductive toxicity, endocrine
disruption, cognitive and behavioral impairment in children, cancer,
asthma, immune dysfunction, and chronic disease.(7-9)
Many
emerging and current use consumer product chemicals of concern are
semivolatile organic compounds (SVOCs), which exist in the gas and
condensed phase and redistribute from their original source over time,
partitioning between indoor air, dust, and surfaces. Consequently,
exposure to SVOC chemicals in the indoor environment may occur from air,
dust, and dermal pathways.(10-13) For some phthalate diesters, the use of consumer products and indoor exposures are major contributors to human exposure.(14-17) Similarly, for some flame retardants, dust is a significant contributor to exposure,(18-20) while the contribution of the indoor environment to total exposure of PFASs is less well characterized.(21, 22)
The chemical properties, sources, exposure pathways and major health
effects associated with each chemical class are reviewed in the
following sources: phthalates,(16, 23-26) flame retardants,(25, 27, 28) environmental phenols,(25, 26, 29, 30) synthetic fragrances,(29, 31, 32) and PFASs.(25, 33, 34)
Dust can provide critical information on consumer product chemicals in the indoor environment.(35) First, it is a window into which chemicals are present indoors.(36, 37)
Second, because SVOCs partition between air and dust in the indoor
environment, dust concentrations can be used in equilibrium partitioning
models to estimate air concentrations and characterize total
residential intake with reasonable accuracy.(38, 39)
Finally, characterizing exposures from indoor dust may have important
implications for children’s health. Young children are particularly
vulnerable to chemical exposures from dust since they crawl, play on the
floor, and frequently put their hands in their mouths.(40)
Increased dust contact likely plays a role in the higher body burden of
flame retardants in young children compared to their parents.(18, 19, 41-44)
However,
it is difficult to obtain a comprehensive assessment of current use
chemicals in dust because few indoor dust studies report on a broad
range of consumer product chemicals.(26, 39, 45-47)
Most studies measure only one or two chemical classes, and a number
emphasize legacy chemicals like PBDEs. Further, the small sample sizes
and convenience populations used in most studies make it difficult to
assess generalizability to a broader population. Comprehensive estimates
of consumer product chemical concentration patterns and common
coexposures in environmental media are needed to prioritize chemicals
and better understand potential cumulative exposures and impacts.(48-51)
Given
these existing data gaps, the objective of our study is to synthesize
indoor dust data for a wide suite of consumer product chemicals and
assess implications for human exposure and health. Specifically, we
aggregate dust data measured in US indoor environments, focusing on the
following SVOC classes: phthalates, RFRs (also known as novel FRs),
environmental phenols, synthetic fragrance, and PFASs. From this data
aggregation, we calculate pooled concentrations, use these pooled
concentrations to estimate residential intake, and then describe hazard
traits of the chemicals to provide context for the potential health
effects. Finally, we make recommendations for future exposure research.
Materials and Methods
Systematic Literature Search
We
first conducted a screening-level literature search to identify
current-use classes of SVOC consumer product chemicals measured in dust.
The preliminary search identified five chemical classes: phthalates and
phthalate replacements, RFRs, PFAS, synthetic fragrances, and
environmental phenols. Other chemical classes in dust that were not
included in this analysis include legacy chemicals, combustion
byproducts, pharmaceuticals, pesticides, and metals. Legacy chemicals,
such as PBDEs and polychlorinated biphenyls (PCBs), are not currently
used in U.S. commerce. Combustion byproducts (e.g, polycyclic aromatic
hydrocarbons) are not commonly found in consumer products.
Pharmaceuticals and pesticides are not “consumer products” as defined in
the U.S. Consumer Product Safety Act, and metals are not SVOCs.(3, 52, 53)
We
then conducted a comprehensive literature search for dust analysis
studies in February 2015, using PubMed and Web of Science databases for
all document types. In order to capture data that would be most
informative on contemporary dust composition, we limited our search to
studies published during or after the year 2000. The search terms used
were [flame retardant*] AND [dust*], [fragrance*] OR [musk*] AND
[dust*], [perfluorin*] OR [polyfluorin*] AND [dust*], [phthalate*] AND
[dust*], [alkylphenol*] OR [BPA*] OR [paraben*] AND [dust*], and
[“semivolatile organic”] OR [“semivolatile organic”] OR [SVOC] AND
[dust*]. We also included two unpublished data sets provided by members
of our study team.
Studies met the eligibility
criteria if samples were collected: during or after the year 1999, in
the United States, indoors (residential, nonresidential, and vehicle
environments), and using a vacuum cleaner (either study vacuumed or from
an existing used bag). Studies were excluded if: they collected samples
in an international airplane, did not measure chemicals of interest,
did not report on primary data, or were not in English. Three additional
studies and 1 unpublished data set were excluded during preparations
for the meta-analysis because samples measured in the U.S. were not
analyzed separately from international samples; they relied on data
previously published in another study; or no quantitative data were
reported (Figure 1).
Figure 1. Exclusion criteria and number of studies or chemicals included at each stage of the analysis.
Meta-Analysis
Only
chemicals that were measured in ≥3 data sets were eligible for
inclusion in the meta-analysis. We collected descriptive information
from the eligible published and unpublished studies, including chemicals
measured; geographic location; microenvironment sampled, study year;
dust collection and storage methods (Supporting Information (SI) Table S1); analytical methods; and quality control measures (SI Table S2).
We also collected quantitative information, including sample size,
method detection limits (MDL), percent of samples above the MDL, and the
following summary statistics: minimum; 10th, 25th, 50th, 75th, 90th,
and 95th percentiles; maximum; mean; standard deviation; geometric mean
(GM); and geometric standard deviation (GSD). Twenty-nine of 31 papers
and the one unpublished data set were missing information determined
critical for between-study comparison (collection method, sieve size,
storage method, MDL, percent detected, maximum, median, GM, GSD, or
study geographic location), so we contacted the corresponding authors to
collect that information. The corresponding authors provided the needed
information for 20 of the papers and the unpublished data set. In order
to use all available data, we included in the meta-analysis all studies
that reported the GM and GSD for the chemicals of interest, even if the
paper was missing other information. Five studies were excluded at this
stage because they did not report GM and GSD, or because they examined
chemicals measured by fewer than two other studies (so pooled GM
estimates from three data sets could not be calculated).(54-58) In total, we were able to include data from 26 papers and one unpublished data set in the meta-analysis (Figure 1).
We estimated pooled GM dust concentrations for 45 chemicals using GMs and GSDs (Figure 1, SI Table S3).
GMs and GSDs were used whenever data were available, including cases in
which fewer than 50% of the data were > MDL (12 instances total).
Below-MDL values were imputed by each study’s author, and most commonly
were assigned the value of MDL/2 or MDL/√2 (SI Table S4).
Pooled GMs and 95% confidence intervals (CI) were generated for all
chemicals measured in at least three data sets using random effects
models in the R Metafor package (version 1.9–7). In cases where a study
contained more than one geographic location (e.g., sampling in two
states), each study location was counted as a separate data set. In
cases where a corresponding author sent GM or GSD estimates that
differed from the published data, we used the estimates sent by the
author.
We additionally used random effects
models in the R Metafor package to test whether concentrations differed
by microenvironment (residential (n = 29 data sets) versus nonresidential (n = 13 data sets)) or sieve size (≤150 μm (n = 15 data sets) versus >150 μm (n
= 21 data sets); testing RFRs and PFASs only). Only chemicals measured
in at least three data sets collected from each type of microenvironment
were compared.
Intake Assessment
To
provide context to the pooled dust concentrations, we estimated
residential intake (mg/kg-d) for each chemical and used these estimates
to rank chemicals from low to high intake. We queried EPA’s EPI Suite
(v4.11) for physicochemical data, including octanol–water (Kow) and octanol–air (Koa)
partitioning coefficient and Henry’s law constant, for each chemical,
using estimated values rather than empirical values for consistency. One
chemical (EH-TBB) was excluded because it lacked physicochemical data
in EPISuite (Figure 1).
Using the modeled GM dust concentrations and available physicochemical
data, we estimated daily residential intake for an adult female and
child (3–6 years old) from three exposure routes: dermal uptake from gas
phase, inhalation of air, and dust ingestion. We excluded the dermal
intake through dust adherence route since it is typically minor(59)
and the intake parameters required to estimate this route were not
consistently available across all chemicals of interest. For TDCIPP
where residential dust concentrations differed significantly from
nonresidential concentrations, we excluded nonresidential concentrations
from the intake calculations. We estimated indoor air concentrations
from pooled GM dust concentrations using partitioning theory first
proposed by Weschler and Nazaroff and further empirically validated by
Dodson et al., which relies on Koa and assumptions about the organic content of air and dust.(38, 39)
While this method of estimating air concentrations from dust
concentrations may be applied to most chemicals in our analysis, it
cannot be applied to the PFASs, which are typically found at lower
relative levels in air (pg/m3 range).(60)
Thus, for PFASs, we estimated residential intake only via the dust
ingestion pathway. For all other chemicals, we estimated gas-phase air
concentrations for the dermal uptake and total air concentrations, that
is, gas- and particulate-phase concentrations, for air inhalation intake
(SI Table S5). Exposure factors are listed in SI Table S6 and equations used to estimate intake are further described in the SI.
Hazard Identification
To
provide summary information on each chemical’s potential hazard(s), we
used the California Safer Consumer Products Candidate Chemical (SCP CC)
list, which is compiled from existing authoritative lists established by
federal and state governmental bodies in North America and Europe, and
identifies hazard trait(s) for each chemical.(61)
While the particular criteria used by each authoritative body differ,
all are science-based consensus processes that require some form of
comprehensive review of the evidence in order to support regulatory
decision-making (SI Table S7).
Hazard traits identified by authoritative bodies fall into broad
general categories (e.g., carcinogenic, reproductive toxicity), with
each body providing further detailed documents on the particular
chemical.
Results and Discussion
Systematic Literature Search
The
literature search identified 1538 published papers and abstracts, of
which 34 papers and two unpublished data sets met the inclusion criteria
(Figure 1).
One hundred seventy-two chemicals from the five classes of interest
were measured in at least one study, including 13 phthalates, 24 PFAS,
25 fragrances, 47 RFRs, and 62 phenols. Of these chemicals, fewer than
half (74 chemicals) were measured in two or more studies (SI Table S8).
The total number of studies available differed by chemical class:
phthalates and certain RFRs were the most likely to be measured (up to
10 studies per chemical), while phenols and fragrances were measured
less frequently. The large proportion of fragrances (96%), phenols
(65%), and RFRs (57%) measured in only one study indicates that further
research is needed to characterize these chemical classes in dust.
There
was considerable variability in the abbreviations and acronyms used to
identify the various chemicals across studies and CAS Registry Numbers
(CAS RN) were rarely reported. In order to ensure correct chemical
identification, we relied on the full chemical names identified in each
paper at the first introduction of the acronym. The inconsistency of
acronym use necessitated the manual matching of each acronym to a full
chemical name, and precluded quick comparison across studies. We present
CAS RNs, full chemical names, and abbreviations in Table 1.
Table
1. Chemicals in the Meta-Analysis (45 Chemicals), Intake Assessment (44
Chemicals), and Hazard Identification (35 Chemicals)a
| chemical common abbreviation | common name(s), other abbreviations | CAS RN | meta-analysis | intake assessment | hazard identification |
|---|---|---|---|---|---|
| Replacement Flame Retardants (RFRs)b | |||||
| TDCIPP | chlorinated tris; Tris(1,3-dichloroisopropyl) phosphate; TDCPP | 13674–87–8 | X | X | X |
| TCIPP | tris(2-chloroisopropyl) phosphate; TCPP | 13674–84–5 | X | X | X |
| TCEP | tris(2-chloroethyl) phosphate | 115–96–8 | X | X | X |
| TPHP | triphenyl phosphate; TPP; TPhP | 115–86–6 | X | X | X |
| HBCDD | hexabromocyclododecane; HBCD; includes alpha-, beta- and gamma- (aHBCD, bHBCD, gHBCD) isomers | 25637–99–4; 3194–55–6c | X | X | X |
| aHBCDD | alpha- hexabromocyclododecane (aHBCD) | 134237–50–6; 3194–55–6c | X | X | |
| bHBCDD | beta- hexabromocyclododecane (bHBCD) | 134237–51–7; 3194–55–6c | X | X | |
| gHBCDD | gamma- hexabromocyclododecane (gHBCD) | 134237–52–8; 3194–55–6c | X | X | |
| BEH-TEBP | bis(2-ethylhexyl) tetrabromophthalate; TBPH | 26040–51–7 | X | X | X |
| BTBPE | 1,2-Bis(2,4,6-tribromophenoxy)ethane | 37853–59–1 | X | X | X |
| DBDPE | decabromodiphenyl ethane | 84852–53–9 | X | X | X |
| TBBPA | tetrabromobisphenol A | 79–94–7 | X | X | X |
| EH-TBB | (2-ethylhexyl)tetrabromobenzoate; 2-Ethylhexyl 2,3,4,5-tetrabromobenzoate | 183658–27–7 | X | i | |
| aDDC–CO | anti-dechlorane plus (aDP) | 135821–74–8; 13560–89–9d | X | X | X |
| sDDC–CO | syn-dechlorane plus (sDP) | 135821–03–3; 13560–89–9d | X | X | X |
| Phthalates and Phthalate Alternativese | |||||
| BBzP | butyl benzyl phthalate; BBzP | 85–68–7 | X | X | X |
| DEHA | bis(2-ethylhexyl) adipate; di (2-ethylhexyl adipate) | 103–23–1 | X | X | X |
| DEHP | di-2-ethylhexyl phthalate; bis(2-ethylhexyl) phthalate; dioctyl phthalate; DOP | 117–81–7 | X | X | X |
| DnBP | dibutyl phthalate, di-n-butyl phthalate | 84–74–2 | X | X | X |
| DEP | diethyl phthalate | 84–66–2 | X | X | X |
| DiBP | diisobutyl phthalate; DiBP | 84–69–5 | X | X | X |
| DnHP | di-n-hexyl phthalate; DnHP; DNHP; DHEXP | 84–75–3 | X | X | X |
| DnOP | di-n-octyl phthalate; DOP | 117–84–0 | X | X | X |
| Environmental Phenols | |||||
| BPA | bisphenol A | 80–05–7 | X | X | X |
| MeP | methyl paraben; Me-PHBA; methyl p-hydroxybenzoate; methyl 4-hydroxybenzoate | 99–76–3 | X | X | X |
| EtP | Ethyl paraben; Et-PHBA; Ethyl p-hydroxybenzoate; ethyl 4-hydroxybenzoate | 120–47–8 | X | X | X |
| BuP | butyl paraben; bu-PHBA; butyl p-hydroxybenzoate; butyl 4-hydroxybenzoate | 94–26–8 | X | X | X |
| NP | 4-nonylphenol; nonylphenol; 4-NP | 25154–52–3; 84852–15–3f | X | X | X |
| NP1EO | nonylphenol monoethoxylate | 9016–45–9g | X | X | X |
| NP2EO | nonylphenol diethoxylate | 9016–45–9g | X | X | X |
| 2,4-DHBZON | BP-1; 2,4-dihydroxybenzophenone; benzophenone-1; (2,4-dihydroxyphenyl)phenyl methanone | 131–56–6 | X | X | |
| OP1EO | octylphenol monoethoxylate; 4-tert-octylphenol monoethoxylate | 2315–67–5; 9036–19–5h | X | X | |
| OP2EO | octylphenol diethoxylate; 4-tert-octylphenol diethoxylate | 2315–61–9; 9036–19–5h | X | X | |
| Perfluoroalkyl Substances (PFAS) | |||||
| PFOA | Perfluorooctanoic acid; Perfluorooctonoate (C8) | 335–67–1 | X | X | X |
| PFHxS | Perfluorohexanesulfonate, perfluorohexanesulfonic acid (C6) | 355–46–4 | X | X | X |
| PFOS | Perfluorooctanesulfonate; Perfluorooctanesulfonic acid (C8) | 1763–23–1 | X | X | X |
| PFNA | Perfluorononanoic Acid; Perfluorononanoate (C9) | 375–95–1 | X | X | X |
| PFDA | perfluoro-n-decanoic acid; perfluorodecanoic acid; perfluorodecanoate; PfDeA (C10) | 335–76–2 | X | X | X |
| PFBS | perfluorobutanesulfonate; perfluorbutanesulfonic acid; nonafluorobutanesulfonic acid; nonafluorobutanesulfonic acid; PFBuS (C4) | 375–73–5 | X | X | X |
| PFHpA | perfluoroheptanoic acid (C7) | 375–85–9 | X | X | X |
| PFDoA | perfluoro-n-dodecanoic acid; perfluorododecanoate (C10) | 307–55–1 | X | X | X |
| PFHxA | perfluorohexanoic acid; PFHA (C6) | 307–24–4 | X | X | |
| PFBA | perfluorobutyric acid; heptafluorobutyric acid; perfluorobutanoic acid (C4) | 375–22–4 | X | X | |
| 8:2 FTOH | 1H,1H,2H,2H-perfluorodecanol; 2-(perfluorooctyl)ethanol; 1-Decanol, 3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,10-heptadecafluoro- | 678–39–7 | X | X | X |
| Fragrance | |||||
| HHCB | galaxolide; 1,3,4,6,7,8-hexahydro-4,6,6,7,8,8-hexamethylcyclopenta-γ-2-benzopyran | 1222–05–5 | X | X | |
a
CAS RN used for intake assessment is bolded, CAS RN used for hazard identification is italicized.
b
Abbreviations used are “Practical Abbreviations (PRAB)” according to Bergman, et al. (2012).(85)
c
3194–55–6
is the most accurate CAS RN to use for the HBCD technical mixture.
However, it has historically also been referred to with the CAS RN
25637–99–4, and is referenced with this number in a variety of
regulatory documents and authoritative lists.(93) Hazards listed for HBCDD in the hazard table (Figure 4)
reflect hazards associated with both CAS RNs in the SCP CC list.
3194–55–6 was used for the intake assessment for aHBCDD, bHBCDD and
gHBCDD as physicochemical properties for individual isomers were not
available in EpiSuite, but are not expected to differ significantly from
the technical mixture for the properties under consideration.
d
Hazard
information for DDC–CO (Dechlorane Plus;
Bis(hexachlorocyclopentadieno)cyclooctane; DP including syn- (sDP and
sDDC–CO) and anti- (aDP and aDDC–CO) isomers) is available from the SCP
CC list, but studies measured individual isomers (aDDCO–CO, sDDC–CO).
Because isomers are measured together in every study, exposure to the
mixture is likely and thus we have provided the hazard information for
the mixture and added intakes for the individual isomers together in
order to rank the mixture by intake in the hazard table.
e
Abbreviations from the Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES).(94)
f
84852–15–3
is the most accurate CAS RN to use for nonylphenol. However, it has
historically also been referred to with the CAS RN 25154–52–3 and is
referenced with this number in a variety of regulatory documents and
authoritative lists.(95)
g
9016–45–9 is the CAS RN for a mixture of ethoxylated nonylphenols with lower numbers of ethoxylation (EO) units.(96) NPEs with 8 or fewer EO units are typically grouped together as the most toxic forms.(97)
Hazard information for NPEO (Mixture of 4-nonylphenol ethoxylates;
includes 4-nonylphenol monoethoxylate AND 4-nonylphenol diethoxylate;
NP1EO and NP2EO) is available from the SCP CC list, but studies measured
individual isomers (NP1EO, NP2EO). Because isomers are measured
together in every study, exposure to the mixture is likely and thus we
have provided the hazard information for the mixture and added intakes
for the individual isomers together in order to rank the mixture by
intake in the hazard table.
h
9036–19–5
is the CAS RN for a mixture of ethoxylated octylphenols (OPEO, includes
octylphenol monoethoxylate AND octylphenol diethoxylate; OP1EO and
OP2EO).(98)
9036–19–5 was used for the intake assessment for OP1EO and OP2EO as
physicochemical properties for individual isomers were not available in
EpiSuite, but are not expected to differ significantly from the mixture
for the properties under consideration.
i
Hazard information for EH-TBB is available from the SCP CC list, but physicochemical properties are not available in EpiSuite.
Studies
differed in the methods used to collect the dust (study vacuumed or
existing used bag), in the size of the sieve used to sift the dust
samples collected, and in storage temperature (SI Table S1). Analytical methods also differed across studies (SI Table S2), as well as the statistical treatment of values below the limit of detection (SI Table S4).
Because sorption of organic compounds to dust vary by particle size,
the differences in sieve size used between studies likely increased
interstudy variation in measured chemical concentrations.(62, 63)
Indeed, we found that DBDPE concentrations were significantly higher in
dust sieved with >150 μm sieves, compared to samples sieved with
≤150 μm sieves. No other RFRs or PFASs differed significantly by sieve
size. Additionally, because some previous research has suggested that
dust collected by a researcher may contain different chemical
concentrations than dust in a home vacuum bag,(64) the heterogeneity in dust collection method likely also added variability to our sample.
Meta-Analysis
We identified 45 chemicals to include in the meta-analysis (Figure 1, Table 1). Data collection sites spanned 14 states and tended to cluster around research universities, particularly for RFRs (SI Figure S1).
Therefore, the data may not be nationally representative. Though dust
was collected from a variety of indoor environments including homes,
schools, daycare centers, cars, gymnasiums, and occupational settings,
nonresidential environments were less frequently sampled (SI Figure S2).
Chemicals and chemical classes also co-occurred in dust: studies that
reported sample-specific concentrations consistently found multiple
chemicals in each dust sample.(36, 65-67)
The
detection frequency of each chemical varied between studies, sometimes
widely, and did not seem to be solely due to varying detection limits.
However, of the 45 chemicals included in the meta-analysis, 10 were
detected in over 90% of samples (SI Table S9),
indicating that indoor dust contains a mixture of chemicals, and that
these particular consumer product chemicals may be ubiquitous. Products
or building materials found in almost all indoor environments, such as
cables/wires, electronics, and upholstered furniture, may be sources of
phthalates and RFRs in a typical U.S. indoor environment.(3, 37, 68)
Phthalates
were measured in the highest concentrations in indoor dust, several
orders of magnitude above the other chemical classes. Phenols, RFRs, and
fragrances were measured in similar concentrations, while PFAS were
measured in the lowest concentrations (Figure 2; SI Table S10).
The relative ranking of chemical classes from highest to lowest
according to the maximum GM concentrations was phthalates, phenols,
RFRs, fragrances, and PFASs.
Figure
2. Pooled geometric means (GM) and 95% confidence intervals (CI) for
the 45 chemicals whose GM and geometric standard deviation (GSD) were
reported in at least three data sets. Gray lines added after every five
chemicals are intended to aid legibility. See Table 1 for identification of abbreviations. See SI Table S3 for information on the studies from which data on each chemical was compiled. See SI Table S10 for the values plotted.
We
compared concentrations of TDICPP, EH-TBB, BEH-TEBP, PFOS, PFOA, PFNA
between residential and nonresidential environments. TDCIPP and EH-TBB
were significantly higher in nonresidential than residential
environments (p = 0.0043 and p = 0.026, respectively), while no differences were found for the other chemicals (SI Table S11).
The differences noted for TDCIPP and EH-TBB were likely driven by very
high RFR levels in gym and fire station dust. However, no differences by
environment were found for BEH-TEBP, which is a component of the
Firemaster 550 mixture along with EH-TBB. This is consistent with other
studies that report ratios of EH-TBB and BEH-TEBP in dust differ from
those in the commercial flame retardant mixture.(18, 68, 69)
Intake Assessment
Intake estimates, like modeled GM concentrations, spanned orders of magnitude (Figure 3).
As expected, estimated intakes, normalized by body weight, were higher
for the child compared to the adult, although the relative ranking of
the chemicals was similar (data not shown).
Figure
3. Top panel of the graph shows the estimated daily residential intake
of each chemical for a 3–6 year old child (mg/kg/day), based on the
pooled GM concentrations of each chemical in dust from the
meta-analysis. The bottom panel shows the proportion of intake from
three pathways: ingestion, inhalation, and dermal exposure from air. In
both panels, PFAS intake estimates were based solely on estimated
ingestion.
Those chemicals with the
highest dust concentrations also tended to have the highest intake
estimates, with the exception of the RFR TCEP, which was driven by its
relatively high predicted air concentration (>50 μg/m3).
TCEP had the highest estimated intake (>1 mg/kg/day) followed by four
phthalates: DEP, DEHP, BBzP, and DnBP (>0.1 mg/kg/day) (Figure 3).
Exposure to indoor air seemed to drive the intake estimates, since
those chemicals with the highest intake estimates also tended to have
the largest proportion of intake from inhalation and dermal uptake of
indoor air. These modeled results agree with recent evidence of high
inhalation exposure to chlorinated RFRs.(70)
In contrast, for those chemicals where the dust ingestion route
dominated, the intake estimates were lower. Compared to other classes,
phthalates generally had the highest intake estimates; RFRs’ intake
estimates varied widely from approximately 100 ng/kg-day for the
Dechlorane isomers to >1 mg/kg-day for TCEP; the fragrance HHCB
ranked seventh of 44 in total intake; phenols had midrange estimates;
and PFASs, which only rely on the dust ingestion route, had the lowest
residential intake estimates (Figure 3).
These
residential intake estimates do not account for exposures in
microenvironments other than the home. While the data on nonresidential
environments were limited (SI Figure S2),
the data suggest that other indoor microenvironments will contribute to
total exposure. For example, concentrations of the flame retardants
TDCIPP and EH-TBB were higher in nonresidential microenvironments (SI Table S11)
and we would expect that people spending substantial time in such
spaces would have higher exposures than the estimates presented in Figure 3.
In
addition to calculating the pooled GM and GSD, we examined the maxima
relative to the GM for representative chemicals from four classes
(TDCIPP, DEHP, MeP, PFOS) (SI Figure S3).
The maxima reported by each data set were, in many cases, at least 1
order of magnitude greater than the reported pooled GM. Thus, focusing
only on the central tendency metric could obscure the very high
exposures, and potential associated health risks, experienced by a
fraction of the population.
We estimated
residential intakes in order to provide context to the pooled dust
concentrations and link concentration and hazard information. However,
this intake assessment is not comprehensive in that it does not quantify
total intakes of every chemical, does not account for variability in
exposure factors (e.g., dust ingestion rates), and relies on estimated
physicochemical data. In addition to the contribution of other
microenvironments on intake, for many of these chemicals, particularly
the phthalates and parabens, actual intakes may be much higher since
these chemicals are widely used in personal care products applied
directly to the skin,(29) some medications;(71) and diet has also been shown to be an important route of exposure.(72, 73)
Exposure factors vary by individuals; for example, the upper percentile
dust ingestion rate for children is 100 mg/day compared to the central
tendency of 60 mg/day used here, which may substantially increase intake
of chemicals in dust for some individuals.(74)
Finally, we relied on estimated physicochemical properties from EPA’s
EpiSuite, which provides one data source for all chemicals; however, the
algorithms used to estimate properties may vary in accuracy across the
chemicals of interest.(38, 39)
Similarly, the application of the partitioning model to estimate indoor
air concentrations may be less reliable for some chemicals since it has
not been fully validated across all of the chemicals of interest.
Hazard Identification
The authoritative body listings aggregated in the SCP CC list (SI Table S7)
identified nine general hazard traits associated with the 35 chemicals
that were both included in the intake assessment and had SCP CC listings
(Figure 1). Reproductive toxicity, endocrine toxicity, developmental toxicity, and carcinogenicity were the most common hazard traits (Figure 4).
These hazard trait categorizations are broad, but do identify chemicals
within and across classes that warrant further investigation for
potential cumulative exposures and impacts, as our analysis indicates
that coexposure to multiple chemicals is likely (SI Table S9).
Previous studies have quantified cumulative risk from a number of
phthalates or PFASs, some of which are included in the current study.(16, 75-77)
Figure
4. Each row on the chart represents potential chemical hazard traits,
and each column represents a chemical. Chemicals are listed in order of
estimated adult daily residential intake (lowest intake on the left-hand
side). For hazard traits associated with a mixture rather than
individual chemicals (HBCDD, DDC–CO, NPEO) the intakes of the chemicals
composing the mixture were added to generate the mixture’s position in
the intake ranking. The hazard traits associated with each chemical,
according to the SCP CC list are represented by filled cells; hazard
traits not associated with the chemical are left blank. Chemicals not
included in the intake assessment and those not listed in SCP CC list
are not shown in the chart.
This
hazard identification approach and ranking by estimated intake does not
account for bioavailability, pharmacokinetics, dose–response and other
myriad factors that influence the toxicity of chemicals. However, the
ranking of chemicals by intake estimate does point to chemicals with
high intake estimates that were associated with multiple hazard traits
of concern (DEP, BBzP, DEHP, DnBP, DiBP), suggesting that these
chemicals could be prioritized for exposure reduction.
Six chemicals (2,4-DHBZON, OP1EO, OP2EO, PFHxA, PFBA, HHCB) were not found on the SCP CC list (Table 1),
which may reflect lack of data, rather than lack of hazard. A search of
ACToR (the Aggregated Computational Toxicology Resource, a
comprehensive database of in vivo and in vitro chemical toxicity data
from U.S. EPA(78))
reveals that while multiple toxicity studies are available for HHCB,
toxicity tests for DHBZON, PFBA and PFHxA are limited to lethal dose
and/or in vitro studies, and no toxicity testing is reported for OP1EO
and OP2EO. A disadvantage of our use of the SCP CC list as the sole
source of hazard information is the limited information both on emerging
chemicals(54, 79) and the emerging toxicities of known chemicals,(80, 81) due to the time needed to amass and assess evidence before these are included in authoritative lists.
Recommendations for Indoor Dust Studies
While
our analysis represents one of the first attempts to aggregate data
across multiple studies for consumer product chemicals, it was somewhat
limited by heterogeneity of the available data sets, as well as
inconsistent or incomplete collection and/or reporting of important
information. We present the following recommendations to maximize the
usefulness and comparability of future empirical dust studies and
advance the field of exposure science.
Dust Collection and Demographic Data
Future
studies should consider standardizing methods found to introduce
variability including dust sample collection method and sieve size. To
collect dust, we suggest following the standards set by ASTM Standard
Practice D5438: Standard Practice for Collection of Floor Dust for
Chemical Analysis.(82)
At the minimum, we suggest that studies should collect fresh dust using
a vacuum cleaner with an extraction thimble in the crevice tool, as
opposed to sampling from used bags, to improve methodological
consistency and ensure thorough sampling. Because the optimal sieve size
might vary by chemical of interest, we do not recommend a single sieve
size for all studies. The two most common sieve sizes in the studies we
collected here were 150 μm (12 studies) and 500 μm (10 studies). In the
interest of comparability with prior work, future studies should
consider using one of these sieve sizes.
Studies
should also collect detailed demographic information on the individuals
occupying the indoor environments to enable assessment of demographic
factors that are associated with chemical exposures in dust. For
example, higher PBDE exposures have been reported in California and in
low-income communities.(83, 84)
This information is critical in shaping future studies of exposure and
health, and in providing an evidence base for intervention development
and public policy.
Reporting
Many
studies included here did not report descriptive information that could
be used to pool findings or compare across data sets. Future studies
should report: the month, year, and location of sample collection,
central tendency measures (e.g., median, GM), measurements of error
(e.g., GSD, 95% CI), MDLs, frequencies of detection above the MDL,
maxima, CAS RNs for each chemical, and acronyms according to established
standards like those developed by Bergman et al. 2012.(85)
Because a chemical’s dust concentration may change over time, studies
should report summary statistics by sample collection year for multiyear
studies.
Indoor Environment and Geography
Most
studies measured residential environments. Vehicles, workplaces, and
other indoor environments where people may spend many hours should be an
emphasis of future research. Moreover, important insights could be
gained by studying environments with high chemical concentrations in
dust, as well as people with elevated exposures, since these studies
could help characterize source contributions to chemical exposures.(86, 87) Future studies should also target dust sampling in less highly sampled regions of the United States.
Study Design
Future
studies should focus on better characterizing emerging consumer product
chemicals in the phenol and fragrance classes which were the least
studied. Nontargeted analytical methods may provide a more accurate
picture of the true landscape of current consumer product chemicals in
dust. We were also not able to fully assess impacts of changing product
formulations on indoor dust, since most studies were cross-sectional and
only included one sample per home. Longitudinal studies with repeated
dust measures over time are needed to detect changes in product
formulation or use, such as was seen with PBDEs and RFRs in Dodson et
al., 2012.(36)
SVOC Dynamics Indoors
Our
intake assessment is based on current understanding of the dynamics of
SVOCs in the indoor environment, which is largely based on partitioning
theory.(38)
Future studies should further validate these theoretical models with
empirical indoor air, dust, and surface measurements of a range of
chemicals. Moreover, partitioning models are most appropriate for
nonpolar chemicals and do not appear to apply to chemicals like PFASs.(60) Future research should investigate the indoor dynamics of PFASs and similar chemicals to develop accurate exposure models.
Broader Research Needs and Implications
Cumulative exposures have more often been discussed in the context of ambient outdoor pollution.(88)
Our results highlight the co-occurrence of chemicals in the indoor
environment that may contribute to common adverse outcomes and thus
advance the rapidly emerging field of cumulative exposure assessment.
However, quantifying the health risks of chemical mixtures will also
require new study designs and tools in toxicology, risk assessment, and
epidemiology.(89)
Our findings inform the next generation of chemical mixture studies by
highlighting chemicals found at high concentrations indoors, identifying
chemical combinations that reflect typical indoor scenarios, and
indicating emerging chemicals of concern for biomonitoring.
We
also know that pooling GMs across studies does not adequately capture
within-cohort subpopulations that may be highly exposed. Based on the
large difference between the maxima and GMs presented here, we suggest
that, consistent with recommendations from the National Research
Council, risk assessments should quantitatively characterize exposure
variability and include central tendency estimates as well as “worst
case” (maximum) scenarios.(90)
It is likely that some households experience excess exposure due to
shared exposure pathways related to building characteristics, occupant
behavior or other modifiable factors.(12)
A full understanding of these household-level drivers of high exposure
is critical to the development of mitigation strategies including
regulatory interventions. However, when identifying compounds to remove
from consumer products, we also need to ensure that replacements are
safer alternatives in order to guard against “regrettable” substitutions
that may not reduce risk.(91)
We
focused our analysis on indoor dust reservoirs, which reflect long-term
exposures that may be the best measure of household exposure to SVOCs,
given their partitioning between indoor air, dust, and surfaces. This
analysis does not, however, consider other important exposure routes.
For example, close contact with flame-retarded products may result in
inhalation and dermal exposures that significantly contribute to total
exposure for the more volatile organophosphate RFRs.(87, 92)
Biomonitoring data, combined with environmental measures, could be used
to further evaluate the contribution of indoor exposures to total
exposures.
In conclusion, this
comprehensive analysis of consumer product chemicals in U.S. indoor dust
suggests that a wide array of chemicals used in everyday products find
their way into indoor environments across the country where people,
including vulnerable subpopulations like children, are continuously
exposed. In this way, the indoor environment is a haven for chemicals
associated with reproductive and developmental toxicity, endocrine
disruption, cancer and other health effects. Additionally, despite the
observed variability across studies, the existing dust literature has
allowed us to identify the chemicals and chemical classes found at the
highest levels indoors (phthalates and phenols), with the highest
estimated intakes (phthalates and RFRs), and associated with the most
hazard traits (phthalates and PFASs). These findings can be used to
improve population health by informing future exposure assessment and
epidemiologic studies of chemical mixtures as well as individual and
regulatory exposure reduction strategies.
Supporting Information
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.6b02023.
- Descriptive statistics, information about data collection and analysis, studies used for meta-analysis data, pooled GMs, chemicals measured in one or two studies, number of samples in various environments, chemicals with highest detection frequencies, comparison of dust levels by environment, input values for intake estimates, identification of authoritative sources used to describe hazard traits, comparisons of geometric mean and maximum values, and exposure factors used for intake estimates. Please contact authors if additional information on the meta-analysis input data or results is desired (PDF)
The authors declare no competing financial interest.
Acknowledgment
We
thank all the researchers who provided data to us for this study: D.
Bennett, A. Bradman, C. Carignan, R. Castorina, F. Gaspar, S. Harrad, K.
Hoffman, H-M Hwang, K. Kannan, C. Liao, J. Meeker, M. Morgan, R. Rudel,
E. Schreder, H-M Shin, H. Stapleton, M. Strynar, and L. Wang. We would
also like to acknowledge the contributions made by Biruk Tammru and
Angela Stoehr, and the advice and feedback provided by Erika Houtz,
June-Soo Park, and their analytical chemist colleagues at the California
Department of Toxic Substances Control. Funding provided by the NRDC
Science Opportunity Fund, the National Institute of Environmental Health
Sciences (R00ES019881), and the U.S. Department of Housing and Urban
Development (Grant No. MALHH0139-05).
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