Scholarly article on topic 'Chemical exposures in recently renovated low-income housing: Influence of building materials and occupant activities'

Chemical exposures in recently renovated low-income housing: Influence of building materials and occupant activities Academic research paper on "Environmental engineering"

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Abstract of research paper on Environmental engineering, author of scientific article — Robin E. Dodson, Julia O. Udesky, Meryl D. Colton, Martha McCauley, David E. Camann, et al.

Abstract Health disparities in low-income communities may be linked to residential exposures to chemicals infiltrating from the outdoors and characteristics of and sources in the home. Indoor sources comprise those introduced by the occupant as well as releases from building materials. To examine the impact of renovation on indoor pollutants levels and to classify chemicals by predominant indoor sources, we collected indoor air and surface wipes from newly renovated “green” low-income housing units in Boston before and after occupancy. We targeted nearly 100 semivolatile organic compounds (SVOCs) and volatile organic compounds (VOCs), including phthalates, flame retardants, fragrance chemicals, pesticides, antimicrobials, petroleum chemicals, chlorinated solvents, and formaldehyde, as well as particulate matter. All homes had indoor air concentrations that exceeded available risk-based screening levels for at least one chemical. We categorized chemicals as primarily influenced by the occupant or as having building-related sources. While building-related chemicals observed in this study may be specific to the particular housing development, occupant-related findings might be generalizable to similar communities. Among 58 detected chemicals, we distinguished 25 as primarily occupant-related, including fragrance chemicals 6-acetyl-1,1,2,4,4,7-hexamethyltetralin (AHTN) and 1,3,4,6,7,8-hexahydro-4,6,6,7,8,8-hexamethylcyclopenta[g]-2-benzopyran (HHCB). The pre- to post-occupancy patterns of the remaining chemicals suggested important contributions from building materials for some, including dibutyl phthalate and xylene, whereas others, such as diethyl phthalate and formaldehyde, appeared to have both building and occupant sources. Chemical classification by source informs multi-level exposure reduction strategies in low-income housing.

Academic research paper on topic "Chemical exposures in recently renovated low-income housing: Influence of building materials and occupant activities"

Environment International xxx (xxxx) xxx-xxx

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Environment International

ELSEVIER journal homepage: www.elsevier.com/locate/envint

Chemical exposures in recently renovated low-income housing: Influence of building materials and occupant activities

Robin E. Dodsona'*, Julia O. Udeskya, Meryl D. Coltonb, Martha McCauleyc, David E. Camannd, Alice Y. Yaud, Gary Adamkiewiczb, Ruthann A. Rudela

a Silent Spring Institute, 320 Nevada Street, Newton, MA 02460, USA b Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, MA 02215, USA c Battelle Memorial Institute, 505 King Ave., Columbus, OH 43201, USA d Southwest Research Institute, P.O. Drawer 28510, San Antonio, TX 78228, USA

ABSTRACT

Health disparities in low-income communities may be linked to residential exposures to chemicals infiltrating from the outdoors and characteristics of and sources in the home. Indoor sources comprise those introduced by the occupant as well as releases from building materials. To examine the impact of renovation on indoor pollutants levels and to classify chemicals by predominant indoor sources, we collected indoor air and surface wipes from newly renovated "green" low-income housing units in Boston before and after occupancy. We targeted nearly 100 semivolatile organic compounds (SVOCs) and volatile organic compounds (VOCs), including phthalates, flame retardants, fragrance chemicals, pesticides, antimicrobials, petroleum chemicals, chlorinated solvents, and formaldehyde, as well as particulate matter. All homes had indoor air concentrations that exceeded available risk-based screening levels for at least one chemical. We categorized chemicals as primarily influenced by the occupant or as having building-related sources. While building-related chemicals observed in this study may be specific to the particular housing development, occupant-related findings might be generalizable to similar communities. Among 58 detected chemicals, we distinguished 25 as primarily occupant-related, including fragrance chemicals 6-acetyl-1,1,2,4,4,7-hexamethyltetralin (AHTN) and 1,3,4,6,7,8-hexahydro-4,6,6,7,8,8-hexamethylcyclopenta[g]-2-benzopyran (HHCB). The pre- to post-occupancy patterns of the remaining chemicals suggested important contributions from building materials for some, including dibutyl phthalate and xylene, whereas others, such as diethyl phthalate and formaldehyde, appeared to have both building and occupant sources. Chemical classification by source informs multi-level exposure reduction strategies in low-income housing.

ARTICLE INFO

Keywords:

Semivolatile organic compounds (SVOCs)

Volatile organic compounds (VOCs)

Indoor air

Surface wipes

Consumer products

Building materials

Abbreviations: 13DC2P, 1,3-dichloro-2-propanol; 22BBM13P, 2,2-bisbromomethyl-1,3-propanediol; 23DB1P, 2,3-dibromo-1-propanol; 4,4'-DDT, 4,4'-DDT dichlorodiphenyltri-chloroethane; ACE, acetone; AER, air exchange rate; AHTN, 6-acetyl-1,1,2,4,4,7-hexamethyltetralin (Tonalide); BBP, butylbenzyl phthalate; BDE, brominated diphenyl ether; BEH-TEBP, bis(2-ethylhexyl)tetrabromophthalate; BENZ, benzene; BP, benzophenone; BP-3, benzophenone-3; BTEX, benzene, toluene, ethylbenzene and xylene; BuAc, butyl acetate; BuOH, 1-butanol; BuPa, butyl paraben; CFORM, chloroform; CHEX, cyclohexanone; DBP, di-n-butyl phthalate; DCHP, dicyclohexyl phthalate; DEET, N,N-diethyl-meta-toluamide; DEHA, bis(2-ethylhexyl) adipate; DEHP, bis(2-ethylhexyl) phthalate; DEP, diethyl phthalate; DINP, diisononyl phthalate; EBENZ, ethylbenzene; EH-TBB, 2-ethylhexyl 2,3,4,5-tetrabromobenzoate; EOH, ethyl alcohol; EPA, Environmental Protection Agency; EtOAc, ethyl acetate; FORM, formaldehyde; GC/MS, gas chromatography/mass spectrometry; GM, geometric mean; HEPT, heptane; HEXA, hexane; HHCB, 1,3,4,6,7,8-hexahydro-4,6,6,7,8,8-hexamethylcyclopenta[g]-2-benzopyran (Galaxolide); IARC, International Agency for Research on Cancer; IOH, iso-propyl alcohol; MECL, methylene chloride; MEK, methyl ethyl ketone; MePa, methyl paraben; MIONE, methyl isobutyl ketone; MK, musk ketone; MMA, methyl methacrylate; MRL, method reporting limit; MX, musk xylene; NAP, naphthalene; NIC, nicotine; NO2, nitrogen dioxide; NP, 4-t-nonylphenol; PCB, polychlorinated biphenyl; PERC, perchloroethylene; PM, particulate matter; PMCH, perfluoromethyl cyclohexane; PVC, polyvinyl chloride; QA/QC, quality assurance/quality control; RPD, relative percent difference; SES, socioeconomic status; STYR, styrene; SVOCs, semivolatile organic compounds; TBOEP, tris(2-butoxyethyl) phosphate; TBPP, tris(4-butylphenyl) phosphate; TCA, 1,1,1-trichloroethane; TCE, trichloroethylene; TCEP, tris(2-chloroethyl) phosphate; TCIPP, tris(1-chloro-2-propyl) phosphate; TCP, tricresyl phosphate; TCS, triclosan; TDCIPP, tris(1,3-dichloroisopropyl) phosphate; THF, tetra-hydrofuran; TOL, toluene; TPHP, triphenyl phosphate; TXIB, 2,2,4-trimethyl-1,3-pentanediol di-isobutyrate; UV, ultraviolet; VOCs, volatile organic compounds; XYL, xylenes

* Corresponding author.

E-mail addresses: dodson@silentspring.org (R.E. Dodson), udesky@silentspring.org (J.O. Udesky), gadamkie@hsph.harvard.edu (G. Adamkiewicz), rudel@silentspring.org (R.A. Rudel).

http://dx.doi.org/10.1016/jj.envint.2017.07.007

Received 9 May 2017; Received in revised form 12 July 2017; Accepted 13 July 2017

0160-4120/ © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/BY-NC-ND/4.0/).

Please cite this article as: Dodson, R.E., Environment International (2017), http://dx.doi.Org/10.1016/j.envint.2017.07.007

1. Introduction

Potential health impacts of chemical exposures in homes are of particular concern in low-income communities, where there is disproportionate exposure to pollutants from industry and traffic-related sources (Miranda et al., 2011), and where behaviors (e.g. smoking) and housing characteristics (e.g. smaller home size) linked with socioeconomic status (SES) are also associated with exposure to a greater number and magnitude of indoor pollutants (Adamkiewicz et al., 2011). Since residential exposures can dominate total exposures for many chemicals of health concern, including semivolatile organic compounds (SVOCs) and volatile organic compounds (VOCs) (Rudel and Perovich, 2009; Shin et al., 2014; Dodson et al., 2007a), identifying major sources and opportunities to reduce exposure is a priority. SVOCs and VOCs have been linked to a range of health effects including hormone disruption (Rudel and Perovich, 2009), cancer (NTP, 2014; Rudel et al., 2007), neurotoxicity (Grandjean and Landrigan, 2014), and respiratory health (Hulin et al., 2012). In addition, there is a higher prevalence of health conditions that are sensitive to the environment, such as asthma, among low SES communities (Akinbami et al., 2016), increasing susceptibility to some of these indoor chemical exposures in low-income homes.

Residential design practices aimed at reducing environmental im-pacts—"green" building—present one opportunity to significantly change chemical levels in homes. For example, lower emissions from the materials used in green buildings could reduce some indoor exposures. In recent years, green design has been widely implemented in housing construction and renovation, and several studies have found improvements in health after residents in low-income communities moved into newly constructed (Colton et al., 2014; Jacobs et al., 2015) or renovated green buildings (Breysse et al., 2014; Breysse et al., 2015; Colton et al., 2015). This apparent health benefit may reflect improvements in indoor air quality, as evidenced by reduced levels of particulate matter (PM2.5) (Frey et al., 2015), black carbon (Coombs et al., 2016), nitrogen dioxide (NO2), and allergens (Jacobs et al., 2014) following implementation of green renovations.

However other elements of green design, such as tightening the building envelope to reduce energy loss, could have varying impacts on indoor exposures: lower air exchange could reduce infiltration of outdoor pollutants but could also increase exposure to chemicals originating indoors. Indoor sources encompass not only the building structure and materials but also occupant products and activities, including cooking activities (Baxter et al., 2007), use of personal care and cleaning products (Dodson et al., 2012a), smoking (Arku et al., 2015; Kraev et al., 2009), and actions that influence air exchange. Despite the potential for occupants' behavior to influence indoor air quality, there is a lack of data to guide the design of interventions based primarily on occupant education. Air quality measurements in homes are generally obtained when occupants are present, limiting the ability to distinguish which sources - occupant, building, and/or outdoor - are most important for a particular chemical or class of chemicals.

We thus designed our study to characterize occupant contributions to indoor air quality in a community of recently renovated Boston low-income housing units by sampling these units pre- and post-occupancy. To our knowledge, our innovative design is the first to allow evaluation of the occupant contribution to indoor air quality in green-renovated homes by measuring PM2.5 and a large suite of VOCs and SVOCs both before and after occupancy. We targeted chemicals that we expected to be present in the indoor environment and have potentially significant occupant sources, based on our previous research (Dodson et al., 2012a; Rudel et al., 2003; Rudel et al., 2010). This study is also part of a larger investigation of how green renovation affects indoor pollution levels and asthma symptoms in public housing (Coombs et al., 2016; Ponder-Brookins et al., 2014). Except for limited and conflicting data on levels of formaldehyde (Colton et al., 2014; Frey et al., 2015; Coombs et al., 2016; Xiong et al., 2015), there have been few measurements of the

effect of green construction on levels of VOCs (Jacobs et al., 2015; Noris et al., 2013), and no investigation of SVOCs. Our goal was thus to expand knowledge about the impacts of green renovation on the indoor environment and to inform development of more comprehensive interventions to improve indoor environmental quality, especially in low-income communities.

2. Methods

2.1. Study site

In 2011, the Boston Housing Authority began redeveloping several properties according to "green" standards with support from the American Recovery and Reinvestment Act. At one federally subsidized housing development in Boston's South End, 13% of residential units were renovated. Improvements focused mainly on energy efficiency, including high efficiency windows, additional insulation, energy star appliances, low energy lighting, and low VOC paints, but also aimed to modernize the units by installing new flooring, baseboards and cabinets. The buildings were awarded a U.S. Green Building Council Leadership in Energy and Environmental Design (LEED) for Homes certification and Homes Energy Rating System (HERS) tier II energy rating of 65, meaning these units were designed to be 35% more efficient than a reference home.

Of the 30 unique units sampled, all but three were single-story, two-or three-bedroom units with average sizes of 700 ft2 (6 units) and 850 ft2 (21 units) respectively, in three- or four-story multi-family walk-up buildings (Table 1). We also sampled from three four-bedroom townhouse units, which averaged 1200 ft2 in size. All units were heated with baseboard radiators controlled by the residents, and some had window air conditioners. There was no mechanical ventilation in these units.

The study population comprised mostly younger (18-39 years old) Hispanic females (Table 1). Twenty (74%) classified themselves as Hispanic or Latino and, of the 16 born outside of the United States, most (81%) were from the Dominican Republic. The majority of the participants (67%) were the only adult living in the unit, and there were at least three children living in most (82%) of the units. Most participants (89%) were new to the housing development.

2.2. Sample collection

We collected indoor air and surface wipes from 10 newly renovated units before occupancy (June to July 2013) and from 27 units one to nine months after occupants moved in (July 2013 to January 2014) (Fig. 1). We selected pre-occupancy units from the 13% renovated units in the development, which received certificate of occupancies in April 2013, mostly based on availability for sampling, as renovations had to be completed and the unit unoccupied for at least one week. For post-occupancy sampling, we recruited seven participants living in units we had sampled pre-occupancy, as well as an additional 20 participants through door knocking at the other newly renovated units. We intentionally sampled fewer pre-occupancy than post-occupancy units; we hypothesized that variability of chemical concentrations in pre-oc-cupancy units would be low, given that all units were renovated to the same specifications, so that we would not need as many samples to characterize the concentration distributions. Study protocols were reviewed by the Office of Human Research Administration at the Harvard T.H. Chan School of Public Health.

We targeted nearly 100 SVOCs and VOCs as well as PM2.5. Specifically, we analyzed for 35 SVOCs in indoor air and 46 on floor wipes, including phthalates, flame retardants, pesticides, antimicrobials, and fragrances, with 23 SVOCs targeted both in air and on wipes, 12 SVOCs in air only, and 23 on wipes only. Formaldehyde, chlorinated solvents, BTEX chemicals, and nicotine were among the 26 VOCS analyzed in indoor air.

R.E. Dodson et al. Table 1

Characteristics of study participants and housing units.

Participant characteristics (N = 27) Age

18-29 11 41

30-39 12 44

40-49 3 11

50-59 0 0

60-69 1 4

Gender

Female 25 93

Male 2 7

Race/ethnicity (not exclusive)

Non-Hispanic White 1 4

Non-Hispanic Black 6 22

Hispanic 20 74

Other 1 4

Country of origin

United States 11 41

Dominican Republic 13 48

Other central American 3 11

Highest level of education

< High School 5 19

High school/GED 11 41

Some college 7 26

Associate's degree 2 7

Bachelor's degree 2 7

Hours employed outside of home

0 14 52

> 0 13 48

Number of adults living in unit

1 18 67

2 6 22

> 3 1 4

Number of children living in unit

2 4 15

3 15 56

> 3 7 26

Housing characteristics (N = 30)

Building type

Walk up building, single floor unit 27 90

Townhouse 3 10

Total number of bedrooms

2 (~ 700 ft2) 6 20

3 (~ 850 ft2) 21 70

4 (townhouse, ~ 1200 ft2) 3 10

Total number of bathrooms

1 27 90

2 3 10

All air samples were collected over a seven day period. Detailed sampling and analytical methods are provided in Supplemental Information (SI). Briefly, we collected SVOCs from main living area indoor air using URG personal pesticide sampling cartridges (University Research Glassware; Chapel Hill, NC) as previously described (Rudel et al., 2003; Rudel et al., 2010; Rudel et al., 2001) and analyzed via gas chromatography/mass spectrometry (GC/MS) at Battelle Memorial Institute, and surface wipes from the kitchen floor using the protocol established in the American Healthy Home Survey (Stout et al., 2009), with analysis via GC/MS at Southwest Research Institute. We passively collected VOCs from indoor air using commercially-available activated carbon badges. Badges were purchased from and analyzed by Assay Technology (Boardman, OH) using gas chromatography with flame ionization detector (GC/FID). We used separate passive samplers to capture formaldehyde, which were purchased from and analyzed by Assay Technology with high performance liquid chromatography (HPLC) using a UV detector. We used a passive diffusion monitor to target vapor-phase nicotine. Nicotine badges were analyzed via GC/MS

at University of California, Berkeley using previously established methods (Hammond and Leaderer, 1987). We collected PM2.5 using a Harvard Personal Exposure Monitor at a target flow rate of 1.8 L/min. We weighed filters before and after sampling using a Mettler MTS microbalance (Mettler-Toledo, Columbus, OH).

We estimated air exchange rate (AER) in each unit using per-fluorocarbon tracer gases (Dietz et al., 1986). Perfluoromethyl cyclo-hexane gas (PMCH) was released continuously from sources with known release rates that were placed near exterior walls. The PMCH gas was then sampled via diffusion on capillary absorption tubes placed in the center of the living space. Capillary absorption tubes were analyzed at Harvard by GC. We calculated AER by assuming a well-mixed interior, which is reasonable over seven days in small mostly single-level housing units.

We also conducted a visual home inspection pre- and post-occupancy, and administered a survey to the new residents post-occupancy. The survey focused on satisfaction with the unit, thermal comfort, mold/moisture, pests, appliance and product use, and self-reported health.

2.3. Quality assurance and control

We employed several quality assurance/quality control (QA/QC) measures to evaluate the reliability of the data. We pooled QA/QC data from this study with that from two of our other studies in low-income housing. All studies used the same sampling procedures and we batched the samples together for chemical analysis. Across all studies, duplicate samples were collected to assess overall measurement precision for SVOCs (n = 15 pairs), VOCs (n = 3 pairs) and PM2 5 (n = 2 pairs) in air, SVOCs on surface wipes (n = 9 pairs), and PMCH for estimation of air exchange (n = 47 pairs). To evaluate potential contamination from the lab and from the sample matrices, at least one solvent blank and one matrix blank were included with each lab batch, except for nicotine analyses, which did not include a solvent blank. We also analyzed field blanks (n = 14 SVOC air samples; n = 3 VOC air badges; n = 5 SVOC wipes; n = 6 PMCH) to evaluate contamination that could have occurred during sampling and transport. Recovery of at least one lab control spike per batch was used to assess accuracy of the multi-residue analytic method for each targeted SVOC, while recoveries of surrogate compounds (n = 4 for SVOCS in air; n = 3 for SVOCs on wipes) helped to characterize extraction efficiency for each sample.

Based on QA/QC results, we focused our analysis on indoor air. This is because duplicate precision, measured as Relative Percent Difference (RPD), was much better for chemicals measured in air (mean RPD < 30% for all but one chemical) compared to chemicals measured on wipes (mean RPD > 30% for all but three chemicals, ranging from 38% for BDE 100 up to 137% for carbaryl). Additional QA/QC information and results are presented in SI.

2.4. Data analysis

We evaluated the impact of occupancy on indoor pollutant levels using two main approaches. First, we compared detection frequencies and visualized concentrations and source rates (mass of chemical emitted per hour) by occupancy status. Source rates were calculated by multiplying the measured concentration (ng or |jg/m3) by the volume of the unit (m3) and estimated air exchange (h- 1) (see SI for calculation of AER). Second, we used linear mixed effects models, with housing unit as a random effect, to identify significant differences in pre- to post-occupancy levels and source rates. We ran separate models with each chemical concentration as the outcome, with concentrations log-transformed because of the skewness of the data. Except where indicated, we assigned a value of method reporting limit (MRL)/2 to results reported by the lab as nondetect. We pursued the mixed effects modeling approach in order to explore the influence of potentially confounding seasonal factors (see Fig. 1) by modeling log-transformed source rate as

Fig. 1. Conceptual model showing factors that varied with occupancy, including (A) time since renovation and heating season (indicated by vertical dashed line), (B) air exchange, (C) temperature, (D) off-gassing from building materials, (E) occupant behavior and activities, and sorption of chemicals to/from surfaces. Pre- and post-occupancy visits can be distinguished by color in the air exchange plot (purple = pre-occupancy, orange = post-occupancy). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

the outcome variable and by considering models restricted to non-heating months only (June-September). Because substitution methods are known to introduce bias (Helsel, 2006), we also conducted sensitivity analyses using methods for mixed effects censored regression proposed by Vaida and Liu (2009) (R package: lmec). We chose not to rely on the censored regression method as our primary modeling approach as it is not well tested with environmental data and to our knowledge cannot currently handle completely uncensored data.

We used the approaches described above to categorize 45 chemicals measured in indoor air as showing evidence of either (a) a predominant occupant source or (b) some contribution from a building-related source. We classified chemicals as having a predominant occupant source if they were only ever detected post-occupancy, if most (at least five) of the seven paired samples showed increases with occupancy, or if a chemical was detected in only one home pre-occupancy but > 10% of homes post-occupancy. On the other hand, chemicals detected in at least 60% of homes pre-occupancy, chemicals for which at least half of the pairs showed decreases or remained constant with occupancy suggested at least some building-related source(s). A few chemicals that we

would have classified as having a predominant occupant source based on air concentrations were re-categorized as have some contribution from the building because the surface wipes indicated higher concentrations pre- compared to post-occupancy. We did not categorize 10 chemicals that were never detected above the MRL at either time point and 3 chemicals that were detected infrequently (< 10% of homes) pre-and post-occupancy and did not show any clear pattern among those few detects. In support of our main analysis, we also characterized and compared variability pre- and post-occupancy. With uncensored lognormal data, we could quantify a change in variability with a statistical measure like the coefficient of variation. However, because of the degree of left censoring - a high frequency of < MRL values - in our data, usual statistical tests of variability are not appropriate. Instead, we characterized spread in the upper half of the distribution by calculating the ratio of the 95th to 50th percentiles of source rate. The characterization of spread complements regression modeling by highlighting chemicals that may not have changed "on average," but that had notably higher values in a few homes, suggesting important sources of chemicals - products or activities - in those homes.

Regression modeling and calculation of the 95th/50th percentile ratios were only done for chemicals with at least 60% detects above the MRL for at least one time point (pre- or post-occupancy). We focused our analysis on measured air concentrations because our wipe measurements showed inadequate precision, which may reflect the spatial variability of settled dust and surface residues (i.e. measurements may be sensitive to the particular area wiped). Wipes may also represent the surface material itself rather than typical house dust exposures. Nevertheless, we considered wipe mass loadings when available, especially for chemicals that were not targeted in air.

To provide context to our measurements, we compared our measured air concentrations to available risk-based screening values for indoor air developed under U.S. EPA's Superfund program (U.S. Environmental Protection Agency, 2017). We compared concentrations to both the carcinogenic (target risk of 1 in 106) and noncarcinogenic (hazard index of 1) levels, noting the exceedances based on the lower value. Screening levels were available for 16 target chemicals, mostly VOCs.

Data processing and statistical analyses were performed using R v3.2.4, an open-source statistical software environment.

3. Results and discussion

As is typical, VOCs, if detected, were at higher concentrations (|jg/ m3 range) than SVOCs (ng/m3 range) (Table 2). We found 29 of 35 (83%) targeted SVOCs in air, 26 of 46 (57%) targeted SVOCs on wipes, and 18 of 26 (69%) targeted VOCs in air at levels above the MRL. As expected, we detected more chemicals post-occupancy (29 SVOCs and 18 VOCs in air; 26 SVOCs on wipes) than pre-occupancy (20 SVOCs and 11 VOCs in air; 18 SVOCs on wipes).

AERs were higher post- compared to pre-occupancy as expected given that all pre-occupancy sampling took place in the summer months with the windows closed and without air conditioning. Variation in the pre-occupancy AERs may be related to variation in temperature (see Fig. 1). After substituting the MRL for two unrealistically low PMCH post-occupancy measurements (see SI, Section 2), median AER remained higher post-occupancy (1.1 h-1) compared to pre-occupancy (0.80 h-1), although not statistically significant (p > 0.05; Wilcoxon rank sum test). When we separated post-occupancy samples by heating season (before and after September 30, 2013), AER estimates were significantly higher in non-heating season post-occupancy units compared to heating season post-occupancy units (1.5 vs 0.57 h-1), consistent with previous estimates in low-income housing (Zota et al., 2005). Seasonal variation in AER could be related to occupants opening windows or installing window air conditioner units during the non-heating season. Variation in indoor temperature relative to outdoor temperature with season could also be a factor. The AERs in this study are on par with previous estimates in Boston green housing units, which are lower than in typical non-green housing units (Colton et al., 2014).

3.1. Source characterization

Our unique study design allowed us to distinguish chemicals predominantly introduced by the occupant from those influenced by building sources. After controlling for the influence of emissions from building materials - that is, establishing approximate baseline levels of chemicals before occupants were present - our analysis identified 25 targeted chemicals for which occupants were the predominant source of concentrations in indoor air (Fig. 2). These chemicals showed obvious and often statistically significant increases in concentrations post-compared to pre-occupancy. On the other hand, many of the remaining chemicals were present before occupants moved in, and levels were either similar or lower post- compared to pre-occupancy (Fig. 3A and B); we classified these as having at least some contribution from building-related sources.

Several factors, illustrated in Fig. 1, may have affected our

characterization of sources for each chemical. AER, which can influence chemical concentrations (Liu et al., 2015; Su et al., 2013), varied not just with occupancy but with season; because our post-occupancy measurements spanned across seasons - summer through winter - an observed increase in levels could thus be explained by the lower AER for samples collected during the heating season. On the other hand, levels of chemicals that appeared to decrease or remain constant with occupancy may have been driven down by decreasing temperature post-occupancy, which affects chemical emission rates for some chemicals (Xiong et al., 2013), or significant sorption onto surfaces including furniture and the occupants themselves (i.e. skin, clothing) (Weschler et al., 2015; Weschler and Nazaroff, 2008).

3.1.1. Occupant as predominant source

Our study demonstrates that residential exposure to a number of chemicals with known or suspected health effects, including anti-mi-crobial and flame retardant chemicals, plastics chemicals, and fragrances, appear to be predominately related to occupant behavior and product use in the home (Fig. 2). Flame retardant tris(2-chloroethyl) phosphate (TCEP), for example, was detected more frequently in post-occupancy air samples and is classified as a carcinogen under California's Proposition 65 program (CA OEHHA, 2017a).

Several chemicals never detected in air above the MRL pre-occu-pancy were present in at least one home after occupants moved in. These included anti-microbial triclosan; flame retardant BDE 47; nicotine; four chemicals classified by the International Agency for Research on Cancer (IARC) as possible human carcinogens (flame retardants 2,3-dibromo-1-propanol (23DB1P) (IARC, 2017a) and 2,2-bisbromomethyl-1,3-propanediol (22BBM13P) (IARC, 2017a) and solvents methylene chloride (IARC, 2014) and perchloroethylene (PCE) (IARC, 2017b); and IARC probable carcinogen chloroform (IARC, 2017c). To our knowledge, these are the first measurements of triclosan, 23DB1P, and 22BBM13P in air in US homes. Triclosan has been used in consumer products such as toothpaste, antibacterial soaps, and children's toys (Halden, 2014), however in 2016 the Food and Drug Administration (FDA) banned its use in over-the-counter soaps and body washes (FDA, 2016). In a recent systematic review, Johnson et al. concluded that triclosan is 'possibly toxic' to reproductive and developmental health, based on animal evidence for thyroid effects (Johnson et al., 2016). BDE 47 has also been linked to thyroid hormone levels during pregnancy (Chevrier et al., 2010; Stapleton et al., 2011) and to neurode-velopmental effects (Herbstman et al., 2007). A major congener of the PentaBDE flame retardant mixture, BDE 47 was once widely used in upholstered furniture before its phase-out in 2005 (Dodson et al., 2012b). The post-occupancy BDE 47 levels in this study are similar to previous measurements in California homes (Rudel et al., 2010) and somewhat lower than those measured in California child care centers and home-based child care facilities (Bradman et al., 2014). 23DB1P and 22BBM13P were detected in just one home post-occupancy (at 0.58 and 0.74 ng/m3, respectively), but the potential health effects of these chemicals are concerning. Brominated 'tris', the parent compound of 23DB1P, was banned from children's pajamas in the late 1970s due to mutagenicity concerns yet continues to be used in furniture, in acrylic carpets and sheets, and materials such as resins and paints (NTP, 2017). 22BBM13P is used in molded plastics and rigid polyurethane foam (NTP, 2017).

As expected, several targeted fragrance chemicals and chemicals used in beauty products appeared to be strongly associated with occupancy. Fragranced products have been linked to health effects including exacerbation of asthma symptoms (Steinemann, 2009) and 6-acetyl-1,1,2,4,4,7-hexamethyltetralin (AHTN), musk xylene and musk ketone have been shown to be weakly estrogenic in vitro (Bitsch et al., 2002). In this housing development, musk ketone was only detected after occupants moved in. We found AHTN and 1,3,4,6,7,8-hexahydro-4,6,6,7,8,8-hexamethylcyclopenta[g]-2-benzopyran (HHCB) at significantly higher concentrations in air post- compared to pre-

Table 2

Summary statistics for air and wipe samples in all homes.

Analyte (Abbreviation) Pre

No.a % > MRLb Min. Median GMC 95th %ile Max.

SVOCs in air (ng/m3)

Phthalates/plastics chemicals diethyl phthalate" (DEP) di-n-butyl phthalate" (DBP) butylbenzyl phthalate" (BBP) bis(2-ethylhexyl) adipate (DEHA) bis(2-ethylhexyl) phthalate" (DEHP) dicyclohexyl phthalate (DCHP) diisononyl phthalate (DINP) 4-t-Nonylphenol (NP) Fragrances, preservatives, UV filters & disinfectants Tonalide (AHTN) Galaxolide (HHCB) musk ketone (MK) musk xylene® (MX) methyl paraben (MePa) benzophenonee (BP) benzophenone-3 (BP-3) triclosan (TCS) Historic building contaminants

PCB 52 Flame retardants BDE 47e

2,3-dibromo-l-propanol (23DB1P)

2.2-bisbromomethyl-l, 3-propanediol(22BBM13P)

1.3-dichloro-2-propanolg (13DC2P) tris(l-chloro-2-propyl) phosphate (TCIPP) tris(l,3-dichloroisopropyl) phosphatef (TDCIPP) tris(2-chloroethyl) phosphate (TCEP) tris(2-butoxyethyl) phosphate (TBOEP) triphenyl phosphate (TPHP)

VOCs in air (ng/m3) Petroleum products benzene (BENZ) toluene (TOL) xylenes (XYL) ethylbenzene (EBENZ) Chlorinated water

chloroform (CFORM) Chlorinated solvents methylene chloride (MECL) perchloroethylene (PERC) Lifestyle ethyl alcohol (EOH) nicotine (NIC) Other

formaldehyde" (FORM) acetone (ACE) methyl ethyl ketone (MEK) cyclohexanone (CHEX) ethyl acetate (EtOAc) butyl acetate (BuAc)

10 100 140 250 270 500 530

10 100 350 610 630 1100 1300

10 100 16 19 22 47 57

10 60 - 4.6 4.4 7.3 8.4

10 100 21 39 41 170 260

10 10 - - - 7.1 10

10 100 50 65 69 110 120

10 100 26 34 37 65 85

10 90 _ 5.7 5.3 8.8 11

10 100 6.4 9.9 9.7 14 14

10 0 - - - - -

10 10 - - - 1.5 2.7

10 90 - 2.4 2 4 4.4

10 100 290 470 450 600 600

10 100 4 8.7 8.8 17 19

10 0 - - - - -

10 10 0.39 0.4h

10 0 _ _ _ _ _

10 0 - - - - -

10 0 - - - - -

10 70 - 1.4 1.2 3.1 3.3h

10 100 11 25 25 43 53h

10 100 2.1 14 8.9 22 24h

10 10 - - - 20 37

10 0 - - - - -

10 80 - 0.84 1.1 6.3 10

10 70 0.73 0.79 1.2 1.3

10 70 - 8.3 4.6 11 12

10 90 - 6 4.8 10 11

10 60 - 1.2 0.96 2 2.2

10 0 " " " "

10 0 _ _ _ _ _

10 0 - - - - -

10 0 _ _ _ _ _

10 0 - - - - -

10 100 4.4 17 16 26 27

10 90 - 4.9 4 8.3 8.7

10 10 - - - 1.1 1.3

10 60 - 2.3 2 3.9 4.2

10 30 - - - 4.4 4.9

10 40 - - - 3.4 4.3

) change in median

> MRLb Min. Median GMC 95th %ile Max.

25 100 110 280 300 560 1800 25 12

25 100 190 340 410 1400 1700 65 - 44

25 96 - 17 15 44 46 2.3 - 11

25 88 - 7.8 7.7 18 22 4.4 70

25 96 - 77 69 160 280 15 97

25 4 - - - - 4.9 4.4 -

25 100 37 85 90 160 180 15 31

25 100 4.5 27 27 57 99 3.2 - 21

25 100 10 28 26 52 93 2.8 390

25 100 39 160 130 280 390 0.35 1500

25 24 - - - 8.6 13 0.22 -

25 16 - - - 6.8 9.8h 0.22 -

25 92 - 8.6 6.1 18 28 0.3 260

25 100 120 340 310 470 490 0.81 - 28

25 96 - 15 13 50 100 1.5 72

25 36 - - - 2.9 3.7 0.73 -

25 4 - " l.lh 0.37

25 44 _ _ _ 0.59 3 0.058 _

25 4 - - - - 0.58 0.22 -

25 4 - - - - 0.74 0.34 -

25 68 - 3.8 2.3 20 22 0.8 170

25 100 5.1 13 17 50 150h 2.2 - 48

25 100 0.8 4.2 5.4 35 98 0.37 - 70

25 20 - - - 35 92 7.3 -

25 16 - - - 11 14 7.3 -

25 88 - 1.6 1.6 4 7.3 0.73 90

26 69 0.74 0.72 1.1 1.8 0.75 1.4

26 46 - - - 8.1 11 3.1 -

26 46 - - - 4.7 12 1.8 -

26 27 - - - 1.6 2.2 0.79 -

26 3.8 " 9.6 4.7

26 3.8 _ _ _ _ 4.7 4.7 _

26 3.8 - - - - 22 3.1 -

26 96 _ 82 98 440 1500 16 _

27 11 - - - 0.18 3.5 0.028 -

24 100 1.5 11 10 26 28 0.056 - 35

26 92 - 9.8 13 120 180 3.1 100

26 7.7 - - - 1.2 1.4 1.3 -

26 3.8 - - - - 3.1 1.8 -

26 85 - 8.1 8.9 52 80 3.1 -

26 73 - 6.1 4.7 12 52 1.5 -

(continued on next p

Table 2 (continued)

Analyte (Abbreviation)

Pre No.a

> MRLb Min. Median GMC 95th %ile Max.

> MRLb Min. Median GMC 95th %ile Max.

3 change in median

methyl methacrylate (MMA) heptane (HEPT) tetrahydrofuran (THF) Particulate matter (tig/m3) PM2.5

SVOCs on surface wipes (tig/ft2) Plastics chemicals

diethyl phthalate (DEP) di-n-butyl phthalate (DBP) butylbenzyl phthalate (BBP) bis(2-ethylhexyl) adipate (DEHA) bis(2-ethylhexyl) phthalate (DEHP) diisononyl phthalate (DINP)

2,2,4-Trimethyl-l,3-pentanediol di-isobutyrate (TXIB) Fragrances & disinfectants Tonalide (AHTN) Galaxolide (HHCB) triclosan (TCS) Flame retardants BDE 47

tris(l-chloro-2-propyl) phosphate (TCIPP) tris(l,3-dichloroisopropyl) phosphate (TDCIPP) tris(2-butoxyethyl) phosphate (TBOEP) triphenyl phosphate (TPHP) tris(4-butylphenyl) phosphate (TBPP) Combustion-related naphthalene" (NAP) phenanthrene pyrene diazinon

piperonyl butoxide

propoxur

lindane

ÎV.ÎV-diethyl-meta-toluamide (DEET)

10 10 10

10 10 10 10 10 10 10

10 10 10

10 10 10 10 10 10

10 10 10 10 10 10 10 10

10 70 30 0 100 100 20

70 100 0

50 100 40 100 100 0

100 100 20 0 0 80 0 80

0.018 0.018 0.057

0.026 0.38 0.97

650 12

0.013 0.017 0.022 0.051

630 11

0.019 0.051

2.8 2.9 26 15 - - - 4.7 28 1.6 -

- - 26 3.8 - - - - 5.3 0.97 -

- - 26 3.8 - - - - 1.2 1.3 -

21 21 27 100 0.9 11 11 39 46 " - 39

1 1.9 27 19 2.2 2.6 1

2.8 2.9 27 93 - 2.5 2.5 8.5 14 1 56

1.7 2 27 89 - 6.4 6.4 44 52 1 -

- - 27 26 - - - 6.3 12 1.7 -

6.3 6.5 27 100 3.8 23 24 77 100 1 690

18,000 18,000 27 100 2100 10,000 9100 15,000 20,000 10 - 33

1.8 2.2 27 52 - 1 - 2.1 4.8 1 -

0.026 0.027 27 100 0.013 0.12 0.12 0.9 1.4 0.01 570

0.11 0.12 27 100 0.11 2 1.5 4.4 4.9 0.01 3400

- - 27 26 - - - 0.45 0.67 0.01 -

0.085 0.1 27 15 _ _ _ 0.047 0.13 0.02 _

3 4.1 27 100 0.12 0.47 0.51 1.3 2.4 0.05 - 52

0.21 0.31 27 74 - 0.12 0.11 0.35 0.88 0.05 -

1900 2200 27 100 23 140 140 430 1200 1 - 78

22 24 27 100 1.2 6.4 5.6 10 11 0.01 - 47

- - 27 41 - - - 0.021 0.059 0.01 -

0.032 0.035 27 78 _ 0.01 0.011 0.029 0.034 0.0062 - 41

0.1 0.12 27 100 0.012 0.047 0.049 0.13 0.18 0.0077 - 7.8

0.032 0.034 27 37 - - - 0.087 0.14 0.005 -

- - 27 3.7 - - - - 0.046 0.01 -

- - 27 7.4 - - - 0.1 0.64 0.01 -

0.064 0.082 27 48 - - - 0.033 0.046 0.01 -

- - 27 3.7 - - - - 0.01 0.01 -

0.038 0.045 27 100 0.015 0.05 0.066 0.39 2.9 0.01 130

The following chemicals were never detected in air samples: preservative butyl paraben, historic building contaminants PCB 11 and PCB 153, flame retardants bis(2-ethylhexyl) tetrabromophthalate (BEH-TEBP), 2-ethylhexyl 2,3,4,5-tetra-bromobenzoatef (EH-TBB) and tris(4-butylphenyl) phosphate (TBPP), chlorinated solvents trichloroethylene (TCE) and 1,1,1-trichloroethane (TCA), and other VOCs 1-butanol (BuOH), methyl isobutyl ketone (MIONE), hexane (HEXA), styrene (STYR), 4-phenylcyclohexane (PCH), and naphthalene (NAP). The following chemicals were never detected on surface wipes: plastics chemical dicyclohexyl phthalate (DCHP), historic building contaminants PCB 11, PCB 52, PCB 95, PCB 105, and PCB 153, flame retardants BDE 28, tris(2-chloroethyl) phosphate (TCEP), and tricresyl phosphate (TCP), combustion-related benzo(a)pyrene (B(a)P), pesticides 4,4'-DDT, carbaryl, chlorpyrifos, cis-permethrin, cypermethrin, deltamethrin/ tralomethrin, /ran.s-permethrin1. methoxychlor, fipronil, and lifestyle-related chemical cotinine. indicates insufficient number of detects to calculate summary statistic. a Number of analyzed samples.

b MRL = method reporting limit (defined as the maximum of the analytical detection limit and the 90th percentile of the blanks). c GM = geometric mean, computed at each time point for compounds with > 60% detects, with non-detects set to the MRL/2. d Compound-specific MRL, determined using the median volume in the samples (13.7 m3). e Value subject to blank correction by subtracting the median blank value. f Average matrix spike recovery was high (> 150%). g Average matrix spike recovery was low (< 50%).

h Surrogate recovery was high (> 150%) for the sample from which we are reporting the maximum value.

R.E. Dodson et al.

Fig. 2. Increased concentrations or detection frequencies post- compared with pre-occupancy indicates predominant occupant sources. Regression models are shown for chemicals detected in at least 60% of samples. Paired samples (n = 7) indicated by light gray lines. See Table 2 for full chemical names and Supplemental information for CAS numbers.

occupancy, with an over 400% increase (95% CI: 240, 670) in the modeled geometric mean (GM) AHTN air concentration (5.1 to 26 ng/ m3) and a 1300% increase (95% CI: 690, 2200) in the modeled GM HHCB air concentration (9.7 to 130 ng/m3). Both AHTN (commercial name Tonalide) and HHCB (commercial name Galaxolide) are synthetic fragrances that have previously been found in a range of consumer products, including detergents and personal care products such as perfume (Dodson et al., 2012a). We are not aware of other measurements of fragrance chemicals in air in US homes. The presence of these chemicals pre-occupancy may reflect infiltration of fragranced products used in other units or in hallways, or use by construction workers or building staff previously working in the unit. Musk xylene was detected least often and at the lowest levels among the four targeted fragrance chemicals (Table 2), consistent with findings from our consumer product testing (Dodson et al., 2012a). Other beauty product chemicals with evidence for significant occupant sources included ethyl alcohol (ethanol), which is also found in cleaning and pest control products (Goldsmith et al., 2014); acetone and ethyl and butyl acetate, commonly found in nail products and paints (Goldsmith et al., 2014); and methyl paraben, a preservative used in many personal care products including makeup, shampoo, and lotions (Dodson et al., 2012a). Levels of these chemicals showed statistically significant increases of > 200% at the modeled GM from pre- to post-occupancy (Table S2). Given that some of these chemicals - ethanol, acetone, and ethyl and butyl acetate - are especially volatile, we also modeled changes in source rate, as described in Section 2.4, before and after occupancy. Findings from source rate models were consistent with concentration-based models.

Plasticizers such as phthalates and adipates are used in a wide range of products including building materials (e.g. vinyl flooring); in this housing complex, levels of two plasticizers, bis(2-ethylhexyl) phthalate (DEHP) and bis(2-ethylhexyl) adipate (DEHA), increased significantly with occupancy (Table S2). Both are used in polyvinyl chloride (PVC; vinyl) and could have been present in furniture, toys, and clothing brought in by occupants (Dionisio et al., 2015). DEHP, an anti-androgen, affects reproductive development primarily in boys, and has

also been linked to asthma (Gray et al., 2000; Bornehag et al., 2004; ECHA, 2012). The median post-occupancy air concentration in our study (77 ng/m3) was in the range of concentrations measured in homes in Albany, NY (Tran and Kannan, 2015) and in Northern Manhattan and the South Bronx (Just et al., 2015). The toxicity of DEHA has been less well studied; however, it has been associated with reproductive toxicity (Dalgaard et al., 2003; Miyata et al., 2006). To our knowledge, DEHA has not been measured recently (i.e. within the past 5 years) in US homes, however the median concentration in air after occupants moved in was comparable to our previous measurements in homes on Cape Cod (Rudel et al., 2003) and lower than the median level we found in homes in Richmond and Bolinas, California (Rudel et al., 2010).

Of the 14 pesticides targeted only on surface wipes and not in air samples, we found piperonyl butoxide (PiPBO), shown to enhance allergic airway inflammation in rodents (Nishino et al., 2013); diazinon, associated with neurodevelopmental effects in prenatally exposed animals (Eskenazi et al., 1999); and lindane, a California Proposition 65 (CA OEHHA, 2017b) and IARC carcinogen (IARC, 2015), in post-occupancy but not pre-occupancy samples. Of note, diazinon was tentatively detected pre-occupancy - meaning that the lab was not confident in the identification of the compound - in the same unit where it was detected with more certainty post-occupancy. The maximum level of PiPBO detected (0.64 |Jg/ft2) was approximately an order of magnitude lower than the mean loading on wipes collected in the American Healthy Housing Survey (AHHS; 4.18 |Jg/ft2) (Stout et al., 2009). On the other hand, we detected diazinon in one sample at a mass loading of 0.046 |Jg/ft2, approximately 2 x the mean reported in the AHHS (0.0278 |Jg/ft2) (Stout et al., 2009) and slightly higher than the median found in another study of Boston low income housing (0.037 |Jg/ft2) (Julien et al., 2008). Mass loadings of DEET, a common broad spectrum insect repellant, showed a significant increase of 200% (95% CI 44%, 540%) at the modeled GM.

Fig. 3. Chemicals for which concentrations decreased post-occupancy or showed a mix of increases and decreases likely have a source unrelated to the occupant. Panel A shows chemicals whose levels in indoor air appear to be primarily influenced by building materials. Panel B shows chemicals that appear to have mixed building and occupant sources. Regression models are shown for chemicals detected in at least 60% of samples. Paired samples (n = 7) indicated by light gray lines. See Table 2 for full chemical names and Supplemental information for CAS numbers.

3.1.2. Building as contributing source

The presence of several chemicals pre-occupancy, whose levels decreased or remained constant after occupants moved in, provided evidence of at least some building-related sources for several flame retardants, plastics chemicals, and VOCs (Fig. 3A and B). However, the number and complexity of changes that occurred with occupancy, as illustrated in Fig. 1, precluded identification of 'predominant' building sources with the same level of confidence as for chemicals with predominant occupant-related sources.

Given their use profiles, we were not surprised to find that four target VOCs - cyclohexanone, toluene, ethylbenzene and xylenes - and plastics chemical butylbenzyl phthalate (BBP) appeared to have building-related sources in this development. Cyclohexanone, used in excess of 2 billion lbs/year in the U.S. (U.S. EPA, 2016), is found in a

range of products including adhesives, paints and lacquers (Dionisio et al., 2015; EPA, U.S., 2016). This chemical showed the greatest percent decrease in modeled GM from pre- to post-occupancy (— 69%; 95% CI: — 85%, — 35%), a finding that was consistent across models exploring the potential influence of air exchange and season (Table S2). Toluene, ethylbenzene, and xylenes, all common solvents used in paints, decreased post-occupancy, with significant decreases for xylene and ethylbenzene. Toluene and ethylbenzene are both listed by California's Proposition 65 as a developmental toxicant (CA OEHHA, 2017c) and carcinogen (CA OEHHA, 2017d), respectively. The maximum levels of these chemicals detected in our study were lower than those measured in a green high-rise building in New York (Xiong et al., 2015), but higher than median air concentrations in California child care centers (Hoang et al., 2016). Of note, the renovation specifications

for this housing complex included use of low-VOC paints. BBP, a California Proposition 65 reproductive toxicant (CA OEHHA, 2017e) known for its use in vinyl flooring, adhesives and glues, showed a significant 34% decline in modeled GM air concentration, but exploration of seasonal factors and wipe data complicated the interpretation somewhat. Air concentrations of BBP have been shown to vary significantly with temperature in a test house, with higher concentrations measured at higher temperatures (Bi et al., 2015). Given our sampling scheme (see Fig. 1), we restricted our regression models to samples collected in the summer to examine whether levels in some post-occupancy samples collected at cooler temperatures (i.e. during the heating season) were driving our findings. Indeed, the estimated decrease in BBP levels in this model was attenuated and no longer significant (Table S2). Furthermore, mass loadings of BBP on wipes actually increased significantly post-occupancy (650%; 95% CI: 180%, 1900%). Post-occupancy BBP mass loadings (GM: 6.4 |Jg/ft2) were lower than reported in the AAHS (GM: 10.2 |Jg/ft2) (Hagan et al., 2014). Potential explanations include

(1) both the building and the occupants were important sources of BBP, but air concentrations decreased overall because of substantial sorption by additional surfaces (such as people and furniture) post-occupancy or

(2) building materials - such as paint - were the primary source of BBP, and the levels increased on wipes as the chemical migrated onto floor surfaces over time.

Two chlorinated organophosphate flame retardants, tris (1-chloro-2-propyl) phosphate (TCIPP) and tris (1,3-dichloroisopropyl) phosphate (TDCIPP), have known uses in furniture (Cooper et al., 2016; Stapleton et al., 2012), but the lower GM concentrations in air post-occupancy suggest a building source, such as insulation added during renovation of this development. TDCIPP is a California Proposition 65 carcinogen (CA OEHHA, 2017f) and has been associated with hormonal effects in men (Meeker and Stapleton, 2010), whereas TCIPP is structurally similar to California Proposition 65 carcinogen TCEP (CA OEHHA, 2017a). The GM TCIPP air concentration decreased significantly from 26 to 17 ng/ m3 post-occupancy and the GM TDCIPP air concentration decreased by nearly half, although not significantly, from 10 to 5.5 ng/m3. Both post-occupancy GMs were slightly higher than previously reported in the respirable (< 4 |jm) fraction of 10 personal air samples (Schreder et al., 2016). The surface wipe model results for TCIPP were consistent with air, while TDCIPP was detected less frequently on wipes pre-occupancy (pre: 40% > MRL; post: 74% > MRL) and the GM mass loading significantly increased. The discrepancy between air and surface wipes for TDCIPP may be a result of equilibrium dynamics - TDCIPP may be released into the air and over time settles onto surfaces.

Interestingly, our data suggested that for several chemicals with known uses in personal care and cleaning products, building-related sources made a strong contribution to air concentrations in this development. The GM concentrations of di-n-butyl phthalate (DBP), used in personal care products such as nail polish and perfumes (Dodson et al., 2012a), nonylphenol, used in cleaning products (Dodson et al., 2012a), and benzophenone (BP), a UV filter, decreased in air after occupants moved in, with statistically significant reductions of 44% (760 ng/m3 to 430 ng/m3) for DBP and 25% (420 ng/m3 to 320 ng/m3) for BP (Table S2). Animal (Gray et al., 2006) and human (Pan et al., 2006) evidence suggests that DBP, which could have been present in adhesives or paint in these units, may be a reproductive toxicant. The median DBP post-occupancy air concentration was greater than the median in homes in Albany, NY (Tran and Kannan, 2015) but somewhat lower than measures in California child care centers (Gaspar et al., 2014). DBP also showed the same puzzling pattern as phthalate BBP, wherein the mass loadings increased non-significantly on post-occupancy surface wipes. Again, this could be a reflection of the dynamics -changing temperature, passage of time, absorption onto skin and furniture - discussed above. Post-occupancy DBP mass loadings (GM: 2.5 |Jg/ft2) were almost half of AAHS mass loadings (GM: 4.7 |Jg/ft2) (Hagan et al., 2014). Benzophenone, which to our knowledge has not been previously measured in US homes, has been detected in emissions

from low- or no-VOC paints (Schieweck and Bock, 2015), and could also be used in plastics and/or surface coatings and adhesives (NTP, 2006). Benzophenone-3 (BP-3), another UV filter used in personal care products (Dodson et al., 2012a), also appeared to have building-related sources, though the contribution of the building to overall levels of this chemical may not have been quite as strong as for BP; levels of BP-3 were relatively similar comparing pre- to post-occupancy, and some pairs increased (see Fig. 3B). Still, BP-3's detection in 100% of units pre-occupancy suggests that workers may have used products - such as sunscreen - containing this chemical, which impacted the space and lingered, or it may have some as yet not well recognized uses in building materials.

Additional chemicals that did not follow the patterns we expected included PM25 and formaldehyde (Fig. 3B). Specifically, we expected that PM25 - as well as benzene - would be influenced largely by infiltration of ambient air (e.g. from motor vehicles) while formaldehyde would arise primarily from building-related sources that would deplete over the course of sampling. However measured PM25 increased in one pre- to post-occupancy pair, and measured indoor levels (median 11 |Jg/m3) were slightly higher than the annual average ambient concentration (7.6 |Jg/m3) recorded at an air quality monitoring site in nearby Roxbury, MA. Comparing our measured indoor PM25 concentrations, which are integrated over 7 days, to the average of one to three 24-hour integrated ambient PM25 concentrations collected every three days at an air quality monitoring site over the same time period, indoor concentrations appear generally higher than outdoor concentrations (see Fig. S4). Activities such as cooking and smoking in the home (EPA, U, 2016) may have influenced indoor levels of PM25 above and beyond ambient contributions; indeed, the home with one of the highest PM2.5 concentrations also had the highest benzene and nicotine concentrations. Overall, the pattern for benzene was more in keeping with our expectations, with levels remaining relatively stable from pre-to post-occupancy and comparable (median ~ 0.7 |Jg/m3) to the reported annual mean ambient concentration in Roxbury, MA (Strum and Scheffe, 2016). The range of formaldehyde concentrations, on the other hand, were more consistent pre- and post-occupancy than we would have predicted, suggesting that its presence as an additive in personal care products (ATSDR, 2008) may have kept indoor air levels steady even as emissions from building materials decreased. The median post-occupancy formaldehyde concentration in our study (11 ng/m3) was on par with findings from another study of green-renovated public housing in Boston (Colton et al., 2014), but somewhat lower than in green-renovated low-income/subsidized housing in California (Noris et al., 2013), Ohio (Coombs et al., 2016), and Phoenix (Frey et al., 2015).

Most of the remaining target chemicals appeared to have 'mixed' building and occupant sources (Fig. 3B), characterized either by (1) notably higher levels in just one or a few post-occupancy units or (2) a pattern wherein levels increased in air with occupancy but decreased on wipes. Among chemicals in the first group, we expected diethyl phthalate (DEP), a lower molecular weight phthalate used in personal care products, including fragranced products, to be strongly influenced by occupancy. However, measured GM air concentrations of DEP increased only slightly with occupancy (pre: 270 |Jg/m3; post: 300 |Jg/ m3), while the maximum concentration increased substantially from 530 |Jg/m3 pre-occupancy to 1800 ng/m3 post-occupancy (Table 2). Investigation of survey data showed that the unit with the highest source rate for DEP - as well as DBP and BBP - reported frequent use of several fragranced products, including daily air freshener and perfume use. Our previous finding of a positive correlation between DEP and several fragrance chemicals in consumer products (Dodson et al., 2012a), in addition to reports of higher MEP, a urinary biomarker of DEP, in perfume users compared to non-users (Braun et al., 2014; Parlett et al., 2013), support the hypothesis that DEP is a common carrier for fragrances. Our analysis of the 95th/50th percentile ratio (Fig. S5) supports our interpretation of substantial skew in the post-occupancy distribution of DEP, and measurements in air in homes in

Albany, NY (Tran and Kannan, 2015) and in California child care centers (Gaspar et al., 2014) showed similar skewness. Methyl metha-crylate (MMA), used in nail products (Goldsmith et al., 2014) as well as in adhesives, paints and coatings, and flame retardant 1,3-dichloro-2-propanol (13DC2P), a flame retardant breakdown product (NTP, 2005), were also measured at notably higher levels in one or a few post-occupancy units. We are not aware of previous measurements of either of these chemicals in residential indoor air. The occupant in the unit with the highest MMA concentration in air (5 x higher than in other units) reported using nail polish a few times a week, while the occupant in the unit with the highest source rate for 13DC2P reported having many new pieces of upholstered furniture, including a couch, upholstered chairs, and upholstered dining chairs. However, levels of other flame retardants were not as elevated in this unit.

Mixed-source chemicals in the second group (levels increased in air with occupancy but decreased on wipes) included plastics chemical diisononyl phthalate (DINP), a higher molecular weight phthalate, and flame retardants tris (2-butoxyethyl) phosphate (TBOEP) and triphenyl phosphate (TPHP). One possible explanation for the increase in air concentrations and decrease in mass loadings for these chemicals is that they were all present in the floor or floor finish and that the mass loading decreased on surface wipes over time with cleaning and everyday wear. Indeed, the surface wipe mass loadings of DINP were 2 to 3 orders of magnitude higher than other phthalates we targeted and 200 x higher at the median than in the AHHS (Hagan et al., 2014). The increase in DINP air concentrations post-occupancy may also reflect occupants bringing in vinyl products such as shower curtains. Increases in air concentrations of TBOEP and TPHP may be a result of their use in products and furnishings brought in by the occupant. TBOEP is used in plastics (van der Veen and de Boer, 2012) and TPHP, for which the increase was statistically significant (Table S2), is used in both foam furniture (Stapleton et al., 2009) and nail polish (Mendelsohn et al., 2016).

3.2. Implications for health

Of the 16 targeted chemicals with U.S. EPA risk-based screening levels for indoor air (Table S4), measured concentrations of 6 chemicals exceeded these risk-based levels during post-occupancy. Forty-four targeted VOCs and SVOCs did not have available screening levels. All units had formaldehyde indoor air concentrations that exceeded the carcinogenic screening level of 0.22 |Jg/m3. At least 69% of units had indoor air concentrations of benzene that exceeded the carcinogenic target risk level of 0.36 |Jg/m3. The likely source is benzene from gasoline and vehicle pollution entering from outdoors. The post-occupancy concentrations of ethylbenzene in four homes slightly exceeded screening levels for residential air. The MRL for chloroform (4.7 |Jg/m3) is higher than the target risk level of 0.12 |jg/m3; therefore, the one unit with detectable levels of chloroform post-occupancy exceeded the screening level. One unit had ethyl acetate and perchloroethylene levels above available screening levels.

A few of the chemicals we detected were banned or are associated with banned activities. Specifically, we detected pesticide propoxur, a California Proposition 65 carcinogen (CA OEHHA, 2017g) that has not been approved for residential use since 2007 (U.S. EPA, 2007), on pre-occupancy kitchen floor wipes in these units, which were renovated in 2013. Another pesticide, diazinon, banned from home use in 2004 because of neurodevelopmental effects in children (Eskenazi et al., 1999), was found on kitchen floor wipes in one unit pre- and post-occupancy (pre-occupancy detect was tentative, see Fig. S6). The observed post-occupancy increase in air concentrations of flame retardant BDE 47, phased out of use in upholstered furniture in 2005 (Dodson et al., 2012b), highlights a potential issue of legacy pollutants in older and possibly second-hand furniture owned by lower income residents. We also detected nicotine in 44% of post-occupancy samples despite a smoking ban in Boston public housing. This is consistent with our other

findings that show measurable levels of airborne nicotine following the ban (MacNaughton et al., 2016).

We detected several chemicals associated with allergy and asthma; for example, formaldehyde (McGwin et al., 2010), particulate matter (Khreis et al., 2017), fragrances (Steinemann, 2009), triclosan (Savage et al., 2014), pesticides diazinon (Eskenazi et al., 1999) and PiPBo (Nishino et al., 2013), and phthalates (Bornehag et al., 2004). These findings are especially relevant in our study community. For example, our recent survey-based study of families living in Boston public housing showed that 21% of adults self-reported as having asthma (Colton et al., 2015).

3.3. Implications for building design and use

Our results also have implications for how new or renovated residential buildings are designed. For example, strategies to reduce chemicals exposures in the home should broaden the scope beyond the current focus on VOCs. Our data suggest that architects and contractors need to consider phthalates, flame retardants, and phenolic compounds such as benzophenone and nonylphenol in material specifications. Active consumer campaigns such as those for flame retardants in upholstered furniture could enhance their impact by considering building materials, such as insulation, as well, given that our data indicate these chemicals are introduced even before furnishings are present. We also found propoxur and naphthalene on floor wipes prior to occupancy, suggesting that these chemicals are either used during renovation or tracked in. Further development of safer, low-cost alternatives that meet the same performance standards is urgently needed so that they can be implemented in affordable housing units under construction or planned for construction.

With regard to building use, our data indicate that occupant education campaigns have the potential to reduce predominant exposure sources in the home for several chemicals. For example, purchasing personal care and cleaning products without synthetic fragrance will reduce exposures to chemicals linked with asthma, respiratory symptoms and hormone disruption (Dodson et al., 2012a). Based on the skewness of the concentration distributions of some chemicals-such as DEP-it is apparent that some occupants have fairly unique product use behaviors that contribute substantially to their exposures. Further investigation of those chemicals with higher concentrations in a few units provides an opportunity for source identification and targeted intervention. However the onus to reduce exposures should not rest solely on the occupant as there may not always be affordable alternatives. Instead, stronger policies and regulations upstream of the consumer are needed to ensure that consumers are not purchasing products containing chemicals with known health effects or that have not been thoroughly tested for safety.

3.4. Limitations

This study, to our knowledge, is the first of its kind to measure SVOC and VOC concentrations before and after occupancy in newly renovated low-income housing. It provides a first look at the impact of the occupant relative to the impact of the building materials on indoor exposures. Because our study took place in one renovated housing development, our study is ideal for evaluating occupant-related effects; however, this limits the generalizability of findings related to the building-related chemicals since our categorization likely reflects specific materials used in this development's renovation. We also collected pre- and post-measurements in different seasons because of delayed recruiting, making the seasonal effects, including temperature and AER, hard to disentangle.

We collected a smaller number of pre-occupancy samples because we hypothesized a priori that the variability in chemical levels would be low. While variability appeared to increase with occupancy as we expected, it was larger than expected pre-occupancy given the

similarity of the units located in one housing development. It is possible that the pre-occupancy levels reflect chemical sources that are not directly building material-related. For example, chemicals - including personal care product chemicals such as fragrances - could have been introduced by building staff accessing the units after the renovations were complete. Except for the three townhouses, all of these units were located in multi-unit buildings and pre-occupancy levels may also reflect air infiltration from other units (Dodson et al., 2007b). Contributions from other units are likely only relevant for measured air concentrations and not wipe levels.

Our unique study design included a limited set of samples from paired pre- and post-occupancy units (n = 7). While our study represents some of the first examples of paired pre-and post-occupancy measurements, the small sample size may limit the stability of our model estimates. One or a few data points could have an exaggerated influence on the results, so the model estimates and p-values should be treated with caution.

In identifying occupants' contribution to indoor air quality, we assumed that measured concentrations related to off-gassing from building material sources would decrease over time as a result of degradation and ventilation out of the unit. Further, we assumed that by the time of our post-occupancy sampling, chemical levels were at steady state, and levels would not increase due to further off-gassing. However, it is possible that the levels had not reached steady state and that even if the units had been left undisturbed - that is, if no residents had moved in - we still would have observed an increase in levels until they reached steady state.

4. Conclusion

Reduction of exposures to chemicals in homes requires multi-faceted approaches that acknowledge the various factors driving their use and resulting exposures in the home. These approaches must consider both design and behavior and must involve designers, occupants, and building management. For example, reducing use of fragranced products in the home will help to reduce levels of DEP, but this chemical may also be present in other products such as insecticides and building materials, including paints and adhesives. Integrated Pest Management (IPM), an approach shown to control pests without relying on the routine application of pesticides in multi-family housing, embraces this type of multi-level approach (Brenner et al., 2003; Scammell et al., 2011). Subsidized housing may be particularly suited for these multi-faceted approaches since building and property management is centralized and can effectively engage with and educate tenants about best practices for proper maintenance and mitigation of in-unit pollutant exposures.

Acknowledgments

We thank the Boston Housing Authority and the study participants for their collaboration.

Funding

This study was funded by the U.S. Department of Housing and Urban Development (Grant Nos. MALHH0229-10 and MAHHU0005-12).

Appendix A. Supplementary data

Additional analytical and QA/QC details; pre- and post-occupancy summary statistics; air exchange measurements; regression modeling results; ratio of 95th to 50th percentile plots; pre- and post-occupancy plots for surface wipes; scatterplots of indoor air and surface wipe levels; residential air screening levels. Supplementary data associated with this article can be found in the online version, at http://dx.doi.

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