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Indoor Air Quality in Green Vs Conventional Multifamily Low-Income Housing Meryl D. Colton,*,† Piers MacNaughton,† Jose Vallarino,† John Kane,‡ Mae Bennett-Fripp,§ John D. Spengler,† and Gary Adamkiewicz† †

Department of Environmental Health, Harvard School of Public Health, Boston 02215, Massachusetts, United States The Boston Housing Authority, Boston 02111, Massachusetts, United States § The Committee for Boston Public Housing, Boston 02120, Massachusetts, United States ‡

S Supporting Information *

ABSTRACT: Indoor air quality is an important predictor of health, especially in low-income populations. It is unclear how recent trends in “green” building affect the indoor exposure profile. In two successive years, we conducted environmental sampling, home inspections, and health questionnaires with families in green and conventional (control) apartments in two public housing developments. A subset of participants was followed as they moved from conventional to green or conventional to conventional housing. We measured particulate matter less than 2.5 μm aerodynamic diameter (PM2.5), formaldehyde, nitrogen dioxide (NO2), nicotine, carbon dioxide (CO2), and air exchange rate (AER) over a sevenday sampling period coincident with survey administration. In multivariate models, we observed 57%, 65%, and 93% lower concentrations of PM2.5, NO2, and nicotine (respectively) in green vs control homes (p = 0.032, p < 0.001, p = 0.003, respectively), as well as fewer reports of mold, pests, inadequate ventilation, and stuffiness. Differences in formaldehyde and CO2 were not statistically significant. AER was marginally lower in green buildings (p = 0.109). Participants in green homes experienced 47% fewer sick building syndrome symptoms (p < 0.010). We observed significant decreases in multiple indoor exposures and improved health outcomes among participants who moved into green housing, suggesting multilevel housing interventions have the potential to improve long-term resident health.

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drive disparities in exposure.2,12 Poorly maintained housing, the presence of a smoker in the home, and proximity to industrial sources of pollution are all examples of drivers of exposure that vary by socioeconomic status.13−15 These environmental health risks may be compounded by the presence of crime and violence, which increase time spent indoors16 and consequently exposure to indoor pollutants. Insufficient or impaired ventilation may be the shared determinant of exposure clustering between pollutants of indoor origin and can thus increase the cumulative health risk in residences and commercial spaces.17 Inadequate ventilation has been associated with higher concentrations of NO2,18 VOCs,6 and indoor-sourced PM,19 as well as poor health outcomes such as sick building syndrome (SBS) (the development of several general symptoms including headaches, sneezing attacks, itchy/burning eyes, skin rashes, blurred vision, and drowsiness).20,21 The relationship between ventilation and

INTRODUCTION Adults in the United States spend approximately 80% of their time indoors, 65% being in their own residence,1 and it has been long established that the home environment can present significant social, physical, and environmental health risks.2−4 Although the majority of research has focused on exposure to outdoor air pollution, both theoretical and empirical research show that indoor environmental exposures can exceed outdoor concentrations.5−8 Exposures to indoor air pollutants such as particulate matter (PM), nitrogen dioxide (NO2), environmental tobacco smoke (ETS), and volatile organic compounds (VOCs) have been associated with poor health outcomes ranging from asthma exacerbation to cancer.9,10 Many indoor pollutants originate from common sources determined by housing conditions or occupant behavior, leading indoor exposures to cluster by building or household. A dose-response relationship between self-reported health and the degree of clustering among poor environmental conditions was observed in a recent survey of 828 public housing residents.11 Poor indoor air quality has increased health implications in low-income communities where socioeconomic factors can © 2014 American Chemical Society

Received: Revised: Accepted: Published: 7833

March 26, 2014 June 13, 2014 June 18, 2014 June 18, 2014 dx.doi.org/10.1021/es501489u | Environ. Sci. Technol. 2014, 48, 7833−7841

Environmental Science & Technology

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year after moving into either green (n = 18) or control (n = 6) apartments. We recruited an additional seven participants living in conventional housing in the second year to balance the control group at follow-up. Home visits in both years occurred between February and early May during the public housing heating season. Participants were recruited in 2012 by door-knocking from a list of approximately 50 households that were designated to move into the green-renovated buildings. Participants were eligible if they were over 18 years of age and spoke English, Spanish, Mandarin Chinese, or Cantonese. If more than one person in the apartment matched the selection criteria, we interviewed the first available, eligible participant. However, at follow-up, the same participant was interviewed. Additional control participants in 2013 were recruited using the same criteria from a list of occupants designated to move into the second phase of green buildings in late 2013. The green buildings were located at the OC housing development in South Boston and consisted of newly constructed town-homes and a six-story building. The buildings were designed with many green features and were certified Leadership in Energy and Environmental Design (LEED) platinum in July, 2012. In brief, these features included the following: • Photovoltaics, cogeneration, and electricity produced by the local utility; • High-efficiency, gas-fired hydronic heat and hot water; • Heat-recovery ventilator (HRV) or energy-recovery ventilator (ERV) that continuously exhaust air outdoors from bathrooms to capture and reuse vertical heat loss from kitchen, bathroom, elevator, and stairs; • “Green” envelopes/exterior including cellulose insulation, high performance windows, high albedo roofs, and fiberglass roof insulation; • “Green” interior finishes including fiberglass window shades, low VOC paints, recycled corridor carpet, and formaldehyde-free adhesives and materials; • HUD’s Sustainability Principles such as re-establishing walkable streets and safe, reliable public transportation at the site; • Smoke-free policy; and • Integrated Pest Management (IPM). The control buildings at OC were one of several three-story walk-up buildings built in 1940. At the second development, the Ruth Lillian Barkley (RLB) development in the South End neighborhood, the control population lived in either a six-story or 14-story building built in similar fashion in 1950. All participants were administered informed consent and compensated for their participation in accordance with the Harvard School of Public Health Institutional Review Board. Sampling and Analytical Methods. We measured particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5), nitrogen dioxide (NO2), formaldehyde, nicotine, carbon dioxide (CO2), and air exchange rate (AER) over a mean seven-day sampling period (range: 6−10 days). All samples were collected in the main living space of the apartment. On the first day of the sampling period, participants were administered a survey covering topics including thermal comfort and satisfaction, observation of mold and pests, smells, product and appliance use, and self-reported health. While the survey was being administered, a trained research assistant conducted a visual inspection of the apartment. This inspection

indoor exposures is complicated by behavioral patterns that impact ventilation and exposure. This is especially relevant in low-income housing where residents typically do not control their heating and may manage their thermal comfort by opening and closing windows. The majority of interventions to improve indoor air quality have focused on a single exposure, policy, or behavior change owing to the complex and costly nature of large-scale home interventions.22 Many of these interventions, such as lead abatement,23 Integrated Pest Management (IPM),24,25 healthy home education,26,27 and weatherization28 have successfully reduced indoor environmental exposures in low-income housing. These approaches are now common elements of healthy home “best practices,” but it has been suggested that multilevel intervention is necessary to significantly improve the indoor environment of low-income residents.29,30 “Green” housing is designed to use low-impact materials, increase energy efficiency and improve occupant health. However, few studies have measured how improvements in energy efficiency impact indoor levels of environmental pollutants, and it is unclear how green building trends and the emergence of new household products will change indoor exposure profiles, especially within low-income housing. Significant tightening of buildings in the late 70s was associated with a dramatic rise in sick building syndrome symptoms,3 but recent studies reported positive respiratory outcomes associated with new or renovated green improvements.31,32 In 2011, The Boston Housing Authority began redeveloping several properties according to green standards, including Old Colony (OC), one of the largest developments in the historic South Boston neighborhood. This redevelopment was coupled with housing design and policy changes supported by previous housing intervention research including the change from gas to electric stoves, implementation of Integrated Pest Management (IPM), and a prohibition on smoking indoors. In 2012, a portion of residents were moved into the green buildings at OC, creating a rare occurrence where half of the development was newly built green construction while half of the development remained as traditional units built in the 1940s. This natural experiment provided a unique opportunity to isolate the effects of housing on health while minimizing selection bias and exposure misclassification from spatial differences in ambient pollution and unmeasured neighborhood effects. In this manuscript, we compare the indoor exposure profiles of conventional and newly constructed green, low-income public housing to understand how comprehensive improvements in development-level policies, building-level structures, and participant-level behaviors affect indoor air quality. We present the first semiexperimental, longitudinal exposure assessment of low-income families moving from one home to another and explore the success of this “intervention” on selfreported health.

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MATERIALS AND METHODS Study Design. In 2012, we conducted environmental sampling and home inspections and administered a health questionnaire with 30 families in conventional (control) apartments in two public housing developments in the Boston area. This visit occurred in the month before each participant moved into either green or conventional apartments. We collected the same environmental measures, inspection, and health questionnaire with 24/30 participants approximately one 7834

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between green and conventional units. The housing type was modeled as a fixed effect with subject-specific random intercepts. We used an unstructured covariance matrix to model the random effect. We estimated the final percent change in environmental measures after including variables for ambient temperature and year to control for weather and any unmeasured temporal trends. The mean ambient temperature was calculated by averaging all 24 h mean samples that corresponded with the seven-day sampling period (range: 3−7 samples) from the Environmental Protection Agency (EPA) state and local air monitoring (SLAM) station at Dudley Square Roxbury (AQS site ID: 25-025-0043; available at http://www.epa.gov/airdata/ ad_maps.html). To control for potential differences in outdoor pollution between the “green” and control visits, we included terms for outdoor PM2.5 and NO2 in the corresponding pollutant models using all 24 h integrated (PM2.5) and 24 h average hourly (NO2) measurements (range: 2−3 and 4−9 samples, respectively) within the sampling period from the same EPA monitoring station at Dudley Square. To illustrate the longitudinal change in a subset of pollutants, we compared the mean change in PM2.5, NO2, and formaldehyde from 2012 to 2013 between participants moving into green homes compared to those moving into conventional units and tested these differences using t tests. We also compared two measures of self-reported health between participants in green and conventional units. The first was an ordinal measure of self-reported health (1 = excellent; 2 = very good; 3 = good; 4 = fair; 5 = poor), which has been shown to be associated with morbidity in adults.35 The second measure was a summation of the number of symptoms reported to be experienced in the last month from a list of 14 common SBS symptoms, which was used in a previous study in Boston public housing.36 The symptoms were categorized as two lower-respiratory, six mucosal, three neurological, and three fatigue symptoms according to factors established using the EPA’s Building Assessment and Survey Evaluation (BASE) study.37 This score was skewed and consequently log transformed for analyses. Comparisons were performed using univariate and multivariate linear mixed effects models with random intercepts to control for correlation in self-reporting. Temperature and year were included in the multivariate model to mimic the analysis performed on the environmental measures. To explore whether a set of symptoms were driving the results, we compared differences in the symptom summation by each symptom factor (lower-respiratory, mucosal, neurological, and fatigue), and compared the multivariate effect estimates of the overall symptom summation with symptoms from each factor removed. All analyses were performed using SAS (version 9.4; Cary, NC).

recorded the presence and function of ventilation in the kitchen and bathroom and the presence of any individual mold colonies, pests, candles, and fragrance products throughout the apartment. Inadequate ventilation was defined as the absence of functional exhausts in the kitchen and/or bathroom. We collected PM2.5 using a Harvard Personal Exposure Monitor (HPEM) with a cut point of 2.5 μm at a flow rate of 1.8 L per minute. Samples were analyzed gravimetrically using a Mettler MT5 microbalance (Mettler-Toledo, Columbus OH). NO2 and formaldehyde were collected using passive badges from Assay Technology, Inc. (Livermore, CA). NO2 badges were analyzed by NIOSH method 6014. Formaldehyde badges were analyzed using OSHA method 1007. We deployed passive air nicotine badges to collect vaporphase nicotine concentrations. Badges were analyzed at the University of California, Berkeley following the method of Hammond and Leader.33 CO2 was measured in 5 min intervals throughout the sampling period using K-33 ELG CO 2 data loggers (CO2meter; Ormand Beach, FL), which employ nondispersive infrared (NDIR) wavelength technology with automatic background calibration for relative humidity. The data were positively skewed so all analyses used the median value for the week. AER was measured using the perfluorocarbon (PFT) technique34 and only collected in the second year when both green and control homes were sampled concurrently. Briefly, sources of perfluoromethyl cyclohexane gas (PMCH) were placed on external walls of the investigated apartment and a capillary absorption tube (CAT) was placed in the center of the home to sample the gas by diffusion. The PFT was quantified by gas chromatography. We calculated the AER by assuming a well-mixed interior using the formula outlined by Dietz et al. We expect this to be a valid assumption over a seven-day sampling period and within the generally small apartments evaluated in this study. Field blanks and duplicates were collected for environmental measures and made up at least 5% of the total samples collected. Analytical limits of detection can be found in the Supporting Information. Statistical Analysis. For the purposes of this analysis, survey questions ascertaining the timing, magnitude, and frequency of pest observation, mold observation, tobacco smoke smell, candle use, and air freshener use were coded as binary variables (“occurred in last 12 months [yes/no]”). For these questions, report of exposures from the survey and observation of the exposure from the inspection were combined to provide the most robust measure of occurrence. Differences in housing characteristics assumed to be independent of participant behavior (e.g., type of building, number of rooms) were labeled “building-level” characteristics and tested using Fisher’s exact test. Differences in housing conditions that were related to resident behavior were labeled “participant-level” housing conditions and were tested using longitudinal generalized marginal models to account for correlation in participant behaviors. The distribution was assumed binomial, the link specified logistic, and within-subject association assumed to have an unstructured pairwise log odds ratio pattern. PM2.5, NO2, formaldehyde, nicotine, median CO2, and AER were all right skewed and log transformed for all analyses. We used univariate, linear mixed effects models with random intercepts to test differences in pollutant concentrations

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RESULTS Demographics and Apartment Characteristics. We performed 61 home visits with 37 unique participants in two Boston public housing developments (Figure 1); Twenty four participants (18 “green”; 6 control) participated in two visits over the successive years. Thirty visits occurred in 2012 and 31 occurred in 2013. Baseline participant demographics are summarized in Table 1. The population was predominantly female (78%) and selfidentified as Hispanic or Latino (56%) with 40% performing the questionnaire in Spanish. The age range was 23−82 years.

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Table 1. Participant Demographics and Self-Reported Health for 37 Unique Participants participant characteristics

Figure 1. Timeline of sample size in each housing type from 2012 to 2013.

The majority of participants were not born in the U.S. (62%); the most common foreign country of origin was the Dominican Republic (24%). The prevalence of current smokers (38%) and asthmatics (24%) was higher than the background prevalence of smoking and asthma in the U.S. (19.3%38 and 8.4%,39 respectively). We compared building-level characteristics and participantlevel characteristics between green and control homes and these results are summarized in Table 2. Thirty seven (86%) control homes had inadequate ventilation as compared to two (11%) of the green homes (p < 0.001). This difference was driven by the absence of mechanical ventilation in the kitchen in all of the control apartments at OC. There were more reports of work orders, stuffiness, pests and mold in the control homes. Several behaviors such as appliance use, candle use, and air-freshener use were not different between control and green homes. Environmental Exposure Profile. The geometric mean (GM) and standard deviation (GSD) of environmental conditions in green and control homes are detailed in Table 3. The percent differences in pollutant concentrations and AERs between green and control homes in the multivariate models are presented in Figure 2. In multivariate models, we observed 57%, 65%, and 93% lower concentrations of PM2.5, NO2, and nicotine (respectively) in green compared to control homes (p = 0.032, p < 0.001, p = 0.003, respectively). The concentrations of formaldehyde and CO2 were higher among the green homes in the univariate model, but this difference was not statistically significant for formaldehyde (p = 0.3389). Additionally, we did not see greater concentrations of formaldehyde nor CO2 in green homes after adjusting for temperature and year. The geometric mean air exchange was 0.8 h−1 in the green homes and 2.0 h−1 in the control homes. In the model adjusted for temperature, the green homes experienced 54% fewer changes per hour (p = 0.109). The concentrations of PM2.5, NO2, and formaldehyde for each home visit for green and control homes are presented longitudinally in Figure 3. Six participants were only sampled in 2012; seven participants were only sampled in 2013; and 24 participants were sampled in both years. For the paired participants, the mean change from 2012 to 2013 in PM2.5 was −10.55 μg/m3 (green) and 3.96 μg/m3 (control) (p = 0.009). The mean change in NO2 was −55.86 μg/m3 (green) and 0.61 μg/m3 (control) (p = 0.001), and for formaldehyde was −0.09 (green) and 1.47 (control) (p = 0.765). Health Outcomes. Differences in self-reported health and SBS symptoms are summarized in Table 3. Compared to

n (%)

N

37

Age (Years) 65

12 (32) 10 (27) 9 (24) 6 (16)

Self-Reported BMI normal (30 kg/m2)

12 (32) 11 (30) 14 (37)

Gender female male

29 (78) 18 (22)

Language English Spanish Chinese

19 (51) 15 (40) 3 (8)

Race/Ethnicity non-Hispanic white non-Hispanic black Hispanic Chinese

7 (19) 10 (27) 17 (56) 3 (8)

Country of Origin United States Puerto Rico Dominican Republic other Latin America other

14 (38) 3 (8) 9 (24) 2 (5) 9 (24)

Highest Level of Education less than high school high school/GED some college associate degree

17 (47) 15 (42) 3 (8) 1 (3)

Hours Employed Outside of Home 0 >0

22 (60) 15 (40)

Current Smoker yes no

14 (38) 23 (62)

Self-Reported Asthmatic Yes No

9 (24) 28 (76)

participants in control homes, participants in green homes experienced a 47% reduction in symptoms (p =