CC BY-NC-ND
0Accepted Manuscript
Indoor air quality and occupant comfort in homes with deep versus conventional energy efficiency renovations
Ellen M. Wells, Matt Berges, Mandy Metcalf, Audrey Kinsella, Kimberly Foreman, Dorr G. Dearborn, Stuart Greenberg
PII: S0360-1323(15)30035-4
DOI: 10.1016/j.buildenv.2015.06.021
Reference: BAE 4165
To appear in: Building and Environment
Received Date: 17 March 2015 Revised Date: 21 June 2015 Accepted Date: 22 June 2015
Please cite this article as: Wells EM, Berges M, Metcalf M, Kinsella A, Foreman K, Dearborn DG, Greenberg S, Indoor air quality and occupant comfort in homes with deep versus conventional energy efficiency renovations, Building and Environment (2015), doi: 10.1016/j.buildenv.2015.06.021.
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Indoor air quality and occupant comfort in homes with deep versus conventional energy efficiency renovations
1* 2 2 2 2 Ellen M. Wells, Matt Berges, Mandy Metcalf, Audrey Kinsella, Kimberly Foreman, Dorr
G. Dearborn,3 Stuart Greenberg2
1. School of Health Sciences, Purdue University, West Lafayette, Indiana 47907, USA
2. Environmental Health Watch, Cleveland, Ohio 44113, USA
3. Department of Environmental Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
Corresponding author: Ellen M. Wells; 550 Stadium Mall Drive, HAMP 1269, School of Health Sciences, Purdue University; West Lafayette, Indiana 47907, USA. Phone: 765-4963535; fax: 765-496-1377; email: wells54@purdue. edu
Abstract
Deep energy retrofits (DER) for residential housing have been proposed to reduce greenhouse gas emissions; these result in ~50% additional energy efficiency compared to standard renovations, energy star (ES) renovations. However, the impact of increased energy efficiency on indoor air quality (IAQ) is poorly understood. We conducted a longitudinal study to compare IAQ and occupant comfort in 12 low income single-family homes renovated to a DER or ES standard. Quarterly visits were conducted for a median of 18 months post-renovation; IAQ was assessed in 4 rooms per visit for a total of 237 measurements. Multivariable regression models accounted for repeated measurements and controlled for house- and family-related covariates. In fully adjusted models, average difference (95% confidence interval) in IAQ parameters in DER homes versus ES homes were: temperature: -0.3 C (-1.2, 0.6); relative humidity: 0.4 % (-1.1, 1.8); carbon dioxide: 43.7 ppm (-18.8, 106.2); and total volatile organic compounds: 198 ppb (224, 620). Residents in DER homes were significantly less likely to report their homes were comfortable, most likely due to initial difficulties with new heating system technology. We found no differences in IAQ between DER and ES homes; however, education is strongly recommended when incorporating new technology into residences.
Keywords: Indoor Air Pollution; Conservation of Energy Resources; Ventilation; Carbon dioxide; Volatile organic hydrocarbons; Thermal comfort
1. Introduction
Reduction of greenhouse gas emissions is an important strategy to mitigate global climate change. One method to reduce emissions is through improving energy efficiency of buildings, as building energy use contributes 20- 40% of all greenhouse gas emissions [1, 2]. However, there is substantial concern that methods to increase energy efficiency, such as increased air tightness, may also lead to worse indoor air quality (IAQ) [3, 4]. Poor IAQ has been associated with development or exacerbation of respiratory diseases including asthma and chronic obstructive pulmonary disease [4-8]. Thus, attaining clean indoor air is an important public health goal [9]. Modeling studies suggest that both improved efficiency as well as good IAQ can both be achieved; but, a key factor is ensuring that adequate ventilation is supplied [1, 10-12]. However, there is limited data on whether this is achieved in practice, and even less for extremely efficient homes, such as deep energy retrofit or net zero energy homes [3, 13-16].
Several studies have explored the relationship between energy efficient homes and occupant health. The majority report energy efficient homes are associated with health benefits [17-24], although Sharpe and colleagues report higher physician-diagnosed adult asthma cases among those living in energy efficient dwellings [25]. A recent meta-analysis by Maidment and colleagues found a small, but statistically significant improvement in health associated with energy efficient housing, but also acknowledge the need for additional research in this area [26].
Fewer studies to date have included data on air quality with somewhat mixed results. Jones et al. compared air quality in one new and one recently renovated energy efficient housing developments with an existing housing development in Chicago; although carbon dioxide, carbon monoxide, formaldehyde, total volatile organic compounds, and particulate concentrations were higher in the efficient developments all measurements were well within
recommended IAQ guidelines [22]. Two energy efficient single-family homes in France did not demonstrate any air quality issues three years post-renovation [27]. Frey et al. measured IAQ before, during, and after efficiency renovations in senior housing in Arizona; they found significant decreases in formaldehyde, but not particulates or other aldehydes [28]. Colton et al. found statistically significant reductions in particulate matter, nitrogen oxide, and nicotine in energy efficient apartments compared to controls [19].
However, the impact of a newer advancement in energy efficiency retrofits, the deep energy retrofit strategy, has not been thoroughly evaluated in terms of IAQ and health. A recent report from France found reductions in small particles, radon, and some volatile organic compounds but increases in other volatile organic compounds when comparing air quality among seven newly constructed, highly efficient homes with the national averages [29]. This initial work is promising, but limited: more work needs to be conducted to fully understand the implications of highly efficient homes on indoor air quality [15, 30].
Therefore, the goal for this project was to compare IAQ and occupant comfort for one year among low-income homes renovated using Energy Star (ES) and Deep Energy Retrofits (DER) renovation methods. ES renovations generally achieve ~50% reduction in energy use compared to typical existing houses, whereas DER renovations typically result in a ~70% reduction compared to typical existing houses [30, 31]. Ventilation requirements for both renovation types were designed to meet American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) standards [32]. Our hypothesis was that there would be no difference in air quality or comfort between these two types of energy efficient renovations and that these would be comfortable places to live.
2. Methods
In order to evaluate our hypothesis, we completed a longitudinal study which assessed indoor air quality and self-reported occupant comfort comparing homes renovated using either ES (n=6) or DER (n=6) approaches. Renovation methods were based on existing Energy Star guidelines [33] and deep energy reduction principles [34]; these were further adapted for this study as described in detail previously [35] and summarized in Table 1. Briefly, all renovations incorporated increased insulation, improved thermal barriers and incorporation of energy efficient appliances; although DER improvements were designed to exceed the efficiency of ES homes (i.e., ACH50 of < 5 in ES versus < 3 in DER). ES homes used an exhaust-only ventilation system using continuously running bathroom fans whereas DER homes used energy recovery ventilators to provide a balanced ventilation system. Both ventilation systems were designed to meet air exchange requirements described by ASHRAE 62.2 [32]. Standard furnaces were installed into ES homes; in contrast, all DER homes had air source heat had pumps for heating and cooling, with back-up heating systems specified and provided. In three DER homes the air source heat pumps were tied to a ducted system; in the other three, the pumps were used with single point, non-ducted mini-splits.
All twelve homes included in the study were low-income, approximately 100 year-old single family houses located in Cleveland, Ohio, USA. Homes were American Colonial or Bungalows, detached with 1-2 stories and basements. Cleveland is located on the southern shore of Lake Erie, which influences the climate. Typical outdoor temperatures in Cleveland range from -2.2° C in January to 23.1° C in July; average annual precipitation is 105.7 cm and snowfall is 173.0 cm [36]. However, the study period (2011-2013) was somewhat warmer and had more precipitation than average; thus, winter heating loads were lighter than normal.
All homes were scheduled to undergo gut renovation to ES standards by Cleveland Housing Network, a not-for-profit affordable housing developer. Through inclusion in this study six of these homes were further renovated to DER standards. Assignment of homes to the more energy efficient DER guidelines was based on consideration of the feasibility and cost; i.e., smaller homes are more readily renovated to meet DER standards. Following renovation, homes were available to occupants through a lease-purchase program where occupants have the option to purchase the home following 15 years of occupancy. 2.1. Study population and visits.
The study was advertised through flyers posted at Cleveland Housing Network's office and website. Other homes, also renovated to ES standards, were available for individuals who did not wish to participate in the study. Any individual eligible to enter a lease-purchase agreement was eligible to participate in this study. Eligibility criteria to enter a lease-purchase agreement are based on housing regulations; these include a low-to-moderate income requirement as well as financial and criminal background checks. Participants signed an informed consent document prior to enrollment. This study was conducted with approval of the Case Western Reserve University Institutional Review Board.
Participants completed a baseline questionnaire shortly before they moved into the study home. A visual inspection of the home was completed by study staff following renovation but before occupancy. Quarterly follow-up visits were scheduled with participants to ensure that they would be at their homes during the time of the visit. At each follow-up visit study staff administered a questionnaire to determine occupant reported-data, completed a visual inspection of the home to document staff-reported observations, and measured indoor air quality in the
living room/foyer, kitchen, master bedroom, basement, and outside the home. A timeline of data collection is shown in Figure 1.
Data for each home was collected over a 9 to 21 month period (median=18). Baseline data were not included in the present analysis, as they were collected prior to when participants moved into these homes. At one study visit, staff found the ventilation system was malfunctioning. As these data were not representative of the intended renovation they were excluded from analyses. In total, up to 63 study visits (ES=38, DER=25) and up to 237 individual-room air quality measurements (144 ES, 93 DER) were used in analyses. 2.2. Data collection.
House characteristics were assessed by study staff just after renovation was completed. A certified home energy assessor completed the Home Energy Rating System (HERS) index and air tightness tests [37]. The HERS index was developed by Residential Energy Services Network, a nonprofit organization, and is used widely to assess energy efficiency in the United States, including federal government users such as the United States Environmental Protection Agency. Scores of 100 indicate efficiency typical of a newly-built home; a score of 130 indicates 30% less efficiency and a score of 70 indicates 30% increased efficiency [37]. Trained study staff used portable indoor air quality monitors to measure temperature (T), relative humidity (RH), carbon dioxide (CO2), (TSI VelociCalc 9555 with probe 982; TSI Incorporated; Shoreview, Minnesota) as well as total volatile organic compounds (tVOCs) (ppbRAE 3000; RAE Systems; San Jose, California, calibrated with isobutylene). Range and precision of these measurements were T: -10 to 60 °C, ±0.3°C; RH: 0 to 95%, ±3%; CO2: 0 to 5000 ppm, ±3% or ±50 ppm; and tVOCs: 1 to 10,000 ppb. Instrumentation was maintained and calibrated according to manufacturer instructions. Instruments were placed on top of a short stepladder at
an elevation of four feet and data were collected each second over a nine minute period; the median value was used as a "snapshot" of air quality conditions.
Both a participant questionnaire and a staff visual inspection form were used to collect data during study visits: staff-administered questionnaires were used to collect self-reported data from participants, whereas inspections reflected study staff observations. Both the questionnaire and visual inspection were modified from those used previously [38]. Some items, such as whether individuals were smoking in the home, were included on both the questionnaire and the inspection. In the questionnaire this was phrased as "In the past three months, has anyone smoked cigarettes, cigars, or pipes inside your home?" whereas in the inspection staff participants were asked "Is there evidence of smoking in the house?"
Other data collected via the questionnaire included who was living in the home, selected activities, and perceived comfort over the past three month period at every visit. Temporary living arrangements are not unusual for this population: 50% of the households reported changes in household residents during the study period. Demographic information collected includes: the number, age, sex, and race/ethnicity of individuals living in the home. Family members were categorized into adults (>18 years old) and children (<18 years old). Activities in the home included presence of pets, use of incense/scented candles or space heaters, and smoking by residents or visitors. Participants were also asked if they used space heaters, humidifiers, or often left their windows open. Occupant comfort was assessed by asking the participant a series of questions to determine how frequently their home was "too hot", "too cold", "drafty", "stuffy", "humid", "dry", or "just right" over the past three months. Responses were recorded as "often", "sometimes" or "rarely". Responses were categorized as "often" and "sometimes/rarely" for analysis.
Trained study staff conducted a visual inspection of the home at every study visit using a standardized checklist. The assessment included questions designed to assess signs of flooding, clutter, pests, pets and smoking at the time of the study visit. Data for smoking and pets were obtained both from the questionnaire as well as the visual inspection; for analysis we created one variable indicating their presence if this was reported either from the questionnaire or inspection. 2.3. Statistical analysis.
All data were analyzed using Stata 13.1 (StataCorp; College Station, Texas). We used generalized estimating equation (GEE) models, which account for the correlation within repeated measurements for each home over time and provide estimates of the average response across all homes [39]. As indoor air quality measurements were taken in multiple rooms in each home, 2-level multilevel (mixed) models were also considered to incorporate repeated measurements over time and multiple measurements per home; however, between-room variance was extremely small, indicating that this additional component was not necessary to describe relationships within the dataset [40].
Descriptive analyses were conducted to describe the cross-sectional and longitudinal distributions of each variable. Distributions for CO2 and tVOCs were lognormal; therefore geometric means are presented in results and regression models were tested to determine if log-transformed versions of these variables provided improved model fit. Indoor air quality parameters, housing characteristics, and family characteristics were compared with renovation type on a cross-sectional basis using Fisher's exact tests. Cross-sectional bivariate comparisons of indoor air quality parameters by room and by calendar month were also assessed. We additionally created a psychrometric chart to compare observed air quality with comfort recommendations described in ASHRAE Standard 55-2013.
Multivariable linear or logistic GEE regression models were constructed to assess the relationship between renovation type with indoor air quality parameters (T, RH, CO2, and tVOCs) and self-reported occupant comfort (too hot, too cold, just right). There was very little between-renovation variability within "drafty", "stuffy", "humid" or "dry", so these outcomes were not included in multivariable analyses. We constructed an unadjusted and an adjusted model which controlled for date/time (cubic spline or linear), amount of conditioned space (quadratic), temperature (continuous), relative humidity (continuous), self-reported family size (continuous), self-reported percentage of males (continuous), presence of pets (yes/no), presence of smokers (yes/no), use of incense or scented candles (yes/no), self-reported frequency of opening windows (ordinal: never/sometimes/frequently). All covariates were time-varying except for the amount of conditioned space. Use of space heaters, heating and air conditioning were considered for inclusion, but not included due to the likelihood of their colinearity with temperature. Results are presented as change in air quality parameter or odds ratio for self-reported comfort comparing DER homes to ES homes.
3. Results
Post-renovation home characteristics are presented in Table 2. There was no significant difference in home age or size of conditioned space between the two renovation types; however, as expected given that smaller homes were preferentially assigned to the DER arm of the study, the point estimates for size indicate the DER homes were somewhat smaller. There are fewer observations for DER homes compared to ES homes, likely because it took longer to complete the more comprehensive renovations in DER homes as well as the fact that DER participants were more likely to be lost to follow-up compared to participants living in ER homes. DER
homes, on average, had a 33.3 lower HERS index score, 1035 lower cubic feet/minute airflow at a difference of 50 Pascale (cfm50) [35], and used 71 MM Btu less energy over a 12-month period versus ES homes, confirming that DER homes were substantially more energy efficient.
Family characteristics and activities are reported in Table 3. We did not observe any significant differences in these characteristics by renovation type, although there was a borderline association for those in DER homes to more often report "never" or "usually/always" compared to "sometimes" for frequency of opening windows. All of the families in the study were African American; the majority was female, single-parent families. 3.1. Indoor air quality.
The median and range of the 237 indoor air quality measurements including all homes were T: 23.0 C (range: 15.2, 27.9), RH: 47.1% (22.0, 77.5), CO2: 716 ppm (476, 2284), and tVOCs: 346 ppb (0, 3279). A cross-sectional description of indoor air quality parameters stratified by renovation type, room, and calendar month, is shown in Figure 2. There was little difference in air quality parameters by room type. Temperature and relative humidity varied by season, with higher measurements occurring during the summer.
Results from multivariable GEE models are presented in Table 4. We did not observe any difference in T, RH, or tVOCs between homes of different renovation types for any model. For CO2, we observed a borderline significant increase in the unadjusted model but these changes were attenuated in the adjusted model. 3.2.Occupant comfort.
A cross-sectional depiction of self-reported occupant comfort is shown in Figure 3. Overall, families generally reported favorably regarding household conditions: 60.3% reported that their home as often 'just right' during the past three months. With regards to less favorable
conditions, 19.4% reported their homes were often too hot; 14.4% too cold; 5.1% too drafty; 4.9% too stuffy and 6.6% too dry. There were no reports of homes being too humid. In the cross-sectional analysis, those in DER homes were more likely to report that their homes were too cold, and less likely to report that their homes were too hot or just right.
This is consistent with the results from the multivariable GEE models reported in Table 5. In these, participants living in DER homes were 85% less likely (95% confidence interval: 41%, 96%) to report that their home was "just right". These results are also consistent with our measurements for temperature: the lowest temperatures we recorded were from DER homes.
Temperature and associated relative humidity measurements were plotted on a psychrometric chart (Table 4). Roughly 47 observations, or 19.8%, fall outside of the graphical comfort zone as defined by ASHRAE Standard 55. The majority (n=34, 14.3%) suggest cold conditions; most often recorded in the living room.
4. Discussion
We renovated 12 low-income homes to an Energy Star or Deep Energy Retrofit energy efficiency standard and followed residents to assess indoor air quality and resident comfort for up to 24 months post-renovation. In models adjusted for both housing characteristics and resident activities, we did not observe any statistically significant difference in indoor air quality between the different renovation types. However, residents in the more highly energy efficient homes were significantly less likely to report that the comfort of their home was "just right".
On average, indoor CO, CO2, T, and RH concentrations were consistent with or lower than published guidelines [32, 41]; we could not compare tVOCs with these guidelines, as the guidelines exist for separate volatile organic compounds. Although on average parameters met
generally accepted standards for indoor air quality, we did observe a few measurements of elevated tVOCs and reduced temperatures. Some individual measurements of tVOCs were substantially higher than the median value; this frequently correlated with a staff observation of some activity (such as use of air fresheners immediately prior to the study visit) that could result in the introduction of VOCs into the home. Other studies have also reported elevated concentrations of some VOCs following renovations for energy efficiency [29, 42].
Some measurements for temperature in this study were lower than recommended values. Notably, the majority of these were only recorded in one room but not the other rooms: there were only 5 out of 62 study visits where cold conditions were reported in multiple rooms within a single house. A recent literature survey of factors which contribute to occupant comfort concluded that providing occupants' control of the indoor environment was important; additionally thermal comfort took precedence over visual comfort, acoustic comfort or IAQ [43]. This is consistent with the discomfort reported by our participants on some occasions. We did investigate the causes of these colder temperatures; issues relating to this were addressed and are discussed in more detail below.
Several environmental health studies on energy efficient retrofits have focused on measures of health [18-26] or housing conditions other than indoor air quality [20, 23]. Although our results are not directly comparable to these studies, the fact that the majority of our measured air quality concentrations met published air quality guidelines is consistent with these reports. Our results are also consistent with studies which directly report air quality concentrations [19, 22, 29]. Energy efficient housing developments in Chicago were reported to have higher 24-hour CO2 (839 and 777 ppm vs. 635 ppm in controls), CO (0.43 and 0.44 ppm vs. 0.31 ppm in controls), and tVOCs (93 and 64 ppb vs. 47 in controls) [22]. Our estimates for
CO2 were similar to this study, although we observed substantially higher tVOC concentration; overall though, the observation that more efficient homes had slightly higher pollutant concentrations reflects our observations. Colton et al. followed residents in multifamily public housing in Boston; some residents moved to energy efficient buildings and others moved to conventional buildings [19]. They reported that median CO2 in energy efficient buildings was 1208 ppm, which is comparable with our observations. Additionally, they also report a non-statistically significant increase in CO2 in energy efficient vs. conventional buildings [19], which is similar to our findings of a non-statistically significant increase in DER homes compared to ES homes. In a different study, Derbez et al. measured air quality during summer and winter seasons within seven newly-built, highly energy efficient homes in France; energy efficiency gains were similar to DER homes in our study [29]. Their reported median temperature (20.325.7 C, summer; 19-21.6 C, winter), relative humidity (45-58%, summer; 29-34% winter), and carbon dioxide concentrations (351- 811 ppm) are similar to our observations, including increased T and RH concentrations during summer months [29].
After reviewing staff notes as well as measured indoor air quality parameters, it seems likely that the reported occupant discomfort with temperature in DER homes may be related to initial difficulties with incorporating new heating system technology. As noted earlier, half of the DER homes used unducted mini-split heat pumps. These unducted heat pumps do not operate in the same manner as conventional heaters: it takes a longer period for a temperate adjustment on the thermostat to result in changed conditions within the house. Additionally, backup heating systems were not always installed or working correctly. It is possible that a lack of familiarity with installation or maintenance personnel as well as participants resulted in
incomplete installation and/or use of this technology, which could result in colder houses or houses with more temperature variability.
On several occasions study staff identified problems with the installation or operation of these units, which were immediately corrected. To further investigate whether this may have been the source of the discomfort reported by residents we conducted a post-hoc analysis by rerunning models from Table 5 while excluding homes that solely relied on the mini-splits and found no difference in perceived comfort between the DER and ES homes (data not shown). Difficulty in achieving energy savings or indoor air quality goals due to a lack of education regarding new technology has been reported previously [29, 44]. Walker et al. undertook an education intervention among residents in newly renovated energy efficient units, and recommended that future efforts include tailored education for residents and improved oversight to ensure proper installation of equipment [44]; importance of training methodology was also emphasized elsewhere [45]. Our staff incorporated informal training during the home visual inspections and discussed an informational pamphlet with participants. However, it is possible that these efforts could have been better tailored for our study population.
A limitation of this design is that we were not able to collect robust data on occupant health; therefore a health evaluation was not included in this analysis. We also do not have any measurements from the housing previously occupied by our participants, or from houses that did not undergo an intervention. This does not affect the internal validity of this study. However, our data should be interpreted accordingly as comparisons between two types of energy efficiency renovations as opposed to comparing energy efficient homes with standard housing. The relatively short period each observation represents (<10 minutes) is another weakness of this research; the brevity of this measurements limits our capacity to extrapolate results to other dates
and times of day. As a result, our results should be interpreted as initial evidence which recommends the value of a similar study with a larger sample size and increased time period of air sample collection.
This study has several strengths, including a multidisciplinary research group and a focus on older, affordable single family housing. The expense of conducting these renovations limits the number of homes that can be included in studies of this type; however, our longitudinal study design with repeated measurements in each home allowed us to collect a sufficient quantity of data for statistical analyses. We also obtained time-varying data on household characteristics in addition to indoor air quality, and had more frequent follow-up visits than other studies. However, perhaps the main strength of the study is its focus on the impact of DER renovations in the context of affordable housing; these result in greater energy efficiency than more commonly used ES or similar guidelines and demonstrate that this energy use reduction can be accomplished within a low-income population.
In conclusion, we found no differences in indoor air quality between DER and ES homes. Our results suggest that it may be possible to achieve greater reductions in greenhouse gas emissions via residential energy efficiency renovations without degrading indoor air quality; however this should be confirmed in larger studies. Our results also suggest that careful attention should be paid to ensure sufficient education of installation and maintenance personnel as well as occupants, particularly when incorporating novel technology or systems.
Acknowledgements
The authors would like to thank William Hutzel, Mike Piepsny, Fatima Allen, Akbar Tyler, Debrah Mohammed, George Trappe, Jody Lavrich, Kate Monter-Durban, Jim Todt and Linda Wigington for their contributions to this research. This study was supported by the United States Department of Housing and Urban Development Healthy Homes Technical Studies Grant# OHLHH0203-09 and a grant from the Cleveland Foundation. The Mary Ann Swetland Center for Environmental Health also provided partial support (EMW; DGD).
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Table 1. Renovation design specifications
Design parameter Deep Energy Reduction Energy Star
ACH50 < 3 < 5
ACH < 0.2 < 0.35
HERS score > 60 > 85
Basement Insulation R10 to R20 R10
Wall insulation R23 to R40 R19
Roof insulation R50 to R60 R38
Thermal barrier Contiguous alignment of thermal and air barrier Thermal bypass checklist
Ventilation type Energy recovery balanced ventilation Whole house exhaust
Furnace type Air source heat pump, <40k Btu; > 90% efficient gas furnace,
some homes also had a backup > 40k Btu
98% efficient tankless hot water
/heat coil system
Heating load, Btu/hr/ft2 < 12 < 16
Reduction of heating load, % > 75 > 50
ACH = air changes per hour; ACH50 = air changes per hour at 50 pascals of pressure; HERS = home energy rating system.
Table 2. Mean (95% confidence interval) of post-renovation house characteristics
House characteristic
Deep Energy Reduction (N=6)
Energy Star (N=6)
All (N=12)
Year built
Size of conditioned space (ft)
HERS score
cfm50 (ft3/minute)
12-month energy use (MM Btu)
12-month energy use/space (MM Btu/ft2)
1905 (1897, 1913) 2260 (2106, 2414) 38.0 (35.0, 41.0) 623 (478, 768) 83.8 (63.0, 104.6) 0.037 (0.027, 0.048)
1912 (1906, 1919) 2428 (2185, 2671) 71.3 (66.6, 76.0) 1658 (1042, 2274) 154.8 (116.6, 193.1) 0.063 (0.052, 0.075)
1909 (1903, 1914) 2344 (2196, 2492) 54.7 (43.3, 66.0) 1141 (683, 1598) 119.3 (87.9, 150.7) 0.050 (0.039, 0.062)
HERS = Home Energy Rating Scale; cfm50=airflow at a Values are mean (95% confidence interval). Energy use was calculated using conditioned space post-renovation.
pressure difference of 50 Pascals. estimates were based on 12 months Bold type indicates Fischer's exact
of utility bills, adjusted for degree days. Energy use per square foot test p<0.05; italic type indicates Fisher's exact test p<0.10
Table 3. Number (percent) of households reporting selected family characteristics and activities, by renovation status
Family characteristic/ activity Deep Energy Reduction (N = 6) Energy Star (N = 6) All (N = 12)
Average size of household
2.0 - 3.0 people 4 (66.7) 2 (33.3) 6 (50.0)
3.1 - 5.0 people 0 (0.0) 4 (66.7) 4 (33.3)
> 5 people 2 (33.3) 0 (0.0) 2 (16.7)
Average # of adults / household
1.0 adult 5 (83.3) 2 (33.3) 7 (58.3)
1.1 - 1.9 adults 0 (0.0) 3 (50.0) 3 (25.0)
2.0 adults 0 (0.0) 1 (16.7) 1 (8.3)
> 2.0 adults 1 (16.7) 0 (0.0) 1 (8.3)
Average # of children / household
0.0 - 1.0 child 0 (0.0) 1 (16.7) 1 (8.3)
1.1 to 2.0 children 5 (83.3) 1 (16.7) 6 (50.0)
2.1 to 3.0 children 0 (0.0) 3 (50.0) 3 (25.0)
> 3 children 1 (16.7) 1 (16.7) 2 (16.7)
Average % males / household
8.0% - 25.0% 2 (33.3) 2 (33.3) 4 (33.3)
25.1% - 40.0% 2 (33.3) 1 (16.7) 3 (25.0)
40.1% - 50.0% 1 (16.7) 3 (50.0) 4 (33.3)
> 50% 1 (16.7) 0 (0.0) 1 (8.3)
Used incense/scented candles
Never reported, N (%) 0 (0.0) 1 (16.7) 1 (8.3)
Sometimes reported, N (%) 4 (66.7) 4 (66.7) 8 (66.7)
Always reported, N (%) 2 (33.3) 1 (16.7) 3 (25.0)
Pet present
Never reported, N (%) 2 (33.3) 4 (66.7) 6 (50.0)
Sometimes reported, N (%) 1 (16.7) 2 (33.3) 3 (25.0)
Always reported, N (%) 3 (50.0) 0 (0.0) 3 (25.0)
Smoking occurred in home
Never reported, N (%) 2 (33.3) 1 (16.7) 3 (25.0)
Sometimes reported, N (%) 2 (33.3) 3 (50.0) 5 (41.7)
Always reported, N (%) 2 (33.3) 2 (33.3) 4 (33.3)
Windows open
Never reported, N (%) 0 (0.0) 0 (0.0) 0 (0.0)
Sometimes reported, N (%) 4 (66.7) 4 (66.7) 8 (66.7)
Always reported, N (%) 2 (33.3) 2 (33.3) 4 (33.3)
Bold type indicates Fischer's exact test p<0.05; italic type indicates Fisher's exact test p<0.10. Data include multiple study visits per house. All values are reported by study participant except for 'have pet' and 'smoker present' which were classified as 'yes' if reported by participant or observed by study staff. Smokers were either household members or household visitors.
Table 4. Average difference (95% confidence interval) for indoor air quality parameters in DER compared to ES homes
IAQ parameter N Unadjusted Adjusted
Temperature, C 237 -0.80 (-1.88, 0.29) -0.30 (-1.21, 0.61)
Relative humidity, % 237 1.50 (-1.42, 4.42) 0.38 (-1.08, 1.83)
Carbon dioxide, ppm 237 71.4 (-7.5, 150.2) 43.7 (-18.8, 106.2)
Total VOCs, ppb 201 224 (-140, 587) 198 (-224, 620)
CI = confidence interval; C = Celsius; CO2 = carbon dioxide; tVOC=total volatile organic compounds. Models are linear generalized estimating equations (GEE) accounting for correlation within homes; carbon monoxide uses a logistic function. Models include observations from 12 houses, including multiple visits and measurements from multiple rooms. Covariates were included as time-varying covariates (assessed at every visit) with the exception of the amount of conditioned space. Adjusted models controlled for date (cubic spline), amount of conditioned space (quadratic), temperature (except for the temperature model), and relative humidity (except for the relative humidity model), family size, proportion male, presence of pets, presence of smokers, using incense/scented candles, and having windows open..Bold type indicates that the covariate was statistically significant using a likelihood ratio test.
Table 5. Odds ratio (95% CI) for self-reported comfort in DER compared to ES homes
Comfort N Unadjusted Adjusted
parameter
Too hot 61 0.24 (0.05, 1.08) 0.14 (0.01, 1.63)
Too cold 62 3.43 (0.89, 13.17) 2.74 (0.46, 16.24)
Just right 62 0.30 (0.07, 1.40) 0.15 (0.04, 0.59)
Models are generalized estimating equations (GEE) accounting for correlation within homes, using a logistic function. Families living in 12 homes were included; multiple visits were included in the dataset. Covariates are included as time-varying covariates (assessed at every visit) with the exception of the amount of conditioned space. Bold type indicates that the covariate was statistically significant using a likelihood ratio test. Adjusted model controls for date, amount of conditioned space (quadratic), temperature, and relative humidity, household size, proportion male, presence of pets, presence of smokers, and using incense/scented candles and frequently having windows open.
1 Mar 11 1 Jun 11 1 Sep 11 1 Dec 11 1 Mar 12 1 Jun 12 1 Sep 12 1 Dec 12 1 Mar 13
Date of study visit
■ Energy Star
Deep Energy Reduction
Figure 1: Dates of data collection for each house included in the study. Diamonds indicate Energy Star homes; squares indicate Deep Energy Retrofit homes.
Figure 2. Geometric mean and 95% confidence interval for temperature (C) (n observations=237), relative humidity (%) (n=237), carbon dioxide (ppm) (n=237) and total volatile organic compounds (VOCs) (ppb) (n=201); stratified by renovation type, room, and month. ES = energy star; DER = deep energy reduction; LR = living room; KI = kitchen; BR = master bedroom; BA = basement.
Often hot
Often cold
Often drafty
Often stuffy
Often dry
Often 'just right'
Figure 3: Mean and 95% confidence interval for the percent reporting a specific comfort parameter, stratified by renovation type. N varies from 59-63 due to missing values.
o ro àî
u IE u 01 CL l/l
Dry bulb temperature, C
Figure 4. Psychrometric chart depicting 237 observations of comfort conditions in Energy Star (N=144; light blue circles) and Deep Energy Reduction (N=93; dark blue squares) homes. Winter and summer comfort zones, defined by ASHRAE Standard 55, are located within the green parallelogram.
Indoor air quality and occupant comfort in homes with deep versus conventional energy efficiency renovations
Highlights
• Achieved low-cost, highly energy efficient renovations in low-income housing.
• Increased energy efficiency was not associated with changes in indoor air quality.
• Occupants reported less comfort in more energy efficient homes.
• Training on installation, maintenance of new technology is strongly recommended.
