Experimental and Modeled Assessment of Interventions to Reduce PM2.5 in a Residence during aWildfire Event

Increasingly large and frequent wildfires affect air quality even indoors by emitting and dispersing fine/ultrafine particulate matter known to pose health risks to residents. With this health threat, we are working to help the building science community develop simplified tools that may be used to estimate impacts to large numbers of homes based on high-level housing characteristics. In addition to reviewing literature sources, we performed an experiment to evaluate interventions to mitigate degraded indoor air quality. We instrumented one residence for one week during an extreme wildfire event in the Pacific Northwest. Outdoor ambient concentrations of PM2.5 reached historic levels, sustained at over 200 μg/m3 for multiple days. Outdoor and indoor PM2.5 were monitored, and data regarding building characteristics, infiltration, and mechanical system operation were gathered to be consistent with the type of information commonly known for residential energy models. Two conditions were studied: a high-capture minimum efficiency rated value (MERV 13) filter integrated into a central forced air (CFA) system, and a CFA with MERV 13 filtration operating with a portable air cleaner (PAC). With intermittent CFA operation and no PAC, indoor corrected concentrations of PM2.5 reached 280 μg/m3, and indoor/outdoor (I/O) ratios reached a mean of 0.55. The measured I/O ratio was reduced to a mean of 0.22 when both intermittent CFA and the PAC were in operation. Data gathered from the test home were used in a modeling exercise to assess expected I/O ratios from both interventions. The mean modeled I/O ratio for the CFA with an MERV 13 filter was 0.48, and 0.28 when the PAC was added. The model overpredicted the MERV 13 performance and underpredicted the CFA with an MERV 13 filter plus a PAC, though both conditions were predicted within 0.15 standard deviation. The results illustrate the ways that models can be used to estimate indoor PM2.5 concentrations in residences during extreme wildfire smoke events

Heat, Wildfire and Energy Demand: An Examination of Residential Buildings and Community Equity

Extreme heat and wildfire events are becoming more prolific and exacerbated by climate change, carrying significant implications for environmental and social systems. Residential buildings play a central role in protecting people from heat and pollutant exposure during extreme weather events, but the level of protection varies dramatically depending on building energy efficiency and technology availability. Low-income and communities of color have higher energy burdens compared to affluent populations, and underserved communities often do not have financial resources for, or access to, advanced building technologies. This dissertation explores the impacts of extreme heat and wildfire on residential buildings, focused specifically on occupant exposure risks related to energy performance and indoor air quality (IAQ). The research presented explores the complex influences that location and socio-demographics play on residential energy burdens, with a particular focus on how low-income households are impacted by inequitable energy systems. This dissertation presents three essays that cover related aspects of IAQ, energy efficiency and equity. The first essay employs a dataset of over 16,000 homes to investigate the relationship between urban heat and residential building energy use, with a particular focus on access to air conditioning and the influence of building characteristics. The second essay presents an experimental assessment of interventions to reduce fine particulate matter (PM2.5) in a home during a wildfire event, using data gathered during a large wildfire in Portland, Oregon in September 2020. The third essay uses data from a low-income energy efficiency program to explore how building characteristics impact energy burdens in low-income housing. Collective findings from the research highlights the need for an energy efficient, resilient housing stock, and supports policies to advance energy equity as a top priority for decarbonizing the building sector

Diffusion of Energy Efficient Technology in Commercial Buildings: An Analysis of the Commercial Building Partnerships Program

This study presents findings from survey and interview data investigating replication of green building measures by Commercial Building Partnership (CBP) partners that worked directly with the Pacific Northwest National Laboratory (PNNL). PNNL partnered directly with 12 organizations on new and retrofit construction projects, which represented approximately 28 percent of the entire U.S. Department of Energy (DOE) CBP program. Through a feedback survey mechanism, along with personal interviews, quantitative and qualitative data were gathered relating to replication efforts by each organization. These data were analyzed to provide insight into two primary research areas: 1) CBP partners\u27 replication efforts of green building approaches used in the CBP project to the rest of the organization\u27s building portfolio, and, 2) the market potential for technology diffusion into the total U.S. commercial building stock, as a direct result of the CBP program. The first area of this research focused specifically on replication efforts underway or planned by each CBP program participant. The second area of this research develops a diffusion of innovations model to analyze potential broad market impacts of the CBP program on the commercial building industry in the United States. Findings from this study provided insight into motivations and objectives CBP partners had for program participation. Factors that impact replication include motivation, organizational structure and objectives firms have for implementation of energy efficient technologies. Comparing these factors between different CBP partners revealed patterns in motivation for constructing energy efficient buildings, along with better insight into market trends for green building practices. The optimized approach to the CBP program allows partners to develop green building parameters that fit the specific uses of their building, resulting in greater motivation for replication. In addition, the diffusion model developed for this analysis indicates that this method of market prediction may be used to adequately capture cumulative construction metrics for a whole-building analysis as opposed to individual energy efficiency measures used in green building

The Role of Building Characteristics, Demographics, and Urban Heat Islands in Shaping Residential Energy Use

As global temperatures continue to rise, questions about infrastructure capacity to keep up with energy demand are increasingly germane. Energy demand is mediated by several structural and environmental conditions, though we have a limited understanding about the role of differences in local ambient temperatures as a predictor for energy demand. This study assesses the effects of residential building structure, socio-demographics, and ambient temperature conditions of a neighborhood to overall energy expenditures of a household. Using annual utility billing and demographic data, existing tax-lot records, and an unusually high-resolution description of ambient temperatures, we ask two research questions: (1) What role do differences in neighborhood ambient temperatures, building characteristics and demographics play in helping to explain residential energy use? And (2) In what ways do energy expenditures spatially cluster in relation to urban heat islands? Using linear regression, spatial regression, and spatial clustering techniques, we evaluated the role of physical location of these households in relation to ambient temperatures. Corroborating the existing literature, the results indicate significant positive relationships among energy expenditures and building size, average household size, and White population; also suggest significant negative relationships among energy expenditures and building density, renter population and population with a bachelor\u27s degree or higher. Negative relationships among morning and afternoon measures of heat suggest that some areas spend less on energy use as a result of the neighborhood scale thermal environment. Spatial analysis of ambient temperature and energy expenditures show that energy use throughout the city is spatially clustered, with more affluent areas using more energy, regardless of whether heat islands are present. The results help to frame the role that increasing ambient temperatures in cities play in the spatial disparity in energy demand