Simulating Energy Use, Indoor Temperatures, and Utility Cost Impacts Amidst a Warming Climate in a Multi-family Housing Model.

Rising ambient temperatures due to climate change will impact both indoor temperatures and heating and cooling utility costs. In traditionally colder climates, there are potential tradeoffs in how to meet the reduced heating and increased cooling demands, and issues related to lack of air conditioning (AC) access in older homes and among lower-income populations to prevent extreme heat exposure. We modeled a typical multi-family home in Boston (MA) in the building simulation program EnergyPlus to assess indoor temperature and energy consumption in current (2020) and projected future (2050) weather conditions. Selected households were those without AC (no AC), those who ran AC sometimes (some AC), and those with sufficient resources to run AC always (full AC). We considered stylized cooling subsidy policies that allowed households to move between groups, both independently and in conjunction with energy efficiency retrofits. Results showed that future weather conditions without policy changes yielded an increase in indoor summer temperatures of 2.1 °C (no AC), increased cooling demand (range: 34-50%), but led to a decrease in net yearly total utility costs per apartment (range: - $21 to - $38). Policies that allowed households to move to greater AC utilization yielded average indoor summer temperature decreases (- 3.5 °C to - 6.2 °C) and net yearly total utility increases (range: + $2 to + $94) per apartment unit, with greater savings for retrofitted homes primarily due to large decreases in heating use. Our model results reinforce the importance of coordinated public policies addressing climate change that have an equity lens for both health and climate goals.

In-home environmental exposures predicted from geospatial characteristics of the built environment and electronic health records of children with asthma.

PURPOSE: Children may be exposed to numerous in-home environmental exposures (IHEE) that trigger asthma exacerbations. Spatially linking social and environmental exposures to electronic health records (EHR) can aid exposure assessment, epidemiology, and clinical treatment, but EHR data on exposures are missing for many children with asthma. To address the issue, we predicted presence of indoor asthma trigger allergens, and estimated effects of their key geospatial predictors. METHODS: Our study samples were comprised of children with asthma who provided self-reported IHEE data in EHR at a safety-net hospital in New England during 2004-2015. We used an ensemble machine learning algorithm and 86 multilevel features (e.g., individual, housing, neighborhood) to predict presence of cockroaches, rodents (mice or rats), mold, and bedroom carpeting/rugs in homes. We reduced dimensionality via elastic net regression and estimated effects by the G-computation causal inference method. RESULTS: Our models reasonably predicted presence of cockroaches (area under receiver operating curves [AUC] = 0.65), rodents (AUC = 0.64), and bedroom carpeting/rugs (AUC = 0.64), but not mold (AUC = 0.54). In models adjusted for confounders, higher average household sizes in census tracts were associated with more reports of pests (cockroaches and rodents). Tax-exempt parcels were associated with more reports of cockroaches in homes. Living in a White-segregated neighborhood was linked with lower reported rodent presence, and mixed residential/commercial housing and newer buildings were associated with more reports of bedroom carpeting/rugs in bedrooms. CONCLUSIONS: We innovatively applied a machine learning and causal inference mixture methodology to detail IHEE among children with asthma using EHR and geospatial data, which could have wide applicability and utility.

Sensitivity of modeled residential fine particulate matter exposure to select building and source characteristics: A case study using public data in Boston, MA.

Many techniques for estimating exposure to airborne contaminants do not account for building characteristics that can magnify contaminant contributions from indoor and outdoor sources. Building characteristics that influence exposure can be challenging to obtain at scale, but some may be incorporated into exposure assessments using public datasets. We present a methodology for using public datasets to generate housing models for a test cohort, and examined sensitivity of predicted fine particulate matter (PM2.5) exposures to selected building and source characteristics. We used addresses of a cohort of children with asthma and public tax assessor's data to guide selection of floorplans of US residences from a public database. This in turn guided generation of coupled multi-zone models (CONTAM and EnergyPlus) that estimated indoor PM2.5 exposure profiles. To examine sensitivity to model parameters, we varied building floors and floorplan, heating, ventilating and air-conditioning (HVAC) type, room or floor-level model resolution, and indoor source strength and schedule (for hypothesized gas stove cooking and tobacco smoking). Occupant time-activity and ambient pollutant levels were held constant. Our address matching methodology identified two multi-family house templates and one single-family house template that had similar characteristics to 60 % of test addresses. Exposure to infiltrated ambient PM2.5 was similar across selected building characteristics, HVAC types, and model resolutions (holding all else equal). By comparison, exposures to indoor-sourced PM2.5 were higher in the two multi-family residences than the single family residence (e.g., for cooking PM2.5 exposure, by 26 % and 47 % respectively) and were sensitive to HVAC type and model resolution. We derived the influence of building characteristics and HVAC type on PM2.5 exposure indoors using public data sources and coupled multi-zone models. With the important inclusion of individualized resident behavior data, similar housing modeling can be used to incorporate exposure variability in health studies of the indoor residential environment.

The impact of energy retrofits on pediatric asthma exacerbation in a Boston multi-family housing complex: a systems science approach.

BACKGROUND: Pediatric asthma is currently the most prevalent chronic disease in the United States, with children in lower income families disproportionately affected. This increased health burden is partly due to lower-quality and insufficient maintenance of affordable housing. A movement towards 'green' retrofits that improve energy efficiency and increase ventilation in existing affordable housing offers an opportunity to provide cost-effective interventions that can address these health disparities. METHODS: We combine indoor air quality modeling with a previously developed discrete event model for pediatric asthma exacerbation to simulate the effects of different types of energy retrofits implemented at an affordable housing site in Boston, MA. RESULTS: Simulation results show that retrofits lead to overall better health outcomes and healthcare cost savings if reduced air exchange due to energy-saving air tightening is compensated by mechanical ventilation. Especially when exposed to indoor tobacco smoke and intensive gas-stove cooking such retrofit would lead to an average annual cost saving of over USD 200, while without mechanical ventilation the same children would have experienced an increase of almost USD 200/year in health care utilization cost. CONCLUSION: The combination of indoor air quality modeling and discrete event modeling applied in this paper can allow for the inclusion of health impacts in cost-benefit analyses of proposed affordable housing energy retrofits.

Modeling Environmental Tobacco Smoke (ETS) Infiltration in Low-Income Multifamily Housing before and after Building Energy Retrofits.

Secondhand exposure to environmental tobacco smoke (ETS) in multifamily housing remains a health concern despite strong recommendations to implement non-smoking policies. Multiple studies have documented exposure to ETS in non-smoking units located in buildings with smoking units. However, characterizing the magnitude of ETS infiltration or measuring the impact of building interventions or resident behavior on ETS is challenging due to the complexities of multifamily buildings, which include variable resident behaviors and complex airflows between numerous shared compartments (e.g., adjacent apartments, common hallways, elevators, heating, ventilating and air conditioning (HVAC) systems, stack effect). In this study, building simulation models were used to characterize changes in ETS infiltration in a low income, multifamily apartment building in Boston which underwent extensive building renovations targeting energy savings. Results suggest that exterior wall air sealing can lead to increases in ETS infiltration across apartments, while compartmentalization can reduce infiltration. The magnitude and direction of ETS infiltration depends on apartment characteristics, including construction (i.e., level and number of exterior walls), resident behavior (e.g., window opening, operation of localized exhaust fans), and seasonality. Although overall ETS concentrations and infiltration were reduced post energy-related building retrofits, these trends were not generalizable to all building units. Whole building smoke-free policies are the best approach to eliminate exposure to ETS in multifamily housing.