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.

Impact of meteorology on indoor air quality, energy use, and health in a typical mid-rise multi-family home in the eastern United States.

Heating and cooling requirement differences across climates not only have carbon emissions and energy efficiency implications but also impact indoor air quality (IAQ) and health. Energy and IAQ building simulation models help understand tradeoffs or co-benefits, but these have not been applied to evaluate climate zone or multi-family home differences. We modeled a four-story multi-family home in six U.S. climate zones and quantified energy, IAQ, and health outcomes with EnergyPlus, CONTAM, and a pediatric asthma systems science model. Pollutant sources included cooking and ambient. Outputs were daily PM2.5 and NO2 indoor concentrations, infiltration, energy for heating and cooling, and asthma exacerbations, which were compared across climate zones, apartment units, and resident behaviors. Daily ambient-sourced PM2.5 decreased and cooking-sourced PM2.5 increased with higher ambient temperatures. Infiltration air changes per hour were higher on the first versus the fourth floor and in colder climates. Window opening during cooking led to decreases in total pollutant concentrations (11%-18% for PM2.5 and 9%-15% for NO2 ), 3%-4% decreases in asthma exacerbations within climate zones, and minimal impacts on cooling, but led to increased heating demand (4%-8%). Our results demonstrate the influence of meteorology, multi-family building characteristics, and resident behavior on IAQ, energy, and health, focused on multi-zone methodology.

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.