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Abstract: Sixteen apartments serving low-income populations in three buildings were retrofit with the goal of simultaneously reducing energy consumption and improving indoor environmental quality (IEQ). Retrofit measures varied among apartments and included, among others, envelope sealing, installation of continuous mechanical ventilation systems, upgrading bathroom fans and range hoods, attic insulation, replacement of heating and cooling systems, and adding wall-mounted particle air cleaners. IEQ parameters were measured, generally for two one-week periods before and after the retrofits. The measurements indicate an overall improvement in IEQ conditions after the retrofits. Comfort conditions, bathroom humidity, and concentrations of carbon dioxide, acetaldehyde, volatile organic compounds, and particles generally improved. Formaldehyde and nitrogen dioxide levels decreased in the building with the highest concentrations, were unchanged in a second building, and increased in a third building. IEQ parameters other than particles improved more in apartments with continuous mechanical ventilation systems installed. In general, but not consistently, larger percent increases in air exchange rates were associated with larger percent decreases in indoor levels of the pollutants that primarily come from indoor sources. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Abstract: The current focus on building energy retrofit provides an opportunity to simultaneously improve indoor environmental quality (IEQ). Toward this end, we developed a protocol for selecting packages of retrofits that both save energy and improve IEQ in apartments. The protocol specifies the methodology for selecting retrofits from a candidate list while addressing expected energy savings, IEQ impacts, and costs in an integrated manner. Interviews, inspections and measurements are specified to collect the needed input information. The protocol was applied to 17 apartments in three buildings in two different climates within California. Diagnostic measurements and surveys conducted before and after retrofit implementation indicate enhanced apartment performance. [Copyright &y& Elsevier]