Abstract
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 (PM(2.5)) 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 PM(2.5) 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 PM(2.5) was similar across selected building characteristics, HVAC types, and model resolutions (holding all else equal). By comparison, exposures to indoor-sourced PM(2.5) were higher in the two multi-family residences than the single family residence (e.g., for cooking PM(2.5) 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 PM(2.5) 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.