Development of a nationally representative set of combined building energy and indoor air quality models for U.S. residences

Abstract

Americans spend most of their time inside residences where they are exposed to a number of pollutants of both indoor and outdoor origin. Residential buildings also account for ∼20% of the primary energy consumed in the U.S. To provide a tool for future investigations of interactions between energy use and indoor air quality (IAQ) in homes across the U.S. population, we developed a custom set of nationally representative building energy and IAQ mass balance models that predict annual energy use for space conditioning and indoor concentrations of a number of pollutants of both indoor and outdoor origin across the U.S. residential building stock. The residential energy and indoor air quality (REIAQ) model framework is built in Python and integrates between EnergyPlus and a dynamic mass balance model. REIAQ utilizes historical weather data to predict hourly energy consumption, air change rates, and HVAC system runtimes, which are coupled with historical outdoor pollutant concentration data and assumptions for indoor emission sources and other factors to predict hourly indoor pollutant concentrations. Modeled indoor pollutants include PM2.5, UFPs, O3, NO2, and several volatile organic compounds (VOCs) and aldehydes. The REIAQ model set successfully predicted annual space conditioning energy consumption for the U.S. residential building stock within ∼2% of historical data. Modeled indoor concentrations, infiltration factors for outdoor contaminants, and indoor/outdoor ratios of each pollutant all matched closely with observations from prior field studies. Population-weighted annual average indoor pollutant concentrations were also used to estimate the chronic health burden of residential indoor exposures.