Changing the air exchange rate of a home (the sum of the infiltration and mechanical ventilation airflow rates) affects the annual thermal conditioning energy. Large-scale changes to air exchange rates of the housing stock can significantly alter the residential sector's energy consumption. However, the complexity of existing residential energy models is a barrier to the accurate quantification of the impact of policy changes on a state or national level.The Incremental Ventilation Energy (IVE) model developed in this study combines the output of simple air exchange models with a limited set of housing characteristics to estimate the associated change in energy demand of homes. The IVE model was designed specifically to enable modellers to use existing databases of housing characteristics to determine the impact of ventilation policy change on a population scale. The IVE model estimates of energy change when applied to US homes with limited parameterisation are shown to be comparable to the estimates of a well-validated, complex residential energy model.

Cooking has a significant impact on indoor air quality. When cooking occurs, how foods are cooked, and the types of food that are cooked have all been shown to impact the rate at which occupants are exposed to pollutants. Home occupancy characteristics impact how concentrations in the home translate into exposures for the occupants. With the intent of expanding our understanding of cooking behavior in the U.S., we developed and advertised an online survey to collect household cooking behavior for the 24 hrs prior to taking the survey. The survey questions were designed to address gaps in knowledge needed to predict the impact of cooking on indoor concentrations of PM2.5 and other pollutants. The survey included the following questions: 1) which meals households ate at home; 2) number of household members at home during cooking; 3) the type of oil used for cooking; 4) the type of foods cooked at each meal; 5) the type of cooking devices used; and 6) the methods selected for food preparation. We also collected information on household characteristics such as their location (zip code), ethnicity, and ages of family members. We analyzed the variability in home cooking characteristics for households in different climate zones and with four different types of family compositions: 1 senior living alone, 1 adult living alone, 2 or more adults/seniors, and families with children. We used simple statistical tests to determine if the probability of certain cooking behaviors differed between these subgroups.