W. Max Kirk

Efforts to improve energy efficiency in homes and buildings have led to tighter structures. However, these changes can also produce negative consequences for indoor air quality and human health. One of the dramatic effects of climate change and weather is the increase in destructive wildfires, such as those experienced in the Pacific Northwest during the summer of 2015. The current article presents data for measurements at two houses during periods with and without high levels of wildfire smoke outdoors. For each house, indoor and outdoor pollutant measurements were obtained for ozone (O₃), fine particulate matter (PM_2.5), and volatile organic compounds along with outdoor weather conditions and occupant activities including the use of windows and doors. The volatile organic compound measurements were obtained using a Proton Transfer Reaction Mass Spectrometer. Compounds monitored included acetonitrile (a biomass burning tracer), formaldehyde, acetaldehyde, methanol, acetone, benzene, toluene, and C2-alkylbenzenes (i.e., sum of xylenes and ethylbenzene), C3-alkylbenzenes (i.e., sum of trimethylbenzene, ethyltoluene, and propylbenzene isomers), and C4-alkylbenzenes (i.e., sum of tetramethylbenzene and its isomers). A carbon dioxide tracer method was used to measure in situ ventilation rates, and blower door tests were also completed to determine standard ventilation rates. For smoky periods with elevated outdoor pollutant levels, penetration factors, defined as the ratio of indoor/outdoor concentrations were quite low. Penetration factors for PM_2.5 were 11% for H2 and 15% for H3, except when windows or doors were open. The penetration factors for O₃ were also low at 24% for H2 and 5% for H3. Elevated indoor volatile organic compound levels were not typically associated with outdoor levels, but reflected significant indoor sources. During smoke events, acetonitrile, a biomass burning tracer compound, was elevated outdoors and indoors in both houses, and benzene was elevated outdoors and indoors in H3.

In the coming decades, as we experience global population growth and global aging issues, there will be corresponding concerns about the quality of the air we experience inside and outside buildings. Because we can anticipate that there will be behavioral changes that accompany population growth and aging, we examine the relationship between home occupant behavior and indoor air quality. To do this, we collect both sensor-based behavior data and chemical indoor air quality measurements in smart home environments. We introduce a novel machine learning-based approach to quantify the correlation between smart home features and chemical measurements of air quality, and evaluate the approach using two smart homes. The findings may help us understand the types of behavior that measurably impact indoor air quality. This information could help us plan for the future by developing an automated building system that would be used as part of a smart city.