People in developed countries spend the majority of their time indoors. Therefore, studying the effect of outdoor air quality on indoor air is of a great importance to human health. This thesis presents several dynamic computer models that were developed to study this effect. They estimate indoor pollutant levels based on outdoor levels, ventilation rate, and other factors. Also, an analysis method is presented that allows for quantifying the effect of outdoor air quality on indoor air at a given building based on measured real-time outdoor and indoor pollutant levels. An important part of this method is separating the measured indoor level into two components - a component caused by indoor sources and a component caused by pollutants penetrating from outdoors. This separation is accomplished using a dynamic model, which, unlike some other methods, also allows for processing transient samples and thus simplifies the needed measurements. Outdoor and indoor pollutant levels were measured at eight buildings in Fairbanks, Alaska and the developed method was used to analyze the data. The main focus was on fine particulate matter (PM2.5) and carbon monoxide (CO) - the pollutants of major concern in Fairbanks. The effective penetration efficiency for PM2.5 ranged from 0.16 to 0.69, and was close to unity for CO. The outdoor generated PM2.5 was responsible in average for about 67% of the indoor PM2.5 in residences, and close to 100% in office environments. These results imply that reducing outdoor pollution can have significant health benefits even for people spending the majority of their time indoors. An air-quality control algorithm for a Heating, Ventilation, and Air Conditioning (HVAC) system was developed and tested using one of the models. This algorithm was shown to reduce indoor PM2.5 levels by 65%. Another model was used to study various ventilation options for a typical Fairbanks home with respect to indoor air quality, energy consumption, overall economy, and environmental impact. Using a Heat Recovery Ventilator (HRV) with an additional filter was shown to be the best option. Another model was successfully used to address key factors for radon mitigation in a home located in a radon-prone area.
Thesis (Ph.D.) University of Alaska Fairbanks, 2007
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