Damp building materials are subjected to degradation processes due to moisture and also microbial growth, with both of these giving rise to emissions of volatile organic compounds (VOCs) that may contribute to indoor air health problems. The overall aim of this thesis was to investigate emissions of reactive and non-reactive VOCs from damp building materials and from the microorganisms growing on them, and also to investigate the possible health impact of these compounds. Three studies were carried out in order to study emissions of VOCs. The first investigated emissions from a mixture of five fungi (Aspergillus versicolor, Fusarium culmorum, Penicillium chrysogenum, Ulocladium botrytis and Wallemia sebi) and the second emissions from the bacterium Streptomyces albidoflavus. In both studies the microorganisms were cultivated on three different building materials (pine wood, particle board and gypsum board) and one synthetic media, MEA and TGEA respectively. The bacterium was also cultivated on sand. Air samples from the cultures were collected on six different adsorbents and chemosorbents to sample a wide range of compounds such as VOCs, aldehydes, amines and light-weight organic acids. The samples were analyzed with gas chromatography, high-pressure liquid chromatography and ion chromatography. Mass spectrometry was used for identification of the compounds. Alcohols and ketones were the predominant compound groups identified. The bacterial culture growing on TGEA emitted ammonia, methylamine, diethylamine and ethylamine. The third study dealt with secondary emissions collected from buildings with moisture and mould problems. Samples were taken when the materials were dry and also after they had been wet for a week. Most alcohols and ketones could be identified from the wet materials. Trimethylamine and triethylamine, were identified from sand contaminated by Bacillus. One study looked at the development of a method for analysis of primary and secondary amines with LC-MS/MS. A three-step process was developed, with the first step screening the samples for NIT derivatives with selected reaction monitoring, SRM. In the second step a precursor ion scan gave the [M+H]+ ion, and the last step involved fragmentation with a product ion scan. It was possible to separate and identify all the investigated amines, which showed that the method was both specific and selective and therefore well suited for the analysis of amines in complex environments. The last study comprised two exposure studies. In study 1 each participant took part in two exposure conditions, one with air from mouldy building materials and one with blank air for a 60 minute period. In study 2 each participant was exposed four times (for a period of 10 min) at random to air from mouldy building materials and blank air, with and without nose-clip. The participants rated air quality and symptoms before, during and after each exposure. Exposure to moderate VOC levels resulted in reports of perceived poor air quality, but no such results were received when exposing the participants to low VOC levels.

The population and economic growth experienced by Canadian metropolitan areas in the past twenty years, has been associated with increased levels of car ownership and vehicle kilometres travelled leading to a deterioration of air quality and public health and an increase in greenhouse gas emissions. The need to modify urban growth patterns has motivated planning agencies in Canada to develop a broad range of policies aiming at achieving a more sustainable transportation sector. The challenge however, remains in the ability to test the effectiveness of proposed policy measures. This situation has led to a renewed interest in integrated land-use and transport models to support transport policy appraisal. This research is motivated by the need to improve transport policy appraisal through the use of integrated land-use and transport models linked with a range of sub-models that can reflect transport externalities. This research starts with an exploration of the transport policy environment in Canada through a questionnaire-based survey conducted with planners and policy-makers. The survey results highlight the need for tools reflecting the sustainability impacts of proposed policies. While the second part of this research explores sustainability indicators and recommends a set of social, economic, and environmental measures, linked with integrated land-use and transport models; effort is dedicated to estimate the environmental indicators as part of this thesis. As such, the third part of this research involves the development of an emission-dispersion-exposure modelling framework. The framework includes a suite of sub-models including an activity-based travel demand model (TASHA), an emission factor model (Mobile6.2C), a meteorological model (CALMET), and a dispersion model (CALPUFF). The framework is used to estimate link-based emissions of light-duty vehicles in the Greater Toronto Area under a base scenario for 2001. Dispersion of emissions is then conducted and linked with population in order to estimate exposure to air pollution.

To address odour and gas problems generated by livestock facilities, air dispersion models have been used to determine reasonable science-based setback distances between the livestock operations and the neighbouring residences. However, none of the existing models consider diurnal, seasonal and climate variations of odour and gas (ammonia, hydrogen sulphide, carbon dioxide) concentrations and emission rates (OGCER), which may result in great uncertainties in setback distance calculations. Thus, the purpose of this project was to monitor and model diurnal and seasonal OGCER from swine grower/finisher rooms. Specifically, this research was conducted to: 1) characterize diurnal OGCER between two different flooring systems (fully and partially slatted floorings) under three different weather conditions (August, October and February); 2) identify seasonal OGCER over a 12-month measuring period; and 3) develop mathematical models to predict the OGCER. <p>A two-factorial strip-block experiment was designed for measuring diurnal OGCER in two grower/finisher rooms. It was found that: 1) the diurnal OGCER in the fully slatted flooring system was 27.6 to 39.5% higher than that in the partially slatted flooring system; however, no significant differences in the diurnal OGCER were found between the two rooms, except for the NH3 concentrations in August, the NH3 and H2S concentrations and emissions in October, and odour concentrations and emissions in February (P > 0.05), and 2) significant diurnal variations in the OGCER (except for the odour concentrations and H2S emissions) have been observed in August (P < 0.05); only gas emissions showed significant fluctuation patterns in October (P < 0.05); no significant variations in the OGCER (except for the CO2 concentrations and emissions) were found in February (P > 0.05). <p>A repeated measurement method was used to monitor seasonal OGCER in four grower/finisher rooms over a period of 12 months. It was found that: 1) the seasonal OGCER from the fully slatted flooring system was 2.9 to 40.6% higher than that from the partially slatted flooring system; however, the seasonal OGCER (except for the NH3 concentrations in October, November and January; the CO2 concentrations in August and the CO2 emissions in December) between the two different floors for each measuring month did not differ significantly (P > 0.05); and 2) the seasonal OGCER was significantly affected by the sampling month (P < 0.05), and no specific seasonal pattern was observed. <p> The statistical models developed for each type of the flooring system determined the OGCER based on the room and ambient temperatures, the ventilation rates and the animal units. The predicted results showed good agreement with measured values for most of OGCER (r2: 0.67-0.95). In order to improve odour and gas prediction models, animal activity and dirtiness of pens should be further investigated.

The main objectives of this thesis are to determine if the addition of an energy wheel in an HVAC system can improve the indoor air relative humidity (RH), and perceived air quality (PAQ), as well as reduce energy consumption. An energy wheel is an air-to-air energy exchanger that transfers heat and moisture between the outdoor air entering and the exhaust air leaving a building. This thesis uses the TRNSYS computer package to model two buildings (an office and a school) in four different cities (Saskatoon, Saskatchewan; Vancouver, British Columbia; Tampa, Florida and Phoenix, Arizona).<p>The results with and without an energy wheel are compared to see if the energy wheel has a significant impact on the RH and PAQ in the buildings. The energy wheel reduces peak RH levels in Tampa, (up to 15% RH), which is a humid climate, but has a smaller effect on the indoor RH in Saskatoon (up to 4% RH) and Phoenix (up to 11% RH), which are dry climates. The energy wheel also reduces the number of people that are dissatisfied with the PAQ within the space by up to 17% in Tampa. <p>The addition of the energy wheel to the HVAC system creates a reduction in the total energy consumed by the HVAC system in Saskatoon, Phoenix and Tampa (2% in each city). There is a significant reduction in the size of the heating equipment in Saskatoon (26%) and in the size of the cooling equipment in Phoenix (18%) and Tampa (17%). A cost analysis shows that the HVAC system including an energy wheel has the least life-cycle costs in these three cities, with savings of up to 6%. In Vancouver, the energy wheel has a negligible impact on the indoor RH, PAQ and energy consumption.

This dissertation investigates whether higher levels of “governmental fragmentation” in metropolitan statistical areas (MSA) leads to worse environmental outcomes. Fragmentation refers to the number of local governments in a given region or MSA as defined by the census. This research contributes to two bodies of literature, that of environmental federalism and that of urban growth and local government form. In the area of environmental federalism this dissertation extends the collective action model to include local governments. An empirical framework is developed that includes cross-sectional and panel data. In the urban growth and local government form literature, this dissertation comprehensively tests many existing measures of local government fragmentation within an environmental policy framework. It also modifies and extends some of the fragmentation variables. The results suggest that local government fragmentation does hinder MSAs from attaining the ozone standard. This dissertation extends the literature by examining the effect that local government fragmentation has on regional environmental quality. Six local government structure variables, jurisdiction count, special district dominance, central city dominance, county primacy, central city growth, and metropolitan power diffusion index are comprehensively tested to determine which might affect regional environmental quality. In addition, this research extends the use of the computationally complex measure of metropolitan power diffusion index to include additional local government expenditures as well as additional years of panel data. Two empirical estimation strategies were implemented, a cross-sectional approach and a panel data approach. The cross-sectional approach estimates the effects that long-term changes in local government structure have on attaining the ozone standard by measuring differences across MSAs. The panel data model’s primary purpose was that of a robustness check on the cross-sectional results. Three of the six tested fragmentation variables were found to have statistically significant effects on MSA attainment of the ozone standard in the cross-sectional model. Higher levels of metropolitan power diffusion index and jurisdiction count were found to hinder attainment of the ozone standard, while greater values of central city growth aided in reaching the attainment standard. Generally, the panel data results’ supported the results from the cross-sectional models. In addition, the panel model resolved some important estimation issues. Metropolitan power diffusion index was found to be correlated with unobservables in the random effects model, indicating that the cross-sectional results for metropolitan power diffusion index may be biased as well. This was not an issue for the variable jurisdiction count. Metropolitan power diffusion index and jurisdiction count are highly correlated with each other and this relationship was used to estimate a reasonable range for the effect metropolitan power diffusion index might have on the attainment of the ozone standard.

This thesis has investigated the cascade impactor with its fractionating particulate capabilities for monitoring respirable particulate matter. Additionally, the cascade impactor was compared with the high volume sampler, the present acceptable method of measuring total suspended particulates.This two-year study analyzed the quarterly and annual geometric means, geometric standard deviations and mass median diameters of the cascade impactor concentrations. Correlation coefficients between the cascade impactor and high volume sampler were analyzed to reveal the relationship between the two sampling methods.Ball State UniversityMuncie, IN 47306

The population and economic growth experienced by Canadian metropolitan areas in the past twenty years, has been associated with increased levels of car ownership and vehicle kilometres travelled leading to a deterioration of air quality and public health and an increase in greenhouse gas emissions. The need to modify urban growth patterns has motivated planning agencies in Canada to develop a broad range of policies aiming at achieving a more sustainable transportation sector. The challenge however, remains in the ability to test the effectiveness of proposed policy measures. This situation has led to a renewed interest in integrated land-use and transport models to support transport policy appraisal. This research is motivated by the need to improve transport policy appraisal through the use of integrated land-use and transport models linked with a range of sub-models that can reflect transport externalities. This research starts with an exploration of the transport policy environment in Canada through a questionnaire-based survey conducted with planners and policy-makers. The survey results highlight the need for tools reflecting the sustainability impacts of proposed policies. While the second part of this research explores sustainability indicators and recommends a set of social, economic, and environmental measures, linked with integrated land-use and transport models; effort is dedicated to estimate the environmental indicators as part of this thesis. As such, the third part of this research involves the development of an emission-dispersion-exposure modelling framework. The framework includes a suite of sub-models including an activity-based travel demand model (TASHA), an emission factor model (Mobile6.2C), a meteorological model (CALMET), and a dispersion model (CALPUFF). The framework is used to estimate link-based emissions of light-duty vehicles in the Greater Toronto Area under a base scenario for 2001. Dispersion of emissions is then conducted and linked with population in order to estimate exposure to air pollution.

Air pollution sampling site selection is one of the most important and yet most vexing of the problems faced by those responsible for regional and urban air quality management and for the attainment and maintenance of national ambient air quality standards. Since one cannot hope to monitor air quality at all locations at all times, selection of sites to give a reliable and realistic picture of air quality becomes a major issue and at the same time a difficult task. The location (configuration) and the number of stations may be based on many factors, some of which may depend on limited resources, federal and state regulations and local conditions. The combination of these factors has made air quality surveys more complex; requiring comprehensive planning to ensure that the prescribed objectives can be attained in the shortest possible time and at the least cost. Furthermore, the choice and siting of the measuring network represents a factor of significant economic relevance for policymakers. In view of the fact that equipment, maintenance and operating personnel costs are increasing dramatically, the possibility of optimizing the monitoring design, is most attractive to the directors of air quality management programs. In this work a methodology that is able to design an optimal air quality monitoring network (AQMN) is described. The objective of the optimization is to provide maximum information about the presence and level of atmospheric contaminants in a given area and with a limited budget. A criterion for assessing the allocation of monitoring stations is developed by applying a utility function that can describe the spatial coverage of the network and its ability to detect violations of standards for multiple pollutants. A mathematical model based on the Multiple Cell Approach (MCA) was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different emission sources. This data was used to train artificial neural networks (ANN) that were proven to be able to predict very well the pattern and violation scores at different potential locations. These neural networks were embedded within a mathematical programming model whose objective is to determine the best monitoring locations for a given budget. This resulted in a nonlinear program (NLP). The proposed model is applied to a network of existing refinery stacks and the locations of monitoring stations and their area coverage percentage are obtained.

A major criticism of the environmental behavior literature is the nearly exclusive focus on the role of attitudes and individual-level characteristics. Despite this concentration on individual-level causes, variation in environmental behavior remains. As individual behavior becomes an increasingly significant source of pollution, a better understanding of the influences individual behavior is critical to addressing environmental degradation. This research re-directs the focus on individual-level influences on environmental behaviors by building models examining the varying dimensions of environmental behaviors as influenced by community characteristics. This is accomplished by testing a series of hypotheses under the auspices of two theoretical frameworks: the neoclassical economic theory and a social contextual model of environmental actions. Using individual-level data from the 1993 and 2000 General Social Survey and MSA data from the U.S. Census and the Environmental Protection Agency, I estimate two-level hierarchical models for three environmentally sensitive behaviors (environmentally sensitive food consumption, environmentally sensitive automobile use, and environmental activism). Multi-level analyses yield models revealing significant associations between MSA measures and individual environmental behaviors. Objective environmental conditions, region of MSA and MSA education level are significantly associated with environmentally sensitive food consumption behaviors, environmentally sensitive automobile use, and environmental activism behaviors, though their influence assumes diverse forms. Among the community measures, MSA education level is the primary social process that produces change in all environmental behaviors. In each of the models, MSA education level exhibits effects on all three behavioral measures and significant cross-level effects on automobile use behaviors. Living in a well educated MSA, particularly in the West or Northeast suggests higher environmental participation. Region of MSA is also a characteristic that must be considered when evaluating environmental behaviors, particularly for those living in the West and Northeast. Theoretical conclusions suggest that individual environmental behavior decision making is not simply a market exchange, but social forces are at work in the individual decision-making process.

Air pollution sampling site selection is one of the most important and yet most vexing of the problems faced by those responsible for regional and urban air quality management and for the attainment and maintenance of national ambient air quality standards. Since one cannot hope to monitor air quality at all locations at all times, selection of sites to give a reliable and realistic picture of air quality becomes a major issue and at the same time a difficult task. The location (configuration) and the number of stations may be based on many factors, some of which may depend on limited resources, federal and state regulations and local conditions. The combination of these factors has made air quality surveys more complex; requiring comprehensive planning to ensure that the prescribed objectives can be attained in the shortest possible time and at the least cost. Furthermore, the choice and siting of the measuring network represents a factor of significant economic relevance for policymakers. In view of the fact that equipment, maintenance and operating personnel costs are increasing dramatically, the possibility of optimizing the monitoring design, is most attractive to the directors of air quality management programs. In this work a methodology that is able to design an optimal air quality monitoring network (AQMN) is described. The objective of the optimization is to provide maximum information about the presence and level of atmospheric contaminants in a given area and with a limited budget. A criterion for assessing the allocation of monitoring stations is developed by applying a utility function that can describe the spatial coverage of the network and its ability to detect violations of standards for multiple pollutants. A mathematical model based on the Multiple Cell Approach (MCA) was used to create monthly spatial distributions for the concentrations of the pollutants emitted from different emission sources. This data was used to train artificial neural networks (ANN) that were proven to be able to predict very well the pattern and violation scores at different potential locations. These neural networks were embedded within a mathematical programming model whose objective is to determine the best monitoring locations for a given budget. This resulted in a nonlinear program (NLP). The proposed model is applied to a network of existing refinery stacks and the locations of monitoring stations and their area coverage percentage are obtained.