South African Air Quality Units are continuously undergoing changes, and improving their performance remains a constant endeavour. In addition, these units are also experiencing several challenges in terms of improving communication across the different spheres, accessing air quality data and using the information to support the decision-making required for efficient management of air quality in South Africa. This study investigated the concept of output efficiency within the South African air quality management context. Models that enable efficient resource allocation can be used to assist managers in understanding how to become efficient. There are, however, few models that focus on the output efficiency of the public sector and air quality management units. The primary purpose of the study was to develop a model to predict the extent to which organisational efficiency could be explained by the percentage of man-hours allocated to a range of management activities. In this study, the development of a model using the logistic regression technique is discussed. Data was collected for two financial years (2005/6 and 2006/7) from the air quality officers in the national, provincial and local spheres of government (N=228). The logistic regression model fitted indicates that the proportion of time spent on knowledge management activities contributes the most to the likelihood of an Air Quality Unit being efficient. The resource allocation model developed will ensure that air quality officers allocate resources appropriately and improve their output performance. / Graduate School for Business Leadership / D.B. L.

South African Air Quality Units are continuously undergoing changes, and improving their performance remains a constant endeavour. In addition, these units are also experiencing several challenges in terms of improving communication across the different spheres, accessing air quality data and using the information to support the decision-making required for efficient management of air quality in South Africa. This study investigated the concept of output efficiency within the South African air quality management context. Models that enable efficient resource allocation can be used to assist managers in understanding how to become efficient. There are, however, few models that focus on the output efficiency of the public sector and air quality management units. The primary purpose of the study was to develop a model to predict the extent to which organisational efficiency could be explained by the percentage of man-hours allocated to a range of management activities. In this study, the development of a model using the logistic regression technique is discussed. Data was collected for two financial years (2005/6 and 2006/7) from the air quality officers in the national, provincial and local spheres of government (N=228). The logistic regression model fitted indicates that the proportion of time spent on knowledge management activities contributes the most to the likelihood of an Air Quality Unit being efficient. The resource allocation model developed will ensure that air quality officers allocate resources appropriately and improve their output performance. / Graduate School for Business Leadership / D.B. L.

Traffic-related air pollution harms health, so whilst it would be advantageous to improve air quality, the socioeconomic impact of air pollution mitigation in Scotland is not fully understood. Evidence from research literature, current regulatory and policy directives and a socioeconomic analysis are required to assess the true health impact. This thesis presents the first health and socioeconomic analysis of traffic-related air pollution and health for Scotland. A critique of the literature was undertaken to determine the evidence base and the strength of evidence in terms of association and causation, between air pollution and ill health. The evidence was subsequently applied in epidemiological studies of Scottish residents, to assess the actual impact on health in Scotland. The perception of barriers and incentives for change were investigated to understand behavioural influences. Recent policy development in Scotland was reviewed, and a socioeconomic analysis of a proposed air pollution strategy in Scotland, was undertaken. The evidence from 30 cohort studies and nine literature reviews demonstrated a link between poor air quality, mortality and respiratory ill health, but the results for other health conditions were inconsistent. The links were associative rather than causal and therefore might be attributable to other factors other than air pollution. Furthermore, epidemiological studies on Scottish populations did not show health effects from traffic-related air pollution. The socioeconomic analysis suggested that an initial investment of between £27m and £44m to introduce Low Emission Zones (LEZ), and an effective active travel programme, might result in a saving of £38m in terms of Years of Life Lost (YLL) and reduction in sickness absence. It is unlikely that the Clean Air For Scotland Strategy will deliver improved air quality and health without substantial investment, better alignment of planning, and a greater public engagement to support public and active transport options.

What makes a community sustainable? Is it the effective management of local environmental resources? Or meeting the social, economic and health needs of its population? For the five UBC researchers in the following pages, the answer is unequivocally both. From tackling water scarcity to environmental health and planning, these researchers are individually working to ensure local communities are equipped with the necessary knowledge to remain sustainable for generations to come.

The Atlanta Inspection and Maintenance program ultimately aims to reduce on-road vehicular emission, a major source of air pollution. The program enforces eligible vehicles to be inspected and repaired, if necessary, before the annual registration renewal. However, various factors can influence the program implementation with respect to the motorists, inspectors, and testing technology. This research explores some of these factors by using empirical data from the Continuous Atlanta Fleet Evaluation project, the inspection transaction records, the Atlanta Household Travel Survey, and the U.S. Census Bureau. The study discusses policy implications of findings from the three essays and offers related recommendations. The first essay examines whether the higher income of a vehicle owner decreases the odds of the vehicle failing the first inspection. Findings show that vehicles owned by low-income households are more likely to fail the first inspection of the annual test cycle. However, after controlling for the vehicle characteristics, the odds of failing the first inspection are similar across households. This suggests that the maintenance behaviors are approximately the same for high- and low-income households. The second essay explains the motorists' decisions in selecting their inspection stations using a random utility model. The study finds that motorists are likely to choose the inspection stations that are located near their houses, charge lower fees, and can serve a large number of customers. Motorists are less likely to choose the stations with a relatively high failure ratio especially in an area of low station density. Moreover, motorists do not travel an extra mile to the stations with lower failure ratio. Understanding choices of vehicle owners can shed some light on the performance of inspection stations. The third essay investigates the validity and reliability of the on-board diagnostic generation II (OBD II) test, a new testing technology required for 1966 and newer model year vehicles. The study compares the inspection results with the observed on-road emission using the remote sensing device (RSD) of the same vehicles. This research finds that the agreement between the RSD measurement and the OBD II test is lower for the relatively older or higher use vehicle fleets

Air pollution of late has been the focus of many studies due to the detrimental health risks that it poses to individuals. University environments have several academic departments with peculiar activities that could be affecting the indoor and outdoor air quality (AQ) of these environments. University settings differ from other environments because of the variety of activities and different lines of work that go on inside buildings housing academic departments and their surroundings, which are likely to have an impact on indoor air quality (IAQ) and outdoor air quality (OAQ) in this environment. Only a few AQ studies have been done in university sites and surrounds worldwide and in these studies, IAQ was given primary importance; whereas, the outdoor environment was and is often neglected. A study comparing both IAQ and OAQ is critical to further understand the relationship between IAQ and OAQ within a university campus. The University of Limpopo (UL) in the Mankweng township of South Africa has been undergoing some refurbishments with numerous construction activities going on in addition to the academic activities of UL. These activities may be affecting the AQ in this unique environment. The main aim of this study was to determine differences between indoor and outdoor AQ in a university environment and to understand how AQ in this unique environment varies with seasons and building function. The study was carried out in three buildings housing three different academic departments in UL namely: Department of Physiology and Environmental Health (PEH), Department of Biochemistry, Microbiology, and Biotechnology (BMBT) and the Department of Biodiversity (BIOD). Twenty indoor and 20 outdoor measuring sites were identified per departmental building from where real-time measurements of 11 AQ parameters (linear air velocity (LAV), dry-bulb temperature (Tdb), relative humidity (RH), carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), sulphur dioxide (SO2), nitrogen dioxide (NO2), hydrogen sulphide (H2S), non-methane hydrocarbons (NMHCs) and volatile organic compounds (VOCs)) were taken over three consecutive days per season. Thus, a total of 60 indoor and 60 outdoor measurements were taken for each parameter in each of the three buildings of interest per season, leading to 360 measurements per season and 1440 measurement per parameter over the one-year period of study across the study area. A hot-wire anemometer was used to measure LAV, whereas the Q-Trak indoor AQ monitor was used in the measurement of Tdb, RH, CO and CO2. Aeroqual AQ monitors were employed in the measurement of O3, SO2, NO2, H2S, NMHCs and VOCs. The Wilcoxon signed ranks test was used to determine differences between indoor and outdoor environments. Significant differences were found between the indoor and outdoor environments for LAV (all three buildings), Tdb (PEH and BMBT), RH (BIOD), O3 (all three buildings), NO2 (all three buildings), CO (all three buildings), CO2 (all three buildings), NMHCs (BMBT and BIOD), and VOCs (all three buildings) (p < 0.05). Linear air velocity, O3, SO2, CO, CO2, and H2S values/concentrations across the indoor/outdoor environments were within the ASHRAE/DEA/WHO guidelines/standards, whereas Tdb, RH and NO2 values/concentrations were not. Air quality in the study area varied with building, with the best AQ across both the indoor and outdoor environments being within the BIOD building, whilst the worst AQ across both environments was encountered in the PEH building. Seasonal differences between buildings were also identified between indoor and outdoor environments among the PEH, BMBT and BIOD buildings (p < 0.008). Across the indoor environment, the winter season was found to be the season with the best AQ, since all the pollutants were found at minimum concentrations. Factors affecting AQ in the study area included thermal comfort, occupant densities, building function, laboratory emissions, renovation activities, generators, vehicular emissions, among others. The best AQ across the outdoor environment occurred during the autumn season, since all the air pollutants were present at minimal concentrations during this time. The best predictors of LAV, Tdb, CO, CO2, NO2, and NMHCs were seasons (R2 = 1.000, p < 0.01). For the parameters RH, H2S, and VOCs, the best predictor was building type (R2 = 1.000, p < 0.01). The indoor and outdoor environment were the best predictors for SO2 (R2 = 0.999, p < 0.01). Ozone had no single predictor that was found to significantly influence its concentration in this study. In relation to an air pollution index (API), generally all pollutant indices fell within the fair, good to very good range when using mean and maxima concentrations, whereas, corresponding NO2 concentrations throughout the study fell within the poor to very poor range (105.660–250.000). University management should take into consideration ventilation in laboratories, occupant densities and location of standby generators and car parks in the management of AQ on the university campus. All heating, ventilation and air conditioning (HVAC) systems need to be upgraded and work in tandem with natural ventilation when having high occupant densities within buildings. Future studies in this sector could incorporate larger sample sizes, be designed as a longitudinal study, and make use of questionnaires and sample more AQ parameters to get a detailed understanding of a university site and its surrounds. / Environmental Sciences / Ph. D. (Environmental Science)

Indiana University-Purdue University Indianapolis (IUPUI)