Since 1992 the Department of Environmental Affairs and Tourism (DEAT), now the Department of Environmental Affairs (DEA), acknowledged that pollution and waste management governance was inadequate in dealing with South Africa’s changing social and industrial context. This triggered an extensive legislative revision, with the new National Environmental Management: Air Quality Act (No. 39 of 2004) (AQA) being partially implemented on 11 September 2005 and full implementation expected by 1 April 2010. The goal of this research was to develop a methodology for the delineation of the boundaries of air quality management areas in South Africa. The preliminary objective of the research was to identify the specific criteria that should be considered when developing the methodology. A review of the methodologies used internationally was undertaken, looking specifically at regions and countries with similar effects-based air quality legislation. The review concluded that the international practice regarding boundary determination was data intensive, relying heavily on the results of ambient air quality monitoring and the results of dispersion modelling based on comprehensive emissions inventories. Another commonality between the methodologies was the use of administrative boundaries as the borders of air quality management areas. South Africa has limited ambient air quality monitoring and there is no national emissions inventory for criteria pollutants. In the absence of this information an alternative approach was required. The next objective of the research was to identify or develop a proxy methodology for assessing the impact of each of these criteria to be used in the boundary determination. The criteria assessed as part of this research included, population density, emission criteria (industrial, mining and domestic), topography and administrative boundaries. A further objective of the research was to combine all the criteria to produce a single indicator or value as to the air pollution impact potential of the area under consideration. This methodology was then applied in the South African context. The final objective of the research was to assess the results of the application of the methodology on the regulatory framework proposed by the AQA, at the national, provincial and local government levels. The methodology has proved successful in the identification of areas with high air pollution impact potential in South Africa. This has allowed for a review of the boundaries proclaimed for the Vaal Triangle Airshed Priority Area and the Highveld Priority Area. In both cases significant revisions of the boundaries are recommended, however due to the controversial nature of these recommendations, it is proposed that these revisions are deferred until the five- yearly review phase of the priority area management plan. The results also recommended the proclamation of two additional national priority areas. The first was the proposed Magaliesberg Priority Area, which covers the north-western areas of Gauteng and the eastern areas of the North-West. This area combines the high density residential, commercial and industrial areas of Gauteng with the high density mining and industrial areas of the North-West. However, it is recommended that further ambient air quality monitoring and research is required prior to the proclamation of this national priority area. The second new national priority area proposed is the Waterberg Priority Area. This proclamation is a proactive declaration based on the proposed industrial developments earmarked for this area. Due to extensive coal reserves in the area, the development of additional coal-fired power generation, a coal to liquid facility and other coal beneficiation projects are currently under consideration. The research has identified five potential provincial priority areas. The provincial priority areas are associated with the major metropolitan centres in the country and their adjacent district municipalities. All of the proposed provincial priority areas, with the exception of the one proposed in Gauteng, require further ambient air quality monitoring and research prior to their proclamation. It is recommended that the City of Johannesburg / City of Tshwane provincial priority area be considered for immediate declaration. The review of the district and local municipalities identified in Table 24 of the National Framework highlighted the conservative nature of the initial assessment. The review amended the classification of 33 of the local municipalities, with 32 being reclassified downwards and only one being reclassified upwards. This also highlighted the subjective nature of the initial assessment. It is recommended that the local municipalities identified as having “Poor” or “Potentially Poor” air quality rating, be prioritised as potential sites in the national ambient air quality monitoring network and receive assistance in the development of their air quality management plans. This ensures that the limited financial and human resources assigned to air quality management in South Africa are deployed in those areas with the greatest need. / Thesis (Ph.D.)-University of KwaZulu-Natal, Westville, 2010.

The Air Quality Act (AQA) Act No.39 of 2004 promulgated in 2004 follows the outdated Atmospheric Pollution Prevention Act (APPA) (Act No.45 of 1965). The legislative approach shifted from a source- based, end of pipe, command and control, guideline principle to ambient air quality management and improvement of compliance to standards through a consultative process. The AQA’s management framework incorporates a co-operative and integrated approach with government, communities and polluters to look at the holistic management of ambient air quality and the identified roles and responsibilities for all stakeholders. The AQA branched from the National Environmental Management Act (NEMA) 107 of 1998, which is the first piece of legislation formalizing the principles of the Integrated Pollution Waste Management (IPWM) Policy published in 2000 and the Bill of Rights. Government and Industry have a role to play in the implementation of the AQA. Government’s role covers the management and enforcement aspects, whilst industries’ role includes the management of air emissions and compliance reporting to improve the overall ambient air quality. The AQA’s industrial requirements range from compliance and reporting by ensuring emission licenses are in place, compliance with standards set by different spheres of government and the management of these emissions. The management of these requirements includes understanding the legislation, its implications and the provision of other financial, human and technological resources. Industry needs to consider the impacts of these legislative changes and how it may impact business as a whole. The aim of this study is to analyze the industrial perspectives of the AQA and its implementation through the use of a questionnaire. Open-ended questionnaires were administered to a total of forty industrial companies in the chemical, petrochemical, energy and mining sectors in the Gauteng, North West and Durban industrial areas. Industries were identified as those which have scheduled process certificates or companies that will be impacted by the impending changes as a result of the AQA. The overall outcome of the industrial responses revealed poor general knowledge of the principles, purpose and the reasons for the transition from APPA to AQA. Few industries had insight into the type of challenges they may face from the AQA’s listed control measures and the control measures that would apply to their particular industry. There is a general concern surrounding the government’s lack of support and the essential enforcement that is required to ensure ambient air quality compliance. These challenges and recommendations are discussed in the thesis. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2009.

M.Sc. / The evaluation of population exposure to air pollution is a fundamental reason for management and control of regional air quality. The purpose of this study was to determine the exposure of the local population to PM-10 emissions from sources within the Vaal Triangle using a Geographic Information System (GIS). The emission inventory compiled by van Nierop for the calendar year of 1992 (van Nierop, 1994) was used as input data for these calculations. The Industrial Source Complex Short Term Model (ISCST) was applied for dispersion calculations of annual PM-10 emissions. The ReGIS package was applied to determine the applicability of GIS as a management tool. Annual average PM-10 concentration contours were calculated for the different air pollution source groups within the Vaal Triangle. The combined source group resulted in the highest population exposure from annual average PM-10 concentrations. Population exposure from high- (> 200 m), medium- (10 to 200 m) and low- (< 10 m) elevation air pollution source groups were determined. The medium-elevation source group resulted in high population exposure followed by the low-elevation source group. The high-elevation source group had very low population exposure as a result. The population exposures from all the industrial sources within the Vaal Triangle were calculated and found to be very high. Annual average PM-10 concentrations from domestic fuel combustion sources were surprisingly low, resulting in low population exposure. ReGIS was found to be inadequate for the task and is not recommended for further use. Despite this, GIS was found to be a powerful decision-making tool and other GIS software packages should be explored for future research.

Highly accurate analysis for the quantification of sulphur compounds and oxygenated volatile organic compounds are crucial for the adherence of the legislation in different environmental sectors. The sulphur compounds and oxygenated volatile organic compounds measurements are challenging, due to various factors such as molecules being adsorbed on the inner surfaces of cylinders. It is therefore important to produce accurate and reliable reference gas mixtures with mole fraction at ambient levels for the air quality monitoring and field of gas sensing in South Africa. The challenges in producing sulphur compounds and oxygenated volatile organic compounds reference gas mixtures are that the overall process from gravimetric preparation steps until the comparison analysis process and the stability of mixture in the gas cylinder, results in the large measurement uncertainties. In order to produce reference gas mixtures of the highest level, three important steps are followed: purity assessment of starting material, gravimetric preparation, and verification/validation of prepared gas mixtures. The purity analysis of high purity starting materials was determined using gas chromatography coupled with various detectors and Karl Fischer for determination of moisture content in high purity chemicals. The sulphur compounds and oxygenated volatile organic compounds to be developed in this study were hydrogen sulphide, sulphur dioxide, acetone, methanol, ethanol, isopropanol, and n-butanol. These components were produced following the International Organisation for Standardisation documents at mole fraction of 10 µmol/mol for sulphur compounds and 5 µmol/mol for oxygenated volatile organic compounds. The preparation of sulphur compounds reference gas mixtures was done with a static gravimetric method using a direct method where a target component was transferred directly into the cylinder. The preparation of oxygenated volatile organic compounds used an indirect method whereby a target liquid component from high purity chemicals was transferred into a cylinder using a gas-tight syringe.The comparison between the reference gas mixtures was validated using Non-Dispersive Ultra-Violet analysers (NDUV), gas chromatograph coupled with pulsed discharge helium ionisation detector (GC-PDHID, UV fluorescence analysers for sulphur compounds and gas chromatograph coupled with flame ionisation detector (GC-FID) for the oxygenated volatile organic compounds. A multi-point calibration method was used to analyse sulphur dioxide and hydrogen sulphide on the NDUV analyser, and the single-point calibration method was used for analysis on the gas chromatography and UV fluorescence where a sample mixture is analysed against a reference mixture with a similar mole fraction. The statistical data considered during analysis included calculation of the instrument drift and percentage relative standard deviation to check measurements repeatability, reliability, and measurement uncertainty. The gravimetric results of prepared sulphur compounds at 10 µmol/mol gave a percentage relative expanded uncertainty of 0.041 % REU for hydrogen sulphide, 0.12 % REU for sulphur dioxide. The gravimetric results of prepared oxygenated volatile organic compounds at 5 µmol/mol showed a percentage relative expanded uncertainty 0.068 to 0.35 % REU for isopropanol and ethanol respectively and less than 2.4 % REU for multi component of oxygenated volatile organic compounds. Finally, the primary standard gas mixtures of sulphur compounds and oxygenated volatile organic compounds were developed with the highest metrological measurement uncertainty level of (k=2). / Environmental Sciences / M. Sc. (Environmental Sciences)

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.

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)