A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science June 2015 / Lephalale is the home of Matimba, one of Eskom’s coal-fired power stations. Matimba is the biggest power station with a dry cooling system in the world. There are other industries (including coal mines) currently in operation in close proximity to the station. This industrial area is expected to grow as more industrial activities are planned for the following years. These activities will aggravate the levels of air pollution and possibly result in it being a “hot spot” for air pollution. The impact of air quality on health is covered by the National Ambient Air Quality Standards (NAAQS), but the impact of air quality on the terrestrial and aquatic ecosystem is not known. Therefore, this study focuses on the deposition of nitrogen oxides (NOx) (as nitrogen dioxide (NO2)) and sulphur dioxide (SO2) within Lephalale in the Waterberg-Bojanala Priority Area. Additionally, inter-annual variability of NOx and SO2 ambient concentrations and back trajectories of air masses were analysed. The study obtained ambient air quality data and meteorological data from Eskom for the period 2008–2012, while additional meteorological data were obtained from the Agricultural Research Council (ARC) and the South African Weather Service (SAWS). An inferential model was used to estimate the dry deposition flux of SO2 and NOx (as NO2), and the Hybrid Single Particle Langrangian Integrated Trajectory (Hysplit) Model was used to cluster back trajectories of air masses. The results of the seasonal dry deposition velocities of SO2 (0.17 to 0.23 cm/s) and NOx (0.10 to 0.15 cm/s) (as NO2) were higher in summer and lower in winter. They were also within the magnitude of the deposition velocities found in previous studies in the Highveld. The high deposition velocities in summer were attributed to photosynthetically active vegetation, turbulence and solar radiation. However, seasonal dry deposition fluxes of SO2 and NOx were higher in winter across the years. The higher flux values in winter were attributed to higher ambient concentrations of the trace gases. Additionally, the annual dry deposition flux of SO2 ranged between 0.43 and 0.67 kg S ha-1 yr-1, while NOx (as NO2) ranged between 0.84 and 1.05 kg N ha-1 yr-1 over the period studied. The annual deposition flux values found in the current study are lower than those found in previous studies in the Highveld. This difference could be because of the lower ambient concentrations of SO2 and NOx observed in this study. There is an inter-annual variability of the ambient concentrations of SO2 and NOx during the period 2008–2012. However, the difference is not large or statistically significant. The dominant direction of the back trajectories of air masses is east and southeast across all seasons for the entire period of 2008–2012. This lack of seasonal pattern in back trajectories and source regions cannot explain the seasonal changes in ambient concentrations (SO2 and NOx). Hence, climatic factors (e.g. change in weather) or seasonal changes in combustion source intensity must be responsible.

Thesis (MTech. degree in Environmental Health)--Tshwane University of Technology, 2012. / Fall out dust sampling and monitoring is becoming one of the preferred methods to determine dust pollution impact from industries/or mines on the receiving environment. Fall out dust monitoring is a useful and cost effective method of providing trend analysis of dust deposition over a period of time. It also provides an indication of the main areas of dust generation and can be conducted for both health and nuisance purposes.The aim of the study was to develop a positioning guideline for fall out dust monitoring equipment in order to optimise the existing fall out dust programme.

M.Sc. / Please refer to full text to view abstract

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.

Thesis (MTech (Environmental Health))--Cape Peninsula University of Technology, 2019. / Background: A recent review on the effects of ambient air pollution on human health in sub-Saharan Africa, specifically calls for an urgent need for more epidemiological studies in developing countries due to a lack of data in these countries. Air pollution information on exposure is important for understanding and addressing its public health impact in developing countries. In many African countries, the spatial distribution of air pollutants has not been quantified even though air pollution is a global public health risk. The main goal of the study was to quantify and compare the seasonal spatial variation of household air pollution in the 4 Western Cape neighbourhoods. Methods: Weekly indoor and outdoor measurements of Particulate Matter (PM2.5), Sulphur dioxide (SO2), Ozone (O3), Carbon monoxide (CO) and Nitrogen dioxide (NO2) were conducted at 127 households in four informal settlement areas (Khayelitsha, Marconi-Beam, Masiphumulele and Oudtshoorn) during one month each in summer and winter. PM2.5 measurements were conducted using Mesa Labs GK2.05 (KTL) cyclone with the GilAir Plus Air Sampling Pump, Gases were measured using Passam passive samplers. Statistical analyses were performed using Stata V12. Simple linear regression was used to evaluate the relationship between continuous exposure levels and the respective predictor variables. These include distance to major roads, bus routes, open grills and waste burning sites. Results: The highest average weekly outdoor PM2.5 and NO2 concentrations for summer were recorded in Milnerton (8.76 µg/m3 and 16.32 µg/m3 respectively). However, the highest average concentrations during winter for PM2.5 were recorded in Oudtshoorn (PM2.5: 16.07 µg/m3), whilst the highest NO2, was recorded in Khayelitsha (NO2: 35.69 µg/m3). SO2 levels were consistently low during both seasons. Noordhoek generally recorded the lowest average levels for all pollutants. Winter average weekly concentrations were generally higher than the levels recorded in summer for all pollutants. In a sub-sample of indoor and outdoor measurements, the results were comparable for PM2.5, NO2 and CO. However, the results of Ozone (O3) showed relatively higher (~10 times) outdoor compared to indoor levels. Linear regression modelling results revealed that significant predictors of elevated exposure to PM2.5 were proximity to construction activities and open grills. Analysis demonstrated a clear dose-response relationship with distance, with open grills within 1000m associated with a 0.33 µg/m3 increase in PM2.5 to 6.77 µg/m3 at a distance of 25 meters. Results from the linear regression modelling revealed that significant predictors of exposure to NO2 were proximity to rapid transport bus stops, bus routes, taxi routes and major routes. Distance to rapid transport bus stops demonstrated an increase in NO2 between 0.09 µg/m3 (at 1km) to 2.16 µg/m3 (at 50m) during summer. A similar pattern was observed for taxi routes and bus routes displaying an increase of 6.26 μg/m3and 6.82 μg/m3 respectively within the proximity of 1000 meters. / MAUERBERGER Foundation Scholarship

The deteriorating air quality in Cape Town (CT) is a threat to the social and economic development of the city. Although previous studies have shown that most of the pollutants are emitted in the city, it is not clear how the transport of pollutants from neighbouring cities may contribute to the pollution. This thesis studies the transport of atmospheric nitrogen oxides (NOx) and nitric acid (HNO3) pollutants over South Africa and examines the role of pollutant transport from the Mpumalanga Highveld on pollution in CT. The study analysed observation data (2001 - 2008) from the CT air quality network and from regional climate model simulation (2001 - 2004) over South Africa. The model simulations account for the influences of complex topography, atmospheric conditions, and atmospheric chemistry on transport of the pollutants over South Africa. Flux budget analysis was used to examine whether the city is a net source or sink for NOx and HNO3. The results show that north-easterly flow transports pollutants (NOx and HNO3) at low level (i.e., surface to 850 hPa) from the Mpumalanga Highveld towards CT. In April, a tongue of high concentration of HNO3 extends from the Mpumalanga Highveld to CT, along the southern coast. The flux budget analysis shows that CT can be a net sink for NOx and HNO3 during extreme pollution events. The study infers that, apart from the local emission of the pollutants in CT, the accumulation of pollutants transported from other areas may contribute to pollution in the city. / Environmental Sciences / M. Sc. (Environmental Science)

The deteriorating air quality in Cape Town (CT) is a threat to the social and economic development of the city. Although previous studies have shown that most of the pollutants are emitted in the city, it is not clear how the transport of pollutants from neighbouring cities may contribute to the pollution. This thesis studies the transport of atmospheric nitrogen oxides (NOx) and nitric acid (HNO3) pollutants over South Africa and examines the role of pollutant transport from the Mpumalanga Highveld on pollution in CT. The study analysed observation data (2001 - 2008) from the CT air quality network and from regional climate model simulation (2001 - 2004) over South Africa. The model simulations account for the influences of complex topography, atmospheric conditions, and atmospheric chemistry on transport of the pollutants over South Africa. Flux budget analysis was used to examine whether the city is a net source or sink for NOx and HNO3. The results show that north-easterly flow transports pollutants (NOx and HNO3) at low level (i.e., surface to 850 hPa) from the Mpumalanga Highveld towards CT. In April, a tongue of high concentration of HNO3 extends from the Mpumalanga Highveld to CT, along the southern coast. The flux budget analysis shows that CT can be a net sink for NOx and HNO3 during extreme pollution events. The study infers that, apart from the local emission of the pollutants in CT, the accumulation of pollutants transported from other areas may contribute to pollution in the city. / Environmental Sciences / M. Sc. (Environmental Science)

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)