Faculty of Science (ETDs)
Permanent URI for this communityhttps://hdl.handle.net/10539/37933
For queries relating to content and technical issues, please contact IR specialists via this email address : openscholarship.library@wits.ac.za, Tel: 011 717 4652 or 011 717 1954
Browse
20 results
Search Results
Item Capability of multi-remote sensing satellite data in detecting and monitoring cyanobacteria and algal blooms in the Vaal dam, South Africa(University of the Witwatersrand, Johannesburg, 2024-03) Obaid, Altayeb Adam Alsafi; Adam, Elhadi M.I.; Ali, Khalid A.Vaal Dam is a large dam in South Africa. It is the primary source of potable water for the metropolitan and industrial areas of Gauteng province and other surrounding areas. The dam's surface area is about 320 km². It’s the second biggest dam in South Africa in terms of surface area, and it drains a catchment area of approximately 38,000 km². The dam's total capacity is about 2.603 × 10⁶ m³ (Haarhoff and Tempelhoff, 2007). The dam catchment area holds various anthropogenic activities, including major agricultural activities, mining, and some industrial activities (Obaid et al., 2023, Du Plessis, 2017), as well as many formal and informal settlements. The dam water is strongly affected by such activities, releasing chemical, physical, and biological contaminants and dissolved urban effluents, most of which enrich the nutrients that reach the dam water in some way. Water resources assessment and monitoring are crucial practices due to their direct contribution to the effective use of such resources. They require precise information about the water quantity and quality. Monitoring of inland water resources has been conducted using in-situ sampling and in-vitro measurement of the water quality constituents. However, these methods have limitations such as high cost, labor-intensive limited spatial and temporal coverage, and time consumption. Over the last few years, remote sensing has been examined for water quality monitoring as a cost- effective system. This research has tested satellite remote sensing to detect some water quality parameters in the Vaal Dam of South Africa. The main objective of this research is to examine the recent generation multispectral satellite sensors, Sentinel-2 MSI, and Landsat-8 OLI data to detect and assess chlorophyll-a and cyanobacteria in the Vaal Dam, South Africa to be used as a cost-effective monitoring tool. To achieve the objective, the research first aimed to understand how the spatial and temporal dynamics of land use, and land cover (LULC) impact algal growth in the dam reservoir. Land use land cover classification was conducted in the catchment area of the Vaal Dam using a pixel-based classification method. Landsat data for the period from 1986 to 2021 were classified using a random forest (RF) classifier in seven-year intervals (1986, 1993, 2000, 2007, 2014, and 2021). Applying the RF classifier revealed that overall classification accuracies (OA) ranged from 87% in the 2014 classified image to 95% in the 2007 image. The change-detection analysis revealed the continuous increase of the settlement class owing to the continuous population growth. A lot of anthropogenic activities associated with population growth have been recognized to release contaminants into the surrounding environment and might end up reaching the water resources causing significant deterioration. As a result, Vaal Dam encounters significant nutrient input from multiple sources within its catchment. This situation raised the frequency of the Harmful Algal Blooms (HABs) within the dam reservoir during recent years. The study also performed a time series analysis for the potential nutrients expected to be the enhancing factors for algal blooms in the Vaal Dam. Using chlorophyll−a (Chl−a) as a proxy of HABs, along with the concentrations of potential nutrients, statistical measures, and water quality data were applied to understand the trend of selected water quality parameters. These parameters were: Chl−a, total phosphorus (TP), nitrate and nitrite nitrogen NO₃NO₂_N), organic nitrogen (KJEL_N), ammonia nitrogen (NH₄_N), dissolved oxygen (DO) and the water temperature. The results reveal that the HAB productivity in the Vaal Dam is influenced by the levels of TP and KJEL_N, which exhibited a significant correlation with Chl−a concentrations. From the Long- term analysis of Chl−a and its driving factors, some very high values of Chl−a concentrations and its driving factors TP and KJEL_N were recorded in erratic individual dates which suggested some nutrients rich in wastes find their way to the dam. Another important notice was that the average Chl-a concentration significantly increased during the period of the study (1986 to 2023) it increased from 4.75 μg/L in the first decade (1990–2000) to 10.51 μg/L in the second decade (2000–2010) and reaching 16.7 μg/L in the last decade (2010–2020). Additionally, Chl−a data extracted from Landsat-8 satellite images was utilized to visualize the spatial distribution of HABs in the reservoir. The satellite data analysis during the last decade revealed that the spatial dynamics of HABs are influenced by the dam’s geometry and the levels of discharge from its two feeding rivers, with higher concentrations observed in meandering areas of the reservoir, and within zones of restricted water circulation. These spatial distribution patterns of HABs are associated with spatial variations of algal species in term of domination through the seasons of the year. The research also examined the utility of remote sensing techniques for mapping algal blooms using the current generation Sentinel-2 and Landsat-8 data. The effectiveness of some band ratio indices in the blue-green and red-near infrared wavelengths was tested. The results suggested that the blue-green band ratio of Landsat-8 [Rrs(560)/Rrs(443)], and red/NIR of Sentinel-2 [Rrs(705)/Rrs(665)] were found to be the best indices for Chl-a retrieval in the Vaal Dam. Results for the Landsat OLI dataset showed R² = 0.89; RMSE = 0.36 μg/L, P < 0.05, and the Sentinel MSI dataset revealed R² = 0.75; RMSE = 0.48 μg/L, P < 0.05 which is a high degree of accuracy. As the potential toxicity comes from the cyanobacterial bloom, the study examines different models to assess and map cyanobacteria concentration in the dam reservoir. Sentinel-2 and in-situ hyperspectral data have been used. None of the Sentinel-2 band ratios showed a significant correlation with the laboratory-measured values of the cyanobacteria. The in-situ measured Hyperspectra showed strong correlations between the band ratios Rrs(705)/Rrs(655) and Rrs(705)/Rrs(620), and the measured cyanobacteria (R² = 0.96 and R² = 0.95 respectively). Chlorophyll−a concentration was retrieved using band ratio indices in the red-NIR region. The strongest correlation was found between the retrieved Chl−a of band ratio Rrs(705)/Rrs(665) and the laboratory-measured Chl−a concentrations for both reflectance datasets. This correlation resulted in an R² value of 0.78 for Sentinel-2 reflectance data and an R² value of 0.93 for in-situ hyperspectral data. A Semi-analytical algorithm for estimating the Chl−a and phycocyanin (PC) pigments has also been examined. The algorithm uses the ratio of the calculated Chl−a absorption at 665 and phycocyanin absorption at 620 nm to their specific absorption coefficients a∗ (655) and a∗ (620) to estimate the concentration of Chl−a and phycocyanin respectively. It resulted in a strong correlation with measured chlorophyll-a, R² = 0.95. The algorithm also strongly correlated with measured cyanobacteria using the absorption to specific absorption ratio at 620 nm (R² = 0.97). However, the estimated values of cyanobacteria using a Semi-analytical algorithm resulted in cyanobacterial concentration values a little bit higher compared to the measured ones, hence, some factors used by the model need to be adjusted to the Vaal Dam site for better estimations. This research revealed that using band ratio indices of Landsat-8 and Sentinel-2 data are valuable tools for mapping chlorophyll-a in the Vaal Dam, a key indicator of phytoplankton biomass. Furthermore, using the semi-analytical algorithm with hyperspectral data is key for estimating the cyanobacteria concentration in the dam water. Models developed in this research will significantly improve near-real-time and long-term chlorophyll-a monitoring of the Vaal Dam. It will effectively help researchers and environmental agencies monitor changes in algal biomass of the dam water to address public health issues related to water quality. It helps to identify areas of high nutrient input and assess the effectiveness of water quality management strategies. It is of prime importance that the developments within the catchment of the Vaal Dam be carefully considered as it is one of the primary sources of dam water. The research recommends implementing the existing regulatory policies for effluent dispersal within the catchment to protect ecosystem functioning and water resources from further deterioration in their quality. It also recommends regular monitoring to detect real-time changes in HABs using satellite remote sensing.Item Evaluating the impact of land use activities in and around Lake Kariba on the presence and levels of anions and cations in the water body(University of the Witwatersrand, Johannesburg, 2024-09) Monyai, Mokgaetji Andelina; Chimuka, Luke; Tutu, Hlanganani; Cukrowska, Ewa; Richards, Heidi L.Huge seas, lakes, and rivers come to mind when we think of surface water. Surface water is vulnerable to water pollution, with consequential repercussions for the well-being of both human and aquatic environments. Furthermore, the diminishing levels of oxygen have a profound effect on the natural ecological equilibrium within river and lake ecosystems. Lake Kariba, situated in the Southern African region, is a vital freshwater ecosystem supporting local communities, wildlife, and regional economies. However, it faces threats from human activities and erratic weather. This study investigated the influence of land use activities in and around Lake Kariba on water composition and the concentration of anions and cations. The research employed a combination of field surveys and laboratory experiments to identify potential sources of ions. Sixty-nine (69) water samples (53 downstream and 16 upstream) were collected during different seasons in October 2021, July 2022 and April 2023. The Ion Chromatography, Inductively Coupled Plasma equipped with Optical Emission and Mass Spectroscopy detectors were used to concentrations of various anions (Fˉ, Clˉ, NO3ˉ, SO4 2ˉ, and PO4 3ˉ) and cations (Ca, K, Mg, Na, Si, Al, Cr, Fe, Mn, As, Cu, Ni, Ti, and Zn) respectively. Acidic water was notably observed upstream in two sampling areas, namely the Malasha and Kanzinze rivers. The Malasha River exhibited pH levels ranging from 3.71 to 4.81, while the Kanzinze River showed a pH of 6.01. The electrical conductivity (EC) for Malasha ranged from 1035 to 1484 µS/cm, whereas for Kanzinze, it measured 878.0 µS/cm. These areas exhibited significantly elevated levels of both anions and cations. In the Kanzinze River, the detected concentrations showed the following descending order: SO4 2ˉ> Clˉ > NO3ˉ> Fˉ> PO4 3ˉ (anions); Ca > Mg > Na > K > Si > Fe > Al > Zn > Cu > Mn > Ni > Cr > Ti > As (cations). Conversely, the Malasha River, exhibited the following order for anions: SO4 2ˉ > Clˉ > NO3 ˉ > Fˉ > PO4 3ˉ, and for cations: Ca > Fe > Mg > Na > Si > K > Al > Mn > Zn > Cr > Cu> Ni > Ti > As. The significant presence of SO4 2- and NO3 - indicates that human activities and agricultural practices in certain areas of Lake Kariba's catchment can have a considerable impact on the lake's water quality. Despite this, the corresponding Water Quality Index (WQI) indicated that the water quality from Kanzinze and Malasha rivers was unsuitable for drinking purposes. The findings revealed variations in ions concentration at different sampling points, with discernible patterns corresponding to specific land use types, such as mining in the upstream that elevated the levels of SO4 2- and some heavy metals and also NO3 - levels in the downstream due to commercial cage fish farming. Statistical analysis showed significant downstream variations (p < 0.05) in water chemistry parameters related to land use, while upstream areas exhibited no significant differences (p > 0.05). Water quality index ranged from 13.1 to 230.0, categorizing water quality from "excellent" to "very poor." The study underscores the complex interplay between land use activities and water chemistry in Lake Kariba, emphasizing downstream impacts. These findings contribute valuable insights for sustainable management and conservation efforts in the region, considering the dynamic nature of the ecosystem and potential threats posed by anthropogenic activities. Continuous monitoring and mitigation strategies are crucial to reserving the ecological balance of Lake Kariba and safeguarding the well-being of its surrounding communities and wildlife.Item Surface water dependencies and activity patterns of mammalian herbivores in South Africa(University of the Witwatersrand, Johannesburg, 2024-10) Padayachy, Janiel; Hetem, Robyn; Strauss, Willem Maartin; Venter, JanAn increase in aridity in Africa may make water availability more variable, thus understanding how animals respond to these conditions is important for future wildlife management and conservation. However, mammalian herbivores with varying water requirements may respond differently to changes in water availability and predation. Using camera trap data, I analysed the spatial distribution relative to surface water sources and the 24-hour activity of 16 mammalian herbivores across 10 sites in South Africa. As expected, water dependent herbivores were generally closer to water, but only at sites where lions were absent. Herbivores with low water requirements were more nocturnal, potentially reducing water required to cool themselves evaporatively when active during the heat of the day. But that nocturnal activity was reduced when lions were present, likely reducing predation risk, increasing water requirements to dissipate heat and forcing herbivores to remain close to water. Nocturnal activity increased with body size in mixed-feeders and grazers, but decreased with body size in browsers, potentially reflecting more time spent foraging by large browsers. Using a novel approach of multistate diel occupancy models I showed that herbivores were generally active during both the day and night, and that the presence of lions impacted occupancy of preferred prey species (blue wildebeest, gemsbok and zebra). Diel occupancy of water-dependent prey (blue wildebeest) was influenced by an interaction between lion presence and distance to water sources. Thus environmental factors as well as physiological and morphological features affected the timing of activity and spatial distribution of several mammalian herbivore species in South African wildlife areas, which should be considered for future research and management of these species.Item Flood Susceptibility Modeling in the uMhlatuzana River Catchment using Computer Vision-Based Deep Learning Techniques(University of the Witwatersrand, Johannesburg, 2024-10) Chirindza, Jonas; Ajoodha, Ritesh; Knight, JasperIn this study, covolutional neural networks (CNN) models are employed for flood susceptibility modeling in the uMhalatuzana River catchment in KwaZulu-Natal, South Africa. The CNN models, including 1D-CNN, 2D-CNN, and 3D-CNN, pro-vide a detailed assessment of flood vulnerability in the region. The models use di- verse spatial information, such as topography, land use, and hydrological features, to estimate the likelihood of flooding in different areas of the catchment. The flood susceptibility maps within the uMhalatuzana River catchment, classified into five risk zones namely, ‘very low’, ‘low’, ‘moderate’, ‘high’ and ‘very high’ susceptibility zone, serve as proactive instruments for risk mitigation and disaster management. The 1D-CNN model displays strong overall performance in flood susceptibility modeling, evident in key metrics such as accuracy, precision, recall, area under curve (AUC) score, and F1-score. The results suggest that the model effectively captures patterns in the input data, emphasizing its potential for flood susceptibility modeling. Moreover, the 2D-CNN model outperforms the 1D-CNN, achieving higher values when evaluated using various performance metrics. Finally, the 3D-CNN model outperformed both the 1D-CNN and 2D-CNN, emphasizing its predictive abilities in flood susceptibility modelling. The flood susceptibility maps produced by the 1D-CNN model, shows that most of the study area exhibits very low flood susceptibility (96.4%), with localized areas of higher susceptibility, particularly in the very high-risk category (2.53%). The 2D CNN model demonstrates a more diverse risk distribution, with a substantial portion having very low susceptibility (74.19%) and significant areas of higher risk, notably in the very high-risk category (10.93%). The 3D-CNN model emphasizes a spatial pattern where a large portion has very low susceptibility (84.10%), but with a concentration of high and very high-risk areas, comprising 12.34% of the total area. Finally, the consistent identification of higher risk susceptibility areas enhances the robustness of the assessments. The models’ high accuracy and detailed risk assessments provide valuable tools for decision-makers, urban planners, and emergency response teams in the uMhalatuzana River catchment. The precision of the models facilitates informed strategies for flood risk management, including targeted interventions such as improved drainage systems and early warning systems.Item Nutrient and Salinity Loading Based On The Temporal And Spatial Water Quality Data In The Upper Crocodile River Basin(University of the Witwatersrand, Johannesburg, 2024-03) Mistry, Nikhil Jayant; Ali, K. Adam; Abiye, TamiruThe Upper Crocodile River Basin has undergone a drastic change through anthropogenic factors such as rapid urban growth, industrial activities, agriculture and mining in the past thirty-eight years. This has led to an increase in nutrient and salinity loads with decreasing water quality. The Upper Crocodile River Basin wastewater treatment works struggle to maintain loading rates, causing partially treated wastewater to enter the river systems that increased the salinity loads. Water chemistry and discharge data from the DWS were collected, cleaned and processed; data were summated across the necessary river channels in which they are located to determine the nutrient and salinity loads in all rivers in the Upper Crocodile River Basin. The results indicated that the Hennops, Jukskei and Crocodile Rivers are responsible for the largest nutrient and salinity loading rates. Changes in land use activities and climate over the past thirty-eight years, since 1980, have drastically impacted the rate at which nutrient and salinity loads enter into the UCRB. During the early 1980s to 1990s a significant drop was observed in nutrient and salinity loading rates, spiking in the late 1990s and early 2000s, influenced by changes in water management and climatic events like the La Niña and the El Niño phenomena. The inter-basin transfer in the early 2000s and subsequent two decades have led to an overall rise in nutrient and salinity loading rates, posing serious water quality and health risks to people in the UCRB area. Mining activities, poor landfill management and leaking tailing storage facilities have resulted in increased sulphate loading rates into the UCRB. Nitrogen loading has risen due to uncontrolled waste disposal from informal settlements, industrial activities and sewage spills in the Johannesburg region. Phosphorus loading rates have risen due to agricultural fertiliser runoff, with the Jukskei River being the largest contributor to these loads in the Upper Crocodile River Basin. The loads entering the Hartbeespoort dam during summer and winter seasons in the 2016-2018 period for sulphate is 6819.24 kg/hr, 4873.62kg/hr; for nitrogen 4179.24 kg/hr, 4021.55 kg/hr and for phosphorus 40.08 kg/hr, 34.724 kg/hr, respectively. Salinity loads entering the Hartbeespoort dam during summer and winter are 42952.87 kg/hr and 27548.39 kg/hr, respectively. According to the findings, water resource management must act quickly to improve the overall quality of the water; in the upcoming ten years, as loading rates are expected to rise exponentially as a result of increased demand and stressed water use, which will lead to poor water quality. This will pose serious health and economic risks to the people of the Upper Crocodile River Basin and the populace of South Africa.Item Baseline Hydrogeology of Dolerite Dykes in Lesotho, Mafeteng District as a Case Study(University of the Witwatersrand, Johannesburg, 2024-06-10) Monyane, Napo Shadrack; Shakhane, Teboho; Abiye, TamiruGroundwater is a vital alternative resource due to the increasing demand for water supply in Lesotho’s rural and urban areas as surface water faces threats from population growth and climate variability. For instance, groundwater serves the demands of Lesotho's growing textile industry and agricultural sectors. The 3D form of the dolerite dykes widespread throughout the Karoo rocks in Lesotho may have an impact on the groundwater occurrence, flow, and yield characteristics of the region. However, specific research on their hydrogeology has not been extensively undertaken. This study aimed at characterising the hydrogeology of the dolerite dykes in the lowlands of Lesotho using selected places namely, Boluma-Tau, Ha Lumisi, Ha Mofota, Ha Maoela, Ha Mofo, Malumeng, Qalabane, Matlapaneng, Thabana Mohlomi, Ha Mohlehli, Malimong, Tsoeneng and Ha Lenonyane as case studies for the research. This research adopted the desktop and walkover survey in developing information on the region’s broad geological and hydrogeological setting within the Karoo lithologies. Included also was the use of ground magnetics in ascertaining the existence and determining the geometry of the dykes, using the D-8 algorithm for flow directions, and drilling and pumping tests for aquifer analysis. The dykes dominating the focal area in the Mafeteng District generally trend NE-SW, NNE-SSW and NW-SE. The magnetic results outlined negative anomalies along the dyke’s contact with the country rocks as surveyed from Qalabane, Mafeteng Lesotho, these magnetic lows imply fracture gaps along the strike of the dyke. A generalised dip, width, and depth could not be easily inferred due to inconsistent magnetic anomaly shapes, but forward modelling indicated a thin (10 m wide) shallow (10°) dyke trending NE-SW intruding both the Burgersdorp and Molteno Formations at Qalabane, Mafeteng. As per the D-8 results, the dolerite dykes in Mafeteng are distributed in the intermediate basin flow values due to a gentle hydraulic gradient. Certain dyke sites exhibit a groundwater flow direction towards the north, whilst others display a radial groundwater flow direction. The derivative analysis revealed the boreholes were drilled in a fractured dyke system, also the dominant radial flow regime and double porosity dip at different pumping durations, and the possible recharge boundary were revealed in some drilled dyke sites. Further analysis from the drawdown versus time curves resulted in average yields of 0.1 – 1.25 l/s with transmissivities ranging from 1 – 14 m2/day, insinuating that a limited extraction of the local water supply is suggested from the dolerite dyke lithologies in the lowlands of Mafeteng Lesotho, given the groundwater yielding capacity and magnitude of the transmissivities. Even though the drilled boreholes from the dyke sites indicated a fractured dyke setting, estimated transmissivity values are variable and low, this is indicative of the inconsistent apertures and lack of interconnectivity of the available secondary hydrogeological features in the lowlands of Lesotho.Item Spatiotemporal characteristics of surface water in Sua Pan, Botswana, using Earth Observation data: 1992–2022(University of the Witwatersrand, Johannesburg, 2024-10) Peplouw, Muchelene Tiara; Adam, Elhadi; Grab, StefanSurface water is a critical resource for sustaining both human and ecological health. However, climate change and human actions threaten its availability in semi-arid regions like Botswana. In addition, current research on monitoring and understanding surface water dynamics in Botswana lacks the application of remote sensing and machine learning. This highlights a crucial gap in knowledge that this study aims to address. This study investigates the spatiotemporal dynamics of land use/land cover (LULC) and surface water extent changes in Sua Pan, Botswana, from 1992 to 2022. Employing remote sensing, machine learning, and statistical techniques, the research offers valuable insights into the intricate relationships between land cover modifications, surface water variations, and climatic variables. Google Earth Engine (GEE) facilitated efficient analysis of Landsat imagery for LULC mapping. Random Forest (RF) effectively classified several land cover types within Sua Pan. To address the challenges of saline environments, a novel water index, the Saline Water Index (SWI), was developed specifically for Sua Pan. The McNemar statistical test compared the performance of SWI to established indices like the Modified Normalised Difference Water Index (MNDWI) and the Normalised Difference Salinity Index (NDSI). Surface water variations were analysed using homogeneity tests and the Mann-Kendall trend test. The relationships between hydro climatic data (rainfall, evapotranspiration, land surface temperature) retrieved from GEE and surface water area for both wet and dry seasons were evaluated using Pearson correlation coefficients and visualised by line and area graphs. Additionally, the influence of the El Niño Southern Oscillation (ENSO) on rainfall and surface water area was assessed using Analysis of Variance (ANOVA) to identify the specific ENSO phases that exert an influence. The findings demonstrate the effectiveness of GEE for LULC mapping with the RF algorithm, achieving moderate to high classification accuracy (65.2% - 90.69%) and Kappa coefficients (0.54 - 0.85). Surface water and bare area exhibited increasing trends (coefficients: 13.017 and 9.0609, respectively), whereas vegetation and salt hard pan showed decreasing trends (-16.786 and -5.3081, respectively). The newly developed SWI outperformed MNDWI and NDSI in detecting surface water, achieving the highest overall accuracy (94%) compared to MNDWI (64%) and NDSI (59%). The McNemar test confirmed no significant statistical difference between the SWI map and the validation dataset (p = 0.2673), while both MNDWI and NDSI maps showed significant differences (p < 0.0001). Utilising SWI, the study revealed that surface water was most prevalent in central and northeastern regions, with an average coverage of 33%. Seasonal homogeneity tests indicated a non-homogenous distribution of surface water area in wet seasons, with abrupt changes in 1994 and 2003. Conversely, dry seasons exhibited a homogenous distribution. The Mann-Kendall trend test identified a statistically significant (p-value = 0.01) but weak positive trend (tau = 0.329) for surface water areas in wet seasons. In contrast, the dry seasons displayed a non-significant (p-value = 0.734) and a very weak positive trend (tau = 0.043). Surface water area, rainfall, evapotranspiration, and temperature consistently increase during the wet seasons compared to the dry seasons. Notably, increased evapotranspiration significantly impacted surface water presence. ENSO exhibited no significant influence on either rainfall or surface water extent (p-value > 0.05 for both). These findings highlight the potential of earth observation data for real-time surface water monitoring in salt pans. The developed techniques offer valuable insights for policy decisions regarding environmental management and conservation efforts in Sua Pan. In addition, the study emphasises the importance of cost-effective approaches for water change assessment, particularly appropriate for under-resourced regions.Item Synthesis and characterization of onion-like carbons for adsorption of tartrazine dye in water(University of the Witwatersrand, Johannesburg, 2024-08) Cwayi, Herbert Qaqambile; Maubane-Nkadimeng, Manoko S.; Coville, Neil J.; Maboya, Winny K.Industrial effluent often can contain a significant amount of synthetic dyes. The discharge of wastewater containing dyes into water streams without proper treatment consequently enters the soil and disturbs the aquatic and terrestrial life. Several wastewater treatment technologies have been proposed that can efficiently reduce the amount of synthetic dyes from the environment, in particular azo dyes. Among all the existing technologies for wastewater treatment, physical adsorption is a popular technology because it is inexpensive, simple, and efficient. The aim of this study is to synthesize, modify, and characterize onion-like carbons (OLCs) derived from four different waste oils for the adsorption of tartrazine dye in water. The OLCs derived from different carbon precursors (waste household oil, restaurant waste oil, engine waste oil, and paraffin oil bath waste) were synthesized using a flame pyrolysis method. The synthesized materials were doped with nitrogen using a chemical vapor deposition technique using 10% ammonia gas as a source of nitrogen. The N-doped OLCs were attached with hydroxyl groups through oxidation reactions to improve their solubility and adsorption efficacy. According to the high-resolution transmission electron microscopy and scanning electron microscopy images, the OLCs from all four-carbon precursor were quasi-spherical, agglomerated, and presented a chain-like structures of multi-layers. The distance between the graphitic layers was found to be 0.32 nm. The average particle size of OLCs was calculated to be 40.2 ± 2.5 nm. Adsorption studies revealed that the initial dye concentration, contact time, and pH of the dye solutions influenced the adsorption capacity of the tetrazine. Nitrogen doping of OLCs increased its capacity to adsorb the tartrazine dye. The nitrogen doped OLCs from household waste oil (H-N-OLCs) and engine waste oil (E-N- OLCs) were used in equilibrium adsorption studies in this work. For a concentration of 20 mg/L of tartrazine dye, an adsorption capacity of 28.9 mg/g was achieved using the N- doped OLCs from household waste oil. The adsorption process follows the pseudo second- order kinetic model. The adsorption isotherm is best fitted to the Freundlich mathematical model. The results obtained show that, the source of oil did not have major effect on the physicochemical properties of OLCs and that incorporation of nitrogen onto carbon matrix enhanced the adsorption of the anionic tartrazine dye in aqueous solution.Item Assessing and comparing the performance of different machine learning regression algorithms in predicting Chlorophyll-a concentration in the Vaal Dam, Gauteng(University of the Witwatersrand, Johannesburg, 2024-03) Mahamuza, Phemelo Hope; Adam, ElhadiThe state of Vaal Dam is influenced by various land uses surrounding the Dam, including agricultural activities, mining operations, industrial enterprises, urban settlements, and nature reserves. Mining activities, farming practices, and sewage outflows from nearby villages led to access contamination within the Dam, increasing algal bloom levels. Sentinel-2 MSI data were utilized to forecast and comprehend the spatial pattern of Chlorophyll-a concentration, indicating algal bloom occurrence in the Vaal Dam. Targeting Sentinel-2 Level-1C, the image was preprocessed on the Google Earth Engine (GEE) with acquisition dates from 25 – 26 October 30, 2016, corresponding to the on-site data collection between October 26 and October 28, 2016. Due to limited resources, up-to-date data on the Vaal Dam could not be collected. However, since this study focuses on applying various machine learning regression models to predict chlorophyll-a levels in waterbodies, the dataset is used to test the models rather than reflect the current state of the Vaal Dam. The dataset, comprising 23 samples, was divided into 70% training and 30% test sets, allowing for comprehensive model evaluation. Band ratio reflectance values were extracted from the satellite image and correlated with in-field Chlorophyll-a values. The highest correlation coefficient values were utilized to train five machine-learning models employed in this study: Random Forest (RF), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression, and Multilinear Regression (MLR). Each model underwent training with ten iterations each; the best learning iteration was then used to generate the final Chlorophyll-a predictive model. The predictive models were validated using the Sentinel-2 MSI satellite data and in-situ measurements using R2, RMSE, and MAPE. Among the five machine learning algorithms trained, RF performed the best, with an R2 of 0.86 and 0.95, an RMSE of 1.38 and 0.8, and MAPE of 15.09% and 10.92% for the training and testing sets, respectively, indicating its ability to handle small, non-linear datasets. SVR also demonstrated a fair performance, particularly in handling multicollinearity in the data points with an R2 of 0.68 and 0.87, an RMSE of 2.37 and 1.56, and MAPE of 18.13% and 19.28% for the training and testing sets, respectively. The spatial pattern of Chlorophyll-a concentrations, mapped from the RF model, indicated that high concentrations of Chlorophyll-a are along the Dam shorelines, suggesting a significant impact of land use activities on pollution levels. This study emphasizes the importance of selecting suitable machine learning algorithms tailored to the dataset's characteristics. RF and SVR demonstrated proficiency in handling nonlinearity, with RF displaying enhanced generalization and resistance to overfitting. Limited field data evenly distributed across the Dam and satellite overpass dates may affect result accuracy. Future research should align satellite pass dates with fieldwork dates and ensure an even distribution of in-field samples across the Dam to represent all land uses and concentration levels.Item The Effects of Land Use Change on Water Quality in the upper Berg - and Breede River catchments, Western Cape, South Africa(University of the Witwatersrand, Johannesburg, 2024-08) van Wyngaard, Zahn; Sheridan, CraigPollution of surface water resources is gaining global attention due to increasing freshwater stress and scarcity. This study assessed how land use changes in the upper catchments of the Berg and Breede rivers affect water quality. Land Cover Data, covering a 22-year period, was prepared, categorised, and analysed. Land use classes include “natural”, “urban”, “agricultural”, “water bodies”, “mining” as well as “degraded land, bare rock, and soil”. Changes of these land use classes were analysed to establish their influence on water quality parameters such as electrical conductivity, pH, total nitrogen including ammonium, nitrate and nitrite, orthophosphate, and sulfate. In the Berg River catchment, urban, natural, water bodies and degraded land, bare rock, and soil increased while agricultural and mining decreased. In the Breede River catchment, urban, water bodies and degraded land, bare rock, and soil as well as mining increased while agricultural and natural decreased. In the Berg River catchment, Dissolved Inorganic Nitrogen (ammonium, nitrate and nitrite), as well as pH increased while electrical conductivity, sulfate, and orthophosphate decreased. In the Breede River catchment, ammonium and orthophosphate increased while a decrease in electrical conductivity, nitrate and nitrite, pH, and sulfate was noted. In the Berg River catchment, the following correlations, or relationships, were noted. Urban land was correlated with ammonium and sulfate; agricultural land was correlated with electrical conductivity and sulfate, natural land cover was correlated with electrical conductivity, orthophosphate, and sulfate. Water bodies were correlated with orthophosphate, sulfate, degraded land, bare rock, and soil was correlated with ammonium and mining was correlated with electrical conductivity, orthophosphate, and sulfate. In the Breede River catchment, urban land was correlated with ammonium and orthophosphate, agricultural land was correlated with nitrate and nitrite and pH, and natural land cover was correlated with electrical conductivity, ammonium, and sulfate. Water bodies were correlated with electrical conductivity, nitrate and nitrite, and sulfate, degraded land, bare rock, and soil were correlated with electrical conductivity, ammonium, orthophosphate, and sulfate, and mining was correlated with electrical conductivity, ammonium and sulfate. The study therefore recommends that we mitigate land use change impacts on water quality by enforcing strict land-use regulations, promote sustainable agricultural practices, protect riparian areas and wetlands, implement better stormwater and wastewater management, educate the public, and coordinate integrated water resource management efforts to reduce pollution of scarce surface water resources.