School of Geography, Archaeology and Environmental Studies (ETDs)
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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 GIS-Based Location-Allocation Modelling of School Accessibility in the Bojanala Platinum District Municipality, South Africa(University of the Witwatersrand, Johannesburg, 2024-09) Molefe, Kebarileng Christinah; Atif, IqraSchool accessibility modelling performs a crucial part in guaranteeing that educational institutions are physically and practically reachable by every student, irrespective of their abilities, disabilities, or socioeconomic status. Neglecting school accessibility perpetuates inequality, reinforces negative stereotypes, and isolates affected students. Therefore, the principal goal of this research was to evaluate the distribution of schools across the Bojanala Platinum District Municipality, focusing on their accessibility to local communities. The study employed an integrated approach, combining geostatistical techniques, location-allocation modelling, and multicriteria decision analysis. By considering both quantitative data and spatial relationships, these methodologies contributed to robust decision-making and informed policy recommendations. The study utilized population data and school-related information sourced from the Department of Education and the HUMDATA websites, both dated to the year 2020. The study examined the distribution of schools in the Bojanala Platinum District Municipality. It was discovered that the schools were clustered, with a concentration in the Rustenburg local municipality, followed by Madibeng. However, a significant inequality in school access was evident. Secondary school students faced the greatest vulnerability, as most accessible schools primarily served primary students. To address this, potential school sites were proposed across the district. The study emphasizes the need for effective interventions by educational administrators and policymakers to eliminate this inequality. This study recommends the establishment of new schools in underserved regions, strategically enhance existing schools, and maximize school accessibility for all residents. Adequate school provision promotes equity, reduces travel burdens, and strengthens community bonds.Item Remote sensing-based assessment of mangrove forest changes and related regulatory frameworks for the sustainability and conservation of coastal ecosystems in Zanzibar Island, Tanzania-East Africa(University of the Witwatersrand, Johannesburg, 2024-10) Mohamed, Mohamed Khalfan; Adam, ElhadiMangroves are vital components of the world's coastal ecosystems, yet they face significant threats from storm surges, tidal waves, commercial aquaculture, and expanding human settlements. These challenges have heightened the need for accurate mangrove maps to gauge ecosystem degradation. However, mapping mangroves at species and community levels is challenging due to the inaccessibility of these environments. Remote sensing offers an efficient alternative to conventional field-based methods by enabling data collection in these challenging ecosystems. This study aimed to apply remote sensing techniques to map mangrove forest changes and species in two protected bays in Zanzibar, Tanzania. The thesis focuses on four key areas. First, it examines the history of mangrove management in Zanzibar, from colonial times (1890) to the present, highlighting policies, laws, and community involvement in conservation. The colonial authority implemented several land administration laws and regulations to protect mangrove forests. However, mangrove forests suffered significant degradation from 1930 to the end of World War II. The post-independence policy framework established the legal foundation for the introduction of community involvement in mangrove conservation. The legal foundation for introducing community participation in mangrove protection was established by post-independence policy structures such as the National Forest Conservation and Management Act of 1996. Nevertheless, sustainable mangrove use remains inadequate. Second, the study compared community perceptions of mangrove ecosystem services using chi-squared tests and one-way ANOVA. Household surveys showed that provisioning services (PS) were the most identified (84%). Supporting (SS), regulating (RS), and cultural services (CS) were rated by 46.2%, 45.4%, and 21.0%, respectively. Statistical analyses indicated significant differences in the awareness of RS (χ2 = 6.061, p = 0.014) and SS (χ2 = 6.006, p = 0.014) between Chwaka, Charawe, Ukongoroni, Unguja Ukuu, and Uzi wards. There were no significant differences in the identification of PS (χ2 = 1.510, p = 0.919) and CS (χ2 = 1.601, p = 0.901). The study found that residents’ occupations did not determine their reliance on mangrove ecosystem services (χ2 = 8.015; p = 0.1554). Third, changes in mangrove cover in Menai Bay and Chwaka Bay between 1973 and 2020 were analyzed using Landsat data. TerrSet geospatial software was used to classify land cover. The SEGMENTATION module grouped pixels based on spectral similarity, and the images segments were transformed into training sites and signature classes using the SEGTRAIN module. Finally, the segments were classified with the SEGCLASS module into a pixel-based land cover map. Separation of land cover classes was determined using the Jeffries–Matusita (J-M) distance and the transformed divergence (TD) index. For Chwaka Bay, overall classification accuracy ranged from 82.5% to 92.7%, while for Menai Bay, it ranged between 85.5% and 94.5%. Producer and user accuracies ranged from 72% to 100%, with kappa coefficients (κ) between 0.72 and 0.90. Menai Bay experienced a 6.8 ha yearly decline in mangrove cover between 1973 and 2020, while Chwaka Bay saw a 48.5 ha annual decrease. Fourth, the study aimed to map mangrove species in Menai Bay using metrics extracted from the Landsat 9 OLI-2 dataset, i.e., vegetation indices (VIs) and gray-level co-occurrence matrices (GLCMs). A critical step in this study was identifying the contribution of vegetation indices and texture features to classifying mangroves. Training data from very high-resolution (VHR) unmanned aerial vehicle (UAV) data covering parts of the study area helped identify five major mangrove species, i.e., Rhizophora mucronata, Ceriops tagal, Sonneratia alba, Avicennia marina, and Bruguira gymnorrhiza. Results showed that textural features attained overall classification accuracy of 68.29% (kappa = 0.62) and 67.07% (kappa = 0.60) for random forest (RF) and support vector machine (SVM), respectively. Vegetation indices (VIs) recorded overall accuracy of 72.64% (kappa = 0.67) and 67.78% (kappa = 0.61) for RF and SVM. Overall, this study demonstrates the potential of remote sensing technologies for mapping mangrove forest changes and species in challenging environments like Zanzibar’s protected bays. By integrating historical policy analysis with modern geospatial techniques, the research highlights the significant role of both legal frameworks and community involvement in mangrove conservation. The community surveys underscore the varying perceptions of mangrove ecosystem services across different wards, with provisioning services being the most recognized. These findings underscore the importance of advancing remote sensing applications and refining conservation strategies to ensure the sustainability of mangrove ecosystems. Additionally, the analysis of long-term changes in mangrove cover from 1973 to 2020 reveals a concerning decline, particularly in Chwaka Bay. Lastly, the study’s classification of mangrove species using Landsat 9 OLI-2 data, vegetation indices, and texture metrics achieved notable accuracy, emphasizing the value of remote sensing in distinguishing species-level characteristics.Item Integrating Sentinel-1/2 and machine learning models for mapping fruit tree species in heterogeneous landscapes of Limpopo(University of the Witwatersrand, Johannesburg, 2024-10) Chabalala, Yingisani Winny; Adam, ElhadiFrom ancient times to this century, Africa has relied chiefly on agriculture for survival. Crop type maps are crucial for agricultural management, sustainable farming systems, and realizing food security. Agronomists, agricultural extension officers, policymakers, and the government rely on crop type spatial distribution information to make informed decisions and optimize resource allocation for sustainable agricultural management. Attaining food security for all is an urgent need in Africa. However, the farming landscapes predominately comprise fragmented smallholder heterogeneous farms. The farming systems include intercropping and cultivating different crops that require different management strategies. This results in within-class spectral similarities and intra-spectral variability due to similar canopy structures and different phenologies, which complicates the application of remote sensing in crop type mapping. The free availability of Copernicus products such as Sentinel 1 and 2 have high temporal, spectral, and spatial resolution suitable for mapping smallholder agriculture. Thus, this research aimed to integrate Sentinel-1/2 and machine learning models for mapping fruit tree species in heterogeneous landscapes of Limpopo. First, the research tested the applicability of sampling techniques and five mapping classifiers (i.e., Random Forest (RF), Support vector Machine (SVM), Adaptive Boosting (AdaBoost), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGBoost) in mapping fruit trees and co-existing land use types. The original dataset was under-sampled randomly into two balanced datasets (i.e., Dataset 1 and Dataset 2) consisting of 100 and 150 sample points. Furthermore, the imbalanced ratio from the original dataset was reduced by applying different sampling strategies to extract four imbalanced datasets (i.e., at 40%, 50%, 60%, and 70%), which resulted in the formation of Dataset 3, Dataset 4, and Dataset 5, respectively. These samples, together with the original dataset (i.e., Dataset 7), were used as input to Sentinel‑2 (S2) data using adaptive boosting (AdaBoost), gradient boosting (GB), random forest (RF), support vector machine (SVM), and eXtreme gradient boost (XGBoost) machine learning algorithms. The results showed that reducing the amount of imbalanced ratio by randomly under-sampling the original imbalanced dataset could increase the classification accuracy to 71% using the SVM classifier and 60% of the original dataset. Individually, the majority of the crop types were classified with an F1 score of between 60% and 100%. Secondly, the research independently assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for fruit tree mapping using random forest (RF) and support vector machine (SVM) classifiers. Four models were tested using each sensor independently and fusing both sensors. From the fused model, features were ranked using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. The best fruit tree map with an overall accuracy (OA) of 0.91.6% with a kappa coefficient of 0.91% was produced using the RF MDA and FVS model and SVM classifier. The application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; A = 87.01%, Kappa coefficient = 87%, respectively. The green (B3), SWIR_2 (B10), and vertical horizontal (VH) polarization bands were identified as the optimal spectral features for S2 and S1 data, respectively. The third part of the research identified the optimal growth window period in which fruit trees can be detected with high accuracy. Phenological metrics were extracted from 12 months (i.e., January to December) of Sentinel-2 (S2) data and were used to classify fruit trees using a random forest (RF) classifier in a Google Earth Engine environment. The results showed that fruit trees can be detected and mapped with high accuracy during winter months (i.e., April-July) with an overall accuracy (OA) of 84.89% and a kappa coefficient of 83%. The user accuracy ranged from 62 to 100%, while the producer accuracy ranged from 60 to 100%. The fruit trees were mostly differentiated from co-existing land use types using the short infrared and the red-edge bands. The fourth part of the thesis attempted to increase fruit tree classification accuracy by classifying optimal Sentinel-2 images acquired during the fruit trees' critical growth stages using a Deep Neural Network (DNN) model. This was achieved by applying phenological metrics derived from Sentinel-2 images acquired during optimal crop-growing seasons (i.e., flowering, fruiting, harvesting). The DNN models were optimized by tuning the hyperparameters to achieve the best classification results. The DNN produced an OA of 86.96%, 88.64%, 86.76%, and 87.25% for April, May, June, and July images, respectively. The results indicate the DNN models were robust and stable across the selected fruit growth periods. This research has shown that earth observation (EO) data such as Sentinel 1 and 2 can be used to map fruit trees in fragmented sub-tropical horticultural landscapes characterized by different environmental conditions and different crop cultivars operating under different management practices. The research results will assist agricultural stakeholders (i.e., farm managers, agronomists, agricultural extension officers, and policymakers) in allocating agricultural resources, devising effective agricultural management strategies, and attaining sustainable agriculture and food security.Item Assessing the inter-annual and inter-seasonal climate-induced variation in caseload of respiratory diseases(University of the Witwatersrand, Johannesburg, 2024-06) Motlogeloa, OgoneIn South Africa, acute upper respiratory diseases pose a significant public health challenge, influenced heavily by climatic factors. Recognizing the critical need for detailed seasonal analysis. This thesis delves into the inter-annual and inter-seasonal impacts of climate on disease caseloads, offering four pivotal contributions to health biometeorology. The first contribution refines the understanding of the acute upper respiratory disease season in South Africa, previously recognized as the winter months of May to September. This research provides a more granular analysis by pinpointing specific onset timings and fluctuations within the season that are crucial for optimizing healthcare responses, particularly in vaccination schedules. The second contribution is an in-depth analysis of climatic variables affecting acute upper respiratory disease prevalence. Utilizing Spearman's correlation analyses and the Distributed Lag Non-linear Model across Johannesburg, Cape Town, and Gqeberha, this study identifies negative correlations between temperature and disease cases, pinpointing significant risk thresholds most prevalent during the winter peak. The third contribution investigates the impact of extreme climate events (ECEs) over twelve years, elucidating how, while individual ECEs influence medical aid claims and disease incidence, it is the broader seasonal patterns that predominantly dictate acute upper respiratory disease prevalence. The fourth contribution offers a nuanced exploration of the climate-health nexus, demonstrating that routine weather variations play a more significant role in the peak transmission of acute upper respiratory viruses than extreme events. This thesis elucidates the substantial yet nuanced influence of climate on respiratory health in South Africa. By specifying the disease season with greater precision and clarifying the relationship between temperature variations and disease prevalence, the research provides essential data for health practitioners to plan targeted interventions. This study moves beyond the focus on extreme weather events to expose the subtler, yet more consistent, impact of seasonal climate shifts on health outcomes, enriching our understanding and serving as a vital reference for enhancing disease preparedness in an era marked by climatic uncertainty.Item Detecting Disease in Citrus Trees using Multispectral UAV Data and Deep Learning Algorithm(University of the Witwatersrand, Johannesburg, 2024-06) Woolfson, Logan Stefan; Adam, ElhadiThere is a high prevalence, in South Africa, of fruit tree related diseases infesting lemon trees, subsequently affecting overall crop yield and quality. Ultimately, the income for the farmers is significantly diminished and limits the supply of nutritional food crops for the South African population, who already suffer from a high incidence of malnutrition. Currently, there are various methods utilized to detect diseases in fruit trees, however they pose limitations in terms of efficiency and accuracy. By employing the use of drones and machine learning methods, fruit tree diseases could be detected at an earlier stage of development and with a much higher level of accuracy. Consequently, the chances of remedying the trees before the disease spreads is greatly improved, and the supply of nutritious fruit within South Africa is increased. This research report’s aim is to investigate the effectiveness of a deep learning algorithm for detecting and classifying diseases in lemon orchards using multispectral drone imagery. This entails assessing the performance of a pretrained ResNet-101 model, fine-tuned with additional sample images, in accurately identifying and classifying diseased lemon trees, specifically those affected by Phytophthora root rot. The methodology involves the utilization of a pretrained ResNet-101 model, a deep learning architecture, and the retraining of its layers with an augmented dataset from multispectral aerial drone images of a lemon orchard. The model is fine-tuned to enhance its ability to discern subtle spectral variations indicative of disease presence. The selection of ResNet-101 is grounded in its proven success in image recognition tasks and transfer learning capabilities. The results obtained demonstrated an impressive accuracy of 80%. The deep learning algorithm exhibited notable performance in distinguishing root rot-affected lemon trees from their healthy counterparts. The findings indicate the promise of utilizing advanced deep learning methods for timely and effective disease detection in agricultural farmlands, facilitating orchard management.Item The Wind Energy Potential of South Africa’s Eastern Cape Province in a Changing Climate(University of the Witwatersrand, Johannesburg, 2024-10) Landwehr, Gregory Brent; Engelbrecht, Francois; Lennard, ChrisDue to the abundance of wind and solar renewable energy resources across South Africa, and the comparative low cost of installation and operation of wind and solar energy infrastructure, it is inevitable that the country’s dependence on fossil fuels for energy will decline in the future. At a practical level, developing wind energy facilities entails a complex array of activities and the ~20-30 year life spans of such facilities intrinsically implies that they will experience climate change. However, insufficient research and related modelling have been undertaken in South Africa to quantify future variability and systematic changes in the wind resource as it relates to specific synoptic weather types and wind energy production. The aim of this thesis is to develop methodologies to understand the synoptic drivers of regional wind energy production potential and in turn assess how and why South Africa’s wind energy production potential may change as a function of changing circulation patterns in a changing climate. The wind energy potential of the Eastern Cape Province of South Africa is quantified using energy yield analysis techniques. These results are mapped onto commonly occurring synoptic types for the region to assign an energy potential to each. When the changing frequency of these synoptic weather types is calculated in a climate change impacted future using Global Climate Models, it is possible to quantify the change in wind energy potential in the long term. Results show that the synoptic-circulation pattern with the highest wind energy potential is the Atlantic Ocean ridging High with its centre at about 30 °S, behind a northward displaced mid-latitude cyclone. Global Climate Model projections of the frequency occurrence of these high energy synoptic states show a decrease in frequency at all global warming temperature thresholds and in turn a decrease in wind energy production. The likely cause of this being the poleward expansion of the descending limb of the Hadley circulation which shifts these synoptic systems southwards. The methodologies presented in this thesis provide South Africa with the necessary climate change risk assessment and mitigation capability to address these impacts on the wind energy sector in South Africa.Item Study of the influence of gust fronts and topographical features in the development of severe thunderstorms across South Africa(University of the Witwatersrand, Johannesburg, 2024) Mofokeng, Puseletso Samuel; Engelbrecht, Francois A.; Bopape, Mary-Jane M.; Grab, Stefan W.South Africa experiences a variety of severe thunderstorms which occasionally leads to a large quantity of small-sized or large-sized hailstones, heavy rain and flash flooding, strong damaging straight-line winds, and/or even tornadoes. For the base period, June 2016 to June 2021, a significant percentage of these severe storms was triggered by topographic features. The Unified Model (UM) at 4 km horizontal grid resolution was used and found to be unable to predict topography-generated vertical wind shear and the associated severe thunderstorms. This inability of the model necessitated the development of a conceptual model by relating the rapid cooling of the cloud-top temperatures with high resolution topographic maps. This means, satellite images were used to deduce the connection of atmospheric fluids (gust fronts) with near linear, concave and/or downslope topographical features. Severe thunderstorms included those connected to the large amounts of vorticity advection (e.g. 500 hPa level), development of low-level mesoscale circulations within the synoptic settings and the resultant vertical wind shear in the lower tropospheric levels. Large amounts of negative vorticity advection are typical with strong horizontal shear and curvature; they are often correlated with trough axes that lean from the south-west to north-east. The usage of large amounts of negative vorticity advection transcends to whether discrete severe thunderstorms will be characterised by heavy rain and flash-flooding or hail with damaging winds. Moreover, the interaction of topography with gust fronts of the upwind thunderstorms linked with large amounts of negative vorticity advection is also investigated. The impacts of storms studied in this dissertation posed a major threat to property, livelihood, agriculture, human and animal lives or even immediate to residual economic loss. This research is aimed at improving the service level for the benefit of disaster management agencies and the public at large. An in-depth study of microscale events such as tornadoes and landspouts was also conducted to improve lead-time for their nowcasting.Item Peat dynamics in the Angolan Highlands(University of the Witwatersrand, Johannesburg, 2023-03) Lourenco, Mauro Cesar; Woodborne, Stephan; Fitchett, JenniferThe Angolan Highlands is a war stricken, threatened, and under-studied area. The region is hydrologically and ecologically important and supports extensive tropical peatland deposits. Peatland preservation has been acknowledged to address climate change, is sensitive to drought and fire, and is directly influenced by vegetation and hydrological conditions. However, little research has been conducted in the Angolan Highlands. This study addresses gaps in the literature through four key contributions. The first is a critical review of peat definitions: the implications of disparate definitions are detailed, and a new proposed definition for peatlands in the interest of climate science is provided. The second is the first map of peatland extent in the Angolan Highlands, containing details on the age and growth dynamics. The study presents a conservative estimate of peatland extent that is much larger than previously estimated for Angola and is a crucial first step in facilitating the preservation of this deposit. The third contribution is the first historical assessment of drought and vegetation response in the region. This contains a 40-year drought and 20-year vegetation history, demonstrating that drought occurrence is increasing and there is a strong relationship between precipitation and the peatland vegetation region. The fourth contribution is the first assessment of the contemporary (2001-2020) fire regime of these peatlands, and reveals that among all land cover classes, peatlands burn more frequently and at a higher proportion. Investigation into the peat dynamics of the Angolan Highlands indicate that they have critical importance and are naturally resistant to both droughts and fire. Failure to preserve these deposits will have direct implications on the communities, environment, and surrounding areas.Item Assessing the effectiveness of wetlands in the Krugersdorp Game Reserve in attenuating pollution from mines on the West Rand, South Africa(University of the Witwatersrand, Johannesburg, 2023) Sawuka, Noluthando Thulisile; Evans, Mary; Masindi KhulisoIn South Africa, 48% of the country’s wetlands are critically endangered because of anthropogenic activities. Wetlands are an important part of the landscape and play a critical role including but not limited to improving water quality, habitat provision, and water storage. This research aimed to assess the effectiveness of wetland systemsin attenuating pollution from water discharged from abandoned gold mines in the Krugersdorp Game Reserve (KGR), West Rand. Eight (8) water samples were collected in the study site. Physico-chemical parameters were measured in situ, and chemical parameters were measured in the lab. The measured physico–chemical parameters from the majority of the sampled wetlands exceeded at least one of the stipulated water quality legislations, which included the General Authorization Limit Section 21f and h, 2013; Unit for TWQGR; Mine Health and Safety Act; and WUL wastewater in terms of the recorded pH, total dissolved solids, and salinity variables. Overall, a decreasing trend in pH level was observed from wetlands sampled upstream of the KGR to wetlands sampled downstream of the KGR, with the highest recorded pH level (Alkalinity: 8.9) obtained from the sampled wetland that was closest to the adjacent mining site upstream of theKGR whilst the lowest recorded pH level (Acidity: 3.9) obtained from a wetland sampling point that was further from the adjoining mine and downstream in the KGR. A weak and positive correlation (r=0.040) was obtained between the measured total dissolved solids and pH levels from the sampled wetlands, indicating minimal spatial variability. However, a strong positive correlation (r=0.999, Correlation is significant at the 0.01 level) was obtained between the measured total dissolved solids and salinity from the sampled wetlands. At least one of the limits stipulated by the water quality legislation was exceeded in terms of the analysed inorganic constituents from the sampled wetlands. The dominant ions recorded in the wetlands in increasing order are F, K, Cl, Mg, Na, Ca, and SO4. Mn and Si were the dominant metal concentrations recorded in most wetlands, with the former also showing exceedances when compared to the stipulated water quality guidelines. The recorded data from the measured physico–chemical parameters and analysed chemical variables indicated poor water quality in wetlands sampled downstream of the KGR and upstream of the KGR. Stringent measures in water quality monitoring need to be implemented to mitigate the environmental impacts associated with wastewater discharge into the receiving environment.