Illegal disposal of solid waste is a significant issue in many parts of the world, particularly in urban areas. Because of unprecedented urbanization, these areas are crowded, putting pressure on the already inadequate municipal services such as waste management. As municipalities fail to provide adequate and effective waste management services, the expansion and proliferation of informal settlements contribute to the problem of illegal dumping. Apartheid spatial planning produced environmentally unsustainable cities characterized by glaring disparities in municipal resource allocation, disturbingly inefficient transportation systems, and widespread urban insecurity. Therefore, this study examines how the expansion of informal settlement at an unprecedented rate contributes to illegal dumping post-apartheid by conducting a systematic review. Using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, a literature search from 1994 to 2023 was conducted in the field of waste management. The review focuses on three key aspects: (1) factors that contribute to illegal dumping in townships; (2) waste management laws during apartheid and post apartheid; and (3) waste collection system in townships and spatial planning. South Africa’s inequality is a major contributor to environmental degradation. The situation was exacerbated by efforts to prevent urban migration and forcefully remove black people from cities; apartheid ideology justified the dormitory-like nature of urban townships. In the South African context, the literature reveals that there is a research gap in the application of technologies and effective waste management plans to keep up with the growing number of informal settlements. Furthermore, despite progressive environmental policies, implementation has been ineffective. This study offers valuable evidence on the spatial and temporal dynamics of informal settlements, addressing both the location and time aspects. A robust government-led Community Participation in Solid Waste Management program is required immediately.

Excessive discharge of poorly treated effluent has impacted global water resource systems intensely. The declining state of wastewater treatment plants (WWTPs) is a significant source of pollution in water resources. There is evidence of water resource quality deterioration in natural environments caused by effluent discharges. We assessed the impact of wastewater treatment effluent on the quality of the Crocodile River. For spatial distribution, we collected data from three WWTPs discharging effluent into the Crocodile River and from three points situated downstream of each WWTP. Physicochemical and microbiological parameters such as pH, electrical conductivity, chemical oxygen demand, phosphates, nitrates, ammonia, and Escherichia coli were analysed using standard methods of the American Public Health Association. The water quality index was also calculated to give an overall indication of pollution within the catchment. The results show that WWTPs were not complying with the effluent standards set out in their water use licence. The WWTP effluent had a negative impact on downstream water quality, with the water quality index indicating low quality of discharged effluent. It is recommended that a regular and consistent water resource quality monitoring programme be implemented, particularly in areas where effluent discharges are prevalent. Significance: In many African nations, water pollution is a serious problem that may be traced to a variety of sources. Surface water pollution has adverse effects on aquatic ecosystems and reduces the availability of clean water. In most semi-arid to dry southern African regions (e.g. South Africa), water scarcity is a significant concern. In these regions, water is a vital resource that must be protected at all times, given that the inadequate infrastructure of wastewater treatment facilities adds to the decline in South Africa’s water quality standards.

This paper investigates the impact of ENSO on water quality by looking at COD, SS, and Escherichia coli along the Crocodile River. Water samples were collected between 2016 and 2021 at three strategic sites on the river and were tested in an accredited laboratory. Python (version 3.8), Spyder and Microsoft Excel 2019 were used to analyze the data. The highest COD concentration (800 mg/L) was recorded at the White River site during El Niño, followed by 600 mg/L during the normal period, and 240 mg/L during the La Niña period. In 2019 during La Niña and the normal period, the E. coli levels were centered in one place at 60 cfu/100 mL, while in 2021 no E. coli levels were detected from the La Niña, El Niño, and normal periods. The suspended solids in this study were more prevalent in the White River (upstream) during the El Niño period. These analyses demonstrate that it is possible to evaluate the local effects associated with large-scale climate variability.

Water quality problems are a major challenge that humans encounter in the 21st Century. Water security presents a pressing concern in South Africa due to persistent difficulties in ensuring water availability, accessibility, and quality. Given the numerous activities that are taking place adjacent to riverbanks, there has been a decline in water quality. This study determined the state of the Crocodile River’s intermediate water quality and assessed the effects of various human-induced activities. Water samples were collected during 2016–2023 in different sites within the Crocodile River, and physicochemical parameters were analysed in an accredited laboratory. The Python package Seaborn was used for statistical analysis of the data to provide visualizations and data variations. Overall, the results show that elevated concentrations of electrical conductivity (EC) and sulphate (SO₄²⁻) were recorded in the Crocodile River. The presence of elevated EC levels in R571 Bridge exceeded 133.90 µs/cm between 2020 and 2021, whilst the highest concentration levels (114 mg/L) for SO₄²⁻ were recorded in Malelane in the year 2020. The elevated concentrations of concern were recorded during the dry season (winter) and the wet season (spring). Overall, the comparisons of the spatiotemporal concentration indicate that the water quality parameters fluctuate over time. Moreover, the monitoring practice of detecting the water quality change over time is also fundamental for providing valuable information necessary for the management of integrated water resources.

The exploitation of resources and industrialized and societal activities has resulted in global freshwater contamination, which is a serious environmental concern. Climate variability exerts a significant influence on the metal concentrations in water sources. The monitoring of metal concentrations in surface water systems amidst climate change and variability has received limited attention, especially in southern Africa. Therefore, the objective of this study is to evaluate temporal variations and seasonal patterns that impact metal levels in the Kaapmuiden River. The results show that the river observed higher iron in the autumn season of 2021 and 2016, recording 0.90 mg/l and 0.64 mg/l, respectively. Manganese was high in two different seasons: spring (2016) recording 0.060 mg/l and winter (2021) recording 0.053 mg/l. The result of this study shows that iron concentration was within the acceptable level of approximately 0.5–1 mg/l set for the ecological health of the river. Subsequently, results show high levels of aluminium in autumn for 2017 and 2021 with a concentration of 0.59 mg/l and 1.10 mg/l, respectively. High concentrations of manganese and aluminium in water resources have an ecotoxicological effect on aquatic biota; hence, adequate measures should be placed to minimize the introduction of these metals from anthropogenic activities.

Chemicals of emerging concern (CECs) pose major challenges for wastewater treatment plants because they are difficult to biodegrade, persist in the environment, and occur at trace concentrations. This review evaluates modelling methodologies for simulating the behaviour and fate of CECs during biological wastewater treatment processes. A systematic review of peer-reviewed literature published between 2000 and 2025 was conducted to assess statistical, mechanistic, machine learning, and hybrid models, focusing on predictive capability, modelling trends, and research gaps. The findings indicate a transition from mechanistically based models, particularly the Activated Sludge Models (ASMs), towards data-driven and hybrid approaches capable of capturing the nonlinear complexities of wastewater treatment systems. Machine learning models demonstrated strong predictive capability and effectively identified operational parameters influencing treatment efficiency, including dissolved oxygen, pH, temperature, solids retention time, and chemical oxygen demand. However, these models remain limited by their reliance on large datasets, reduced transferability across treatment systems, and the interpretability challenges associated with "black box" predictions. Mechanistic models accurately represent biochemical processes but are less effective in simulating dynamic CEC interactions. Hybrid models integrating mechanistic and machine learning approaches emerged as the most promising methodology by combining predictive accuracy with improved process representation. Future research should focus on Per- and polyfluoroalkyl substances (PFAS) and microplastics modelling, uncertainty analysis, explainable artificial intelligence, and region-specific datasets, particularly in developing countries.