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Artificial neural networks (ANNs) are powerful data-oriented "black-box" algorithms capable of assessing and delineating linear and multifaceted non-linear correlations between the dependent and explanatory variables. Through the years, neural networks have proven to be effective and robust analytical techniques for establishing artificial intelligence-based tools for modelling, estimating, and projecting spatial and temporal variations in water bodies. Accordingly, ANN-based algorithms gained increased attention and have emerged as practical alternatives to traditional approaches for hydro-chemical analysis. ANNs are among the widely used computer systems for modelling surface water quality. Considering their wide recognition, resilience, flexibility, and accuracy, the current study employs a neural network-based methodology to construct a novel water quality index (WQI) model suitable for analysing South African rivers. The feed-forward, back-propagated multilayered perceptron model has three parallel-distributed neuron layers interconnected with seventy weighted links orientated laterally from left to right. First, the input layer includes thirteen neuro-nodes symbolising thirteen explanatory variables, including NH3, Ca, Cl, Chl-a, EC, F, CaCO3, Mg, Mn, NO3, pH, SO4, and turbidity (NTU). Second, the hidden layer consists of eleven neuro-nodes accountable for computational tasks. Lastly, the output layer features one neuron responsible for conveying network outcomes using a single-digit WQI rating extending from zero to one hundred, where zero represents substandard water quality and one hundred denotes exceptional water quality. The AI-based model was developed using water quality data obtained from six monitoring locations within four drainage basins under the management of the Umgeni Water Board in the KwaZulu-Natal Province of South Africa. The dataset comprises 416 samples randomly divided into training, testing, and validation sets using a proportional split of 70:15:15%. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) technique was utilised to conduct backpropagation training and adjust synapse weights. The dependent variables are the WQI scores from the universal water quality index (UWQI) model developed specifically for South African river basins. The ANN demonstrated enhanced efficiency through an overall correlation coefficient (R) of 0.985. Furthermore, the neural network attained R-values of 0.987, 0.992, and 0.977 for the training, testing, and validation intervals. The ANN model achieved a Nash–Sutcliffe efficiency (NSE) value of 0.974 and coefficient of determination (R2) of 0.970. Sensitivity analysis provided additional validation of the preparedness and computational competence of the ANN model. The typical target-to-output error tolerance for the ANN model is 0.242, demonstrating an adequate predictive ability to deliver results comparable with the target UWQI, having the lowest and highest index ratings of 75.995 and 94.420, respectively. Accordingly, the three-layer neural network is scientifically sound, with index values and water quality evaluations corresponding to the UWQI results. The current research project seeks to document the processes used and the outcomes obtained. [ABSTRACT FROM AUTHOR]

There is a rapid spread of antibiotic resistance in the environment. However, the impact of antibiotic resistance in drinking water is relatively underexplored. Thus, this study aimed to quantify antibiotic resistance genes (ARGs) and antibiotic residues in two drinking water production facilities (NW-E and NW-C) in North West Province, South Africa and link these parameters to bacterial communities. Physicochemical and ARG levels were determined using standard procedures. Residues (antibiotics and fluconazole) and ARGs were quantified using ultra-high performance liquid chromatography (UHPLC) chemical analysis and real-time PCR, respectively. Bacterial community compositions were determined by high-throughput 16S rRNA sequencing. Data were analysed using redundancy analysis and pairwise correlation. Although some physicochemical levels were higher in treated than in raw water, drinking water in NW-E and NW-C was safe for human consumption using the South African Water Quality Guideline (SAWQG). ARGs were detected in raw and treated water. In NW-E, the concentrations of ARGs (sul1, intl1, EBC, FOX, ACC and DHA) were higher in treated water than in raw water. Regarding antimicrobial agents, antibiotic and fluconazole concentrations were higher in raw than in treated water. However, in NW-C, trimethoprim concentrations were higher in raw than in treated water. Redundancy analysis showed that bacterial communities were not significantly correlated (Monte Carlo simulations, p-value >0.05) with environmental factors. However, pairwise correlation showed significant differences (p-value <0.05) for Armatimonas, CL500-29 marine group, Clade III, Dickeya and Zymomonas genera with environmental factors. The presence of ARGs and antibiotic residues in the current study indicated that antibiotic resistance is not only a clinical phenomenon but also in environmental settings, particularly in drinking water niches. Consumption of NW-E and NW-C treated water may facilitate the spread of antibiotic resistance among consumers. Thus, regulating and monitoring ARGs and antibiotic residues in drinking water production facilities should be regarded as paramount. [ABSTRACT FROM AUTHOR]

This study reviews worldwide wetland park research from 1996 to 2022. A bibliometric analysis is conducted on 591 wetland park studies indexed in the Web of Science and Scopus databases. The study utilizes CiteSpace and VOSviewer tools to visualize and explore influential research focuses, themes, directions, and countries. The citation burst indicates that from 1996 to 2022, research on wetland parks transited from exploring basic community structures to complex ecosystem service assessments and the formulation of management strategies. Furthermore, over the past three years, wetland park research has seen a significant surge in studies investigating water quality, ecosystem services, and spatiotemporal analysis. Notably, the three most frequent keywords in research on wetland parks were China, South Africa, and biodiversity. These keywords reflect regions that enhance biodiversity via wetland parks. The spectral-clustering algorithm identifies carbon sequestration as a research focus, highlighting the vital role of wetlands in the carbon cycle. Most authors work in developed countries' institutions, but some are from developing countries like China, South Africa, and India. The findings suggest that economic development is crucial in wetland park construction and significantly influences related research. Developed countries may offer more PhD positions to developing countries' researchers in the field and raise their awareness about wetland conservation. Given the holistic requirements of wetlands, this research recommends that educators should adopt an interdisciplinary approach in the future when nurturing wetland staff. Additionally, the study maps out the primary areas of interest in wetland park research, including environmental science, ecological economics, forestry, wetlands, tourism, and management. New artificial intelligence and digital technologies should be developed for wetland park research. This study fills a research gap: quantitative and visualized knowledge-mapping and bibliometrics on wetland parks are scarce. Additionally, no previous study has explored the relationship between wetland park research and the economic development of countries. [ABSTRACT FROM AUTHOR]

River water quality modelling relies on a good dataset for model input, calibration and validation to develop a reliable model. Determination of data requirements, and data sourcing towards this purpose in South Africa, are not elementary. Data availability is key throughout the modelling processes, making the sourcing of good data equally imperative. This paper provides starting considerations, and compiles a list of available data sources applicable to river water quality modelling in South Africa. It also provides a background on the included data sources and a general overview of the extent of the databases. [ABSTRACT FROM AUTHOR]

The Vaal Dam catchment, which is the source of potable water for Gauteng province, is characterized by diverse human activities, and the dam encounters significant nutrient input from multiple sources within its catchment. As a result, there has been a rise in Harmful Algal Blooms (HABs) within the reservoir of the dam. In this study, we employed time series analysis on nutrient data to explore the relationship between HABs, using chlorophyll-a (Chl−a) as a proxy, and nutrient levels. Additionally, Chl−a data extracted from Landsat-8 satellite images was utilized to visualize the spatial distribution of HABs in the reservoir. Our findings revealed that HAB productivity in the Vaal Dam is influenced by the levels of total phosphorus (TP) and organic nitrogen (KJEL_N), which exhibited a positive correlation with chlorophyll-a (Chl−a) concentration. Long-term analysis of Chl−a in-situ data (1986–2022) collected at a specific point within the reservoir showed an average concentration of 11.25 μg/L. However, on certain stochastic dates, Chl−a concentration spiked to very high values, reaching a maximum of 452.8 μg/L, coinciding with elevated records of TP and KJEL_N concentrations on those dates, indicating their effect on productivity levels. The decadal time series and trend analysis demonstrated an increasing trend in Chl−a productivity over the studied period, rising 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). The rising averages of the decadal values were associated with increasing decadal averages of its driving factors, TP from 0.1043 to 0.1096 to 0.1119 mg/L for the three decades, respectively, and KJEL_N from 0.80 mg/L in the first decade to 1.14 mg/L in the last decade. 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. [ABSTRACT FROM AUTHOR]

Rivers in Africa have experienced dire pollution as a result of the poor management of wastewater effluent emanating from water resource recovery facilities (WRRFs). An integrated wastewater resource recovery model was developed and applied to identify ideal wastewater resource recovery technologies that can be used to recover valuable resources from a mixture of wastewater effluents in a case study in the Burgersfort WRRF in the Limpopo province, South Africa. This novel model incorporates the process of biological nutrient removal (BNR) with an extension of conventional methods of resource recovery applicable to wastewater. The assessment of results of effluent quality from 2016 to 2022 revealed that ammonia, chemical oxygen demand, total coliform, fecal coliform, and Escherichia coli levels were critically non-compliant with the permissible effluent guidelines, indicating a stable upward trend in terms of concentrations, and scored a very bad wastewater quality index rating. All variables assessed showed a significant loading, except for orthophosphates, and significant correlations were observed among the variables. The results of the integrated wastewater resource recovery model revealed a high probability of reclaiming recoverable resources such as nutrients, sludge, bioplastics, biofuel, metals, and water from wastewater, which have economic, environmental, and social benefits, thereby improving the effluent quality of a WRRF. [ABSTRACT FROM AUTHOR]

While natural floods play a crucial role in shaping the composition of aquatic communities, the most rivers worldwide are regulated or dammed for anthropogenic purposes, resulting in alterations to the biological and chemical composition of these ecosystems. Studies have demonstrated various negative effects of river regulation on aquatic invertebrate communities globally. However, there is a scarcity of research in Africa, despite its vulnerability to anthropogenic impacts. This study aimed to compare aquatic invertebrate communities in the Phongolo River, an impacted regulated river, and the Usuthu River, a less impacted unregulated river, in South Africa. It further aimed to ascertain whether Lake Nyamithi, a naturally saline lake receiving water from both of the aforementioned systems, exhibited a stronger similarity to one of the two rivers in terms of its aquatic invertebrate composition. Aquatic invertebrate and water samples were collected from 2012 to 2018 over several surveys. The Usuthu River demonstrated a diverse and sensitive aquatic invertebrate community, emphasising its high conservation value. The Phongolo River demonstrated effects of anthropogenic impact, with taxa more resilient to changes in water quality and flow compared to the Usuthu River. Mismanagement and excessive water use may lead to the loss of any remaining sensitive aquatic invertebrate communities in this river. The presence of invasive molluscan in the Phongolo River and Lake Nyamithi also poses a threat to the native aquatic invertebrate communities. These invasive species are currently absent from the Usuthu River although other invasive species, such as the Australian redclaw crayfish, are found in both river systems. Lake Nyamithi displayed a unique aquatic invertebrate community, distinct from both rivers and their floodplains. This study provides important baseline information on the Usuthu River's aquatic invertebrates and emphasises the need to maintain adequate water flow in rivers and floodplains to protect biodiversity and sensitive species. [ABSTRACT FROM AUTHOR]

The Oceans and Coastal Information Management System (OCIMS) was launched by the South African Government in 2015 to support the development and governance of the South African ocean economy. The OCIMS has established knowledge tools for marine spatial planning, maritime domain awareness, search and rescue, water quality and harmful algal bloom monitoring. Those tools are used daily by stakeholders across government departments, industry, and civil society. Unlike many other operational oceanographic and coastal systems around the world, the OCIMS was designed from its inception using inputs from stakeholders. Continuous engagements between developers and stakeholders have ensured that the system remains fit for purpose. The OCIMS is both locally relevant and globally cognizant. Developments are undertaken to ensure inter-operability with other systems in the world and promote the exchange and discovery of data. The OCIMS project was able to leverage co-funding and the sharing of data and expertise through partnerships across the public and private sectors. These partnerships have been essential to the success of OCIMS and would not have been possible without continued engagements and the sustained funding provided by the South African national government. The development pathway followed to establish the OCIMS could benefit other countries looking to implement their own operational ocean and coastal system knowledge platform. • The Ocean Information Management System supports Blue growth. • The formalization of stakeholder's engagement ensures co-design and relevance. • Adherence to international best practices promotes global visibility. • Co-funding from partners help leverage cost and human capacity. • Secure funding streams from government are essential to ensure sustainability. [ABSTRACT FROM AUTHOR]