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Review

Advancing Water Quality Monitoring in eThekwini, South Africa: Integrating Water 4.0, Automation, and AI for Real-Time Surveillance

by
Owen Rubaba
1,* and
Tom Walingo
2
1
Independent Institute of Education Emeris, Durban 4016, South Africa
2
Department of Electrical and Electronics Engineering, University of Kwa Zulu Natal, Durban 4041, South Africa
*
Author to whom correspondence should be addressed.
Water 2025, 17(22), 3299; https://doi.org/10.3390/w17223299
Submission received: 8 October 2025 / Revised: 7 November 2025 / Accepted: 11 November 2025 / Published: 18 November 2025
(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)
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Abstract

Global strategies for ensuring access to clean and safe drinking water are increasingly shifting toward a preventive approach based on risk assessment and risk management of the entire water supply and production chain. However, many developing countries, including South Africa, still lag in adopting advanced real-time water monitoring technologies aligned with Water 4.0 principles. To transition to these innovative technologies, it is essential to understand current gaps in water monitoring and the challenges to adopting these systems. This systemic review aims to assess current monitoring practices, identify implementation challenges, and explore strategic pathways for adopting smart water infrastructure in eThekwini Municipality, South Africa. This review identifies critical gaps in eThekwini’s water quality monitoring, including limited real-time surveillance, fragmented data systems, budgetary constraints, cybersecurity vulnerabilities, uneven rural–urban access, slow commercialization of academic innovations, policy misalignment, and insufficient technical capacity. It emphasizes the potential of real-time monitoring systems, automation, and artificial intelligence (AI) to address existing water quality monitoring challenges. Additionally, special focus is given to the role of electronic sensors in measuring physicochemical parameters like turbidity, pH, and dissolved oxygen as cost-effective indicators for detecting microbial contaminants. Implementing Water 4.0 strategies provides eThekwini and similar municipalities an opportunity to develop a more proactive, resilient, and sustainable approach to water quality management.

1. Introduction

Access to safe drinking water is a basic human right enshrined under the United Nations Sustainable Development Goal (SDG) 6. However, due to rapid urbanization and climate change the availability and predictability of safe drinking water have become a global challenge [1,2,3]. The World Health Organization [4] estimates that in 2022 alone, at least 1.7 billion people used a drinking water source contaminated with feces, and that 485,000 deaths occur each year associated with diarrheal diseases such as cholera, dysentery, typhoid, and polio, primarily linked to pathogens such as Escherichia coli (E. coli). The majority of these cases are in developing countries [5]. Monitoring the safety and quality of drinking water is, therefore, an important step in preventing waterborne diseases. Water monitoring involves the systematic assessment of a water body’s physical, chemical, and biological properties to evaluate its quality and ensure it meets established standards. The common standard is the WHO’s Guidelines for Drinking-Water Quality [6,7]. It is estimated that water scarcity affects more than 40% of the world’s population [8], and global water demand for all users is expected to increase by 20–30% by 2050, with significant differences across global regions [4,9]. To meet this demand for access to drinking water, disruptive technologies are needed, and Water 4.0 has been suggested as a comprehensive approach to water management. The evolution of water management has progressed through distinct technological eras. Water 1.0 was rooted in the pre-industrial age and relied on rudimentary infrastructure such as aqueducts, wells, and gravity-fed systems to meet basic supply and sanitation needs [10]. With the Industrial Revolution, Water 2.0 introduced centralized treatment plants, piped distribution networks, and chemical disinfection, marking a shift toward public health-driven infrastructure and in the late 20th century, Water 3.0 emphasized environmental protection, wastewater recycling, and regulatory oversight [11]. Water 4.0 represents a transformative leap, integrating digital technologies such as IoT sensors, artificial intelligence, and cloud-based analytics [12].
By leveraging smart sensors, Internet of Things (IoT) devices, and data analytics, artificial intelligence and cloud platforms, this approach enables real-time data acquisitions, predictive maintenance, and automated decision-making, and this has been a key feature in more developed countries [13,14]. While Water 4.0 has gained traction in high-income countries, its application in developing urban contexts remains limited. In South Africa, municipalities like eThekwini face unique challenges: budget constraints, fragmented data systems, and limited technical capacity. Yet, the region also presents opportunities for innovation, particularly through partnerships with research institutions such as the University of KwaZulu-Natal (UKZN), the Council for Scientific and Industrial Research (CSIR), Durban University of Technology (DUT), and pilot programs supported by donor agencies. This review explores how Water 4.0 principles can be adapted to the eThekwini context, offering a roadmap for integrating smart technologies into water quality monitoring systems in resource-constrained environments.

2. Methodology

This review employed a systematic approach to explore recent developments in water quality monitoring, with a particular emphasis on the eThekwini Municipality in South Africa. Umgeni Water is the primary water service provider in the region, supplying potable water to residential, commercial, and industrial sectors, while also managing the quality of coastal ocean beaches in Durban. Given the municipality’s strategic importance and vulnerability to waterborne risks, this review used it as a focal point for assessing the feasibility and relevance of emerging technologies in water surveillance. This review was guided by four central research questions: (1) What are the current water quality monitoring practices in eThekwini Municipality? (2) What challenges hinder effective real-time water surveillance in the region? (3) How can Water 4.0 technologies, including automation and artificial intelligence (AI), be integrated into existing systems to enhance monitoring? (4) What global best practices can inform local implementation strategies?
To address these questions, a systematic literature search was conducted using Google Scholar and PubMed, supplemented by publicly available government and municipal records. The search strategy combined keywords such as “water quality monitoring,” “Water 4.0,” “real-time water surveillance,” “eThekwini Municipality,” and “Umgeni Water.” The search was restricted to English-language sources published between 2013 and 2025 to ensure relevance to recent technological and policy developments.
Inclusion criteria focused on studies that addressed water quality monitoring technologies, implementation frameworks, or case studies relevant to urban municipalities, particularly within developing country contexts. Excluded materials included opinion pieces, non-technical articles, and studies unrelated to water quality or digital integration.
The selection process was guided by an adapted PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. An initial pool of 156 records was identified. After removing duplicates, 115 records were screened based on titles and abstracts. Of these, 77 full-text articles were assessed for eligibility, and 44 were ultimately included in the final synthesis. Snowballing was used to gather more literature based on information from the 38 full texts. From the selected literature, key themes were extracted through thematic analysis. These included the types of sensors and automation tools used in water monitoring, the integration of AI and machine learning in water quality surveillance, policy and governance frameworks, and infrastructure challenges and opportunities for real-time monitoring. These themes were analyzed to identify prevailing trends, critical gaps, and actionable insights relevant to the eThekwini context.

3. Review of the Literature

The unavailability and predictability of safe drinking water have been attributed to the global rapid urbanization and climate change, leading to public health challenges [1,2]. This has brought to the fore the need for enhanced water quality monitoring as an important component of public health and environmental management, to protect freshwater resources. The importance of water quality in developing countries cannot be overemphasized, given that some waterborne epidemics, such as cholera, have virtually disappeared from developed countries due to high hygiene standards and water quality [15].
Adu-Manu et al. [16] trace the history of global water quality monitoring (WQM) technology and show that from the 1960s to 2000, WQM mainly relied on a manual approach for water sampling and analysis, where a water sample would be collected manually from the source and analyzed in the laboratory. From the late 2000s, new technologies such as new sensors were developed that utilize fiber optics, laser technology, biosensors, optical sensors, and microelectromechanical systems to detect different water quality parameters in situ, where computing and telemetry technologies were introduced to support the data acquisition and monitoring processes. Satellite image acquisition to remotely estimate some water quality parameters was gradually added as a tool for WQM in more developed countries [16].

3.1. Overview of Current Challenges and Gaps in Water Quality Monitoring

To mitigate public health problems associated with contaminated water, various international bodies such as the WHO, the United States Environmental Protection Agency (US EPA), and the European Union, as well as other respective countries, have delineated specific acceptable limits of pathogens in water [17,18]. Notably, E. coli is the WHO’s preferred marker for fecal contamination of drinking water [17], while Enterococci serve as indicator bacteria for evaluating the quality of bathing water [18,19]. Escherichia coli constitutes part of the natural gut microbiota in humans and other warm-blooded animals; hence, its presence in water suggests recent contamination by fecal matter [20,21]. However, direct monitoring of these biological contaminants and confirming their presence in the laboratory is time consuming, typically taking 18–48 h [21].

3.2. Key Technologies in Real-Time Monitoring and AI Applications

Conventional water quality assessment involving manual sampling lacks automated systems, real-time monitoring, which would allow prompt detection and response to water quality issues [11]. This has necessitated the development of real-time technologies for water quality monitoring. For example, the U.S. Environment Protection Agency (EPA) has implemented real-time water quality surveillance in key water bodies to mitigate risks from industrial discharges of heavy metals, organic compounds, and excess nutrients [22,23]. In Europe, projects such as the DigitalWater2020 (DW2020) synergy group, with digital-water city, ScoreWater, Fiware4Water, NAIADES, and aqua3s initiatives, integrate technologies from various fields, including sensors, IoT, satellite data analysis, decision support systems, and crisis management to address water sector issues [24].
This has been supported by recent advances in electrochemical detection, colorimetry, and optical methods using absorbance or fluorescence offering increasing opportunities for automated in situ determinations of macronutrient concentrations, species, and compositions [25]. Biosensors have been considered as potential on-site monitoring devices for the rapid and selective monitoring of a variety of analytes in the water. Several biosensor technologies have been investigated such as microbial fuel cells [26] complementary metal-oxide-semiconductor as demonstrated by the PlomBox project where fluorescing lead-sensing bacteria is used to assay lead in drinking water [26]. Notably, some biosensor technologies have also been commercialized and demonstrated to be a powerful technology of choice for the detection of pathogens with the ability to carry out on-site measurements, high sensitivity, real-time measurement capacity, and simplicity [27].
AI and ML are being increasingly being used to analyze large datasets generated by sensors and monitoring systems (Table 1). These tools can predict contamination events, identify pollution sources, and optimize water treatment processes while an ML-based technique can be used to predict the quality of the water, using historical water quality data [28]. Cloud-based platforms and open-data initiatives are increasingly being used to share water quality information, enabling better collaboration between stakeholders. In Southern Africa, Malawi, drone technology such as the Mavic 2 Pro Drone has been tested for the collection and delivery of river water samples for basic water quality assessments [29]. The technology not only reduces the time taken for water collection and eases water collection but will simply replace the traditional human going to collect water; however, the basic water tests will still need human input.
To assess the feasibility of implementing Water 4.0 technologies in eThekwini Municipality, a “strengths, weaknesses, opportunities and threats” (SWOT) analysis was conducted (Table 2). This framework identifies the internal strengths and weaknesses of the current water monitoring system, alongside external opportunities and threats that may influence the adoption of smart technologies. The analysis provides strategic insight into how eThekwini can leverage its existing assets while addressing key barriers to digital transformation in water governance.

3.3. Focus on Proxy Parameters for Microbial Contaminants

Correlations between enteric bacteria and physicochemical parameters such as pH, temperature, salinity, conductivity, and turbidity have long been demonstrated [45]. Monitoring these parameters as proxies for microbial contamination has gained traction [23]. Different microbes thrive under specific optimum conditions relating to temperature, pH, dissolved oxygen, and turbidity. Monitoring these parameters offers cost-effective and practical solutions for regions with limited access to advanced microbiological testing [46,47].

3.4. Water 4.0: Revolutionizing Water Management

The advent of Water 4.0, a paradigm that emphasizes the integration of digital technologies, automation, and artificial intelligence (AI) in water management, offers an opportunity to revolutionize water quality monitoring involving remote technologies generally referred to as Smart Water Quality Monitoring (SWQM). However, Singh & Walingo [48] suggest that there are limited instances of practical applications that collectively utilize the available SWQM systems tools. By leveraging these advancements, cities in developing countries such as Durban can transition from reactive to proactive water management, enhancing the detection and mitigation of contamination events.
Water 4.0 is linked to the fourth industrial revolution (4IR) and is indeed the fourth water revolution, and arguably, all four industrial revolutions correspond to respective four water revolutions [49]. Industry 4.0 is characterized by an elevated level of digitalization, organization, and control throughout the entire product lifecycle’s value chain; hence, the ability to use artificial intelligence knowledge and techniques in water monitoring can be time and cost-saving compared to traditional water collection approaches [14,21,50].
To align with 4IR, Water 4.0 has to match the attributes of 4IR, such as the following: interoperability, virtualization, decentralization, real-time capability, service orientation, and modularity [51,52,53]. Briefly, interoperability is the capacity of people, smart factories, and cyber–physical systems to connect and interact with one another through the IoT and the Internet of Services; virtualization: a digital replica of the smart factory that is produced by combining virtual plant and simulation models with sensor data; decentralization: the capacity for autonomous decision-making by cyber–physical systems in smart factories; real-time capability: the ability to gather, examine, and deliver knowledge instantly; service orientation: using the Internet of Services to offer services (of people, cyber–physical systems, and smart factories); modularity: the ability to easily modify Smart Factories to meet the evolving needs of certain modules.

3.5. Advances in Sensor Technology and Integration with AI

The need for a rapid and effective method of water monitoring has long been recognized; however, traditional tests are time-consuming and require manual sampling and equipped laboratories necessitating research into low-cost, rapid, and real-time technologies [54]. Over the past two decades, interest has been increasing in the development of simple, inexpensive, and disposable biosensors for in-field clinical and environmental analysis. Electrochemical immunosensors have become very popular due to their relatively low costs and rapid turnaround time compared to conventional biological culture methods [55], resulting in the development of ultrasensitive electrochemical DNA biosensors for rapid detection of pathogens such as Vibrio cholerae in environmental samples [56].
Kruger et al. [57] suggest using Effect-Based Methods (EBMs) to determine water quality. These are methods that measure the biological effects of chemicals on organisms or cells rather than identifying specific chemicals in the water. They are important in WQM as they can detect the mixture effects of all active known and unknown chemicals in a sample, which cannot be addressed by chemical analysis alone, but the methods have not been commercialized [58]. For instance, histopathologic and biochemical biomarkers looking at fish health have been used as proxies to detect water pollution in the Umgeni catchment area including the Inanda dam, one of eThekwini’s primary water sources [59]. These tests were able to suggest which tributaries were contributing to higher pollution in the dam.
Use of machine learning algorithms has been shown to improve the accuracy of predicting E. coli concentration using physicochemical parameter data [60]. This is because at below or above certain thresholds, physicochemical parameters do not have a linear relationship with E. coli concentration, making simple calculations problematic. The presence of non-human fecal matter such as that from cattle and wildlife is a common problem in low-to-medium-income countries [20]. Hence, ML, with its advanced algorithms and ability to learn from complex data patterns, offers a powerful advantage in accurately identifying non-human fecal sources, an important consideration in informing management and research priorities.

3.6. Perspectives on Real-Time and AI-Driven Monitoring/Smart Cities

There has been tremendous research and advances in WQM, including wireless water technologies, hardware equipment development, and data analytics, with three elements together creating a basic network to monitor water quality remotely (Figure 1). These elements are the sensing system, the communication system, and the head end system [48]. The new age information technology has given rise to the smart city concept, which is an area populated with electronic sensors providing data that controls assets and resources effectively using information and communication technology (ICT) and sensors together with IoT to improve quality of life, the efficiency of urban operation and services, and competitiveness, including with respect to water quality management [61,62,63]. This is a key theme in the South African Smart Cities Framework (SCF) developed by the Department of Cooperative Governance to provide municipalities, national and provincial government, the private sector, civil society, and other role players with impartial, factual information about smart cities in South Africa [64].
It has been suggested that partnerships with these technology hubs and accelerators and unique funding partners further should be pursued to build out the digital water ecosystem and create opportunities for digital water solutions [65].

3.7. Water 4.0 and Water Quality Monitoring in South Africa

The South African Department of Water and Sanitation [66] cites water quality management challenges such as vandalism of infrastructure, lack of sufficient maintenance plans, technical and financial capacity, use of inappropriate land management practices, uncontrolled discharges from abandoned mines, and ineffective monitoring. The DWS operates national monitoring programs that assess raw surface water quality in rivers and dams in collaboration with other government agencies, municipalities, academic institutions, and private entities [66]. The national benchmark, the Blue Drop System, has shown that urban municipalities are improving their water quality management whilst rural municipalities are falling behind [31,67].
South Africa is prone to waterborne epidemics and recently experienced a cholera outbreak with more than a thousand suspected cases and 42 deaths in 2023 [68]. Traditional monitoring systems, reliant on periodic sampling and delayed laboratory analysis, often fail to detect contamination events in time to prevent public exposure. These challenges are not unique to South Africa [54] and can be broadly categorized into technical, logistical, financial, and policy-related issues, which collectively hinder the ability to ensure safe and sustainable water supplies.
South Africa has embraced the era of Industry 4.0 with different innovative solutions across various industrial sectors, and the Internet of Things (IoT) has provided new solutions to the water industry, improving water management and reducing operational expenses relating to water infrastructure maintenance, with the challenge in the water industry being the ability to turn the available data into insightful information, permitting effective decision making [69]. Reliable information about the origin of high water treatment costs is required to inform both policy and planning decisions [70]; hence, machine learning coupled with real-time monitoring can be used to identify the main contaminants responsible for high water treatment costs in major water treatment entities such as the Umgeni catchment area and predict water treatment costs from observed levels of contaminants.
South Africa employs the Colilert® 18 system for microbial water quality monitoring for rapid detection of contaminants like E. coli [71]. The integration of advanced technologies, such as IoT-based monitoring systems and AI-driven data analysis, holds promise for enhancing the effectiveness and responsiveness of water quality management in major urban centers like Durban, Cape Town, and Johannesburg [36,72,73]. The national research Council for Scientific and Industrial Research (CSIR) satellite-based observations of water quality form a key component of current and ongoing monitoring programs in South Africa with projects such as the satellite remote sensing Sentinel-3 Ocean and Land Colour Instrument (OLCI) and its validation program, the S3VAL project, supporting water-related monitoring applications in South Africa [74]. This includes the integration of earth observation into the national eutrophication monitoring program, utilizing satellite-based monitoring of eutrophication, cyanobacteria, and algal blooms. The CSIR has also deployed, Gizmo, Africa’s only hyperspectral radiometric buoy, to study microalgae biodiversity at the Theewaterskloof Dam in Western Cape in 2023 [36].

3.8. eThekwini Municipality

The eThekwini Metropolitan area is located on the east coast of South Africa in the Province of KwaZulu-Natal (KZN) and is the third largest metropolitan municipality in the country following Johannesburg and Cape Town. It has a steadily growing population of 3.9 million people [75]. Durban is the biggest city in the eThekwini Municipality and is straddled with several water bodies such as the Umgeni River system, which supplies water to the Inanda, Nagle, Midmar, and Albert Falls dams. The Umbilo and Umhlathuza rivers also play a vital role in the municipality. The municipality has a large coastline, and Durban is euphorically referred to as the playground of South Africa in part due to its warm, clean beaches attracting tourists from within and outside South Africa, making the safety of the coastal sea water an important consideration [76,77]. Umgeni Water is a state-owned entity that supplies water to eThekwini Metropolitan Municipality and several smaller municipalities in KwaZulu-Natal province (Figure 1). It oversees approximately 930 km of pipelines, 53 km of tunnels, 14 impoundments, 48 water treatment works, and 11 wastewater treatment works [78].

3.9. Municipal Monitoring Programs

The eThekwini Water and Sanitation Scientific Services program conducts regular sampling from the rivers at different points monthly and tests for a variety of parameters and detects contaminants. The city has developed a color-coded Esri ArcGIS software-generated spatial map detailing the level of pollution and water quality to inform stakeholders of the water quality of major rivers for that month [75]. The municipality also monitors its 100 km coastline beach water quality, collecting samples fortnightly, making results publicly available to inform residents and visitors about the safety of recreational waters [75]. Academic partners such as the Durban University of Technology’s Institute for Water and Wastewater Technology (IWWT) test the beach water quality as well, using traditional water quality monitoring such as periodic sampling and laboratory analysis [75,79,80].
Physicochemical parameters such as temperature, electrical conductivity, and pH have been shown to have positive relationships with the microbial communities in the uMgeni river [80]. The presence of toxic chemicals such as polycyclic aromatic hydrocarbons and polychlorinated biphenyls has been demonstrated in the sediment of aquatic systems in Durban [81]. eThekwini is highly industrial, with a large manufacturing base as the largest municipality in the province of KwaZulu-Natal, and its water sources are exposed to different industrial waste. These compounds typically accumulate in sediment but can become bioavailable through remobilization events, such as floods or dredging or through bioturbation by benthic organisms. Real-time monitoring and use of AI and ML to predict the occurrence of such events is necessary for protecting human health and creating cost effective treatment regimes, since treating drinking water contaminated with sediments and chemical contaminants is more expensive in the Umgeni catchment area [70,82]. A study carried out in the upper uMngeni catchment looking at a 28-year time-series of water quality data from 11 sampling stations assessing pH, electrical conductivity, temperature, turbidity, total suspended solids, and E. coli counts showed that E. coli and turbidity were the most influential variables affecting the recreational and eutrophication WQIs, respectively [83]. Pathogens in the Umgeni river have also been studied using several methods such as membrane filtration for quantifying bacterial indicators, COD, dissolved nutrients, fluorescence such as SYBY Gold staining and transmission electron microscopy, and DNA-based methods for virus detection along physicochemical parameters [84]. While most studies have been conducted on the Umgeni river which is the main contributor for the city’s drinking water and is well regulated, some studies have also looked at water quality in other notable rivers in Durban such as the Umbilo and Umhalangane Rivers, using DNA-PCR-based methods to study viral diversity and abundance [85] and the relationships between physicochemical parameters and microbes [86].
The ongoing CSIR project “Sea Disposal of Sewage: Environmental Surveys in the Durban Outfalls Region” assesses water quality, in terms of chemical, physical, and bacteriological aspects, and other marine life to assist in the regulation of outfall permits providing a four-decade-long dataset [87].

3.10. Smart Water Quality Monitoring Initiatives

Interdisciplinary collaborations are gaining traction in the eThekwini Municipality, with universities and research institutions working together to address water quality challenges. The integration of electronic engineering innovations with microbiological insights holds promise for developing efficient, real-time, and cost-effective water quality monitoring solutions tailored to local needs. The Council for Scientific and Industrial Research (CSIR) has used remote sensing techniques, such as satellite imagery, to study the 50 biggest South African dams, including eThekwini’s Inanda Dam, showing widespread presence of harmful cyanobacteria [72].
Singh & Walingo [48] note that there has been no real-time commercial product for water quality monitoring deployed in South African major water sources and have developed a pilot Smart IOT-WSN Water Quality Monitoring and Pollution Assessment Framework (SWMPAF), a collaboration project between the University of Kwa Zulu Natal and the Water Research Commission of South Africa in the Umgeni River Catchment area. Partnerships with the municipality between the University of KwaZulu-Natal’s Centre of Radio and Rural Access Technologies are using an IOT real-time water monitoring and pollution assessment system targeting emergencies such as crude oil spillage in the Umgeni River, flooding from weather storms into cities main water sources, and other emergencies requiring an immediate response, and the results show promise in transiting to SWQM. [39]. The project aims to develop remote water monitoring using sensors to measure physicochemical parameters and wirelessly transmit data to a control room for management via custom software, even in rugged environments [39]. The CSIR’s Oceans and Coastal Information System (OCIMS) uses satellite remote sensing and geospatial information to provide real-time data on several aspects of the oceans and coastline such as water quality and flooding events [73].

3.11. Challenges

Kruger et al. [57] acknowledge that in South Africa there are good governing laws and guidelines set in place to help protect the water resources and ensure it is of good quality such as the Water Services Act (WSA) (Act 108 of 1997) and the National Water Act (NWA) (Act 36 of 1998), but the country faces a progressively deteriorating infrastructure due to corruption and insufficient funds, an ever-increasing number of toxicants, as well as the identification of emerging chemicals of concern. According to Corruption Watch [88] (2020), mismanagement and misuse of financial resources in the water sector have led to delayed infrastructure upgrades, inflated procurement costs, and poor service delivery. The Daily Maverick [89] reports that over 26% of South Africa’s municipalities have experienced water-related corruption scandals, with funds earmarked for maintenance and monitoring systems often diverted or misappropriated. These governance failures directly undermine the feasibility of implementing Water 4.0 technologies, which require transparent procurement, skilled personnel, and sustained investment.
Deploying state-of-the-art monitoring systems in eThekwini, particularly those involving automation, AI, and IoT-enabled devices, requires significant upfront investment; yet the municipality faces budgetary constraints that limit the adoption of such technologies, a situation that has been worsened by the 2022 severe floods that destroyed infrastructure [78]. Alabe et al. [69] suggest that there is a need to address various limitations in terms of Water 4.0 in South Africa to prepare the country’s water sector for the Industry 4.0 paradigm shift. The region’s vulnerability to flooding, particularly in low-lying informal settlements, raises concerns about the resilience of sensor networks and data transmission infrastructure [90]. Cybersecurity risks are also amplified in decentralized systems, especially when real-time data flows through multiple unsecured endpoints across municipal departments [91,92]. These vulnerabilities include exposure to hacking, ransomware, and data manipulation, which could compromise public health and environmental safety.
Moreover, interoperability between legacy infrastructure and modern IoT platforms may be hindered by fragmented data governance and limited technical capacity [93]. Despite these barriers, eThekwini’s existing partnerships with research institutions and its track record in piloting smart city initiatives suggest a foundation for phased adoption. Strategic planning, robust cybersecurity protocols, and climate-resilient infrastructure design will be essential to ensure that Water 4.0 technologies are not only deployed but sustained in this flood-prone, resource-constrained urban environment.

4. Future Directions and Recommendations

Given that available international SWQM solutions are expensive and difficult to maintain for most African countries, Mukuyu et al. [94] suggest that innovations related to water quality monitoring should be considered in the context of affordability, scalability and flexibility.
To extend the benefits of real-time water quality monitoring across South Africa, scalable solutions are necessary. This involves standardizing monitoring protocols, investing in adaptable technologies, and fostering collaborations between government agencies, research institutions, and the private sector. Rural and peri-urban areas require decentralized, low-cost sensor networks that operate on solar power and transmit data via GSM or LoRaWAN protocols. Modular SWQM kits designed for easy installation and maintenance can be deployed at boreholes, community taps, and informal settlements. These kits should prioritize essential parameters (e.g., turbidity, pH, and E. coli) and be integrated with mobile dashboards accessible to local health workers and water stewards [95]. Affordability can also be enhanced through pooled procurement models, where municipalities collaborate to purchase SWQM technologies in bulk, and through donor-backed pilot programs that subsidize initial deployments. Public–private partnerships with local tech firms and universities can further reduce costs by localizing manufacturing and maintenance. Ultimately, ensuring affordability requires not just cheaper devices but a governance model that aligns funding, training, and community engagement across diverse geographies. The National Water Security Framework emphasizes the importance of integrated water resource management and the adoption of innovative technologies to ensure water security nationwide [96].
Aptly put, “these technologies demand huge investment costs together with high level skills sets which South Africa should have. The fourth industrial revolution (IR4.0) brings with it opportunities to address various aspects of water management and provision of services” [96]. Municipal interactive maps that show beach water quality rely on fortnightly sampling and would benefit from real-time, continuous sampling using fixed sensors recording physicochemical parameters. This is an important consideration given the increase in frequencies and duration of beach water contamination [97]. For eThekwini Municipality, a strong policy framework and the adoption of Water 4.0 principles are essential to bridge existing gaps and shift toward a more proactive, technology-driven, and sustainable approach to water quality management. By aligning with Water 4.0 global trends, municipalities such as eThekwini can adopt innovative and scalable solutions to improve their water quality monitoring systems. Leveraging automation, AI, and cost-effective sensor technologies will be critical in addressing local challenges while contributing to broader efforts in sustainable water management. Research hubs are vital for advancing IoT and Big Data tools to enable real-time monitoring, analysis, and reporting in diverse water management fields through electronic sensor technologies.
This review suggests a set of strategic implementation pathways to operationalize Water 4.0 technologies within eThekwini’s water monitoring system (Table 3). These pathways reflect a blend of technical planning, institutional collaboration, and financial innovation tailored to the municipality’s unique context. The table below summarizes three core models: a phased rollout strategy that enables incremental adoption and learning; public–private partnerships (PPPs) that mobilize expertise and investment; and diversified funding mechanisms designed to overcome cost barriers and ensure long-term sustainability. Together, these approaches offer a roadmap for transitioning from pilot projects to full-scale smart water governance across urban and peri-urban zones.

5. Conclusions

While specific comparative studies within Durban or other South African metros remain limited, global and local advancements in AI-driven and automated water quality monitoring reveal significant potential for transforming water governance. The transition from traditional methods to real-time, data-driven systems offers improved accuracy, responsiveness, and cost-efficiency which are critical attributes for ensuring safe and sustainable water resources in rapidly urbanizing environments.
However, most cutting-edge technologies in South Africa remain confined to academic and research institutions such as the University of KwaZulu-Natal and the Council for Scientific and Industrial Research (CSIR). This disconnect between innovation and implementation is driven by several factors. First, municipalities often lack the technical capacity and digital infrastructure needed to deploy and maintain smart systems. Second, procurement processes are slow and risk-averse, favoring legacy solutions over experimental technologies. Third, there is limited policy alignment between national innovation strategies and local water service mandates, resulting in fragmented funding and unclear accountability. Finally, the absence of commercialization pathways such as local manufacturing, public–private partnerships, and regulatory incentives hinders the scaling of academic prototypes into operational municipal tools.
To overcome these barriers, a forward-looking strategy is essential. Municipalities like eThekwini must prioritize pilot programs that bridge research and practice, supported by innovation grants and donor-backed initiatives. National water policies should be revised to include digital readiness benchmarks and incentives for smart infrastructure adoption. Capacity-building programs must be launched to train municipal staff in data analytics, sensor maintenance, and cybersecurity. Additionally, partnerships with local tech firms can accelerate the development of affordable, modular SWQM kits tailored to South Africa’s diverse urban and rural contexts.
Existing research hubs play a pivotal role in this transition and should be expanded to fully harness IoT, machine learning, and Big Data for real-time monitoring, analysis, and reporting. Future work should focus on developing scalable commercialization models, evaluating the long-term cost-benefit of Water 4.0 systems, and preparing the South African water sector for the broader Industry 4.0 paradigm shift. By aligning innovation with governance, South Africa can move from reactive water management to a proactive, resilient, and equitable future.

Author Contributions

Conceptualization, O.R. and T.W.; methodology, O.R.; validation, O.R. and T.W.; formal analysis, O.R.; writing—original draft preparation, O.R.; writing—review and editing, O.R. and T.W.; funding acquisition, O.R. and T.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Water 17 03299 g001
Figure 1. The components of remote SWMQ.
Figure 1. The components of remote SWMQ.
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Table 1. Comparison of water monitoring methods: global vs. eThekwini Municipality.
Table 1. Comparison of water monitoring methods: global vs. eThekwini Municipality.
MethodDescriptionAdvantagesDisadvantagesGlobal UsageUsage in eThekwiniCost/Scalability in eThekwini
Manual Sampling and Laboratory AnalysisCollection of water samples for lab-based analysis of parameters like pH, DO, and contaminants [22,30].High accuracy; comprehensive parameter analysis.Time-consuming; labor-intensive; not real-time.Standard for regulatory compliance worldwide [17].Routine monitoring by local authorities [31].Low cost, but low scalability due to labor and time constraints.
On-site Testing with Portable KitsPortable kits for field testing of basic water quality parameters [32].Rapid results; cost-effective.Limited parameter range; user error potential.Used in field studies worldwide [33].Applied in quick assessments [34].Moderate cost; moderate scalability in informal settlements and rural areas.
Remote Sensing TechnologiesSatellites and drones to assess water parameters like turbidity [35].Large-area monitoring; trend analysis.Surface observations only; requires validation.Common in environmental monitoring [35].Limited implementation; future potential [36].High cost, low scalability due to technical and data-processing demands.
In situ Sensor Networks (IoT-based)Real-time water monitoring via wireless sensor networks [37].Continuous data; anomaly detection.High setup costs; sensor fouling.Expanding in smart water management [38].Pilot projects exist; broader adoption possible [39].High initial cost, but high scalability if donor-funded or phased in.
AI and Machine Learning ModelsAI for analyzing water data and predicting trends [40].Handles big data; predictive accuracy.Needs high-quality data; complex to develop.Used in predictive water quality management [40].Emerging field; potential future use [41].Moderate cost; scalable if integrated with existing municipal data systems.
Cyber–Physical Systems (CPS)Smart systems integrating real-time monitoring and control [42].Adaptive responses; efficiency improvements.High costs; cybersecurity concerns.Used in smart city water systems [43].Future potential in Durban’s water infrastructure [44].Very high cost; low scalability in current municipal budget context.
Table 2. SWOT Analysis for the feasibility of implementing Water 4.0 technologies in eThekwini Municipality.
Table 2. SWOT Analysis for the feasibility of implementing Water 4.0 technologies in eThekwini Municipality.
StrengthsWeaknesses
Real-time data acquisition improves responsiveness and early warning systemsHigh initial costs for infrastructure and skilled personnel
Integration with AI enables predictive analytics and anomaly detectionLimited interoperability between legacy systems and new technologies
Automation reduces human error and enhances consistencyData privacy and cybersecurity concerns
Scalable platforms adaptable to urban and rural settingsMaintenance and calibration challenges in harsh environments
OpportunitiesThreats
Public-private partnerships can accelerate deploymentBudget constraints in municipalities like eThekwini
International donor support for digital water initiativesResistance to change from traditional operators
Policy alignment with UN SDGs and Water 4.0 frameworksRisk of technological obsolescence without continuous investment
Capacity-building programs through universities and research councilsEnvironmental factors (e.g., floods, droughts) disrupting sensor networks
Table 3. Implementation pathways and strategic models.
Table 3. Implementation pathways and strategic models.
StrategyDescriptionKey ActionsLocal Relevance (eThekwini)
Phased Rollout StrategyGradual implementation to manage risk and build capacity.Phase 1: Pilot in high-priority zones (e.g., industrial discharge, coastal beaches).
Phase 2: Evaluate performance and refine systems.
Phase 3: Scale across municipality with interoperability planning.		Aligns with existing pilot projects and allows incremental investment and learning.
Public-Private Partnerships (PPPs)Collaborative model to mobilize resources and expertise.Partner with Umgeni Water, UKZN, CSIR
Facilitate technology transfer from global vendors.
Offer tax incentives or co-financing for private investment.		Leverages Durban’s strong academic and research ecosystem; mitigates budget constraints.
Funding Models & Financial SustainabilityDiverse financing pathways to overcome cost barriers.Apply for grants from USAID, GIZ, World Bank
Introduce tiered service models.
Explore green bonds and innovation funds.		Supports long-term affordability and aligns with South Africa’s green finance initiatives.
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Rubaba, O.; Walingo, T. Advancing Water Quality Monitoring in eThekwini, South Africa: Integrating Water 4.0, Automation, and AI for Real-Time Surveillance. Water 2025, 17, 3299. https://doi.org/10.3390/w17223299

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Rubaba O, Walingo T. Advancing Water Quality Monitoring in eThekwini, South Africa: Integrating Water 4.0, Automation, and AI for Real-Time Surveillance. Water. 2025; 17(22):3299. https://doi.org/10.3390/w17223299

Chicago/Turabian Style

Rubaba, Owen, and Tom Walingo. 2025. "Advancing Water Quality Monitoring in eThekwini, South Africa: Integrating Water 4.0, Automation, and AI for Real-Time Surveillance" Water 17, no. 22: 3299. https://doi.org/10.3390/w17223299

APA Style

Rubaba, O., & Walingo, T. (2025). Advancing Water Quality Monitoring in eThekwini, South Africa: Integrating Water 4.0, Automation, and AI for Real-Time Surveillance. Water, 17(22), 3299. https://doi.org/10.3390/w17223299

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