On the Use of an IoT Integrated System for Water Quality Monitoring and Management in Wastewater Treatment Plants

Bioelectrochemical biosensors for water quality assessment and wastewater monitoring

Bioelectrochemical biosensors offer a promising approach for real-time monitoring of industrial bioprocesses. Many bioelectrochemical biosensors do not require additional labelling reagents for target molecules. This simplifies the monitoring process, reduces costs, and minimizes potential contamination risks. Advancements in materials science and microfabrication technologies are paving the way for smaller, more portable bioelectrochemical biosensors. This opens doors for integration into existing bioprocessing equipment and facilitates on-site, real-time monitoring capabilities. Biosensors can be designed to detect specific heavy metals such as lead, mercury, or chromium in wastewater. Early detection allows for the implementation of appropriate removal techniques before they reach the environment. Despite these challenges, bioelectrochemical biosensors offer a significant leap forward in wastewater monitoring. As research continues to improve their robustness, selectivity, and cost-effectiveness, they have the potential to become a cornerstone of efficient and sustainable wastewater treatment practices.

Evaluation of low-cost sensors to integrate in a water quality monitor for real-time measurements

Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from - 0.33 to 33.77%, and most of them were ≤ 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors.

Data-driven approaches linking wastewater and source estimation hazardous waste for environmental management

Industrial enterprises are major sources of contaminants, making their regulation vital for sustainable development. Tracking contaminant generation at the firm-level is challenging due to enterprise heterogeneity and the lack of a universal estimation method. This study addresses the issue by focusing on hazardous waste (HW), which is difficult to monitor automatically. We developed a data-driven methodology to predict HW generation using wastewater big data which is grounded in the availability of this data with widespread application of automatic sensors and the logical assumption that a correlation exists between wastewater and HW generation. We created a generic framework that used representative variables from diverse sectors, exploited a data-balance algorithm to address long-tail data distribution, and incorporated causal discovery to screen features and improve computation efficiency. Our method was tested on 1024 enterprises across 10 sectors in Jiangsu, China, demonstrating high fidelity (R² = 0.87) in predicting HW generation with 4,260,593 daily wastewater data.

Portable IC system enabled with dual LED-based absorbance detectors and 3D-printed post-column heated micro-reactor for the simultaneous determination of ammonium, nitrite and nitrate

BACKGROUND

The on-site and simultaneous determination of anionic nitrite (NO2-) and nitrate (NO3-), and cationic ammonium (NH4+), in industrial and natural waters, presents a significant analytical challenge. Toward this end, herein a 3D-printed micro-reactor with an integrated heater chip was designed and optimised for the post-column colorimetric detection of NH4+ using a modified Berthelot reaction. The system was integrated within a portable and field deployable ion chromatograph (Aquamonitrix) designed to separate and detect NO2- and NO3-, but here enabled with dual LED-based absorbance detectors, with the aim to provide the first system capable of simultaneous determination of both anions and NH4+ in industrial and natural waters.

RESULTS

Incorporating a 0.750 mm I.D. 3D-printed serpentine-based microchannel for sample-reagent mixing and heating, the resultant micro-reactor had a total reactor channel length of 1.26 m, which provided for a reaction time of 1.42 min based upon a total flow rate of 0.27 mL min-1, within a 40 mm2 printed area. The colorimetric reaction was performed within the micro-reactor, which was then coupled to a dedicated 660 nm LED-based absorbance detector. By rapidly delivering a reactor temperature of 70 °C in just 40 s, the optimal conditions to improve reaction kinetics were achieved to provide for limits of detection of 0.1 mg L-1 for NH4+, based upon an injection volume of just 10 μL. Linearity for NH4+ was observed over the range 0-50 mg L-1, n = 3, R2 = 0.9987. The reactor was found to deliver excellent reproducibility when included as a post-column reactor within the Aquamonitrix analyser, with an overall relative standard deviation below 1.2 % for peak height and 0.3 % for peak residence time, based upon 6 repeat injections.

SIGNIFICANCE

The printed post-column reactor assembly was integrated into a commercial portable ion chromatograph developed for the separation and detection of NO2- and NO3-, thus providing a fully automated system for the remote and simultaneous analysis of NO2-, NO3-, and NH4+ in natural and industrial waters. The fully automated system was deployed externally within a greenhouse facility to demonstrate this capability.

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These predictive insights play a crucial role in decision-making processes aimed at safeguarding water quality, such as identifying areas in need of immediate attention and implementing preventive measures to avert contamination. This paper aims to provide a comprehensive review of the current state of the art in water quality monitoring, with a specific focus on the employment of IoT wireless technologies and ML techniques. The study examines the utilization of a range of IoT wireless technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, in the context of monitoring water quality. Furthermore, it explores the application of both supervised and unsupervised ML algorithms for analyzing and interpreting the collected data. In addition to discussing the current state of the art, this survey also addresses the challenges and open research questions involved in integrating IoT wireless technologies and ML for water quality monitoring (WQM).

Dynamic Modelling, Process Control, and Monitoring of Selected Biological and Advanced Oxidation Processes for Wastewater Treatment: A Review of Recent Developments

This review emphasizes the significance of formulating control strategies for biological and advanced oxidation process (AOP)-based wastewater treatment systems. The aim is to guarantee that the effluent quality continuously aligns with environmental regulations while operating costs are minimized. It highlights the significance of understanding the dynamic behaviour of the process in developing effective control schemes. The most common process control strategies in wastewater treatment plants (WWTPs) are explained and listed. It is emphasized that the proper control scheme should be selected based on the process dynamic behaviour and control goal. This study further discusses the challenges associated with the control of wastewater treatment processes, including inadequacies in developed models, the limitations of most control strategies to the simulation stage, the imperative requirement for real-time data, and the financial and technical intricacies associated with implementing advanced controller hardware. It is discussed that the necessity of the availability of real-time data to achieve reliable control can be achieved by implementing proper, accurate hardware sensors in suitable locations of the process or by developing and implementing soft sensors. This study recommends further investigation on available actuators and the criteria for choosing the most appropriate one to achieve robust and reliable control in WWTPs, especially for biological and AOP-based treatment approaches.

Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication

The increasing demand for water and worsening climate change place significant pressure on this vital resource, making its preservation a global priority. Water quality monitoring programs are essential for effectively managing this resource. Current programs rely on traditional monitoring approaches, leading to limitations such as low spatiotemporal resolution and high operational costs. Despite the adoption of novel monitoring approaches that enable better data resolution, the public's comprehension of water quality matters remains low, primarily due to communication process deficiencies. This study explores the advantages and challenges of using Internet of Things (IoT) and citizen science as alternative monitoring approaches, emphasizing the need for enhancing public communication of water quality data. Through a systematic review of studies implemented on-field, we identify and propose strategies to address five key challenges that IoT and citizen science monitoring approaches must overcome to mature into robust sources of water quality information. Additionally, we highlight three fundamental problems affecting the water quality communication process and outline strategies to convey this topic effectively to the public.

An IoT-based water contamination analysis for aquaculture using lightweight multi-headed GRU model

Maintaining the quality of water is essential for the health and productivity of aquatic organisms, including fish in aquaculture ponds. However, water contamination can severely impact fish health and survival, making it necessary to develop monitoring systems that can detect early signs of water contamination. Initial deep learning models had limitations in capturing the temporal and spatial dependencies of time-series data, which can lead to inaccurate predictions. In this paper, we propose a smart monitoring system that uses IoT devices to collect water quality data and segment it into contaminated and non-contaminated categories based on a water toxic index (WTI), a measure of water contamination levels. To address the limitations of early deep learning models for classification of toxic and non-toxic water quality, an enhanced light-weight multi-headed gated recurrent unit (MHGRU) model that captures the spatial and temporal dependencies of water quality parameters. Our study demonstrates that the proposed model outperforms existing models, achieving an impressive accuracy of 99.7% when evaluated on real-time data. Notably, our model also excels when tested on a public dataset, achieving an accuracy of 99.12%. In comparison, best performed existing ANN models achieve accuracies of 99.52% and 98.71% on the respective datasets.

Resource Allocation Optimization in IoT-Enabled Water Quality Monitoring Systems

Water quality monitoring systems that are enabled by the Internet of Things (IoT) and used in water applications to collect and transmit water data to data processing centers are often resource-constrained in terms of power, bandwidth, and computation resources. These limitations typically impact their performance in practice and often result in forwarding their data to remote stations where the collected water data are processed to predict the status of water quality, because of their limited computation resources. This often negates the goal of effectively monitoring the changes in water quality in a real-time manner. Consequently, this study proposes a new resource allocation method to optimize the available power and time resources as well as dynamically allocate hybrid access points (HAPs) to water quality sensors to improve the energy efficiency and data throughput of the system. The proposed system is also integrated with edge computing to enable data processing at the water site to guarantee real-time monitoring of any changes in water quality and ensure timely access to clean water by the public. The proposed method is compared with a related method to validate the system performance. The proposed system outperforms the existing system and performs well in different simulation experiments. The proposed method improved the baseline method by approximately 12.65% and 16.49% for two different configurations, demonstrating its effectiveness in improving the energy efficiency of a water quality monitoring system.

Automated Drone-Delivery Solar-Driven Onsite Wastewater Smart Monitoring and Treatment System

Treating potential polluted water sources is urgent and challenging, especially for natural water sources. Numerous research groups focus on either smart water monitoring or new adsorbent. However, either aspect alone is insufficient for complex nature water source treatment. Here, integrating the state-of-art machine learning technique, a sustainable silk-based bioadsorbent, and wireless Internet of Things, an integrated automated drone-delivery solar driven onsite water monitoring & treatment system (WMTS) for the contaminated nature water sources is developed. In short, the embedded sensors and microprogrammed control unit capture and upload the real-time monitoring data to the cloud server for data analysis and optimized treatment strategy. Meanwhile, a grid map system based on the satellite remote sensing images directs the minimum number of WMTS units to cover the entire polluted region. Finally, unmanned aerial vehicles provide autonomous dispatch, operation, and maintenance, especially in hard-to-reach sites. Overall, this work offers a general, sustainable, energy-efficient, and closed-loop solution toward efficiently alerting and on-site treating nature water source contamination.

Low-Cost Water Quality Sensors for IoT: A Systematic Review

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices.

Design of Water Quality Monitoring System in Shaanxi Section of Weihe River Basin Based on the Internet of Things

Monitoring environmental water quality in an efficient, cheap, and sustainable way can better serve the country's strategic requirements for water resources and water ecological protection. This paper takes the Shaanxi section of the Weihe River Basin as a pilot project and aims to use the Internet of Things technology to develop water quality monitoring sensors, so as to realize the construction of low-cost, high-reliability water quality monitoring demonstration applications. First of all, we established the design of the water quality collection terminal, designed the low-power water quality sensor node, supported the Internet of Things protocol and the collection of various water quality parameters, and used networking for data transmission. Secondly, we use the ant colony algorithm-based system clustering model to obtain a cluster map of water quality monitoring tasks in a certain section of the Weihe River Basin. We take the task clustering graph as an example for analysis, optimize the monitoring model through the ant colony algorithm, and obtain the weight of the optimization index. The weight of the scheduled task limit of the monitoring point becomes larger, so the release of the monitoring task mainly affects the limit of the scheduled task of the monitoring point. Through the above work, we designed and implemented a set of online water quality monitoring system based on the Internet of Things and data mining technology. The system can provide reference for large-scale water resource protection and water environment governance.

Modelling of wastewater treatment, microalgae growth and harvesting by flocculation inside photo bioreactor using machine learning technique

Biological wastewater treatment with the use of algae-bacteria consortia for the uptake of nutrient and recovery of resource is considered as the ‘paradigm shift’ from the process of mainstream wastewater treatment plants (WWTPs) so as to mitigate the pollution and thus promoting the circular economy. In this regard, the application of machine learning algorithms (MLAs) was found to be effectual and beneficial for the prediction of uncertain performances in the process of treatment and it shows a satisfactory result for the effective optimization, monitoring, uncertainty prediction and so on in the environment systems. The proposed approach aims at modelling the treatment of wastewater, growth of micro algae and flocculation harvesting at the photobioreactor (PBR) along with the utilization of machine learning techniques. Initially, the raw data from the PBR was taken and is pre-processed using z-score normalization technique followed by extraction and selection of features that are more appropriate. The Adaptive neuro-fuzzy inference system (ANFIS) model is built along with the modified Fuzzy C-Means algorithm (MFCM) so as to cluster the huge amount of data. ANFIS is employed for the estimation of controller output parameters and for controlling the temperature inside the reactor. The output controller parameter performance can be enhanced by the use of optimization approach. The discrete Multilayer perceptron (DMLP) with the hyper tuning parameters of Iterative Levi’s Flight Dependent Cuckoo search optimization algorithm (ILF-CSO) is employed for the prediction purpose of attained cultivation growth rate and the pH of treated wastewater. The optimization technique based on machine learning model in turn offers the best possible solution needed for the estimation of output parameters. Thus, the removal rate of effluent T-N concentrations from the wastewater treatment is predicted with some intervals of day. At last, the performance is estimated in terms of growth rate, temperature variations, biomass, nitrate and phosphate concentrations, and error rates (RMSE, APE), and determination coefficient (R2). The attained outcome shows that the presented model is effectual and has the potential to apply for controlling and predicting the biological wastewater treatment plants.

A New Area of Utilizing Industrial Internet of Things in Environmental Monitoring

Due to environmental regulations continually reducing emission quantity allowed over time, there is a growing need for adaptable and feasible environmental monitoring, such as emission, wastewater quality, and air pollution monitoring, for the process industry (and surrounding communities). Alternative environmental monitoring and process monitoring technologies based on industrial internet of things (IIoT) and artificial intelligence (AI) enable the process industry to take a proactive approach toward the environment and asset integrity management. The monitoring devices can be deployed in a stationary or dynamic manner. In this study, the emerging trend and various applications of IIoT and advanced data analytics methodologies in environmental monitoring are reviewed. An example showing challenges and research needs in sensor placement is given. Future directions in technology, regulation, and application have been discussed as well.

The Effects of Wastewater Treatment Plant Failure on the Gulf of Gdansk (Southern Baltic Sea)

In August 2019 and during August/September 2020, the main collection system of the Wastewater Treatment Plant (WWTP) in Warsaw, Poland, malfunctioned. During that system failure, over 4.8 million m³ of untreated wastewater was dropped directly into the Vistula River in just a few days. It is currently considered as one of the largest known failures of WWTP worldwide. In order to assess the environmental impact, water samples were collected from 2 spots at the Vistula river estuary (406 and 415 km from the discharge location, respectively), and 4 spots at the Gulf of Gdansk, situated on the southern shore of the Baltic Sea. The sampling was conducted before the wastewater wave reached the Vistula river’s mouth, followed by daily sampling during 21 days after the malfunction occurred. The study showed the decline in water quality at the Vistula river estuary and the Baltic shore waters as the wave of wastewater reached those points, despite being situated over 400 km downstream from the place of the accident. Those changes included the reduction in the dissolved oxygen content (by 0.69-fold at its peak), the increase in Total Organic Carbon (TOC) (by 1.28-fold at its peak), nitrate-nitrogen (N-NO₃) (by 1.68-fold at its peak), phosphorous (P) (by 2.41-fold at its peak), conductivity (by 16.8-fold at its peak), and Chemical Oxygen Demand (COD) (by 1.84-fold). In the samples from the Vistula river, the decline in water quality was seen as incidental and lasted 2–3 days. Subsequently, the levels of physical and chemical parameters returned to the levels from before the accident. However, the changes in the Gulf of Gdańsk lasted significantly longer, especially on the West side of the Vistula river, where, even after 21 days from the initial accident, some parameters remained altered.

IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies

In recent years, smart cities have been significantly developed and have greatly expanded their potential. In fact, novel advancements to the Internet of things (IoT) have paved the way for new possibilities, representing a set of key enabling technologies for smart cities and allowing the production and automation of innovative services and advanced applications for the different city stakeholders. This paper presents a review of the research literature on IoT-enabled smart cities, with the aim of highlighting the main trends and open challenges of adopting IoT technologies for the development of sustainable and efficient smart cities. This work first provides a survey on the key technologies proposed in the literature for the implementation of IoT frameworks, and then a review of the main smart city approaches and frameworks, based on classification into eight domains, which extends the traditional six domain classification that is typically adopted in most of the related works.

Internet of things for secure surveillance for sewage wastewater treatment systems

IoT is a secure communication technology used to transfer data from a physical entity to a device with intelligent analysis tools through a wireless channel. The wastewater treatment method extracts pollutants and transforms them into effluents added to the water supply with minimal environmental effects or recovered directly. The major issue is monitoring the disposal of sewage in the treatment plants. Hence, this paper, Surveillance-based Sewage Wastewater Monitoring System (SSWMS) with IoT, has been proposed for monitoring wastewater treatment and improving water quality. A smart water sensor enabled by IoT monitors water quality, water pressure, and water temperature and quantifies water dynamics to map water flow through the entire treatment facility. The proposed method calculates the wastewater treatment facility's effectiveness and ensures that chemical releases are maintained below allowable levels. Thus, the experimental results show the improved recycling water quality level is raised to 97.98%, enhancing secure communication and less moisture content when compared to other methods.

Sensors for Sustainable Smart Cities: A Review

Experts confirm that 85% of the world’s population is expected to live in cities by 2050. Therefore, cities should be prepared to satisfy the needs of their citizens and provide the best services. The idea of a city of the future is commonly represented by the smart city, which is a more efficient system that optimizes its resources and services, through the use of monitoring and communication technology. Thus, one of the steps towards sustainability for cities around the world is to make a transition into smart cities. Here, sensors play an important role in the system, as they gather relevant information from the city, citizens, and the corresponding communication networks that transfer the information in real-time. Although the use of these sensors is diverse, their application can be categorized in six different groups: energy, health, mobility, security, water, and waste management. Based on these groups, this review presents an analysis of different sensors that are typically used in efforts toward creating smart cities. Insights about different applications and communication systems are provided, as well as the main opportunities and challenges faced when making a transition to a smart city. Ultimately, this process is not only about smart urban infrastructure, but more importantly about how these new sensing capabilities and digitization developments improve quality of life. Smarter communities are those that socialize, adapt, and invest through transparent and inclusive community engagement in these technologies based on local and regional societal needs and values. Cyber security disruptions and privacy remain chief vulnerabilities.

Perspective of Spirulina culture with wastewater into a sustainable circular bioeconomy

Spirulina biomass accounts for 30% of the total algae biomass production globally. In conventional process of Spirulina biomass production, cultivation using chemical-based culture medium contributes 35% of the total production cost. Moreover, the environmental impact of cultivation stage is the highest among all the production stages which resulted from the extensive usage of chemicals and nutrients. Thus, various types of culture medium such as chemical-based, modified, and alternative culture medium with highlights on wastewater medium is reviewed on the recent advances of culture media for Spirulina cultivation. Further study is needed in modifying or exploring alternative culture media utilising waste, wastewater, or by-products from industrial processes to ensure the sustainability of environment and nutrients source for cultivation in the long term. Moreover, the current development of utilising wastewater medium only support the growth of Spirulina however it cannot eliminate the negative impacts of wastewater. In fact, the recent developments in coupling with wastewater treatment technology can eradicate the negative impacts of wastewater while supporting the growth of Spirulina. The application of Spirulina cultivation in wastewater able to resolve the global environmental pollution issues, produce value added product and even generate green electricity. This would benefit the society, business, and environment in achieving a sustainable circular bioeconomy.

Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data

As cities grow in size and number of inhabitants, continuous monitoring of the environmental impact of sound sources becomes essential for the assessment of the urban acoustic environments. This requires the use of management systems that should be fed with large amounts of data captured by acoustic sensors, mostly remote nodes that belong to a wireless acoustic sensor network. These systems help city managers to conduct data-driven analysis and propose action plans in different areas of the city, for instance, to reduce citizens’ exposure to noise. In this paper, unsupervised learning techniques are applied to discover different behavior patterns, both time and space, of sound pressure levels captured by acoustic sensors and to cluster them allowing the identification of various urban acoustic environments. In this approach, the categorization of urban acoustic environments is based on a clustering algorithm using yearly acoustic indexes, such as Lday, Levening, Lnight and standard deviation of Lden. Data collected over three years by a network of acoustic sensors deployed in the city of Barcelona, Spain, are used to train several clustering methods. Comparison between methods concludes that the k-means algorithm has the best performance for these data. After an analysis of several solutions, an optimal clustering of four groups of nodes is chosen. Geographical analysis of the clusters shows insights about the relation between nodes and areas of the city, detecting clusters that are close to urban roads, residential areas and leisure areas mostly. Moreover, temporal analysis of the clusters gives information about their stability. Using one-year size of the sliding window, changes in the membership of nodes in the clusters regarding tendency of the acoustic environments are discovered. In contrast, using one-month windowing, changes due to seasonality and special events, such as COVID-19 lockdown, are recognized. Finally, the sensor clusters obtained by the algorithm are compared with the areas defined in the strategic noise map, previously created by the Barcelona city council. The developed k-means model identified most of the locations found on the overcoming map and also discovered a new area.

Smart Earth Technologies: a pressing need for abating pollution for a better tomorrow

Standing at the cusp of an augmented age facilitates a glance into the future of a cybernetic world aligned with planetary wellbeing. The era of exponential technological revolutions has brought with it a plethora of opportunities expanding in a multi-faceted dimension with an added emphasis towards nurturing a mutual synergy of nature with a daily dose of digitalization. The paper is written with an intent to lay out an accumulated comprehensive review of different literary works which lay the grounds for how different Smart Earth Technologies aid in monitoring and tackling the degradation of air and water resources. If an intertwined state-of-the-art centralized research source could be created, it would become a boon for seasoned researchers and neophytes succeeding portion of the article expands itself to a wide variety of research literature complimented with real-time models, case, and empirical studies which help heighten the previous limit to the research done on these Technologies tinkering the present monitoring systems. The primary aim of this work is to fuel the need of theoretical, practical, and empirical evolution in the ways the intelligent technologies help blossom a pollution-free environment. The secondary intention was to ensure that in-depth study of Smart Environmental Pollution the Monitoring Systems provisioned a multitude of prospects for upgrading one's knowledge on environmental management through current world technologies. By looking at these trends of the past, the enthusiast of technology could collaborate with the researchers of Environmental Pollution to assist in proliferation of diverse 'smart' solutions creating a Smarter, Greener, and Brighter future for research and developments in Sustainable Technologies devising a pollution-free environment.

Smart Urban Water Networks: Solutions, Trends and Challenges

This Editorial presents the paper collection of the Special Issue (SI) on Smart Urban Water Networks [...]