The concept of digital twins is one of the fundamental pillars of Industry 4.0. Digital twin allows the realization of a virtual model of a real system, enhancing the relevant performance (e.g., in terms of production rate, risk prevention, energy saving, and maintenance operation). Current literature presents many contributions pointing out the advantages that may be achieved by the definition of a digital twin of a water supply system. The Reference Architecture Model for Industry 4.0 introduces the concept of the Asset Administration Shell for the digital representation of components within the Industry 4.0 ecosystem. Several proposals are currently available in the literature considering the Asset Administration Shell for the realization of a digital twin of real systems. To the best of the authors\' knowledge, at the moment, the adoption of Asset Administration Shell for the digital representation of a water supply system is not present in the current literature. For this reason, the aim of this paper is to present a methodological approach for developing a digital twin of a water supply system using the Asset Administration Shell metamodel. The paper will describe the approach proposed by the author and the relevant model based on Asset Administration Shell, pointing out that its implementation is freely available on the GitHub platform.

Gadolinium-based contrast agents (GBCA) are essential for diagnostic MRI examinations. GBCA are only used in small quantities on a per-patient basis; however, the acquisition of contrast-enhanced MRI examinations worldwide results in the use of many thousands of litres of GBCA per year. Data shows that these GBCA are present in sewage water, surface water, and drinking water in many regions of the world. Therefore, there is growing concern regarding the environmental impact of GBCA because of their ubiquitous presence in the aquatic environment. To address the problem of GBCA in the water system as a whole, collaboration is necessary between all stakeholders, including the producers of GBCA, medical professionals and importantly, the consumers of drinking water, i.e. the patients. This paper aims to make healthcare professionals aware of the opportunity to take the lead in making informed decisions about the use of GBCA and provides an overview of the different options for action.In this paper, we first provide a summary on the metabolism and clinical use of GBCA, then the environmental fate and observations of GBCA, followed by measures to reduce the use of GBCA. The environmental impact of GBCA can be reduced by (1) measures focusing on the application of GBCA by means of weight-based contrast volume reduction, GBCA with higher relaxivity per mmol of Gd, contrast-enhancing sequences, and post-processing; and (2) measures that reduce the waste of GBCA, including the use of bulk packaging and collecting residues of GBCA at the point of application.Critical relevance statement This review aims to make healthcare professionals aware of the environmental impact of GBCA and the opportunity for them to take the lead in making informed decisions about GBCA use and the different options to reduce its environmental burden.Key points• Gadolinium-based contrast agents are found in sources of drinking water and constitute an environmental risk.• Radiologists have a wide spectrum of options to reduce GBCA use without compromising diagnostic quality.• Radiology can become more sustainable by adopting such measures in clinical practice.

This study examined the factors that affect the private sectors\' willingness to invest in rural water supply. The study applied a mixed methods approach, including an overview of relevant studies, expert consultation, exploratory factor analysis using SPSS software, and a fuzzy-analytic hierarchy process to identify and evaluate the factors applicable to Ha Nam province in Vietnam. Some factors were distinguished that are significant to private investors\' rural water supply investment decisions, including tax incentive policy, policies to support preferred access to loans and credit, a state risk-sharing mechanism, a mechanism to adjust water price, community support, high community demand for clean water, and input water quality. In addition, the study constructed an investment attractiveness index to evaluate the attractiveness of private sector investment for two typical rural water supply projects in Ha Nam province. This index can be used as a basis for the government to design appropriate incentives to attract investment from private investors and construct an investment attractiveness map.

Water quality extremes, which water quality models often struggle to predict, are a grave concern to water supply facilities. Most existing water quality models use mean error functions to maximize the predictability of water quality mean value. This paper describes a composite quantile regression neural network (CQRNN) model, which simultaneously estimates non-crossing regression quantiles by minimizing the composite quantile regression error function. This method can improve the prediction of extremes. This paper evaluates the performance of CQRNN for predicting extreme values of turbidity and total organic carbon (TOC) and compares with quantile regression (QR), linear regression (LR), and k-nearest neighbors (KNN) in an application to the Hetch Hetchy Regional Water System, which is the primary water supply for San Francisco, CA. CQRNN is superior to QR, LR, and KNN for predicting the mean trend and extremes of turbidity and TOC, especially for the non-Gaussian turbidity data. The performance of CQRNN is the most stable relative to other methods over different training sample sizes.

Pathogenic Legionella species grow optimally inside free-living amoebae to concentrations that increase risks to those who are exposed. The aim of this study was to screen a complete drinking water system and cooling towers for the occurrence of Acanthamoeba spp. and Naegleria fowleri and their cooccurrence with Legionella pneumophila, Legionella anisa, Legionella micdadei, Legionella bozemanii, and Legionella longbeachae. A total of 42 large-volume water samples, including 12 from the reservoir (water source), 24 from two buildings (influents to the buildings and exposure sites (taps)), and six cooling towers were collected and analyzed using droplet digital PCR (ddPCR). N. fowleri cooccurred with L. micdadei in 76 (32/42) of the water samples. In the building water system, the concentrations of N. fowleri and L. micdadei ranged from 1.5 to 1.6 Log10 gene copies (GC)/100 mL, but the concentrations of species increased in the cooling towers. The data obtained in this study illustrate the ecology of pathogenic Legionella species in taps and cooling towers. Investigating Legionella\'s ecology in drinking and industrial waters will hopefully lead to better control of these pathogenic species in drinking water supply systems and cooling towers.

Microplastic (MP) pollution has received widespread attention; however, its occurrence and distribution in water supply systems, particularly in pipe scales, remains unclear. In this study, MPs were observed in water and pipe scale samples from the drinking water treatment plant (DWTP) and distribution system (DWDS), respectively. The MP concentrations ranged from 13.23 to 134.79 n/L and 569.99 to 751.73 n/kg in the water and pipe scale samples, respectively. The predominant particles in the pipe scales (50-100 μm) were smaller than those in the water samples (> 200 μm). Overall, MP fragments were the most abundant. Of all the identified MPs, nylon and polyvinyl chloride were predominant in the water and pipe scale samples, respectively. Furthermore, the DWTP and DWDS both prevented MPs from entering the tap water, thereby reducing their risk. The results of this study provide direct evidence for the strong adsorption of MPs onto pipe scales, indicating that pipe scale stability may play a role in improving water quality and security. However, the abundance of MPs in pipe scales cannot be ignored. Additionally, the results provide valuable background information on MP pollution in water supply systems.

Monitoring of disinfection by-products (DBPs) in water supply system is important to ensure safety of drinking water. Yet it is a laborious job. Developing predictive DBPs models using simple and easy parameters is a promising way. Yet current models could not be well applied into practice because of the improper dataset (e.g. not from real tap water) they used or involving the parameters that are difficult to measure or require expensive instruments. In this study, four simple and easy water quality parameters (temperature, pH, UVA254 and Cl2) were used to predict trihalomethane (THMs) occurrence in tap water. Linear/log linear regression models (LRM) and radial basis function artificial neural network (RBF ANN) were adopted to develop the THMs models. 64 observations from tap water samples were used to develop and test models. Results showed that only one or two parameters entered LRMs, and their prediction ability was very limited (testing datasets: N25 = 46-69%, rp = 0.334-0.459). Different from LRM, the prediction accuracy of RBF ANNs developed with pH, temperature, UVA254 and Cl2 can be improved continuously by tweaking the maximum number of neuron (MN) and Gaussian function spread (S) until it reached best. The optimum RBF ANNs of T-THMs, TCM and BDCM were obtained when setting MN = 20, S = 100, 100.1 and 60, respectively, where the N25 and rp values for testing datasets reached 85-92% and 0.813-0.886, respectively. Accurate predictions of THMs by RBF ANNs with these four simple and easy parameters paved an economic and convenient way for THMs monitoring in real water supply system.

Drinking water provision has been a constant challenge in the Sahrawi refugee camps, located in the desert near Tindouf (Algeria). The drinking water supply system is itself divided in three zones which pump groundwater from different deep aquifers. It is equipped with reverse osmosis plants and chlorination systems for treating water. The allocation of water supplied to the Saharawi refugees for human consumption in 2016 has been estimated at between 14 and 17 L/person/day on average. This supplied water volume is below recommended standards, and also below the strategic objective of the Sahrawi government (20 L/person/day). Yet the local groundwater resources are huge in comparison with estimated consumption, and hence there is great potential for increasing the supplied volume through effecting improvements in the supply system. The physico-chemical quality of the raw and supplied water between 2006 and 2016 has been assessed according to Algerian standards for human consumption. The raw water of two zones of the supply system presents a very high conductivity and high concentrations of chloride, nitrate, fluoride, sulfate, sodium, calcium, potassium and iodide concentrations of natural origin, which may entail health risks. The treatment of water in a reverse osmosis plant greatly improves its quality and osmosed water met the standards. However, the supply of osmosed and raw water needs to be combined in Zone 1, to avoid an excessive reduction in water volume, and the supplied raw water poses a risk to the health of the refugees. The present study provides an example of a drinking water supply system under extreme drought conditions and in the political and social conditions of a refugee camp. Furthermore, it establishes a reference for supplied water allocation and quality in the Sahrawi refugee camps.

Despite the existence of significant knowedge on complex mechanisms of THM formation, a simple kinetic model to predict THM species concentration is not available, hindering application of knowlwdge for regulatory, monitoring and operational control. The parallel second order reaction (2R) model containing fast and slow reactants has been well established to describe the chlorine decay kinetics under distribution conditions. The proposed THM species model expands the 2R model by systematically incorporating the initial unproductive (not forming THM) chlorine consumption and assuming each THM species is formed at a fixed yield (µg-THM species/mg- productive chlorine consumption). The model concept is tested on 15 water samples that contain a wide range of dissolved organic carbon, specific UV absorbence, and bromide concentrations collected from Australia and US. In all samples, the model describes the THM species concentrations well (error < 3 µg/L in 84% of model estimates) as long as the chlorine profile is described accurately (R2 > 0.984). The model formulated from the minimum data (initial and two other data points of Cl2 and THM species) predicts the rest of concentrations of THM species from only chlorine measurements. To fully optimise the system or adopt in regulatory monitoring, the effect of changes due to bulk water quality, operational conditions and wall (and biofilm) effects on THM formation kinetics should be established. A similar concept could be extended to other DBP, but rigorous testing is needed.