Around 30–40 years ago, principles and methods were developed to conceptualise, assess and manage risk. These principles and methods are still, to a large extent, the foundation of the field. Over the past decade, many tools have been developed for risk analysis of water systems. Many advances have been made, both in the theoretical platform and in practical models and procedures. Various risk analysis approaches have been used to identify potential hazards, calculate the probability of accidents and assess the severity of consequences. The objective of this paper is to review these developments, focusing on the fundamental ideas and thinking behind them, considering their application at strategic, programmatic and operational levels of decision-making, in order to improve the understanding of stakeholders (researchers, regulators, etc.). To achieve this objective, scientific papers on risk analysis associated with water treatment systems were identified and reviewed, with particular focus on risk assessment methods (qualitative, semi-qualitative or quantitative, deterministic or probabilistic, etc.), tools (ETA, FTA, FMEA/FMECA, QMRA, HRA, Markov, etc.), applicability of these tools and results of case studies. A total of 141 references were selected on the basis of title sorting from databases as ScienceDirect, PubMed, Scopus, ISI Web of Science or SpringerLink, and a total of 68 articles were selected for full-text analysis. Main conclusions of this review and analysis efforts are as follows: (1) the scientific foundation of risk assessment and risk management is still an open issue; (2) principles, theories and base methods applicable to water supply systems are in continuous development, existing tools are suitable, and a growing number of applications are available and of great interest; and (3) risk analysis methodologies are in their journey to gain the necessary broad technical, community and political acceptance in the water treatment sector, and some gaps and opportunities have been included in the discussion.

Water-related diseases, particularly waterborne diseases, remain significant sources of morbidity and mortality worldwide but especially in developing countries. Emerging waterborne pathogens represent a major health risk. Cryptosporidium is one such pathogen which is globally recognized as a major cause of diarrhea in children and adults. The objective of this paper is not only to review published studies on the impact of these emerging waterborne pathogens but also to identify the various risk factors that favor their transmission. A number of envisaged and needed actions to tackle the challenge of these pathogens in Africa have also been discussed. We have searched the web of ScienceDirect, PubMed, Scopus. ISI Web of Science, SpringerLink, and Google Scholar. The first database search yielded 3099 articles. As a result, 141 studies were submitted for abstract review. A total of 68 articles were selected for full text analysis. After evaluating a considerable number of articles on this topic, the following results were obtained. A number of pathogens are likely to present public health risks, including Cryptosporidium. The contaminating potential of these pathogens is associated with a multitude of factors, such as the effects of climate change, social and behavioral aspects of local populations, water issues, geographical locations that may cause isolation, and inequalities due to lack of transparency of governments in the distribution of financial resources. We stress the need to maintain and strengthen real-time surveillance and rapid epidemiological responses to outbreaks and the emergence of new waterborne pathogens in all countries. African governments, for their part, should be aware of future risks of waterborne protozoan diseases. They must provide immediate and effective responses by establishing technical and financial mechanisms to ensure sufficient quantities of safe drinking water, sewage disposal, and hygiene for all.

Risk-informed decision making permits a more effective water safety management. In this framework, this article introduces the rationale and proposes a new approach to carry out a quantitative risk assessment along the water chain, from river source to tap water, by integrating predictive modelling combined with event-tree and fault-tree techniques. The model developed by this approach could not only account for normal but also for abnormal process conditions in the water treatment plant, as well as assess the real impact of the applied safety controls, such as turbidity control. A sensitivity study was conducted to determine the effect of considering a typical drinking water treatment plant (DWTP), i.e. coagulation, sedimentation and filtration with two turbidity controls (on intake and after filtration) on the risk of infection due to exposure to Cryptosporidium in tap water. The results showed that, with the current effectiveness of turbidity reduction in the DWTP, the first control did not minimise the annual risk of Cryptosporidium infection (3.6E-04) and only limiting turbidity after filtration to below 0.01NTU provided a clear reduction in risk (7.7E-05) at the cost of rejecting 60 % of the water after the control. The lowest risk was found when turbidity reduction was set at 4 logs (8.48E-06), although this means that the effectiveness of turbidity reduction should be greatly improved. It was therefore concluded that supplementing the current treatment with alternative barriers such as UV or ozone disinfection and/or implementing direct control of Cryptosporidium concentration should be considered.

In developing countries, diseases related to lack of water or inadequate water quality cause the death of approximately 5 million people annually, of whom about 1.8 million die from diarrhoeal diseases, 90% of them being children under five, which is equivalent to 4,500 children per day (WHO, 2004; Rojas, 2006). The WHO reports that improvements in water quality alone reduce morbidity from diarrhoeal diseases by a third or more (WHO, 2007), and drinking water treatment plants  are currently the focus of Protozoan studies on water supply. Giardia and Crytosporidium are two of the protozoa that are currently of greatest interest due to their resistance to conventional disinfection processes (Johnson et al., 2003), and they can exist in the presence of high concentrations of free chlorine (Corbitt, 1999). A reality that confirms this fact is that 98% of the individuals affected by epidemic outbreaks in the United States were supplied by drinking water plants using a conventional treatment system. The DWTP of the Dam that caters 859885 people, corresponds to a series of municipalities in the northern area of the metropolitan area of Valencia (Spain), and the central-north-western districts of the city of Valencia face this problem. In a study conducted from 2006 to 2010, in the water used for human consumption in the city of Valencia, some positive concentrations of networked oocysts were detected. In conclusion, it is possible that the protozoa entered the network, as the analyses indicate, and that the pathology exists in the environment although no outbreaks have been recorded to date. Given that it is present in raw water, it is very convenient and interesting to develop a tool capable of evaluating the water treatment process, from production to the consumer, a useful tool for operators as a support for decision-making. The campaign was carried out throughout the year, taking a weekly sample, the analyses were made with the EPA1623 method. A survey was also carried out in person on volunteers who indicate their age, sex, postal code o and number of glasses of tap water ingested. The purpose of this study was to show how an artificial neural network  can be useful to predict the consumption of pathogenic microorganisms. More specifically, the aim is to develop a backpropagation type neuronal network capable of discriminating between those who consume and those who do not, based on the answers given by the subjects to a questionnaire, with the main objective of demonstrating the usefulness of the methodology based on neuronal networks for risk stratification, applying it to the calculation of the probability of the annual risk of individual contamination of the population supplied