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The volume of cold tap water consumed is an essential element in quantitative microbial risk assessment. This paper presents a review of tap water consumption studies. Study designs were evaluated and statistical distributions were fitted to water consumption data from The Netherlands, Great Britain, Germany and Australia. We conclude that the diary is to be preferred for collecting water consumption data. If a diary is not feasible, a 24 h recall would be the best alternative, preferably repeated at least once. From the studies evaluated, the mean daily consumption varies from 0.10 L to 1.55 L. No conclusions could be drawn regarding the effects of season, age and gender on tap water consumption. Physical activity, yearly income and perceived health status were reported to influence water consumption.Comparison of the different statistical probability distribution functions of the datasets demonstrated that the Poisson distribution performed better than the lognormal distribution as suggested by Roseberry and Burmaster.For quantitative microbiological risk assessment (QMRA) it is recommended to use country-

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Water temperature is often monitored at water sources and treatment works; however, there is limited monitoring of the water temperature in the drinking water distribution system (DWDS), despite a known impact on physical, chemical and microbial reactions which impact water quality. A key parameter influencing drinking water temperature is soil temperature, which is influenced by the urban heat island effects. This paper provides critique and comprehensive summary of the current knowledge, policies and challenges regarding drinking water temperature research and presents the findings from a survey of international stakeholders. Knowledge gaps as well as challenges and opportunities for monitoring and research are identified. The conclusion of the study is that temperature in the DWDS is an emerging concern in various countries regardless of the water source and treatment, climate conditions, or network characteristics such as topology, pipe material or diameter. More research is needed, especially to determine (i) the effect of higher temperatures, (ii) a legislative limit on temperature and (iii) measures to comply with this limit.

KEywords: drinking water temperature, soil temperature model, water quality modelBlokker & Pieterse-Quirijns | http://dx.

Quantitative Microbial Risk Assessments (QMRA) have focused on drinking water system components upstream of distribution to customers, for nominal and event conditions. Yet some 15-33% of waterborne outbreaks are reported to be caused by contamination events in distribution systems. In the majority of these cases and probably in all non-outbreak contamination events, no pathogen concentration data was available. Faecal contamination events are usually detected or confirmed by the presence of E. coli or other faecal indicators, although the absence of this indicator is no guarantee of the absence of faecal pathogens. In this paper, the incidence and concentrations of various coliforms and sources of faecal contamination were used to estimate the possible concentrations of faecal pathogens and consequently the infection risks to consumers in event-affected areas. The results indicate that the infection risks may be very high, especially from Campylobacter and enteroviruses, but also that the uncertainties are very high. The high variability of pathogen to thermotolerant coliform ratios estimated in environmental samples severely limits the applicability of the approach described.Importantly, the highest ratios of enteroviruses to thermotolerant coliform were suggested from soil and shallow groundwaters, the most likely sources of faecal contamination that are detected in distribution systems. Epidemiological evaluations of non-outbreak faecal contamination of drinking water distribution systems and thorough tracking and characterisation of the contamination sources are necessary to assess the actual risks of these events.

A method for optimizing sensor locations to effectively and efficiently detect contamination in a water distribution network is presented here. The problem is formulated and solved as a twin-objective optimization problem with the objectives being the minimization of the number of sensors and minimization of the risk of contamination. Unlike past approaches, the risk of contamination is explicitly evaluated as the product of the likelihood that a set of sensors fails to detect contaminant intrusion and the consequence of that failure ͑expressed as volume of polluted water consumed prior to detection͒. A novel importance-based sampling method is developed and used to effectively determine the relative importance of contamination events, thus reducing the overall computation time. The above problem is solved by using the nondominated sorting genetic algorithm II. The methodology is tested on a case study involving the water distribution system of Almelo ͑The Netherlands͒ and the potential intrusion of E. coli bacteria. The results obtained show that the algorithm is capable of efficiently solving the above problem. The estimated Pareto front suggests that a reasonable level of contaminant protection can be achieved using a small number of strategically located sensors.

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