Climate-driven QMRA model for selected water supply systems in Norway accounting for raw water sources and treatment processes

Hadi, Muhamad Sukri; Seidu, Razak

doi:10.1016/j.scitotenv.2018.12.460

Cited by 20 publications

(3 citation statements)

References 66 publications

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“…Therefore, we also perform work to assess how future climate scenarios will challenge raw water quality and the water treatment capacity in a follow up study that is a part of the same research project. Using the data from this study and future climate scenarios combined with modelling of treatment effect and QMRA [ 31 ] to assess probability of disease is needed to address and prepare for a changing climate with more extreme weather events and potential need for increased treatment capacity. This will be central to minimize the risk and burden of waterborne disease in the future.…”

Section: Discussionmentioning

confidence: 99%

Heavy weather events, water quality and gastroenteritis in Norway

et al. 2021

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Climate change will lead to more extreme weather events in Europe. In Norway, little is known about how this will affect drinking water quality and population's health due to waterborne diseases. The aim of our work was to generate new knowledge on the effect of extreme weather conditions and climate change on drinking water and waterborne disease. In this respect we studied the relationship between temperature, precipitation and runoff events, raw and treated water quality, and gastroenteritis consultations in Norway in 2006–2014 to anticipate the risk with changing climate conditions. The main findings are positive associations between extreme weather events and raw water quality, but only few with treated drinking water. Increase in maximum temperature was associated with an increase in risk of disease among all ages and 15–64 years olds for the whole year. Heavy rain and high runoff were associated with a decrease in risk of gastroenteritis for different age groups and time periods throughout the year. No evidence was found that increase in precipitation and runoff trigger increased gastroenteritis outbreaks. Large waterworks in Norway currently seem to manage extreme weather events in preventing waterborne disease. However, with more extreme weather in the future, this may change. Therefore, modelling future climate scenarios is necessary to assess the need for improved water treatment capacity in a future climate.

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Section: Discussionmentioning

confidence: 99%

Heavy weather events, water quality and gastroenteritis in Norway

et al. 2021

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“…was determined using bacteria and virus data measured by Haramoto et al (2006), Katayama et al (2008), La Rosa et al (2010), and Silverman et al (2013), and is similar to the range of raw wastewater R NoV:FIB determined in the present study. Similarly, Eregno et al (2016) and Mohammed & Seidu (2019) assumed a conversion factor of 10 À1.06 NoV per E. coli in QMRA of surface waters used for recreational purposes and drinking water, respectively, based on norovirus concentrations measured in nearby wastewater effluent.…”

Section: Influence Of R Nov:fib On Qmra-estimated Risksmentioning

confidence: 99%

Systematic review of the relative concentrations of noroviruses and fecal indicator bacteria in wastewater: considerations for use in quantitative microbial risk assessment

Hamadieh

Hamilton

Silverman

2021

Journal of Water and Health

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Human noroviruses are a leading cause of food- and water-borne disease, which has led to an interest in quantifying norovirus health risks using quantitative microbial risk assessment (QMRA). Given the limited availability of quantitative norovirus data to input to QMRA models, some studies have applied a conversion factor to estimate norovirus exposure based on measured fecal indicator bacteria (FIB) concentrations. We conducted a review of peer-reviewed publications to identify the concentrations of noroviruses and FIB in raw, secondary-treated, and disinfected wastewater. A meta-analysis was performed to determine the ratios of norovirus-FIB pairs in each wastewater matrix and the variables that significantly impact these ratios. Norovirus-to-FIB ratios were found to be significantly impacted by the norovirus genotype, month of sample collection, geographic location, and the extent of wastewater treatment. Additionally, we evaluated the impact of using a FIB-to-virus conversion factor in QMRA and found that the choice of conversion ratio has a great impact on estimated health risks. For example, the use of a conversion ratio previously used in the World Health Organization (WHO 2006) Guidelines for the Safe Use of Wastewater, Excreta and Greywater predicted health risks that were significantly lower than those estimated with measured norovirus concentrations used as inputs. This work emphasizes the gold standard of using measured pathogen concentrations directly as inputs to exposure assessment in QMRA. While not encouraged, if one must use a FIB-to-virus conversion ratio to estimate norovirus dose, the ratio should be chosen carefully based on the target microorganisms (i.e., strain, genotype, or class), prevalence of disease, and extent of wastewater treatment.

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“…A learning algorithm that is widely used in predictive modelling, even when the number of variables exceeds the sample size greatly, is the random forest (RF) algorithm (Tyralis and Papacharalampous, 2017). It has already been applied successfully in various microbiological studies to solve classification problems (Baudron et al, 2013;Peters et al, 2007), for nowcasting (Vincenzi et al, 2011) and forecasting (Mohammed and Seidu, 2019;Parkhurst et al, 2005). The studies revealed the potential of RF for microbial predictive modelling and this explains the choice of the learning algorithm for the current study.…”

Section: Introductionmentioning

confidence: 97%