A theoretical approach to using faecal indicator data to model norovirus concentration in surface water for QMRA: Glomma River, Norway.

Monitoring of faecal indicator organisms, such as Escherichia coli, in environmental and drinking waters is inadequate for the protection public health, primarily due to the poor relationship between E. coli and the occurrence of human pathogens, especially viruses, in environmental samples. Nevertheless, measurements of faecal indicator organisms within the risk based approach, can provide valuable information related to the magnitude and variability of faecal contamination, and hence provide insight into the expected level of potential pathogen contamination. In this study, a modelling approach is presented that estimates the concentration of norovirus in surface water relying on indicator monitoring data, combined with specific assumptions regarding the source of faecal contamination. The model is applied to a case study on drinking water treatment intake from the Glomma River in Norway. Norovirus concentrations were estimated in two sewage sources discharging into the river upstream of the drinking water offtake, and at the source water intake itself. The characteristics of the assumed source of faecal contamination, including the norovirus prevalence in the community, the size of the contributing population and the relative treatment efficacy for indicators and pathogens in the sewage treatment plant, influenced the magnitude and variability in the estimated norovirus concentration in surface waters. The modelling exercise presented is not intended to replace pathogen enumeration from environmental samples, but rather is proposed as a complement to better understand the sources and drivers of viruses in surface waters. The approach has the potential to inform sampling regimes by identifying when the best time would be to collect environmental samples; fill in the gaps between sparse datasets; and potentially extrapolate existing datasets in order to model rarer events such as an outbreak in the contributing population. In addition, and perhaps most universally, in the absence of pathogen data, this approach can be used as a first step to predict the source water pathogen concentration under different contamination scenarios for the purpose of quantifying microbial risks.

Evaluation of three full-scale stormwater treatment systems with respect to water yield, pathogen removal efficacy and human health risk from faecal pathogens.

In this study, three full-scale, operational stormwater harvesting systems located in Melbourne, Australia were evaluated with respect to water yields; pathogen removal performance by analysis of native surrogate data (Escherichiacoli, somatic coliphages and Clostridium perfringens); and potential human health risk associated with exposures to faecal pathogens using Quantitative Microbial Risk Assessment (QMRA). The water yield assessment confirmed variation between design and measured yields. Faecal contamination of urban stormwater was site specific and variable. Different treatment removal performance was observed between each of the microbial surrogates and varied between event and baseline conditions, with negligible removal of viruses during event conditions. Open storages that provide a habitat for waterfowl may lead to elevated risk due to the potential for zoonotic transmission. Nevertheless, in the Australian urban setting studied, the potential for human faecal contamination of the separated stormwater system was a critical driver of risk. If the integrity of the sewerage system can be ensured, then predicted health risks are dramatically reduced.

Microbial risk assessment of drinking water based on hydrodynamic modelling of pathogen concentrations in source water.

Norovirus contamination of drinking water sources is an important cause of waterborne disease outbreaks. Knowledge on pathogen concentrations in source water is needed to assess the ability of a drinking water treatment plant (DWTP) to provide safe drinking water. However, pathogen enumeration in source water samples is often not sufficient to describe the source water quality. In this study, the norovirus concentrations were characterised at the contamination source, i.e. in sewage discharges. Then, the transport of norovirus within the water source (the river Göta älv in Sweden) under different loading conditions was simulated using a hydrodynamic model. Based on the estimated concentrations in source water, the required reduction of norovirus at the DWTP was calculated using quantitative microbial risk assessment (QMRA). The required reduction was compared with the estimated treatment performance at the DWTP. The average estimated concentration in source water varied between 4.8×10(2) and 7.5×10(3) genome equivalents L(-1); and the average required reduction by treatment was between 7.6 and 8.8 Log10. The treatment performance at the DWTP was estimated to be adequate to deal with all tested loading conditions, but was heavily dependent on chlorine disinfection, with the risk of poor reduction by conventional treatment and slow sand filtration. To our knowledge, this is the first article to employ discharge-based QMRA, combined with hydrodynamic modelling, in the context of drinking water.

Incorporating method recovery uncertainties in stochastic estimates of raw water protozoan concentrations for QMRA.

The impact of incorporating recovery data on protozoan concentration estimates was investigated for Cryptosporidium and Giardia using a large dataset (n=99) of [oo]cyst assay results with paired recovery estimates. Stochastic [oo]cyst concentration was estimated using three approaches: I-no availability/consideration of recovery, II-limited recovery data, where sample recovery was considered as an independent random variable, and III-every [oo]cyst assay result was adjusted for a concurrently derived recovery estimate. Critically, Approach I underestimated [oo]cyst concentrations by about 100% compared to Approaches II and III, which were similar. The impact of dataset size on statistical uncertainty about the concentration estimate for Approach II was investigated; little improvement in parameter uncertainty was achieved beyond n=20. It is suggested that recovery data be incorporated into source water concentration estimates, especially when used to infer health risks to consumers, so as not to underestimate the risk. Where none is available, conservatively low recoveries should be assumed. When designing monitoring programmes, recovery data should be collected as a pair with [oo]cyst count data for an initial period at least, so that site-specific relationships between those parameters may be ascertained and incorporated into source water concentration estimates.