The impact of incorporating recovery data on protozoan concentration estimates was investigated forCryptosporidium and Giardia using a large dataset (n ¼ 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 ¼ 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.

Concentrations of microbiological contaminants in streams increase during rainfall-induced higher flow ‘event’ periods as compared to ‘baseflow’ conditions. If the stream feeds a drinking water reservoir, such periods of heightened pathogen loads may pose a challenge to the water treatment plant and subsequently a health concern to water consumers downstream. In order to manage this risk, it is desirable to first quantify the differences in surface water quality between baseflow and event conditions. The Event Mean Concentration (EMC) is a flow-weighted average concentration of a contaminant over the duration of a single event, proposed here as a standard parameter for quantifying the net effect of events on microbial water quality. Application of the EMC concept was assessed using flow and quality data for several events from an urbanised catchment. Expected mean EMCs were significantly larger than expected mean baseflow concentrations (p-value≤0.012) for three microbial agents - Escherichia coli (13,000 [n = 7] v. 610 [n = 16] mpn/100 ml), Cryptosporidium (234 [n = 6] v. 51 [n = 16] oocysts/10 litres) and Campylobacter (48 [n = 5] v. 2.1 [n = 16] mpn/100 ml). These parameter estimates were complemented by estimating data variability and uncertainty in the form of second-order random variables. As such the results are in a format appropriate for potential use as components in probabilistic risk assessments evaluating the effect runoff events have on drinking water quality.

Some national drinking water guidelines provide guidance on how to define 'safe' drinking water.Regarding microbial water quality, a common position is that the chance of an individual becoming infected by some reference waterborne pathogen (e.g. Cryptsporidium) present in the drinking water should ,10 24 in any year. However the instantaneous levels of risk to a water consumer vary over the course of a year, and waterborne disease outbreaks have been associated with shorter-duration periods of heightened risk. Performing probabilistic microbial risk assessments is becoming commonplace to capture the impacts of temporal variability on overall infection risk levels. A case is presented here for adoption of a shorter-duration reference period (i.e. daily) infection probability target over which to assess, report and benchmark such risks. A daily infection probability benchmark may provide added incentive and guidance for exercising control over short-term adverse risk fluctuation events and their causes. Management planning could involve outlining measures so that the daily target is met under a variety of pre-identified event scenarios. Other benefits of a daily target could include providing a platform for managers to design and assess management initiatives, as well as simplifying the technical components of the risk assessment process.

Risk mitigation provided by human monitoring and control over a water supply system has been consistently overlooked when estimating pathogen exposure to consumers. The Systems-Actions-Management (SAM) framework lends itself neatly to Quantitative Microbial Risk Assessment (QMRA) as one way to establish this link. The general premise is that an organisational protocol will influence how a human controller behaves, in turn influencing the system performance. For illustrative purposes, the framework was applied to a hypothetical water supply system to quantify the risk reduction offered by routine Cryptosporidium monitoring and the response to oocyst 'detects'. Our findings suggest that infrequent direct pathogen monitoring may provide a negligible risk barrier. The practice of sampling treated water to verify microbiological integrity is also dubious: oocyst densities were largely under-estimated, in part due to the spatial dispersion of oocysts in the waterbody, but predominantly from imperfect detection methods. The development of 'event-driven' monitoring schemes with barrier performance-based treatment verification methods, as promoted in new guidelines, is supported as a pressing issue to reduce the likelihood of undetected pathogen passage through a treatment plant.

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