Characterizing the concentration of Cryptosporidium in Australian surface waters for setting health-based targets for drinking water treatment

Seasonal variations and health risk assessment of microbial contaminations of groundwater in selected coastal communities of Ghana

This study investigated seasonal variations in microbial contaminations of groundwater and associated health risks in four coastal communities (Essiama, Winneba, Accra, and Keta) in Ghana. Membrane filtration methods, sanitary risk inspection, and quantitative microbial risk assessment were employed, respectively, to (i) quantify bacteriological quality, (ii) identify risks to contamination, and (iii) assess health risks associated with Escherichia coli in groundwater. Results showed 70.00%, 53.33%, 70.37% and 90.00% of groundwater sources in Essiama, Winneba, Accra, and Keta, respectively, were at intermediate risk, whereas 3.33%, 40.00%, 14.81%, and 3.33%, respectively, were at high risk. Very high-risk levels of contamination were recorded only in Accra. The presence of animal wastes within a 10 m radius of groundwater collection point, bad drainage systems, collection of spilt water in apron area, the use of ropes and buckets when fetching groundwater, and absence of aprons and well covers put more than 60.00% of the groundwater points in two or more locations at risk of contaminations. Assessment of bacteriological quality of groundwater indicated that mean total coliforms and E. coli ranged, respectively, between 123.40-501.30 and 30.98-141.90 CFU/100 ml for the communities; the highest microbial counts for dry and wet seasons occurred in Winneba and Keta, respectively. Seasonal variations in E. coli counts in Winneba and Accra were significantly higher in the dry season than in the wet season; Essiama and Keta showed no significant seasonal variations. Exposure to E. coli O157:H7 through drinking groundwater ranged between 5 and 23 cells per day. Although exposure to E. coli O157:H7 through bathing was less than 1 cell per day in all communities, residents were exposed to one E. coli, at least, every 62, 141, 237, and 282 days in Winneba, Accra, Keta, and Essiama, respectively. The risk of infection and illness for all communities was 1 for drinking, whereas that for bathing ranged from 0.57 to 0.98. The estimated Disability-Adjusted Life Years (DALY) exceeded the WHO-acceptable DALY. These findings show that groundwater resources in the selected coastal communities were prone to microbial contaminations, and this may be a setback to Sustainable Development Goals 6. Implications of the findings are discussed.

Application of QMRA to prioritise water supplies for Cryptosporidium risk in New South Wales, Australia

A Quantitative Microbial Risk Assessment (QMRA) framework was applied to assess 312 drinking water supply systems across regional New South Wales (NSW). The framework was needed to support the implementation of a recommendation in the Australian Drinking Water Guidelines (ADWG) for appropriate treatment barriers to be operating in systems 'at risk' for Cryptosporidium. The objective was to prioritise systems so that those with the highest risk could be identified and addressed first. The framework was developed in a pilot study of 30 systems, selected to represent the range of water supplies across regional NSW. From these, source water categories were defined to represent local conditions with reference to the literature and Cryptosporidium risk factors. Values for Cryptosporidium oocyst concentration were assigned to the categories to allow quantification of the health risk from those water sources. The framework was then used to assess the risks in all 312 regional drinking water supply systems. Combining the disciplined approach of QMRA with simple catchment and treatment information and categorical risk outputs provided a useful and transparent method for prioritising systems for further investigation and potential risk management intervention. The risk rankings for drinking water supplies from this QMRA process have been used to set priorities for a large State Government funding program.

Importance of Distributional Forms for the Assessment of Protozoan Pathogens Concentrations in Drinking-Water Sources

The identification of appropriately conservative statistical distributions is needed to predict microbial peak events in drinking water sources explicitly. In this study, Poisson and mixed Poisson distributions with different upper tail behaviors were used for modeling source water Cryptosporidium and Giardia data from 30 drinking water treatment plants. Small differences (<0.5-log) were found between the "best" estimates of the mean Cryptosporidium and Giardia concentrations with the Poisson-gamma and Poisson-log-normal models. However, the upper bound of the 95% credibility interval on the mean Cryptosporidium concentrations of the Poisson-log-normal model was considerably higher (>0.5-log) than that of the Poisson-gamma model at four sites. The improper choice of a model may, therefore, mislead the assessment of treatment requirements and health risks associated with the water supply. Discrimination between models using the marginal deviance information criterion (mDIC) was unachievable because differences in upper tail behaviors were not well characterized with available data sets ( n<30 ). Therefore, the gamma and the log-normal distributions fit the data equally well but may predict different risk estimates when they are used as an input distribution in an exposure assessment. The collection of event-based monitoring data and the modeling of larger routine monitoring data sets are recommended to identify appropriately conservative distributions to predict microbial peak events.

Utility of QMRA to compare health risks associated with alternative urban sewer overflow management strategies

Wet weather sewer overflows pose potential short-term public health risks. With increasing populations, aging infrastructure and climate change, utilities are challenged with managing sewerage infrastructure to provide optimum outcomes. This study compared how modelled public health risk profiles could change under alternative sewer overflow management strategies during 12 and 24-month rainfall-runoff events. Specifically, existing conditions were compared with both a 'business-as-usual' (BAU) sewer upgrade and a more holistic 'effects-based planning' (EBP) approach based on pumped wet weather sewage overflows directed to a local receiving waterway. Options were compared based on their efficacy to reduce manhole overflows, recreational waterway guideline exceedances and downstream recreational waterway health risks estimated through a screening-level Quantitative Microbial Risk Assessment (QMRA). Results indicated that the two management strategies would be equally effective in reducing the frequency, duration and volume of manhole sewer overflows, eliminating them in the 12-month scenarios and reducing them from >5000 m³ for the 24-month baseline scenario, to 23 and 35 m³ for BAU and EBP, respectively. Baseline, BAU and EBP scenarios produced similar hours of enterococci guideline exceedances, ranging from 1 to 4 h difference. The QMRA produced similar health risk profiles for downstream recreational waterway users for all design events, suggesting that sewer overflows are not the primary driver of public health risks during and immediately following high rainfall events. As such, QMRA provided evidence that an EBP strategy may be used to manage wet weather sewer overflows in lieu of an expensive BAU upgrade, without exacerbating the public health of downstream waterway users. Further investigation of the broader environmental health impacts of implementing this type of innovative approach is warranted. Nonetheless, this work highlights the value of integrating QMRA with other modelling approaches to guide and inform sewer overflow management.

Implementation of quantitative microbial risk assessment (QMRA) for public drinking water supplies: Systematic review

In the more than 15 years since its introduction, quantitative microbial risk assessment (QMRA) has become a widely used technique for assessing population health risk posed by waterborne pathogens. However, the variation in approaches taken for QMRA in relation to drinking water supply is not well understood. This systematic review identifies, categorises, and critically synthesises peer-reviewed and academic case studies of QMRA implementation for existing distributed public drinking water supplies. Thirty-nine English-language, peer-reviewed and academic studies published from 2003 to 2019 were identified. Key findings were synthesised in narrative form. The overall designs of the included studies varied widely, as did the assumptions used in risk calculation, especially in relation to pathogen dose. There was also substantial variation in the degree to which the use of location-specific data weighed with the use of assumptions when performing risk calculation. In general, the included studies' complexity did not appear to be associated with greater result certainty. Factors relating to pathogen dose were commonly influential on risk estimates whereas dose-response parameters tended to be of low relative influence. In two of the included studies, use of the 'susceptible fraction' factor was inconsistent with recognised guidance and potentially led to the underestimation of risk. While approaches and assumptions used in QMRA need not be standardised, improvement in the reporting of QMRA results and uncertainties would be beneficial. It is recommended that future authors consider the water supply QMRA reporting checklist developed for the current review. Consideration of the broad types of uncertainty relevant to QMRA is also recommended. Policy-makers should consider emergent discussion on acute microbial health-based targets when setting normative guidelines. The continued representation of QMRA case studies within peer-reviewed and academic literature would also enhance future implementation. Further research is needed on the optimisation of QMRA resourcing given the application context.

Can routine monitoring of E. coli fully account for peak event concentrations at drinking water intakes in agricultural and urban rivers?

In several jurisdictions, the arithmetic mean of Escherichia coli concentrations in raw water serves as the metric to set minimal treatment requirements by drinking water treatment plants (DWTPs). An accurate and precise estimation of this mean is therefore critical to define adequate requirements. Distributions of E. coli concentrations in surface water can be heavily skewed and require statistical methods capable of characterizing uncertainty. We present four simple parametric models with different upper tail behaviors (gamma, log-normal, Lomax, mixture of two log-normal distributions) to explicitly account for the influence of peak events on the mean concentration. The performance of these models was tested using large E. coli data sets (200-1800 samples) from raw water regulatory monitoring at six DWTPs located in urban and agricultural catchments. Critical seasons of contamination and hydrometeorological factors leading to peak events were identified. Event-based samples were collected at an urban DWTP intake during two hydrometeorological events using online β-d-glucuronidase activity monitoring as a trigger. Results from event-based sampling were used to verify whether selected parametric distributions predicted targeted peak events. We found that the upper tail of the log-normal and the Lomax distributions better predicted large concentrations than the upper tail of the gamma distribution. Weekly sampling for two years in urban catchments and for four years in agricultural catchments generated reasonable estimates of the average raw water E. coli concentrations. The proposed methodology can be easily used to inform the development of sampling strategies and statistical indices to set site-specific treatment requirements.

Microbial risk associated with CSOs upstream of drinking water sources in a transboundary river using hydrodynamic and water quality modeling

Urban source water protection planning requires the characterization of sources of contamination upstream of drinking water intakes. Elevated pathogen concentrations following Combined Sewer Overflows (CSOs) represent a threat to human health. Quantifying peak pathogen concentrations at the intakes of drinking water plants is a challenge due to the variability of CSO occurrences and uncertainties with regards to the fate and transport mechanisms from discharge points to source water supplies. Here, a two-dimensional deterministic hydrodynamic and water quality model is used to study the fluvial contaminant transport and the impacts of the upstream CSO discharges on the downstream concentrations of Escherichia coli in the raw water supply of two drinking water plants, located on a large river. CSO dynamic loading characteristics were considered for a variety of discharges. As a result of limited Cryptosporidium data, a probability distribution of the ratio of E. coli to Cryptosporidium based on historical data was used to estimate microbial risk from simulated CSO-induced E. coli concentrations. During optimal operational performance of the plants, the daily risk target was met (based on the mean concentration during the peak) for 80% to 90% of CSO events. For suboptimal performance of the plants, these values dropped to 40% to 55%. Mean annual microbial risk following CSO discharge events was more dependent on treatment performance rather than the number of CSO occurrences. The effect of CSO-associated short term risk on the mean annual risk is largely dependent on the treatment performance as well as representativeness of the baseline condition at the intakes, demonstrating the need for assessment of treatment efficacy. The results of this study will enable water utilities and managers with a tool to investigate the potential alternatives in reducing the microbial risk associated with CSOs.

Understanding human infectious Cryptosporidium risk in drinking water supply catchments

Treating drinking water appropriately depends, in part, on the robustness of source water quality risk assessments, however quantifying the proportion of infectious, human pathogenic Cryptosporidium oocysts remains a significant challenge. We analysed 962 source water samples across nine locations to profile the occurrence, rate and timing of infectious, human pathogenic Cryptosporidium in surface waters entering drinking water reservoirs during rainfall-runoff conditions. At the catchment level, average infectivity over the four-year study period reached 18%; however, most locations averaged <5%. The maximum recorded infectivity fraction within a single rainfall runoff event was 65.4%, and was dominated by C. parvum. Twenty-two Cryptosporidium species and genotypes were identified using PCR-based molecular techniques; the most common being C. parvum, detected in 23% of water samples. Associations between landuse and livestock stocking characteristics with Cryptosporidium were determined using a linear mixed-effects model. The concentration of pathogens in water were significantly influenced by flow and dominance of land-use by commercial grazing properties (as opposed to lifestyle properties) in the catchment (p < 0.01). Inclusion of measured infectivity and human pathogenicity data into a quantitative microbial risk assessment (QMRA) could reduce the source water treatment requirements by up to 2.67 log removal values, depending on the catchment, and demonstrated the potential benefit of collating such data for QMRAs.

QMRA and water safety management: review of application in drinking water systems

Quantitative microbial risk assessment (QMRA), the assessment of microbial risks when model inputs and estimated health impacts are explicitly quantified, is a valuable tool to support water safety plans (WSP). In this paper, research studies undertaken on the application of QMRA in drinking water systems were reviewed, highlighting their relevance for WSP. The important elements for practical implementation include: the data requirements to achieve sufficient certainty to support decision-making; level of expertise necessary to undertake the required analysis; and the accessibility of tools to support wider implementation, hence these aspects were the focus of the review. Recommendations to support the continued and growing application of QMRA to support risk management in the water sector are provided.

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.

Variability in the recovery of a virus concentration procedure in water: Implications for QMRA

Methods for analysing water for viruses are known to have variable and relatively poor recovery efficiencies. Quantitative method recovery data are needed to correct virus enumeration results so that estimates of virus concentrations in surface waters for QMRA are not too low. Obtaining quantitative data representing method recoveries for different pathogenic viruses is a significant challenge. In this study, we investigated the use of mengovirus process control data for quantifying recovery efficiency of human adenovirus (AdV) and noroviruses GI (NoVGI) and GII (NoVGII) from surface waters. Samples were collected from the inlet to a drinking water treatment plant on the Glomma River, Norway. Performance of the sample concentration procedure was quantified by comparing the virus concentrations found in concentrated and unconcentrated samples. The mean recovery of viruses (1.2%, 0.31%, 0.15% and 0.053% for mengovirus (n = 86), AdV (n = 20), NoVGI (n = 33) and NoVGII (n = 21) respectively) estimated in this study were lower than expected, and the between sample variability in estimated recovery was very high, spanning around 6 orders of magnitude for mengovirus. Within-sample correlation between the estimated recovery of mengovirus and human viruses was poor, and therefore sample specific mengovirus data could not be used to correct all human virus concentrations. Instead beta distributions were fitted to human virus-specific recovery estimates. The magnitude and variability of virus concentration when corrected for the variable recovery efficiency was orders of magnitude higher than the uncorrected concentration. Better estimates of virus concentration could be achieved if a sample-specific spiking control could be developed that mimicked closely the behaviour of human viruses in environmental samples.