Identifying water quality and environmental factors that influence indicator and pathogen decay in natural surface waters

Comparative experiment to select water quality parameters for modelling the survival of Escherichia coli in lakes

Numerical health risk assessment models have been developed to describe faecal contamination of water using Escherichia coli as an indicator bacterium. Although many previously established numerical models for E. coli in aquatic environments have only considered the effects of one or two water quality parameters such as temperature and sunlight, it is difficult to simulate E. coli survival with only one or two parameters because the aquatic environment is a complex system. This study conducted a series of comparative experiments to select water quality parameters that should be preferentially considered in a numerical model for E. coli survival in lakes. The parameters considered were temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), suspended solids (SS), coexisting microbes, and light intensity. In the laboratory experiments, the survival of E. coli was observed by controlling two of these seven parameters, and the effects of these parameters on the rate of E. coli population change were statistically compared. Consequently, light intensity affected the survival of E. coli most significantly, followed by the presence of coexisting microbes, temperature, pH, and TDS. However, DO and SS had smaller effects on survival than other parameters. High-impact interactions on E. coli survival were observed between temperature and TDS and temperature and coexisting microbes. These results suggest that existing numerical models for simulating E. coli survival in lakes should be modified to consider the independent and interactive effects of multiple parameters such as sunlight, coexisting microbes, temperature, pH, and TDS.

Influence of combined abiotic/biotic factors on decay of P. aeruginosa and E. coli in Rhine River water

Understanding the dynamic change in abundance of both fecal and opportunistic waterborne pathogens in urban surface water under different abiotic and biotic factors helps the prediction of microbiological water quality and protection of public health during recreational activities, such as swimming. However, a comprehensive understanding of the interaction among various factors on pathogen behavior in surface water is missing. In this study, the effect of salinity, light, and temperature and the presence of indigenous microbiota, on the decay/persistence of Escherichia coli and Pseudomonas aeruginosa in Rhine River water were tested during 7 days of incubation with varying salinity (0.4, 5.4, 9.4, and 15.4 ppt), with light under a light/dark regime (light/dark) and without light (dark), temperature (3, 12, and 20 °C), and presence/absence of indigenous microbiota. The results demonstrated that light, indigenous microbiota, and temperature significantly impacted the decay of E. coli. Moreover, a significant (p<0.01) four-factor interactive impact of these four environmental conditions on E. coli decay was observed. However, for P. aeruginosa, temperature and indigenous microbiota were two determinate factors on the decay or growth. A significant three-factor interactive impact between indigenous microbiota, temperature, and salinity (p<0.01); indigenous microbiota, light, and temperature (p<0.01); and light, temperature, and salinity (p<0.05) on the decay of P. aeruginosa was found. Due to these interactive effects, caution should be taken when predicting decay/persistence of E. coli and P. aeruginosa in surface water based on a single environmental condition. In addition, the different response of E. coli and P. aeruginosa to the environmental conditions highlights that E. coli monitoring alone underestimates health risks of surface water by non-fecal opportunistic pathogens, such as P. aeruginosa. KEY POINTS: Abiotic and biotic factors interactively affect decay of E. coli and P. aeruginosa E.coli and P.aeruginosa behave significantly different under the given conditions Only E. coli as an indicator underestimates the microbiological water quality.

Design, optimization, and inference of multiphasic decay of infectious virus particles

The loss of virus particles is typically considered to arise from a first-order kinetic process. Signals of deviations from this exponential decay are often de-prioritized. Here, we propose methods to evaluate if a design is adequate to evaluate evidence for multiphasic virus particle decay and to optimize the sampling times of decay experiments, accounting for uncertainties in viral kinetics. First, we evaluate 1500 synthetic scenarios of biphasic decays, with varying decay rates and initial proportions of subpopulations. Robust inference of multiphasic decay is more likely when the faster decaying subpopulation predominates insofar as early samples are taken to resolve the faster decay rate. Overall, we find that design optimization leads to a better precision of estimation while reducing the number of samples. It helps to estimate adequately the fastest decay in 54% of situations vs. 41% using a non-optimized design. We then apply these methods to infer multiple decay rates associated with the decay of ΦD9, an evolved isolate derived from phage Φ21. A pilot experiment confirmed that ΦD9 decay is multiphasic, but was unable to resolve the rate or proportion of the fast decay subpopulation(s). We then applied optimal design methods to propose new ΦD9 sampling times. Using this strategy, we were able to robustly estimate both decay rates and their respective subpopulations. Notably, we conclude that the vast majority (94%) of the population decays at a rate 16-fold higher than a slow decaying population. Altogether, these results provide methods to quantitatively estimate heterogeneity in viral decay.

Methodological differences between studies confound one-size-fits-all approaches to managing surface waterways for food and water safety

Even though differences in methodology (e.g., sample volume and detection method) have been shown to affect observed microbial water quality, multiple sampling and laboratory protocols continue to be used for water quality monitoring. Research is needed to determine how these differences impact the comparability of findings to generate best management practices and the ability to perform meta-analyses. This study addresses this knowledge gap by compiling and analyzing a data set representing 2,429,990 unique data points on at least one microbial water quality target (e.g., Salmonella presence and Escherichia coli concentration). Variance partitioning analysis was used to quantify the variance in likelihood of detecting each pathogenic target that was uniquely and jointly attributable to non-methodological versus methodological factors. The strength of the association between microbial water quality and select methodological and non-methodological factors was quantified using conditional forest and regression analysis. Fecal indicator bacteria concentrations were more strongly associated with non-methodological factors than methodological factors based on conditional forest analysis. Variance partitioning analysis could not disentangle non-methodological and methodological signals for pathogenic Escherichia coli, Salmonella, and Listeria. This suggests our current perceptions of foodborne pathogen ecology in water systems are confounded by methodological differences between studies. For example, 31% of total variance in likelihood of Salmonella detection was explained by methodological and/or non-methodological factors, 18% was jointly attributable to both methodological and non-methodological factors. Only 13% of total variance was uniquely attributable to non-methodological factors for Salmonella, highlighting the need for standardization of methods for microbiological water quality testing for comparison across studies.IMPORTANCEThe microbial ecology of water is already complex, without the added complications of methodological differences between studies. This study highlights the difficulty in comparing water quality data from projects that used different sampling or laboratory methods. These findings have direct implications for end users as there is no clear way to generalize findings in order to characterize broad-scale ecological phenomenon and develop science-based guidance. To best support development of risk assessments and guidance for monitoring and managing waters, data collection and methods need to be standardized across studies. A minimum set of data attributes that all studies should collect and report in a standardized way is needed. Given the diversity of methods used within applied and environmental microbiology, similar studies are needed for other microbiology subfields to ensure that guidance and policy are based on a robust interpretation of the literature.

Temperature and particles interact to affect human norovirus and MS2 persistence in surface water

Modeling the fate and transport of viruses and their genetic material in surface water is necessary to assess risks associated with contaminated surface waters and to inform environmental surveillance efforts. Temperature has been identified as a key variable affecting virus persistence in surface waters, but the effects of the presence of biological and inert particles and of their interaction with temperature have not been well characterized. We assessed these effects on the persistence of human norovirus (HuNoV) genotype II.4 purified from stool and MS2 in surface water. Raw or filter-sterilized creek water microcosms were inoculated and incubated in the dark at 10 °C, 15 °C, and 20 °C. HuNoV (i.e., genome segments and intact capsids) and MS2 (i.e., infectious MS2, genome segments, and intact capsids) concentrations were followed over 36 days. The range in positive, significant first-order decay rate constants for HuNoV in this study was 0.14 to 0.69 day-1 compared with 0.026 to 0.71 day-1 for that of MS2. Decay rate constants for HuNoV genome segments and infectious MS2 were largest in creek water that included biological and inert particles and incubated at higher temperatures. In addition, for HuNoV and MS2 incubated in raw or filter-sterilized creek water at 15 °C, capsid damage was not identified as a dominant inactivation mechanism. Environmental processes and events that affect surface water biological and inert particles, temperature, or both could lead to variable virus decay rate constants. Incorporating the effects of particles, temperature, and their interaction could enhance models of virus fate and transport in surface water.

The Effect of Protozoa Indigenous to Lakewater and Wastewater on Decay of Fecal Indicator Bacteria and Coliphage

Fecal indicator bacteria (FIB: Escherichia coli and enterococci) are used to assess recreational water quality. Viral indicators (i.e., somatic and F+ coliphage), could improve the prediction of viral pathogens in recreational waters, however, the impact of environmental factors, including the effect of predatory protozoa source, on their survival in water is poorly understood. We investigated the effect of lakewater or wastewater protozoa, on the decay (decreasing concentrations over time) of culturable FIB and coliphages under sunlight and shaded conditions. FIB decay was generally greater than the coliphages and was more rapid when indicators were exposed to lake vs. wastewater protozoa. F+ coliphage decay was the least affected by experimental variables. Somatic coliphage decayed fastest in the presence of wastewater protozoa and sunlight, though their decay under shaded conditions was-10-fold less than F+ after 14 days. The protozoa source consistently contributed significantly to the decay of FIB, and somatic, though not the F+ coliphage. Sunlight generally accelerated decay, and shade reduced somatic coliphage decay to the lowest level among all the indicators. Differential responses of FIB, somatic, and F+ coliphages to environmental factors support the need for studies that address the relationship between the decay of coliphages and viral pathogens under environmentally relevant conditions.

Can water composition and weather factors predict fecal indicator bacteria removal in retention ponds in variable weather conditions?

Retention ponds provide benefits including flood control, groundwater recharge, and water quality improvement, but changes in weather conditions could limit the effectiveness in improving microbial water quality metrics. The concentration of fecal indicator bacteria (FIB), which is used as regulatory standards to assess microbial water quality in retention ponds, could vary widely based on many factors including local weather and influent water chemistry and composition. In this critical review, we analyzed 7421 data collected from 19 retention ponds across North America listed in the International Stormwater BMP Database to examine if variable FIB removal in the field conditions can be predicted based on changes in these weather and water composition factors. Our analysis confirms that FIB removal in retention ponds is sensitive to weather conditions or seasons, but temperature and precipitation data may not describe the variable FIB removal. These weather conditions affect suspended solid and nutrient concentrations, which in turn could affect FIB concentration in the ponds. Removal of total suspended solids and total P only explained 5% and 12% of FIB removal data, respectively, and TN removal had no correlation with FIB removal. These results indicate that regression-based modeling with a single parameter as input has limited use to predict FIB removal due to the interactive nature of their effects on FIB removal. In contrast, machine learning algorithms such as the random forest method were able to predict 65% of the data. The overall analysis indicates that the machine learning model could play a critical role in predicting microbial water quality of surface waters under complex conditions where the variation of both water composition and weather conditions could deem regression-based modeling less effective.

Meta-Analysis Addressing the Implications of Model Uncertainty in Understanding the Persistence of Indicators and Pathogens in Natural Surface Waters

This study evaluates the impact persistence model selection has on the prediction of persistence values of interest and the identification of influential water quality and environmental factors for microorganisms in natural surface waters. Five persistence models representing first-order decay and nonlinear decay profiles were fit to a comprehensive database of 629 data sets for fecal indicator bacteria (FIB), bacteriophages, bacteria, viruses, and protozoa mined from the literature. Initial periods of minimal decay and decay rates tapering off over time were often observed, and a two-parameter model, based on the logistic probability distribution, provided the best fit to the data most frequently. First-order decay kinetics provided the best fit to less than 20% of the analyzed data. Using the best fitting models in this analysis, T90 and T99 metrics were calculated for each data set and used as the dependent variable in a variety of exploratory factor analyses. Random forest methods identified temperature and predation as some of the most important water quality factors influencing persistence, and the protozoa target type differed the most from FIB. This analysis further confirmed the interactions between temperature and predation and suggests that pH and turbidity be more frequently documented in persistence studies to further elucidate their impact on target persistence. The findings from this analysis and the calculated persistence metrics can be used to better inform quantitative microbial risk assessments and may lead to improved predictions of human health risks and water management decisions.

Predicting the dispersal of SARS-CoV-2 RNA from the wastewater treatment plant to the coast

Viral pathogens including SARS-CoV-2 RNA have been detected in wastewater treatment effluent, and untreated sewage overflows, that pose an exposure hazard to humans. We assessed whether SARS-CoV-2 RNA was likely to have been present in detectable quantities in UK rivers and estuaries during the first wave of the Covid-19 pandemic. We simulated realistic viral concentrations parameterised on the Camel and Conwy catchments (UK) and their populations, showing detectable SARS-CoV-2 RNA concentrations for untreated but not for treated loading, but also being contingent on viral decay, hydrology, catchment type/shape, and location. Under mean or low river flow conditions, viral RNA concentrated within the estuaries allowing for viral build-up and caused a lag by up to several weeks between the peak in community infections and the viral peak in the environment. There was an increased hazard posed by SARS-CoV-2 RNA with a T₉₀ decay rate >24 h, as the estuarine build-up effect increased. High discharge events transported the viral RNA downstream and offshore, increasing the exposure risk to coastal bathing waters and shellfisheries – although dilution in this case reduced viral concentrations well below detectable levels. Our results highlight the sensitivity of exposure to viral pathogens downstream of wastewater treatment, across a range of viral loadings and catchment characteristics – with implications to environmental surveillance.

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SARS-CoV-2 RNA from treated sewage unlikely to be detectable in estuaries.

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SARS-CoV-2 RNA from untreated sewage can be detectable in estuaries.

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Peak RNA concentration in estuaries can be delayed from peak community infection.

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RNA concentration is sensitive to viral loading, decay, hydrology, and estuary shape.

A physics-informed statistical learning framework for forecasting local suspended sediment concentrations in marine environment

An in-situ monitoring of water quality (suspended sediment concentration, SSC) and concurrent hydrodynamics was conducted in the subaqueous Yellow River Delta in China. Empirical mode decomposition and spectral analysis on the SSC time series reveal the different periodicities of each physical mechanism that contribute to the SSC variations. Based on this physical understanding, the decomposed SSC time series were trained separately with a newly-proposed augmented lncosh ridge regression, in which (1) a lncosh function was incorporated in traditional ridge regression for handling outliers in original data, and (2) the temporal auto-correlation in the decomposed SSC series was used for augmented regression. Finally, the trained sub-series were added up as the final prediction. The advantages of this decomposition-ensemble framework is that it depends on SSC only, superior to the normal process-based models which need the concurrent hydrodynamics for estimating bed shear stress. This will not only reduce the measurement uncertainties of the input when training the data-driven model, but also save the prediction cost as no other parameters than SSC need to be measured and input for running the model. The framework realized 6-hour-ahead high-accuracy forecasting with mean relative errors of 5.80-9.44% in the present case study. The proposed framework can be extended to forecast any signal that is superposed by components with various timescales (periodicities) which is common in nature.