High resolution characterisation of E. coli proliferation profiles in livestock faeces

Eliciting expert judgements to underpin our understanding of faecal indicator organism loss from septic tank systems

Septic tank systems (STS) in rural catchments represent a potential source of microbial pollution to watercourses; however, data concerning the risk of faecal indicator organism (FIO) export from STS to surface waters are scarce. In the absence of empirical data, elicitation of expert judgements can provide an alternative approach to aid understanding of FIO pollution risk from STS. Our study employed a structured elicitation process using the Sheffield Elicitation Framework to obtain expert judgements on the proportion of FIOs likely to be delivered from STS to watercourses, based on 36 scenarios combining: (i) septic tank effluent movement risk, driven by soil hydro-morphological characteristics; (ii) distance of septic tank to watercourse; and (iii) degree of slope. Experts used the tertile method to elicit a range of values representing their beliefs of the proportion of FIOs likely to be delivered to a watercourse for each scenario. The experts judged that 93 % of FIOs would likely be delivered from an STS to a watercourse under the highest risk scenario that combined (i) very high STS effluent movement risk, (ii) STS distance to watercourse <10 m, and (iii) a location on a steep slope with gradient >25 %. Under the lowest risk scenario, the proportion of FIOs reaching a watercourse would likely reduce to 5 %. Expert confidence was high for scenarios that represented extremes of risk, while uncertainty increased for scenarios depicting intermediate risk conditions. The behavioural aggregation process employed to obtain a consensus among the experts proved to be useful for highlighting both areas of strong consensus and high uncertainty. The latter therefore represent priorities for future empirical research to further improve our understanding of potential pollution risk from septic tanks and in turn enable better assessments of potential threats to water quality in rural catchments throughout the world where decentralised wastewater systems are common.

Time since faecal deposition influences mobilisation of culturable E. coli and intestinal enterococci from deer, goose and dairy cow faeces

Mobilisation is a term used to describe the supply of a pollutant from its environmental source, e.g., soil or faeces, into a hydrological transfer pathway. The overarching aim of this study was to determine, using a laboratory-based approach, whether faecal indicator bacteria (FIB) are hydrologically mobilised in different quantities from a typical agricultural, wildlife and wildfowl source, namely dairy cattle, red deer and greylag goose faeces. The mobilisation of FIB from fresh and ageing faeces under two contrasting temperatures was determined, with significant differences in the concentrations of both E. coli and intestinal enterococci lost from all faecal sources. FIB mobilisation from these faecal matrices followed the order of dairy cow > goose > deer (greatest to least, expressed as a proportion of the total FIB present). Significant changes in mobilisation rates from faecal sources over time were also recorded and this was influenced by the temperature at which the faecal material had aged over the course of the 12-day study. Characterising how indicators of waterborne pathogens are mobilised in the environment is of fundamental importance to inform models and risk assessments and develop effective strategies for reducing microbial pollution in catchment drainage waters and associated downstream impacts. Our findings add quantitative evidence to support the understanding of FIB mobilisation potential from three important faecal sources in the environment.

Biological Indicators for Fecal Pollution Detection and Source Tracking: A Review

Fecal pollution, commonly detected in untreated or less treated sewage, is associated with health risks (e.g., waterborne diseases and antibiotic resistance dissemination), ecological issues (e.g., release of harmful gases in fecal sludge composting, proliferative bacterial/algal growth due to high nutrient loads) and economy losses (e.g., reduced aqua farm harvesting). Therefore, the discharge of untreated domestic sewage to the environment and its agricultural reuse are growing concerns. The goals of fecal pollution detection include fecal waste source tracking and identifying the presence of pathogens, therefore assessing potential health risks. This review summarizes available biological fecal indicators focusing on host specificity, degree of association with fecal pollution, environmental persistence, and quantification methods in fecal pollution assessment. The development of practical tools is a crucial requirement for the implementation of mitigation strategies that may help confine the types of host-specific pathogens and determine the source control point, such as sourcing fecal wastes from point sources and nonpoint sources. Emerging multidisciplinary bacterial enumeration platforms are also discussed, including individual working mechanisms, applications, advantages, and limitations.

Integrative Survey of 68 Non-overlapping Upstate New York Watersheds Reveals Stream Features Associated With Aquatic Fecal Contamination

Aquatic fecal contamination poses human health risks by introducing pathogens in water that may be used for recreation, consumption, or agriculture. Identifying fecal contaminant sources, as well as the factors that affect their transport, storage, and decay, is essential for protecting human health. However, identifying these factors is often difficult when using fecal indicator bacteria (FIB) because FIB levels in surface water are often the product of multiple contaminant sources. In contrast, microbial source-tracking (MST) techniques allow not only the identification of predominant contaminant sources but also the quantification of factors affecting the transport, storage, and decay of fecal contaminants from specific hosts. We visited 68 streams in the Finger Lakes region of Upstate New York, United States, between April and October 2018 and collected water quality data (i.e., Escherichia coli, MST markers, and physical–chemical parameters) and weather and land-use data, as well as data on other stream features (e.g., stream bed composition), to identify factors that were associated with fecal contamination at a regional scale. We then applied both generalized linear mixed models and conditional inference trees to identify factors and combinations of factors that were significantly associated with human and ruminant fecal contamination. We found that human contaminants were more likely to be identified when the developed area within the 60 m stream buffer exceeded 3.4%, the total developed area in the watershed exceeded 41%, or if stormwater outfalls were present immediately upstream of the sampling site. When these features were not present, human MST markers were more likely to be found when rainfall during the preceding day exceeded 1.5 cm. The presence of upstream campgrounds was also significantly associated with human MST marker detection. In addition to rainfall and water quality parameters associated with rainfall (e.g., turbidity), the minimum distance to upstream cattle operations, the proportion of the 60 m buffer used for cropland, and the presence of submerged aquatic vegetation at the sampling site were all associated based on univariable regression with elevated levels of ruminant markers. The identification of specific features associated with host-specific fecal contaminants may support the development of broader recommendations or policies aimed at reducing levels of aquatic fecal contamination.

Exploration of E. coli contamination drivers in private drinking water wells: An application of machine learning to a large, multivariable, geo-spatio-temporal dataset

Groundwater resources are under increasing threats from contamination and overuse, posing direct threats to human and environmental health. The purpose of this study is to better understand drivers of, and relationships between, well and aquifer characteristics, sampling frequencies, and microbiological contamination indicators (specifically E. coli) as a precursor for improving knowledge and tools to assess aquifer vulnerability and well contamination within Ontario, Canada. A dataset with 795, 023 microbiological testing observations over an eight-year period (2010 to 2017) from 253,136 unique wells across Ontario was employed. Variables in this dataset include date and location of test, test results (E. coli concentration), well characteristics (well depth, location), and hydrogeological characteristics (bottom of well stratigraphy, specific capacity). Association rule analysis, univariate and bivariate analyses, regression analyses, and variable discretization techniques were utilized to identify relationships between E. coli concentration and the other variables in the dataset. These relationships can be used to identify drivers of contamination, their relative importance, and therefore potential public health risks associated with the use of private wells in Ontario. Key findings are that: i) bedrock wells completed in sedimentary or igneous rock are more susceptible to contamination events; ii) while shallow wells pose a greater risk to consumers, deep wells are also subject to contamination events and pose a potentially unanticipated risk to health of well users; and, iii) well testing practices are influenced by results of previous tests. Further, while there is a general correlation between months with the greatest testing frequencies and concentrations of E. coli occurring in samples, an offset in this timing is observed in recent years. Testing remains highest in July while peaks in adverse results occur up to three months later. The realization of these trends prompts a need to further explore the bases for such occurrences.

Impact of Freeze-Thaw Cycles on Die-Off of E. coli and Intestinal Enterococci in Deer and Dairy Faeces: Implications for Landscape Contamination of Watercourses

Characterising faecal indicator organism (FIO) survival in the environment is important for informing land management and minimising public health risk to downstream water users. However, key gaps in knowledge include understanding how wildlife contribute to catchment-wide FIO sources and how FIO survival is affected by low environmental temperatures. The aim of this study was to quantify E. coli and intestinal enterococci die-off in dairy cow versus red deer faecal sources exposed to repeated freeze–thaw cycles under controlled laboratory conditions. Survival of FIOs in water exposed to freeze–thaw was also investigated to help interpret survival responses. Both E. coli and intestinal enterococci were capable of surviving sub-freezing conditions with the faeces from both animals able to sustain relatively high FIO concentrations, as indicated by modelling, and observations revealing persistence in excess of 11 days and in some cases confirmed beyond 22 days. Die-off responses of deer-derived FIOs in both faeces and water exposed to low temperatures provide much needed information to enable better accounting of the varied catchment sources of faecal pollution and results from this study help constrain the parameterisation of die-off coefficients to better inform more integrated modelling and decision-making for microbial water quality management.