Pathogen Concentrations In Water Research Articles

Host and Water Microbiota Are Differentially Linked to Potential Human Pathogen Accumulation in Oysters.

Oysters play an important role in coastal ecology and are a globally popular seafood source. However, their filter-feeding lifestyle enables coastal pathogens, toxins, and pollutants to accumulate in their tissues, potentially endangering human health. While pathogen concentrations in coastal waters are often linked to environmental conditions and runoff events, these do not always correlate with pathogen concentrations in oysters. Additional factors related to the microbial ecology of pathogenic bacteria and their relationship with oyster hosts likely play a role in accumulation but are poorly understood. In this study, we investigated whether microbial communities in water and oysters were linked to accumulation of Vibrio parahaemolyticus, Vibrio vulnificus, or fecal indicator bacteria. Site-specific environmental conditions significantly influenced microbial communities and potential pathogen concentrations in water. Oyster microbial communities, however, exhibited less variability in microbial community diversity and accumulation of target bacteria overall and were less impacted by environmental differences between sites. Instead, changes in specific microbial taxa in oyster and water samples, particularly in oyster digestive glands, were linked to elevated levels of potential pathogens. For example, increased levels of V. parahaemolyticus were associated with higher relative abundances of cyanobacteria, which could represent an environmental vector for Vibrio spp. transport, and with decreased relative abundance of Mycoplasma and other key members of the oyster digestive gland microbiota. These findings suggest that host and microbial factors, in addition to environmental variables, may influence pathogen accumulation in oysters. IMPORTANCE Bacteria in the marine environment cause thousands of human illnesses annually. Bivalves are a popular seafood source and are important in coastal ecology, but their ability to concentrate pathogens from the water can cause human illness, threatening seafood safety and security. To predict and prevent disease, it is critical to understand what causes pathogenic bacteria to accumulate in bivalves. In this study, we examined how environmental factors and host and water microbial communities were linked to potential human pathogen accumulation in oysters. Oyster microbial communities were more stable than water communities, and both contained the highest concentrations of Vibrio parahaemolyticus at sites with warmer temperatures and lower salinities. High oyster V. parahaemolyticus concentrations corresponded with abundant cyanobacteria, a potential vector for transmission, and a decrease in potentially beneficial oyster microbes. Our study suggests that poorly understood factors, including host and water microbiota, likely play a role in pathogen distribution and pathogen transmission.

Some aspects of deep-bed filtration dynamics in QMRA for drinking water

Unlike most unit processes in drinking water treatment, the performance of deep-bed filtration processes vary systematically on short time-scales; the particle removal capacity changes with time since the previous backwash, even when the influent water quality is stable. For microorganisms, the removal efficiency may vary by orders of magnitude. In this note, the potential impact of such dynamics on microbial risk estimates is studied, using representative experimental filtration data for viruses and bacteria in conjunction with single-hit dose-response models for microbial infection.Assuming that filtration is the only source of variation in pathogen concentrations on the time-scale of a single filter cycle, it is concluded that such variations are unlikely to substantially affect risk estimates, except possibly in an outbreak situation with extremely high pathogen concentrations; it is generally sufficient to know the mean pathogen concentrations. Future studies should include concurrent variation in the performance of other unit processes and raw water pathogen concentrations. Experimental work should focus on capturing the variation in filtration performance in order to correctly estimate mean removal rates.

Sign-constrained linear regression for prediction of microbe concentration based on water quality datasets.

This study presents a novel methodology for estimating the concentration of environmental pollutants in water, such as pathogens, based on environmental parameters. The scientific uniqueness of this study is the prevention of excess conformity in the model fitting by applying domain knowledge, which is the accumulated scientific knowledge regarding the correlations between response and explanatory variables. Sign constraints were used to express domain knowledge, and the effect of the sign constraints on the prediction performance using censored datasets was investigated. As a result, we confirmed that sign constraints made prediction more accurate compared to conventional sign-free approaches. The most remarkable technical contribution of this study is the finding that the sign constraints can be incorporated in the estimation of the correlation coefficient in Tobit analysis. We developed effective and numerically stable algorithms for fitting a model to datasets under the sign constraints. This novel algorithm is applicable to a wide variety of the prediction of pollutant contamination level, including the pathogen concentrations in water.

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.

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

It is proposed that the next revision of the Australian Drinking Water Guidelines will include 'health-based targets', where the required level of potable water treatment quantitatively relates to the magnitude of source water pathogen concentrations. To quantify likely Cryptosporidium concentrations in southern Australian surface source waters, the databases for 25 metropolitan water supplies with good historical records, representing a range of catchment sizes, land use and climatic regions were mined. The distributions and uncertainty intervals for Cryptosporidium concentrations were characterized for each site. Then, treatment targets were quantified applying the framework recommended in the World Health Organization Guidelines for Drinking-Water Quality 2011. Based on total oocyst concentrations, and not factoring in genotype or physiological state information as it relates to infectivity for humans, the best estimates of the required level of treatment, expressed as log10 reduction values, ranged among the study sites from 1.4 to 6.1 log10. Challenges associated with relying on historical monitoring data for defining drinking water treatment requirements were identified. In addition, the importance of quantitative microbial risk assessment input assumptions on the quantified treatment targets was investigated, highlighting the need for selection of locally appropriate values.

Investigating source water Cryptosporidium concentration, species and infectivity rates during rainfall-runoff in a multi-use catchment

Protozoan pathogens present a significant human health concern, and prevention of contamination into potable networks remains a key focus for drinking water providers. Here, we monitored the change in Cryptosporidium concentration in source water during high flow events in a multi-use catchment. Furthermore, we investigated the diversity of Cryptosporidium species/genotypes present in the source water, and delivered an oocyst infectivity fraction. There was a positive and significant correlation between Cryptosporidium concentration and flow (ρ = 0.756) and turbidity (ρ = 0.631) for all rainfall-runoff events, despite variable source water pathogen concentrations. Cell culture assays measured oocyst infectivity and suggested an overall source water infectious fraction of 3.1%. No infectious Cryptosporidium parvum or Cryptosporidium hominis were detected, although molecular testing detected C. parvum in 7% of the samples analysed using PCR-based molecular techniques. Twelve Cryptosporidium species/genotypes were identified using molecular techniques, and were reflective of the host animals typically found in remnant vegetation and agricultural areas. The inclusion of molecular approaches to identify Cryptosporidium species and genotypes highlighted the diversity of pathogens in water, which originated from various sources across the catchment. We suggest this mixing of runoff water from a range of landuses containing diverse Cryptosporidium hosts is a key explanation for the often-cited difficulty forming strong pathogen–indicator relationships.

Exploring global Cryptosporidium emissions to surface water

The protozoan parasite Cryptosporidium is a major cause of diarrhoea worldwide. This paper presents the first model-based inventory with 0.5 by 0.5degree resolution of global Cryptosporidium emissions for the year 2000 from humans and animals to surface water. The model is based on nutrient distribution modelling, because the sources and transport of oocysts and nutrients to the surface water are comparable. Total emissions consist of point source emissions from wastewater and nonpoint source emissions by runoff of oocysts in manure from agricultural lands. Results indicate a global emission of 3×1017 oocysts per year, with comparable contributions from point and nonpoint sources. Hot-spot areas for point sources are big cities in China, India and Latin America, while the area with the largest nonpoint source emissions is in China. Uncertainties in the model are large. Main areas for further study are (i) excretion rates of oocysts by humans and animals, (ii) emissions of humans not connected to sewage systems, and (iii) retention of oocysts to determine surface water pathogen concentrations rather than emissions. Our results are useful to health organisations to identify priority areas for further study and intervention.

Microbial risk implications of rainfall-induced runoff events entering a reservoir used as a drinking-water source

Waterborne disease outbreaks have been associated with periods of heightened source water pathogen concentrations in treated drinking-water supplies. For their management it is necessary to identify and quantify the impacts of events which lead to adverse concentration fluctuations. The aim of this work was to estimate relative microbial risks to water consumers arising from one such event: rainfall-induced runoff entering a surface drinking-water reservoir in an Australian agricultural catchment. Runoff events are known to influence both the source water entering a reservoir and the ability of a reservoir to act as a barrier to pathogen progression. Hydrograph separation methods were used to distinguish ‘runoff event’ from baseflow periods and pathogen concentrations of the inflow to the reservoir during runoff and baseflow periods respectively were estimated. Relative impacts of runoff event periods on health risks to consumers were assessed using Quantitative Microbial Risk Assessment principles. Runoff event conditions predominated 14 % of the time. The proportions of infections attributable to runoff event periods from Cryptosporidium , Giardia and Campylobacter spp. were 57 %, 80 %, and 28 % respectively. Daily infection risks were greatest in winter months than other seasons primarily due to the higher frequency of runoff events. Results from this and similar analyses, aiming to assess impacts of explicitly identified events on consumer health risks, provides important information for water system risk management, e.g. identifying periods of heightened risk and setting management priorities.