Now showing 1 - 5 of 5
  • Publication
    Integrated modelling of urban, rural and coastal domains for bathing water quality prediction – Smart Coasts and Acclimatize Projects
    This paper presents the results of the Interreg funded SMARTCOASTS project in which an integrated catchment (MIKE11) and coastal (MIKE21 and MIKE3) modelling tool was developed for predicting the bathing water quality, at Bray, Co. Wicklow, on the east coast of Ireland. The Bray bathing waters had historically been prone to episodic short-term pollution, caused primarily by rainfall related catchment run-off. Accounting fully for the complexity of the pollution inputs for water quality prediction in the system required an integrated modelling approach. The approach for integrating the individual component models (NAM, MIKE 11, and MIKE 3 FM) was simple but efficient. The component models, interfaced to the core of the forecasting system, were run sequentially, i.e. in the form of a cascade with the forcing of each downstream model being the result of the model upstream of it. Rainfall (both forecasted and measured) drives the hydrological processes in the NAM model, which produces runoff that generates sub-catchment inflows into the river network. The output from NAM serves as the input to the MIKE 11 model which routes the flow and water quality variables in the river network and transports them to the coastal waters. Finally, the MIKE 3 FM coastal model uses flow and water quality outputs from MIKE 11, together with tidal and meteorological data, to simulate the current flow, transport and fate of water quality variables in the marine environment. Models were calibrated using measured data. Adjustment of the tidal constituents of the MIKE global model resulted in a markedly improved fit to measured water levels at five reference tidal gauges, used for calibration. Bottom friction was calibrated to produce good correlations of measured and simulated current speed and direction. When applied to water quality prediction, results of the transport model showed that the model adequately replicated measurements of E.coli and Intestinal Enterococci within the coastal domain. Computational simulations of bathing water quality are not without difficulty and a significant challenge in this work involved incorporating real-time meteorological data from a sensor network within the catchment into the model predictions. The work of Smart Coasts is currently being built on in the Interreg funded Acclimatize project. Acclimatize is focussing on the bathing waters of Dublin Bay and involves the development of a modelling platform that will facilitate a longer-term assessment of the likely pressures on bathing water quality in the context of a changed climate.
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  • Publication
    Modelling Quantified Microbial Source Specific Pollution from Domestic Wastewater Treatment Systems during High Flows
    The ability to model the transport of source specific faecal bacteria contamination in river networks can equip water resource managers with information of the different pathogens that are present. Such information can be particularly useful in catchment management plans for rivers from which potable water is extracted or where these rivers discharge to coastal zones where bathing or aquaculture is prominent and where the evaluation of human health risks is of primary importance. This paper presents an application and performance assessment of the commercially available MIKE11 Hydrodynamic model for evaluating the fate of faecal bacteria of human origin, Human Gene Marker HF183f, from Domestic Wastewater Treatment Systems within the Dargle catchment. The Dargle is a spate river and the upper catchment is characterised by steep slopes that incorporates peat bogs and land used for forestry and agricultural purposes. Residential dwellings within the area are predominantly single detached units that rely on septic tanks for wastewater treatment. In the context of faecal bacteria of human origin, malfunctioning systems are of concern, particularly in terms of surface ponding, leakage to groundwater and direct discharge to surface waters. The MIKE11 model was calibrated in a two stages process. Firstly, the model was calibrated for prediction of discharge and microbial water quality parameters, namely E. coli and Intestinal Enterococci (IE), using data from a real-time sensor network within the catchment that comprised rain gauges, weather stations and water level recorders, data from which was used to determine flow records from stage-discharge ratings. E. coli and IE concentrations were determined from high resolution sampling during storm events. Following this, water quality samples taken during storm event sampling were used to identify and quantify the human gene marker HF183f using quantitative polymer chain reaction (qPCR) techniques. Results from the qPCR analysis were used to further calibrate the model at sub-catchment level for the transport of microbial bacteria derived from human origins. Using non-compliance statistics from the EPA National Inspection Plan, domestic sources have been calculated based on the percentage number of malfunctioning septic tank units and average daily faecal gene marker concentrations per household. The study highlights issues with how the fate of the human gene marker is modelled using MIKE11, particularly in terms of advection-dispersion inputs and the requirement to associate microbial concentrations with total runoff when modelling surface and groundwater pathways.
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  • Publication
    Minimizing errors in RT-PCR detection and quantification of SARS-CoV-2 RNA for wastewater surveillance
    Wastewater surveillance for pathogens using reverse transcription-polymerase chain reaction (RT-PCR) is an effective and resource-efficient tool for gathering community-level public health information, including the incidence of coronavirus disease-19 (COVID-19). Surveillance of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) in wastewater can potentially provide an early warning signal of COVID-19 infections in a community. The capacity of the world's environmental microbiology and virology laboratories for SARS-CoV-2 RNA characterization in wastewater is increasing rapidly. However, there are no standardized protocols or harmonized quality assurance and quality control (QA/QC) procedures for SARS-CoV-2 wastewater surveillance. This paper is a technical review of factors that can cause false-positive and false-negative errors in the surveillance of SARS-CoV-2 RNA in wastewater, culminating in recommended strategies that can be implemented to identify and mitigate some of these errors. Recommendations include stringent QA/QC measures, representative sampling approaches, effective virus concentration and efficient RNA extraction, PCR inhibition assessment, inclusion of sample processing controls, and considerations for RT-PCR assay selection and data interpretation. Clear data interpretation guidelines (e.g., determination of positive and negative samples) are critical, particularly when the incidence of SARS-CoV-2 in wastewater is low. Corrective and confirmatory actions must be in place for inconclusive results or results diverging from current trends (e.g., initial onset or reemergence of COVID-19 in a community). It is also prudent to perform interlaboratory comparisons to ensure results' reliability and interpretability for prospective and retrospective analyses. The strategies that are recommended in this review aim to improve SARS-CoV-2 characterization and detection for wastewater surveillance applications. A silver lining of the COVID-19 pandemic is that the efficacy of wastewater surveillance continues to be demonstrated during this global crisis. In the future, wastewater should also play an important role in the surveillance of a range of other communicable diseases.
      36Scopus© Citations 66
  • Publication
    SARS-CoV-2 variant trends in Ireland: Wastewater based epidemiology and clinical surveillance
    SARS-CoV-2 RNA quantification in wastewater is an important tool for monitoring the prevalence of COVID-19 disease on a community scale which complements case-based surveillance systems. As novel variants of concern (VOCs) emerge there is also a need to identify the primary circulating variants in a community, accomplished to date by sequencing clinical samples. Quantifying variants in wastewater offers a cost-effective means to augment these sequencing efforts. In this study, SARS-CoV-2 N1 RNA concentrations and daily loadings were determined and compared to case-based data collected as part of a national surveillance programme to determine the validity of wastewater surveillance to monitor infection spread in the greater Dublin area. Further, sequencing of clinical samples was conducted to determine the primary SARS-CoV-2 lineages circulating in Dublin. Finally, digital PCR was employed to determine whether SARS-CoV-2 VOCs, Alpha and Delta, were quantifiable from wastewater. No lead or lag time was observed between SARS-CoV-2 wastewater and case-based data and SARS-CoV-2 trends in Dublin wastewater significantly correlated with the notification of confirmed cases through case-based surveillance preceding collection with a 5-day average. This demonstrates that viral RNA in Dublin's wastewater mirrors the spread of infection in the community. Clinical sequence data demonstrated that increased COVID-19 cases during Ireland's third wave coincided with the introduction of the Alpha variant, while the fourth wave coincided with increased prevalence of the Delta variant. Interestingly, the Alpha variant was detected in Dublin wastewater prior to the first genome being sequenced from clinical samples, while the Delta variant was identified at the same time in clinical and wastewater samples. This work demonstrates the validity of wastewater surveillance for monitoring SARS-CoV-2 infections and also highlights its effectiveness in identifying circulating variants which may prove useful when sequencing capacity is limited.
      16Scopus© Citations 4
  • Publication
    Delayed differentiation of vaginal and uterine microbiomes in dairy cows developing postpartum endometritis
    Bacterial overgrowth in the uterus is a normal event after parturition. In contrast to the healthy cow, animals unable to control the infection within 21 days after calving develop postpartum endometritis. Studies on the Microbial Ecology of the bovine reproductive tract have focused on either vaginal or uterine microbiomes. This is the first study that compares both microbiomes in the same animals. Terminal Restriction Fragment Length Polymorphism of the 16S rRNA gene showed that despite large differences associated to individuals, a shared community exist in vagina and uterus during the postpartum period. The largest changes associated with development of endometritis were observed at 7 days postpartum, a time when vaginal and uterine microbiomes were most similar. 16S rRNA pyrosequencing of the vaginal microbiome at 7 days postpartum showed at least three different microbiome types that were associated with later development of postpartum endometritis. All three microbiome types featured reduced bacterial diversity. Taken together, the above findings support a scenario where disruption of the compartmentalization of the reproductive tract during parturition results in the dispersal and mixing of the vaginal and uterine microbiomes, which subsequently are subject to differentiation. This differentiation was observed early postpartum in the healthy cow. In contrast, loss of bacterial diversity and dominance of the microbiome by few bacterial taxa were related to a delayed succession at 7DPP in cows that at 21 DPP or later were diagnosed with endometritis.
      293Scopus© Citations 30