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During April–July 2022, outbreaks of severe acute hepatitis of unknown etiology (SAHUE) were reported in 35 countries. Five percent of cases required liver transplantation, and 22 patients died. Viral metagenomic studies of clinical samples from SAHUE cases showed a correlation with human adenovirus F type 41 (HAdV-F41) and adeno-associated virus type 2 (AAV2). To explore the association between those DNA viruses and SAHUE in children in Ireland, we quantified HAdV-F41 and AAV2 in samples collected from a wastewater treatment plant serving 40% of Ireland’s population. We noted a high correlation between HAdV-F41 and AAV2 circulation in the community and SAHUE clinical cases. Next-generation sequencing of the adenovirus hexon in wastewater demonstrated HAdV-F41 was the predominant HAdV type circulating. Our environmental analysis showed increased HAdV-F41 and AAV2 prevalence in the community during the SAHUE outbreak. Our findings highlight how wastewater sampling could aid in surveillance for respiratory adenovirus species.

Wastewater-based epidemiology (WBE) has been employed by many countries globally since the beginning of the COVID-19 pandemic in order to assess the benefits of this surveillance tool in the context of informing public health measures. WBE has been successfully employed to detect SARS-CoV-2 at wastewater treatment plants for community-wide surveillance, as well as in smaller catchments and institutions for targeted surveillance of COVID-19. In addition, WBE has been successfully used to detect new variants, identify areas of high infection levels, as well as to detect new infection outbreaks. However, due to to the large number of inherent uncertainties in the WBE process, including the inherent intricacies of the sewer network, decay of the virus en route to a monitoring point, levels of recovery from sampling and quantification methods, levels of faecal shedding among the infected population, as well as population normalisation methods, the usefulness of wastewater samples as a means of accurately quantifying SARS-CoV-2 infection levels among a population remains less clear. The current WBE programmes in place globally will help to identify new areas of research aimed at reducing the levels of uncertainty in the WBE process, thus improving WBE as a public health monitoring tool for future pandemics. In the meantime, such programmes can provide valuable comparisons to clinical testing data and other public health metrics, as well being an effective early warning tool for new variants and new infection outbreaks. This review includes a case study of sampled wastewater from the sewer network in Dublin, Ireland, during a peak infection period of COVID-19 in the city, which evaluates the different uncertainties in the WBE process. HIGHLIGHTS Essential components of wastewater-based epidemiology as applied to COVID-19 pandemic reviewed.; Challenges facing data interpretation from wastewater sampling, uncertainties in the WBE process, identification & implementation of suitable sampling strategies reviewed.; Environmental engineering perspective given.; Above illustrated with example using raw data from wastewater sampling to quantify infection prevalence.;

Abstract Historical meteorological datasets are indispensable for forming climatic models and the generation of weather forecasts. Such data are core to the training phase of prediction models and may also be harnessed for hydrological and environmental models. GRIB is the most common data format used in meteorology and represents the de facto standard for storing historical weather data. However, GRIB datasets are complex and do not constitute analysis‐ready data without additional preprocessing. The MÉRA dataset of the Met Eireann, the Irish meteorological service, is archetypical of such datasets, emerging from a high‐resolution climatic reanalysis of Irish weather data between 1981 and 2019. This article describes the MÉRA Data Extractor toolkit. This toolkit enables the intuitive, fast extraction and preprocessing of data from this extensive dataset. The toolkit is available as open source and will be of interest to those researching climate modelling in Europe.

An understanding of water exchange processes is essential for assessing water quality management issues in coastal bays. This paper evaluates the impact of water exchange processes on pollution persistence in a macro-tidal semi-closed coastal bay through two transport time scales (TTS), namely residence time and exposure time. The numerical model was calibrated against field-measured data for various tidal conditions. Simulated current speeds and directions were shown to agree well with the field data. By considering different release scenarios of a conservative tracer by the refinement of an integrated hydrodynamic and solute transport model (the EFDC), the two TTS were used for interpreting the water exchange processes in a semi-closed system, and for describing the effects of advective and dispersive processes on the transport and fate of pollutants. The results indicate that the magnitudes of river inflows to the bay, tidal ranges, and tracer release times significantly influence the residence and exposure times. Return coefficients were shown to be variable, confirming the different effects of returning water for the different conditions that were studied. For the tested river flow magnitudes and tide conditions, the exposure times were generally higher than the residence times, but particularly so for neap tide conditions. The results, therefore, highlight the risks associated with pollutants leaving a specified domain on an outgoing tide but re-entering on subsequent incoming tides. The spatial distributions of the exposure and residence times across the model domain confirmed that for the case of Dublin Bay, river inputs have a potentially greater impact on water quality on the northern side of the bay.

Microplastics are a pervasive pollutant of aquatic ecosystems and are reported to interact with a wide range of aquatic biota. The complexities of natural food webs means that the transfer and accumulation of microplastics is difficult to assess, and only a handful of studies have attempted to quantify trophic transfer in freshwater biota. Bioaccumulation models can provide a valuable tool to explore the transfer of microplastics along complex food webs, but such approaches have been rarely applied to freshwater ecosystems. Here, a food web accumulation model was implemented to assess the transfer, bioaccumulation and hence biomagnification potential of microplastics along a food web located in the River Slaney catchment in south-east Ireland. Literature feeding values and environmental field data were used to simulate microplastic uptake in benthic macroinvertebrates, with fish and Eurasian otter (Lutra lutra (Linnaeus, 1758)) comprising the higher trophic levels. Microplastic concentrations from the model were used to form a basis of comparison with empirical data for benthic macroinvertebrates and brown trout (Salmo trutta Linnaeus, 1758). Predicted concentrations were greatest in benthic macroinvertebrates and lowest in fish species such as European eel (Anguilla anguilla (Linnaeus, 1758)) and three-spined stickleback (Gasterosteus aculeatus (Linnaeus, 1758)). Biota magnification factors for fish and L. lutra indicate that microplastic accumulation between the specific predator-prey interactions are not expected. To better inform models such as this, and therefore improve their accuracy, it is important to gain a better understanding of microplastic retention times in biota and the interaction between microplastics and resources utilised by benthic macroinvertebrates and fish, such as plant material, allochthonous detritus as well as terrestrial and aerial prey. Capsule: A bioaccumulation model, used to explore the transfer and possible accumulation of microplastics along a riverine food web, showed that microplastic are not expected to accumulate in the gastrointestinal tracts of higher level biota based on the predator-prey interactions specified.

Abstract The ubiquitous nature of microplastics in aquatic ecosystems may have serious implications for aquatic biota. While microplastic research in freshwater ecosystems is increasing, very few studies have assessed the physical presence of microplastics among top predators. The Eurasian otter (Lutra lutra), a top predator of aquatic ecosystems, is one of the most widely distributed otter species and has a broad habitat niche. The opportunistic collection of otter spraints (i.e., feces) presents a valuable opportunity to assess pollutants of freshwater ecosystems through noninvasive means. Here, we assessed the prevalence, abundance, and concentration of microplastics (100 μm to 5 mm), as well as dietary remains, in 53 spraint samples collected over eight river catchments spanning three regions of Ireland. We found microplastics present in 57% of spraints at an abundance of 1.2 ± 0.1 microplastics (MPs)/spraint (mean ± SE) and a concentration of 3.8 ± 0.6 MPs/g (dry weight). Fibers were the dominant particle type recovered (85%), followed by film (10%). No significant differences in microplastic concentrations were detected between the three regions assessed, or between spraints collected from areas upstream (i.e., “lower” exposure) or downstream (“higher” exposure) of putative microplastic sources, which were defined using spatial vector data. While microplastic concentrations were not explained by spraint condition (i.e., fresh, drying, or dry), spraints collected in autumn had a significantly higher concentration than spring and summer. Furthermore, microplastic abundance or concentration could not be linked to dietary composition based on the items identified. From a trophic perspective, this study showed that the presence of microplastics in the feces of otter is most likely being obtained through its prey (i.e., secondary ingestion). While there may be limitations associated with using spraints as a biomonitoring tool for microplastics in freshwater systems, particularly with respect to otter home range and dietary niche breadth, they could still be employed for a regional assessment of microplastic levels.

Fecal pollution of surface water may introduce bacteria and bacteriophages harboring antibiotic resistance genes (ARGs) into the aquatic environment. Watercourses discharging into the marine environment, especially close to designated bathing waters, may expose recreational users to fecal pollution and therefore may increase the likelihood that they will be exposed to ARGs. This study compares the bacterial and bacteriophage ARG profiles of two rivers (River Tolka and Liffey) and two small urban streams (Elm Park and Trimleston Streams) that discharge close to two marine bathing waters in Dublin Bay. Despite the potential differences in pollution pressures experienced by these waterways, microbial source tracking analysis showed that the main source of pollution in both rivers and streams in the urban environment is human contamination. All ARGs included in this study, blaTEM, blaSHV, qnrS, and sul1, were present in all four waterways in both the bacterial and bacteriophage fractions, displaying a similar ARG profile. We show that nearshore marine bathing waters are strongly influenced by urban rivers and streams discharging into these, since they shared a similar ARG profile. In comparison to rivers and streams, the levels of bacterial ARGs were significantly reduced in the marine environment. In contrast, the bacteriophage ARG levels in freshwater and the marine were not significantly different. Nearshore marine bathing waters could therefore be a potential reservoir of bacteriophages carrying ARGs. In addition to being considered potential additional fecal indicators organism, bacteriophages may also be viewed as indicators of the spread of antimicrobial resistance.

Small urban streams discharging in the proximity of bathing waters may significantly contribute to the deterioration of water quality, yet their impact may be overlooked. This study focuses on the Elm Park stream in the city of Dublin that is subject to faecal contamination by unidentified sources. The aim of the study was to identify a minimum number of “sentinel” sampling stations in an urban catchment that would provide the maximum amount of information regarding faecal pollution in the catchment. Thus, high-resolution sampling within the catchment was carried out over the course of 1 year at 11 stations. Faecal indicator bacteria were enumerated and microbial source tracking (MST) was employed to evaluate human pollution. In addition, ammonium, total oxidised nitrogen, and phosphorus levels were monitored to determine if these correlated with faecal indicator and the HF183 MST marker. In addition, the effect of severe weather events on water quality was assessed using automated sampling at one of the identified “sentinel” stations during baseflow and high flow conditions over a 24-h period. Our results show that this urban stream is at times highly contaminated by point source faecal pollution and that human faecal pollution is pervasive in the catchment. Correlations between ammonium concentrations and faecal indicator bacteria (FIB) as well as the human MST marker were observed during the study. Cluster analysis identified four “sentinel” stations that provide sufficient information on faecal pollution in the stream, thus reducing the geographical complexity of the catchment. Furthermore, ammonium levels strongly correlated with FIB and the human HF183 MST marker under high flow conditions at key “sentinel” stations. This work demonstrates the effectiveness of pairing MST, faecal indicators, and ammonium monitoring to identify “sentinel” stations that could be more rapidly assessed using real-time ammonium readouts to assess remediation efforts.

In the context of “green” approaches to coastal engineering, the term “eco-engineering” has emerged in recent years to describe the incorporation of ecological concepts (including artificially water-filled depressions and surface textured tiles on seawalls and drilled holes in sea structures) into the conventional design process for marine infrastructures. Limited studies have evaluated the potential increase in wave energy dissipation resulting from the increased hydraulic roughness of ecologically modified sea defences which could reduce wave overtopping and consequent coastal flood risks, while increasing biodiversity. This paper presents results of small-scale laboratory investigations of wave overtopping on artificially roughened seawalls. Impulsive and non-impulsive wave conditions with two deep-water wave steepness values (=0.015 and 0.06) are evaluated to simulate both swell and storm conditions in a two-dimensional wave flume with an impermeable 1:20 foreshore slope. Measurements from a plain vertical seawall are taken as the reference case. The seawall was subsequently modified to include 10 further test configurations where hydraulic effects, reflective of “eco-engineering” interventions, were simulated by progressively increasing seawall roughness with surface protrusions across three length scales and three surface densities. Measurements at the plain vertical seawall compared favorably to empirical predictions from the EurOtop II Design Manual and served as a validation of the experimental approach. Results from physical model experiments showed that increasing the length and/or density of surface protrusions reduced overtopping on seawalls. Benchmarking of test results from experiments with modified seawalls to reference conditions showed that the mean overtopping rate was reduced by up to 100% (test case where protrusion density and length were maximum) under impulsive wave conditions. Results of this study highlight the potential for eco-engineering interventions on seawalls to mitigate extreme wave overtopping hazards by dissipating additional wave energy through increased surface roughness on the structure.

Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making.

AIM:To investigate atrial flow patterns in the normal adult heart, to explore whether caval vein arrangement and patency of the foramen ovale (PFO) may be associated with flow pattern. MATERIALS AND METHODS:Time-resolved, three-dimensional velocity encoded magnetic resonance imaging (4D flow) was employed to assess atrial flow patterns in thirteen healthy subjects (6 male, 40 years, range 25-50) and thirteen subjects (6 male, 40 years, range 21-50) with cryptogenic stroke and patent foramen ovale (CS-PFO). Right atrial flow was defined as vortical, helico-vortical, helical and multiple vortices. Time-averaged and peak systolic and diastolic flows in the caval and pulmonary veins and their anatomical arrangement were compared. RESULTS:A spectrum of right atrial flow was observed across the four defined categories. The right atrial flow patterns were strongly associated with the relative position of the caval veins. Right atrial flow patterns other than vortical were more common (p = 0.015) and the separation between the superior and inferior vena cava greater (10±5mm versus 3±3mm, p = 0.002) in the CS-PFO group. In the left atrium all subjects except one had counter-clockwise vortical flow. Vortex size varied and was associated with left lower pulmonary vein flow (systolic r = 0.61, p = 0.001, diastolic r = 0.63 p = 0.002). A diastolic vortex was less common and time-averaged left atrial velocity was greater in the CS-PFO group (17±2cm/sec versus 15±1, p = 0.048). One CS-PFO subject demonstrated vortical retrograde flow in the descending aortic arch; all other subjects had laminar descending aortic flow. CONCLUSION:Right atrial flow patterns in the normal heart are heterogeneous and are associated with the relative position of the caval veins. Patterns, other than 'typical' vortical flow, are more prevalent in the right atrium of those with cryptogenic stroke in the context of PFO. Left atrial flow patterns are more homogenous in normal hearts and show a relationship with flow arising from the left pulmonary veins.