Predicting faecal indicator fluxes using digital land use data in the UK's sentinel Water Framework Directive catchment: the Ribble study

A new Bayesian approach for managing bathing water quality at river bathing locations vulnerable to short-term pollution

Short-term fecal pollution events are a major challenge for managing microbial safety at recreational waters. Long turn-over times of current laboratory methods for analyzing fecal indicator bacteria (FIB) delay water quality assessments. Data-driven models have been shown to be valuable approaches to enable fast water quality assessments. However, a major barrier towards the wider use of such models is the prevalent data scarcity at existing bathing waters, which questions the representativeness and thus usefulness of such datasets for model training. The present study explores the ability of five data-driven modelling approaches to predict short-term fecal pollution episodes at recreational bathing locations under data scarce situations and imbalanced datasets. The study explicitly focuses on the potential benefits of adopting an innovative modeling and risk-based assessment approach, based on state/cluster-based Bayesian updating of FIB distributions in relation to different hydrological states. The models are benchmarked against commonly applied supervised learning approaches, particularly linear regression, and random forests, as well as to a zero-model which closely resembles the current way of classifying bathing water quality in the European Union. For model-based clustering we apply a non-parametric Bayesian approach based on a Dirichlet Process Mixture Model. The study tests and demonstrates the proposed approaches at three river bathing locations in Germany, known to be influenced by short-term pollution events. At each river two modelling experiments ("longest dry period", "sequential model training") are performed to explore how the different modelling approaches react and adapt to scarce and uninformative training data, i.e., datasets that do not include event pollution information in terms of elevated FIB concentrations. We demonstrate that it is especially the proposed Bayesian approaches that are able to raise correct warnings in such situations (> 90 % true positive rate). The zero-model and random forest are shown to be unable to predict contamination episodes if pollution episodes are not present in the training data. Our research shows that the investigated Bayesian approaches reduce the risk of missed pollution events, thereby improving bathing water safety management. Additionally, the approaches provide a transparent solution for setting minimum data quality requirements under various conditions. The proposed approaches open the way for developing data-driven models for bathing water quality prediction against the reality that data scarcity is common problem at existing and prospective bathing waters.

The relationship between environmental parameters and microbial water quality at two Costa Rican beaches from 2002 to 2017

Environmental conditions influence fecal indicator bacteria (FIB) levels, which are routinely used to characterize recreational water quality. This study examined 15 years of environmental and FIB data at Puntarenas and Jacó beach, Costa Rica. FIB relationships with sea level, wave height, precipitation, direct normal irradiance (DNI), wind, and turbidity were analyzed. Pearson's correlations identified lags between 24 and 96 h among environmental parameters and FIB. Multiple linear regression models composed of environmental parameters explained 24% and 27% of fecal coliforms and enterococci variability in Jacó, respectively. Puntarenas's models explained 17-26% of fecal coliforms and 12-18% enterococci variability. Precipitation, sea level anomalies, and wave height most frequently explained FIB variability. Hypothesis testing often identified significant differences in precipitation, wave height, daily sea level anomalies, and maximum sea level 24 h prior between days with and without FIB threshold exceedance. Unexpected FIB interactions with DNI, sea level, and turbidity highlight the importance of future investigations.

Rainfall-driven E. coli transfer to the stream-conduit network observed through increasing spatial scales in mixed land-use paddy farming karst terrain

Karst aquifers have distinctive hydrology and supply 25% of the world's population with drinking water, making them a critical geological setting for understanding and managing microbial water pollution. Rainfall causes elevated concentrations and loading of faecal microorganisms, e.g. E. coli, in catchment surface and groundwater systems, increasing the risk of human exposure to faecally-contaminated water. However, effective management of microbial water quality in complex karst catchments is constrained by limited understanding of E. coli - discharge responses to rainfall. We analysed how rainfall events of varying magnitude (2.4-100 mm) control E. coli-discharge dynamics at increasing spatial scales in a mixed land-use karst catchment in southwest China. During the wet season, hourly water sampling was undertaken throughout five storm events to characterise in high detail E. coli emergence with resulting flow across multiple sites of varying catchment area, stream order, and land-use. E. coli concentration was found to increase by 1-3 orders of magnitude following rainfall events. Maximum E. coli concentration and speed of E. coli recession were influenced by rainfall (amount, intensity), timing of agricultural activities, and position in the hydrological system. For high intensity events ∼90% of the cumulative E. coli export occurred within 48 h. E. coli concentration increased with increasing discharge at all sites. E. coli concentration at low discharge was higher in the headwaters than at the catchment outlet, while the rate of increase in E. coli concentration with increasing discharge appears to follow the opposite trend, being higher at the catchment outlet than the headwaters. This was attributed to the decreasing flow path gradient and increasing degree of development of the fissure network, but further event monitoring at varying catchment scales is required to confirm this relationship. The results provide novel insight into how rainfall characteristics combine with land-use and catchment hydrology to control E. coli export in karst landscapes.

On the implementation of reliable early warning systems at European bathing waters using multivariate Bayesian regression modelling

For ensuring microbial safety, the current European bathing water directive (BWD) (76/160/EEC 2006) demands the implementation of reliable early warning systems for bathing waters, which are known to be subject to short-term pollution. However, the BWD does not provide clearly defined threshold levels above which an early warning system should start warning or informing the population. Statistical regression modelling is a commonly used method for predicting concentrations of fecal indicator bacteria. The present study proposes a methodology for implementing early warning systems based on multivariate regression modelling, which takes into account the probabilistic character of European bathing water legislation for both alert levels and model validation criteria. Our study derives the methodology, demonstrates its implementation based on information and data collected at a river bathing site in Berlin, Germany, and evaluates health impacts as well as methodological aspects in comparison to the current way of long-term classification as outlined in the BWD.

Evaluation of the Cleaning Procedure Efficacy in Prevention of Nosocomial Infections in Healthcare Facilities Using Cultural Method Associated with High Sensitivity Luminometer for ATP Detection

In healthcare facilities, environmental surfaces may be a reservoir of infectious agents even though cleaning and disinfection practices play a role in the control of healthcare-associated infections. In this study, the effectiveness of cleaning/disinfection procedures has been evaluated in two hospital areas, which have different risk category classifications. According to the contract with the cleaning service, after the daily ambulatory activities, the housekeeping staff apply an alcohol-based detergent followed by a chlorine-based disinfectant (2% Antisapril, Angelini; 540 mg/L active chlorine), properly diluted and sprayed. The contract provides for the use of disposable microfiber wipes which must be replaced with new ones in each health out-patient department. Surface contamination was analyzed using cultural methods and ATP detection, performed with a high-sensitivity luminometer. The values 100 CFU/cm² and 40 RLU/cm² were considered as the threshold values for medium-risk category areas, while 250 CFU/cm² and 50 RLU/cm² were defined for the low-risk category ones. Air quality was evaluated using active and passive sampling microbiological methods and particle count (0.3 μm⁻10 μm) detection. The cleaning/disinfection procedure reduced the medium bacterial counts from 32 ± 56 CFU/cm² to 2 ± 3 CFU/cm² in the low-risk area and from 25 ± 40 CFU/cm² to 7 ± 11 CFU/cm² in the medium-risk one. Sample numbers exceeding the threshold values decreased from 3% and 13% to 1% and 5%, respectively. RLU values also showed a reduction in the samples above the thresholds from 76% to 13% in the low-risk area. From the air samples collected using the active method, we observed a reduction of 60% in wound care and 53% in an ambulatory care visit. From the air samples collected using the passive method, we highlighted a 71.4% and 50% reduction in microbial contamination in the medium-risk area and in the low-risk one, respectively. The 10 μm size particle counts decreased by 52.7% in wound care and by 63% in the ambulatory care visit. Correct surface sanitation proved crucial for the reduction of microbial contamination in healthcare settings, and plays an important role in ensuring air quality in hospital settings.

Using spatial-stream-network models and long-term data to understand and predict dynamics of faecal contamination in a mixed land-use catchment

An 11year dataset of concentrations of E. coli at 10 spatially-distributed sites in a mixed land-use catchment in NE Scotland (52km²) revealed that concentrations were not clearly associated with flow or season. The lack of a clear flow-concentration relationship may have been due to greater water fluxes from less-contaminated headwaters during high flows diluting downstream concentrations, the importance of persistent point sources of E. coli both anthropogenic and agricultural, and possibly the temporal resolution of the dataset. Point sources and year-round grazing of livestock probably obscured clear seasonality in concentrations. Multiple linear regression models identified potential for contamination by anthropogenic point sources as a significant predictor of long-term spatial patterns of low, average and high concentrations of E. coli. Neither arable nor pasture land was significant, even when accounting for hydrological connectivity with a topographic-index method. However, this may have reflected coarse-scale land-cover data inadequately representing "point sources" of agricultural contamination (e.g. direct defecation of livestock into the stream) and temporal changes in availability of E. coli from diffuse sources. Spatial-stream-network models (SSNMs) were applied in a novel context, and had value in making more robust catchment-scale predictions of concentrations of E. coli with estimates of uncertainty, and in enabling identification of potential "hot spots" of faecal contamination. Successfully managing faecal contamination of surface waters is vital for safeguarding public health. Our finding that concentrations of E. coli could not clearly be associated with flow or season may suggest that management strategies should not necessarily target only high flow events or summer when faecal contamination risk is often assumed to be greatest. Furthermore, we identified SSNMs as valuable tools for identifying possible "hot spots" of contamination which could be targeted for management, and for highlighting areas where additional monitoring could help better constrain predictions relating to faecal contamination.

The INCA-Pathogens model: An application to the Loimijoki River basin in Finland

Good hygienic quality of surface waters is essential for drinking water production, irrigation of crops and recreation. Predictions of how and when microbes are transported by rivers are needed to protect downstream water users. In this study we tested the new process-based INCA-Pathogens model in the agricultural Loimijoki River basin (3138km²) in Finland, and we quantified ecosystem services of water purification and water provisioning for drinking and recreation purposes under different scenarios. INCA is a catchment scale process based model to calculate pollutant transfer from terrestrial environment and point sources to the catchment outlet. A clear gradient was observed in the numbers of faecal coliforms along the River Loimijoki. The highest bacterial counts were detected in the middle part of the main stream immediately after small industries and municipal sewage treatment plants. In terms of model performance, the INCA-Pathogen model was able to produce faecal coliform counts and seasonality both in the low pollution level sampling points and in the high pollution level sampling points. The model was sensitive to the parameters defining light decay in river water and in soil compartment, as well as to the amount of faecal coliforms in the manure spread on the fields. The modeling results showed that the number of faecal coliforms repeatedly exceeded 1000 bacteria 100ml^-1. Moreover, results lead to the following conclusions: 1) Climate change does not cause a major threat to hygienic water quality as higher precipitation increases runoff and causes diluting effect in the river, 2) Intensification of agriculture is not a threat as long as animal density remains relatively low and environmental legislation is followed, 3) More intensive agriculture without environmental legislation causes a threat especially in tributaries with high field percentage and animal density, and 4) Hygienic water quality in the River Loimijoki can best be improved by improving sewage treatment. We conclude that this catchment scale model is a useful tool for addressing catchment management and water treatment planning issues.

A physical descriptive model for predicting bacteria level variation at a dynamic beach

A rational-based physical descriptive model (PDM) has been developed to predict the levels of Escherichia coli in water at a beach with dynamic conditions in the Greater Toronto Area (GTA), Ontario, Canada. Bacteria loadings in the water were affected not only by multiple physical factors (precipitation, discharge, wind, etc.), but also by cumulative effects, intensity, duration and timing of storm events. These may not be linearly related to the observed variations in bacteria levels, and are unlikely to be properly represented by a widely used multiple linear regression model. In order to account for these complex relationships, the amounts of precipitation and nearby creek discharge, the impact of various time-related factors, lag time between events and sample collection, and threshold for different parameters were used in determining bacteria levels. This new comprehensive PDM approach improved the accuracy of the E. coli level predictions in the studied beach water compared to the previously developed statistical predictive and presently used geometric mean models. In spite of the complexity and dynamic conditions at the studied beach, the PDM achieved 75% accuracy overall for the five case years examined.

Predicting fecal indicator organism contamination in Oregon coastal streams

In this study, we used publicly available GIS layers and statistical tree-based modeling (CART and Random Forest) to predict pathogen indicator counts at a regional scale using 88 spatially explicit landscape predictors and 6657 samples from non-estuarine streams in the Oregon Coast Range. A total of 532 frequently sampled sites were parsed down to 93 pathogen sampling sites to control for spatial and temporal biases. This model's 56.5% explanation of variance, was comparable to other regional models, while still including a large number of variables. Analysis showed the most important predictors on bacteria counts to be: forest and natural riparian zones, cattle related activities, and urban land uses. This research confirmed linkages to anthropogenic activities, with the research prediction mapping showing increased bacteria counts in agricultural and urban land use areas and lower counts with more natural riparian conditions.

Modelling the fate and transport of faecal bacteria in estuarine and coastal waters

This paper details a numerical model developed to predict the fate and transport of faecal bacteria in receiving surface waters. The model was first validated by comparing model predicted faecal bacteria concentrations with available field measurements. The model simulations agreed well with the observation data. After calibration, the model was applied to investigate the effects of different parameters, including: tidal processes, river discharges from the upstream boundaries and bacteria inputs from the upstream boundaries, wastewater treatment works (WwTWs), rivers and combined sewer overflows (CSO), on the concentrations of faecal bacteria in the Ribble Estuary. The results revealed that the tide and upstream boundary bacteria inputs were the primary factors controlling the distribution of faecal bacteria. The bacteria inputs from the WwTWs in the model domain were generally found not to have a significant impact on distribution of faecal bacteria in the estuary.

Spatial and temporal heterogeneity of bacteria across an intertidal shellfish bed: implications for regulatory monitoring of faecal indicator organisms

Routine bacterial monitoring of shellfish beds using indicator species is a common global practice designed to prevent human consumption of contaminated shellfish products. However, current bacteriological monitoring procedures which focus on the quantification of faecal indicator organisms (FIOs) as a proxy for microbial pollution may not be representative of total bacterial contamination levels present in shellfish harvesting areas. The objective of this study was to critically assess the accuracy of current monitoring strategies by quantifying the spatial (lateral and longitudinal distance) and temporal (seasonality and tidal state) concentrations of FIOs (Escherichia coli and total coliforms) within a single intertidal commercially harvested shellfish bed. Spatial and temporal FIO dynamics, including the effects of tidal state and seasonality, were quantified in mussel flesh and sediment samples from a single intertidal mussel (Mytilus edulis) bed. Our results confirmed that FIO concentrations across a shellfish bed were heterogeneous over larger spatial and temporal scales, but showed no relation to the concentrations of autochthonous bacteria, such as Vibrio spp., or the physico-chemical parameters of the sediment. These results have important implications for both public health and the economic prosperity of the shellfish industry, and demonstrate the importance of accommodating both spatial and temporal fluctuations in routine bacteriological monitoring protocols. We conclude that current FIO monitoring procedures may not accurately represent levels of microbial contamination within shellfish harvesting areas and that more robust microbiological testing procedures need developing.

The impact of land use on microbial surface water pollution

Our knowledge relating to water contamination from point and diffuse sources has increased in recent years and there have been many studies undertaken focusing on effluent from sewage plants or combined sewer overflows. However, there is still only a limited amount of microbial data on non-point sources leading to diffuse pollution of surface waters. In this study, the concentrations of several indicator micro-organisms and pathogens in the upper reaches of a river system were examined over a period of 16 months. In addition to bacteria, diffuse pollution caused by Giardia lamblia and Cryptosporidium spp. was analysed. A single land use type predestined to cause high concentrations of all microbial parameters could not be identified. The influence of different land use types varies between microbial species. The microbial concentration in river water cannot be explained by stable non-point effluent concentrations from different land use types. There is variation in the ranking of the potential of different land use types resulting in surface water contamination with regard to minimum, median and maximum effects. These differences between median and maximum impact indicate that small-scale events like spreading manure substantially influence the general contamination potential of a land use type and may cause increasing micro-organism concentrations in the river water by mobilisation during the next rainfall event.

Land use and hydroclimatic influences on Faecal Indicator Organisms in two large Scottish catchments: towards land use-based models as screening tools

Faecal Coliform (FC) bacteria were used as Faecal Indicator Organisms (FIOs) for assessment of microbiological pollution risk in two large, mixed land use catchments in Scotland. FC counts varied spatially in relation to land use and human population and resulting trophic status. These were highest in catchments with a high cover of improved pasture (which was assumed to be a proxy for cattle and sheep grazing densities) and significant human populations. FC counts were lowest in oligotrophic upland areas, where domesticated animal populations were low. In both lowland and upland catchments, peak FC counts occurred under periods of elevated flows during summer. However, in lowland agricultural catchments of higher trophic status, contamination appears to be chronic and occurs all year round. In contrast, upland headwater catchments exhibit more episodic peaks in relation to high flow events. Larger scale catchments integrate the inputs from contrasting head water streams. Spatial variations in stream FC concentrations can be predicted to a first approximation using multiple regression based on catchment characteristics. Land cover was the most important factor, with percentage improved pasture being the primary control and human population being of secondary importance. These two factors could explain 78% of the variation in mean annual FC concentrations and 65% of the 95th percentile. This simple linear model provides a screening tool for rapid assessment of pollution risk in unmonitored catchments. However, improved prediction of short-term dynamics and peak values requires higher resolution sampling and process-based models of FC production, survival and transport. A particularly important need is an improved characterisation of the hydrological connectivity which controls the flux from pollutant reservoirs on the landscape into river channel networks.

Detection and forecasting of oyster norovirus outbreaks: recent advances and future perspectives

Norovirus is a highly infectious pathogen that is commonly found in oysters growing in fecally contaminated waters. Norovirus outbreaks can cause the closure of oyster harvesting waters and acute gastroenteritis in humans associated with consumption of contaminated raw oysters. Extensive efforts and progresses have been made in detection and forecasting of oyster norovirus outbreaks over the past decades. The main objective of this paper is to provide a literature review of methods and techniques for detecting and forecasting oyster norovirus outbreaks and thereby to identify the future directions for improving the detection and forecasting of norovirus outbreaks. It is found that (1) norovirus outbreaks display strong seasonality with the outbreak peak occurring commonly in December-March in the U.S. and April-May in the Europe; (2) norovirus outbreaks are affected by multiple environmental factors, including but not limited to precipitation, temperature, solar radiation, wind, and salinity; (3) various modeling approaches may be employed to forecast norovirus outbreaks, including Bayesian models, regression models, Artificial Neural Networks, and process-based models; and (4) diverse techniques are available for near real-time detection of norovirus outbreaks, including multiplex PCR, seminested PCR, real-time PCR, quantitative PCR, and satellite remote sensing. The findings are important to the management of oyster growing waters and to future investigations into norovirus outbreaks. It is recommended that a combined approach of sensor-assisted real time monitoring and modeling-based forecasting should be utilized for an efficient and effective detection and forecasting of norovirus outbreaks caused by consumption of contaminated oysters.

Hypothesis-driven approach for the identification of fecal pollution sources in water resources

Water resource management must strive to link catchment information with water quality monitoring. The present study attempted this for the field of microbial fecal source tracking (MST). A fecal pollution source profile based on catchment data (e.g., prevalence of fecal sources) was used to formulate a hypothesis about the dominant sources of pollution in an Austrian mountainous karst spring catchment. This allowed a statistical definition of methodical requirements necessary for an informed choice of MST methods. The hypothesis was tested in a 17-month investigation of spring water quality. The study followed a nested sampling design in order to cover the hydrological and pollution dynamics of the spring and to assess effects such as differential persistence between parameters. Genetic markers for the potential fecal sources as well as microbiological, hydrological, and chemo-physical parameters were measured. The hypothesis that ruminant animals were the dominant sources of fecal pollution in the catchment was clearly confirmed. It was also shown that the concentration of ruminant markers in feces was equally distributed in different ruminant source groups. The developed approach provides a tool for careful decision-making in MST study design and might be applied on various types of catchments and pollution situations.

Microbial water pollution: a screening tool for initial catchment-scale assessment and source apportionment

The European Union Water Framework Directive requires that Management Plans are developed for individual River Basin Districts. From the point of view of faecal indicator organisms (FIOs), there is a critical need for screening tools that can provide a rapid assessment of the likely FIO concentrations and fluxes within catchments under base- and high-flow conditions, and of the balance ('source apportionment') between agriculture- and sewage-derived sources. Accordingly, the present paper reports on: (1) the development of preliminary generic models, using water quality and land cover data from previous UK catchment studies for assessing FIO concentrations, fluxes and source apportionment within catchments during the summer bathing season; (2) the calibration of national land use data, against data previously used in the models; and (3) provisional FIO concentration and source-apportionment assessments for England and Wales. The models clearly highlighted the crucial importance of high-flow conditions for the flux of FIOs within catchments. At high flow, improved grassland (and associated livestock) was the key FIO source; FIO loadings derived from catchments with high proportions of improved grassland were shown to be as high as from urbanized catchments; and in many rural catchments, especially in NW and SW England and Wales, which are important areas of lowland livestock (especially dairy) farming, ≥ 40% of FIOs was assessed to be derived from agricultural sources. In contrast, under base-flow conditions, when there was little or no runoff from agricultural land, urban (i.e. sewerage-related) sources were assessed to dominate, and even in rural areas the majority of FIOs were attributed to urban sources. The results of the study demonstrate the potential of this type of approach, particularly in light of climate change and the likelihood of more high-flow events, in underpinning informed policy development and prioritization of investment.

Predicting microbial pollution concentrations in UK rivers in response to land use change

The Water Framework Directive has caused a paradigm shift towards the integrated management of recreational water quality through the development of drainage basin-wide programmes of measures. This has increased the need for a cost-effective diagnostic tool capable of accurately predicting riverine faecal indicator organism (FIO) concentrations. This paper outlines the application of models developed to fulfil this need, which represent the first transferrable generic FIO models to be developed for the UK to incorporate direct measures of key FIO sources (namely human and livestock population data) as predictor variables. We apply a recently developed transfer methodology, which enables the quantification of geometric mean presumptive faecal coliforms and presumptive intestinal enterococci concentrations for base- and high-flow during the summer bathing season in unmonitored UK watercourses, to predict FIO concentrations in the Humber river basin district. Because the FIO models incorporate explanatory variables which allow the effects of policy measures which influence livestock stocking rates to be assessed, we carry out empirical analysis of the differential effects of seven land use management and policy instruments (fiscal constraint, production constraint, cost intervention, area intervention, demand-side constraint, input constraint, and micro-level land use management) all of which can be used to reduce riverine FIO concentrations. This research provides insights into FIO source apportionment, explores a selection of pollution remediation strategies and the spatial differentiation of land use policies which could be implemented to deliver river quality improvements. All of the policy tools we model reduce FIO concentrations in rivers but our research suggests that the installation of streamside fencing in intensive milk producing areas may be the single most effective land management strategy to reduce riverine microbial pollution.

Parameter uncertainty analysis of non-point source pollution from different land use types

Land use type is one of the most important factors that affect the uncertainty in non-point source (NPS) pollution simulation. In this study, seventeen sensitive parameters were screened from the Soil and Water Assessment Tool (SWAT) model for parameter uncertainty analysis for different land use types in the Daning River Watershed of the Three Gorges Reservoir area, China. First-Order Error Analysis (FOEA) method was adopted to analyze the effect of parameter uncertainty on model outputs under three types of land use, namely, plantation, forest and grassland. The model outputs selected in this study consisted of runoff, sediment yield, organic nitrogen (N), and total phosphorus (TP). The results indicated that the uncertainty conferred by the parameters differed among the three land use types. In forest and grassland, the parameter uncertainty in NPS pollution was primarily associated with runoff processes, but in plantation, the main uncertain parameters were related to runoff process and soil properties. Taken together, the study suggested that adjusting the structure of land use and controlling fertilizer use are helpful methods to control the NPS pollution in the Daning River Watershed.

Heavy metals in potable groundwater of mining-affected river catchments, northwestern Romania

Groundwater, accessed using wells and municipal springs, represents the major source of potable water for the human population outside of major urban areas in northwestern Romania, a region with a long history of metal mining and metallurgy. The magnitude and spatial distribution of metal contamination in private-supply groundwater was investigated in four mining-affected river catchments in Maramureş and Satu Mare Counties through the collection of 144 groundwater samples. Bedrock geology, pH and Eh were found to be important controls on the solubility of metals in groundwater. Peak metal concentrations were found to occur in the Lapuş catchment, where metal levels exceed Dutch target and intervention values in up to 49% and 14% of samples, respectively. A 700 m wide corridor in the Lapuş catchment on either side of the main river channel was identified in which peak Cd (31 μg l(-1)), Cu (50 μg l(-1)), Pb (50 μg l(-1)) and Zn (3,000 μg l(-1)) concentrations were found to occur. Given the generally similar bedrock geologies, lower metal levels in other catchments are believed to reflect differences in the magnitude of metal loading to the local environment from both metal mining and other industrial and municipal sources. Sampling of groundwater in northwestern Romania has indicated areas of potential concern for human health, where heavy metal concentrations exceed accepted environmental quality guidelines. The presence of elevated metal levels in groundwater also has implications for the implementation of the EU Water Framework Directive (WFD) and achieving 'good' status for groundwater in this part of the Danube River Basin District (RBD).

Nowcasting and forecasting concentrations of biological contaminants at beaches: a feasibility and case study

Public concern over microbial contamination of recreational waters has increased in recent years. A common approach to evaluating beach water quality has been to use the persistence model which assumes that day-old monitoring results provide accurate estimates of current concentrations. This model is frequently incorrect Recent studies have shown that statistical regression models based on least-squares fitting often are more accurate. To make such models more generally available, the Virtual Beach (VB) tool was developed. VB is public-domain software that prescribes site-specific predictive models. In this study we used VB as a tool to evaluate statistical modeling for predicting Escherichia coli (E. coli levels at Huntington Beach, on Lake Erie. The models were based on readily available weather and environmental data, plus U.S. Geological Service onsite data. Although models for Great Lakes beaches have frequently been fitted to multiyear data sets, this work demonstrates that useful statistical models can be based on limited data sets collected over much shorter time periods, leading to dynamic models that are periodically refitted as new data become available. Comparisons of the resulting nowcasts (predictions of current, but yet unknown, bacterial levels) with observations verified the effectiveness of VB and showed that dynamic models are about as accurate as long-term static models. Finally, fitting models to forecasted explanatory variables, bacteria forecasts were found to compare favorably to nowcasts, yielding adjusted coefficients of determination (adjusted R2) of about 0.40.

Spatial analysis of water quality trends in the Han River basin, South Korea

Spatial patterns of water quality trends for 118 sites in the Han River basin of South Korea were examined for eight parameters-temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (TP), and total nitrogen (TN). A non-parametric seasonal Mann-Kendall's test determined the significance of trends for each parameter for each site between 1993 and 2002. There are no significant trends in temperature, but TN concentrations increased for the majority of the monitoring stations. DO, BOD, COD, pH, SS, and TP show increasing or decreasing trends with approximately half of the stations exhibiting no trends. Urban land cover is positively associated with increases in water pollution and included as an important explanatory variable for the variations in all water quality parameters except pH. Topography and soil factors further explain the spatial variations in pH, COD, BOD, and SS. BOD, COD, SS, and TP variations are consistently better explained by 100m buffer scale analysis, but DO are better explained by the whole basin scale analysis. Local water quality management or geology could further explain some variations of water quality. Non-point-source pollution exhibits strong positive spatial autocorrelation as measured by Moran's I, indicating that the incorporation of spatial dimensions into water quality assessment enhances our understanding of spatial patterns of water quality. The spatial regression models, compared to ordinary least square (OLS) models, always better explain the variations in water quality. This study suggests that spatial analysis of watershed data at different scales should be a vital part of identifying the fundamental spatio-temporal distribution of water quality.

Quantitative catchment profiling to apportion faecal indicator organism budgets for the Ribble system, the UK's sentinel drainage basin for Water Framework Directive research

Under the EU Water Framework Directive (WFD) 20/60/EC and the US Federal Water Pollution Control Act 2002 management of water quality within river drainage basins has shifted from traditional point-source control to a holistic approach whereby the overall contribution of point and diffuse sources of pollutants has to be considered. Consequently, there is a requirement to undertake source-apportionment studies of pollutant fluxes within catchments. The inclusion of the Bathing Water Directive (BWD), under the list of 'protected areas' in the WFD places a requirement to control sources of faecal indicator organisms within catchments in order to achieve the objectives of both the BWD (and its revision - 2006/7/EC) and the WFD. This study was therefore initiated to quantify catchment-derived fluxes of faecal indicator compliance parameters originating from both point and diffuse sources. The Ribble drainage basin is the single UK sentinel WFD research catchment and discharges to the south of the Fylde coast, which includes a number of high profile, historically non-compliant, bathing waters. Faecal indicator concentrations (faecal coliform concentrations are reported herein) were measured at 41 riverine locations, the 15 largest wastewater treatment works (WwTWs) and 15 combined sewer overflows (CSOs) across the Ribble basin over a 44-day period during the 2002 bathing season. The sampling programme included targeting rainfall-induced high flow events and sample results were categorised as either base flow or high flow. At the riverine sites, geometric mean faecal coliform concentrations showed statistically significant elevation at high flow compared to base flow. The resultant faecal coliform flux estimates revealed that over 90% of the total organism load to the Ribble Estuary was discharged by sewage related sources during high flow events. These sewage sources were largely related to the urban areas to the south and east of the Ribble basin, with over half the load associated with the relatively small subcatchment of the River Douglas. The majority of this load was attributed to two WwTWs that discharge through a common outfall close to the tidal limit of this catchment. Budgets adjusted to accommodate the impact of proposed UV disinfection of these effluents showed that the load from these sources would be reduced significantly during base flow conditions. However, during high flow events loads would still remain high due to the operation of storm sewage overflows from stormwater retention tanks. The study identified untreated storm sewage spills from urban infrastructure and WwTW stormwater retention tanks as the dominant component of the high flow flux of faecal indicators to receiving waters of the Fylde coast and the associated bathing waters.

Characteristics and significance of liquid effluent from woodchip corrals in Scotland

Woodchip corrals are increasingly used as cost effective means of over-wintering livestock in temperate regions but there is little information on their potential environmental impact. Four woodchip corrals of varying characteristics were instrumented to capture and quantify the flows reaching the base, where pollutant fluxes may move either vertically to groundwater, or laterally to a water course. Samples for chemical analysis were collected daily by auto-sampler. Samples for bacterial analysis were aseptically hand-sampled. Sampling frequency was increased during high flow events and sampling was conducted over a 12-month period. Microbiological samples were analysed for total coliform (TC), presumptive Escherichia coli (EC) and intestinal enterococci (IE). Leachate was also analysed for total phosphorus, phosphate, total nitrogen, ammonium, total oxidised nitrogen, nitrite and nitrate. Each corral had a recording rain gauge sited within 10 m of the corral surface. Mean total nitrogen concentration in leachate was 339.5 mg l(-1), of which ammoniacal-N comprised approximately 57%. Mean total phosphorus concentration was 94.7 mg l(-1). Geometric mean concentrations of TC, EC and IE were 95,461, 94,983 and 55,552 cfu100 ml(-1), respectively. Significant flows of leachate occurred at the base of the corrals on most days during the 1-year sampling period and flow rate increased with stocking density. Strong positive linear relationships were found between the concentrations of the nutrient parameters and discharge. Strong positive curvilinear relations were found between faecal indicator concentrations and discharge. Different relationships were observed in the stocked and unstocked corrals. The resulting fluxes are sufficient to give concern and to indicate that corral development is worthy of regulatory attention.

Faecal indicator organism concentrations in sewage and treated effluents

The importance of faecal indicator organism (FIO) fluxes within drainage basins is increasing as the European Union (EU) Water Framework Directive and the United States Clean Water Act place requirements on regulators to manage point and diffuse sources of microbial pollution causing non-compliance (EU) or impairment (US) of receiving waters. Central to this management task is knowledge of the likely FIO concentrations in raw sewage and treated effluents, but few empirical data have been published in the peer-reviewed literature. Accordingly, this paper presents results for 1933 samples from 162 different sewage discharge sites in the UK and Jersey, which encompass 12 types of sewage-related discharge, representative of untreated sewage and primary-, secondary- and tertiary-treated effluents. Geometric means (GMs) and 95% confidence intervals (CIs) have been used to characterise base- and high-flow FIO concentrations. The data sets and sub-sets are mostly quite large (n 40) and may therefore be applied with some confidence to comparable discharge sites in similar geographical regions. Very marked, statistically significant reductions in GM FIO concentrations result from secondary and tertiary treatment, and there are statistically significant differences between some secondary and some tertiary treatments. Flow conditions are also shown to be important: untreated sewage and effluent from primary treatment plant have lower concentrations at high flow, due to dilution within combined sewerage systems, whereas some treated effluents (e.g. from activated sludge plant) have higher concentrations at high flow because of the shorter residence time within the plant. Under base-flow conditions, secondary treatments result in estimated GM FIO reductions of 95.22-99.29% (cf. primary-treated effluent). Corresponding figures for tertiary treatment plants (cf. secondary-treated effluent) are 93.24-96.59% for reedbed/grass plots and 99.71-99.92% for UV disinfection. Results suggest that secondary and tertiary treatment plants are less effective under high-flow conditions, but further high-flow sampling is required to confirm this.

Enterococci concentrations in diverse coastal environments exhibit extreme variability

Fecal indicator bacteria (FIB) concentrations in a single grab sample of water are used to notify the public about the safety of swimming in coastal waters. If concentrations are over a single-sample standard, waters are closed or placed under an advisory. Previous work has shown that notification errors occur often because FIB vary more quickly than monitoring results can be obtained (typically 24 h). Rapid detection technologies (such as quantitative polymerase chain reaction) that allow FIB quantification in hours have been suggested as a solution to notification errors. In the present study, I explore variability of enterococci (ENT) over time scales less than a day that might affect interpretation of FIB concentrations from a single grab sample, even if obtained rapidly. Five new data sets of ENT collected at 10 and 1 min periodicities for 24 and 1 h, respectively, are presented. Data sets are collected in diverse marine environments from a turbulent surf zone to a quiescent bay. ENT vary with solar and tidal cycles, as has been observed in previous studies. Over short time scales, ENT are extremely variable in each environment even the quiescent bay. Changes in ENT concentrations between consecutive samples (1 or 10 min apart) greater than the single-sample standard (104 most probable number per 100 mL) are not unusual. Variability, defined as the change in concentration between consecutive samples, is not distinct between environments. ENT change by 60% on average between consecutive samples, and by as much as 700%. Spectral analyses reveal no spectral peaks, but power-law decline of spectral density with frequency. Power-law exponents are close to 1 suggesting ENT time series share properties with 1/f noise and are fractal in nature. Since fractal time series have no characteristic time scale associated with them, it is not obvious how the fractal nature of ENT can be exploited for adaptive sampling or management. Policy makers, as well as scientists designing field campaigns for microbial source tracking and epidemiology studies, are cautioned that a single sample of water reveals little about the true water quality at a beach. Multiple samples must be taken to gain a snapshot into the patchy structure of microbial water quality and associated human health risk.

Microbial impact of small tributaries on water and shellfish quality in shallow coastal areas

This study aimed to investigate the impact of small tributaries on seawater and shellfish quality in coastal area subjected to brief episodes leading to fecal contamination. Escherichia coli and F-RNA-specific bacteriophages were selected as fecal indicators and astroviruses were chosen as being representative of pathogens in the human population during winter viral epidemics. A two-dimensional hydrodynamic model was built to simulate the current and dispersion in the model domain, which includes areas uncovered at low tide. The model also includes decay rates to simulate microorganism behavior and assess the influence of fecal input on shellfish quality. The originality lies in the fact that specific features of the study area were considered. Modeling results indicate limited particle movements and long flushing times at the back of the bay, where shellfish are farmed. Computational results showed that under normal conditions, i.e. 94% of the time, when rainfall was less than 10 mm per day, the sector shows acceptable water quality. These results are in agreement with shellfish concentration measured in the field. Under high flow conditions, high concentrations of fecal indicators and astrovirus were measured in the river and tributaries. The corresponding fluxes were over 50 times higher than under normal weather conditions. The location of the shellfish beds near the coast makes them vulnerable and fecal indicators and viruses were detected in shellfish after short rainfall events. Our modeling approach makes a contribution to shellfish management and consumer protection, by indicating the "risk period" as defined by EU regulations. Molecular development such as viral quantification in conjunction with model developments will help to prevent shellfish contamination and thus provide safer products to consumers and an effective tool for shellfish producers.

Reducing fluxes of faecal indicator compliance parameters to bathing waters from diffuse agricultural sources: the Brighouse Bay study, Scotland

The European Water Framework Directive requires the integrated management of point and diffuse pollution to achieve 'good' water quality in 'protected areas'. These include bathing waters, which are regulated using faecal indicator organisms as compliance parameters. Thus, for the first time, European regulators are faced with the control of faecal indicator fluxes from agricultural sources where these impact on bathing water compliance locations. Concurrently, reforms to the European Union (EU) Common Agricultural Policy offer scope for supporting on-farm measures producing environmental benefits through the new 'single farm payments' and the concept of 'cross-compliance'. This paper reports the first UK study involving remedial measures, principally stream bank fencing, designed to reduce faecal indicator fluxes at the catchment scale. Considerable reduction in faecal indicator flux was observed, but this was insufficient to ensure bathing water compliance with either Directive 76/160/EEC standards or new health-evidence-based criteria proposed by WHO and the European Commission.

Microbial source tracking: a forensic technique for microbial source identification?

As the requirements of the Water Framework Directive (WFD) and the US Clean Water Act (USCWA) for the maintenance of microbiological water quality in 'protected areas' highlight, there is a growing recognition that integrated management of point and diffuse sources of microbial pollution is essential. New information on catchment microbial dynamics and, in particular, the sources of faecal indicator bacteria found in bathing and shellfish harvesting waters is a pre-requisite for the design of any 'programme of measures' at the drainage basin scale to secure and maintain compliance with existing and new health-based microbiological standards. This paper reports on a catchment-scale microbial source tracking (MST) study in the Leven Estuary drainage basin, northwest England, an area for which quantitative faecal indicator source apportionment empirical data and land use information were also collected. Since previous MST studies have been based on laboratory trials using 'manufactured' samples or analyses of spot environmental samples without the contextual microbial flux data (under high and low flow conditions) and source information, such background data are needed to evaluate the utility of MST in USCWA total maximum daily load (TMDL) assessments or WFD 'Programmes of Measures'. Thus, the operational utility of MST remains in some doubt. The results of this investigation, using genotyping of Bacteroidetes using polymerase chain reaction (PCR) and male-specific ribonucleic acid coliphage (F + RNA coliphage) using hybridisation, suggest some discrimination is possible between livestock- and human-derived faecal indicator concentrations but, in inter-grade areas, the degree to which the tracer picture reflected the land use pattern and probable faecal indicator loading were less distinct. Interestingly, the MST data was more reliable on high flow samples when much of the faecal indicator flux from catchment systems occurs. Whilst a useful supplementary tool, the MST information did not provide quantitative source apportionment for the study catchment. Thus, it could not replace detailed empirical measurement of microbial flux at key catchment outlets to underpin faecal indicator source apportionment. Therefore, the MST techniques reported herein currently may not meet the standards required to be a useful forensic tool, although continued development of the methods and further catchment scale studies could increase confidence in such methods for future application.