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Introduction: Advances in molecular methods provide new opportunities for directly measuring pathogens or host-associated markers of fecal pollution instead of relying on fecal indicator bacteria (FIB) alone for beach water quality monitoring. Adoption of new indicators depends on identifying relationships between either the presence or concentration of the indicators and illness among swimmers. Here we present results from three epidemiologic studies in which a broad range of bacterial and viral indicators of fecal contamination were measured simultaneously by either culture or molecular methods along with Enterococcus to assess whether they provide better health risk prediction than current microbial indicators of recreational water quality., Methods: We conducted prospective cohort studies at three California beaches -- Avalon Bay (Avalon), Doheny State Beach (Doheny), Surfrider State Beach (Malibu) -- during the summers of 2007, 2008 and 2009. The studies enrolled 10,785 swimmers across the beaches and recorded each swimmer's water exposure. Water and sand samples were collected several times per day at multiple locations at each beach and analyzed for up to 41 target indicators using 67 different methodologies. Interviewers contacted participants by phone 10-14 days later and recorded symptoms of gastrointestinal illness occurring after their beach visit. Regression models were used to evaluate the association between water quality indicators and gastrointestinal illness among swimmers at each beach., Results: F+ coliphage (measured using EPA Method 1602) exhibited a stronger association with GI illness than did EPA Method 1600 at the two beaches where it was measured, while a molecular method, F+ RNA Coliphage Genotype II, was the only indicator significantly associated with GI illness at Malibu. MRSA, a known pathogen, had the strongest association with GI illness of any microbe measured at Avalon. There were two methods targeting human-associated fecal anaerobic bacteria that were more strongly associated with GI illness than EPA Method 1600, but only at Avalon. No indicator combinations consistently had a higher odds ratio than EPA Method 1600, but one composite indicator, based on the number of pathogens detected at a beach, was significantly associated with gastrointestinal illness at both Avalon and Doheny when freshwater flow was high., Discussion: While EPA Method1600 performed adequately at two beaches based on its consistency of association with gastrointestinal illness and the precision of its estimated associations, F+ coliphage measured by EPA Method 1602 had a stronger association with GI illness under high risk conditions at the two beaches where it was measured. One indicator, F+ Coliphage Genotype II was the only indicator significantly associated with GI illness at Malibu. Several indicators, particularly those targeting human associated bacteria, exhibited relationships with GI illness that were equal to or greater than that of EPA Method 1600 at Avalon, which has a focused human fecal source. Our results suggest that site-specific conditions at each beach determine which indicator or indicators best predict GI illness., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Bathing beaches are monitored for fecal indicator bacteria (FIB) to protect swimmers from unsafe conditions. However, FIB assays take ∼24 h and water quality conditions can change dramatically in that time, so unsafe conditions cannot presently be identified in a timely manner. Statistical, data-driven predictive models use information on environmental conditions (i.e., rainfall, turbidity) to provide nowcasts of FIB concentrations. Their ability to predict real time FIB concentrations can make them more accurate at identifying unsafe conditions than the current method of using day or older FIB measurements. Predictive models are used in the Great Lakes, Hong Kong, and Scotland for beach management, but they are presently not used in California - the location of some of the world's most popular beaches. California beaches are unique as point source pollution has generally been mitigated, the summer bathing season receives little to no rainfall, and in situ measurements of turbidity and salinity are not readily available. These characteristics may make modeling FIB difficult, as many current FIB models rely heavily on rainfall or salinity. The current study investigates the potential for FIB models to predict water quality at a quintessential California Beach: Santa Monica Beach. This study compares the performance of five predictive models, multiple linear regression model, binary logistic regression model, partial least square regression model, artificial neural network, and classification tree, to predict concentrations of summertime fecal coliform and enterococci concentrations. Past measurements of bacterial concentration, storm drain condition, and tide level are found to be critical factors in the predictive models. The models perform better than the current beach management method. The classification tree models perform the best; for example they correctly predict 42% of beach postings due to fecal coliform exceedances during model validation, as compared to 28% by the current method. Artificial neural network is the second best model which minimizes the number of incorrect beach postings. The binary logistic regression model also gives promising results, comparable to classification tree, by adjusting the posting decision thresholds to maximize correct beach postings. This study indicates that predictive models hold promise as a beach management tool at Santa Monica Beach. However, there are opportunities to further refine predictive models., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ)--a direct classification approach--for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Background: Traditional fecal indicator bacteria (FIB) measurement is too slow (>18 h) for timely swimmer warnings., Objectives: Assess relationship of rapid indicator methods (qPCR) to illness at a marine beach impacted by urban runoff., Methods: We measured baseline and two-week health in 9525 individuals visiting Doheny Beach 2007-08. Illness rates were compared (swimmers vs. non-swimmers). FIB measured by traditional (Enterococcus spp. by EPA Method 1600 or Enterolert™, fecal coliforms, total coliforms) and three rapid qPCR assays for Enterococcus spp. (Taqman, Scorpion-1, Scorpion-2) were compared to health. Primary bacterial source was a creek flowing untreated into ocean; the creek did not reach the ocean when a sand berm formed. This provided a natural experiment for examining FIB-health relationships under varying conditions., Results: We observed significant increases in diarrhea (OR 1.90, 95% CI 1.29-2.80 for swallowing water) and other outcomes in swimmers compared to non-swimmers. Exposure (body immersion, head immersion, swallowed water) was associated with increasing risk of gastrointestinal illness (GI). Daily GI incidence patterns were different: swimmers (2-day peak) and non-swimmers (no peak). With berm-open, we observed associations between GI and traditional and rapid methods for Enterococcus; fewer associations occurred when berm status was not considered., Conclusions: We found increased risk of GI at this urban runoff beach. When FIB source flowed freely (berm-open), several traditional and rapid indicators were related to illness. When FIB source was weak (berm-closed) fewer illness associations were seen. These different relationships under different conditions at a single beach demonstrate the difficulties using these indicators to predict health risk., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Fecal indicator microbes, such as enterococci, are often used to assess potential health risks caused by pathogens at recreational beaches. Microbe levels often vary based on collection time and sampling location. The primary goal of this study was to assess how spatial and temporal variations in sample collection, which are driven by environmental parameters, impact enterococci measurements and beach management decisions. A secondary goal was to assess whether enterococci levels can be predictive of the presence of Staphylococcus aureus, a skin pathogen. Over a ten-day period, hydrometeorologic data, hydrodynamic data, bather densities, enterococci levels, and S. aureus levels including methicillin-resistant S. aureus (MRSA) were measured in both water and sand. Samples were collected hourly for both water and sediment at knee-depth, and every 6 h for water at waist-depth, supratidal sand, intertidal sand, and waterline sand. Results showed that solar radiation, tides, and rainfall events were major environmental factors that impacted enterococci levels. S. aureus levels were associated with bathing load, but did not correlate with enterococci levels or any other measured parameters. The results imply that frequencies of advisories depend heavily upon sample collection policies due to spatial and temporal variation of enterococci levels in response to environmental parameters. Thus, sampling at different times of the day and at different depths can significantly impact beach management decisions. Additionally, the lack of correlation between S. aureus and enterococci suggests that use of fecal indicators may not accurately assess risk for some pathogens., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The US EPA BEACH Act requires beach managers to issue swimming advisories when water quality standards are exceeded. While a number of methods/models have been proposed to meet the BEACH Act requirement, no systematic comparisons of different methods against the same data series are available in terms of relative performance of existing methods. This study presents and compares three models for nowcasting and forecasting enterococci levels at Gulf Coast beaches in Louisiana, USA. One was developed using the artificial neural network (ANN) in MATLAB Toolbox and the other two were based on the US EPA Virtual Beach (VB) Program. A total of 944 sets of environmental and bacteriological data were utilized. The data were collected and analyzed weekly during the swimming season (May-October) at six sites of the Holly Beach by Louisiana Beach Monitoring Program in the six year period of May 2005-October 2010. The ANN model includes 15 readily available environmental variables such as salinity, water temperature, wind speed and direction, tide level and type, weather type, and various combinations of antecedent rainfalls. The ANN model was trained, validated, and tested using 308, 103, and 103 data sets (collected in 2007, 2008, and 2009) with an average linear correlation coefficient (LCC) of 0.857 and a Root Mean Square Error (RMSE) of 0.336. The two VB models, including a linear transformation-based model and a nonlinear transformation-based model, were constructed using the same data sets. The linear VB model with 6 input variables achieved an LCC of 0.230 and an RMSE of 1.302 while the nonlinear VB model with 5 input variables produced an LCC of 0.337 and an RMSE of 1.205. In order to assess the predictive performance of the ANN and VB models, hindcasting was conducted using a total of 430 sets of independent environmental and bacteriological data collected at six Holly Beach sites in 2005, 2006, and 2010. The hindcasting results show that the ANN model is capable of predicting enterococci levels at the Holly Beach sites with an adjusted RMSE of 0.803 and LCC of 0.320 while the adjusted RMSE and LCC values are 1.815 and 0.354 for the linear VB model and 1.961 and 0.521 for the nonlinear VB model. The results indicate that the ANN model with 15 parameters performs better than the VB models with 6 or 5 parameters in terms of RMSE while VB models perform better than the ANN model in terms of LCC. The predictive models (especially the ANN and the nonlinear VB models) developed in this study in combination with readily available real-time environmental and weather forecast data can be utilized to nowcast and forecast beach water quality, greatly reducing the potential risk of contaminated beach waters to human health and improving beach management. While the models were developed specifically for the Holly Beach, Louisiana, the methods used in this paper are generally applicable to other coastal beaches., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Enterococci are used to assess the risk of negative human health impacts from recreational waters. Studies have shown sustained populations of enterococci within sediments of beaches but comprehensive surveys of multiple tidal zones on beaches in a regional area and their relationship to beach management decisions are limited. We sampled three tidal zones on eight South Florida beaches in Miami-Dade and Broward counties and found that enterococci were ubiquitous within South Florida beach sands although their levels varied greatly both among the beaches and between the supratidal, intertidal and subtidal zones. The supratidal sands consistently had significantly higher (p < 0.003) levels of enterococci (average 40 CFU/g dry sand) than the other two zones. Levels of enterococci within the subtidal sand correlated with the average level of enterococci in the water (CFU/100mL) for the season during which samples were collected (r(s) = 0.73). The average sand enterococci content over all the zones on each beach correlated with the average water enterococci levels of the year prior to sand samplings (r(s) = 0.64) as well as the average water enterococci levels for the month after sand samplings (r(s) = 0.54). Results indicate a connection between levels of enterococci in beach water and sands throughout South Florida's beaches and suggest that the sands are one of the predominant reservoirs of enterococci impacting beach water quality. As a result, beaches with lower levels of enterococci in the sand had fewer exceedences relative to beaches with higher levels of sand enterococci. More research should focus on evaluating beach sand quality as a means to predict and regulate marine recreational water quality., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Efforts to improve public health protection in recreational swimming waters have focused on obtaining real-time estimates of water quality. Current monitoring techniques rely on the time-intensive culturing of fecal indicator bacteria (FIB) from water samples, but rapidly changing FIB concentrations result in management errors that lead to the public being exposed to high FIB concentrations (type II error) or beaches being closed despite acceptable water quality (type I error). Empirical predictive models may provide a rapid solution, but their effectiveness at improving health protection has not been adequately assessed. We sought to determine if emerging monitoring approaches could effectively reduce risk of illness exposure by minimizing management errors. We examined four monitoring approaches (inactive, current protocol, a single predictive model for all beaches, and individual models for each beach) with increasing refinement at 14 Chicago beaches using historical monitoring and hydrometeorological data and compared management outcomes using different standards for decision-making. Predictability (R(2)) of FIB concentration improved with model refinement at all beaches but one. Predictive models did not always reduce the number of management errors and therefore the overall illness burden. Use of a Chicago-specific single-sample standard-rather than the default 235 E. coli CFU/100 ml widely used-together with predictive modeling resulted in the greatest number of open beach days without any increase in public health risk. These results emphasize that emerging monitoring approaches such as empirical models are not equally applicable at all beaches, and combining monitoring approaches may expand beach access., (Published by Elsevier Ltd.)

There has been an ongoing dilemma for agencies that set criteria for safe recreational waters in how to provide for a seasonal assessment of a beach site versus guidance for day-to-day management. Typically an overall 'safe' criterion level is derived from epidemiologic studies of sewage-impacted beaches. The decision criterion is based on a percentile value for a single sample or a moving median of a limited number (e.g. five per month) of routine samples, which are reported at least the day after recreator exposure has occurred. The focus of this paper is how to better undertake day-to-day recreational site monitoring and management. Internationally, good examples exist where predictive empirical regression models (based on rainfall, wind speed/direction, etc.) may provide an estimate of the target faecal indicator density for the day of exposure. However, at recreational swimming sites largely impacted by non-sewage sources of faecal indicators, there is concern that the indicator-illness associations derived from studies at sewage-impacted beaches may be inappropriate. Furthermore, some recent epidemiologic evidence supports the relationship to gastrointestinal (GI) illness with qPCR-derived measures of Bacteroidales/Bacteroides spp. as well as more traditional faecal indicators, but we understand less about the environmental fate of these molecular targets and their relationship to bather risk. Modelling pathogens and indicators within a quantitative microbial risk assessment framework is suggested as a way to explore the large diversity of scenarios for faecal contamination and hydrologic events, such as from waterfowl, agricultural animals, resuspended sediments and from the bathers themselves. Examples are provided that suggest that more site-specific targets derived by QMRA could provide insight, directly translatable to management actions., (Published by Elsevier Ltd.)

Quantitative assessment of multiple sources to short-term variations in recreational water quality, as indexed by faecal indicator organism (FIO) concentrations, is becoming increasingly important with adoption of modern water quality standards and catchment-based water quality management requirements (e.g. the EU Water Framework Directive, Article 11 'Programmes of Measures' and the US Clean Water Act, 'Total Maximum Daily Loads'). This paper describes a study combining microbial tracers, intensive FIO measurement, open channel hydrology and molecular microbial source tracking (MST) to enhance understanding of recreational water quality at Amroth in southwest Wales, UK. Microbial tracers were released from four stream inputs during a moderate hydrograph event. Tracers from two local streams impacted simultaneously with a period of maximum FIO concentrations at the near-shore compliance monitoring site. Connection between these inputs and this site were rapid (9-33 min). Water quality impairment from a more remote stream input followed, 12.85 h after tracer release, sustaining FIO concentrations above desired compliance levels. MST analysis showed dominance of ruminant Bacteroidales genetic markers, associated with agricultural pollution. This integration of tracers and MST offers additional information on the movement and individual sources causing water quality impairment., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

The European Community Bathing Water Directive (European Parliament, 2006) set compliance standards for bathing waters across Europe, with minimum standards for microbiological indicators to be attained at all locations by 2015. The Directive allows up to 15% of samples affected by short-term pollution episodes to be disregarded from the figures used to classify bathing waters, provided certain management criteria have been met, including informing the public of short-term water pollution episodes. Therefore, a scientifically justifiable discounting limit is required which could be used as a management tool to determine the samples that should be removed. This paper investigates different methods of obtaining discounting limits, focusing in particular on extreme value methodology applied to data from Scottish bathing waters. Return level based limits derived from threshold models applied at a site-specific level improved the percentage of sites which met at least the minimum required standard. This approach provides a method of obtaining limits which identify the samples that should be removed from compliance calculations, although care has to be taken in terms of the quantity of data which is removed., ((c) 2009 Elsevier Ltd. All rights reserved.)

Beach advisories are issued to the public in California when the concentration of fecal indicator bacteria (FIB), including total coliform, fecal coliform (or Escherichia coli), and Enterococcus, exceed their recreational water health standards, or when the amount of a rainfall event is above the pre-determined threshold. However, it is not fully understood about how and to what degree stormwater runoff or baseflow exerts impacts on beach water quality. Furthermore, current laboratory methods used to determine the FIB levels take 18-96 h, which is too slow to keep pace with changes in FIB levels in water. Thus, a beach may not be posted when it is contaminated, and may be posted under advisory when bacterial levels have already decreased to within water quality standards. The study was designed to address the above critical issues. There were large temporal and spatial variations in FIB concentrations along two popular State Beaches in San Diego, CA, USA. The rainstorm-induced runoff from the watersheds exerts significant impacts on the marine recreational water quality of the beaches adjacent to lagoons during the first 24-48 h after a rain event. The large volume of stormwater runoff discharging to beaches caused high FIB concentrations in beach water not only at the lagoon outlet channel and the mixing zone, but also at the locations 90 m away from the channel northward or southward along the shoreline. The geomorphology of beach shoreline, distance from the outlet channel, wind strength, wind direction, tide height, wave height, rainfall, time lapse after a rainstorm, or channel flow rate played a role in affecting the distribution of FIB concentrations in beach water. Despite the great temporal and spatial variability of FIB concentrations along a shoreline, the artificial neural network-based models developed in this study are capable of successfully predicting FIB concentrations at different beaches, different locations, and different times under baseflow or rainstorm conditions. The models are based on readily measurable variables including temperature, conductivity, pH, turbidity, channel water flow, rainfall, and/or time lapse after a rainstorm. The established models will help fill the current gap between beach posting and actual water quality and make more meaningful and effective decisions on beach closures and advisories.

Two public beaches (Anderson and Hilton) in Newport News, Virginia, were frequently closed to swimming in 2004 due to high Enterococcus spp. counts that exceeded the regulatory standard. The microbial source tracking (MST) methods of antibiotic resistance analysis (ARA) and fluorometry (to detect optical brighteners) were used in the summer of 2004 to determine the origins of fecal pollution at the two beaches. Both MST methods detected substantial human-origin pollution at the two beaches, in locations producing consistently high levels of Enterococcus spp. Investigations by municipal officials led to the fluorometric detection and subsequent repair of sewage infrastructure problems at both beaches. The success of the mitigation efforts was confirmed during the summer of 2005 using ARA and fluorometry, with the results cross-validated by pulsed-field gel electrophoresis (PFGE).

The recent implementation of the Revised Bathing Water Directive in the European Union has highlighted the need for development of effective methods to differentiate between sources of faecal contamination. It had previously been shown that amplification of 16S rRNA genes of host-specific Bacteriodales species using the HF183F and CF128F primers could be used as markers for human and bovine faecal contamination in the United States. This paper determined the sensitivity and specificity of these markers in four Atlantic Rim countries (France, Ireland, Portugal and the United Kingdom) to evaluate their usefulness in determining the origin of faecal contamination. It was shown that the HF183F marker displayed high sensitivity (80-100%) and specificity (91-100%), and is reliable as an indication of human faecal contamination. The CF128F marker displayed 100% sensitivity in all four countries. However, strong regional variations in specificity (41-96%) were observed, highlighting the need for local validation before this marker is employed in source tracking of faecal contamination.

The effect of a stormwater conveyance system on indicator bacteria levels at a Florida beach was assessed using microbial source tracking methods, and by investigating indicator bacteria population structure in water and sediments. During a rain event, regulatory standards for both fecal coliforms and Enterococcus spp. were exceeded, contrasting with significantly lower levels under dry conditions. Indicator bacteria levels were high in sediments under all conditions. The involvement of human sewage in the contamination was investigated using polymerase chain reaction (PCR) assays for the esp gene of Enterococcus faecium and for the conserved T antigen of human polyomaviruses, all of which were negative. BOX-PCR subtyping of Escherichia coli and Enterococcus showed higher population diversity during the rain event; and higher population similarity during dry conditions, suggesting that without fresh inputs, only a subset of the population survives the selective pressure of the secondary habitat. These data indicate that high indicator bacteria levels were attributable to a stormwater system that acted as a reservoir and conduit, flushing high levels of indicator bacteria to the beach during a rain event. Such environmental reservoirs of indicator bacteria further complicate the already questionable relationship between indicator organisms and human pathogens, and call for a better understanding of the ecology, fate and persistence of indicator bacteria.

While the US Environmental Protection Agency's (EPA) Beaches Environmental Assessment and Coastal Health (BEACH) Act requires coastal and Great Lakes' states to implement plans for monitoring bacterial contamination of recreational beach water, exactly how this monitoring should occur has not been regulated. This study examined differences in concentration of Escherichia coli in water collected from different depths and from different horizontal locations across the beach. E. coli concentrations were significantly different (p<0.05), when water from different depths was compared. Sampling water at depths of 30, 60, and 120 cm resulted in significantly lower E. coli concentrations as depth increased. Had the State of Wisconsin chosen to collect beach water monitoring samples at a shallower or deeper depth, numbers of beach closures and the potential risk to public health would have changed substantially. These data imply that a revised and standardized protocol for monitoring beach water should be adopted by all states of a monitoring region to better compare microbial contamination of beaches and protect public health.

Analyses of Irish bathing water quality data sets are reported to investigate whether the parametric calculations proposed in the draft Bathing Water Directive are valid. Faecal coliforms (assumed to be Escherichia coli) and faecal streptococci (assumed to be intestinal enterococci) have been analysed separately. It was noted that classifications based on the parametric 95th percentile calculations disagreed with those based on percentage compliance with the standards on 13.8% of occasions. When these disagreements were studied, it was found that the datasets frequently contained many censored data points (Result < 1). Also, the datasets were not log normally distributed on at least 85% of occasions. Both these findings fatally undermine the validity of using a parametric method for calculating 95th percentiles to classify bathing water quality. By contrast the non-parametric Hazen method is a better estimate of true population 95th percentiles, but essentially gives very similar classifications to the percentage compliance approach, fully agreeing on over 95% of occasions. The same is also true when considering 90th percentiles. A series of Monte Carlo studies were also conducted to determine the impact of small numbers. It was ascertained that small sample sizes are very undependable in determining bathing water classification and the parametric method in particular is particularly unreliable. In conclusion, the parametric method for calculating bathing water compliance is so severely and fatally flawed statistically that it should not form the basis of any legislation. The Hazen method gives a better estimate of true 90th or 95th percentiles, though as the resultant classifications agree with percentage compliance so closely it is doubtful that there is any statistical value in using a percentile approach over the long established and well understood percentage compliance approach.

'Natural' treatment systems such as wetlands and reed beds have been proposed as sustainable means of reducing fluxes of faecal indicator organisms (FIOs) to recreational and shellfish harvesting waters. This is because FIO fluxes to coastal waters from both point (effluent) and diffuse (catchment) sources can cause non-compliance with microbiological standards for bathing and shellfish harvesting waters. The Water Framework Directive requires competent authorities in the member states to manage both point and diffuse sources of FIOs in an integrated manner to achieve compliance with 'good' water quality as defined in a series of daughter Directives. This study was undertaken to investigate the relative sources of FIOs to the popular bathing waters around Clacton, UK. In this predominantly arable (mainly cereal cropping) farming area, the principal land use predictor, explaining 76% of the variance in geometric mean presumptive Escherichia coli concentration at sub-catchment outlets during the bathing season, was the proportion of built-up (i.e. urbanised) land in each sub-catchment. This new finding contrasts with earlier studies in livestock farming regions where the proportion of improved grassland has proven to be the strongest predictor of microbial concentration. Also novel in this investigation, a flood defence wall has been built creating a wetland area which discharges every tidal cycle. The wetland produces over 97% reduction in the flux and concentrations of FIOs to the marine recreational waters. Also, FIO concentrations in water draining through the wetland to the sea were similar to concentrations measured in six UK sewage treatment plant effluents subject to secondary (biological) treatment followed by UV disinfection.