Predicting water quality at Santa Monica Beach: evaluation of five different models for public notification of unsafe swimming conditions.

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.

A multi-criteria evaluation system for marine litter pollution based on statistical analyses of OSPAR beach litter monitoring time series.

During the last decades, marine pollution with anthropogenic litter has become a worldwide major environmental concern. Standardized monitoring of litter since 2001 on 78 beaches selected within the framework of the Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) has been used to identify temporal trends of marine litter. Based on statistical analyses of this dataset a two-part multi-criteria evaluation system for beach litter pollution of the North-East Atlantic and the North Sea is proposed. Canonical correlation analyses, linear regression analyses, and non-parametric analyses of variance were used to identify different temporal trends. A classification of beaches was derived from cluster analyses and served to define different states of beach quality according to abundances of 17 input variables. The evaluation system is easily applicable and relies on the above-mentioned classification and on significant temporal trends implied by significant rank correlations.

Urbanised beaches of the Ligurian coastal area (NW Mediterranean): a classification based on organic-matter characteristics and hydrolytic enzymatic activities.

The beaches of Liguria have been intensively affected by human activities for over a century, transforming nearly the entire coastline from natural to urbanised and significantly upsetting beach ecological properties. The present study aims to investigate 9 Ligurian beaches characterised by different degree of urbanisation, to test if and to what extent the organic-matter (OM) recycling processes can be linked to the human activity. Swash zone sediment, sampled during the spring-summer-autumn period, when the anthropogenic influence is at its maximum due to tourism, was analysed for OM features and recycling processes. Multivariate statistical analyses showed that huge amounts of detrital OM accumulated in the more urbanised sites, where the anthropogenic influence was at its peak, deriving from higher inhabitant number and density, from the presence of crowded roads very near to the swash zone and sewage treatment plants. The presence of torrent outlets on the beaches provided further OM accumulation. Lipids, carbohydrates and degraded autotrophic pigments were the OM fractions mainly responsible of the differentiation, and rather constant, high labile phosphorus contents were found in the more urbanised sites. The high activity values of the hydrolytic enzymes indicate the response of the microbial system to the OM accumulation in the urban sites. However, a decoupling of the trends of some enzymatic activities (namely glucosidase and lipase) and their target OM was observed in the highly urbanised conditions.

Does the bathing water classification depend on sampling strategy? A bootstrap approach for bathing water quality assessment, according to Directive 2006/7/EC requirements.

Data on the 95th and 90th percentiles of bacteriological quality indicators are used to classify bathing waters in Europe, according to the requirements of Directive 2006/7/EC. However, percentile values and consequently, classification of bathing waters depend both on sampling effort and sample-size, which may undermine an appropriate assessment of bathing water classification. To analyse the influence of sampling effort and sample size on water classification, a bootstrap approach was applied to 55 bacteriological quality datasets of several beaches in the Balearic Islands (Spain). Our results show that the probability of failing the regulatory standards of the Directive is high when sample size is low, due to a higher variability in percentile values. In this way, 49% of the bathing waters reaching an "Excellent" classification (95th percentile of Escherichia coli under 250 cfu/100 ml) can fail the "Excellent" regulatory standard due to sampling strategy, when 23 samples per season are considered. This percentage increases to 81% when 4 samples per season are considered. "Good" regulatory standards can also be failed in bathing waters with an "Excellent" classification as a result of these sampling strategies. The variability in percentile values may affect bathing water classification and is critical for the appropriate design and implementation of bathing water Quality Monitoring and Assessment Programs. Hence, variability of percentile values should be taken into account by authorities if an adequate management of these areas is to be achieved.

Association of gastrointestinal illness and recreational water exposure at an inland U.S. beach.

Recent epidemiology studies examining U.S. recreational water exposure and illness relationships have focused primarily on coastal and Great Lakes beaches. Human-made lakes in the U.S. have received little attention in epidemiology studies despite contributing to more waterborne disease epidemics annually than coastal U.S. waters. In a comprehensive beach cohort study, we examined relationships between water quality indicators and reported adverse health outcomes among users of a beach at an inland U.S. lake. Human health data was collected over 26 swimming days during the 2009 swimming season in conjunction with water quality measurements. Adverse health outcomes were reported 8-9 days post-exposure via a phone survey. Wading, playing or swimming in the water was observed to be a significant risk factor for GI illness (adjusted odds ratio (AOR) of 3.2; CI 1.1, 9.0). Among water users, Escherichia coli density was significantly associated with elevated GI illness risk where the highest E. coli quartile was associated with an AOR of 7.0 (CI 1.5, 32). GI illness associations are consistent with previous freshwater epidemiology studies. Our findings are unique in that our observations of positive associations with GI illness risk are based upon a single daily E. coli measurement. Lastly, this study focused on an understudied issue, illness risk at inland reservoirs. Our results support the usefulness of E. coli as a health-relevant indicator of water quality for this inland U.S. beach.

Efeito do grau de exposição às ondas sobre a comunidade de peixes juvenis em praias arenosas do Município do Rio de Janeiro, Brasil / Effect of the wave exposure gradient on juvenile fish community in sandy beaches of the Rio de Janeiro City, Brazil

A distribuição e abundância da ictiofauna na zona de arrebentação de praias arenosas do Município do Rio de Janeiro, Brasil, foram estudadas objetivando detectar variações sazonais ou influências do grau de exposição na estrutura da comunidade de peixes jovens. Amostras trimestrais foram realizadas em 10 praias sendo agrupadas de acordo com o nível de exposição (abrigada, exposta e muito exposta), entre Janeiro e Novembro de 2004, com os arrastos tendo sido efetuados perpendiculares à linha de costa. Foram registrados 6.464 peixes, distribuídos em 38 espécies e 22 famílias, com o predomínio de formas juvenis ou de pequeno porte. As famílias Clupeidae, Carangidae e Sciaenidae representaram 79 por cento do número total de indivíduos e 61 por cento da biomassa total. As espécies de maior participação na biomassa, representando 81 por cento do peso total, foram Harengula clupeola (36 por cento), Orthopristis ruber (14 por cento), Mugil liza (10 por cento), Trachinotus goodei (8 por cento), Trachinotus carolinus (7 por cento) e Umbrina coroides (6 por cento). Três espécies apresentaram maior abundância relativa, tendo cada uma, contribuído acima de 10 por cento do número total (Harengula clupeola, Umbrina coroides e Trachinotus carolinus), mas somente T. carolinus apresentou um padrão bem definido, sendo mais abundante nas zonas abrigadas. As maiores CPUEs (número de indivíduos e biomassa) e o número de espécies foram encontradas nas zonas abrigadas (p < 0,05); sazonalmente nenhum destes indicadores apresentou diferença significativa (p > 0,05). O grau de exposição às ondas foi confirmado como um fator primário na estruturação da comunidade de peixes, com os locais mais abrigados provavelmente associados à maior disponibilidade de organismos planctônicos, menores turbulências e maior estabilidade do substrato.

Distribution and abundance of the ichthyofauna of surf zone in sandy beaches of the Rio de Janeiro Municipality, Brazil, were studied aiming to detect seasonal and wave exposure gradient influences on the fish community structure. Quarterly samplings were performed at 10 sites according to the exposure degree (sheltered, exposed and very exposed) between January and November 2004, by using beach seines with hauls carried out perpendicular to the coastline. A total of 6,464 fishes in 38 species and 22 families were identified, mainly juveniles or small sized individuals. The families Clupeidae, Carangidae and Sciaenidae amounted to 79 percent of the total fish number and 61 percent of the total biomass. The following species amounted to 81 percent of the total weight were: Harengula clupeola (36 percent), Orthopristis ruber (14 percent), Mugil liza (10 percent), Trachinotus goodei (8 percent), Trachinotus carolinus (7 percent) and Umbrina coroides (6 percent). Three species showed the highest relative abundance, contributing each one more than 10 percent of the total number (Harengula clupeola, Umbrina coroides, e Trachinotus carolinus); but only T. carolinus showed a clear pattern, being more abundant in the sheltered beaches. The highest CPUEs (number of individuals and biomass) and number of species values occurred in the sheltered beaches (p < 0.05); none of these indicators showed significant seasonal differences (p > 0.05), although some trends were detected. The wave exposure degree was confirmed as a primary factor influencing the fish community structure, with sheltered sites probably associated to higher plankton availability, lesser turbulence and more substrate stability.

Classification of bathing water quality based on the parametric calculation of percentiles is unsound.

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.

An assessment of the impact of the proposed EU bathing water directive on Irish coastal bathing area compliance.

An assessment of the impact of the new microbial water quality standards of the proposed EU Bathing Water Directive on the classification of designated Irish coastal bathing areas is presented. The new standards are applied retrospectively to the microbial water quality results for the bathing seasons of 1999, 2000 and 2001, and the outcome is compared with that recorded under the present Bathing Water Directive. A Microsoft EXCEL application was developed to generate the retrospective bathing area classifications according to the proposed Directive (Excellent, Good, Poor). It was found that the number of Irish coastal bathing areas not attaining 'Excellent' classification (as would be required at present for the Blue Flag award) was trebled; the number attaining 'Good' classification was increased by about 50%, and the number attracting 'Poor' classification (equivalent to 'Fail' under the present Directive) was increased nine-fold. Some of the shortcomings of the proposed Directive and suggestions for its revision are discussed.