RESUMEN
Hong Kong's beach water quality classification scheme, used effectively for >25â¯years in protecting public health, was first established in local epidemiology studies during the late 1980s where Escherichia coli (E. coli) was identified as the most suitable faecal indicator bacteria. To review and further substantiate the scheme's robustness, a performance check was carried out to classify water quality of 37 major local beaches in Hong Kong during four bathing seasons (March-October) from 2010 to 2013. Given the enterococci and E. coli data collected, beach classification by the local scheme was found to be in line with the prominent international benchmarks recommended by the World Health Organization and the European Union. Local bacteriological studies over the last 15â¯years further confirmed that E. coli is the more suitable faecal indicator bacteria than enterococci in the local context.
Asunto(s)
Playas , Calidad del Agua , Enterococcus , Monitoreo del Ambiente , Escherichia coli , Heces/microbiología , Hong Kong , Humanos , Estaciones del Año , Microbiología del AguaRESUMEN
Traditional beach management that uses concentrations of cultivatable fecal indicator bacteria (FIB) may lead to delayed notification of unsafe swimming conditions. Predictive, nowcast models of beach water quality may help reduce beach management errors and enhance protection of public health. This study compares performances of five different types of statistical, data-driven predictive models: multiple linear regression model, binary logistic regression model, partial least-squares regression model, artificial neural network, and classification tree, in predicting advisories due to FIB contamination at 25 beaches along the California coastline. Classification tree and the binary logistic regression model with threshold tuning are consistently the best performing model types for California beaches. Beaches with good performing models usually have a rainfall/flow related dominating factor affecting beach water quality, while beaches having a deteriorating water quality trend or low FIB exceedance rates are less likely to have a good performing model. This study identifies circumstances when predictive models are the most effective, and suggests that using predictive models for public notification of unsafe swimming conditions may improve public health protection at California beaches relative to current practices.
Asunto(s)
Playas , Modelos Estadísticos , Microbiología del Agua , Calidad del Agua , California , Enterobacteriaceae , Enterococcus , Ambiente , Heces/microbiología , Gastroenteritis , Humanos , Análisis de los Mínimos Cuadrados , Modelos Lineales , Modelos Logísticos , Modelos Teóricos , Redes Neurales de la Computación , Sensibilidad y Especificidad , AguaRESUMEN
Bathing beaches are monitored for fecal indicator bacteria (FIB) to protect swimmers from unsafe conditions. However, FIB assays take â¼24 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.
Asunto(s)
Playas/normas , Difusión de la Información/métodos , Modelos Teóricos , Calidad del Agua/normas , Playas/clasificación , California , Modelos Logísticos , Redes Neurales de la ComputaciónRESUMEN
Bacterial level (e.g. Escherichia coli) is generally adopted as the key indicator of beach water quality due to its high correlation with swimming associated illnesses. A 3D deterministic hydrodynamic model is developed to provide daily water quality forecasting for eight marine beaches in Tsuen Wan, which are only about 8 km from the Harbour Area Treatment Scheme (HATS) outfall discharging 1.4 million m(3)/d of partially-treated sewage. The fate and transport of the HATS effluent and its impact on the E. coli level at nearby beaches are studied. The model features the seamless coupling of near field jet mixing and the far field transport and dispersion of wastewater discharge from submarine outfalls, and a spatial-temporal dependent E. coli decay rate formulation specifically developed for sub-tropical Hong Kong waters. The model prediction of beach water quality has been extensively validated against field data both before and after disinfection of the HATS effluent. Compared with daily beach E. coli data during August-November 2011, the model achieves an overall accuracy of 81-91% in forecasting compliance/exceedance of beach water quality standard. The 3D deterministic model has been most valuable in the interpretation of the complex variation of beach water quality which depends on tidal level, solar radiation and other hydro-meteorological factors. The model can also be used in optimization of disinfection dosage and in emergency response situations.