Wai Thoe

Real-time forecasting of Hong Kong beach water quality by 3D deterministic model

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.

Impacts of land use and water quality on macroinvertebrate communities in the Pearl River drainage basin, China

The East River (Dong Jiang), a major tributary of the Pearl River (Zhu Jiang, the second largest river in China by discharge), is situated in southern China, which has the highest rates of urbanization and development on Earth. The East River also provides 80% of Hong Kong’s water supply. However, there have been no ecological studies to examine the combined impacts of changes in land use and water quality degradation on this river ecosystem. We tested the hypothesis that land-use disturbance and water quality degradation would significantly reduce benthic biodiversity in the East River by investigating macroinvertebrate community composition and relating it to data on water quality and catchment condition. The percentage of total impervious area within each catchment (%TIA—an indicator of land-use disturbance) was negatively related to a composite water quality index—the ERWQI—we developed for the East River. Modeling by partial least squares projection to latent structures (PLS) showed that family richness and relative abundance index (RAI) of macroinvertebrates were strongly influenced by both %TIA and ERWQI. Multi-response permutation procedure (MRPP) tests showed highly significant differences in family richness composition and RAI of macroinvertebrates among sites in the upper, middle, and lower course of the East River. MRPP also revealed differences in the family richness composition of nighttime drift samples between upper and middle site groups. Abundance (individuals m−3) and total family richness of drifting macroinvertebrates at each site were positively related to %TIA (range: 1.0–8.5%), while drift biomass was negatively related to dissolved oxygen and positively related to total suspended solids. Thus, human disturbances associated with land-use changes (increasing %TIA) and nutrient inputs severely degraded ecosystem integrity and the water quality of the East River and thereby reduced aquatic biodiversity.

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 worlds 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.

Daily Forecasting of Hong Kong Beach Water Quality by Multiple Linear Regression Models

AbstractA daily forecast system of marine beach water quality (WATERMAN) has been developed for Hong Kong. Individual multiple linear regression (MLR) models were developed to forecast daily water quality (lnEC concentration) at each of the 41 beaches. The forecast system was developed through data assimilation with the most recent water quality data, and supported by field studies and deterministic hydrological, hydrodynamic, and water quality models. Model input parameters included three rainfall variables (daily rainfall in the previous three days): the previous day’s solar radiation, onshore wind speed, and water temperature; tide level and predicted salinity at sampling time; and the geometric mean of the five most recent E. coli measurements. The forecasts were disseminated through the Internet and smart phone apps. The operational performance of the beach water quality forecast system during 2010–2011 is presented for 16 representative beaches. An overall accuracy of 78–100% in forecasting complian...

Sunny with a chance of gastroenteritis: predicting swimmer risk at California beaches.

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.

NITROGEN SOURCE APPORTIONMENT OF THE EAST RIVER (DONGJIANG), CHINA

Abstract The East River, locally known as Dongjiang, is the major source of potable water supply for Hong Kong. Because of the rapid economic development in the basin, concerns have been raised regarding the water quality in the river. In this study, total nitrogen (TN) export from the basin in 1998 is estimated for various sources based on a simple correlative approach utilizing measured concentrations in the river, together with some mass balance constraints based on socio-economic data. A modeling tool, HSP-F, is used for discharge calculation. The export coefficients for four land-use classes are calibrated and compared with literature. It is found that municipal input together with the contribution from farm animals are the major sources of nitrogen in the basin. A total load of 113, 000 t N/a has been estimated, giving an average specific export coefficient of 41.5 kg/ha/a, of which 68% are from non-point sources from the urban land-use and 17% from farm animals. The spatial distribution of nitrogen provenance is highly uneven and is concentrated in the downstream areas near Huizhou, Dongguan and Shenzhen.