Daily prediction of marine beach water quality in Hong Kong

Bacterial concentration (Escherichia coli) is generally adopted as a key indicator of beach water quality. Currently the beach management system in Hong Kong relies on past water quality data sampled at intervals between 3 and 14 days. Beach advisories are issued when the geometric mean E. coli level of the past five samples exceeds the beach water quality objective (WQO) of 180 counts/100 mL. When the E. coli level varies dynamically, the system is not able to track the daily bacterial variation. And yet worldwide there does not exist a generally accepted method to predict beach water quality in a marine environment, which is influenced by hydro-meteorological variables, catchment characteristics, as well as complicated tidal currents and wave effects. A comprehensive study of beach water quality prediction has been carried out for four representative beaches in Hong Kong: Big Wave Bay (BW), Deep Water Bay (DW), New Cafeteria (NC) and Silvermine Bay (SIL). Statistical analysis of the extensive regular monitoring data was carried out for two periods before and after the commissioning of the Harbour Area Treatment Scheme (HATS): (1990–1997) and (2002–2006) respectively. The data analysis shows that E. coli is strongly correlated with seven hydro-environmental variables: rainfall, solar radiation, wind speed, tide level, salinity, water temperature and past E. coli concentration. The relative importance of the parameters is beach-specific, and depends on the local geographical and hydrographical characteristics as well as location of nearby pollution sources. Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are developed from the sparsely sampled regular monitoring data (2002–2006) to predict the next-day E. coli concentration using the key hydro-environmental variables as input parameters. The models are validated against daily monitoring data in the bathing seasons of 2007 and 2008. The models are able to track the dynamic changes in E. coli concentration and predict WQO compliance/exceedance with an overall accuracy of 70–96%. Both the MLR and ANN models are superior to the current beach advisories in capturing water quality variations, and in predicting WQO exceedances. For example, the models predict around 80% and 50% of the exceedances at BW and NC respectively in June–July 2007, as compared to 0% and 14% based purely on past data. Similarly, observed exceedances are predicted with success rates of 71%, 42%, and 53% at BW, NC, and SIL respectively during July–October 2008, as compared with 0%, 0%, and 6% using the current water quality assessment criterion. The MLR and ANN models have similar performances; ANN model tends to be better in predicting the high-end concentrations, with however a greater number of false positive predictions (false alarms). This work demonstrates the practical feasibility of predicting bacterial concentration based on the critical hydro-environmental variables, and paves the way for developing a real time water quality forecast and management system for Hong Kong.