Water Quality Beaches Modeling Based on Environmental Parameters for Coastal City Public Management in Santos Bay

The use of modeling techniques for the fecal bacteria concentration forecasting has been used as a practicable tool by the beach managers, whose main objective is to reduce the occurrence of bacterial diseases in humans due to contaminated beach waters by simply avoiding the contact between them. In fact, there is an inherent difficulty in this public health policy associated to the current sampling and laboratorial post processing methodology to label some beach area as unappropriated for human contact, as it may require one day or more to be done. It means that the current beach statuses shown are often based on samples collected at least 24 hours before.

Therefore, the referred work creates a tool which correlates the daily beach water quality (measured by the fecal coliform concentration in water) and the accumulated precipitation in the previous days, with the assumption that the diffuse pollution caused by the surface runoff and associated to the lack of sanitation is the responsible for the low water quality level, frequently faced in large cities worldwide.

The current tool was applied to Santos bay, in São Paulo, Brazil, which not only frequently has its beaches associated to high levels of pollution, as well is an overpopulated touristic Brazilian city with the largest port in Latin America. There, an oceanic interceptor catches the water from Santos drainage channels, including the water pollution diffusion, and conducts it to a pre-conditioning treatment station before disposing into the ocean through a submarine outfall. However, during some intense raining events, the drainage channels gates are opened to avoid flooding the urban area, and then this collected water goes directly to the sea.

In this work a statistical approach model correlation between precipitation and fecal coliform data was developed to estimate the beach water quality in real time, and its results show values of correlation coefficient R² of the statistical model above 0.6 and the capacity of identifying the beach water quality status in almost 90% of the days in 2018. Although other parameters must be incorporated to the statistical model to improve the results, as the gates operation, the transient flow in the channels and the dynamic interaction of ocean and coastal area reflected by waves, tide and wind, these results justify the use of the statistical tool as a complement criteria for a fast available extra source of water level quality information for the beach managers.