Researchers have developed a new computer tool that can identify harmful bacteria levels from all potential pollution sources on recreational beaches.
The predictive model uses information on waves, tides, rainfall and solar radiation to more accurately predict harmful bacteria concentration and movement along the shore.
The team led by researchers at the University of Miami (UM) Rosenstiel School of Marine and Atmospheric Science, optimised and validated their model using a 10-day monitoring dataset from the popular Virginia Beach in Miami.
US federal and state laws require water monitoring of fecal indicator bacteria, such as enterococci and fecal coliform, at recreational beaches to protect beachgoers from harmful water contamination levels.
Excess levels of these harmful bacteria prompt beach advisories and closures to minimise human health risks.
Water contamination from fecal indicator bacteria can result from "point-source" pollution, such as a sewage outfall, or "nonpoint source" pollution from storm-water runoff, or animal and human inputs.
Current methods assess fecal bacteria contamination levels by direct sampling of water from beaches, as well as by using complex computer modelling.
Direct sampling methods require a one-day laboratory analysis to access the health risk to humans at a particular beach. Therefore, a 24 to 48 hours wait period after sampling is required before any beach closure or advisory is issued.
In addition, the current computer-based model requires high computing power, which is often inaccessible to beach closure decision managers, and can only predict contaminates from known sources of pollution, such as sewage outfalls.
"The development of this new model has allowed us, for the first time, to estimate contamination levels on beaches subject to nonpoint source pollution, in particular from beach sand and runoff from storms," researchers said.
