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Intelligent Sensors Detect Ocean Pollutants

Oil spills are among the biggest potential threats to coastal water and water supplies. In response to this environmental threat, researchers at the University of Kentucky developed a data-driven pollution model. The model will be useful in proposing countermeasures to general biochemical and chemical contaminants, from oil spills to chemicals used in terrorist attacks. This project is developing a new class of intelligent sensors and predictive computational models for pollutant tracking and identification that can dynamically adapt to unexpected and changing water bodies in remote areas. This tool will help to mitigate damage in the water and surrounding ecosystems, or even act as an early warning system forecasting events such as chemical spills or algae blooms.

The project uses a variable, light-wave sensor array that is integrated into an ocean observational system. The multi-scale mathematical models and computer simulations are both wind and transport driven, and are dynamically accumulating observed ocean data. This sensing system is able to go beyond just sensing to prevention, for example, a connection program enables small oil tank leaks to be tracked and repaired before major spills occur.

These intelligent sensors pick up data that was previously too elusive to detect, or required on-site personnel to physically test or observe. This technology will improve water quality and enable surveying of remote bodies of water, keeping current data on chemicals in each location. The system may aid in preventing biochemical terrorist attacks from reaching our water supply.


  • Photo of oil tanker out at sea
Oil spills are among the biggest threats to marine and coastal ecosystems. Sensors are being developed to monitor and identify pollutants, from oil spills to chemicals used in terrorist attacks.
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