Forecasting sudden changes in environmental pollution patterns.

Area of Science:

• Environmental science
• Fluid dynamics
• Applied mathematics

Background:

• Reliable forecasting of oceanic and atmospheric contamination spread is lacking, hindering effective protection of ecosystems, society, and economies from environmental disasters.
• The Deepwater Horizon oil spill exemplifies the severe consequences of such environmental disasters and the need for improved predictive capabilities.

Purpose of the Study:

• To present a novel methodology for predicting short-term changes in environmental contamination patterns, including oil spills and atmospheric ash clouds.
• To leverage mathematical insights into Lagrangian coherent structures (LCSs) for enhanced environmental disaster forecasting.

Main Methods:

• Utilizing new mathematical results for objective identification of material surfaces driving tracer mixing in unsteady flow data.
• Identifying Lagrangian coherent structures (LCSs) with attracting cores that indicate inevitable material instabilities.
• Developing a model-independent forecasting scheme based on observed flow velocities and LCS-core analysis.

Main Results:

• Demonstrated the potential of LCS cores to forecast imminent shape changes in contamination patterns before significant motion occurs.
• Achieved high-precision forecasts of two major instabilities in the Deepwater Horizon oil spill shape.
• Validated the methodology using simulated surface currents and treating oil as a passive tracer.

Conclusions:

• The developed LCS-core analysis offers a powerful tool for model-independent, short-term prediction of environmental contamination spread.
• This approach enhances the ability to protect ecosystems, society, and economies by providing timely warnings of contamination pattern changes.
• The methodology shows promise for improving response strategies to environmental disasters like oil spills and volcanic ash clouds.