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Intercept the Drift: A Novel NOAA-Data-Driven Lagrangian Model for Marine Debris Interception, Cleanup, and Source Reduction in a Chesapeake Bay Case Study

Booth Id:
EAEV068

Category:
Earth and Environmental Sciences

Year:
2026

Finalist Names:
Hui, Lilian (School: Albemarle High School)

Abstract:
My project is a Chesapeake Bay case study simulation built using an existing Lagrangian tracking model, driven by real National Oceanic and Atmospheric Association (NOAA) current and wind data for velocities and coastline boundaries. I released and tracked debris particles over 72 hours from the 8 major pollutant contributing tributaries in the Chesapeake Bay. I first placed 10 interceptor sites by geographic intuition (for example, large cities or at the end of rivers). The simulation demonstrated that these were largely ineffective, with 8 of the 10 sites capturing <10 of the total 1,600 particles released. I also ran 100 random 5-site configurations, which yielded a mean capture rate of 12.0%, only slightly lower than the geographically intuition based sites that captured 15.6% of the debris particles; Geographic intuition is barely better than random placement. The model then found the 5 optimal sites through the regions of highest trajectory density, capturing 56.5% of the debris - a 261% improvement using half the interception devices. While a significant improvement in debris collection was shown, the model still reveals that interception alone is not enough. A three part strategy must be used: optimizing interception, source reduction at tributary mouths, and a targeted beach cleanup in the south-eastern bay. This simulation was run under calm and storm wind conditions, across all four seasons, and used Monte Carlo statistics and confidence intervals to confirm the results are consistent.

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