Solutions for Modelling the Marine Oil Spill Drift

Oil spills represent a critical environmental hazard with far-reaching ecological and economic consequences, necessitating the development of sophisticated modelling approaches to predict, monitor, and mitigate their impacts. This study presents a computationally efficient and physically grounded modelling framework for simulating oil spill drift in marine environments, developed using Python coding. The proposed model integrates core physical processes—advection, diffusion, and degradation—within a simplified partial differential equation system, employing an integrator for numerical simulation. Building on recent advances in marine pollution modelling, the study incorporates real-time oceanographic data, satellite-based remote sensing, and subsurface dispersion dynamics into an enriched version of the simulation. The research is structured in two phases: (1) the development of a minimalist Python model to validate fundamental oil transport behaviours, and (2) the implementation of a comprehensive, multi-layered simulation that includes NOAA ocean currents, 3D vertical mixing, and support for inland and chemical spill modelling. The results confirm the model’s ability to reproduce realistic oil spill trajectories, diffusion patterns, and biodegradation effects under variable environmental conditions. The proposed framework demonstrates strong potential for real-time decision support in oil spill response, coastal protection, and environmental policy-making. This paperwork contributes to the field by bridging theoretical modelling with practical response needs, offering a scalable and adaptable tool for marine pollution forecasting. Future extensions may incorporate deep learning algorithms and high-resolution sensor data to further enhance predictive accuracy and operational readiness.