Chronic oil pollution in small ports: A review of the role and potential of AUVs

Chronic oil pollution in small ports are typically low-level, repeated, and long-lasting, making them difficult to monitor with conventional approaches. Autonomous Underwater Vehicles (AUVs) provide a mobile means of high-resolution in-situ observation. This review evaluates recent progress in sensor integration, data fusion, spatial interpolation, and machine learning methods that improve AUV-based detection. Fluorescence sensors, mass spectrometers, and hybrid payloads expand chemical monitoring capacity, while fusion techniques mitigate the weaknesses of individual sensors. Interpolation approaches such as Gaussian Process Regression (GPR) address spatial gaps in survey data, and physics-informed models add physical constraints. Together, these advances highlight the potential for AUVs to serve as practical tools for monitoring chronic oil pollution in confined, dynamic port environments.