INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025
Comprehensive Study on Oil Spills: Causes, Impacts, Detection, and
Mitigation Techniques
Sameesha S S Sreepa
Centre for Pollution Control & Environmental Engineering, Kalapet, Pondicherry University
Pondicherry
Received: 18 November 2025; Accepted: 24 November 2025; Published: 01 December 2025
ABSTRACT:
Oil spills pose severe threats to marine ecosystems, human health, and global economies. The paper examines
oil spill causes, environmental impacts, detection technologies, and mitigation strategies. It also evaluates
modern approaches such as remote sensing, AI-enabled spill detection, nanotechnology-based cleanup, and
emerging eco-friendly solutions. The findings highlight global challenges and propose sustainable solutions for
improved preparedness and response.
Keywords— Oil spills, Marine pollution, Remote sensing, Bioremediation, Environmental impact, Spill
detection.
INTRODUCTION
Oil spill contamination remains one of the most critical environmental challenges across the globe. Offshore
drilling, pipeline ruptures, shipping accidents, and storage failures contribute significantly to large-scale spills.
The persistence of oil in marine water affects aquatic life, food chains, coastal biodiversity, and human
livelihoods. Despite advancements in monitoring systems and remediation technologies, incidents continue to
occur due to aging infrastructure, operational errors, and natural hazards.
The motivation for this study is to examine the technological evolution in oil spill detection and response,
evaluate the environmental consequences, and explore the potential for applying modern predictive systems.
This paper aims to provide a consolidated review suitable for researchers, policymakers, and environmental
engineers.
BACKGROUND AND LITERATURE REVIEW
Oil spill research has advanced significantly in the last decade. However, disparities in technology access and
implementation efficiency remain.Key contributions include:
Traditional Detection Methods: Surface visual observation, aerial surveillance, and vessel-based patrolling.
The drawbac involve Subject to human error, weather limitations, and slow response time.
Remote Sensing oTechnolgy: SAR (Synthetic Aperture Radar), hyperspectral imaging, and thermal infrared
scanning are frequently used for identifying hydrocarbon signatures. Comparative Insight: are SAR remains
the most reliable for rapid, large-scale detection. Optical sensors offer detail but are weather-dependent.
Machine Learning Techniques: Deep learning algorithms perform classification of oil slicks in satellite images,
reducing human intervention and error.Predictive Drift Models: are Forecast spill movement for early
intervention. The limitation of Drift Models are extensive datasets and high computational resources—often
lacking in developing nations.
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