Navigating microplastic-related challenges in the Arabian Gulf: Prospects of artificial intelligence and bioremediation

Abstract

Microplastics (MPs) have emerged as contaminants of growing concern due to their widespread distribution, high mobility, and ability to act as vectors for pollutants in marine ecosystems. This review examines MP contamination in the Arabian Gulf, one of the world’s most environmentally vulnerable semi-enclosed seas. The Gulf’s extreme conditions, including high salinity, elevated temperatures, restricted water circulation, and intensive coastal development, promote MP accumulation and biological exposure, increasing potential risks to marine organisms, aquaculture, and human health. Conventional detection and quantification techniques, including Fourier-transform infrared (FTIR) and Raman spectroscopy, as well as pyrolysis-gas chromatography/mass spectrometry, are critically assessed with emphasis on limitations related to size detection thresholds, analytical throughput, and processing efficiency. The review highlights artificial intelligence (AI) as a transformative approach for MP analysis. Machine-learning algorithms applied to FTIR and Raman spectral data improve polymer classification accuracy, whereas computer-vision models such as U-Net and Mask R-convolutional neural network enable automated particle segmentation and sizing. These tools reduce manual bias, enhance reproducibility, and facilitate high-throughput analysis across laboratories. Meanwhile, eco-friendly bioremediation strategies are reviewed. Microorganisms, algae, and aquatic plants have demonstrated the ability to adsorb, colonize, or partially degrade MPs, offering sustainable alternatives to conventional remediation methods. However, the effectiveness of these biological approaches under the harsh environmental conditions of the Arabian Gulf remains limited. Finally, this review proposes a Gulf-specific roadmap that includes standardized monitoring protocols and shared spectral and image databases to support AI-based detection, interlaboratory proficiency testing, and pilot-scale bioremediation studies tailored to regional conditions.

Keywords

Microplastic / Arabian Gulf / Marine pollution / Artificial intelligence prediction / Artificial intelligence in pollution management / Machine learning / Bioremediation / Aquatic ecosystems

Funding

None.

Conflict of interest

The authors declare they have no competing interests.

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