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A novel polymer-sensitive index coupled with multivariate and machine learning modeling for microplastic risk assessment in coastal sediments of the bay of Bengal
Summary
Scientists found that popular tourist beaches in Bangladesh have much higher levels of tiny plastic particles (called microplastics) in the sand compared to less-visited areas, with some of the most dangerous types of plastics concentrated where people spend the most time. The researchers discovered that simply counting plastic particles isn't enough—the type of plastic matters more for health risks, since some plastics are more toxic than others. This research shows that heavily-used beaches need better waste management to protect both tourists and local communities from potentially harmful plastic pollution.
Microplastic (MP) contamination in coastal sediments poses growing ecological and human health concerns, yet data for developing nations remain limited. This study provides a comprehensive assessment of MPs along the Cox's Bazar shoreline, the world's longest natural sea beach and a rapidly expanding tourism hub in Bangladesh. Tourism-dominated beaches showed significantly higher abundances (up to 111 items kg-1 dw) compared to rural low-use sites, with fibres and fragments representing the dominant morphotypes. Polymer analysis identified polyethylene (PE) and polypropylene (PP) as the most common constituents, reflecting consumer packaging waste and fishing-related debris as major sources. Risk evaluation demonstrated that abundance alone underestimates potential ecological hazard. Novel, hazard-weighted indices developed in this study, the Sediment Polymer Hazard Index (SPHI) and Microplastic Pollution Risk Index (MPRI), identified tourism hotspots as high-risk zones due to elevated contributions from toxic polymers (e.g., PS, PET) and ingestion-prone particle characteristics. Multivariate analyses further indicated that site-use category significantly constrain MP composition, confirming the influence of direct human pressure. Machine learning models, applied to classify MP contamination in coastal sediments, demonstrated that polymer-specific composition outperforms total abundance in predicting high-risk sites, with Random Forest achieving the highest classification accuracy. These results highlight the need for targeted coastal management prioritizing tourism-intensive zones, improved waste handling, and sustainable fishing practices. Integrating hazard-based indices and advanced predictive tools into long-term monitoring frameworks will be essential to protect the ecological and socioeconomic value of Cox's Bazar as coastal development accelerates.