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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
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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.
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Researchers investigated the occurrence, abundance, distribution, and risk of microplastics in beach sediments at two Bangladeshi coastal sites — Cox's Bazar and Kuakata — finding widespread contamination and raising concern about microplastic pollution in the Northern Bay of Bengal.
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Scientists took a fresh look at microplastic pollution in Bangladesh's rivers and coastal waters, but instead of just counting plastic particles like most studies do, they ranked the different plastic types by how toxic they actually are. This matters because not all plastics are equally harmful—some, like polyamide (used in things like textiles and fishing nets), are far more dangerous than others, meaning water samples with fewer plastic particles could still pose greater health risks depending on which types of plastic are present. This more precise approach could help identify the real pollution hotspots that deserve the most attention for protecting drinking water and food safety.
Occurrence, spatial distribution, and risk assessment of microplastics in surface water and sediments of Saint Martin Island in the Bay of Bengal
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Researchers surveyed microplastic occurrence, spatial distribution, and pollution risk in surface water and beach sediments of Saint Martin Island in the Bay of Bengal, finding 3,166 particles/kg in beach sediments and elevated polymer risk indices indicating significant coastline pollution despite a low overall risk category.
Polymer‑specific hazard profiling and risk indexing of microplastics in coastal sediments of St. Martin’s Island: A multivariate and machine learning approach
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This study provided the first polymer-resolved evaluation of microplastic contamination in coastal sediments from a small island in the Bay of Bengal, finding polyethylene and polypropylene fibres and fragments most abundant. A novel Microplastic Pollution Risk Index (MPRI) was proposed to support standardized risk communication for island ecosystems.
Characterization and Spatial Abundance of Microplastics in the Coastal Regions of Cox’s Bazar, Bangladesh: An Integration of Field, Laboratory, and GIS Techniques
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This large-scale field and GIS study characterized microplastics in coastal sediments of Cox's Bazar, Bangladesh, finding high concentrations linked to tourism pressure, inadequate waste management, and the proximity of rivers carrying inland plastic waste.
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