We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Polymer‑specific hazard profiling and risk indexing of microplastics in coastal sediments of St. Martin’s Island: A multivariate and machine learning approach
Summary
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.
• This study presents the first polymer-resolved evaluation of microplastic contamination in surface sediments from a small island ecosystem in the Bay of Bengal. • Microplastic assemblages were dominated by fibres and fragments, with polyethylene and polypropylene most abundant, reflecting persistent inputs from fishing gear and ropes. • We propose a novel Microplastic Pollution Risk Index (MPRI) that integrates polymer hazard scores with persistence, morphology, and color, providing a comprehensive hazard profile. • Tourism-exposed beaches exhibited higher diversity and shares of polystyrene, PET, and PVC, linking packaging and consumer waste to localized contamination hotspots. • Multivariate analyses (Pearson correlation, PCA, RDA, HCA) and machine learning models (Random Forest, SVM, KNN) clearly separated tourism and fishing sites, underscoring distinct contamination pathways. Microplastic (MP) contamination poses an emerging ecological threat in small-island environments, yet polymer-specific risk assessments remain limited for the Bay of Bengal region. This study provides the first integrated, polymer-resolved evaluation of microplastics in coastal sediments of St. Martin’s Island, Bangladesh. A total of 298 suspected MPs were isolated through stereomicroscopy, of which 250 particles were confirmed through ATR-FTIR (≥89% spectral match). Fibres and fragments dominated the assemblage, with high abundances observed in tourism-intensive beaches (S1–S4, S12) and fishing-dominated zones (S8–S11). Polymer profiles were characterized by the predominance of PE, PP, PET, and PVC. Four complementary ecological risk metrics, Pollution Load Index (PLI), Polymer Hazard Index (PHI), Sediment Polymer Hazard Index (SPHI), and the Microplastic Pollution Risk Index (MPRI), identified localized hotspots of elevated risk, particularly at tourism and active fishing sites. Multivariate analyses (PCA, HCA) revealed clear clustering patterns associated with site-use categories, while machine-learning classifiers (Random Forest, SVM, KNN) accurately distinguished tourism, fishing, and low-use zones based on MP morphology, color, polymer type, and abundance. Collectively, these findings demonstrate that anthropogenic pressure strongly shapes microplastic composition and hazard profiles on St. Martin’s Island. The integrated risk-index and ML framework presented here provides a robust, reproducible approach for coastal microplastic monitoring in data-limited regions and can support targeted management and mitigation strategies in vulnerable island ecosystems.
Sign in to start a discussion.
More Papers Like This
Occurrence, spatial distribution, and risk assessment of microplastics in surface water and sediments of Saint Martin Island in the Bay of Bengal
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.
A novel polymer-sensitive index coupled with multivariate and machine learning modeling for microplastic risk assessment in coastal sediments of the bay of Bengal
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.
Occurrence, spatial distribution, and risk assessment of microplastics in surface water and sediments of Saint Martin Island in the Bay of Bengal
Researchers surveyed microplastic occurrence in surface water and sediments around Saint Martin Island in the Bay of Bengal, finding widespread contamination with spatial distribution patterns linked to tourism and fishing activities, and conducted ecological risk assessment.
Occurrence, spatial distribution, and risk assessment of microplastics in surface water and sediments of Saint Martin Island in the Bay of Bengal
Researchers surveyed microplastic occurrence and spatial distribution in surface water and sediments around Saint Martin Island in the Bay of Bengal, identifying multiple morphological types including expanded polystyrene, foam, filaments, fragments, and fibers, and conducted risk assessments finding sediment concentrations of 3,166 particles/kg.
Occurrence and spatial distribution of microplastics in water and sediments of Hatiya Island, Bangladesh and their risk assessment
Researchers assessed microplastic distribution in surface water and sediments around Hatiya Island, Bangladesh, finding higher contamination in sediments than in water. Fibers were the most common type, and polymer analysis identified potentially hazardous plastics including polyurethane, polycarbonate, and polyvinyl chloride. While basic pollution indices showed minimal levels, hazard-based risk assessments indicated severe contamination due to the presence of these hazardous polymer types.