The Sylhet district in Bangladesh experiences frequent flooding due to its geographic setting and heavy monsoon rainfall, further intensified by human-driven activities, including rapid urban expansion, land-use/land-cover alterations, and river encroachment, which collectively cause significant damage to communities, infrastructure, and agriculture. This study aims to delineate flood risk in the district by integrating flood hazard and vulnerability maps using an enhanced GIS-AHP (Analytical Hierarchy Process) approach that incorporates 14 hazard and 8 vulnerability factors. Employing AHP, influencing weights were assigned to hazard and vulnerability defining factors, thereafter mapped in GIS to assess the spatial characteristics of the flood hazard index (FHI) and flood vulnerability index (FVI), thus collectively producing the flood risk index (FRI). Validation of the FHI map was conducted through Receiver Operating Characteristic curve analysis, yielding an Area Under the Curve of 0.858. The results also indicated a high level of correspondence with the flood inundation maps for 2004 and 2022. The FVI map was validated by qualitative analysis, confirming its contextual reliability. The assessment reveals that moderate flood hazard dominates across the district, with upazilas like Companiganj, Gowainghat, Bishwanath, and Balaganj having moderate hazard levels above 80%. However, vulnerability levels are higher in densely populated settlement areas, particularly in Sylhet Sadar, Golapganj, Dakshin Surma, and Biswanath upazilas. Moreover, 74.26% of Sylhet falls within the moderate-risk category. The high-risk zones are mainly clustered in Sylhet Sadar, Biswanath, Dakshin Surma, and Golapganj, where over 40% of the land area is classified as high-risk. The FRI map’s performance was further evaluated using recent flood event information, revealing that flooded areas mostly correspond to moderate- and high-risk zones.

The rapid growth in plastic production and consumption has led to the widespread contamination of the environment with microplastics (MPs), raising increasing concerns about their potential impacts on human health. In recent years, advances in analytical techniques have revealed the presence of MPs in a wide range of human biological samples, suggesting that these particles can enter, circulate within, and accumulate throughout the human body. This narrative review provides a comprehensive overview of the current evidence on the occurrence and distribution of MPs in human tissues, organs, and biological fluids. MPs have been detected in blood, urine, stool, semen, breast milk, amniotic fluid, ocular fluids, and lavage fluids, as well as in several organs and tissues including the lungs, placenta, heart, kidneys, liver, vascular tissues, and reproductive organs. Among the identified polymers, polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polystyrene (PS), and polyvinyl chloride (PVC) are reported most frequently. This narrative review also summarizes the major analytical techniques used for MP detection and characterization, including Raman spectroscopy, μ-FTIR, LDIR spectroscopy, and Py-GC/MS, which highlight their advantages and limitations. Collectively, the available evidence demonstrates that MPs are not confined to the external environment but are now detectable in multiple compartments of the human body, including highly protected biological systems. However, important questions remain regarding their long-term persistence, transport mechanisms, biological fate, and potential health effects. Further methodological standardization and large-scale human studies are needed to better understand the extent of human exposure and the implications of MP accumulation for human health.

Microplastics (MPs) have been reported in commercial beverages, but substantial differences in study design make direct comparisons difficult. This non-systematic, focused narrative review critically synthesizes a purposively selected core set of 17 particle-count studies published from 2020 to 2026 on soft drinks, beer, wine, tea, coffee, juices, energy drinks, and related beverages, while considering recent complementary thermal-analysis evidence. Data were compared for study location, beverage type, analytical method, abundance, particle size, morphology, color, polymer composition, and packaging. Fourier-transform infrared spectroscopy-based methods were most common; Raman spectroscopy, fluorescence microscopy, scanning electron microscopy, and laser direct infrared imaging were used in selected studies. Reported soft-drink concentrations ranged from approximately 0.30 particles/L to 166 ± 62 particles/100 mL (1660 ± 620 particles/L), but these values cannot support a geographic ranking because minimum particle-size thresholds, confirmation criteria, blank corrections, sample volumes, and reporting units differed. Fibers and fragments were the dominant morphologies, and polyethylene terephthalate, polyethylene, polypropylene, and polyamide were frequently identified. Findings from beverages packaged in glass and aluminum, as well as plastic, indicate that source water, ingredients, processing equipment, filtration, closures, ambient deposition, and packaging can all contribute. Current intake estimates describe potential particle ingestion rather than absorbed dose or toxicological impact. Because current data largely reflect analytical sensitivity rather than true contamination gradients, this review demonstrates that reliable cross-study exposure assessments currently remain associated with considerable uncertainty, and this uncertainty will be difficult to resolve until particle-count data are normalized to harmonized size thresholds and paired with mass-based thermal analyses.

The Surma River Basin in northeastern Bangladesh is highly vulnerable to recurrent flash and riverine floods, yet discharge-based flood frequency estimates remain scarce. This study addresses that gap by analyzing 40 years (1985–2024) of annual maximum discharge from two key stations: Kanairghat (SW266) and Sylhet (SW267). Nine probability distributions were evaluated using Product Moments and L-moments, with goodness-of-fit tests (Chi-Square, Kolmogorov-Smirnov, Anderson-Darling) guiding model selection. At Kanairghat, the Generalized Extreme Value distribution with L-moments (GEV-LM) ranked first, capturing the heavy upper tail of flash-driven regimes. At Sylhet, the Log-Pearson Type-III with L-moments (LP3-LM) was optimal, reflecting downstream attenuation by Haor wetlands and channel sedimentation. Trend analysis revealed contrasting stationarity: Kanairghat exhibited a declining signal with a structural break around 1995, while Sylhet remained stationary. Non-stationary GEV modeling projected the 100-year flood at Kanairghat to decrease by roughly 22% by 2030 relative to 1985, underscoring the need to reassess stationary design values. Bootstrap confidence intervals and tail sensitivity analysis showed substantially wider uncertainty bounds at the upstream station. Design water levels consistently exceeded observed rating-curve ranges, indicating that current infrastructure may be under-designed for moderate return periods. The principal methodological contribution is a five-component uncertainty framework that simultaneously quantifies model, estimator, stationarity, sampling, and tail sensitivity uncertainty—a combination not previously applied to discharge-based flood frequency analysis in Bangladesh, offering a fuller characterization of design flood uncertainty than single-component assessments typically provide, within the two-station scope analyzed here. The methodology is directly applicable to other flash-flood-prone regions of Bangladesh, supporting more resilient hydraulic design and flood risk planning.