Search

Abstract Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond.

Microplastic pollution has a significant threat to marine ecosystems, yet its impact on spineless cuttlefish (Sepiella inermis) remains under-researched. This study aims to address this gap by analysing microplastic contamination in Sepiella inermis from the North-East Bay of Bengal. This species is widely consumed and transported globally as food, thus holding significant health concerns. A total of 40 adult female cuttlefish were collected from two sampling sites (18°36′31.35″N 87°48′10.63″E and 15°43′35.37″N 88°12′07.01″E) in the Bay of Bengal. Tissue samples from tentacles, gut, and nidamental glands were analysed for microplastic content, alongside sediment and surface water samples. Parameters such as microplastic abundance, size, shape, and colour were recorded. The average abundance of microplastic particles was measured at 2.003 particles per gram in tentacle tissue, 2.31 particles per gram in gut tissue, and 0.99 particles per gram in nidamental gland tissue. The gut tissue exhibited the highest abundance of microplastics per gram. Chemical characterization using FT-IR and confocal Raman spectroscopy identified 11 types of microplastic polymers. Of the 11 types of plastic polymers identified, PVC was the most prevalent, accounting for 17.64 % of the microplastics found across all tissues. PVC microplastics can cause significant harm to marine life and human health by accumulating in the food chain and releasing harmful chemicals like phthalates, which can lead to endocrine disruption. ABS, PET, PP, PE, and PA microplastic polymers are highly persistent in environment, leading to long-term pollution in oceans. When ingested by marine organisms, they can disrupt entire ecosystems. In humans, the accumulation of these microplastics can impair the immune system and contribute to chronic diseases. The Pollution Load Index (PLI) was calculated for each tissue type, revealing that gut tissue is more prone to microplastic pollution compared to the nidamental gland and tentacles. The average PLI per gram of gut tissue was 2.26, which was significantly higher than 1, indicating substantial pollution. This research highlights the urgent need for comprehensive strategies to mitigate microplastic pollution, given the potential health risks associated with the consumption of contaminated marine species.