Trophic status and its regulating factor determination at the Rewalsar Lake, northwest Himalaya (HP), India, based on selected parameters and multivariate statistical analysis

Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches

Assessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, and complex classification schemes. To tackle these issues, we developed a novel tool that harnesses machine learning (ML) and artificial intelligence (AI), enhancing the reliability and accuracy of trophic status assessments. Our research introduces an improved data-driven methodology specifically tailored for transitional and coastal (TrC) waters, with a focus on Cork Harbour, Ireland, as a case study. Our innovative approach, named the Assessment Trophic Status Index (ATSI) model, comprises three main components: the selection of pertinent water quality indicators, the computation of ATSI scores, and the implementation of a new classification scheme. To optimize input data and minimize redundancy, we employed ML techniques, including advanced deep learning methods. Specifically, we developed a CHL prediction model utilizing ten algorithms, among which XGBoost demonstrated exceptional performance, showcasing minimal errors during both training (RMSE = 0.0, MSE = 0.0, MAE = 0.01) and testing (RMSE = 0.0, MSE = 0.0, MAE = 0.01) phases. Utilizing a novel linear rescaling interpolation function, we calculated ATSI scores and evaluated the model's sensitivity and efficiency across diverse application domains, employing metrics such as R2, the Nash-Sutcliffe efficiency (NSE), and the model efficiency factor (MEF). The results consistently revealed heightened sensitivity and efficiency across all application domains. Additionally, we introduced a brand new classification scheme for ranking the trophic status of transitional and coastal waters. To assess spatial sensitivity, we applied the ATSI model to four distinct waterbodies in Ireland, comparing trophic assessment outcomes with the Assessment of Trophic Status of Estuaries and Bays in Ireland (ATSEBI) System. Remarkably, significant disparities between the ATSI and ATSEBI System were evident in all domains, except for Mulroy Bay. Overall, our research significantly enhances the accuracy of trophic status assessments in marine ecosystems. The ATSI model, combined with cutting-edge ML techniques and our new classification scheme, represents a promising avenue for evaluating and monitoring trophic conditions in TrC waters. The study also demonstrated the effectiveness of ATSI in assessing trophic status across various waterbodies, including lakes, rivers, and more. These findings make substantial contributions to the field of marine ecosystem management and conservation.

Spatial distribution and characteristics of microplastics and associated contaminants from mid-altitude lake in NW Himalaya

Despite the worldwide concern over the impact of microplastics (MPs) and associated organic contaminants, the information regarding the occurrence and characteristics of these emerging class of pollutants is limited in freshwater environment. We present data on the distribution and concentration levels of MPs and phthalate esters (PAEs) from Rewalsar Lake, a shallow eutrophic lake in Northwest Himalaya. The MPs were identified in all samples, with concentration of 13-238 particles L^-1 and 750 to 3020 particles kg^-1 dry weight (dw) in surface water and sediments respectively. Majority of MPs were dominated by polystyrene, polyethylene, polypropylene polymers that principally occurred in the form of pellets and fragments. The MPs distribution was different among sampling sites, being more abundant to sites in the proximity of domestic sewage effluents and high level of religious and tourist activities. The di-isobutyl phthalate, dibutyl phthalate (DBP) and its branched isomer, di(2-ethylhexyl) phthalate (DEHP) are the identified PAE congeners in sediments, and the measured total concentrations of Σ₃PAE ranged from 1.69 μg/g to 4.03 μg/g dw. Notably, concentration values of DEHP were higher as compared to other detected phthalates, and exceeded recommended environmental risk limit. The findings of this study emphasize the requirement for proper waste management measures in the region to reduce entry of these pollutants into the ecosystem. Further, this work contributes to the understanding of MPs and PAEs potential contamination profiles and sources in freshwater environments, and provides valuable information for future management decisions.