Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches.

The Rooppur Nuclear Power Plant (RNPP) at Ishwardi, Bangladesh is planning to go into operation within 2024 and therefore, adjacent areas of RNPP is gaining adequate attention from the scientific community for environmental monitoring purposes especially for water resources management. However, there is a substantial lack of literature as well as environmental datasets for earlier years since very little was done at the beginning of the RNPP's construction phase. Therefore, this study was conducted to assess the potential toxic elements (PTEs) contamination in the groundwater and its associated health risk for residents at the adjacent part of the RNPP during the year of 2014-2015. For the purposes of achieving the aim of the study, groundwater samples were collected seasonally (dry and wet season) from nine sampling sites and afterwards analyzed for water quality indicators such as temperature (Temp.), pH, electrical conductivity (EC), total dissolved solid (TDS), total hardness (TH) and for PTEs including Iron (Fe), Manganese (Mn), Copper (Cu), Lead (Pb), Chromium (Cr), Cadmium (Cd) and Arsenic (As). This study adopted the newly developed Root Mean Square water quality index (RMS-WQI) model to assess the scenario of contamination from PTEs in groundwater whereas the human health risk assessment model was utilized to quantify the risk of toxicity from PTEs. In most of the sampling sites, PTEs concentration was found higher during the wet season than the dry season and Fe, Mn, Cd and As exceeded the guideline limit for drinking water. The RMS score mostly classified the groundwater in terms of PTEs contamination into "Fair" condition. The non-carcinogenic risks (expressed as Hazard Index-HI) revealed that around 44% and 89% of samples for adults and 67% and 100% of samples for children exceeded the threshold limit set by USEPA (HI > 1) and possessed risks through the oral pathway during dry and wet season, respectively. Furthermore, the calculated cumulative HI score was found higher for children than the adults throughout the study period. In terms of carcinogenic risk (CR) from PTEs, the magnitude of risk decreased following the pattern of Cr > As > Cd. Although the current study is based on old dataset, the findings might serve as a baseline for monitoring purposes to reduce future hazardous impact from the power plant.

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.

Assessment of Tc-99 monitoring within the western Irish Sea using a numerical model.

Water circulation patterns and associated material transport within a highly dynamic system such as the Irish Sea are complex phenomena. Although Tc-99 monitoring programme undertaken by the Radiological Protection Institute of Ireland provides a good insight to the material distribution on the east coast of Ireland, transport patterns within the Irish Sea have not been fully explored. In this study a validated transport model was used to hindcast transport of Tc-99 discharged from the Sellafield plant to determine extents of Tc-99 migration within the Irish Sea and reassess transit times to east coast of Ireland. Transit times are also estimated within a context of changes in meteorological conditions and fluctuations in discharges. Additionally, seasonal and inter-annual circulation patterns were examined. Relationships between discharge times and timing of far field concentrations are highly variable and are dependent on sea dynamics controlling the accumulation and removal of Tc-99 mass. Transport towards the Irish east coast, and consequently transit times, vary intra- and inter-annually, and depend on the prevailing hydrodynamic conditions resulting from meteorological conditions. The transit times from Sellafield to Balbriggan fall within the wide range of 30-240 days; with summer releases resulting in the shortest transit times. The model also indicated a strong relationship between summer concentration peaks on the east coast of Ireland and the strength of the Western Irish Gyre. Sudden increases of Tc-99 concentrations at Balbriggan coincide with peak of sea surface temperatures when the gyre is strongest and when advection is fastest. The adequacy of the current radionuclide monitoring programme within the western Irish Sea is evaluated, and recommendations are made for the development of a more optimised monitoring programme.