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.

Evaluation of Darbandikhan Lake and its tributaries' water quality in the Sulaymaniyah Province in Iraqi Kurdistan, using the water quality index model and multivariate statistical analysis

This study evaluated the characteristics of the water in Darbandikhan Lake and its rivers in the Kurdistan Region of Iraq. For this purpose, 25 samples were collected seasonally and analysed for 36 physiochemical parameters. The proportions of physiochemical parameters exceeding the WHO standards in the samples with their highest exceedances were 9894% for Al, 198% for Mn, 40% for Pb, 1.6% for pH, 3250% for PO4, 11.8% for Sr, 155% for T.Alk, 7813% for turbidity, 1188% for Ti, 1033% for Tl and 1293% for V in the river water and 120% for Co, 74% for Cr, 4485% for Fe and 9% for K in the lake water. The pollution sources were designated by multivariate statistical analysis as being related to industrial and domestic waste, solid waste disposal, fertilisers and organic contamination from agricultural and natural sources. The water quality index (WQI) results were 22.3 to 721.3 for drinking, 13.9 to 86.2 for irrigation, 1.4 to 299.5 for livestock, 71.5 to 1754.4 for the textile industry, 20.7 to 237.9 for recreation and 64.6 to 1867.4 for aquatic life. The irrigation water quality index (IWQI) results were excellent for sodium adsorption ratio (SAR), and for the US salinity scale, all water samples fell into the medium salinity-low sodium category (C2-S1) in all seasons, except for all Chaqan River samples. The Tanjaro River sample in spring fell in the relatively high salinity-low sodium category (C3-S1), excellent and good for sodium percentage (Na%), suitable to moderate for permeability index (PI%), suitable to unsuitable for magnesium hazard percentage (MH%), suitable for Kelly Index (KI) and safe to unsuitable for residual sodium carbonates (RSC). The Sirwan River, Tanjaro River and Zmkan River took first to third place in both the annual average pollution share ratio and the discharge. While the Zalm River ranked fourth in discharge and fifth in pollution share ratio, the Chaqan River was the reverse. The highest pollution share ratio was 64.3 for the Sirwan River in summer, and the lowest was 0.7 for the Zalm River in autumn.

A comprehensive method for improvement of water quality index (WQI) models for coastal water quality assessment

Here, we present an improved water quality index (WQI) model for assessment of coastal water quality using Cork Harbour, Ireland, as the case study. The model involves the usual four WQI components - selection of water quality indicators for inclusion, sub-indexing of indicator values, sub-index weighting and sub-index aggregation - with improvements to make the approach more objective and data-driven and less susceptible to eclipsing and ambiguity errors. The model uses the machine learning algorithm, XGBoost, to rank and select water quality indicators for inclusion based on relative importance to overall water quality status. Of the ten indicators for which data were available, transparency, dissolved inorganic nitrogen, ammoniacal nitrogen, BOD₅, chlorophyll, temperature and orthophosphate were selected for summer, while total organic nitrogen, dissolved inorganic nitrogen, pH, transparency and dissolved oxygen were selected for winter. Linear interpolation functions developed using national recommended guideline values for coastal water quality are used for sub-indexing of water quality indicators and the XGBoost rankings are used in combination with the rank order centroid weighting method to determine sub-index weight values. Eight sub-index aggregation functions were tested - five from existing WQI models and three proposed by the authors. The computed indices were compared with those obtained using a multiple linear regression (MLR) approach and R² and RMSE used as indicators of aggregation function performance. The weighted quadratic mean function (R² = 0.91, RMSE = 4.4 for summer; R² = 0.97, RMSE = 3.1 for winter) and the unweighted arithmetic mean function (R² = 0.92, RMSE = 3.2 for summer; R² = 0.97, RMSE = 3.2 for winter) proposed by the authors were identified as the best functions and showed reduced eclipsing and ambiguity problems compared to the others.