Analysis of uranium and other water quality parameters in drinking water sources of 5 districts of Kerala in southern India and potability estimation using water quality indexing method

Quantification of natural uranium and its risk evaluation in groundwater of Chikkaballapur district in Karnataka, India

The present study focused on the distribution of uranium in groundwater samples collected from various sources in the Chikkaballapur district and its associated risk in humans. Seventy-five groundwater samples were collected during pre-monsoon and post-monsoon seasons and were analysed for uranium concentration along with different water quality parameters. The uranium concentration ranged from 0.23 to 285.23 µg/L in the pre-monsoon season and from 0.02 to 107.87 µg/L in the post-monsoon season. More than 90% of samples, except a few, were under the safe limits of 60 µg/L as directed by the Department of Atomic Energy (DAE) of India's Atomic Energy Regulatory Board (AERB). The study analysed physicochemical parameters like pH, total dissolved solids (TDS), nitrate, total hardness, phosphate, sulphate and fluoride in collected water samples. Out of all samples, few samples noted higher values of TDS, nitrate and fluoride. Their correlation along with uranium is detailed in the study. Owing to its slightly elevated content, an evaluation of the radiological and chemical hazards associated with uranium consumption was analysed. When the risk resulting from chemical toxicity was evaluated, relatively few samples had a hazard quotient (HQ) score higher than 1, which suggested that the people were vulnerable to chemical danger. This study also evaluates the dangers of elevated uranium levels in groundwater samples to the general public's health. It also acknowledges the importance of routinely evaluating and treating the drinking water sources in the region.

Mapping of uranium concentrations in groundwater samples of Davanagere district, Karnataka, India, and assessment of effective dose to the population

The geomorphology, geohydrology, lithology and ecological features of the area influence the uranium content in groundwater. The groundwater samples were collected from 75 locations of Davanagere district, Karnataka, India. Uranium analysis in the water samples was done using LED fluorimeter, based on fluorescence of dissolved uranyl salts. The uranium concentration in water samples varied from 18.41 to 173.21 μg L-1 with a geometric mean of 39.69 μg L-1. Higher uranium concentration in groundwater was observed in Harapanahalli and Jagalur taluk of Davanagere district, which falls in the Eastern Dharwar Craton, which is generally known to contain more radioactive minerals than the Western Dharwar Craton. The effective ingestion dose and lifetime cancer risk to the population were calculated using the obtained uranium concentration in drinking water.

Inconsistency of PCA-based water quality index - Does it reflect the quality?

The formalization of a stable water quality index (WQI) from measured hydrogeochemical parameters is essential for the identification and classification of water resources. In the principal component analysis (PCA) based WQI approach, the parameter weight is derived using either PC loading or rotated factor loading from a large number of samples pooled for WQI measurement. The PCA-based approach is paradoxical, as the calculated WQI rating of a sample would rather be dependent on the size, and composition of the population. Though this issue is well anticipated, no attempt has been made to regularize or measure the extent of WQI disagreement. In the present study, the WQI of 106 groundwater samples analyzed for 12 different hydrochemical parameters were modelled using PC loading or rotated factor loading (referred to as PCQ-1, PCQ-2, respectively) approach. Analysis reveals PCQ-1 to be positively biased in 78 % of samples and rating disagreements were evident in 9.43 % of samples. WQI of the data set was estimated using repeated (1000) random non-overlapping 2 to 5-fold data partitioning (containing 21 to 83 samples in each fold) adopting either an in-sample (test set) or out-sample (train set) modelling approach. The mean of WQI deviations in repeated resampling from the reference (i.e., using the entire dataset) has been positive in most of the samples using the PCQ-1 model, irrespective of the fold partition size. The median root mean square deviation values of the data set increased with the number of fold partitioning for in-sample calibration for both PCQ-1 and PCQ-2 approaches. The exclusion of a single water quality parameter from the PCA model can cause up to a 60 % deviation of the WQI score in some water samples. The cross-validation and Monte Carlo resampling approach can serve as a framework to test the stability of PCA-based WQI.