Water quality index and health risk assessment for heavy metals in groundwater of Kashiani and Kotalipara upazila, Gopalganj, Bangladesh

Abstract

The groundwater quality of the Gopalganj district in Bangladesh was not well documented. Therefore, this research was done to determine the groundwater quality in the study area. The water quality index and the human health risk for heavy metal ingestion were used to describe the water quality. The water quality was performed through the estimation of turbidity, pH, electrical conductivity, total dissolved solids, and concentration of sodium, potassium, ammonium, nitrate, iron, manganese, zinc, copper, and arsenic. The mean turbidity, pH, and total dissolved solids in the two upazila were within the permissible limit. However, the electrical conductivity in both the upazila was higher than the WHO-prescribed value in which the higher concentration was in Kotalipara. Excess concentrations of iron and arsenic were found in the Kashiani upazila, but these were below the detection limit in Kotalipara upazila. The water quality index revealed that roughly 61.0% of samples of Kashiani upazila were of poor quality. However, about 96.0% of samples of Kotalipara upazila were of excellent quality. Chronic health risks due to the revelation of drinking have also been determined by assessing the hazard quotient and hazard index. In Kashiani, almost 85.0% of samples were elevated chronic risks for adults and 100.0% of the samples were very high chronic risks for children. In Kotalipara, all the samples (almost 100%) were suggested to have a lower chronic risk for adults and children. The results suggested that the carcinogenic risk of arsenic via oral exposure was very high for both adults and children in Kashiani upazila.

Assessment of hydrochemical characteristics, health risks and quality of groundwater for drinking and irrigation purposes in a mountainous region of Pakistan

“…In total, 40 composite groundwater samples ( n = 3) were collected in 1.5L HDP bottles from distinct locations across district Zhob. Before sampling, the water was run for 10 min to purge any potential foreign contaminants and establish a steady state, ensuring that the obtained sample accurately represents the water quality (Omeka & Egbueri 2023 ; Shaibur et al 2024 ). The collection bottles were immersed in 10% nitric acid for 24 h, followed by thorough washing using distilled water.…”

“…Consequently, depending exclusively on conventional methods for assessing water quality may not yield the precise and effective outcomes required for accurately predicting water quality and ensuring resource sustainability (Omeka 2023 ). To address this issue, the water quality index (WQI) comes into play, offering a holistic evaluation by considering multiple physicochemical parameters (Haq & Muhammad 2023 ; Ram et al 2021 ; Shaibur et al 2024 ). WQI serves as an effective tool, simplifying intricate water quality data into a comprehensible single value for policymakers and environmentalists (Jehan et al 2023 ).…”

“…at specific locations and times (Uddin et al 2023 ). The idea is based on the notion that a lower value indicates a smaller deviation from the suggested parameter values, resulting in higher quality water suitable for human consumption and vice versa (Shaibur et al 2024 ). WQI reflects the composite effect of multiple water quality parameters on the overall water quality (Kumar et al 2020 ; Shahab et al 2018 ).…”

“…Recent global attention has focused on groundwater contamination and associated health risks covering various countries including Bangladesh (Islam et al 2018 ; Shaibur et al 2024 ), India (Arumugam et al 2023 ; Khan et al 2023 ; Saraswat et al 2023 ; Selvam et al 2023 ), China (Lan et al 2023 ), Nigeria (Igwe & Omeka 2022 ; Omeka 2023 ), and with a significant focus on Pakistan. Studies have highlighted groundwater contamination issues in various regions of Pakistan, including Balochistan, Larkana, Lahore, Peshawar, Karak, Muzaffarabad, and Gilgit-Baltistan (Ahmad et al 2021 ; Hayder et al 2022 ; Ismail et al 2023 ; Javed et al 2019 ; Khanoranga & Khalid 2019 ; Lanjwani et al 2022 ; Sohail et al 2019 ).…”

Valuation K-Nearest Neighbors and Naïve Bayes for Dringking Water Potability Classification

“…Drinking water quality is measured by various variables and parameters that are used as the basis for model building such as ph, turbidity, metal content etc. (Shaibur et al, 2024). Classification is a stage of data pattern analysis that aims to identify the appropriate class or category in an unrecognised object based on the characteristics of the observed features (Ainurrohma, 2021).…”