Mohammadpour Amin, Keshtkar Mahsa, Samaei Mohammad Reza, Isazadeh Siavash, Mousavi Khaneghah Amin
Department of Environmental Health Engineering, School of Public Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Department of Environmental Health Engineering, School of Public Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Environmental Health Engineering, School of Public Health, Hormozgan University of Medical Sciences, Hormozgan, Iran.
Chemosphere. 2024 Mar;352:141284. doi: 10.1016/j.chemosphere.2024.141284. Epub 2024 Feb 7.
The present study employed deterministic and probabilistic approaches to determine the Water Quality Index (WQI) and assess health risks associated with water consumption in Darab County, Iran. Additionally, pollution levels were predicted using a machine-learning algorithm. The study's findings indicate that certain physicochemical parameters of water in some locations exceeded permissible limits (WHO or EPA), with 79.00 % of total hardness (TH) and 21.74 % of Total dissolved solids (TDS) levels exceeding standard values. The WQI for drinking water was determined to be 94.56 % using the deterministic approach, and 98.4 % of samples included the excellent and good categories according to the WQI classification system using the probabilistic approach. Fluoride (F) exhibited the most substantial impact on WQI values. The Artificial Neural Network (ANN) analysis findings suggest that the pH, nitrate (NO), and TDS are the most significant factors affecting the prediction of F concentration in water. Multivariate analysis demonstrated that anthropogenic, especially agriculture and geogenic factors, contributed to the water quality in this area. The health risk assessment (HRA) using deterministic methods revealed that water consumption posed a relatively high risk in certain areas. However, Monte Carlo simulation demonstrated that the 5th and 95th percentiles of Hazard Index (HI) for children, teenagers, and adults were within limits of (0.14-2.38), (0.09-1.29), and (0.10-1.00) respectively, with a certainty level of 70 %, 91 %, and 95 %. Interactive indices revealed that the intake of IR and NO-IR in children, BW and F-BW in teenagers, and NO and NO-IR in adults significantly impacted health risks. Based on these findings, augmenting water treatment processes, regulating fluoride concentrations, and advocating for sustainable agricultural practices complemented by continuous monitoring is imperative.
本研究采用确定性和概率性方法来确定水质指数（WQI），并评估伊朗达拉布县与用水相关的健康风险。此外，还使用机器学习算法预测污染水平。研究结果表明，某些地点的水的某些理化参数超过了允许限值（世界卫生组织或美国环境保护局的标准），总硬度（TH）的79.00%和总溶解固体（TDS）水平的21.74%超过了标准值。使用确定性方法确定的饮用水WQI为94.56%，根据概率性方法的WQI分类系统，98.4%的样本属于优良类别。氟化物（F）对WQI值的影响最大。人工神经网络（ANN）分析结果表明，pH值、硝酸盐（NO）和TDS是影响水中氟浓度预测的最重要因素。多变量分析表明，人为因素，尤其是农业和地质因素，对该地区的水质有影响。使用确定性方法进行的健康风险评估（HRA）表明，在某些地区用水存在相对较高的风险。然而，蒙特卡罗模拟表明，儿童、青少年和成年人的危害指数（HI）的第5和第95百分位数分别在（0.14 - 2.38）、（0.09 - 1.29）和（0.10 - 1.00）范围内，确定性水平分别为70%、91%和95%。交互指数表明，儿童摄入的碘和非碘、青少年的体重和氟 - 体重以及成年人的硝酸盐和非碘显著影响健康风险。基于这些发现，加强水处理过程、调节氟化物浓度以及倡导可持续农业实践并辅以持续监测势在必行。
