Scholarlink

<p>Rising nitrate levels in groundwater have emerged as a major global concern due to their adverse health impacts through multiple exposure pathways. This study assessed the nitrate concentrations and associated non-carcinogenic health risks for different population groups (males, females, and children) in the Munneru Basin, India. A total of 175 groundwater samples were analysed for nitrate and physicochemical parameters. Nitrate concentrations ranged from 0 to 493&#xa0;mg/L, with 71 samples (41%) exceeding the Bureau of Indian Standards (BIS) permissible limit of 45&#xa0;mg/L. Oral ingestion poses a substantially higher risk of exposure than dermal contact. The mean total Hazard Index (HI) values were 1.373 for males, 1.622 for females, and 2.385 for children, with 42.28%, 47.42%, and 60% of samples exceeding the safe limit (HI &gt; 1), respectively. Children were identified as the most susceptible group because of their higher exposure potential and lower body weight relative to adults. Monte Carlo Simulations (MCS) were conducted to assess the probabilistic distribution of nitrate-related health risks. A robust simulation framework was implemented in Python, incorporating the exposure parameters recommended by the United States Environmental Protection Agency (USEPA). The probabilistic model produced slightly higher HI estimates and indicated a high probability of HI &gt; 1 across all population groups—57.96% for males, 61.01% for females, and 65.96% for children. Kernel Density Estimation (KDE) plots and MCS histograms were used to visualise the probabilistic distribution and variability of health risk outcomes. Sensitivity analysis identified the nitrate concentration and water ingestion rate as the most influential parameters controlling health risk outcomes. Principal Component Analysis (PCA) showed strong positive loadings for NO<sub>3</sub><sup>−</sup>, Cl<sup>−</sup>, and SO<sub>4</sub><sup>2−</sup> in PC2, indicating anthropogenic contributions from agricultural runoff and untreated sewage effluents. These findings highlight the critical need for targeted nitrate management strategies and public health interventions, particularly to protect vulnerable populations, such as children, in nitrate-affected regions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrated deterministic and probabilistic framework for non-carcinogenic health risk assessment of groundwater nitrate in the Munneru Basin, India: a Monte Carlo Simulation approach

  • 期刊论文

  • 发表日期：

    2025年11月25日

查看全文
  • Bijay Ketan Mohanta,
  • S. Srinivasa Vittala,
  • Monika Singh,
  • G. Krishnamurthy

摘要

Rising nitrate levels in groundwater have emerged as a major global concern due to their adverse health impacts through multiple exposure pathways. This study assessed the nitrate concentrations and associated non-carcinogenic health risks for different population groups (males, females, and children) in the Munneru Basin, India. A total of 175 groundwater samples were analysed for nitrate and physicochemical parameters. Nitrate concentrations ranged from 0 to 493 mg/L, with 71 samples (41%) exceeding the Bureau of Indian Standards (BIS) permissible limit of 45 mg/L. Oral ingestion poses a substantially higher risk of exposure than dermal contact. The mean total Hazard Index (HI) values were 1.373 for males, 1.622 for females, and 2.385 for children, with 42.28%, 47.42%, and 60% of samples exceeding the safe limit (HI > 1), respectively. Children were identified as the most susceptible group because of their higher exposure potential and lower body weight relative to adults. Monte Carlo Simulations (MCS) were conducted to assess the probabilistic distribution of nitrate-related health risks. A robust simulation framework was implemented in Python, incorporating the exposure parameters recommended by the United States Environmental Protection Agency (USEPA). The probabilistic model produced slightly higher HI estimates and indicated a high probability of HI > 1 across all population groups—57.96% for males, 61.01% for females, and 65.96% for children. Kernel Density Estimation (KDE) plots and MCS histograms were used to visualise the probabilistic distribution and variability of health risk outcomes. Sensitivity analysis identified the nitrate concentration and water ingestion rate as the most influential parameters controlling health risk outcomes. Principal Component Analysis (PCA) showed strong positive loadings for NO3−, Cl−, and SO42− in PC2, indicating anthropogenic contributions from agricultural runoff and untreated sewage effluents. These findings highlight the critical need for targeted nitrate management strategies and public health interventions, particularly to protect vulnerable populations, such as children, in nitrate-affected regions.