Natural Resource and Environmental Management Group, CSIR - Central Institute of Mining and Fuel Research, Barwa Road, Dhanbad, 826015, India.
Department Environmental Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India.
Environ Sci Pollut Res Int. 2021 May;28(20):25798-25807. doi: 10.1007/s11356-021-12352-8. Epub 2021 Jan 20.
The contribution of heavy metal to the groundwater pollution index (m-HPI) is dependent on other heavy metals present in it. This contribution may be synergistic or anti-synergistic (antagonistic) depending upon the constituent matrix. Both heavy metal type and its concentration are important. m-HPI, a variant of heavy metal pollution index in water, may be calibrated against USEPA hazard index (HI) using a generic multivariate non-linear regression (MVNLR) model. Excellent correlation may be obtained between HI and m-HPI through optimization of normalized weightage factors of constituent metals that contribute to m-HPI. MVNLR model was employed on groundwater samples of ten sites having different heavy metal matrix. The synergistic/antagonistic contribution of heavy metals to m-HPI was well discernible at each site. This study clearly showed that the individual contribution of a particular heavy metal to pollution index might be altered (enhanced or reduced) in the presence of other heavy metals. A calibrated MVNLR model was successfully used for predicting the hazard index (HI) of water samples.
重金属对地下水污染指数（m-HPI）的贡献取决于其中存在的其他重金属。这种贡献可能是协同的或拮抗的（对抗的），具体取决于组成矩阵。重金属的类型及其浓度都很重要。m-HPI 是水中重金属污染指数的一种变体，可以使用通用的多元非线性回归（MVNLR）模型根据美国环保署危害指数（HI）进行校准。通过优化构成 m-HPI 的金属的归一化权重因子，可以在 HI 和 m-HPI 之间获得极好的相关性。MVNLR 模型应用于具有不同重金属矩阵的十个地点的地下水样本。在每个地点，重金属对 m-HPI 的协同/拮抗贡献都可以明显区分。这项研究清楚地表明，在存在其他重金属的情况下，特定重金属对污染指数的个别贡献可能会发生变化（增强或减少）。经过校准的 MVNLR 模型成功用于预测水样的危害指数（HI）。
