A study on the interdependence of heavy metals while contributing to groundwater pollution index

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

Fluvial Geochemistry of Subarnarekha River basin, India

The fluvial geochemistry of the Subarnarekha River and its major tributaries has been studied on a seasonal basis in order to assess the geochemical processes that explain the water composition and estimate solute fluxes. The analytical results show the mildly acidic to alkaline nature of the Subarnarekha River water and the dominance of Ca2+Ca2+ and Na+Na+ in cationic and HCO−3HCO3− and Cl−Cl− in anionic composition. Minimum ionic concentration during the monsoon and maximum concentration in the pre-monsoon seasons reflect concentrating effects due to decrease in the river discharge and increase in the base flow contribution during the pre-monsoon and dilution effects of atmospheric precipitation in the monsoon season. The solute acquisition processes are mainly controlled by weathering of rocks, with minor contribution from marine and anthropogenic sources. Higher contribution of alkaline earth (Ca2++Mg2+)(Ca2++Mg2+)to the total cations (TZ+)(TZ+) and high (Na++K+)/Cl−(Na++K+)/Cl−, (Na++K+)/TZ+(Na++K+)/TZ+, HCO−3/(SO2−4+Cl−)HCO3−/(SO42−+Cl−) and low (Ca2++Mg2+)/(Na++K+)(Ca2++Mg2+)/(Na++K+) equivalent ratios suggest that the Subarnarekha River water is under the combined influence of carbonate and silicate weathering. The river water is undersaturated with respect to dolomite and calcite during the post-monsoon and monsoon seasons and oversaturated in the pre-monsoon season. The pH–log H4SiO4H4SiO4 stability diagram demonstrates that the water chemistry is in equilibrium with the kaolinite. The Subarnarekha River annually delivered 1.477×1061.477×106 ton of dissolved loads to the Bay of Bengal, with an estimated chemical denudation rate of 77 ton km−2 yr−177 ton km−2 yr−1. Sodium adsorption ratio, residual sodium carbonate and per cent sodium values placed the studied river water in the ‘excellent to good quality’ category and it can be safely used for irrigation

Hydrogeochemistry of coal mine water of north Karanpura coalfields, india: implication for solute acquisition processes dissolved fluxes and water quality assessment

Mine water samples collected from different mines of the North Karanpura coalfields were analysed for pH, electrical conductivity, total dissolved solids (TDS), total hardness (TH), major anions, cations and trace metals to evaluate mine water geochemistry and assess solute acquisition processes, dissolved fluxes and its suitability for domestic, industrial and irrigation uses. Mine water samples are mildly acidic to alkaline in nature. The TDS ranged from 185 to 1343 mg L−1 with an average of 601 mg L−1. Ca2+ and Mg2+ are the dominant cations, while SO4 2− and HCO3 − are the dominant anions. A high concentration of SO4 2− and a low HCO3 −/(HCO3 − + SO4 2−) ratio (<0.50) in the majority of the water samples suggest that either sulphide oxidation or reactions involving both carbonic acid weathering and sulphide oxidation control solute acquisition processes. The mine water is undersaturated with respect to gypsum, halite, anhydrite, fluorite, aluminium hydroxide, alunite, amorphous silica and oversaturated with respect to goethite, ferrihydrite, quartz. About 40% of the mine water samples are oversaturated with respect to calcite, dolomite and jarosite. The water quality assessment shows that the coal mine water is not suitable for direct use for drinking and domestic purposes and needs treatment before such utilization. TDS, TH, F−, SO4 2−, Fe, Mn, Ni and Al are identified as the major objectionable parameters in these waters for drinking. The coal mine water is of good to suitable category for irrigation use. The mines of North Karanpura coalfield annually discharge 22.35 × 106 m3 of water and 18.50 × 103 tonnes of solute loads into nearby waterways

Exploring new correlation between hazard index and heavy metal pollution index in groundwater

Hazard index and various heavy metal pollution indices in groundwater are generally poorly correlated though all of them aim to address water quality. A semi empirical approach has been proposed for correlating Hazard Index (HI) of groundwater samples with a recently introduced heavy metal pollution index, m-HPI. m-HPI has two components, a positive index (PI) and a negative index (NI). It is possible to correlate HI with PI and NI through multivariate non-linear regression (MVNLR). Correlation performance may be improved by optimizing the weightage factor of each heavy metal. Introduction of USEPA heavy metal reference dose (RfD) in the expression for weightage factor improves the correlation still further. The newly proposed approach has been successfully validated with seven sets of water samples of different origin comprising different sets of heavy metals. The derived correlation function is generic and has global applicability as optimized m-HPI (PI and NI) data of 305 groundwater samples spread over six different locations could be well correlated with corresponding HI through a single generic correlation function employing MVNLR model. The predictive capability of MVNLR model has been demonstrated for each site. This communication has brought for the first time two poorly correlated similar narratives such as HI and heavy metal pollution index (HMPI) on the same page and provided a very useful predictive tool