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Indexical methods assessing PTEs distribution in Mahan river command area, central India’s coal mining zone

Geochemical Transactions volume 26, Article number: 2 (2025) Cite this article

Abstract

The quality of water can significantly affect the regional water resources due to scarcity of potable water in industrial area. The purpose of this study was to explore potentially toxic trace elements (PTEs) contamination and their seasonal variations in different water sources within the coal mining area of the Mahan River command area, Central India. To achieve this, 96 water samples were collected across two distinct seasons and analysed for PTEs. The results indicate that during the pre-monsoon season, the concentrations of Mn (18%), Cu (4%), Pb (8%), Ni (18%), Cd (2%), Al (4%), Cr (2%), and Fe (30%) exceeded permissible limits. In the post-monsoon season, Mn (15%), Pb (6%), Ni (15%), Cd (2%), Al (15%), Fe (46%) and Ba (4%) surpassed the standards. The multiple groundwater pollution indexical methods further revealed that 14% [Heavy metal pollution index (HPI)], 14% [Heavy metal evaluation index (HEI)], 18% [Contamination index (CI)], 14% [the entropy-weight based HM contamination index (EHCI)] and 20% [Heavy metal index (HMI)] of the samples exceeded permissible thresholds during the pre-monsoon season. Similarly, during the post-monsoon period, 10% (HPI), 10% (HEI), 15% (CI), 15% (EHCI) and 17% (HMI) of the samples were above acceptable limits. The relationship between the pH of water and the total load of dissolved metals is established using Caboi plot, confirming that mine water from mine water from Bhatgaon Underground (UG), Mahamaya UG, and Mahan Opencast (OC) [PR40, PR41, PR42, PR43, PR47, and PR48], surrounding rivers, and groundwater sources, exhibited an "Acid-High Metal" characteristic. This suggests significant contamination from acid mine drainage and mineral dissolution. Apart from the anthropogenic inputs, geogenic and environmental processes are responsible for the current distribution of PTEs and their seasonal variations.

Introduction

Global attention has recently been drawn to major environmental contamination as a result of expanding population and industrialization [1]. The indiscriminate disposal of industrial waste in the open environment causes significant environmental degradation [2, 3]. Groundwater is an important component of the environment and the greatest reserve of freshwater available to humans [4]. Groundwater has tremendous economic and social worth. However, groundwater supplies are being consumed at an alarming and unsustainable rate in many parts of the world [5].

As a result, everyone’s primary priorities are sound growth, careful conservation, and constant pollution prevention. India is the world’s greatest user of groundwater, consuming an estimated 230 cubic kilometers per year, or almost one-quarter of the global total [6]. Untreated groundwater or surface-water sources remain the sole water supply for 90% of India’s rural population and 30% of its urban residents [7]. According to the NITI Aayog’s 2018 report [8], about 800 million people reside in rural areas, and approximately two lakh people die each year as a result of a lack of fresh water.

Groundwater pollution is often caused primarily by geogenic causes [9], while anthropogenic activities significantly contribute to the deterioration of groundwater quality [10]. In this aspect, mining activities such as coal mining can promote severe groundwater as well as surface water pollution [11]. However, the coal mining activity is of a dual character: on one hand, it is powering the nation and on the other, coal mining and allied industries inadvertently threaten the water environment both in terms of quality and quantity [12]. Coal, lignite, oil, natural gas, uranium, and water are the main energy inputs for power generation in India [13]. Of these, coal and lignite play an important role in power generation in India, as the other energy minerals are in short supply as regards to their occurrence and utilization in the country [14].

Mine trash, also known as overburden material (OB), is heaped on the surface close to open-cast mines, whereas coal is retrieved directly utilizing various underground mining procedures that do not remove overburden material [11]. This promotes the disintegration of numerous compounds as it travels through coal seams and host rocks, finally transporting vast quantities of suspended particles as well as dissolved materials, which are collected in typical mine drainage sumps [15]. The hydrochemical behaviour of mine seepage water and surrounding water resources must be investigated in order to determine the influence of such mining activities on the water environment. The issue of environmental pollution caused by toxic metals has sparked widespread concern across various regions of the world. Pollution of water resources by heavy metals is a growing problem [16, 17]. Although some heavy metals such as iron (Fe), copper (Cu), and zinc (Zn) are required in trace amount for the normal growth and development of organisms, many other heavy metals including lead (Pb), cadmium (Cd), and mercury (Hg) are serious threats to aquatic ecosystems and human health [18, 19]. Furthermore, in what forms do these heavy metals exist, ions, complexes, organic compounds, particles or settled sediments, highlighting their persistence, stability and resistance to degradation [20]. The term Potentially toxic trace elements (PTEs) are more inclusive and appropriate than heavy metals, as it refers to metals and metalloids with a density above 4 g/cm3, but their toxicity depends on chemical properties rather than density [21]. Due to their persistence and non-degradable nature, these "trace element" (TE) present in low concentrations in water is termed as potentially toxic trace elements (PTEs) [18, 19]. Indexical methods offer a structured approach to evaluating water quality deterioration caused by mining activities by consolidating multiple PTE concentrations into a single numerical index, simplifying interpretation and decision-making. In this regard, many scientists around the world have conducted various studies using various water quality indices, such as the heavy metal pollution index (HPI), heavy metal evaluation index (HEI), and contamination index (CI) [12, 22, 23, 24, 25]. These methods of approach have some shortcoming as the criteria weight assessment is purely based on subjective way [26–27]. To address the subjective approach of assigning weights to quality parameters, researchers have introduced water quality indices based on objective weighting methods [27, 28, 29].

The current study was done to investigate the possibility of PTEs contamination in the mine discharge waters of six mines located in the Mahan River catchment area, as well as the potential impact on groundwater and river water sources. To assess pollution levels, five pollution indices such as Heavy metal pollution index (HPI), Heavy metal evaluation index (HEI) and the Contamination index (CI), entropy-weight based HM contamination index (EHCI), and Principal Component Analysis (PCA)-based HMI Heavy metal index (HMI) were computed. In addition to the conventional indices (HPI, HEI, and CI), which are objective-based methodologies, two recently developed pollution indices (EHCI and HMI) were selected due to their predominantly objective weight computation processes. The primary objectives of current study focused on (1) the distribution of PTEs in various water sources and their seasonal variation, (2) the use of multiple indexical methods to assess PTE distribution within the study region, and (3) the potential impact of mining activity on both groundwater and Mahan River systems. The work’s conclusion will highlight the severity of the coal mine’s impact on surface water chemistry, the effect on aquifer systems in and around the mining sites, and whether or not any other irregularities exist in the area.

Description of study region

The research region is located in the center of Bishrampur Coalfield, between latitudes 23°00’ N and 23°30’ N and longitudes 83°00’ E and 83°45’ E (Fig. 1). Metalled roads connect the region to Bishrampur Coalfield’s other collieries, including Shivani Underground (UG), Jagannathpur Opencast (OC), Kalyani UG, Mahamaya UG, Mahan-II OCP, Bhatgaon UG, Nawapara UG, Mahan OC, and Dugga UG. Geographically, the Mahan River and its tributaries regulate the primary drainage component of the coalfield, which flows mostly in the east-west direction of the area before flowing south-north in the north-western part of the research area (Fig. 1). The research area has gently undulating trends and elevations ranging from 446 m to 672 m. The area has a tropical monsoon climate, with scorching summers and cold winters, as well as substantial rainfall during the monsoon season. The yearly mean daily temperature ranges from 17.8oC to 30.1oC. May and January have the greatest and lowest average monthly temperatures (42.7o C and 4.4o C, respectively). Between June and September, the study area receives around 87% of the total rainfall. The average yearly rainfall from 1991 to 2017 was approximately 1270 mm. Because of the tropical monsoon environment, the area’s relative humidity ranges from moderate to high. During the monsoon season, relative humidity ranges from 55 to 66% in June to 83–88% in August. Throughout the year, relative humidity varies from 22 to 39% in April to 83–88% in August. The yearly mean daily humidity is roughly 68% in the morning and 52% in the evening.

Fig. 1

Map of the study area with mines, sampling locations and sub-watersheds

Materials and methods

Field sampling and PTEs analysis

To achieve the stated objectives, extensive high-resolution sampling was conducted from all available sources, including groundwater (26 Nos), mine water (12 Nos), and river water (12 Nos) samples from various places within and around the Bishrampur coal mining zone (Fig. 1). A total of 96 samples were collected throughout two seasons: pre-monsoon (May 2018) and post-monsoon (November 2017), with 48 from each season. Aside from the research area, two additional samples were taken from separate watersheds with higher altitudes and pollution-free zones/eco zones (i.e., Mainpat) to compare pollution intensity to non-coal mining areas. During fieldwork, polyethylene bottles were used to collect groundwater samples and filtered via 0.45 μm filter paper. Samples were promptly preserved by lowering pH to < 2 with HNO3 and held at 40C until analysis. The concentrations of potentially toxic trace elements (PTEs) such as, Chromium (Cr), Cadmium (Cd), Barium (Ba), Silver (Ag), Copper (Cu), Strontium (Sr), Cobalt (Co), Nickel (Ni), Zinc (Zn), Iron (Fe), Arsenic (As), Led (Pb), Aluminium (Al), Manganese (Mn), Selenium (Se), and Vanadium (V) in water were analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP–MS, Perkin Elmer, Model: ELAN DRCe) by following the standard procedures of APHA (2012) [30]. Strict quality assurance protocols were followed to ensure data reliability, and careful handling procedures were implemented to prevent contamination. Instrument readings were corrected using reagent blanks. Water samples were analyzed without prior digestion and were tested in triplicate for trace element determination. Analytical accuracy was verified by analyzing certified reference standards (NIST 1640a and NIST 1643b). The precision of the measurements was generally within 5% RSD, demonstrating high accuracy and reproducibility. While, in-situ water quality parameters such as pH, total dissolved solids (TDS) and electric conductivity (EC) for all groundwater samples were measured at each site by means of a hand-held measuring device (SPECTRO, SLE-2603).

Heavy metal pollution in groundwater using multiple indexical methods

The pollution evaluation indices used in this study, namely HPI, HEI, CI, EHCI, and HMI were determined to investigate drinking suitability of groundwater by comparing heavy metal constituents with BIS (2012) standards [31].

HPI

HPI assesses the overall relevance of each possible potentially toxic trace elements (PTEs) for water quality assessment. Individual PTE weights are determined based on their relative significance to water quality or by calculating values inversely proportional to the predefined maximum allowable and desirable limits as prescribed in BIS, 2012 guidelines for the corresponding PTEs [31]. The weight of the PTE runs from 0 to 1, with larger values indicating the significance of the specific element in the quality assessment [32]. The following formulae were used to estimate the HPI for each groundwater sample:

$$HPI=\frac{{\sum\limits_{{i=1}}^{n} {{W_i}{Q_i}} }}{{\sum\limits_{{i=1}}^{n} {{W_i}} }}$$
(1)
$${W_i}=\frac{1}{{{S_i}}}$$
(2)
$${Q_i}=\sum\limits_{{i=1}}^{n} {\frac{{\left\{ {{A_i}\left( - \right){I_i}} \right\}}}{{\left( {{S_i} - {I_i}} \right)}} \times 100} $$
(3)

where Wi is the unit weight of ith PTE, the sub-index of ith PTE is represented by Qi, n is the total number of groundwater samples, Ai, Si and Ii is the actual, maximum permissible, and maximum desirable concentration of PTEs, respectively. The critical value for HPI in the current research is set at 100 to assess water’s drinking suitability [14].

HEI

HEI method, was introduced by [33], estimates the overall water quality accounting various PTEs in the similar manner like HPI method. In this method of water quality assessment, the actual or measured values of PTEs are divided by the Si value suggested by [31] as per the Eq. 4.

$$HEI=\sum\limits_{{i=1}}^{n} {\frac{{{A_i}}}{{{S_i}}}} $$
(4)

As HEI lacks a defined threshold value, the contamination level determined by this index remains subjective. However, contamination levels in groundwater samples are classified using a multiple of the mean values approach.

CI

According to [34], CI method estimates the degree of contamination by computing the collective effect of different water quality inputs and summarizing them. The computation of CI involves the summing up of individual contamination factors of selected PTEs exceeding the CSi as per Eq. 5 & Eq. 6.

$$CI=\sum\limits_{{i=1}}^{n} {{C_{Fi}}} $$
(5)
$${C_{Fi}}=\frac{{{C_{Ai}}}}{{{C_{Si}}}} - 1$$
(6)

where the contamination factor ith PTE is represented by CFi, CAi and CSi is measured concentration and the maximum permissible limit of ith PTE.

In the present research work, CI values of groundwater were classified into three contamination levels such as low (CI:<1), moderate (CI:1–3), and high (CI:>3) [34–35].

EHCI

The water quality assessment using EHCI mainly works on the information congregated by the entropy system [36]. The entropy weight (wi) of PTEs is calculated based on Shannon entropy information technique and then coupled with the sub-indices (Qi) of respective PTE provides the EHCI values as per the following Eq.

$$EHCI=\sum\limits_{{j=1}}^{n} {{w_i}{Q_i}} $$
(7)
$${Q_i}=\left( {\frac{{{A_i}}}{{{S_i}}}} \right) \times 100$$
(8)
$${w_j}=\left( {1 - {E_j}} \right)/\sum\limits_{{j=1}}^{m} {\left( {1 - {E_j}} \right)} $$
(9)
$${E_j}= - \frac{1}{{\ln m}}\sum\limits_{{i=1}}^{m} {\left\{ {{P_{ij}} \cdot \ln {P_{ij}}} \right\}} $$
(10)
$${P_{ij}}=\frac{{{y_{ij}}}}{{\sum\limits_{{i=1}}^{m} {{y_{ij}}} }}$$
(11)
$${y_{ij}}=\frac{{x{}_{{ij}} - \mathop {\hbox{min} }\limits_{{i=1,2,3.m}} \left( {{x_{ij}}} \right)}}{{\mathop {\hbox{max} }\limits_{{i=1,2,3.m}} \left( {{x_{ij}}} \right) - \mathop {\hbox{min} }\limits_{{i=1,2,3.m}} \left( {{x_{ij}}} \right)}}$$
(12)

where jth PTE entropy weight is represented by wi, Ej is the entropy of jth PTE, in ith groundwater sample, the probability of occurrence of jth PTE is represented by Pij, the normalized value in ith groundwater sample (i = 1, 2, 3...m and j = 1, 2, 3....n) for jth PTE is represented by yij, and for ith groundwater sample the measured concentration of jth PTE is represented by xij.

The study categorized computed EHCI values into four categories: unsuitable (> 200), poor (150–200), average (100–150), good (50–100), and excellent (< 50) [37].

HMI

As recommended by [29], the Principal Component Analysis (PCA)-based HMI also assesses water quality. The factors bearing with an eigenvalue more than one is extracted in the form of factor loading during the PCA method. In this process, the relative eigenvalues and factor loadings are then multiplied to get heavy metal weights. Later, the calculated weights are further used to estimate the HMI values of water samples as per the equation below.

$$HMI=\sum\limits_{{i=1}}^{n} {\left( {{p_i} \times \frac{{{A_i}}}{{{S_i}}}} \right) \times 100} $$
(13)

where ith heavy metal’s PCA-based weight is represented by pi. In this research work, the estimated HMI values are classified into five sub-categories such as, Unsuitable for Drinking (> 300), Very Poor (200–300), Poor (100–200), Good (50–100), and Excellent (< 50) [29].

Results and discussion

Groundwater compliance W.r.t drinking water standards and statistical analysis of PTEs dynamics

The descriptive statistics of the PTEs concentrations for all locations within the study area illustrates that the average concentration of Mn, Pb, Ni and Fe (Pre-monsoon) and Mn, Pb, Ni, Fe and Al (Post-monsoon) in different water sources of the study area were higher than the permissible limits, as suggested by Bureau of Indian Standards [31]. The mean concentration of all parameters except Al, Fe and Mn were higher during pre-monsoon than that of post-monsoon (Table 1). During the post-monsoon period, the increased water infiltration from rainfall accelerates the oxidation of sulfide minerals (pyrite) in coal seams and associated strata. This oxidation releases sulfuric acid (H2SO4), which enhances the dissolution and mobilization of trace metals such as Fe, Al, Mn, and Ba into groundwater [3].

Table 1 Statistical analysis of PTEs dynamics and comparison with drinking suitability (BIS 2012)

The results of water samples compliance with respect to suitability of drinking water [31] reveal that 48% of water samples during pre-monsoon season fell below the acceptable limit of pH (6.5) and it is 44% in the case of the post-monsoon season. Similarly, the PTEs namely, Fe (30%) > Mn (18%) > Ni (18%) > Pb (8%) > Cu (4%) > Al (4%) > Cd (2%) and Cr (2%) during pre-monsoon and while Fe (46%) > Al (15%) > Mn (15%) > Ni (15%) > Pb (6%) > Ba (4%) > and Cd (2%) samples in post-monsoon season exceeded permissible limits [31]. The remaining elements, such as Zn, As, Se, and Ag, are completely within acceptable limits during both seasons (Table 1). This confirms the seasonal variation in PTE concentrations. The elevated levels of Fe, Al, Mn and Ba observed during the post-monsoon period are attributed to the interaction of mine seepage water and precipitation over the mine catchments, leading to the leaching of these elements from overburden dumps and active mining faces, which subsequently enter the mine discharge water [32]. Conversely, the higher PTE concentrations recorded during the pre-monsoon season are driven by factors such as evaporation, extensive sugarcane cultivation, and coal mining-associated industries [14].

Seasonal dynamics of PTEs and water resource comparison using the Caboi plot

Groundwater PTEs concentration largely governed by the chemical composition of host rock with which it interacts alongside climatic and anthropogenic factors. In general, the pH of the groundwater is one of the factors that drive the mobility of cationic species of the water. Therefore, the relationship between pH and PTEs content of the groundwater has been traced in the present study by using Caboi diagram [38]. The plot classified (Figure. 2) most of the groundwater, river water and mine water samples of both the seasons as near neutral low-metal type and this particular cluster of the samples are free from acid mine drainage and contamination. Whereas, a few samples mostly the mine water samples of Bhatgaon UG, Mahamaya UG, and Mahan OC (PR40, PR41, PR42, PR43, PR47, and PR48) along with a river water samples (PR44) located proximate to these mines were observed as Acid–High-metal. The low pH range could be attributed to the oxidation of pyrite-bearing minerals present in coal cleat faces when exposed to an aeration zone and water, which also accounts for the high concentration of PTEs in the research area. This clearly shows that, in addition to mineral dissolution, AMD affects the mine water samples in this cluster. Most notably, post-monsoon samples have a higher concentration of PTEs than pre-monsoon ones.

Evaluation of PTEs pollution and Spatial distribution of pollution indices

All the pollution evaluation indices have been estimated for both seasons, individually for all the sampling stations (Table 2). Statistically, the 14% (HPI), 14% (HEI), 18% (CI), 14% (EHCI), and 20% (HMI) of the samples were above the standards during the pre-monsoon season. A total of 10% (HPI), 10% (HEI), 15% (CI), 15% (EHCI), and 17% (HMI) of samples exceeded standards during the post-monsoon season. However, the indices for all the groundwater and river water samples baring a few locations in proximity to the mining area, are below the critical limit. Consequently, the majority of the dug wells and bore wells in the study area are not polluted and are safe for human consumption.

Table 2 Summary of multiple PTEs indices of water samples

Furthermore, a Paired t-Test analysis was performed to evaluate the statistical significance of seasonal variations in water quality by comparing the mean values between the pre- and post-monsoon seasons (Table 3). The dataset reveals a significant increase in various water quality indices (HPI, HEI, CI, EHCI, and HMI) from pre- to post-monsoon seasons. Among them, HEI, CI, and EHCI exhibit statistically significant increases post-monsoon (p < 0.05), indicating a notable rise in PTE concentrations following the monsoon. In contrast, HPI and HMI do not show significant seasonal variations, suggesting that heavy metal pollution and overall contamination remain relatively stable across the monsoon transition. These findings highlight the influence of coal mining activities on groundwater and surface water in proximity to the mine, contributing to elevated PTE loads. To examine the spatial distribution of pollution load and seasonal change across various water sources in the study area, a thematic map containing all five indices was developed (Fig. 3). The spatial distribution maps of HPI, HEI, CI, EHCI, and HMI consistently indicate higher metal contamination in the north-western part of the study region throughout the year. This elevated contamination is attributed to geogenic influxes and mining activities, which hastened the dissolution of toxic metals upon contact.

Fig. 2

The Caboi diagram shows the classification of surface and groundwater as a function of the total heavy metal load relative to the pH of the study area

Fig. 3

Thematic map showing the spatiotemporal distribution of the multiple indices

Table 3 Paired t-Test results and comparison of mean values (Pre- vs. Post-Monsoon water Quality)

Among all the indexical methods, the thematic map of the EHCI for different seasons was chosen for evaluation (Fig. 3), as it exhibits the highest mean difference of + 78.06 and a statistically significant p-value (p < 0.05) (Table 3). It is apparent from the EHCI thematic map that the highest pollution anomalies (> 200) were reported in the north-western part of the study area, where Mahan OCP, Bhatgaon UG, and Mahamaya UG mines are in operation. Consequently, the water samples from this region are classified as a very poor and unsuitable category. Moreover, the mine water samples from the northwestern part of the study area exhibit a comparatively lower pollution load in terms of EHCI during the pre-monsoon season (1102) than in the post-monsoon season (2193) (Table 2; Fig. 3). This variation can be attributed to several anthropogenic factors, including the movement of heavy earthmoving machinery (HEMM) at the mine site and the interaction of seepage water with mine workings. Additionally, runoff from rainfall over overburden (OB) dumps is a plausible contributor, as the study area received substantial precipitation (1500 mm).

The groundwater, river water and mine water sample from the rest of the locations within the no-pollution category, with the EHCI value below 50 (Fig. 3). Therefore, it is reasonable to conclude that the mining activities in the Mahan River catchment area do not pose any appreciable threat to the groundwater and surface water system. Their remains the impact is localized to the mine periphery.

Conclusions

This study provides a comprehensive assessment of potentially toxic trace elements (PTEs) in groundwater, river water, and mine water within the Mahan River catchment area, a coal mining zone in Central India. The investigation reveals notable seasonal variations in PTE concentrations, with elevated levels of Fe, Mn, Al, Ba, Pb, and Ni post-monsoon due to increased oxidation of sulfide minerals and enhanced metal leaching. The indexical methods (HPI, HEI, CI, and EHCI) indicate that 85–90% of the water samples fall within permissible limits, signifying minimal contamination risk for most locations and are suitable for consumption. However, 20% of samples (HMI) in the pre-monsoon season and 17% in the post-monsoon season exceed the acceptable thresholds and are unsuitable for immediate consumption, primarily in areas adjacent to active mining operations.

The Caboi plot further confirms that the majority of the groundwater, river water and mine water samples from both seasons exhibit a "near neutral low-metal" characteristic, while mine water from Mahan OC, Mahamaya UG, and Bhatgaon UG shows an "Acid-High Metal" signature, indicating localized acid mine drainage (AMD) effects in addition to mineral dissolution.

Despite these localized impacts, the broader Mahan River system and regional groundwater remain largely unaffected by mining activities, with contamination confined to mine periphery locations. Overall, this study enhances the understanding of hydrogeochemical dynamics in coal mining regions, offering critical insights for policymakers, environmental managers, and stakeholders to ensure sustainable water resource management in mining-affected areas.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Emenike PC, Tenebe I, Ogarekpe N et al (2019) Probabilistic risk assessment and Spatial distribution of potentially toxic elements in groundwater sources in Southwestern Nigeria. Sci Rep 9(1):15920. https://doi.org/10.1038/s41598-019-52325-z

    Article CAS Google Scholar

  2. Gaur VK, Sharma P, Sirohi R et al (2020) Assessing the impact of industrial waste on environment and mitigation strategies: A comprehensive review. J Hazard Mater 5:398:123019. https://doi.org/10.1016/j.jhazmat.2020.123019

    Article CAS Google Scholar

  3. Kumar N, Tiwari MK, Singh R et al (2022) Chemometrics in ascertaining hydrogeochemical characteristics of coal mine discharge vis-à-vis behaviour of surface and groundwater resources of the Mahan river catchment area, central India. Mine Water Environ 41(2):518–532. https://doi.org/10.1007/s10230-02200854-x

    Article CAS Google Scholar

  4. Singh R, Gayen A, Kumar S, Dewangan R (2021) Geo-spatial distribution of arsenic contamination of groundwater resources in intricate crystalline aquifer system of central India: arsenic toxicity manifestation and health risk assessment. Hum Ecol Risk Assess: Int J 27(6):1588–1612. https://doi.org/10.1080/10807039.2020.1865787

    Article CAS Google Scholar

  5. Singh R, Venkatesh AS, Syed TH et al (2018) Stable isotope systematics and geochemical signatures constraining groundwater hydraulics in the mining environment of the Korba coalfield, central India. Environ Earth Sci 77:584–590. https://doi.org/10.1007/s12665-018-7725-7

    Article CAS Google Scholar

  6. Gleeson T, Befus KM, Jasechko S et al (2016) The global volume and distribution of modern groundwater. Nat Geosci 9(2):161–167. https://doi.org/10.1038/NGEO2590

    Article CAS Google Scholar

  7. Siraj G, Khan HH, Khan A (2023) Dynamics of surface water and groundwater quality using water quality indices and GIS in river Tamsa (Tons). Jalalpur India HydroResearch 6:89–107. https://doi.org/10.1016/j.hydres.202302002

    Article Google Scholar

  8. National Institution for Transforming India (NITI) Aayog. Annual Report 2018-19. Available at:chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/niti.gov.in/sites/default/files/2019-11/AnnualReport2019.pdf, accessed: 03.01.2024

  9. Wang Y, Li P (2022) Appraisal of shallow groundwater quality with human health risk assessment in different seasons in rural areas of the Guanzhong plain (China). Environ Res 207:112210. https://doi.org/10.1016/j.envres.2021.112210

    Article CAS Google Scholar

  10. Khan HH, Khan A, Ahmed S et al (2021) GIS-based impact assessment of land-use changes on groundwater quality: study from a rapidly urbanizing region of South India. Environ Earth Sci 63:1289–1302. https://doi.org/10.1007/s12665-010-0801-2

    Article Google Scholar

  11. Zhao L, Ren T, Wang N (2017) Groundwater impact of open cut coal mine and an assessment methodology: A case study in NSW. Int J Min Sci Technol 27(5):861–866. https://doi.org/10.1016/j.ijmst.201707008

    Article Google Scholar

  12. Panigrahy BP, Singh PK, Tiwari AK et al (2015) Assessment of heavy metal pollution index for groundwater around Jharia coalfield region, India. J Bio Env Sci 6(3):33–39

    Google Scholar

  13. Singh R, Syed TH, Kumar S et al (2017) Hydrogeochemical assessment of surface and groundwater resources of Korba coalfield, central India: environmental implications. Arab J Geosci 10:1–6. https://doi.org/10.1007/s12517-0173098-6

    Article Google Scholar

  14. Singh R, Venkatesh AS, Syed TH et al (2017) Assessment of potentially toxic trace elements contamination in groundwater resources of the coal mining area of the Korba coalfield, central India. Environ Earth Sci 76:1–7. https://doi.org/10.1007/s12665-017-6899-8

    Article CAS Google Scholar

  15. Singh R, Narayan ID, Doley T et al (2021) Climate-resilient groundwater rationing in the mining environment: an operational framework of India. Environ Earth Sci 80(12):449. https://doi.org/10.1007/s12665021-09732-1

    Article Google Scholar

  16. Saleh HN, Panahande M, Yousefi M et al (2019) Carcinogenic and non-carcinogenic risk assessment of heavy metals in groundwater wells in Neyshabur plain, Iran. Biol Trace Elem Res 190:251–261. https://doi.org/10.1007/s12011-018-1516-6

    Article CAS Google Scholar

  17. Saleh TA (2015) Isotherm, kinetic, and thermodynamic studies on hg (II) adsorption from aqueous solution by silica-multiwall carbon nanotubes. Environ Sci Pollut Res 22:16721–16731. https://doi.org/10.1007/s11356-015-4866-z

    Article CAS Google Scholar

  18. Jomova K, Alomar SY, Nepovimova E et al (2024) Heavy metals: toxicity and human health effects. Arch Toxicol 20:1–57. https://doi.org/10.1007/s00204-024-03903-2

    Article CAS Google Scholar

  19. Mitra S, Chakraborty AJ, Tareq AM et al (2022) Impact of heavy metals on the environment and human health: novel therapeutic insights to counter the toxicity. J King Saud Univ Sci 34(3):101865. https://doi.org/10.1016/j.jksus.2022.101865

    Article Google Scholar

  20. Pertsemli E, Voutsa D (2007) Distribution of heavy metals in lakes Doirani and Kerkini, Northern Greece. J Hazard Mater 148(3):529–537. https://doi.org/10.1016/j.jhazmat.2007年03月01日9

    Article CAS Google Scholar

  21. Duffus JH (2002) Heavy metals a meaningless term? Pure Appl Chem 74(5):793–807. https://doi.org/10.1351/pac200274050793. IUPAC Technical Report

    Article CAS Google Scholar

  22. Sajil Kumar PJ, Davis Delson P, Thomas Babu P (2012) Appraisal of heavy metals in groundwater in Chennai City using a HPI model. Bull Environ Contam Toxicol 89:793–798. https://doi.org/10.1007/s00128-012-0794-5

    Article CAS Google Scholar

  23. Qu L, Huang H, Xia F et al (2018) Risk analysis of heavy metal concentration in surface waters across the rural-urban interface of the Wen-Rui Tang river, China. Environ Pollut 237:639–649. https://doi.org/10.1016/j.envpol.2018年02月02日0

    Article CAS Google Scholar

  24. Uugwanga MN, Kgabi NA (2021) Heavy metal pollution index of surface and groundwater from around an abandoned mine site. Klein Aub Phys Chem Earth Parts A/b/c 124:103067. https://doi.org/10.1016/j.pce.2021.103067

    Article Google Scholar

  25. Ahirvar BP, Das P, Srivastava V et al (2023) Perspectives of heavy metal pollution indices for soil, sediment, and water pollution evaluation: an insight. Total Environ Res Themes 6:100039. https://doi.org/10.1016/j.totert.2023.100039

    Article Google Scholar

  26. Singha S, Pasupuleti S, Singha SS et al (2020) Effectiveness of groundwater heavy metal pollution indices studies by deep-learning. J Contam Hydrol 235:103718. https://doi.org/10.1016/j.jconhyd.2020.103718

    Article CAS Google Scholar

  27. Singh KR, Dutta R, Kalamdhad AS et al (2020) Review of existing heavy metal contamination indices and development of an entropy-based improved indexing approach. Environ Dev Sustain 22:7847–7864. https://doi.org/10.1007/s10668-019-00549-4

    Article Google Scholar

  28. Giri S, Singh AK (2019) Assessment of metal pollution in groundwater using a novel multivariate metal pollution index in the mining areas of the Singhbhum copper belt. Environ Earth Sci 78(6):192. https://doi.org/10.1007/s12665-019-8200-9

    Article CAS Google Scholar

  29. Dash S, Borah SS, Kalamdhad A (2019) A modified indexing approach for assessment of heavy metal contamination in deepor beel, India. Ecol Indic 106:105444. https://doi.org/10.1016/j.ecolind.2019.105444

    Article CAS Google Scholar

  30. APHA (American Public Health Association) (2012) Standard methods for the examination of water and waste water. APHA, Washington, DC

    Google Scholar

  31. BIS (Bureau of Indian Standards) IS: 10500, Indian Standard for Drinking Water Specification. 2nd, Revision (2012) New Delhi

  32. Prasad B, Kumari P, Bano S, Kumari S (2014) Ground water quality evaluation near mining area and development of heavy metal pollution index. Appl Water Sci 4(1):11–17. https://doi.org/10.1007/s13201-013-0126-x

    Article CAS Google Scholar

  33. Edet AE, Offiong OE (2002) Evaluation of water quality pollution indices for heavy metal contamination monitoring. A study case from Akpabuyo-Odukpani area, lower cross river basin (southeastern Nigeria). GeoJournal 57:295–304. https://doi.org/10.1023/B:GEJO.0000007250.92458.de

    Article Google Scholar

  34. Backman B, Bodiš D, Lahermo P et al (1998) Application of a groundwater contamination index in Finland and Slovakia. Environ Geol 36:55–64. https://doi.org/10.1007/s002540050320

    Article CAS Google Scholar

  35. Prasanna MV, Praveena SM, Chidambaram S et al (2012) Evaluation of water quality pollution indices for heavy metal contamination monitoring: a case study from Curtin lake, Miri City, East Malaysia. Environ Earth Sci 67:1987–2001. https://doi.org/10.1007/s12665-012-1639-6

    Article CAS Google Scholar

  36. Singha S, Pasupuleti S, Singha SS et al (2021) Prediction of groundwater quality using efficient machine learning technique. Chemosphere 276:130265. https://doi.org/10.1016/j.chemosphere.2021.130265

    Article CAS Google Scholar

  37. Amiri V, Rezaei M, Sohrabi N (2014) Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran. Environ Earth Sci 72:3479–3490. https://doi.org/10.1007/s12665-014-3255-0

    Article CAS Google Scholar

  38. Caboi R, Cidu R, Fanfani L et al (1999) Environmental mineralogy and geochemistry of the abandoned Pb-Zn Montevecchio-Ingurtosu mining district, Sardinia, Italy. Chron De La Rechérche Miniére 534:21–28

    Google Scholar

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Acknowledgements

The authors are grateful to the management of CMPDIL, Ranchi, SECL, Bilaspur, and AKS University, Satna for providing facilities for the publishing of this study. We are also grateful to the CSIR-Central Institute of Mining and Fuel Research, Dhanbad, for some analytical assistance. The opinions expressed in this work are solely those of the authors and do not necessarily reflect the views of the organization to which they belong.

Funding

No funding was provided to help in the preparation of this manuscript. The authors disclose no competing interests relevant to the content of this article.

Author information

Authors and Affiliations

  1. Mining Department, Western Coalfield Limited, Nagpur, Maharashtra, 440014, India

    Nirmal Kumar

  2. Department of environmental science, AKS University, Satna, Madhya Pradesh, 485001, India

    Mahendra Kumar Tiwari

  3. Exploration Department, Central Mine Planning and Design Institute Limited, Nagpur, Maharashtra, 440014, India

    Rambabu Singh

  4. Department of Civil Engineering, GITAM (Deemed to be University), Hyderabad, Telangana, 502329, India

    Sudhakar Singha

  5. Department of Civil Engineering, KG Reddy College of Engineering & Technology, Hyderabad, Telangana, 501504, India

    Soumya S. Singha

  6. Department of Chemical Engineering, CBIT, Hyderabad, Telangana, 500075, India

    Prasad Babu.K

Authors
  1. Nirmal Kumar
  2. Mahendra Kumar Tiwari
  3. Rambabu Singh
  4. Sudhakar Singha
  5. Soumya S. Singha
  6. Prasad Babu.K

Contributions

Nirmal Kumar: Have made a substantial contribution to the acquisition of field data, analysis, and interpretation of data for the article.Mahendra Kumar Tiwari: Have made a substantial contribution to the interpretation of data for the article.Rambabu Singh: Have made a substantial contribution to the design of the article, interpretation of data and drafted the article and brought it to current form.Sudhakar Singha: Preparation of various maps/ figures using different software and reviewed the manuscript.Soumya S. Singha: Computation of different indices and their implications and reviewed the manuscript.Prasad Babu K: Associated in interpretation of water quality indices and preparation of various maps/ figures and reviewed the manuscript.

Corresponding author

Correspondence to Rambabu Singh.

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Competing interests

The authors declare no competing interests.

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Kumar, N., Tiwari, M.K., Singh, R. et al. Indexical methods assessing PTEs distribution in Mahan river command area, central India’s coal mining zone. Geochem Trans 26, 2 (2025). https://doi.org/10.1186/s12932-025-00098-y

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  • DOI: https://doi.org/10.1186/s12932-025-00098-y

Keywords

Geochemical Transactions

ISSN: 1467-4866

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