Contamination and Sources of Surface Water in Korba Coal Basin, Chhattisgarh, India

Contamination and Sources of Surface Water in Korba Coal Basin, Chhattisgarh, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Contamination and Sources of Surface Water in Korba Coal Basin, Chhattisgarh, India Khageshwar Singh Patel, Piyush Kumar Pandey, Bharat Lal Sahu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4478468/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In India, surface water reservoirs are widely harnessed to meet a variety of needs, including domestic, agricultural, and industrial applications. The quality of these resources, particularly in coal mine regions, undergoes substantial deterioration due to the discharge of various wastes (industrial, municipal, and runoff water) and coal ash deposition. The Korba basin, shaped by mining activities, shallow groundwater levels, and the flow of the expansive Hasedo River, features numerous ponds, pit lakes, and canals. A significant health concern in this area is the prevalence of fluorosis disease among the local population. The main aim of this study was to evaluate the water quality of reservoirs, including ponds, pit lakes, canals, and rivers, with a focus on identifying contaminant levels and tracing the sources of chemical species such as carbonate and organic carbons, anions, and metals. During the period from 2012 to 2017, elevated carbon contents (varying from 1010 to 4420 mg·L − 1 ) markedly contributed to increased total dissolved solids (TDS), with values ranging between 2865 and 5540 mg·L − 1 . fluoride (F − ), aluminum (Al), iron (Fe), and manganese (Mn) concentrations in all surface water bodies exhibited variations within the ranges of 1.8–4.4, 0.42–1.91, 0.3–1.22, and 1.0–2.1 mg·L − 1 , respectively. This study delves into the temporal and seasonal variations, water quality indices, and toxicities associated with the identified contaminants. Surface water quality sources Korba basin India Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The major sources of pollution are considered to be mining activities, in particular coal mining which cause significant environment damage. The geology, hydrology, geochemistry, climate change, mining processing history control the transport of pollutants in and around mining sites (Sharifi et al. 2023 ; van Vliet et al. 2023 ). Coal, a natural fossil fuel abundantly available in India, possesses inherent drawbacks, including low caloric value and a high ash content exceeding 60%, which has adverse environmental and ecological implications (Mishra, 2004 ). Reports from various coal mines and thermal power plant areas in different countries highlight degraded water quality and elevated concentrations of total dissolved solids (TDS) and chemical species such as fluoride (F − ), nitrate (NO 3 − ), sulfate (SO 4 2− ), iron (Fe), lead (Pb), mercury (Hg), uranium (U), and others (Ayub and Ahmad, 2020 ; Chowdhury, 2017 ; Dewan et al., 2019; Dubey et al., 2022 ; Hossain et al., 2021 ; Kumar and Singh, 2022 ; Mohanty et al., 2018 ; Nihalani et al., 2021 ; Rana et al., 2019 ; Verma, 2018 ; Xue et al 2016 ). The Korba basin in Chhattisgarh, India, characterized by coal mining activities, has witnessed pollution of soil, sediments, and water with F − and heavy metals (Patel 2016, 2024; Sharma et al., 2017 , 2019 ). As in the Korba basin has long history of coal mining activities, discharge of untreated wastewater, mine drainage and water runoff are the primary sources of pollution into nearby surface water bodies (Patel 2016, 2024; Sharma et al., 2017 , 2019 ). However, organic and inorganic carbon in the surface water in coal mine areas is crucial due to thermal plant and coal mining effluents. Consequently, this study aims comprehensively assess water quality, hydrochemistry, delineation of contaminant concentration variations (including carbons, anions, cations and heavy metals) and identify the origins of these contaminants in the surface water bodies of the Korba basin, Chhattisgarh, India. This information may serve to develop viable strategies for ensuring a contaminant-safe water supply for domestic and other purposes. Materials and Methods Study Area The Korba basin, situated in Chhattisgarh, India, within the coordinates 22°20'–25'N and 84°40'–82°45'E, at an average elevation of 252 meters, hosts the largest coal deposit in the country. The climate in this region is characterized by hot temperate conditions, with the maximum rainfall occurring in the summer months. The yearly precipitation in the Korba basin averages around 1000 mm, mainly influenced by the southwest monsoon. The geological formations consist of Proterozoic and Gondwana types, featuring several sinkholes (Sahu et al., 2016 ). The major drainage system in the studied area comprises the Hasdeo River and its tributaries, such as Ahiran, Bating Nala, Chuia Nala, Dhengur Nala, Gagechorai, Jharri Nala, and Tan. The region is marked by the presence of thirteen open and underground coal mines, eight thermal power plants, one aluminum plant, twenty rice mills, and a cement plant. These industrial entities discharge untreated wastewater into the environment, significantly impacting the water quality of the basin (MSME, 2023 ). Water Sample Collection Surface water samples were obtained from seven locations encompassing rivers, canals, natural ponds, and pit lakes during three distinct seasons over the period of May (pre-monsoon), August (monsoon), and December (post-monsoon) from 2012 to 2017 (see Fig. 1 ). Collected samples were carefully filled into cleaned and rinsed polyethylene containers (rinsed thrice with the sample) and promptly transported to the laboratory for subsequent analysis. Analysis Measurement of Physical Parameters On-site measurements using sensors manufactured by Hanna Instruments (Mumbai, Maharashtra, India) included various physical parameters such as dissolved oxygen (DO), electrical conductivity (EC), pH, reduction potential (RP), and temperature (T). Chemical Analysis Prior to chemical analysis, water samples underwent filtration through 0.45 µm glass fiber filters. The divided samples served two purposes: the first portion underwent acidification with a few drops of concentrated HNO 3 acid sourced from Merck, India for metal analysis, whereas the second portion was allocated for the examination of carbons and anions. The TDS value was determined using the evaporation method on the filtered water sample until a constant weight was achieved (APHA, 2017 ). Organic carbon (OC) and inorganic carbon (IC) were analyzed using a CHNSO-IRMS analyzer (SV Instruments Analytica Pvt. Ltd, Gurgaon, Haryana, India). The IC was removed by adjusting the pH of the water sample to 2–3, followed by the removal of formed CO 2 through gas bubbling. The OC value was obtained by subtracting the IC value from the total carbon (TC) value. The method's LOD and LOQ stood at 1.2 and 4 mg L − 1 as carbonate, respectively. For monitoring F − levels, a Metrohm-720 ion meter (Metrohm, Herisau, Switzerland) was employed in the presence of a buffer (TISAB-III) at a pH of 5.2 ± 0.2, using Teflon containers. The LOD and LOQ for this method were 0.02 and 0.10 mg·L − 1 , respectively, with a calibration curve prepared using standard NaF solution. A DX120 ion chromatograph (Dionex, Sunnyvale, CA, USA) equipped with analytical columns (IonPac AS22 and IonPac CS16) was used for the analysis of ions. Eluents (4.5 mmol L − 1 Na 2 CO 3 + 1.0 mmol L − 1 NaHCO 3 in the case of anions and 42 mmol L − 1 methane sulfonic acid (MSA) in the case of cations) were employed for separation. The method's LOD for Cl − , NO 3 − , SO 4 2− , Na, K, Mg, and Ca were 0.01, 0.02, 0.02, 8, 1.5, 0.1, and 0.8 mg·L − 1 , respectively. Inductively coupled plasma optical emission spectroscopy (ICP-OES) and plasma mass spectrometry (ICP-MS) spectrometers from Thermo Fisher Scientific (Waltham, MA, USA) were utilized for the analysis of metals. The LODs for Si, Al, Mn, Fe, Cu, and Zn with ICP-OES were 0.075, 0.03, 0.002, 0.006, and 0.001 mg·L − 1 , respectively, while lower detections of 0.1, 0.2, 0.2, 0.1, 0.3, and 0.2 µg L − 1 for Cr, Cu, Sb, Cd, Pb, and U with ICP-MS were recorded, respectively. The detail operating conditions were provided previously (Patel et al. 2024 ). Analysis of Water Quality Parameters and Source Apportionment Models The exchangeable sodium percentage (ESP), magnesium hazard (MH), sodium adsorption ratio (SAR), sodium hazard (SH), and water quality index (WQI) were calculated based on methodologies established in prior studies (Patel et al., 2017 ). Diagrams illustrating the results were generated using AquaChem software (Waterloo Hydrogeologic, Waterloo, ON, Canada). To identify the sources of chemical species, factor analysis and hierarchical cluster analysis models were employed (Cid et al., 2011 ). Multivariate statistical calculations were conducted using the STATISTICA 7.1 software (StatSoft GmbH, Hamburg, Germany). Quality Assurance (QA)/Quality Control (QC) In the preparation of reagent solutions and reagent blanks, ultra-pure deionized water was utilized. The standardization of instruments was achieved using appropriate reference materials. For the measurement of carbons, F − , ions, and other elements with the carbon analyzer, ion meter, ion chromatography, and ICP-OES/ICP-MS, uncertainties of 3.5%, 3.0%, 5–7%, and 5 − 12%, respectively, were recorded. To ensure accuracy, all instruments underwent cleaning procedures with recommended washing solutions after each analysis. Statistical Analysis The filtered water was used for the chemical analysis. The mean value of three replicate measurements was recorded. Software, SPSS (IBM, SPSS, version 20) and One way ANOVA were used for the calculations. And variation studies. Results and Discussion Physical Characteristics A summary of the physical-geographical attributes of the seven water reservoirs is presented in Table 1 . Notably, among the three ponds, the Dhanras pond exhibited the largest catchment area. During the post-monsoon period, the T, pH, DO, RP, EC, and TDS of water displayed were in the 20.8–22.2°C, 6.7–7.7, 5.8–8.1 mg·L − 1 , 176–210 mV, 172–504 µS cm − 1 , and 2865–8540 mg·L − 1 range, respectively. The corresponding mean values were 21.6 ± 0.4°C, 7.4 ± 0.2, 7.1 ± 0.7 mg·L − 1 , 193 ± 10 mV, 318 ± 99 µS cm − 1 , and 4122 ± 1475 mg·L − 1 , respectively. The water from the pit lake revealed significantly elevated values for EC and TDS, a phenomenon likely stemming from continuous leaching processes associated with coal sediments. In contrast, the mobile water from the river displayed heightened pH and DO values, indicative of frequent occurrences of water (Gupta et al. 2017 ). Table 1 Physical characteristics of surface reservoir. S. No. Location Type Area Depth T pH DO RP EC TDS, m 2 m o C - mg L − 1 mV µS cm L − 1 mg L − 1 1 Bhainskhatal Po 6540 5 22.0 6.7 7.8 182 220 3510 2 Kohadiya Ca - 3 21.6 7.4 6.7 202 331 3140 3 Parsabhantha Po 5232 4 21.4 7.7 5.8 210 493 2865 4 SNR Ca - 2 22.2 7.3 5.9 208 172 3016 5 CR Ri - 4 21.7 7.6 8.1 176 210 4016 6 SB PL 654 2 21.5 7.5 7.4 189 504 5540 7 Dhanras Po 21800 10 20.8 7.3 7.8 182 300 3767 SNR = Satnam Nagar, Risdi; CR = Chhatghat Rampu,r SB = Surakachhar, Bharotal, Po = Pond, Ca = Canal, Ri = River, PL = PL Chemical Characteristics During the post-monsoon period in December 2012, the concentration range and mean values of 25 chemical species (OC, CC, F − , Cl − , NO 3 − , SO 4 2− , SiO 4 4− , Li, Na, K, Rb, Mg, Ca, Ba, Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sb, Pb, and U) in the surface water are summarized in Table 2 . The total concentration of these species in the water varied from 2790 to 7969 mg·L − 1 , with a mean value of 3960 ± 1340 mg·L − 1 . A significant fraction was contributed by OC and CC, with concentrations ranging between 1110–3260 and 1010–4420 mg·L − 1 , respectively. Both carbons exhibited a moderate correlation (r = 0.72), suggesting the origin of CC through the bacterial oxidation of OC. The element abundance in the surface water was categorized into three groups: group I (HCO 3 − , Li, K, Rb, SiO 4 4− , Sb, Pb and U), group II (F − , Cl − , NO 3 − , SO 4 2− , Mg, Ca, and Ba), and group III (Na, Al, Fe, Mn, Zn, Cr, Co, Ni, Cu, OC, and CC), corresponding to where the maximum concentrations were detected, namely in pond, pit lake, and river water, respectively. The occurrence trend of the 24 elements in the water, based on mean values, followed a decreasing order: CC > OC > > NO 3 − > Ca > Na + > Cl − > SO 4 2− > K + > Mg > F − > Al > Fe > Mn > Zn > Ba > > Li > Rb > Cu > Pb > Ni > Cr > Co > Sb > U. Comparable contamination patterns of ions and metals in the surface water were observed at other locations (Hossain et al., 2021 ; Kumar and Singh, 2016 ; Li et al., 2021 ; Varada et al., 2014 ; Verma, 2018 ; Xinyue and Guangzhou, 2022 ). However, the OC content in the studied area was found to be several folds higher than the reported global content of 3.88 mg·L − 1 (Toming et al., 2020 ). Table 2 The concentration of chemical species in surface water. Species Unit Min Max Mean ±Std OC mg L − 1 1110 3260 1737 707 CC mg L − 1 1056 4420 1877 1216 F − mg L − 1 1.8 4.4 2.6 0.9 SO 4 2− mg L − 1 10 70 28 21 Cl − mg L − 1 7 37 17 11 NO 3 − mg L − 1 7 50 20 16 HCOO 3 − mg L − 1 81 113 94 10 SiO 4 4− mg L − 1 3.1 23 9.2 6.4 Na mg L − 1 6 47 27 19 K mg L − 1 4.9 14.6 9.7 4.5 Mg mg L − 1 5 15 7.3 3.7 Ca mg L − 1 16 42 22.3 9.7 Al mg L − 1 0.96 2.14 1.33 0.41 Fe mg L − 1 0.42 1.91 1.03 0.57 Mn mg L − 1 0.29 1.22 0.70 0.38 Ba mg L − 1 0.067 0.215 0.099 0.052 Zn mg L − 1 0.045 0.423 0.181 0.130 Li µg L − 1 5.8 23.6 15.9 6.9 Rb µg L − 1 3.6 25.5 13.9 9.2 Cr µg L − 1 3.0 8.0 3.9 1.9 Co µg L − 1 2.5 5.9 3.6 1.2 Ni µg L − 1 3.2 7.9 5.4 1.6 Cu µg L − 1 5.4 14 8.6 2.8 Sb µg L − 1 1.36 4.43 2.95 1.03 Pb µg L − 1 2.8 6.3 5.2 1.3 U µg L − 1 0.32 0.97 0.74 0.22 Cluster Analysis The hierarchical cluster software was utilized to group water samples, and the results are illustrated in Fig. 2 . The seven surface water samples were effectively categorized into three distinct groups. Group I comprised pit lake water from the Surakachhar Bharotal site. Group II encompassed water from ash dump areas, specifically Dhanras and Chhatghat Rampur. On the other hand, Group III consisted of pond and canal water from Parsabhatha, Satnam Nagar Risdi, Kohadiya, and Bhainskhatal sites. A comparison of median values emphasized Group I as an outlier. Seasonal and Temporal Variations The Parsabhatha pond was chosen for seasonal and temporal variation studies due to its extensive use of water for domestic purposes. The water quality parameters of the Parsabhatha pond are outlined in Table 3 . The pH value of the water reservoir decreased to 6.5 during the rainy season, likely attributed to the discharge of acidic runoff water. However, it slightly increased to 7.8 in the pre-monsoon period. The maximum values for most chemical species were observed during the rainy season, possibly due to the mixing of runoff water. Temporal variations in the water quality of the Parsabhatha pond over the period 2012–17 during the summer season is also documented in Table 3 . An increase in the concentration of chemical species such as OC, CC, F − , SO 4 2− , Cl − , NO 3 − , SiO 2 , Na, K, Mg, Ca, Al, Ba, Fe, Mn, Zn, Li, Rb, Cr, Co, Ni, Cu, Sb, Pb, and U was observed, with an increment rate of approximately 8.2, 1.0, 4.0, 1.8, 8.1, 32, 33, 17, 14, 11, 22, 20, 16, 12, 9, 4.3, 13, 15, 19, 14, 21, 23, 22, 27, and 5%, respectively. Among these, lithium (Li) was the most significant element, and its concentration in the present surface water was higher than those reported in other areas (Ewuzie et al., 2020 ; Giotakos et al., 2013 ; Kostik et al., 2014 ). Table 3 Seasonal variation of physicochemical parameters in Parsabhantha pond during 2012. S. No. Parameter Monsoon, Aug. 12 Post-monsoon, Feb. 12 Pre-monsoon, May 12 1 pH 6.5 7.7 7.8 2 Color YI CL CL 3 T, o C 25.2 21.4 35.6 4 DO* 7.1 6.2 5.6 5 RP, mV 170 210 215 6 EC, µS cm − 1 678 493 517 7 TDS* 5310 3140 4198 8 OC* 1342 1347 1157 9 CC* 2467 1143 2216 10 F −* 4.8 2.4 3.2 11 SO 4 2−* 67 16 21 12 Cl −* 38 14 23 13 NO 3 −* 116 13 37 14 SiO 2 * 14 3.1 5 15 Na* 63 47 51 16 K* 37 14 23 17 Mg* 30 6 9 18 Ca* 91 16 31 19 Al* 1.8 1.0 1.2 20 Ba* 1.11 0.08 0.26 21 Fe* 1.78 0.61 0.75 22 Mn* 0.62 0.35 0.41 23 Zn* 0.27 0.05 0.13 24 Li ≠ 45 24 32 25 Rb ≠ 31 19 22 26 Cr ≠ 13.1 3.1 7.2 27 Co ≠ 11.2 3.9 6.1 28 Ni ≠ 8.1 4.8 4.1 29 Cu ≠ 14.6 7.5 8.4 30 Sb ≠ 9.6 3.5 4.6 31 Pb ≠ 16 5.7 7.1 32 U ≠ 2.3 0.9 0.81 CL = Colorless, YI = Yellowish, * = mg L − 1 , ≠ = µg L − 1 Water Topology and Mechanism Schoeller Diagram (Fig. 3 ) and Piper Diagram (Fig. 4 ) were constructed based on the analyzed water data, revealing the dominance of ions such as HCO 3 , Na, and Ca. The inferred water type is characterized as HCO 3 -Na/-Ca. It seems that Ca-Mg-HCO3 hydro-chemical facies is the predominant (mixed type). This indicates that hydrochemistry of the study area is influenced by mineral dissolution, interaction of other ions present in the water as well as anthropogenic activities such as mining waste runoff. Among cations, most of the samples (sample 1, 2, 3,4 and 5) fall in no dominance zone, sample 6 showed Ca type of water. As per anion triangle, most of the surface water samples (sample 7) were bicarbonate type water followed by non-dominance zone. In a study, it was revealed that 94% and 92% of the samples were in no dominance type in cation and anion facies, respectively based on surface water analyzed from Singrauli coalfield, India. Only 6% and 5% of the samples fall in Ca type and bicarbonate type, respectively (Varshney et al. 2022 ). Future investigation should include large number of surface water samples to obtain the clear scenario of hydrochemical facies of the study area. To comprehend the principal mechanisms governing water chemistry in the region, the Gibbs Diagram was also generated (Fig. 5 ). The primary process identified in the studied region is evaporation, evident from the high TDS values. Additionally, the water chemistry within the aquifer framework suggests saltwater intrusion. Correlation and Sources Species F − , Cl − , SO 4 2− , SiO 4 4− , Mg, Ca, and Ba exhibited significant correlation (r = 0.74 − 0.96) at p-value > 0.05, suggesting their presence as corresponding salts. Additional correlations were observed, including HCO 3 − with Mg and Ca; NO 3 − with Al; Na with K; and Fe and Mn with Zn (r = 0.84 − 0.85, 0.72, 0.98, and 0.93–0.99, respectively). To further analyze the measured parameters in surface water reservoirs within the Korba basin, factor analysis (FA) was employed. Three factors with Eigenvalues > 1, contributing to a total variance of 89.01%, were identified (Table 4 ). The first factor (F1) exhibited strong positive loadings for TDS, Ba, Al, Ca 2+ , Mg 2+ , F − , Cl − , SO 4 2− , NO 3 − , and SiO 4 4− . The presence of Ca 2+ and Mg 2+ minerals suggested weathering and dissolution of carbonate minerals through surface runoff (Ateş et al., 2020 ; Brindha et al., 2020 ). Additionally, Cl − , SO 4 2− , and NO 3 − were attributed to agricultural runoff, irrigation water returns, and domestic sewage/wastewater discharges, indicating a mixed factor comprising 34.58% of the total variance from geogenic and anthropogenic sources. The second factor (F2) was associated with natural bedrock weathering and geogenic processes, displaying high loadings of OC, CC, DO, Cd, Co, Cr, Cu, Ni, and Zn. In turn, Cd, Co, Cr, Cu, Ni, Pb, and Zn were primarily linked to industrial sources (Ateş et al., 2020 ). Hence, this factor that accounted for 31.05% of the total variance indicates contributions from domestic and industrial wastewater discharges. The third factor (F3), representing 23.38% of the total variance, displayed strong positive loadings on Fe, Mn, and pH. Fe and Mn were attributed to geogenic processes involving weathering and redox reactions (Brindha et al., 2020 ). Table 4 FA analysis results of the trace metals and ions detected in the surface water reservoirs located in Korba basin during post monsoon season. Parameter Component F1 F2 F3 pH 0.876 DO, mg L − 1 0.251 0.743 RP, mV 0.434 EC, µS cm − 1 0.366 0.366 TDS, mg L − 1 0.951 F − 0.936 0.295 SO 4 2− 0.88 0.418 Cl − 0.932 NO 3 − 0.902 0.324 0.239 SiO 4 4− 0.901 OC 0.93 CC 0.909 0.373 Na 0.255 K 0.285 Mg 0.92 0.245 Ca 0.897 0.289 0.255 Fe 0.554 0.819 Mn 0.482 0.837 Al 0.948 Ba 0.954 Zn 0.747 0.642 Cr 0.94 0.285 Co 0.663 0.26 Ni 0.6 Cu 0.852 Sb 0.376 Pb 0.232 U 0.309 Variance % 34.58 31.05 23.38 Cumulative % 34.58 65.63 89.01 Water Quality Assessment Several chemical species, including OC, F − , Al, Mn, and Fe, were consistently found to exceed the permissible limits of 25, 1.5, 0.5, 0.05, and 0.300 mg·L − 1 of BIS and WHO, respectively, throughout all seasons (BIS 2012 ; WHO, 2012 ) similar to other coal field areas (Sahoo et al. 2022; Zhou et al. 2020 ). Remarkable increases in the concentrations of carbons, ions, and metals were observed during the monsoon period, attributed to the discharge of runoff water. In the rainy season, concentrations of NO 3 − and Pb exceeded the tolerance limits of 45 and 0.01 mg·L − 1 , respectively. The ESP, SH, MH, SAR, and WQI values were assessed for domestic and irrigation purposes. The observed values ranged from 16.4–61.4%, 20.2–72.9%, 31.6–40.0%, 0.32 to 2.56, and 226 to 372, with mean values of 32.2 ± 20.0, 40.3 ± 22.2, 35.7 ± 3.0, 1.40 ± 1.09, and 296 ± 26, respectively. The water quality was thus deemed unsuitable for drinking purposes, as indicated by a very poor WQI, exceeding 200 (WHO, 2012 ). However, the values of ESP (Na%), MH, and SAR were found to be less than 80%, 50%, and 6, respectively. The water quality fell within the C1 − S1 and C2 − S2 categories with low salinity (Fig. 6 ), making it suitable for direct use in irrigation (Bouwer, 1978 ; Paliwal, 1972 ; Richards, 1954 ; Wilcox, 1955 ). Conclusions In this study, we explored the seasonal variation of various physicochemical parameters including various anions, trace elements and carbons and their possible source along with water quality assessment in the surface water of the Korba basin, which was influenced by coal mining activities. The presence of physicochemical parameters such as carbons, F −, Fe, and Mn in the water of the studied area consistently exceeded permissible limits across all seasons. Notably, higher contamination during the rainy season was attributed to the mixing of runoff water and other wastes. The observed temporal influences suggest ongoing coal exploitation, indicating the potential for other elements to surpass limits in the future. Various anthropogenic activities, including alumina smelting, coal burning, ash deposition, runoff water, and municipal sewage mixing, were identified as primary contributors to water pollution in the region. The computed water suitability for drinking and irrigation purposes indicates that a significant portion of the watershed is unsuitable for consumption according to water quality index standards. However, the assessed irrigation water quality suggests its viability for such purposes. To address these concerns, conventional treatment methods must be implemented for the remediation of contaminants at their sources, emphasizing the need for effective control measures to safeguard water quality and to protect the ecosystem of the studied area. Declarations Acknowledgments We are thankful to Mr. S. K. Patel, Amity University, Raipur for preparing of the graph Funding Financial support for this work was provided by the University Grants Commission (UGC), New Delhi, through BSR grant no. F.18–1/2011(BSR)2016. Author Information Authors and Affiliations Amity University, Baloda-Bazar Road, Raipur-493225, CG, India Khageshwar Singh Patel and Piyush Kumar Pandey Guru Ghasidas University, Koni, Bilaspur, Chhattisgarh 495009, India Bharat Lal Sahu School of Studies in Environmental Science, Pt. Ravishankar Shukla University, Raipur, India Shobhana Ramteke Polish Geological Institute, Rakowiecka, Street-00-975, Warsaw, Poland Irena Wysocka Suleyman Demirel University, 32260 Isparta, Turkey Sema Yurdakul National Institute of Technology Raipur, G.E. Road, Raipur, India Dalchand Jhariya Universidad de Valladolid, Avenida de Madrid 44, 34004, Palencia, Spain Pablo Martín-Ramos University Drive, Callaghan, NSW 2308, Australia Mohammad Mahmudur Rahman Author Contributions Khageshwar Singh Patel : Conception and design of experiments; writing and editing assistance. Bharat Lal Sahu and Shobhana Ramteke : Sample collection and experiments. Piyush Kant Pandey : Data evaluation and compilation; writing and editing assistance. Irena Wysocka : ICP-MS data generation. Sema Yurdakul : source analysis. Dalchand Jhariya: graphical assistance. Pablo Martín-Ramos : writing and editing, Mohammad Mahmudur Rahman : Writing and editing. All authors have read and approved the publication. Corresponding Author Correspondence to [email protected] Ethics declarations Conflict of interest The authors declare no competing interests. Data Availability The data is available on request from corresponding author. References APHA (2017) American Public Health Association). Standard methods for the examination of water and wastewater, 21st Edn., APHA, AWWA and WEF, Washington DC, USA. Ateş A, Demirel H, Köklü R, Doğruparmak ŞC, Altundağ, H., Şengörür B (2020) Seasonal source apportionment of heavy metals and physicochemical parameters: a case study of sapanca Lake Watershe. J Spectrosc Article ID: 7601590. https://doi.org/10.1155/2020/7601590 . Ayub A, Ahmad SS (2020) Seasonal assessment of groundwater contamination in coal mining areas of Balochistan. Sustain 12: 6889. https://doi.org/10.3390/su12176889 . BIS (2012). Drinking water – specification, Bureau of Indian Standard, 2nd Edn., New Delhi. Bouwer H (1978) Groundwater Hydrology, McGraw-Hill, New York, 480. Brindha K, Paul R, Walter J, Tan ML, Singh MK (2020) Trace metals contamination in groundwater and implications on human health: comprehensive assessment using hydrogeochemical and geostatistical methods. Environ Geochem Health. 42(11): 3819–3839. doi: 10.1007/s10653-020-00637-9 . Chowdhury AH (2017) Environmental impact of coal based power plant of Rampal on the Sundarbans (world largest mangrove forest) and surrounding areas. MOJ Eco Environ Sci 2(3): 85–98. DOI: 10.15406/mojes.2017.02.00022 . Cid FD, Antón RI, Pardo R, Vega M, Caviedes-Vidal E (2011) Modelling spatial and temporal variations in the water quality of an artificial water reservoir in the semiarid midwest of Argentina, Anal ChimActa. 705(1–2): 243–252. doi: 10.1016/j.aca.2011.06.013 . Dewan Md, Shah A, Redwanur Rahman Md (2019) Environmental impact assessment of Phulbari Coal based thermal power plant of Bangladesh. J Soci Anthropo 3(2): 34–54. http://pubs.sciepub.com/jsa/3/2/1 . Dubey CS, Usham AL, Mishra BK, Singh PK, Singh AK (2022) Anthropogenic arsenic menace in contaminated water near thermal power plants and coal mining areas of India. Environ Geochem Health 44:1099–1127 (2022). https://doi.org/10.1007/s10653-021-01010-0 . Ewuzie U, Nnorom IC, Eze SO (2020) Lithium in drinking water sources in rural and urban communities in Southeastern Nigeria. Chemosphere 245: 125593. https://doi.org/10.1016/j.chemosphere.2019.125593 Giotakos O, Nisianakis P, Tsouvelas G, Giakalou VV (2013) Lithium in the public water supply and suicide mortality in Greece. Biol Trace Element Res156(1–3):376–9. https://doi.org/10.1007/s12011-013-9815-4 . Gupta N, Pandey P, Hussain J (2017) Effect of physicochemical and biological parameters on the quality of river water of Narmada, Madhya Pradesh, India. Water Sci 31(1): 11–23, https://doi.org/10.1016/j.wsj.2017.03.002 . Hossain MS, Ahmed MK, Liyana E, Hossain MS, Jolly YN, Kabir MJ, Akter S, Rahman MS (2021) A case study on metal contamination in water and sediment near a coal thermal power plant on the eastern coast of Bangladesh. Environ 8(10):108. https://doi.org/10.3390/environments8100108 . Kostik V, Bauer B, Kavrakovski Z (2014) Lithium content in potable water, surface water, ground water, and mineral water on the territory of Republic of Macedonia. Int J Med Public Health 3: 189–93. https://doi.org/10.4103/2230-8598.137700 . Kumar A, Singh PK (2016) Qualitative assessment of mine water of the Western Jharia coalfield area, Jharkhand, India. Curr World Environ 11(1). http://dx.doi.org/10.12944/CWE.11.1.37 . Kumar P, Singh AK (2022) Hydro geochemistry and quality assessment of surface and sub-surface water resources in Raniganj coalfield area, Damodar Valley, India. Int J Environ Anal Chem 102:19, 8346–8369, DOI: 10.1080/03067319.2020.1849653 . Li X, Cai J, Chen D, Feng Q (2021) Characteristics of water contamination in abandoned coal mines: a case study on Yudong River area, Kaili, Guizhou Province, China. Int J Coal Sci Technol 8: 1491–1503. https://doi.org/10.1007/s40789-021-00466-w . Mishra UC (2004) Environmental impact of coal industry and thermal power plants in India. J Environ Radioact 72(1–2):35–40. doi: 10.1016/S0265-931X(03)00183-8 . Mohanty AK, Lingaswamy M, Gurunadha Rao VVS, Sankaran S (2018) Impact of acid mine drainage and hydrogeochemical studies in a part of Rajrappa coal mining area of Ramgarh district, Jharkhand state of India, Groundw Sustain Develop 7164–7175, https://doi.org/10.1016/j.gsd.2018.05.005 . MSME (2023) Brief Industrial Profile of Korba District, Government of India Ministry of Ministry of Micro, Small & Medium Enterprises (MSME), msmediraipur.gov.in Nihalani SA, Mishra YD, Meeruty AR (2021) Water Resource Management for Coal-Based Thermal Power Plant. In: Jha R, Singh VP, Singh V, Roy L, Thendiyath R (eds) Water Resources Management and Reservoir Operation. Water Sci Technol Lib 107. Springer, Cham. https://doi.org/10.1007/978-3-030-79400-2_3 . Paliwal KV (1972) Irrigation with saline water. In: Monogram No. 2 (New Series). IARI, Water Technology Centre, New Delhi, 198. Patel K S, Sharma R, Dahariya N, Patel RK, Blazhev B, Laurent M (2016). Black carbon and heavy metal contamination of soil. Polish J Environ Stud 25. 10.15244/pjoes/61062 . Patel KS, Sahu BL, Dahariya NS, Bhatia A, Patel R.K., Laurent M, Sracek O, Bhattacharya P (2017) Groundwater arsenic and fluoride in Rajnandgaon District, Chhattisgarh, northeastern India. Appl Water Sci 7: 1817–1826. https://doi.org/10.1007/s13201-015-0355-2 . Patel KS, Pandey PK, Sahu BL, Patel RK, Wysocka I, Yurdakul S, Martín-Ramos P, Bhattacharya P (2024) Groundwater contamination and sources of potentially toxic elements in Korba Basin, India. In press. https://doi.org/10.1002/tqem.22177 Rana, A., James, A. & Nath, S. 2019. Effect of coal mining on the surface water quality in Northern Coalfield Singrauli, India. Res Review Int J Multidiscipl 4(6): ISSN: 2455–3085 Richards, L.A. 1954. Diagnosis improvement saline alkali soils.US Department of Agriculture Handbook. No. 60. Sahoo BP, Sahu HB (2022) Assessment of metal pollution in surface water using pollution indices and multivariate statistics: a case study of Talcher coalfield area, India. Appl Water Sci 12: 223. https://doi.org/10.1007/s13201-022-01743-y Sahu P, Pradhan M, Jade RK, Lokhande RD (2016) Study the variations of sinkhole depth with respect to working height in underground coal mines. Recent Adv Rock Engin 10.2991/rare-16.2016.88 . Sharifi SA, Zaeimdar M, Jozi SA, Hejazi R (2023) Effects of Soil, Water and Air Pollution with Heavy Metal Ions Around Lead and Zinc Mining and Processing Factories. Water Air Soil Pollut 234: 760. https://doi.org/10.1007/s11270-023-06758-y . Sharma R, Patel KS, Lata L, Milosh H (2017) Contamination of Pond Water and Sediment in Coal Burning Area. J Environ Protect 8: 358–379. https://doi.org/10.4236/jep.2017.83027 Sharma R, Yadav A, Ramteke S, Patel KS, Lata L, Huber M, Corns W, Martin-Ramos P(2019). Heavy metal pollution in surface soil of Korba basin, India. J Hazard Toxic Radioact Waste 23: 05019004. 10.1061/(ASCE)HZ.2153-5515.0000460 . Toming K, Kotta J, Uuemaa E, Sebastian S, Tiit K, Lars JT (2020) Predicting lake dissolved organic carbon at a global scale. Sci Rep 10:8471. https://doi.org/10.1038/s41598-020-65010-3 . van Vliet MTH, Thorslund J, Strokal M, Hofstra N, Flörke M, Macedo HE, Nkwasa A, Tang T, Kaushal SS, Kumar R, van Griensven A, Bouwman L, Mosley LM (2023) Global river water quality under climate change and hydroclimatic extremes. Nat Rev Earth Environ 4, 687–702. https://doi.org/10.1038/s43017-023-00472-3 . Varada VK, Gulwade DP, Deo SS (2014) Quality assessment of surface and ground water around thermal power plants at Warora district, Chandrapur (MS). J Chem Pharmaceut Res. 6(7):1856–1860. Varshney R, Modi P, Sonkar AK, Singh P, Jamal A (2022) Assessment of surface water quality in and around Singrauli coalfield, India and its remediation: An integrated approach of GIS, water quality index, multivariate statistics and phytoremediation. Arab J Geosci 15(18):1530. DOI: 10.1007/s12517-022-10806-y . Verma S, Anju (2018) Assessment of water quality around coal-fired thermal power plant, Bathinda (Punjab), India. J Appl Nat Sci 10(3): 915–924. 10.31018/jans.v10i3.1809 . Zhou M, Li X, Zhang M, Liu B, Zhang Y, Gao Y, Ullah H, Peng L, He A, Yu H (2020) Water quality in a worldwide coal mining city: A scenario in water chemistry and health risks exploration. J Geochem Explor 213:,106513. https://doi.org/10.1016/j. gexplo.2020.106513 Xinyue D, Guangzhou C (2022) Characteristics of water pollution and evaluation of water quality in subsidence water bodies in Huainan coal mining areas, China. J Chem Article ID 2857700, 12 p. https://doi.org/10.1155/2022/2857700 . Xue Y, Song J, Zhang Y, Kong F, Wen M, Zhang G (2016) Nitrate pollution and preliminary source identification of surface water in a Semi-Arid River Basin, using isotopic and hydrochemical approaches. Water 8(8): 328. https://doi.org/10.3390/w8080328 Wilcox LV (1955) Classification and use of irrigation waters. USDA. Circ 969, Washington, DC. WHO (2012) Guideline for drinking water quality, 4th ed., Switzerland? Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4478468","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307115131,"identity":"3a4d3a0c-09bf-4db9-8208-8b9f9c137ce3","order_by":0,"name":"Khageshwar Singh Patel","email":"data:image/png;base64,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","orcid":"","institution":"Amity University","correspondingAuthor":true,"prefix":"","firstName":"Khageshwar","middleName":"Singh","lastName":"Patel","suffix":""},{"id":307115133,"identity":"d848e052-a392-4f22-9b8b-e0186761bd9a","order_by":1,"name":"Piyush Kumar Pandey","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Piyush","middleName":"Kumar","lastName":"Pandey","suffix":""},{"id":307115135,"identity":"05af7c4e-5283-495e-b6cf-67bc5613b2f9","order_by":2,"name":"Bharat Lal Sahu","email":"","orcid":"","institution":"Guru Ghasidas University","correspondingAuthor":false,"prefix":"","firstName":"Bharat","middleName":"Lal","lastName":"Sahu","suffix":""},{"id":307115136,"identity":"e2cf5142-5607-4f9e-b4c9-07da372348ca","order_by":3,"name":"Shobhana Ramteke","email":"","orcid":"","institution":"Pt. Ravishankar Shukla University","correspondingAuthor":false,"prefix":"","firstName":"Shobhana","middleName":"","lastName":"Ramteke","suffix":""},{"id":307115137,"identity":"57647336-f8ed-48ed-87dd-99470949d4f1","order_by":4,"name":"Irena Wysocka","email":"","orcid":"","institution":"Polish Geological Institute","correspondingAuthor":false,"prefix":"","firstName":"Irena","middleName":"","lastName":"Wysocka","suffix":""},{"id":307115138,"identity":"a5fc79c1-e710-44d6-98cb-1a923b224829","order_by":5,"name":"Sema Yurdakul","email":"","orcid":"","institution":"Suleyman Demirel University","correspondingAuthor":false,"prefix":"","firstName":"Sema","middleName":"","lastName":"Yurdakul","suffix":""},{"id":307115144,"identity":"119914a8-8e4d-4e33-9867-a1d2d5436e00","order_by":6,"name":"Dalchand Jhariya","email":"","orcid":"","institution":"National Institute of Technology Raipur","correspondingAuthor":false,"prefix":"","firstName":"Dalchand","middleName":"","lastName":"Jhariya","suffix":""},{"id":307115145,"identity":"b44ac126-ac90-4183-8f2f-479e666acc64","order_by":7,"name":"Pablo Martín-Ramos","email":"","orcid":"","institution":"ETSIIAA, Universidad de Valladolid","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Martín-Ramos","suffix":""},{"id":307115146,"identity":"f59870e8-9d95-4948-970e-89ab144d560d","order_by":8,"name":"Mohammad Mahmudur Rahman","email":"","orcid":"","institution":"Global Centre for Environmental Remediation, College of Engineering, Science and Environment, Adustralia","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Mahmudur","lastName":"Rahman","suffix":""}],"badges":[],"createdAt":"2024-05-26 03:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4478468/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4478468/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57931681,"identity":"fe78bb8c-9c36-4caa-9ec7-c08729623cd4","added_by":"auto","created_at":"2024-06-07 15:50:22","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":707306,"visible":true,"origin":"","legend":"\u003cp\u003eSampling locations of surface water in the Korba Basin, CG, India.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4478468/v1/a8077e4ba535e95b6ce31ca2.jpeg"},{"id":57931288,"identity":"3d25198d-f48a-4bff-9085-01efccaecc6b","added_by":"auto","created_at":"2024-06-07 15:42:22","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":328512,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram of cluster analysis for surface water using Ward’s method and Euclidean distances.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4478468/v1/052a05a9e81d91dabe4ffe86.jpeg"},{"id":57931679,"identity":"c54ae07a-1e8d-4787-bcdf-6109d385c4e9","added_by":"auto","created_at":"2024-06-07 15:50:22","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":319767,"visible":true,"origin":"","legend":"\u003cp\u003eSchoeller Diagram for Post Monsoon Korba.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4478468/v1/0d56ff7f2942f941d35ebdfc.jpeg"},{"id":57931680,"identity":"35f10b0e-bcfc-43c5-b60f-17510c4584aa","added_by":"auto","created_at":"2024-06-07 15:50:22","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":358052,"visible":true,"origin":"","legend":"\u003cp\u003ePiper Diagram for Post Monsoon Korba Basin.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4478468/v1/1884e3e834eb54dd5cf66f4f.jpeg"},{"id":57931291,"identity":"446e19bc-c11f-464d-b91f-b1988f562192","added_by":"auto","created_at":"2024-06-07 15:42:22","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":323932,"visible":true,"origin":"","legend":"\u003cp\u003eGibbs Diagram showing the mechanism behind the water chemistry.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4478468/v1/7e3aad5e148977ed262b22b1.jpeg"},{"id":57931293,"identity":"c1e71649-c2e0-4bd7-97ac-ba5d27495f7f","added_by":"auto","created_at":"2024-06-07 15:42:22","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":328891,"visible":true,"origin":"","legend":"\u003cp\u003eUSSL diagram showing suitability of water for Irrigation purpose.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4478468/v1/a7be9efad46c158fc6d1972c.jpeg"},{"id":58821530,"identity":"fa138057-17b9-48a4-8720-857a51283f1f","added_by":"auto","created_at":"2024-06-21 14:47:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3397615,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4478468/v1/fdf89d10-2d1f-4f5c-abae-a622a50dce1e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contamination and Sources of Surface Water in Korba Coal Basin, Chhattisgarh, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe major sources of pollution are considered to be mining activities, in particular coal mining which cause significant environment damage. The geology, hydrology, geochemistry, climate change, mining processing history control the transport of pollutants in and around mining sites (Sharifi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; van Vliet et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Coal, a natural fossil fuel abundantly available in India, possesses inherent drawbacks, including low caloric value and a high ash content exceeding 60%, which has adverse environmental and ecological implications (Mishra, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Reports from various coal mines and thermal power plant areas in different countries highlight degraded water quality and elevated concentrations of total dissolved solids (TDS) and chemical species such as fluoride (F\u003csup\u003e\u0026minus;\u003c/sup\u003e), nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), sulfate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e), iron (Fe), lead (Pb), mercury (Hg), uranium (U), and others (Ayub and Ahmad, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chowdhury, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dewan et al., 2019; Dubey et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kumar and Singh, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohanty et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nihalani et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rana et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Verma, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xue et al \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Korba basin in Chhattisgarh, India, characterized by coal mining activities, has witnessed pollution of soil, sediments, and water with F\u003csup\u003e\u0026minus;\u003c/sup\u003e and heavy metals (Patel 2016, 2024; Sharma et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As in the Korba basin has long history of coal mining activities, discharge of untreated wastewater, mine drainage and water runoff are the primary sources of pollution into nearby surface water bodies (Patel 2016, 2024; Sharma et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, organic and inorganic carbon in the surface water in coal mine areas is crucial due to thermal plant and coal mining effluents. Consequently, this study aims comprehensively assess water quality, hydrochemistry, delineation of contaminant concentration variations (including carbons, anions, cations and heavy metals) and identify the origins of these contaminants in the surface water bodies of the Korba basin, Chhattisgarh, India. This information may serve to develop viable strategies for ensuring a contaminant-safe water supply for domestic and other purposes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThe Korba basin, situated in Chhattisgarh, India, within the coordinates 22\u0026deg;20'\u0026ndash;25'N and 84\u0026deg;40'\u0026ndash;82\u0026deg;45'E, at an average elevation of 252 meters, hosts the largest coal deposit in the country. The climate in this region is characterized by hot temperate conditions, with the maximum rainfall occurring in the summer months. The yearly precipitation in the Korba basin averages around 1000 mm, mainly influenced by the southwest monsoon. The geological formations consist of Proterozoic and Gondwana types, featuring several sinkholes (Sahu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The major drainage system in the studied area comprises the Hasdeo River and its tributaries, such as Ahiran, Bating Nala, Chuia Nala, Dhengur Nala, Gagechorai, Jharri Nala, and Tan. The region is marked by the presence of thirteen open and underground coal mines, eight thermal power plants, one aluminum plant, twenty rice mills, and a cement plant. These industrial entities discharge untreated wastewater into the environment, significantly impacting the water quality of the basin (MSME, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eWater Sample Collection\u003c/h2\u003e \u003cp\u003eSurface water samples were obtained from seven locations encompassing rivers, canals, natural ponds, and pit lakes during three distinct seasons over the period of May (pre-monsoon), August (monsoon), and December (post-monsoon) from 2012 to 2017 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Collected samples were carefully filled into cleaned and rinsed polyethylene containers (rinsed thrice with the sample) and promptly transported to the laboratory for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eMeasurement of Physical Parameters\u003c/h2\u003e \u003cp\u003eOn-site measurements using sensors manufactured by Hanna Instruments (Mumbai, Maharashtra, India) included various physical parameters such as dissolved oxygen (DO), electrical conductivity (EC), pH, reduction potential (RP), and temperature (T).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eChemical Analysis\u003c/h2\u003e \u003cp\u003ePrior to chemical analysis, water samples underwent filtration through 0.45 \u0026micro;m glass fiber filters. The divided samples served two purposes: the first portion underwent acidification with a few drops of concentrated HNO\u003csub\u003e3\u003c/sub\u003e acid sourced from Merck, India for metal analysis, whereas the second portion was allocated for the examination of carbons and anions. The TDS value was determined using the evaporation method on the filtered water sample until a constant weight was achieved (APHA, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Organic carbon (OC) and inorganic carbon (IC) were analyzed using a CHNSO-IRMS analyzer (SV Instruments Analytica Pvt. Ltd, Gurgaon, Haryana, India). The IC was removed by adjusting the pH of the water sample to 2\u0026ndash;3, followed by the removal of formed CO\u003csub\u003e2\u003c/sub\u003e through gas bubbling. The OC value was obtained by subtracting the IC value from the total carbon (TC) value. The method's LOD and LOQ stood at 1.2 and 4 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e as carbonate, respectively.\u003c/p\u003e \u003cp\u003eFor monitoring F\u003csup\u003e\u0026minus;\u003c/sup\u003e levels, a Metrohm-720 ion meter (Metrohm, Herisau, Switzerland) was employed in the presence of a buffer (TISAB-III) at a pH of 5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, using Teflon containers. The LOD and LOQ for this method were 0.02 and 0.10 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, with a calibration curve prepared using standard NaF solution.\u003c/p\u003e \u003cp\u003eA DX120 ion chromatograph (Dionex, Sunnyvale, CA, USA) equipped with analytical columns (IonPac AS22 and IonPac CS16) was used for the analysis of ions. Eluents (4.5 mmol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;1.0 mmol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NaHCO\u003csub\u003e3\u003c/sub\u003e in the case of anions and 42 mmol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e methane sulfonic acid (MSA) in the case of cations) were employed for separation. The method's LOD for Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, Na, K, Mg, and Ca were 0.01, 0.02, 0.02, 8, 1.5, 0.1, and 0.8 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003eInductively coupled plasma optical emission spectroscopy (ICP-OES) and plasma mass spectrometry (ICP-MS) spectrometers from Thermo Fisher Scientific (Waltham, MA, USA) were utilized for the analysis of metals. The LODs for Si, Al, Mn, Fe, Cu, and Zn with ICP-OES were 0.075, 0.03, 0.002, 0.006, and 0.001 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, while lower detections of 0.1, 0.2, 0.2, 0.1, 0.3, and 0.2 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for Cr, Cu, Sb, Cd, Pb, and U with ICP-MS were recorded, respectively. The detail operating conditions were provided previously (Patel et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalysis of Water Quality Parameters and Source Apportionment Models\u003c/p\u003e \u003cp\u003eThe exchangeable sodium percentage (ESP), magnesium hazard (MH), sodium adsorption ratio (SAR), sodium hazard (SH), and water quality index (WQI) were calculated based on methodologies established in prior studies (Patel et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Diagrams illustrating the results were generated using AquaChem software (Waterloo Hydrogeologic, Waterloo, ON, Canada). To identify the sources of chemical species, factor analysis and hierarchical cluster analysis models were employed (Cid et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Multivariate statistical calculations were conducted using the STATISTICA 7.1 software (StatSoft GmbH, Hamburg, Germany).\u003c/p\u003e \u003cp\u003eQuality Assurance (QA)/Quality Control (QC)\u003c/p\u003e \u003cp\u003eIn the preparation of reagent solutions and reagent blanks, ultra-pure deionized water was utilized. The standardization of instruments was achieved using appropriate reference materials. For the measurement of carbons, F\u003csup\u003e\u0026minus;\u003c/sup\u003e, ions, and other elements with the carbon analyzer, ion meter, ion chromatography, and ICP-OES/ICP-MS, uncertainties of 3.5%, 3.0%, 5\u0026ndash;7%, and 5\u0026thinsp;\u0026minus;\u0026thinsp;12%, respectively, were recorded. To ensure accuracy, all instruments underwent cleaning procedures with recommended washing solutions after each analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe filtered water was used for the chemical analysis. The mean value of three replicate measurements was recorded. Software, SPSS (IBM, SPSS, version 20) and One way ANOVA were used for the calculations. And variation studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":" \u003cp\u003ePhysical Characteristics\u003c/p\u003e \u003cp\u003eA summary of the physical-geographical attributes of the seven water reservoirs is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Notably, among the three ponds, the \u003cem\u003eDhanras\u003c/em\u003e pond exhibited the largest catchment area. During the post-monsoon period, the T, pH, DO, RP, EC, and TDS of water displayed were in the 20.8\u0026ndash;22.2\u0026deg;C, 6.7\u0026ndash;7.7, 5.8\u0026ndash;8.1 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 176\u0026ndash;210 mV, 172\u0026ndash;504 \u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 2865\u0026ndash;8540 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e range, respectively. The corresponding mean values were 21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u0026deg;C, 7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, 7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 193\u0026thinsp;\u0026plusmn;\u0026thinsp;10 mV, 318\u0026thinsp;\u0026plusmn;\u0026thinsp;99 \u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 4122\u0026thinsp;\u0026plusmn;\u0026thinsp;1475 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. The water from the pit lake revealed significantly elevated values for EC and TDS, a phenomenon likely stemming from continuous leaching processes associated with coal sediments. In contrast, the mobile water from the river displayed heightened pH and DO values, indicative of frequent occurrences of water (Gupta et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysical characteristics of surface reservoir.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDepth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTDS,\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003em\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003emV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026micro;S cm L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBhainskhatal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKohadiya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDhanras\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eSNR\u0026thinsp;=\u0026thinsp;Satnam Nagar, Risdi; CR\u0026thinsp;=\u0026thinsp;Chhatghat Rampu,r SB\u0026thinsp;=\u0026thinsp;Surakachhar, Bharotal, Po\u0026thinsp;=\u0026thinsp;Pond, Ca\u0026thinsp;=\u0026thinsp;Canal, Ri = River, PL\u0026thinsp;=\u0026thinsp;PL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eChemical Characteristics\u003c/h2\u003e \u003cp\u003eDuring the post-monsoon period in December 2012, the concentration range and mean values of 25 chemical species (OC, CC, F\u003csup\u003e\u0026minus;\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, SiO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e4\u0026minus;\u003c/sup\u003e, Li, Na, K, Rb, Mg, Ca, Ba, Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sb, Pb, and U) in the surface water are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The total concentration of these species in the water varied from 2790 to 7969 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a mean value of 3960\u0026thinsp;\u0026plusmn;\u0026thinsp;1340 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. A significant fraction was contributed by OC and CC, with concentrations ranging between 1110\u0026ndash;3260 and 1010\u0026ndash;4420 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Both carbons exhibited a moderate correlation (r\u0026thinsp;=\u0026thinsp;0.72), suggesting the origin of CC through the bacterial oxidation of OC.\u003c/p\u003e \u003cp\u003eThe element abundance in the surface water was categorized into three groups: group I (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, Li, K, Rb, SiO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e4\u0026minus;\u003c/sup\u003e, Sb, Pb and U), group II (F\u003csup\u003e\u0026minus;\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, Mg, Ca, and Ba), and group III (Na, Al, Fe, Mn, Zn, Cr, Co, Ni, Cu, OC, and CC), corresponding to where the maximum concentrations were detected, namely in pond, pit lake, and river water, respectively. The occurrence trend of the 24 elements in the water, based on mean values, followed a decreasing order: CC\u0026thinsp;\u0026gt;\u0026thinsp;OC\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e \u0026gt; Ca\u0026thinsp;\u0026gt;\u0026thinsp;Na\u003csup\u003e+\u003c/sup\u003e \u0026gt; Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e \u0026gt; SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e \u0026gt; K\u003csup\u003e+\u003c/sup\u003e \u0026gt; Mg\u0026thinsp;\u0026gt;\u0026thinsp;F\u003csup\u003e\u0026minus;\u003c/sup\u003e \u0026gt; Al\u0026thinsp;\u0026gt;\u0026thinsp;Fe\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;Ba\u0026thinsp;\u0026gt;\u0026thinsp;\u0026gt;\u0026thinsp;Li\u0026thinsp;\u0026gt;\u0026thinsp;Rb\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Co\u0026thinsp;\u0026gt;\u0026thinsp;Sb\u0026thinsp;\u0026gt;\u0026thinsp;U. Comparable contamination patterns of ions and metals in the surface water were observed at other locations (Hossain et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kumar and Singh, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Varada et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Verma, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xinyue and Guangzhou, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the OC content in the studied area was found to be several folds higher than the reported global content of 3.88 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Toming et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe concentration of chemical species in surface water.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026plusmn;Std\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCOO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSiO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e4\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eCluster Analysis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe hierarchical cluster software was utilized to group water samples, and the results are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The seven surface water samples were effectively categorized into three distinct groups. Group I comprised pit lake water from the Surakachhar Bharotal site. Group II encompassed water from ash dump areas, specifically Dhanras and Chhatghat Rampur. On the other hand, Group III consisted of pond and canal water from Parsabhatha, Satnam Nagar Risdi, Kohadiya, and Bhainskhatal sites. A comparison of median values emphasized Group I as an outlier.\u003c/p\u003e \u003cp\u003eSeasonal and Temporal Variations\u003c/p\u003e \u003cp\u003eThe Parsabhatha pond was chosen for seasonal and temporal variation studies due to its extensive use of water for domestic purposes. The water quality parameters of the Parsabhatha pond are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The pH value of the water reservoir decreased to 6.5 during the rainy season, likely attributed to the discharge of acidic runoff water. However, it slightly increased to 7.8 in the pre-monsoon period. The maximum values for most chemical species were observed during the rainy season, possibly due to the mixing of runoff water.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTemporal variations in the water quality of the Parsabhatha pond over the period 2012\u0026ndash;17 during the summer season is also documented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. An increase in the concentration of chemical species such as OC, CC, F\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, SiO\u003csub\u003e2\u003c/sub\u003e, Na, K, Mg, Ca, Al, Ba, Fe, Mn, Zn, Li, Rb, Cr, Co, Ni, Cu, Sb, Pb, and U was observed, with an increment rate of approximately 8.2, 1.0, 4.0, 1.8, 8.1, 32, 33, 17, 14, 11, 22, 20, 16, 12, 9, 4.3, 13, 15, 19, 14, 21, 23, 22, 27, and 5%, respectively. Among these, lithium (Li) was the most significant element, and its concentration in the present surface water was higher than those reported in other areas (Ewuzie et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Giotakos et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kostik et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeasonal variation of physicochemical parameters in Parsabhantha pond during 2012.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonsoon, Aug. 12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost-monsoon, Feb. 12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePre-monsoon, May 12\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT, \u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDO*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRP, mV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC, \u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOC*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csup\u003e\u0026minus;*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSiO\u003csub\u003e2\u003c/sub\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNa*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMg*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBa*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFe*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMn*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZn*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLi\u003csup\u003e\u0026ne;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRb\u003csup\u003e\u0026ne;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCr\u003csup\u003e\u0026ne;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCo\u003csup\u003e\u0026ne;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNi\u003csup\u003e\u0026ne;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCu\u003csup\u003e\u0026ne;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSb\u003csup\u003e\u0026ne;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePb\u003csup\u003e\u0026ne;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU\u003csup\u003e\u0026ne;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCL\u0026thinsp;=\u0026thinsp;Colorless, YI\u0026thinsp;=\u0026thinsp;Yellowish, * = mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, \u0026ne; = \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWater Topology and Mechanism\u003c/h2\u003e \u003cp\u003eSchoeller Diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and Piper Diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) were constructed based on the analyzed water data, revealing the dominance of ions such as HCO\u003csub\u003e3\u003c/sub\u003e, Na, and Ca. The inferred water type is characterized as HCO\u003csub\u003e3\u003c/sub\u003e-Na/-Ca. It seems that Ca-Mg-HCO3 hydro-chemical facies is the predominant (mixed type). This indicates that hydrochemistry of the study area is influenced\u003c/p\u003e \u003cp\u003eby mineral dissolution, interaction of other ions present in the water as well as anthropogenic activities such as mining waste runoff. Among cations, most of the samples (sample 1, 2, 3,4 and 5) fall in no dominance zone, sample 6 showed Ca type of water. As per anion triangle, most of the surface water samples (sample 7) were bicarbonate type water followed by non-dominance zone. In a study, it was revealed that 94% and 92% of the samples were in no dominance type in cation and anion facies, respectively based on surface water analyzed from Singrauli coalfield, India. Only 6% and 5% of the samples fall in Ca type and bicarbonate type, respectively (Varshney et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFuture investigation should include large number of surface water samples to obtain the clear scenario of hydrochemical facies of the study area. To comprehend the principal mechanisms governing water chemistry in the region, the Gibbs Diagram was also generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The primary process identified in the studied region is evaporation, evident from the high TDS values. Additionally, the water chemistry within the aquifer framework suggests saltwater intrusion.\u003c/p\u003e \u003cp\u003eCorrelation and Sources\u003c/p\u003e \u003cp\u003eSpecies F\u003csup\u003e\u0026minus;\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, SiO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e4\u0026minus;\u003c/sup\u003e, Mg, Ca, and Ba exhibited significant correlation (r\u0026thinsp;=\u0026thinsp;0.74\u0026thinsp;\u0026minus;\u0026thinsp;0.96) at p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05, suggesting their presence as corresponding salts. Additional correlations were observed, including HCO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;\u0026minus;\u0026thinsp;with Mg and Ca; NO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;\u0026minus;\u0026thinsp;with Al; Na with K; and Fe and Mn with Zn (r\u0026thinsp;=\u0026thinsp;0.84\u0026thinsp;\u0026minus;\u0026thinsp;0.85, 0.72, 0.98, and 0.93\u0026ndash;0.99, respectively).\u003c/p\u003e \u003cp\u003eTo further analyze the measured parameters in surface water reservoirs within the Korba basin, factor analysis (FA) was employed. Three factors with Eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1, contributing to a total variance of 89.01%, were identified (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The first factor (F1) exhibited strong positive loadings for TDS, Ba, Al, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, F\u003csup\u003e\u0026minus;\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, and SiO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e4\u0026minus;\u003c/sup\u003e. The presence of Ca\u003csup\u003e2+\u003c/sup\u003e and\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e minerals suggested weathering and dissolution of carbonate minerals through surface runoff (Ateş et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Brindha et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e were attributed to agricultural runoff, irrigation water returns, and domestic sewage/wastewater discharges, indicating a mixed factor comprising 34.58% of the total variance from geogenic and anthropogenic sources.\u003c/p\u003e \u003cp\u003eThe second factor (F2) was associated with natural bedrock weathering and geogenic processes, displaying high loadings of OC, CC, DO, Cd, Co, Cr, Cu, Ni, and Zn. In turn, Cd, Co, Cr, Cu, Ni, Pb, and Zn were primarily linked to industrial sources (Ateş et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, this factor that accounted for 31.05% of the total variance indicates contributions from domestic and industrial wastewater discharges.\u003c/p\u003e \u003cp\u003eThe third factor (F3), representing 23.38% of the total variance, displayed strong positive loadings on Fe, Mn, and pH. Fe and Mn were attributed to geogenic processes involving weathering and redox reactions (Brindha et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFA analysis results of the trace metals and ions detected in the surface water reservoirs located in Korba basin during post monsoon season.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO, mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRP, mV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC, \u0026micro;S cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS, mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSiO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e4\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eWater Quality Assessment\u003c/h2\u003e \u003cp\u003eSeveral chemical species, including OC, F\u003csup\u003e\u0026minus;\u003c/sup\u003e, Al, Mn, and Fe, were consistently found to exceed the permissible limits of 25, 1.5, 0.5, 0.05, and 0.300 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eof BIS and WHO, respectively, throughout all seasons (BIS \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; WHO, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) similar to other coal field areas (Sahoo et al. 2022; Zhou et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Remarkable increases in the concentrations of carbons, ions, and metals were observed during the monsoon period, attributed to the discharge of runoff water. In the rainy season, concentrations of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and Pb exceeded the tolerance limits of 45 and 0.01 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003eThe ESP, SH, MH, SAR, and WQI values were assessed for domestic and irrigation purposes. The observed values ranged from 16.4\u0026ndash;61.4%, 20.2\u0026ndash;72.9%, 31.6\u0026ndash;40.0%, 0.32 to 2.56, and 226 to 372, with mean values of 32.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.0, 40.3\u0026thinsp;\u0026plusmn;\u0026thinsp;22.2, 35.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0, 1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09, and 296\u0026thinsp;\u0026plusmn;\u0026thinsp;26, respectively. The water quality was thus deemed unsuitable for drinking purposes, as indicated by a very poor WQI, exceeding 200 (WHO, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the values of ESP (Na%), MH, and SAR were found to be less than 80%, 50%, and 6, respectively. The water quality fell within the C1\u0026thinsp;\u0026minus;\u0026thinsp;S1 and C2\u0026thinsp;\u0026minus;\u0026thinsp;S2 categories with low salinity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), making it suitable for direct use in irrigation (Bouwer, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Paliwal, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Richards, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1954\u003c/span\u003e; Wilcox, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1955\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we explored the seasonal variation of various physicochemical parameters including various anions, trace elements and carbons and their possible source along with water quality assessment in the surface water of the Korba basin, which was influenced by coal mining activities. The presence of physicochemical parameters such as carbons, F\u003csup\u003e\u0026minus;,\u003c/sup\u003e Fe, and Mn in the water of the studied area consistently exceeded permissible limits across all seasons. Notably, higher contamination during the rainy season was attributed to the mixing of runoff water and other wastes. The observed temporal influences suggest ongoing coal exploitation, indicating the potential for other elements to surpass limits in the future. Various anthropogenic activities, including alumina smelting, coal burning, ash deposition, runoff water, and municipal sewage mixing, were identified as primary contributors to water pollution in the region. The computed water suitability for drinking and irrigation purposes indicates that a significant portion of the watershed is unsuitable for consumption according to water quality index standards. However, the assessed irrigation water quality suggests its viability for such purposes. To address these concerns, conventional treatment methods must be implemented for the remediation of contaminants at their sources, emphasizing the need for effective control measures to safeguard water quality and to protect the ecosystem of the studied area.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to Mr. S. K. Patel, Amity University, Raipur for preparing of the graph\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial support for this work was provided by the University Grants Commission (UGC), New Delhi, through BSR grant no. F.18\u0026ndash;1/2011(BSR)2016.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmity University, Baloda-Bazar Road, Raipur-493225, CG, India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKhageshwar Singh Patel\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand Piyush Kumar Pandey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuru Ghasidas University, Koni, Bilaspur, Chhattisgarh 495009, India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBharat Lal Sahu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSchool of Studies in Environmental Science, Pt. Ravishankar Shukla University, Raipur, India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShobhana Ramteke\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePolish Geological Institute, Rakowiecka, Street-00-975, Warsaw,\u0026nbsp;Poland\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIrena\u0026nbsp;Wysocka\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSuleyman Demirel University, 32260 Isparta, Turkey\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSema Yurdakul\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNational Institute of Technology Raipur, G.E. Road, Raipur, India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDalchand Jhariya\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUniversidad de Valladolid, Avenida de Madrid 44, 34004, Palencia, Spain\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePablo Mart\u0026iacute;n-Ramos\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUniversity Drive,\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eCallaghan, NSW 2308,\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eAustralia\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMohammad Mahmudur Rahman\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eKhageshwar Singh Patel\u003c/em\u003e:\u0026nbsp;Conception and design of experiments; writing and editing assistance. \u003cem\u003eBharat Lal Sahu and\u0026nbsp;\u003c/em\u003e\u003cem\u003eShobhana Ramteke\u003c/em\u003e: Sample collection and experiments. \u003cem\u003ePiyush Kant Pandey\u003c/em\u003e: Data evaluation and compilation; writing and editing assistance.\u0026nbsp;\u003cem\u003eIrena\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eWysocka\u003c/em\u003e: ICP-MS data generation.\u0026nbsp;\u003cem\u003eSema Yurdakul\u003c/em\u003e: source analysis.\u003cem\u003e\u0026nbsp;Dalchand Jhariya:\u0026nbsp;\u003c/em\u003egraphical assistance.\u0026nbsp;\u003cem\u003ePablo Mart\u0026iacute;n-Ramos\u003c/em\u003e: writing and editing, \u003cem\u003eMohammad Mahmudur Rahman\u003c/em\u003e: Writing and editing. All authors have read and approved the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to\u0026nbsp;[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data is available on request from corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAPHA (2017) American Public Health Association). Standard methods for the examination of water and wastewater, 21st Edn., APHA, AWWA and WEF, Washington DC, USA.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAteş A, Demirel H, K\u0026ouml;kl\u0026uuml; R, Doğruparmak ŞC, Altundağ, H., Şeng\u0026ouml;r\u0026uuml;r B (2020) Seasonal source apportionment of heavy metals and physicochemical parameters: a case study of sapanca Lake Watershe. J Spectrosc Article ID: 7601590. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2020/7601590\u003c/span\u003e\u003cspan address=\"10.1155/2020/7601590\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyub A, Ahmad SS (2020) Seasonal assessment of groundwater contamination in coal mining areas of Balochistan. Sustain 12: 6889. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su12176889\u003c/span\u003e\u003cspan address=\"10.3390/su12176889\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBIS (2012). Drinking water \u0026ndash; specification, Bureau of Indian Standard, 2nd Edn., New Delhi.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouwer H (1978) Groundwater Hydrology, McGraw-Hill, New York, 480.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrindha K, Paul R, Walter J, Tan ML, Singh MK (2020) Trace metals contamination in groundwater and implications on human health: comprehensive assessment using hydrogeochemical and geostatistical methods. Environ Geochem Health. 42(11): 3819\u0026ndash;3839. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10653-020-00637-9\u003c/span\u003e\u003cspan address=\"10.1007/s10653-020-00637-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury AH (2017) Environmental impact of coal based power plant of Rampal on the Sundarbans (world largest mangrove forest) and surrounding areas. MOJ Eco Environ Sci 2(3): 85\u0026ndash;98. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15406/mojes.2017.02.00022\u003c/span\u003e\u003cspan address=\"10.15406/mojes.2017.02.00022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCid FD, Ant\u0026oacute;n RI, Pardo R, Vega M, Caviedes-Vidal E (2011) Modelling spatial and temporal variations in the water quality of an artificial water reservoir in the semiarid midwest of Argentina, Anal ChimActa. 705(1\u0026ndash;2): 243\u0026ndash;252. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.aca.2011.06.013\u003c/span\u003e\u003cspan address=\"10.1016/j.aca.2011.06.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDewan Md, Shah A, Redwanur Rahman Md (2019) Environmental impact assessment of Phulbari Coal based thermal power plant of Bangladesh. J Soci Anthropo 3(2): 34\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pubs.sciepub.com/jsa/3/2/1\u003c/span\u003e\u003cspan address=\"http://pubs.sciepub.com/jsa/3/2/1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDubey CS, Usham AL, Mishra BK, Singh PK, Singh AK (2022) Anthropogenic arsenic menace in contaminated water near thermal power plants and coal mining areas of India. Environ Geochem Health 44:1099\u0026ndash;1127 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10653-021-01010-0\u003c/span\u003e\u003cspan address=\"10.1007/s10653-021-01010-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEwuzie U, Nnorom IC, Eze SO (2020) Lithium in drinking water sources in rural and urban communities in Southeastern Nigeria. Chemosphere 245: 125593. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chemosphere.2019.125593\u003c/span\u003e\u003cspan address=\"10.1016/j.chemosphere.2019.125593\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiotakos O, Nisianakis P, Tsouvelas G, Giakalou VV (2013) Lithium in the public water supply and suicide mortality in Greece. Biol Trace Element Res156(1\u0026ndash;3):376\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12011-013-9815-4\u003c/span\u003e\u003cspan address=\"10.1007/s12011-013-9815-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta N, Pandey P, Hussain J (2017) Effect of physicochemical and biological parameters on the quality of river water of Narmada, Madhya Pradesh, India. Water Sci 31(1): 11\u0026ndash;23,\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.wsj.2017.03.002\u003c/span\u003e\u003cspan address=\"10.1016/j.wsj.2017.03.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain MS, Ahmed MK, Liyana E, Hossain MS, Jolly YN, Kabir MJ, Akter S, Rahman MS (2021) A case study on metal contamination in water and sediment near a coal thermal power plant on the eastern coast of Bangladesh. Environ 8(10):108. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/environments8100108\u003c/span\u003e\u003cspan address=\"10.3390/environments8100108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKostik V, Bauer B, Kavrakovski Z (2014) Lithium content in potable water, surface water, ground water, and mineral water on the territory of Republic of Macedonia. Int J Med Public Health 3: 189\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/2230-8598.137700\u003c/span\u003e\u003cspan address=\"10.4103/2230-8598.137700\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar A, Singh PK (2016) Qualitative assessment of mine water of the Western Jharia coalfield area, Jharkhand, India. \u003cem\u003eCurr World Environ\u003c/em\u003e 11(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.12944/CWE.11.1.37\u003c/span\u003e\u003cspan address=\"10.12944/CWE.11.1.37\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar P, Singh AK (2022) Hydro geochemistry and quality assessment of surface and sub-surface water resources in Raniganj coalfield area, Damodar Valley, India. Int J Environ Anal Chem 102:19, 8346\u0026ndash;8369, DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/03067319.2020.1849653\u003c/span\u003e\u003cspan address=\"10.1080/03067319.2020.1849653\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Cai J, Chen D, Feng Q (2021) Characteristics of water contamination in abandoned coal mines: a case study on Yudong River area, Kaili, Guizhou Province, China. Int J Coal Sci Technol 8: 1491\u0026ndash;1503. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40789-021-00466-w\u003c/span\u003e\u003cspan address=\"10.1007/s40789-021-00466-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra UC (2004) Environmental impact of coal industry and thermal power plants in India. J Environ Radioact 72(1\u0026ndash;2):35\u0026ndash;40. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0265-931X(03)00183-8\u003c/span\u003e\u003cspan address=\"10.1016/S0265-931X(03)00183-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohanty AK, Lingaswamy M, Gurunadha Rao VVS, Sankaran S (2018) Impact of acid mine drainage and hydrogeochemical studies in a part of Rajrappa coal mining area of Ramgarh district, Jharkhand state of India, Groundw Sustain Develop 7164\u0026ndash;7175, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gsd.2018.05.005\u003c/span\u003e\u003cspan address=\"10.1016/j.gsd.2018.05.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMSME (2023) Brief Industrial Profile of Korba District, Government of India Ministry of Ministry of Micro, Small \u0026amp; Medium Enterprises (MSME), msmediraipur.gov.in\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNihalani SA, Mishra YD, Meeruty AR (2021) Water Resource Management for Coal-Based Thermal Power Plant. In: Jha R, Singh VP, Singh V, Roy L, Thendiyath R (eds) Water Resources Management and Reservoir Operation. Water Sci Technol Lib 107. Springer, Cham. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-79400-2_3\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-79400-2_3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaliwal KV (1972) Irrigation with saline water. In: Monogram No. 2 (New Series). IARI, Water Technology Centre, New Delhi, 198.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel K S, Sharma R, Dahariya N, Patel RK, Blazhev B, Laurent M (2016). Black carbon and heavy metal contamination of soil. Polish J Environ Stud 25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15244/pjoes/61062\u003c/span\u003e\u003cspan address=\"10.15244/pjoes/61062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel KS, Sahu BL, Dahariya NS, Bhatia A, Patel R.K., Laurent M, Sracek O, Bhattacharya P (2017) Groundwater arsenic and fluoride in Rajnandgaon District, Chhattisgarh, northeastern India. Appl Water Sci 7: 1817\u0026ndash;1826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13201-015-0355-2\u003c/span\u003e\u003cspan address=\"10.1007/s13201-015-0355-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel KS, Pandey PK, Sahu BL, Patel RK, Wysocka I, Yurdakul S, Mart\u0026iacute;n-Ramos P, Bhattacharya P (2024) Groundwater contamination and sources of potentially toxic elements in Korba Basin, India. In press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/tqem.22177\u003c/span\u003e\u003cspan address=\"10.1002/tqem.22177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRana, A., James, A. \u0026amp; Nath, S. 2019. Effect of coal mining on the surface water quality in Northern Coalfield Singrauli, India. Res Review Int J Multidiscipl 4(6): ISSN: 2455\u0026ndash;3085\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichards, L.A. 1954. Diagnosis improvement saline alkali soils.US Department of Agriculture Handbook. No. 60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahoo BP, Sahu HB (2022) Assessment of metal pollution in surface water using pollution indices and multivariate statistics: a case study of Talcher coalfield area, India. Appl Water Sci 12: 223. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13201-022-01743-y\u003c/span\u003e\u003cspan address=\"10.1007/s13201-022-01743-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahu P, Pradhan M, Jade RK, Lokhande RD (2016) Study the variations of sinkhole depth with respect to working height in underground coal mines. Recent Adv Rock Engin \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2991/rare-16.2016.88\u003c/span\u003e\u003cspan address=\"10.2991/rare-16.2016.88\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharifi SA, Zaeimdar M, Jozi SA, Hejazi R (2023) Effects of Soil, Water and Air Pollution with Heavy Metal Ions Around Lead and Zinc Mining and Processing Factories. Water Air Soil Pollut 234: 760. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11270-023-06758-y\u003c/span\u003e\u003cspan address=\"10.1007/s11270-023-06758-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma R, Patel KS, Lata L, Milosh H (2017) Contamination of Pond Water and Sediment in Coal Burning Area. J Environ Protect 8: 358\u0026ndash;379. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4236/jep.2017.83027\u003c/span\u003e\u003cspan address=\"10.4236/jep.2017.83027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma R, Yadav A, Ramteke S, Patel KS, Lata L, Huber M, Corns W, Martin-Ramos P(2019). Heavy metal pollution in surface soil of Korba basin, India. J Hazard Toxic Radioact Waste 23: 05019004. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1061/(ASCE)HZ.2153-5515.0000460\u003c/span\u003e\u003cspan address=\"10.1061/(ASCE)HZ.2153-5515.0000460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToming K, Kotta J, Uuemaa E, Sebastian S, Tiit K, Lars JT (2020) Predicting lake dissolved organic carbon at a global scale. Sci Rep 10:8471. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-65010-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-65010-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Vliet MTH, Thorslund J, Strokal M, Hofstra N, Fl\u0026ouml;rke M, Macedo HE, Nkwasa A, Tang T, Kaushal SS, Kumar R, van Griensven A, Bouwman L, Mosley LM (2023) Global river water quality under climate change and hydroclimatic extremes. Nat Rev Earth Environ 4, 687\u0026ndash;702. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43017-023-00472-3\u003c/span\u003e\u003cspan address=\"10.1038/s43017-023-00472-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarada VK, Gulwade DP, Deo SS (2014) Quality assessment of surface and ground water around thermal power plants at Warora district, Chandrapur (MS). J Chem Pharmaceut Res. 6(7):1856\u0026ndash;1860.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarshney R, Modi P, Sonkar AK, Singh P, Jamal A (2022) Assessment of surface water quality in and around Singrauli coalfield, India and its remediation: An integrated approach of GIS, water quality index, multivariate statistics and phytoremediation. Arab J Geosci 15(18):1530. DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12517-022-10806-y\u003c/span\u003e\u003cspan address=\"10.1007/s12517-022-10806-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerma S, Anju (2018) Assessment of water quality around coal-fired thermal power plant, Bathinda (Punjab), India. J Appl Nat Sci 10(3): 915\u0026ndash;924. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31018/jans.v10i3.1809\u003c/span\u003e\u003cspan address=\"10.31018/jans.v10i3.1809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou M, Li X, Zhang M, Liu B, Zhang Y, Gao Y, Ullah H, Peng L, He A, Yu H (2020) Water quality in a worldwide coal mining city: A scenario in water chemistry and health risks exploration. J Geochem Explor 213:,106513. https://doi.org/10.1016/j. gexplo.2020.106513\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXinyue D, Guangzhou C (2022) Characteristics of water pollution and evaluation of water quality in subsidence water bodies in Huainan coal mining areas, China. J Chem Article ID 2857700, 12 p. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2022/2857700\u003c/span\u003e\u003cspan address=\"10.1155/2022/2857700\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue Y, Song J, Zhang Y, Kong F, Wen M, Zhang G (2016) Nitrate pollution and preliminary source identification of surface water in a Semi-Arid River Basin, using isotopic and hydrochemical approaches. Water 8(8): 328. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w8080328\u003c/span\u003e\u003cspan address=\"10.3390/w8080328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilcox LV (1955) Classification and use of irrigation waters. USDA. Circ 969, Washington, DC.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO (2012) Guideline for drinking water quality, 4th ed., Switzerland?\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Surface water, quality, sources, Korba basin, India","lastPublishedDoi":"10.21203/rs.3.rs-4478468/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4478468/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn India, surface water reservoirs are widely harnessed to meet a variety of needs, including domestic, agricultural, and industrial applications. The quality of these resources, particularly in coal mine regions, undergoes substantial deterioration due to the discharge of various wastes (industrial, municipal, and runoff water) and coal ash deposition. The Korba basin, shaped by mining activities, shallow groundwater levels, and the flow of the expansive Hasedo River, features numerous ponds, pit lakes, and canals. A significant health concern in this area is the prevalence of fluorosis disease among the local population. The main aim of this study was to evaluate the water quality of reservoirs, including ponds, pit lakes, canals, and rivers, with a focus on identifying contaminant levels and tracing the sources of chemical species such as carbonate and organic carbons, anions, and metals. During the period from 2012 to 2017, elevated carbon contents (varying from 1010 to 4420 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) markedly contributed to increased total dissolved solids (TDS), with values ranging between 2865 and 5540 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. fluoride (F\u003csup\u003e\u0026minus;\u003c/sup\u003e), aluminum (Al), iron (Fe), and manganese (Mn) concentrations in all surface water bodies exhibited variations within the ranges of 1.8\u0026ndash;4.4, 0.42\u0026ndash;1.91, 0.3\u0026ndash;1.22, and 1.0\u0026ndash;2.1 mg\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. This study delves into the temporal and seasonal variations, water quality indices, and toxicities associated with the identified contaminants.\u003c/p\u003e","manuscriptTitle":"Contamination and Sources of Surface Water in Korba Coal Basin, Chhattisgarh, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 15:42:18","doi":"10.21203/rs.3.rs-4478468/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4fa21c31-9f08-487d-9ee9-8ee9f5452e43","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-09T15:08:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 15:42:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4478468","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4478468","identity":"rs-4478468","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}