Physico-Chemical Characteristics of Groundwater in the Mutoshi Mining Environment in Kolwezi (Lualaba, DR Congo)

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The adopted methodology involved collecting samples from geological formations and groundwater in the study area. Rocks were analyzed to determine concentrations of major and trace elements, while detailed physico-chemical parameter analyses were conducted on groundwater samples. The results highlight high concentrations of Cu, Co, Mn, Fe, Mg, and Al in the rocks. Groundwater also exhibits concentrations of Cu, Mn, Fe, and Al exceeding the WHO's drinking water standards, raising concerns about the quality of water for human consumption. Hydrochemical classification of the waters, performed using the Piper diagram, revealed two distinct hydrofacies. Calcium-magnesium bicarbonate waters dominate in most samples, while sodium-potassium bicarbonate waters are also present. Processes influencing groundwater mineralization include dissolution of carbonate, clay, and evaporite minerals. These findings underscore the importance of understanding these processes to adequately assess and manage groundwater quality in the Mutoshi region. groundwater hydrofacies mining environment physico-chemical characteristics Mutoshi Kolwezi Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 I. Introduction Access to safe drinking water remains a major challenge in many developing countries, including the Democratic Republic of Congo (DRC) (UNICEF, 2019). Despite the DRC's immense water resources, only 26% of the urban population and 7% of the rural population have access to safe drinking water (WaterAid, 2019). In Lualaba Province, and more specifically in the city of Kolwezi, water quality and potability remain a concern. The Mutoshi aquifer, which supplies over half of Kolwezi's drinking water, is seeing its quality deteriorate (Balloy et al., 2018). This phenomenon is partly due to the aquifer's underlying geological formations, which contain metals such as copper and cobalt that can infiltrate groundwater (Ilunga et al., 2013). Groundwater quality depends on the chemical composition of the geological layers it passes through before emerging at the surface or in wells and boreholes (Appelo & Postma, 2005). When rocks or ores rich in metals are exposed to weathering processes, metal ions can be released into groundwater, degrading its quality (Lee et al., 2005). The mobility of heavy metals is influenced by various factors such as pH, redox potential (Eh), the mineral composition of rocks, the presence of organic matter, and anthropogenic pollution (Adhikari et al., 2014; Jiang et al., 2009). It is therefore essential to understand the geochemical characteristics of the overlying layers of the Mutoshi aquifer to assess their potential impact on the quality and potability of the underlying groundwater. While previous studies have reported elevated levels of certain metals such as aluminum, iron, manganese, and lead in some areas of the Mutoshi aquifer, suggesting physico-chemical influences from the overlying copper-cobalt mineralized zones (Ilunga et al., 2013; Balloy et al., 2018), a holistic understanding of the influence of the overlying layers on groundwater quality is still lacking. Therefore, this study aims to characterize the physico-chemical properties of groundwater in the Mutoshi mining environment, with the goal of identifying their nature and understanding the processes responsible for their mineralization. II. Materials and methods 2.1 Study area The Mutoshi aquifer is located approximately 350 meters from the Mutoshi open-pit mine (formerly Ruwe) northeast of the city of Kolwezi, about 8 km from the city center. The area is located between longitudes 25.50° and 25.54° and latitudes -10.66° and -10.70° (Figure 1). Access to the site is via the road that passes through the Mutoshi technical institute. The city of Kolwezi where our study area is located is in Lualaba Province, approximately 320 km from the city of Lubumbashi (Lukamba et al., 2007). François (1973) identified several morphological zones in the Kolwezi mining area: the N'zilo promontory to the northwest, an area of rugged relief where relatively hard Kibarian rocks outcrop (quartzites and metamorphic schists). The Lualaba River (source of the Congo River) and some tributaries cross this massif at the bottom of deep gorges, in a series of waterfalls and rapids before emptying into the Upemba depression. The sandy plateaus of Manika and Biano are located southwest and northeast respectively, separated by the upper Lualaba Valley. Two gently sloping hills, interrupted by small reliefs, connect the Lualaba Valley to the plateaus. According to Placet (1975), this structure serves as a hydrogeological reservoir in a region also characterized by a generally low slope, low surface runoff, and high infiltration. The average latitude of the region varies between 1000 and 1500 m, with depressions occupied by rivers. From a geomorphological perspective, the region exhibits karstic tendencies. The Lualaba is the most important river, originating on the high plateaus of Manika at 1420 m. The Kolwezi region falls entirely within the Lualaba watershed. The Lulua and Musonoie rivers are the two most important waterways in the Kolwezi region among the tributaries of the Lualaba (Geo Ouest, 2007) 2.2 Methodological approaches 2.2.1. Sampling and analysis methods We collected samples from geological formations and groundwater in the study area. The rocks were analyzed to quantify concentrations of major and trace elements, while groundwater samples underwent detailed analysis of their physico-chemical parameters. 2.2.1.1 Sampling and analysis of geological formations This study examined rock samples from the overburden layers of the Mutoshi deposit using lithogeochemical techniques such as X-ray diffraction to determine major and trace elements. First, representative lithological units overlying the Mutoshi aquifer were identified by referring to existing geological maps and conducting field verifications (Reedman, 1979). Rock samples were collected from fresh outcrops using a hammer and chisel. Approximately 2 kg of each lithotype were sealed in plastic bags to prevent contamination (Bowen, 1979). At least 5 samples per unit were collected. Sampling locations were recorded and marked on maps for reference. Samples were also collected from visible mineralized zones, such as faults, veins, or fractures, to study associated alteration (Levinson, 1974). For soil sampling, small pits approximately 5 decimeters in diameter and 75 centimeters deep were dug at each grid point, following a predetermined sampling density of 50x50 meters (Govett, 1983). A total of 30 soil samples were collected. To ensure sample quality, certain key rules were followed, such as recording the location and time of sampling, noting field parameters, using airtight containers to prevent contamination, maintaining a depth of 50 to 75 cm to avoid root effects, and respecting the grid spacing (Reedman, 1979). The soil samples were placed in labeled plastic bags, with a basic description of the terrain noted in a sampling log for easy geological mapping of the encountered formations. Laboratory analysis involved X-ray fluorescence (XRF) to determine the elemental composition of the lithological samples (Bowen, 1979). In the case of X-ray fluorescence, the sample is irradiated with X-rays, making it fluorescent and emitting secondary X-rays characteristic of its constituent elements. From the emitted wavelength spectra, the elements present are identified and quantified (Govett, 1983). This helps understand the enrichment and depletion of major and trace elements associated with mineralization in the overburden layers of the Mutoshi deposit. 2.2.1.2 Sampling and analysis of groundwater Groundwater sampling at Mutoshi was conducted from existing tubular wells and boreholes after adequate purging to ensure collection of formation water (APHA, 1998). Field measurements included pH, electrical conductivity, temperature, and total dissolved solids, taken using calibrated portable meters according to standard methods (Hem, 1985). Samples intended for cation and anion analysis were filtered in the field through 0.45 µm membranes and appropriately preserved. Containers for cation analysis were acidified with ultrapure nitric acid to a pH below 2 (Reedman, 1979). Those not acidified were reserved for anion analysis. Samples were refrigerated and transported to accredited laboratories for analysis by inductively coupled plasma mass spectrometry (ICP-MS), ion chromatography, titration, and spectrophotometry, according to established protocols (Levinson, 1974). 2.2.2 Data processing methods Processing data on Mutoshi's geological formations and groundwater involved hydrochemical and statistical methods. 2.2.2.1. Geochemical characterization of rocks To characterize the studied rocks, geochemical data processing involved descriptive statistical analysis of the various chemical elements analyzed, including minimum, maximum, mean, standard deviation, and coefficients of variation (CV). This analysis also included comparing the mean values to Clark standards to assess the enrichment of chemical elements in the rock samples collected at Mutoshi. This statistical analysis was conducted on 30 samples and 10 elements (Cu, Co, Mn, Fe, S, Si, Ca, Mg, Al, and K). 2.2.2.2. Physico-chemical characterization of groundwater Regarding the physico-chemical parameters of groundwater, their processing also involved descriptive statistical analysis, including the measurement of minimum, maximum, mean, standard deviation, and coefficients of variation (CV). The values of these parameters were compared to each other and also to WHO standards (Djahadi, et al., 2021). This statistical analysis was conducted on 20 samples and 21 physico-chemical variables (T°C, pH, Eh, CE, TDS, MES, MOS, Cu, Co, Mn, Fe, S, Al, Si, Ca, Mg, Na, K, HCO3, Cl, and SO4). To assess the relationship between these physico-chemical parameters of groundwater, a correlation analysis was conducted. This bivariate statistical analysis method allowed us to determine if variations in one parameter are associated with variations in the other. For this purpose, correlation coefficients (r), ranging from -1 to +1, were calculated to assess the strength and direction of the statistical link between the analyzed parameters (Mashauri, et al., 2023a). The hydrochemical study of the waters required the use of tools such as the Piper diagram (Cidu et al., 2009; Bashir et al., 2017; Yao and Ahoussi, 2021; Hakim et al., 2022; Barhi et al., 2022) and Stiff diagrams to classify the waters and assess their ionic composition. Finally, using binary diagrams, we examined the origin of water chemistry and base exchange processes to better understand the interaction between groundwater and surrounding geological formations. These diagrams are frequently used in hydrochemical studies by several authors, including (Biemi, 1992; Ahoussi et al., 2012; Benjamin et al., 2023). All these analyses were conducted using Excel 2016, Tanagra 1.4, and Diagramme 6.77 software. The location of measurement points in the study area was represented using QGIS 3.43.5 software. III. Results 3.1 Geochemical characterization of rocks Field observations reveal that the aquifer studied is covered by an alternating sequence of black shales, laterite layers, and Kalahari-type quartz sands. To identify the origin of chemical elements in groundwater, rock samples collected in the field were analyzed to quantify concentrations of major and trace elements (Table 1). Table 1: Statistical analysis of concentrations of major and trace elements in Mutoshi rock samples. Elements (ppm) Minimum Maximum Moyenne Ecart-type CV Clark Cu 127 9930 3012,9 2382 0,8 55 Co 32 3560 580,9 755,1 1,3 25 Mn 0 2182 292,3 612,1 2,1 950 Fe 103 41500 5297,2 10250,6 1,9 56000 S 0 75 16,6 21,3 1,3 350 Si 0 24512 3853,5 6937,3 1,8 288000 Ca 0 5709 1098,2 1793,1 1,6 36000 Mg 0 65437 5468,4 14187,5 2,6 21000 Al 582 90600 59038,2 29213,8 0,5 81000 K 4 188 75,6 37 0,5 25000 The results of the chemical composition analysis of the rocks revealed high concentrations of elements such as Cu, Co, Mn, Fe, Mg, and Al. These elements exceed the reference values established by the Clark scale and can have a negative impact on the quality of Mutoshi groundwater. 3.1 Concentration of chemical elements in groundwater As part of this study, the World Health Organization (WHO) standards were used as a priority to assess the physico-chemical quality of Mutoshi groundwater (Table 2). Table 2: Statistical analysis of physico-chemical parameters of groundwater. Physico-chemical parameters Units Minimum Maximum Moyenne Ecart-type CV WHO standard T °C 22,3 26,7 23,96 1,25 0,05 - pH pH 6,58 8,2 7,08 0,50 0,07 6,5 à 8,5 Eh V 24 92,9 51,59 15,74 0,31 - CE µS/cm 12,32 59,65 35,83 8,03 0,22 250 TDS mg/L 0,89 7,84 3,32 1,66 0,50 1000 MES mg/L 0,019 0,276 0,17 0,08 0,44 - MOS mg/L 0,047 0,241 0,13 0,04 0,35 - Cu mg/L 0,2 32 2,43 6,83 2,81 2 Co mg/L 0,04 2 0,58 0,44 0,76 15 Mn mg/L 0 9,5 1,84 2,37 1,28 0,4 Fe mg/L 0 1,97 0,70 0,61 0,87 0,3 S mg/L 0 7,5 1,21 2,14 1,77 - Al mg/L 0 0,889 0,60 0,27 0,44 0,2 Si mg/L 0,13 4,35 2,27 1,26 0,56 - Ca mg/L 3,5 34,84 21,42 9,56 0,45 200 Mg mg/L 8,9 57,1 23,13 9,95 0,43 150 Na mg/L 24,89 59,59 36,25 8,57 0,24 200 K mg/L 0,004 7 2,44 1,94 0,80 10 HCO 3 mg/L 128 291 179,98 58,68 0,33 - Cl mg/L 5,89 20 10,54 4,47 0,42 250 SO 4 mg/L 26 70 44,47 15,72 0,35 250 The results presented in this table demonstrate that the average concentrations of elements such as Cu, Mn, Fe, and Al exceed the WHO's drinking water standards. 3.2 Chemical facies of groundwater Hydrochemical classification of the studied waters in the Piper diagram (Figure 2) revealed two distinct hydrofacies. On the one hand, we observe calcium-magnesium bicarbonate waters, representing 85% of the analyzed water samples, and on the other hand, sodium-potassium bicarbonate waters. To show and compare the ionic composition of the analyzed water samples, we used a series of 20 Stiff diagrams (Figure 3). Each diagram is a horizontal inverted bar graph with three arms extending to the left and right from a central vertical axis. The left arms represent cations and those on the right represent anions. Each arm is labeled with the name of the ion it represents: Na+K, Ca, Mg, Cl, HCO3+CO3, and SO4+NO3. The concentration of each ion is indicated by the length of the corresponding arm, with scales provided in milliequivalents per liter (meq/L) at the top and bottom of the diagrams. Each Stiff diagram is associated with a sample number (P1 to P20). 3.2 Origin of groundwater chemistry Based on the chemical analyses of groundwater samples collected in the field, we attempted to determine the probable origin of certain ions using binary diagrams (Figure 4). These diagrams allow visualization of the relationships between the different ions present in groundwater, such as calcium (Ca2+), magnesium (Mg2+), bicarbonate (HCO3-), and sulfate (SO42-). They provide information on chemical equilibria and processes of dissolution or precipitation of certain minerals, which is essential for understanding the geological origin of groundwater, rock weathering processes, and chemical reactions that influence water composition. The diagrams (a and b in Figure 4) reveal that all points lie below the line with a slope of 1, suggesting that calcium ions are present in lower quantities than what would be needed to achieve full saturation. This observation could result from processes of carbonate mineral dissolution in groundwater, where calcium ions are released into solution. Undersaturation of calcium ions may indicate that groundwater has already reacted with carbonate minerals and reached a partial chemical equilibrium. This can occur, in particular, in regions where groundwater has passed through geological formations rich in carbonates, such as calcite or dolomite. Conversely, the diagrams (c and d in Figure 4) clearly reveal that the water points lie below, above, near, or on the line with a slope of 1, also called the theoretical mineral dissolution line. This means that, in addition to carbonate dissolution phenomena, the elements (Ca2+ + Mg2+) have a second origin, which could be linked to clay minerals and evaporites. 3.3 Base exchange processes and groundwater mineralization processes During their underground journey, waters come into contact with different geological formations that have the ability to exchange their ions with those contained in the waters. Base exchanges with clay minerals present in aquifer formations and groundwater are often mentioned. These exchanges involve clay minerals capable of fixing ions by adhesion and releasing other ions depending on the electrical charge existing between the clay mineral layers and the saturation state of the solution. Base exchanges characterizing Mutoshi groundwater are highlighted by the relationship between [(Ca²⁺ + Mg²⁺) - (HCO₃- + SO₄²-)] vs [(Na⁺+k⁺) – Cl-] represented by Figure 5. This relationship focuses only on reactions that can exist between clay minerals and the solution, discarding ions potentially coming from other dissolution reactions of carbonate minerals (calcite, dolomite) and evaporites (gypsum). This figure helps to highlight groundwater mineralization processes during rainwater infiltration and/or during its residence within the aquifer itself. In the absence of these reactions, all points representing the samples should be located near the origin (Mclean et al., 2000). The diagram in Figure 5 highlights that all analyzed groundwater samples lie in zones 2 and 4. In zone 2, the concentration of (Ca²⁺+ Mg²⁺) ions is higher than that of (HCO₃⁻ + SO₄²⁻) ions, indicating a predominance of carbonate mineral dissolution processes. Conversely, in zone 4, the concentration of (Ca²⁺+ Mg²⁺) ions is lower than that of (HCO₃⁻ + SO₄²⁻) ions, suggesting significant production of HCO₃⁻ and/or SO₄²⁻ by sulfate mineral dissolution, likely linked to base exchange processes. In this zone, there is a release of Na⁺ and K⁺ ions, while alkaline-earth ions (Ca²⁺ and Mg²⁺) are retained in the clays. 3.3 Relation entre les variables physico-chimiques des eaux souterraines To better understand the degree of dependence between the physico-chemical variables of Mutoshi groundwater, we calculated the correlation coefficients. Table 3 below provides, in matrix form, the correlation coefficient (r) values calculated for the 21 variables taken two by two. This table gives the different correlations between the physico-chemical variables. There is a strong positive or negative correlation between T°C and pH (r= 0,853), T°C and MES (r= -0,713), Eh and MES (r= -0,742), and TDS and MOS (r= -0,743). On the other hand, medium-level positive or negative correlations are observed between T°C and Eh (r= 0,506), T°C and MOS (r= -0,514), T°C and Al (r= -0,579), pH and MES (r= -0,669), Eh and MOS (r= -0,528), CE and MOS (r= 0,538), CE and Na (r= -0,501), TDS and MES (r= 0,503), MES and MOS (r= 0,604), Co and K (r= -0,565), Mn and Fe (r= 0,520), Fe and Cl (r= 0,571), Al and Cl (r= 0,527), Al and SO4 (r= 0,642), Mg and K (r= 0,534), Cl and SO4 (r= 0,543). It should be noted that good positive correlations confirm that all these elements participate in groundwater mineralization. On the other hand, the negative correlation coefficients express an opposition between the variables. This means that the considered variables evolve in opposite directions relative to the center. IV. Discussion The results of the chemical composition analysis of Mutoshi groundwater (Table 2) reveal high average concentrations of elements such as copper (Cu), manganese (Mn), iron (Fe), and aluminum (Al) that exceed the WHO drinking water standards. Rock samples collected in the field also exhibit high concentrations of these elements, exceeding the reference values established by the Clark scale (Table 1). The increased presence of metals such as copper, cobalt, and iron in groundwater could result from the infiltration of these elements from mine waste into underground aquifers. These contaminants can make waters unfit for human consumption and have harmful effects on local ecosystems. These findings highlight the importance of understanding the impact of waste from the Mutoshi mine on groundwater quality in the city of Kolwezi. The high concentrations of these chemical elements in the rocks and groundwater suggest potential contamination due to mining activities. It is imperative to implement monitoring and environmental management measures to mitigate the impact of mine waste on groundwater in the region. Sustainable mining practices and waste treatment technologies can contribute to limiting groundwater contamination and preserving water quality for local communities. To identify the geochemical facies of Mutoshi groundwater, we used the Piper diagram (1944) by calculating the relative proportions of the analyzed elements. This diagram is widely recognized as one of the most commonly used graphical methods in water classification, allowing the determination of similarities and differences between water samples, grouping them into sets of groups. Analysis of the diagram reveals, in order of abundance, the existence of two chemical facies for the measurement campaign (Figure 2). The majority of the analyzed water samples belong to the category of calcium-magnesium bicarbonate waters, while a smaller proportion corresponds to sodium-potassium bicarbonate waters. The enrichment in calcium and magnesium is likely due to exchanges between the alkalis (sodium and potassium) of the aquifer and the alkaline earths (calcium and magnesium) of the Katanga Supergroup rocks, indicating a fixation of alkaline earths (Ca2+, Mg2+) and a solubilization of alkalis (Na+, K+). In terms of origin and mineralization processes, the binary diagrams (Figures 4 and 5) helped to establish different relationships between major ions, revealing that groundwater mineralization is influenced by dissolution phenomena and basic cation exchanges between the aquifer and the rocks of the Katanga Supergroup. These results agree with those obtained by Essouli (2017), Andzaye (2015), and Moukolo (1992) in Congo Brazzaville, which also highlight the influence of dissolution phenomena and basic cation exchanges between the aquifer and the rocks. V. Conclusion This study aims to contribute to the understanding of the physico-chemical characteristics of groundwater in the Mutoshi mining environment in Kolwezi, Lualaba Province, Democratic Republic of Congo. The results of the chemical analysis of the rocks reveal high concentrations of Cu, Co, Mn, Fe, Mg, and Al. Regarding the assessment of the physico-chemical quality of the studied groundwater, it is observed that the concentrations of Cu, Mn, Fe, and Al exceed the WHO drinking water standards, raising major concerns about the quality of water intended for human consumption. The hydrochemical classification of waters in the Piper diagram allowed us to distinguish two distinct hydrofacies, namely calcium-magnesium bicarbonate waters, which dominate in the majority of samples, and sodium-potassium bicarbonate waters. Examining the binary diagrams of the water samples highlighted the different processes that influence groundwater mineralization, including dissolution of carbonate, clay, and evaporite minerals. The correlations between physico-chemical variables reveal significant relationships, both positive and negative, between different parameters, thus confirming the involvement of these elements in groundwater mineralization. Declarations Ethical Approval This declaration is not applicable as this study does not involve any human or animal subjects. References Ahoussi, K. E., Loko S., Adja, M. G., Lasm, T., and Jourda, J. P., (2012). 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International Journal of Geomatics, vol. 32, no. 1, 79-98pp. https://doi.org/10.32604/RIG.2023.044124 Mclean, W., Jankowski, J., Lavitt N. (2000). Groundwater quality and sustainability in an alluvial aquifer, Australia. In Sililo O. et al. (eds). Groundwater, past achievement and future challenges. A. A. Balkema (Rotterdamm), 567- 573pp. Moukolo N. (1992). Current state of knowledge on the hydrogeology of Congo. Journal Hydrogéologie, 1-2, 47-58 pp. Piper AM., (1944). A graphic procedure in the geochemical interpretation of water analyses. Trans. Am. Geophysical Union, 25: 914-928pp. Yao, K.S.A., and Ahoussi, K.E., (2021). Application of multivariate statistical methods to the hydrochemical study of groundwater in a mining environment in the Center-West of Côte d’Ivoire: case of the Divo Department. Afrique Science 18(4), 53 – 68pp. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2025 Read the published version in Advances in Environmental and Engineering Research → 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4558219","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319555806,"identity":"ec10f8b8-feca-4db8-a6c2-2a267456c5ce","order_by":0,"name":"Matthieu Matthieu 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13:06:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4558219/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4558219/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.21926/aeer.2502019","type":"published","date":"2025-04-16T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60477122,"identity":"97664f5a-5b6c-4268-b5c2-6e471e14884c","added_by":"auto","created_at":"2024-07-17 08:00:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1330411,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of the study area with the main sampling points.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4558219/v1/bd530a69a96c4b9a53739cd3.png"},{"id":60477121,"identity":"3219ae1f-48c7-411c-a2f1-9036fbf94ea2","added_by":"auto","created_at":"2024-07-17 08:00:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":206364,"visible":true,"origin":"","legend":"\u003cp\u003eProjection of Mutoshi groundwater in the Piper diagram.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4558219/v1/3db04156e97285a06f616a70.png"},{"id":60477119,"identity":"27294f56-e0ef-4364-bc0f-ad112bd5169c","added_by":"auto","created_at":"2024-07-17 08:00:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":276806,"visible":true,"origin":"","legend":"\u003cp\u003eStiff diagrams of the studied groundwater.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4558219/v1/4e064e4bb74f157152e13d8c.png"},{"id":60477935,"identity":"ea5df95f-3e97-4c8d-bcc8-6386c672ee64","added_by":"auto","created_at":"2024-07-17 08:08:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94258,"visible":true,"origin":"","legend":"\u003cp\u003eBinary diagrams: Ca\u003csup\u003e2+ \u003c/sup\u003evs HCO3\u003csup\u003e-\u003c/sup\u003e\u0026nbsp;(a), Ca\u003csup\u003e2+ \u003c/sup\u003evs HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u0026nbsp;+ SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e\u0026nbsp;(b), Ca\u003csup\u003e2+ \u003c/sup\u003e+ Mg\u003csup\u003e2+ \u003c/sup\u003evs HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e\u0026nbsp;+ SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e\u0026nbsp;(c) et Ca\u003csup\u003e2+ \u003c/sup\u003evs SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e\u0026nbsp;(d).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4558219/v1/399659008df628639a710214.png"},{"id":60477123,"identity":"f6607865-1923-464c-926f-7f61283e773a","added_by":"auto","created_at":"2024-07-17 08:00:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41124,"visible":true,"origin":"","legend":"\u003cp\u003eBinary diagram [(Ca²⁺+ Mg²⁺) - (HCO₃- + SO₄²-)] vs [(Na⁺+k⁺) – Cl-] of Mutoshi groundwater.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4558219/v1/4aea089ee61e4ae8d000e52a.png"},{"id":82111778,"identity":"b1326098-a2b4-4352-8f4f-79117c29a3fa","added_by":"auto","created_at":"2025-05-07 01:18:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2700711,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4558219/v1/c6d8a389-ee6a-44d1-b0cd-9bea9a4189c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePhysico-Chemical Characteristics of Groundwater in the Mutoshi Mining Environment in Kolwezi (Lualaba, DR Congo) \u003c/p\u003e","fulltext":[{"header":"I. Introduction","content":"\u003cp\u003eAccess to safe drinking water remains a major challenge in many developing countries, including the Democratic Republic of Congo (DRC) (UNICEF, 2019). Despite the DRC\u0026apos;s immense water resources, only 26% of the urban population and 7% of the rural population have access to safe drinking water (WaterAid, 2019). In Lualaba Province, and more specifically in the city of Kolwezi, water quality and potability remain a concern. The Mutoshi aquifer, which supplies over half of Kolwezi\u0026apos;s drinking water, is seeing its quality deteriorate (Balloy et al., 2018). This phenomenon is partly due to the aquifer\u0026apos;s underlying geological formations, which contain metals such as copper and cobalt that can infiltrate groundwater (Ilunga et al., 2013).\u003c/p\u003e\n\u003cp\u003eGroundwater quality depends on the chemical composition of the geological layers it passes through before emerging at the surface or in wells and boreholes (Appelo \u0026amp; Postma, 2005). When rocks or ores rich in metals are exposed to weathering processes, metal ions can be released into groundwater, degrading its quality (Lee et al., 2005). The mobility of heavy metals is influenced by various factors such as pH, redox potential (Eh), the mineral composition of rocks, the presence of organic matter, and anthropogenic pollution (Adhikari et al., 2014; Jiang et al., 2009). It is therefore essential to understand the geochemical characteristics of the overlying layers of the Mutoshi aquifer to assess their potential impact on the quality and potability of the underlying groundwater.\u003c/p\u003e\n\u003cp\u003eWhile previous studies have reported elevated levels of certain metals such as aluminum, iron, manganese, and lead in some areas of the Mutoshi aquifer, suggesting physico-chemical influences from the overlying copper-cobalt mineralized zones (Ilunga et al., 2013; Balloy et al., 2018), a holistic understanding of the influence of the overlying layers on groundwater quality is still lacking. Therefore, this study aims to characterize the physico-chemical properties of groundwater in the Mutoshi mining environment, with the goal of identifying their nature and understanding the processes responsible for their mineralization.\u003c/p\u003e"},{"header":"II. Materials and methods","content":"\u003ch3\u003e2.1 Study area\u003c/h3\u003e\n\u003cp\u003eThe Mutoshi aquifer is located approximately 350 meters from the Mutoshi open-pit mine (formerly Ruwe) northeast of the city of Kolwezi, about 8 km from the city center. The area is located between longitudes 25.50\u0026deg; and 25.54\u0026deg; and latitudes -10.66\u0026deg; and -10.70\u0026deg; (Figure 1). Access to the site is via the road that passes through the Mutoshi technical institute. The city of Kolwezi where our study area is located is in Lualaba Province, approximately 320 km from the city of Lubumbashi (Lukamba et al., 2007).\u003c/p\u003e\n\u003cp\u003eFran\u0026ccedil;ois (1973) identified several morphological zones in the Kolwezi mining area: the N\u0026apos;zilo promontory to the northwest, an area of rugged relief where relatively hard Kibarian rocks outcrop (quartzites and metamorphic schists). The Lualaba River (source of the Congo River) and some tributaries cross this massif at the bottom of deep gorges, in a series of waterfalls and rapids before emptying into the Upemba depression. The sandy plateaus of Manika and Biano are located southwest and northeast respectively, separated by the upper Lualaba Valley. Two gently sloping hills, interrupted by small reliefs, connect the Lualaba Valley to the plateaus.\u003c/p\u003e\n\u003cp\u003eAccording to Placet (1975), this structure serves as a hydrogeological reservoir in a region also characterized by a generally low slope, low surface runoff, and high infiltration. The average latitude of the region varies between 1000 and 1500 m, with depressions occupied by rivers. From a geomorphological perspective, the region exhibits karstic tendencies. The Lualaba is the most important river, originating on the high plateaus of Manika at 1420 m. The Kolwezi region falls entirely within the Lualaba watershed. The Lulua and Musonoie rivers are the two most important waterways in the Kolwezi region among the tributaries of the Lualaba (Geo Ouest, 2007)\u003c/p\u003e\n\u003ch3\u003e2.2 Methodological approaches\u003c/h3\u003e\n\u003ch4\u003e\u003cem\u003e2.2.1. Sampling and analysis methods\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eWe collected samples from geological formations and groundwater in the study area. The rocks were analyzed to quantify concentrations of major and trace elements, while groundwater samples underwent detailed analysis of their physico-chemical parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1.1 Sampling and analysis of geological formations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examined rock samples from the overburden layers of the Mutoshi deposit using lithogeochemical techniques such as X-ray diffraction to determine major and trace elements. First, representative lithological units overlying the Mutoshi aquifer were identified by referring to existing geological maps and conducting field verifications (Reedman, 1979). Rock samples were collected from fresh outcrops using a hammer and chisel. Approximately 2 kg of each lithotype were sealed in plastic bags to prevent contamination (Bowen, 1979). At least 5 samples per unit were collected. Sampling locations were recorded and marked on maps for reference. Samples were also collected from visible mineralized zones, such as faults, veins, or fractures, to study associated alteration (Levinson, 1974).\u003c/p\u003e\n\u003cp\u003eFor soil sampling, small pits approximately 5 decimeters in diameter and 75 centimeters deep were dug at each grid point, following a predetermined sampling density of 50x50 meters (Govett, 1983). A total of 30 soil samples were collected. To ensure sample quality, certain key rules were followed, such as recording the location and time of sampling, noting field parameters, using airtight containers to prevent contamination, maintaining a depth of 50 to 75 cm to avoid root effects, and respecting the grid spacing (Reedman, 1979). The soil samples were placed in labeled plastic bags, with a basic description of the terrain noted in a sampling log for easy geological mapping of the encountered formations.\u003c/p\u003e\n\u003cp\u003eLaboratory analysis involved X-ray fluorescence (XRF) to determine the elemental composition of the lithological samples (Bowen, 1979). In the case of X-ray fluorescence, the sample is irradiated with X-rays, making it fluorescent and emitting secondary X-rays characteristic of its constituent elements. From the emitted wavelength spectra, the elements present are identified and quantified (Govett, 1983). This helps understand the enrichment and depletion of major and trace elements associated with mineralization in the overburden layers of the Mutoshi deposit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1.2 Sampling and analysis of groundwater\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGroundwater sampling at Mutoshi was conducted from existing tubular wells and boreholes after adequate purging to ensure collection of formation water (APHA, 1998). Field measurements included pH, electrical conductivity, temperature, and total dissolved solids, taken using calibrated portable meters according to standard methods (Hem, 1985). Samples intended for cation and anion analysis were filtered in the field through 0.45 \u0026micro;m membranes and appropriately preserved. Containers for cation analysis were acidified with ultrapure nitric acid to a pH below 2 (Reedman, 1979). Those not acidified were reserved for anion analysis. Samples were refrigerated and transported to accredited laboratories for analysis by inductively coupled plasma mass spectrometry (ICP-MS), ion chromatography, titration, and spectrophotometry, according to established protocols (Levinson, 1974).\u003c/p\u003e\n\u003ch4\u003e\u003cem\u003e2.2.2 Data processing methods\u003c/em\u003e\u003c/h4\u003e\n\u003cp\u003eProcessing data on Mutoshi\u0026apos;s geological formations and groundwater involved hydrochemical and statistical methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2.1. Geochemical characterization of rocks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize the studied rocks, geochemical data processing involved descriptive statistical analysis of the various chemical elements analyzed, including minimum, maximum, mean, standard deviation, and coefficients of variation (CV). This analysis also included comparing the mean values to Clark standards to assess the enrichment of chemical elements in the rock samples collected at Mutoshi. This statistical analysis was conducted on 30 samples and 10 elements (Cu, Co, Mn, Fe, S, Si, Ca, Mg, Al, and K).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2.2. Physico-chemical characterization of groundwater\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegarding the physico-chemical parameters of groundwater, their processing also involved descriptive statistical analysis, including the measurement of minimum, maximum, mean, standard deviation, and coefficients of variation (CV). The values of these parameters were compared to each other and also to WHO standards (Djahadi, et al., 2021). This statistical analysis was conducted on 20 samples and 21 physico-chemical variables (T\u0026deg;C, pH, Eh, CE, TDS, MES, MOS, Cu, Co, Mn, Fe, S, Al, Si, Ca, Mg, Na, K, HCO3, Cl, and SO4). To assess the relationship between these physico-chemical parameters of groundwater, a correlation analysis was conducted. This bivariate statistical analysis method allowed us to determine if variations in one parameter are associated with variations in the other. For this purpose, correlation coefficients (r), ranging from -1 to +1, were calculated to assess the strength and direction of the statistical link between the analyzed parameters (Mashauri, et al., 2023a).\u003c/p\u003e\n\u003cp\u003eThe hydrochemical study of the waters required the use of tools such as the Piper diagram (Cidu et al., 2009; Bashir et al., 2017; Yao and Ahoussi, 2021; Hakim et al., 2022; Barhi et al., 2022) and Stiff diagrams to classify the waters and assess their ionic composition. Finally, using binary diagrams, we examined the origin of water chemistry and base exchange processes to better understand the interaction between groundwater and surrounding geological formations. These diagrams are frequently used in hydrochemical studies by several authors, including (Biemi, 1992; Ahoussi et al., 2012; Benjamin et al., 2023). All these analyses were conducted using Excel 2016, Tanagra 1.4, and Diagramme 6.77 software. The location of measurement points in the study area was represented using QGIS 3.43.5 software.\u003c/p\u003e"},{"header":"III. Results","content":"\u003ch3\u003e3.1 Geochemical characterization of rocks\u003c/h3\u003e\n\u003cp\u003eField observations reveal that the aquifer studied is covered by an alternating sequence of black shales, laterite layers, and Kalahari-type quartz sands. To identify the origin of chemical elements in groundwater, rock samples collected in the field were analyzed to quantify concentrations of major and trace elements (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1: Statistical analysis of concentrations of major and trace elements in Mutoshi rock samples.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eElements (ppm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.586994727592268%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.114235500878735%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMoyenne\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcart-type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eClark\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.586994727592268%\" valign=\"top\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.114235500878735%\" valign=\"top\"\u003e\n \u003cp\u003e9930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" 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\u003cp\u003e755,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e1,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.586994727592268%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.114235500878735%\" valign=\"top\"\u003e\n \u003cp\u003e2182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e292,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e612,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e2,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.586994727592268%\" valign=\"top\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.114235500878735%\" valign=\"top\"\u003e\n \u003cp\u003e41500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e5297,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e10250,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e1,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e56000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.586994727592268%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.114235500878735%\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e16,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e21,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e1,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.586994727592268%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.114235500878735%\" valign=\"top\"\u003e\n \u003cp\u003e24512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e3853,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e6937,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e1,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e288000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCa\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.586994727592268%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.114235500878735%\" valign=\"top\"\u003e\n \u003cp\u003e5709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e1098,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e1793,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e1,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e36000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMg\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.586994727592268%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.114235500878735%\" valign=\"top\"\u003e\n \u003cp\u003e65437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e5468,4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e14187,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e2,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e21000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAl\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.586994727592268%\" valign=\"top\"\u003e\n \u003cp\u003e582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.114235500878735%\" valign=\"top\"\u003e\n \u003cp\u003e90600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e59038,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e29213,8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e0,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e81000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.586994727592268%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.114235500878735%\" valign=\"top\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e75,6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e0,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0597539543058%\" valign=\"top\"\u003e\n \u003cp\u003e25000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe results of the chemical composition analysis of the rocks revealed high concentrations of elements such as Cu, Co, Mn, Fe, Mg, and Al. These elements exceed the reference values established by the Clark scale and can have a negative impact on the quality of Mutoshi groundwater.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Concentration of chemical elements in groundwater\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs part of this study, the World Health Organization (WHO) standards were used as a priority to assess the physico-chemical quality of Mutoshi groundwater (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2: Statistical analysis of physico-chemical parameters of groundwater.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003ePhysico-chemical parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003eMoyenne\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003eEcart-type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003eWHO standard\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026deg;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e22,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e26,7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e23,96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e1,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e6,58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e8,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e7,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e0,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e6,5 \u0026agrave; 8,5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eEh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003eV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e92,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e51,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e15,74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026micro;S/cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e12,32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e59,65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e35,83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e8,03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eTDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e0,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e7,84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e3,32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e1,66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eMES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e0,019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e0,276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0,17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e0,08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eMOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e0,047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e0,241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eCu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e0,2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e2,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e6,83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e2,81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eCo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e0,04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0,58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e0,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eMn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e9,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e1,84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e2,37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e1,28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e0,4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eFe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e1,97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0,70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e0,61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e0,3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e7,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e1,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e2,14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e1,77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eAl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e0,889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e0,60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e0,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e0,2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eSi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e0,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e4,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e2,27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e1,26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eCa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e3,5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e34,84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e21,42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e9,56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eMg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e8,9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e57,1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e23,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e9,95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eNa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e24,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e59,59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e36,25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e8,57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e0,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e2,44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e1,94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e179,98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e58,68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eCl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e5,89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e10,54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e4,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.772357723577235%\" valign=\"top\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983739837398375%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.008130081300813%\" valign=\"top\"\u003e\n \u003cp\u003e44,47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\" valign=\"top\"\u003e\n \u003cp\u003e15,72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.78048780487805%\" valign=\"top\"\u003e\n \u003cp\u003e0,35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.471544715447154%\" valign=\"top\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe results presented in this table demonstrate that the average concentrations of elements such as Cu, Mn, Fe, and Al exceed the WHO\u0026apos;s drinking water standards.\u003c/p\u003e\n\u003ch3\u003e3.2 Chemical facies of groundwater\u003c/h3\u003e\n\u003cp\u003eHydrochemical classification of the studied waters in the Piper diagram (Figure 2) revealed two distinct hydrofacies. On the one hand, we observe calcium-magnesium bicarbonate waters, representing 85% of the analyzed water samples, and on the other hand, sodium-potassium bicarbonate waters.\u003c/p\u003e\n\u003cp\u003eTo show and compare the ionic composition of the analyzed water samples, we used a series of 20 Stiff diagrams (Figure 3). Each diagram is a horizontal inverted bar graph with three arms extending to the left and right from a central vertical axis. The left arms represent cations and those on the right represent anions. Each arm is labeled with the name of the ion it represents: Na+K, Ca, Mg, Cl, HCO3+CO3, and SO4+NO3. The concentration of each ion is indicated by the length of the corresponding arm, with scales provided in milliequivalents per liter (meq/L) at the top and bottom of the diagrams. Each Stiff diagram is associated with a sample number (P1 to P20).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Origin of groundwater chemistry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the chemical analyses of groundwater samples collected in the field, we attempted to determine the probable origin of certain ions using binary diagrams (Figure 4). These diagrams allow visualization of the relationships between the different ions present in groundwater, such as calcium (Ca2+), magnesium (Mg2+), bicarbonate (HCO3-), and sulfate (SO42-). They provide information on chemical equilibria and processes of dissolution or precipitation of certain minerals, which is essential for understanding the geological origin of groundwater, rock weathering processes, and chemical reactions that influence water composition.\u003c/p\u003e\n\u003cp\u003eThe diagrams (a and b in Figure 4) reveal that all points lie below the line with a slope of 1, suggesting that calcium ions are present in lower quantities than what would be needed to achieve full saturation. This observation could result from processes of carbonate mineral dissolution in groundwater, where calcium ions are released into solution. Undersaturation of calcium ions may indicate that groundwater has already reacted with carbonate minerals and reached a partial chemical equilibrium. This can occur, in particular, in regions where groundwater has passed through geological formations rich in carbonates, such as calcite or dolomite. Conversely, the diagrams (c and d in Figure 4) clearly reveal that the water points lie below, above, near, or on the line with a slope of 1, also called the theoretical mineral dissolution line. This means that, in addition to carbonate dissolution phenomena, the elements (Ca2+ + Mg2+) have a second origin, which could be linked to clay minerals and evaporites.\u003c/p\u003e\n\u003ch3\u003e3.3 Base exchange processes and groundwater mineralization processes\u003c/h3\u003e\n\u003cp\u003eDuring their underground journey, waters come into contact with different geological formations that have the ability to exchange their ions with those contained in the waters. Base exchanges with clay minerals present in aquifer formations and groundwater are often mentioned. These exchanges involve clay minerals capable of fixing ions by adhesion and releasing other ions depending on the electrical charge existing between the clay mineral layers and the saturation state of the solution.\u003c/p\u003e\n\u003cp\u003eBase exchanges characterizing Mutoshi groundwater are highlighted by the relationship between [(Ca\u0026sup2;⁺ + Mg\u0026sup2;⁺) - (HCO₃- + SO₄\u0026sup2;-)] vs [(Na⁺+k⁺) \u0026ndash; Cl-] represented by Figure 5. This relationship focuses only on reactions that can exist between clay minerals and the solution, discarding ions potentially coming from other dissolution reactions of carbonate minerals (calcite, dolomite) and evaporites (gypsum). This figure helps to highlight groundwater mineralization processes during rainwater infiltration and/or during its residence within the aquifer itself. In the absence of these reactions, all points representing the samples should be located near the origin (Mclean et al., 2000).\u003c/p\u003e\n\u003cp\u003eThe diagram in Figure 5 highlights that all analyzed groundwater samples lie in zones 2 and 4. In zone 2, the concentration of (Ca\u0026sup2;⁺+ Mg\u0026sup2;⁺) ions is higher than that of (HCO₃⁻ + SO₄\u0026sup2;⁻) ions, indicating a predominance of carbonate mineral dissolution processes. Conversely, in zone 4, the concentration of (Ca\u0026sup2;⁺+ Mg\u0026sup2;⁺) ions is lower than that of (HCO₃⁻ + SO₄\u0026sup2;⁻) ions, suggesting significant production of HCO₃⁻ and/or SO₄\u0026sup2;⁻ by sulfate mineral dissolution, likely linked to base exchange processes. In this zone, there is a release of Na⁺ and K⁺ ions, while alkaline-earth ions (Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺) are retained in the clays.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Relation entre les variables physico-chimiques des eaux souterraines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better understand the degree of dependence between the physico-chemical variables of Mutoshi groundwater, we calculated the correlation coefficients. Table 3 below provides, in matrix form, the correlation coefficient (r) values calculated for the 21 variables taken two by two.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"937\" height=\"495\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThis table gives the different correlations between the physico-chemical variables. There is a strong positive or negative correlation between T\u0026deg;C and pH (r= 0,853), T\u0026deg;C and MES (r= -0,713), Eh and MES (r= -0,742), and TDS and MOS (r= -0,743). On the other hand, medium-level positive or negative correlations are observed between T\u0026deg;C and Eh (r= 0,506), T\u0026deg;C and MOS (r= -0,514), T\u0026deg;C and Al (r= -0,579), pH and MES (r= -0,669), Eh and MOS (r= -0,528), CE and MOS (r= 0,538), CE and Na (r= -0,501), TDS and MES (r= 0,503), MES and MOS (r= 0,604), Co and K (r= -0,565), Mn and Fe (r= 0,520), Fe and Cl (r= 0,571), Al and Cl (r= 0,527), Al and SO4 (r= 0,642), Mg and K (r= 0,534), Cl and SO4 (r= 0,543). It should be noted that good positive correlations confirm that all these elements participate in groundwater mineralization. On the other hand, the negative correlation coefficients express an opposition between the variables. This means that the considered variables evolve in opposite directions relative to the center.\u003c/p\u003e"},{"header":"IV. Discussion","content":"\u003cp\u003eThe results of the chemical composition analysis of Mutoshi groundwater (Table 2) reveal high average concentrations of elements such as copper (Cu), manganese (Mn), iron (Fe), and aluminum (Al) that exceed the WHO drinking water standards. Rock samples collected in the field also exhibit high concentrations of these elements, exceeding the reference values established by the Clark scale (Table 1). The increased presence of metals such as copper, cobalt, and iron in groundwater could result from the infiltration of these elements from mine waste into underground aquifers. These contaminants can make waters unfit for human consumption and have harmful effects on local ecosystems. These findings highlight the importance of understanding the impact of waste from the Mutoshi mine on groundwater quality in the city of Kolwezi. The high concentrations of these chemical elements in the rocks and groundwater suggest potential contamination due to mining activities. It is imperative to implement monitoring and environmental management measures to mitigate the impact of mine waste on groundwater in the region. Sustainable mining practices and waste treatment technologies can contribute to limiting groundwater contamination and preserving water quality for local communities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo identify the geochemical facies of Mutoshi groundwater, we used the Piper diagram (1944) by calculating the relative proportions of the analyzed elements. This diagram is widely recognized as one of the most commonly used graphical methods in water classification, allowing the determination of similarities and differences between water samples, grouping them into sets of groups. Analysis of the diagram reveals, in order of abundance, the existence of two chemical facies for the measurement campaign (Figure 2). The majority of the analyzed water samples belong to the category of calcium-magnesium bicarbonate waters, while a smaller proportion corresponds to sodium-potassium bicarbonate waters. The enrichment in calcium and magnesium is likely due to exchanges between the alkalis (sodium and potassium) of the aquifer and the alkaline earths (calcium and magnesium) of the Katanga Supergroup rocks, indicating a fixation of alkaline earths (Ca2+, Mg2+) and a solubilization of alkalis (Na+, K+).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of origin and mineralization processes, the binary diagrams (Figures 4 and 5) helped to establish different relationships between major ions, revealing that groundwater mineralization is influenced by dissolution phenomena and basic cation exchanges between the aquifer and the rocks of the Katanga Supergroup. These results agree with those obtained by Essouli (2017), Andzaye (2015), and Moukolo (1992) in Congo Brazzaville, which also highlight the influence of dissolution phenomena and basic cation exchanges between the aquifer and the rocks.\u003c/p\u003e"},{"header":"V. Conclusion","content":"\u003cp\u003eThis study aims to contribute to the understanding of the physico-chemical characteristics of groundwater in the Mutoshi mining environment in Kolwezi, Lualaba Province, Democratic Republic of Congo. The results of the chemical analysis of the rocks reveal high concentrations of Cu, Co, Mn, Fe, Mg, and Al. Regarding the assessment of the physico-chemical quality of the studied groundwater, it is observed that the concentrations of Cu, Mn, Fe, and Al exceed the WHO drinking water standards, raising major concerns about the quality of water intended for human consumption. The hydrochemical classification of waters in the Piper diagram allowed us to distinguish two distinct hydrofacies, namely calcium-magnesium bicarbonate waters, which dominate in the majority of samples, and sodium-potassium bicarbonate waters. Examining the binary diagrams of the water samples highlighted the different processes that influence groundwater mineralization, including dissolution of carbonate, clay, and evaporite minerals. The correlations between physico-chemical variables reveal significant relationships, both positive and negative, between different parameters, thus confirming the involvement of these elements in groundwater mineralization.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis declaration is not applicable as this study does not involve any human or animal subjects.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhoussi, K. E., Loko S., Adja, M. G., Lasm, T., and Jourda, J. P., (2012). Hydrogeochemical study of fracture aquifer waters in the Paleoproterozoic basement of northeastern C\u0026ocirc;te d\u0026rsquo;Ivoire: Case of the Bondoukou region. Afrique Sciences, 08, 51 \u0026ndash; 68p.\u003c/li\u003e\n\u003cli\u003eAndzaye O.A. (2015). Contribution to the hydrochemical study of exploited aquifers in the northern part of the Brazzaville region: between Avenue de la Paix and the Djiri River, Master\u0026apos;s thesis in Geosciences, Faculty of Sciences and Technologies, Marien NGOUABI University of Brazzaville, Congo.\u003c/li\u003e\n\u003cli\u003eBarhi H., El Messari J.E.S., Mansour M.R.O. (2022). Contribution of hydrochemistry, fracturing, and statistical analysis methods to characterize groundwater in the calcareous massif of the Tlata Taghramt region, Haouz limestone chain (Northern Rif, Morocco). ESI Preprints. 785-803p. https://doi.org/10.19044/esipreprint.9.2022.\u003c/li\u003e\n\u003cli\u003eBashir, E., Huda, S. N., Naseem, S., Hamza, S., and Kaleem, M., (2017). Geochemistry and quality parameters of dug and tube w of Khipro, District Sanghar, Sindh, Pakistan. Applied Water Science, 7, 1645 - 1655 p.\u003c/li\u003e\n\u003cli\u003eBenjamin A. B. R.V., Privat T., Issa S.S. \u0026amp; Germain A.M. (2023). Origin and Processes of\u003c/li\u003e\n\u003cli\u003eMineralization of Groundwater in the Southern Part of the Marais Poitevin (PoitouCharentes-France) and its Upper Oxfordian Carbonate Substratum. ESI Preprints. 533-567pp. https://doi.org/10.19044/esipreprint.9.2023\u003c/li\u003e\n\u003cli\u003eBiemi J., (1992). Contribution to the geological, hydrogeological, and remote sensing study of sub-Sahelian watersheds of the Precambrian basement of West Africa: Hydrostructure, Hydrodynamics, Hydrochemistry, and Isotopes of discontinuous aquifers of furrows and granite areas of the upper Marahou\u0026eacute; (C\u0026ocirc;te d\u0026rsquo;Ivoire). State doctorate in Natural Sciences. University of Cocody, Abidjan, 493 p.\u003c/li\u003e\n\u003cli\u003eCidu, R., Biddau, R., and Fanfani, L., (2009). Impact of past m\u0026eacute;nin activity on the quality of groundwater in SW Sardinia (Italy). J. Geoch. Expl, 100, 125 \u0026ndash; 132 p.\u003c/li\u003e\n\u003cli\u003eDjahadi, S.D., Sandao, I., Ahmed, Y., Harouna, M., and Ousmane, B., (2021). Physico-chemical characteristics of groundwater in the basement of the Goroubi watershed in the Torodi /Liptako Niger commune. Int. J. Biol. Chem. Sci. 15(6), 2715-2729pp.\u003c/li\u003e\n\u003cli\u003eEssouli K. P., (2017). Contribution to the hydrochemical study of exploited groundwater in the northern part of Brazzaville: Origin of mineralization and physico-chemical and bacteriological quality\u0026quot;, Master\u0026apos;s thesis in Geosciences, Faculty of Sciences and Technologies, Marien NGOUABI University of Brazzaville, Congo.\u003c/li\u003e\n\u003cli\u003eHakim, D.K., Amina, A., Amina, R., Djouhra, B., Ahmed, F. (2022). Application of Multivariate Statistical Methods to the Hydrochemical Study of Groundwater Quality in the Sahel Watershed, Algeria. Journal of Ecological Engineering, 23(8), 341\u0026ndash;349pp. https://doi.org/10.12911/22998993/151147\u003c/li\u003e\n\u003cli\u003eMashauri, F., Mbuluyo, M, Nkongolo, N., (2023a). Influence of hydro-morphometric parameters on water flow in the sub-basins of the Tshopo, Democratic Republic of Congo. International Journal of Geomatics, vol. 32, no. 1, 79-98pp. https://doi.org/10.32604/RIG.2023.044124\u003c/li\u003e\n\u003cli\u003eMclean, W., Jankowski, J., Lavitt N. (2000). Groundwater quality and sustainability in an alluvial aquifer, Australia. In Sililo O. et al. (eds). Groundwater, past achievement and future challenges. A. A. Balkema (Rotterdamm), 567- 573pp.\u003c/li\u003e\n\u003cli\u003eMoukolo N. (1992). Current state of knowledge on the hydrogeology of Congo. Journal Hydrog\u0026eacute;ologie, 1-2, 47-58 pp.\u003c/li\u003e\n\u003cli\u003ePiper AM., (1944). A graphic procedure in the geochemical interpretation of water analyses.\u003c/li\u003e\n\u003cli\u003eTrans. Am. Geophysical Union, 25: 914-928pp.\u003c/li\u003e\n\u003cli\u003eYao, K.S.A., and Ahoussi, K.E., (2021). Application of multivariate statistical methods to the hydrochemical study of groundwater in a mining environment in the Center-West of C\u0026ocirc;te d\u0026rsquo;Ivoire: case of the Divo Department. Afrique Science 18(4), 53 \u0026ndash; 68pp.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"groundwater, hydrofacies, mining environment, physico-chemical characteristics, Mutoshi, Kolwezi","lastPublishedDoi":"10.21203/rs.3.rs-4558219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4558219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study examines the physico-chemical characteristics of groundwater in the Mutoshi mining environment in Kolwezi, Democratic Republic of Congo. The adopted methodology involved collecting samples from geological formations and groundwater in the study area. Rocks were analyzed to determine concentrations of major and trace elements, while detailed physico-chemical parameter analyses were conducted on groundwater samples. The results highlight high concentrations of Cu, Co, Mn, Fe, Mg, and Al in the rocks. Groundwater also exhibits concentrations of Cu, Mn, Fe, and Al exceeding the WHO's drinking water standards, raising concerns about the quality of water for human consumption. Hydrochemical classification of the waters, performed using the Piper diagram, revealed two distinct hydrofacies. Calcium-magnesium bicarbonate waters dominate in most samples, while sodium-potassium bicarbonate waters are also present. Processes influencing groundwater mineralization include dissolution of carbonate, clay, and evaporite minerals. These findings underscore the importance of understanding these processes to adequately assess and manage groundwater quality in the Mutoshi region.","manuscriptTitle":"Physico-Chemical Characteristics of Groundwater in the Mutoshi Mining Environment in Kolwezi (Lualaba, DR Congo)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-17 08:00:00","doi":"10.21203/rs.3.rs-4558219/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":"96f5eb69-96d3-4c9c-b194-7e32a8a14eac","owner":[],"postedDate":"July 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T01:17:52+00:00","versionOfRecord":{"articleIdentity":"rs-4558219","link":"https://doi.org/10.21926/aeer.2502019","journal":{"identity":"advances-in-environmental-and-engineering-research","isVorOnly":true,"title":"Advances in Environmental and Engineering Research"},"publishedOn":"2025-04-16 00:00:00","publishedOnDateReadable":"April 16th, 2025"},"versionCreatedAt":"2024-07-17 08:00:00","video":"","vorDoi":"10.21926/aeer.2502019","vorDoiUrl":"https://doi.org/10.21926/aeer.2502019","workflowStages":[]},"version":"v1","identity":"rs-4558219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4558219","identity":"rs-4558219","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}