Introduction

Lakes play a crucial role as ecosystems since they support both aquatic and terrestrial biodiversity and offer a range of ecological services that contribute to societal well-being1,2. At present, many urban lakes are at risk of deterioration due to the introduction of various pollutants from surface runoff, discharges of municipal wastewater, and atmospheric deposition3,4. Lake sediments serve as crucial information sources in the investigation of terrestrial environmental alterations5,6,7. The combination of minerals found in lake sediments acts as a sensitive indicator in studies of sediment quality and holds significant information regarding a variety of pollutants8. Understanding the mineral characteristics and geochemical makeup of sediments is vital for evaluating the quality of lake sediments.

The principal components of lake sediments encompass quartz, feldspars, clays, calcium carbonate, and a variety of silicate minerals, in addition to organic matter derived from vegetative detritus, decomposed aquatic flora and fauna, and humic substances9,10. Minerals found in sediments are typically categorized into primary and secondary minerals. Quartz and feldspar are the examples of primary minerals, which mechanically disintegrates into sediments due to the differences in temperature and pressure11. Such alterations enable the development of new types of clay minerals (i.e. secondary minerals) including kaolinite and chlorite12. The mineralogical characterization of complex sediment samples serves as a crucial approach for assessing anthropogenic impacts and alterations in both the chemical and physical properties of lake ecosystems, alongside their functional dynamics13. The mineral composition determines the nature of interaction between the sediment and water and, therefore, holds a central role in the management of several issues related to the geo-environmental status of a lake14. In sediment quality assessment, physicochemical properties are as crucial as mineral composition. Variations in pH and electrical conductivity (EC) can be substantial and are influenced by a range of environmental factors, including climatic conditions, the presence of local biota, geological substrates, and anthropogenic activities. Understanding these parameters is essential for comprehensive sediment analysis.

Significant research has been carried out globally on the variations in the composition of elements and minerals within lacustrine systems, highlighting the weathering processes that affect their geochemical properties. Wang et al.15 investigated eight trace elements in sediments of Lake Aha, an urban lake in Guiyang City, Southwest China, revealing that, apart from Cr and Pb, the median concentrations of Cd, As, Zn, Ni, Co, and Cu exceeded regional background values. Central lake sediments exhibited notably higher trace element concentrations than peripheral cores, highlighting localized contamination from historical coal mining and urbanization. In the Central Gangetic Plain, Dubey et al.16 employed FTIR, XRD, SEM, and EDS to evaluate the impact of intensive aquaculture and land use changes on lake sediments. The study found significant alterations in pH, organic carbon, and phosphorus content in aquaculture-influenced lakes, emphasizing the need to monitor spatial and annual variations in mineral fluxes for understanding sediment dynamics and ecosystem health.

Mir et al.17 analyzed a 753-year sediment core from Honnamanakere Lake, southern India, to assess paleoclimate and pollution history. Their findings show increased heavy metal contamination, especially Cr and Ni, linked to industrialization in the past three centuries. Multivariate analysis indicated contributions from natural weathering, agrochemical use, and atmospheric deposition. Vasistha and Ganguly6 characterized the physicochemical and mineralogical properties of soil and sediment from two lakes in Haryana using SEM, XRD, and EDS. Their results showed permissible limits for most parameters, but morphological differences and elemental accumulation pointed to watershed erosion as a significant driver of sedimentation. Collectively, these studies underscore the importance of integrating mineralogical, chemical, and statistical approaches to trace pollution sources and evaluate ecological risks in lake systems.

Despite the existing study on radionuclide activity in Puliyanthangal Lake18 to the best of our knowledge, this study represents the first comprehensive investigation of the lake’s sediments focusing on physicochemical properties, mineralogical composition, elemental distribution, and morphological changes. The area’s industrial impact on sediment characteristics has not been extensively characterized before. This study fills an important knowledge gap by integrating multiple advanced analytical techniques (FT-IR, XRD, SEM-EDS, and multivariate statistics) to assess sediment contamination and spatial variability in this understudied industrial region of Tamil Nadu, India. The findings will provide valuable insights for environmental management and pollution mitigation in Puliyanthangal Lake.

The main aim of this paper is to identify and examine the significant physicochemical characteristics, as well as the mineral and elemental compositions, of lake sediments, along with the changes in morphology. A thorough understanding of these interrelationships is essential for the effective management and conservation of lake ecosystems, as modifications in sediment mineralogy and elemental make-up can lead to significant repercussions for sediment-water quality and aquatic biota in Puliyanthangal Lake, situated in the Ranipet industrial area of Tamil Nadu, India.

Materials and methods

Study area

Puliyanthangal Lake is situated adjacent to Puliyanthangal village within the periphery of the SIPCOT industrial complex, in the Ranipet district of Tamil Nadu. The industrial area is located around the NH–4 Chennai–Bangalore highway and is identified for its industrial activities namely leather tanning, dyeing and electroplating as well as management of agricultural and domestic waste19. The lake itself is classified as shallow, covering approximately 29.92 hectares18,20 and exhibits a trapezium-like morphology. Geographic coordinates place it at approximately 12.9648° N latitude and 79.2937° E longitude, located about 4 km west of Ranipet.

Puliyanthangal Lake is significantly affected by various pollutants, including industrial waste, organic matter, suspended solids, detergents, and lubricating oils, resulting in substantial environmental degradation. Currently, there is a lack of data regarding its mineral and physicochemical properties in existing literature. Consequently, the site has been selected for an in-depth study to assess the environmental risk exposure experienced by the local population and other biota within the vicinity.

Sample collection

In this study, sediment samples were systematically collected from twenty-one designated points across the study area, as illustrated in Fig. 1. The geographical coordinates of each sampling location were recorded using a handheld GARMIN GPS device, detailed in Supplementary Table S1. A random sampling strategy was employed, following the methodology outlined by Thangam et al.21. The arbitrary collecting of samples within the prescribed boundaries of the area of concern is known as random sampling. The arbitrary selection of sample points requires the selection of each sampling site independently of the location of all other points, resulting in an equal possibility of selection for all places within the area of concern22. At each location, five representative subsamples were obtained: one from the central point and four from the corners of a 1 m2 quadrat, which were subsequently combined to form a composite sample for that specific grid point23. Sediment was extracted using a digging hoe from a depth of 5–10 cm, with each sample weighing approximately 2 kg. The collected sediment was placed in sealed polythene bags, labeled with unique sample IDs (L1–L21), and transported to the laboratory for further analysis.

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Sediment sampling points in the study area.

Sample preparation

Before the drying process, all extraneous materials such as leaves, twigs, and branches from the aquatic macrophytes were meticulously removed from the collected sediments. The samples were air-dried at ambient temperature for 7 days to mitigate the loss of any volatile elements, after that they were subjected to the sieve analysis for the proportion of grain size. Samples were ground using a ceramic mortar and pestle to study the minerals and elements until a uniform sediment powder was achieved. Subsequently, the ground sediments were passed through a 63 μm mesh sieve for further analysis. The sieved sediments were then re-dried in the oven at 60 °C for 2 h to eliminate residual moisture. These prepared sediment samples were subsequently utilized for further laboratory analyses.

Physicochemical analysis of lake sediments

Measurement of sediment characteristics

The grain-size distribution of 21 sediment samples was analyzed using a mechanical sieving technique with a Rotap sieve shaker. This method is known for its precision in determining the proportions of sand, silt, and clay within sedimentary materials24,25. The sieve assembly was organized in order of ascending mesh sizes, culminating in a round-bottom pan, referred to as the receiver, which collects the residues from the sediment samples. Initially, each sample was weighed, then poured onto the pre-assembled sieves placed in the shaker. The shaking process was conducted for a duration of 2–3 min, facilitating the separation of particle sizes. Post-sieving, the mass of the material retained on each sieve and in the receiver pan was measured using an analytical balance, and these weights were meticulously recorded and organized into a table. The percentages of sand, silt, and clay were subsequently calculated using Eq. (1)26,

$$\:\text{P}\text{r}\text{o}\text{p}\text{o}\text{r}\text{t}\text{i}\text{o}\text{n}\:\text{o}\text{f}\:\text{g}\text{r}\text{a}\text{i}\text{n}-\text{s}\text{i}\text{z}\text{e}\:\text{f}\text{r}\text{a}\text{c}\text{t}\text{i}\text{o}\text{n}=\frac{{\text{W}}_{1}}{{\text{W}}_{2}}\:\times\:100.$$
(1)

where W1 denotes the weight of the material retained on each sieve, and W2 represents the total initial weight of the sediment sample.

Measurement of pH

pH was measured to identify the nature of acidity or alkalinity of sediment samples26 through the use of an ELICO LI 120 pH meter with one electrode system. The calibration process in the pH meter was performed with a standard pH 7 buffer solution and then the meter was adjusted with known pH of buffer solutions 4.00 and 10.00. About 25 g of air-dried sediment sample was taken and transferred into a 100 mL glass beaker containing 50 mL of deionized water. The solution is obtained by stirring with a magnetic pellet for 30 min to allow stabilization27. In this way, this dispersion was prepared in a 1:2 ratio of sediment: water, for each of the samples. Then, the electrode was immersed in the solution for 30 s to allow the meter to stabilize and make good contact with the dispersion, taking care not to hit the bottom or side of the glass beaker. Finally, the pH value was read and recorded in the automatic display of the pH meter.

Measurement of electrical conductivity (EC)

Electrical conductivity (EC) quantifies the capacity of an aqueous solution to conduct electrical current, primarily due to the presence of dissolved materials. This measurement is typically expressed in microsiemens per centimeter (µS/cm)27. In this study, the EC of the sediment samples was measured using an EQUIP-TRONICS conductivity meter with a built-in magnetic stirrer model no. EQ-664. Calibration of the meter was performed using a standard 0.005 N KCl solution, achieving a precision of 7200 ± 1 (µS/cm)26. Each sample was prepared by dispersing it in water at a 1:2 ratio prior to conducting the conductivity measurement.

Analytical methods used for the study

The analytical techniques for mineralogical and elemental analysis of lake sediments are briefly described below.

Mineralogical analysis of the lake sediment by fourier‑transform infrared spectroscopy (FT-IR)

Mineral identification in sediment samples was conducted using an FT-IR spectrometer (Perkin-Elmer) with a spectral range of 4000–400 cm− 1 and a resolution of 4 cm− 1, equipped with a LiTaO3 infrared detector (15,700 –370 cm− 1). For the analysis, the KBr pellet method was employed28,29. Approximately 0.1 to 2% of the powdered sediment sample was mixed with analytical-grade KBr at a 1:20 ratio. The mixture was ground in an agate mortar and pestle until the crystallites were sufficiently reduced in size to minimize scattering effects on the infrared beam, which could lead to a distorted baseline in the spectrum. The die was thoroughly cleaned using acetone and water prior to use, and the finely ground powder was prepared within a 7 mm collar. Using a hydraulic press, the sediment was compacted for 2 min to create a thin, transparent KBr pellet, ensuring no white spots were present, as these indicate either insufficient grinding or poor dispersion of the sample. After disassembly of the die set, the collar containing the pellet was placed onto the sediment sample holder, which was subsequently secured within the FT-IR spectrometer. Analysis was performed in the spectral region of 4000–400 cm− 1, with KBr serving as the background reference, using IR solution software (version 1.10)30.

Mineralogical analysis of lake sediment by x‑ray diffraction spectroscopy (XRD)

X-ray diffraction is a powerful non-destructive technique for mineral characterization having wide applications in determining crystal structure and size, identifying phases, assessing crystallographic orientation, and measuring lattice parameters, dislocation density, residual stress/strain, phase transformation, and thermal expansion coefficient31. Each crystalline mineral has a unique XRD (X-ray diffraction) pattern, which acts as a distinctive identifier32. XRD is utilized to analyze the diffraction intensities and peak positions of a sample, while Bragg’s law is employed to compute the corresponding inter-planar spacing. By utilizing computerized searching, unidentified sample components can be recognized by matching the strongest peak intensity line with inter-planar spacing values stored in a database, such as the Joint Committee on Powder Diffraction Standard (JCPDS)33.

The lake sediment samples underwent mineral analysis using the powder X-ray diffraction (XRD) technique at the SSN Research Centre, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam. The dried and well-homogenized milled sediments were examined using the Multifunctional PANalytical EMPREAN X-ray Diffractometer with CuKα1 radiation (λ = 1.5406 Å) at room temperature. The 2θ position values were set from 10–80º. The X-ray reflections were recorded as peaks of varying heights on a strip chart. The height of the peaks corresponded to the intensity of reflection. The characteristics of each mineral phase’s peaks were identified by comparing the 2θ values with the support of accessible research papers34,35,36 and by utilizing the record of the International Centre for Diffraction Data37. In addition, a small crystallinity value means a higher degree of crystallization, or vice versa. Therefore, the degree of crystallinity was calculated by dividing the total number of areas under diffraction peak by total area of diffractrogram38.

Surface morphology of lake sediment using scanning electron microscope (SEM)

The surface morphology of sediment samples from Puliyanthangal Lake was analyzed using a Carl Zeiss Microscopy GmbH scanning electron microscope (SEM, Model EVO 18, UK), which operates with high vacuum and variable pressure modes, achieving a resolution of 3 nm. Specimens were examined under SEM to obtain detailed insights into their shape, size, and surface texture35,39. This technique is widely applied across various environmental science and technology fields to assess microstructural characteristics.

Elemental analysis of lake sediment using energy‑dispersive x‑ray spectroscopy (EDS)

Sediment samples collected from the study area were analyzed using Energy Dispersive X-ray Spectroscopy (EDS) to determine their elemental composition. The EDS system (EDAX Inc., U.S., with instrumentation from Quorum Technologies, U.K.) operates with a resolution of 5.9 keV and utilizes various modes, including spectrum acquisition, quantification, point and identifying analysis, elemental mapping, and line scanning. Essential accessories for the EDS setup included a sputter coater for carbon evaporation and a critical point dryer. The computer-controlled EDS analysis enabled detailed compositional profiling of the sediment particles, facilitating the estimation of relative mineral abundances through elemental analysis39. The EDS technique works by detecting X-rays emitted from the sample upon bombardment with an electron beam, allowing for precise characterization of the sediment’s elemental constituents.

Multivariate statistical analysis

Multivariate statistical techniques such as Pearson’s correlation, cluster analysis, and principal component analysis were employed to interpret the interrelationships among heavy metals and to identify their potential sources40. These statistical approaches are essential in distinguishing between natural (geogenic) and anthropogenic contributions by revealing patterns and associations that are not evident from raw concentration data alone. Pearson’s correlation helps assess linear relationships among metal concentrations, while cluster and principal component analyses group elements with similar behavior, suggesting common origins41. The statistical software IBM-SPSS version 20 was used to perform Pearson’s correlation analysis, cluster analysis, and principal component analysis among the heavy metals, and the ORIGIN PRO (v2021) was used for graphing analysis. These techniques collectively support a more accurate classification of pollution origins and facilitate a detailed evaluation of heavy metal interactions in the sediment environment.

Results and discussion

Physicochemical analysis of lake sediments

The mineral composition of sediments primarily consists of illite (47–55%), kaolinite (25–35%), and montmorillonite (16–26%)34,36,42,43. Typically, the levels of chemical elements in these sediments are influenced by physiographic conditions and hydrodynamic processes associated with fine deposits.

According to the results presented in Supplementary Table S2, the sand content varies from 30% (L8) to 82% (L11) by mass, silt content ranges from 16% (L11) to 60% (L16) by mass, and clay content ranges from 2% (L11) to 13% (L6) by mass. This variation may reflect the geological patterns of the study area, and the irregular distribution suggests that human and industrial activities significantly contribute to the concentration of fine particles in the central, northern, and eastern parts of the Lake. Figure 2, illustrates the spatial distribution of grain-size fractions in the lake sediments: (a) sand, (b) silt, and (c) clay.

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Spatial distribution of grain-size fractions in lake sediments (a) sand, (b) silt and, (c) clay.

From the results of Supplementary Table S2, over 80% of the lake samples consist primarily of sand, while the remaining 20% are dominated by silt. In contrast, the clay content in the lake is quite low, a result of the non-stagnant water conditions present in this main section of the lake42,43. Therefore, the particle size distribution in sediment samples impacts the mineralogical composition of the sediments44.

To assess the acidity or alkalinity of the sediments, pH measurements were conducted, and the results are presented in Supplementary Table S2. Among the 21 samples analyzed, the sample L19 exhibited the highest pH value at 8.26, located near an industrial zone, while the lowest pH of 7.51 was recorded at sample L2, situated close to a roadway. Figure 3, illustrates the spatial distribution of pH across the lake sediments. Notably, all sampled sediments displayed pH values exceeding 7, indicating their alkaline nature, which may be attributed to industrial effluents in the vicinity of the lake45. This observation further implies that the concentration of hydroxide ions (OH−) in the sediments surpasses that of hydrogen ions (H+), likely a result of diminished water input into the lake.

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Spatial distribution of pH in lake sediments.

To identify the materials that can conduct electricity (EC) in lake sediments, we measured the electrical conductivity of the collected sediment samples, with the results presented in Supplementary Table S2. Generally, sediments with an EC below 400 µS/cm are considered marginally non-saline, while those with an EC above 800 µS/cm are classified as severely saline46. In this study, the electrical conductivity of the lake sediments ranged from 2400 to 3600 µS/cm, with an average of 3133.33 µS/cm. Figure 4, illustrates the spatial distribution of electrical conductivities in the lake sediments. The results indicate that the lake sediments are severely saline, suggesting the presence of electrically active materials within them.

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Spatial distribution of electrical conductivity in lake sediments.

Mineralogical analysis of sediments using FT-IR

The best FT-IR spectrum from all the sampling points was selected as the representative spectrum for this study, and the chosen representative FT-IR spectra are displayed in Fig. 5. The FT-IR absorption frequencies of the peaks were recorded in the range of 400–4000 cm− 1, and the corresponding minerals are listed in Supplementary Table S3. These minerals were identified by comparing the observed frequencies with data from the existing literature30,47,48. The band assignments for different minerals found in the lake sediment samples from the Ranipet district are provided in Supplementary Table S4. The identified minerals in the lake sediments include quartz, feldspar, clay minerals (kaolinite and montmorillonite), carbonate minerals (calcite), and organic carbon. A detailed discussion of these minerals follows.

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FT-IR spectrum of representative lake sediments.

Quartz

Among the principal mineral groups, quartz stands out as the most abundant and pervasive. It is a silica mineral, comprising silicon dioxide (SiO2), and crystallizes in a trigonal system. Its specific gravity ranges from 2.60 to 2.65, with an average of approximately 2.62. Analysis of the FT-IR absorption spectra reveals distinctive peaks corresponding to quartz, notably at 779 cm− 1, 780 cm− 1, and 796 cm− 1. The strong absorbance peak at 796 cm− 1, along with the peak at 780 cm− 1, is attributable to the Si–O symmetrical stretching vibrations. Conversely, the frequency of 779 cm− 1 aligns with the Si–O symmetric bending vibration. These FT-IR results unequivocally confirm the presence of quartz minerals across all sediment samples analyzed.

Alkali feldspar

Feldspars are aluminosilicate minerals characterized by a framework crystal structure, where essential cations include Na+, K+, Ca2+, and Ba2+. There are three types of alkali feldspar: orthoclase, microcline, and sanidine. Specifically, orthoclase (common Potash feldspar) has a chemical formula of KAlSi3O8, belongs to the monoclinic crystal system, and exhibits a specific gravity ranging from 2.56 to 2.58. In the analysis of sediment samples, as shown in Supplementary Table S3, peaks at 535 cm− 1, 467 cm− 1 and 468 cm− 1 which indicates the presence of alkali feldspar. The peak at 535 cm− 1 corresponds to the asymmetrical bending vibration of Si–O, while the absorption bands at 467 cm− 1 and 468 cm− 1 are attributed to Si–O–Si bending. These results confirm the presence of alkali feldspar in all sediment samples.

Clay minerals

Clay minerals are primarily categorized into three groups: kaolinite, illite, and montmorillonite. Notably, kaolinite and montmorillonite are consistently present across all sediment samples analyzed, as detailed in Supplementary Table S3. Kaolinite serves as a fundamental raw material in ceramics and is extensively utilized in the production of coated paper49. Its chemical formula is Al2Si2O5(OH)4, and it crystallizes in the triclinic system with a specific gravity of 2.6. FT-IR analysis reveals distinct absorption bands at approximately 1030–1035 cm− 1, 1115 cm− 1, 3620–3623 cm− 1, and 3694 cm− 1, which are indicative of kaolinite’s presence in all samples. The strong bands at 3694 cm− 1 and 3623 cm− 1 confirm the O–H stretching vibrations characteristic of kaolinite. Furthermore, the peak observed at 3620 cm− 1 is associated with the O–H stretching of inner hydroxyl groups50. The Si–O asymmetrical stretching vibrations and Si–O stretching associated with the clay minerals are identified at 1115 cm− 1 and within the range of 1030–1035 cm− 1, respectively. Additionally, peaks at 3440–3445 cm− 1 and 1645 cm− 1 signify the presence of montmorillonite. The absorbance at 3440–3445 cm− 1 corresponds to O–H stretching of water molecules adsorbed onto the mineral’s surface, while the band centred on 3400 cm− 1 pertains to O–H stretching of water within the interlayer spaces of montmorillonite.

Carbonate minerals

Calcite, with the chemical formula CaCO3, crystallizes in the hexagonal system and has a specific gravity of 2.71. It is commonly found in sedimentary rock formations as a secondary mineral, but its primary occurrence as a mineral in some of the igneous rocks is also evident. In the current study, absorption bands observed at 1428 cm− 1, 1432 cm− 1, and 1445 cm− 1 are attributed to calcite, as noted by Ravisankar et al.51. Aragonite is equally significant as calcite and dolomite, and its presence in the samples has been confirmed through X-ray diffraction (XRD) analysis.

Organic carbon

The weak absorbance peaks observed in the ranges of 2850–2855 cm− 1 and 2920–2930 cm− 1 indicate the potential presence of organic carbon52. These vibrational bands are attributed to C-H absorptions from contaminants found in all sediment samples analyzed.

Extinction coefficient of minerals

In order to analyze the relative distribution of minerals, the extinction coefficient was calculated using the below expression53,54.

$$\:\text{E}\text{x}\text{t}\text{i}\text{n}\text{c}\text{t}\text{i}\text{o}\text{n}\:\text{c}\text{o}\text{e}\text{f}\text{f}\text{i}\text{c}\text{i}\text{e}\text{n}\text{t}\:\left(\text{K}\right)=\:\frac{\text{D}\text{A}}{\text{M}}\:\:{\text{c}\text{m}}^{2}/\text{m}\text{g}.$$
(2)

The optical density (D) of light is defined as the logarithm to the base 10 of the reciprocal of the transmitted radiant power (T). Additionally, A represents the area of the prepared sample pellet, and M denote the mass of the sample. In this study, we calculated the extinction coefficients for quartz, orthoclase feldspar, montmorillonite, and illite corresponding to the FT-IR absorption peaks at 779, 535, 3440, and 915 cm− 1, respectively. The determined extinction coefficient values are presented in Supplementary Table S5. The results indicate that quartz is the predominant components, while orthoclase feldspar, montmorillonite, and illite are present in lesser amounts within the analyzed samples. To analyze the spatial distribution of minerals in sediments, we examined the frequency distribution of their extinction coefficients using histograms, as illustrated in Fig. 6. The histograms for quartz, orthoclase feldspar, montmorillonite, and illite revealed a bell-shaped curve, indicating that these minerals exhibit a symmetrical (normal) distribution within the sediment matrix.

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Frequency distribution of extinction coefficient of minerals.

Findings of the mineralogical analysis from XRD

This study aims to investigate the mineralogical composition and analyze the crystalline and non-crystalline nature of minerals in sediment samples. The qualitative mineralogy is assessed using standard XRD interpretation techniques55. The best X-ray diffraction spectrum from all sediment samples is considered representative for the study. A selected representative XRD spectrum of lake sediments is shown in Fig. 7. The mineral phases present in the samples are identified by comparing the XRD patterns with the JCPDS database. In this analysis, minerals are identified using 2θ (degree) values, d-spacing (in Å), and their respective hkl (Miller indices), which are summarized in Supplementary Table S6. The XRD results reveal the presence of several minerals, including chloritoid, quartz, calcite, hornblende, microcline, albite, kyanite, goethite, and aragonite. The absence of certain minerals in the XRD analysis, which are identified through FT-IR, can be attributed to the disordered nature (loss of crystalline structure) of these minerals. Conversely, some minerals, such as chloritoid, hornblende, microcline, albite, kyanite, goethite, and aragonite, are only detected in the XRD analysis. This finding indicates that these minerals maintain a crystalline order, albeit in very small quantities.

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XRD spectrum of representative lake sediments.

During the data analysis, additional steps were taken to accurately determine the crystalline and low-crystalline (amorphous) phases of minerals in lake sediment. The degree of crystallinity for representative lake sediment samples is as follows: 45.49% for L1, 41.61% for L3, 39.81% for L6, and 48.52% for L9. Based on these samples, the order of degree of crystallinity from lowest to highest is L6 < L3 < L1 < L9. This indicates that sample L6 has a higher degree of crystallinity, while sample L9 shows a lower degree. These results highlight the low-crystallinity phases and clearly represent several noise peaks in the sediment samples, as illustrated in Fig. 7. Low crystallinity has been observed in the mineral albite, which shows a peak at 47.16 degrees with a d-spacing of 1.929 Å. Similarly, calcite and aragonite are noted as low crystallinity components in the sediment samples. X-ray diffraction results indicate that goethite (FeO(OH), iron oxide hydroxide) and kyanite (Al2SiO5, aluminium silicate) are recorded at approximately 67.15 degrees with a d-spacing of 1.394 Å and at 54.87 degrees with a d-spacing of 1.675 Å, respectively, due to their low crystallinity. Additionally, the chemical composition associated with low crystallinity can provide insights into the nature of its potential constituents.

SEM analysis of the lake sediments

SEM plays a crucial role in elucidating the characteristics of various sedimentary lacustrine environments, providing insights into the depositional and transport history of clastic sediments12,56. The SEM photomicrographs captured at optical resolution of 50 μm, 30 μm, and 20 μm highlight the sediment’s diverse particle sizes and morphologies57. In Figs. 8 and 9, the SEM images of samples L2 and L7 demonstrate the presence of intricate aggregates, exhibiting shapes such as symmetrical, spheroidal, and ellipsoidal forms. Notably, the pre-treatment applied to the sediment samples effectively standardized them by removing agglomerations and debris without affecting the inherent sediment morphology.

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SEM micrograph for lake sediment sample L2 (a) 50 μm; (b) 30 μm; (c) & (d) 20 μm.

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SEM micrograph for lake sediment sample L7 (a) 50 μm; (b) 30 μm; (c) & (d) 20 μm.

This is the first study represents the sediment morphology in a lake situated in an industrial region of Tamil Nadu, highlighting significant dissimilarities in the sedimentary patterns observed. The analysis revealed variations in both the shape and amorphous character of the sediment within the study area. Notably, temporal fluctuations in the morphology of the lake sediments appear minimal. To elucidate the long-term morphological dynamics of the lake sediments, future research should be conducted over extended periods, as structural changes in sediments typically require considerable timescales to become apparent.

Elemental analysis of the sediments using energy‑dispersive x‑ray spectroscopy (EDS)

Sediment samples were analyzed for various metals, including sodium (Na), aluminum (Al), magnesium (Mg), silicon (Si), potassium (K), and calcium (Ca). Additionally, first-row transition metals such as titanium (Ti), vanadium (V), iron (Fe), cobalt (Co), copper (Cu), and zinc (Zn) were examined, along with heavy metals like lead (Pb), cadmium (Cd), chromium (Cr), mercury (Hg), and arsenic (As)58,59. According to Chandrasekaran et al.35 sediments typically contain high levels of silica, alumina, and iron. The elements mentioned above play a key role in the formation of biological materials: calcium is essential for calcium carbonate, iron contributes to iron oxides, manganese is important for manganese oxides, while aluminum and silicon are integral to biogenic silica (SiO2), quartz, and aluminosilicates found in lake sediments60. The elemental components found in the sediment samples from the lake in the study area were examined to gain insights into the elemental chemistry of the sediment41. The outcomes obtained from the EDS spectra indicated the diverse composition of the samples. Analyzing the sediments with computer-assisted elemental analysis via EDS provides compositional details about the sediment particles and enables assessment of the relative abundance of various minerals39. Figure 10, displays the EDS spectrum for lake sediment samples L2 and L7.

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EDS spectrum with respective SEM images of lake sediment samples L-2 and L-7.

The elemental analysis of the sediments from Puliyanthangal Lake shows a strong correlation with the minerals identified through FT-IR and XRD analyses. The key elements responsible for the formation of quartz, feldspar, kaolinite, kyanite, chloritoid, hornblende, and albite, as reported in the FT-IR and XRD analyses, include silicon (Si) and oxygen (O), along with aluminum (Al), potassium (K), iron (Fe), magnesium (Mg), sodium (Na), and several other trace elements identified in the EDS analysis. Additionally, for the formation of calcite and aragonite (CaCO3), also noted in the FT-IR and XRD analyses, the primary elements involved are carbon (C), oxygen (O), and calcium (Ca), as indicated by the EDS results. These elements interact with various geological and environmental factors, influencing the unique characteristics of the lake sediments and contributing to the overall composition of the lake ecosystem61. Figure 11, shows the elemental overlay of the sediment samples.

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Elemental overlay of the sediment samples L2 and L7.

The geomineral analysis of sediments in Puliyanthangal Lake indicates a significant anthropogenic influence, particularly linked to extensive industrial operations that contribute effluents laden with heavy metals into the sediment matrix62. This anthropogenic impact has altered the elemental composition of the sediments within the lake ecosystem. Supplementary Table S7 presents the weight% of various elements detected in the sediments of study area.

Multivariate statistical analysis

Multivariate statistical techniques are essential tools in environmental geochemistry, as they help to decipher complex datasets and identify potential sources of contamination. In this study, Pearson’s correlation, hierarchical cluster analysis, and principal component analysis (PCA) were employed to examine the relationships among heavy metals in the sediments and to distinguish between natural and anthropogenic sources60.

Pearson correlation analysis was used to assess the strength and direction of linear relationships between metal concentrations. The correlation coefficients (r) range from − 1 to + 1, with higher absolute values indicating stronger associations. As shown in Fig. 12, strong positive correlations were observed between Cr and Pb (r = 0.92), Pb and As (r = 0.74), and As and Cr (r = 0.84), suggesting that these elements likely share a common anthropogenic source, such as industrial effluents or runoff. In contrast, metals such as Cd, Fe, Co, Ni, Cu, Zn, and Hg were intercorrelated among themselves but negatively associated with Cr, Pb, and As, implying different input pathways possibly a combination of natural geogenic processes and human activity63,64.

Fig. 12
Fig. 12The alternative text for this image may have been generated using AI.
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Pearson correlation coefficient matrix of heavy metals in the study area.

Cluster analysis (CA) further supported these findings by grouping metals with similar behaviors. Using the average linkage method and correlation coefficient distance, the dendrogram (Fig. 13) identified two distinct clusters. Cluster I included Pb, Cr, and As, while Cluster II encompassed Cd, Fe, Co, Ni, Cu, Zn, and Hg. This classification suggests differentiated source contributions with Cluster I likely linked to industrial discharge and Cluster II associated with both geogenic sources and diffuse pollution from surrounding catchments60.

Fig. 13
Fig. 13The alternative text for this image may have been generated using AI.
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The results of the dendrogram of hierarchical cluster analysis in studied sediments.

Principal component analysis (PCA) was performed to reduce data dimensionality and identify dominant pollution factors. Two principal components explained a cumulative variance of 94.3% (illustrated in the Fig. 14), with PC1 (86.8%) showing strong positive loadings for Ni, Cd, Cu, Zn, and Fe indicating a mixed origin from weathering and possibly domestic or agricultural inputs. PC2 (7.5%), with positive loadings for As, Cr, and Pb, points toward localized anthropogenic contamination, particularly from industrial sources41. These PCA results corroborate both the correlation matrix and HCA, enhancing the confidence in source apportionment. This integrated statistical approach enables a clearer understanding of the spatial variability and origins of contaminants in Puliyanthangal Lake sediments. By distinguishing between natural and human-induced inputs, it supports more targeted environmental monitoring and pollution control strategies.

Fig. 14
Fig. 14The alternative text for this image may have been generated using AI.
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Principal component analysis of heavy metals: component plot in 2D rotated space.

Conclusion

Anthropogenic activities, such as discharging industrial effluents, domestic wastewater, and conducting aquaculture operations, significantly impact the mineral and elemental composition of the sediments in Puliyanthangal Lake. The results show that the lake sediments are predominantly composed of sand, followed by silt and then clay. Almost all sediment samples exhibit alkaline conditions and are severely saline due to the discharge of effluents from nearby industries. Quartz is the primary mineral identified in all samples, with strong peaks at 779 cm− 1, 780 cm− 1, and 796 cm− 1, confirmed by FT-IR spectroscopy and XRD analysis. Clay minerals are the second most abundant group in the sediment samples, while other minerals are present in smaller quantities. XRD results also indicate the presence of additional minerals, including chloritoid, hornblende, albite, kyanite, goethite, and aragonite. Furthermore, the XRD pattern illustrates the non-crystalline nature of these sediments.

A normal distribution was observed for quartz, orthoclase feldspar, montmorillonite, and illite within the sediments. SEM analysis reveals a symmetrical, spheroidal, and ellipsoidal morphology of the sediments, indicating variations in both shape and amorphous characteristics throughout the study area. EDS analysis highlights the significance of specific elements responsible for forming minerals. Additionally, multivariate statistical analysis provides valuable insights into the presence of heavy metals, concluding that carcinogenic elements such as chromium (Cr), lead (Pb), and arsenic (As) are introduced into the lake due to the anthropogenic impact of discharging industrial effluents. These findings are crucial for characterizing and identifying the minerals in the sediments, offering insights into their structural properties, composition, and potential environmental implications.

The major limitation of this study is the collection and analysis of only surface sediments, which means that potential contamination in deeper and older layers remains unexamined. Additionally, seasonal variations were not accounted for, which may influence the observed levels of pollution. Also, the research did not assess the biological effects of the identified contaminants on human health and the environment. To improve future studies, it is recommended that sediment sampling be conducted across multiple seasons to better understand how contamination levels fluctuate over time. Future research should also consider collecting deeper sediment cores to investigate historical pollution trends and include biological assessments to evaluate the ecological and health impacts of sediment-bound heavy metals.