Effect of minerals and heavy metals in sand samples of Ponnai river, Tamil Nadu, India

River sand samples have been collected from Ponnai river, Tamil Nadu, India for characterization of minerals and heavy metals by different spectroscopic techniques. Initially, the samples were subjected by Fourier Transform-Infra Red (FT-IR) spectroscopic technique and infra-red absorption bands values are observed in the range of 515–520, 695–700, 775–780 cm−1 which shows the presence of quartz in all the samples. Similarly, infra-red peaks were absorbed for feldspar, kaolinite, calcite, gibbsite and organic carbon and confirmed by X-Ray diffraction (XRD) technique. Additionally, zircon, aragonite, magnetite and kyanite minerals were identified in the samples using only the XRD method. The concentration of heavy metals such as Pb, Cr, Zn, Ni, Hg, As, Mn, Cu has been determined by flame atomic absorption spectrometry (FAAS). An average metal concentration measured in mg kg−1 were: Pb 0.12, As 0.15, Hg 0.13, Cu 2.80, Zn 10.15 Cr 12.70, Ni 2.86 and Mn 104.94 and hence found in the order of Mn > Cr > Zn > Ni > Cu > As > Hg > Pb. These average values do not exceed the world average value and hence potentially do not affect the quality of sand in the river. In addition to that, presences of heavy metals are confirmed by scanning electron microscope equipped with energy dispersive X-ray spectrometry (SEM/EDS) analysis. In order to understand the possible natural and anthropogenic sources of heavy metals, multivariate statistical techniques such as Pearson correlation, principal component and cluster analysis were performed. Results obtained from the statistical techniques were good agreement with each other.

chromium can be determined by UV-Vis spectrometer 24,25 . On the other hand, XRF can also be used for quantification of heavy metals and total bromine 26 . In this work, heavy metals such as Pb, Cr, Zn, Ni, Hg, As, Mn, Cu is determined by Flame Atomic Absorption spectroscopic technique (FAAS) since it has high precision and rapid process in elemental analysis 27 . The detection limit for AAS is up to 0.1 μg kg −1 under optimum test conditions. With its high spatial resolution, large depth field, and simple specimen preparation, scanning electron microscopes with energy dispersive X-ray spectrometry (SEM/EDS) are a suitable technique most commonly used in soil, sediment and rock characterization. The SEM/EDS mapping has been integrated to provide a new perspective of the dynamic biogeochemistry processes. Scanning electron microscopy (SEM) was used to examine the morphology of particles and aggregates in sediments of Ponnai river, Tamil Nadu. Energy-dispersive X-ray spectrometry (EDS) was also used to analyze the elemental composition and distribution, with a focus on heavy metals.
A multivariate statistical method such as the principal component analysis (PCA) and cluster analysis (CA) is a powerful tool for evaluating pollution levels among samples 28,29 . The PCA method has been widely applied in geochemical applications to identify the sources of pollution and to distinguish natural pollution from anthropogenic pollution 30,31 . When combined with PCA, CA serves as a check for results and allows for the grouping of individuals parameters and variables [32][33][34][35][36][37] .
Based on the above discussions, the main objectives of the present work is to (i) identify the primary (quartz (SiO 2 ), and feldspar (Na, K)AlSi 3 O 8 ) and secondary minerals (clay, carbonate and Gibbsite minerals) in the sand samples using FT-IR spectroscopic technique (ii) confirm the identified minerals by XRD technique in sand samples (iii) to determine the concentration of heavy metals such as As, Hg, Pb, Cu, Zn, Ni, Cr and Mn in sand samples using Atomic absorption spectrometry (iv) obtained results of heavy metals are compared with toxic reference values given by United State Environment Protection Agency (USEPA), average shale value (ASV) and TRV (toxicity reference value), (v) to confirm the presence of heavy metals using scanning electron microscope equipped with energy dispersive X-ray spectrometry (SEM/EDS) and (vi) to find the possible pollution sources of heavy metals whether natural or anthropogenic using multivariate statistical techniques.

Geology of the study area
Geologically, the Ponnai river flows from the North direction and ends with the East direction and sampling points are depicted in Fig. 1. It passes along the hard rock formation of Archean crystalline and metamorphic complex that overlaid by sedimentary formation. The area underwent numerous tectonic and magmatic activities during the pre-Cambrian period. Fissile hornblende biotite gneiss is the oldest rock in the study area. Charnockites are coarse grained and their color is bluish dark to grey and it occurred a few sq.km where the Ponnai  www.nature.com/scientificreports/ river meets Palar river. It is the second largest rock type present in the area. They are massive and less weathered than the gneisses. As well as the river cross another hard rock terrain which includes Granite that's present next to Charnockite rock. Small patches of pyroxene granulite occurred at the end of the sample location with the district border of Vellore. Geologically, the basin is underlain by rocks of Archean age consisting of granites, granite gneisses, recent alluvium and soils 38 . Sedimentary deposits are seen along the flood plain of the river that is laid on by the various hard rock formations 39,40 .

Materials
Sample collection. Sampling sites were selected to cover the entire stream from its source to its confluence with natural and anthropogenic activities 41 . The geographical information of each location (longitude and latitude) is noted and then stored on the internal memory of the Garmin GPS and given Table 1. The samples were collected at 25 different locations PNR1-PNR25 along the Ponnai River ( Fig. 1) Tamil Nadu using a stainless steel auger which was cleaned between samples and the first subsample at each point was discarded to avoid cross contamination. In each location, five representative sub-samples, one from the center point and four from the four quadrants of the 1 m 2 area of each point, were taken at depth 5 cm from surface of the riverbed and combined to make one composite sample representing each point on the grid. Sand samples were dried at room temperature (33 °C) and stored in clean polythene bags for further studies 42 .

Sample preparation. For FT-IR study.
A fine homogenized powder sample of 2 mg was mixed with 40 mg of KBr in the ratio 1:20 and it's was ground well using mortar and pestle. Before blending, required amount of KBr powder was dried at 120 °C for 6 h in an oven. If not, the broad spectral peak will appear due to unbound OH will consequentially affect the interpretation on the bound hydroxyls associated, with any of the minerals 1 . Materials required for KBr pressed-pellet method are Potassium Bromide (KBr), Acetone; die for making KBr pellets, laboratory hydraulic press for creating pressure on the confined sample, small hand agate mortar and pestle and mechanical vacuum pump. The prepared pellet was stored in a moisture free glass container before it was placed in a suitable sample holder and it was used for infrared beam for analysis.
For XRD study. The collected sand samples were made into fine-grained particles using agate mortar. Powdered samples are dried and sieved through 2 mm sieve. Sieved sample is homogenized in a tumbler mixer for 30 min. The prepared samples are then subjected to analysis.

Methods
FT-IR analysis. The Fourier transform infrared (FT-IR) spectroscopy is a powerful and well known method implemented to identify the mineral constituents present in the river sand samples. The compounds synthesized were characterized by FT-IR spectra in the region 4000-400 cm −1 using Perkin Elmer FT-IR spectrometer (model: Spectrum 2000). KBr was used to make a pellet format. It also has great importance in resolving the order-disorder for identifying the different functional groups in the mid-infrared area and for structural analysis present in the samples 44 . The recorded FT-IR spectrums for sand samples are given in Fig

XRD analysis.
In the present study, X-ray diffraction patterns were recorded for river sand samples and given in Fig. 4. The obtained XRD patterns were compared data provided by international center diffraction data (ICDD) formerly known as joint committee on powder diffraction standards (JCPDS) for mineral identification 45,46 . In this analysis, only those peaks of the minerals, which did not overlap with sufficient intensity, were considered for identification.

AAS analysis.
In the present work, concentrations of heavy metals were determined using a Flame atomic absorption spectrometer (AAS, Analyst iCE3000, Thermo scientific, USA). Qualitative and quantitative of heavy metals are measured based on the measurement of absorption of optical radiation by atoms in the gaseous state 27 . The standard solutions for all the heavy metals under study were prepared in three to five different concentrations to obtain a calibration curve by diluting stock standard solution of concentration 1000 ppm. Regular operating conditions for sample analysis are given in Table 2. The hollow cathode lamps for Pb, As, Hg, Cu, Zn, Cr, Ni and Mn were used as radiation source and fuel was air acetylene. All the samples and standard were analyzed in multiple times and average value taken as a results.
SEM-EDS analysis. The determination of concentration of heavy metals present in the samples was done using the Carl Zeiss Microscopy GmbH Germany (EVO 18) Energy Dispersive Xray spectrometer (SEM-EDS). This technique is being used in numerous applications for environmental science and technology. Energy dispersive X-ray spectrometry is a popular method for the determination of trace elements in geological and environ-  Multivariate statistical methods. Multivariate statistical techniques could be used to identify similar origins or geochemical characteristics between the heavy metals from their inter-relationship. Hence it is performed to identify the origin of heavy metals for river sand samples using SPSS 16.0. Pearson correlation analysis was carried out to find out the relation between heavy metals. Principal component analysis (PCA) was extracted to identify the natural and anthropogenic origins to be distinguished for metals in the samples. Varimax rotation was applied to highlight the contribution of the most important variables. Then, cluster analysis (CA) was applied using average linkage method and the Euclidean distance as a measure of similarity 47 . Both techniques  www.nature.com/scientificreports/ were applied to standardized data in order to improve interpretation and avoid misclassification. All of the results were generally consistent with each other.

Results and discussion
Mineral identification by FT-IR. The observed peak values for the minerals present in the samples are tabulated in Table 3 By using FT-IR absorption peak values; we can identify the major and minor composition in the samples, compared with the already reported literature 2,44,[48][49][50][51][52] . The list of various minerals discussion is presented below. Quartz is the most abundant and widely distributed mineral found at earth's surface. It can be observed from the Table 2, the i.r. absorption bands values appears in the range of 515-520, 695-700, 775-780 cm −1 indicates the availability of quartz in the samples. Microcline (KAlSi 3 O 8 ) is the triclinic low-temperature K-feldspar stable at temperatures lower than 500 °C. It is usually formed by recrystallization from feldspar, and sometimes   53 . Calcite is a carbonate mineral which is also found in the sand samples. It can be observed from the Table 2, that i.r absorption peaks appear at 1440-1445, 2875-2880 cm −1 are assigned to calcite 2,53,54 . Gibbsite is an aluminum hydroxide and secondary mineral which is identified from peak in the range 1000-1005 cm −1 . Band assignment for each frequency and minerals are also given in Table 4.
Mineral confirmation by XRD. Recorded XRD spectrum for samples is given in Fig. 4. Using the XRD parameters, primary and secondary minerals are identified in the river sand samples. Primary mineral such as quartz (SiO 2 ), and feldspar (Na, K)AlSi 3 O 8 are identified and chemical composition of these minerals are not altered by naturally since the time of origin. Quartz is the first most abundant mineral in the all studied samples from PNR1-PNR25.
Feldspar group of minerals such as microcline feldspar, orthoclase feldspar and albite are also impartment mineral in the environment samples. In the present study, microcline and orthoclase feldspar minerals are identified in the samples PNR1-PNR5; PNR21-PNR25 and, microcline feldspar and albite are identified in the samples PNR11-PNR15. All these three feldspar group minerals that is microcline feldspar, orthoclase feldspar and albite are identified in the following samples PNR6-10-PNR15; PNR16-PNR20. Hence, the feldspar group of mineral is the second most abundant minerals in the samples.
Kaolinite is a one of the secondary mineral 55 . This mineral formation is due to the decomposition and chemical alteration of primary minerals in the samples. It is also well known clay mineral. As seen from the XRD results, kaolinite present at only few samples of PNR1-PNR15 and PNR16-20. Hence it is considered as minor distribution in the samples.
Calcite and aragonite are the carbonate minerals which are major component in the igneous rock. In this work, all the samples (PNR1-PNR25) shows the presence of calcite and aragonite found in the samples of PNR16-PNR25. Hence, the calcite is also considered as major component in the samples.
Zircon is one of heavy mineral distributed as minor constituent in earth crust and found as zirconium silicate mineral with a chemical formula ZrSiO 4 . It is also one of the primary accessories mineral and found in the samples of PNR1-PNR20. Magnetite is an Iron-oxide mineral with chemical formula Fe 3 O 4. This mineral is identified in the river sand samples from PNR6 to PNR 25 in the study area.
Kyanite and goethite is common accessory mineral which are found in almost all the river sand samples in the study area. The mineral gibbsite was identified form FT-IR but absent in XRD because this mineral was poor in crystalline nature or not in crystalline structure 55 .
The results obtained from the XRD analysis are good agreement with FT-IR analysis for the minerals quartz, feldspar, kaolinite and calcite. Also these minerals are considered as major component in the river sand samples. Additionally, zircon, aragonite, magnetite and kyanite minerals were identified in the samples using only the XRD method.
Concentration of heavy metals in river sands. The concentration of heavy metals in the river sand samples are reported in Table 5. As seen from Table 5, the concentration of manganese (Mn) was the highest among the heavy metals analyzed from all the sampling locations and the range obtained were found to be 78.05-168.95 mg kg −1 with mean of 104.94 mg kg −1 . Chromium (Cr) is very harmful to living organisms. The hexavalent form of Cr is the most toxic. The minimum level was 5.05 mg kg −1 at PNR13 and the mean value (12.70 mg kg −1 ) of Cr was does not exceeded the standard values set by USEPA and toxicity reference value www.nature.com/scientificreports/ (TRV). The maximum concentration of Cr found in the sample at PNR25 was 31.47 mg kg −1 which is slightly higher than the toxicity reference value which is 26 mg kg −1 . This high concentration of Cr can cause lethality to some aquatic species in the river system. This may be due to contamination of samples by effluents from leather industries 56 . Cu is an essential metal for all living organisms, but is toxic at high levels. Concentration of copper measured in the sample PNR25 showed a maximum value which is the order of 4.99 mg kg −1 . According to United State Environment Protection Agency (USEPA), maximum permissible value for Cu in the river sand is 31.6 mg kg −1 , average shale value (ASV) is 45 mg kg −1 and TRV (toxicity reference value) is 16 mg kg −1 . The observed value for Cu was found below the permissible limit set up by USEPA, ASV and TRV. The concentration of lead (Pb) ranged between 0.11 and 0.14 mg kg −1 with mean value of 0.12 mg kg −1 . The obtained value of Pb in the present study was found below the permissible limit set up by USEPA, ASV, and TRV. Hence, lead (Pb) was the least abundant metal in the river samples and this reveals that few samples are free from automobile exhaust fumes and pesticides. Nickel (Ni) is a highly toxic metal even at low concentration. The concentration of Ni found in the different samples showed maximum value of 4.69 mg kg −1 at sample PNR25 and mean value is 2.86 mg kg −1 . However, the concentration of Ni was found below the permissible limit at all of the sites.
The average value of Zinc (Zn) concentration in the sand samples of the River Ponnai was recorded as 10.15 mg kg −1 . The observed value of Zn was found below the permissible limit at all locations proposed by USEPA, ASV, TRV and hence no adverse effect on aquatic biota 57 . Arsenic (As) is a highly toxic element that exists in various species and its average value found to be 0.15 mg kg −1 and Mercury (Hg) is a persistent environmental pollutant with bioaccumulation ability in fish, animals, and human beings and its range between 0.11 and 0.16 mg kg −1 with mean value of 0.13 mg kg −1 . The concentration of As and Hg at all the locations showed that below the permissible limit given by USEPA, ASV, TRV and this reveals that there is absence of As and Hg contamination found in the study area.
The average concentration of heavy metals of Ponnai river sediments were compared with similar other works in the world and given in Table 6. The mean concentrations of all heavy metals measured in this study were significantly lower than those in the Xiangjiang River, which is one of the most polluted rivers in China 58 . The concentrations of Pb and Ni measured in this study were lower than those detected in the Gorges River in Australia 59 and the Nile River in Egypt, all of which are heavily polluted. www.nature.com/scientificreports/ Spatial distribution of heavy metals. Spatial distribution of heavy metals was studied from sampling location PNR1 to PNR25 and shown in Fig. 5. It is observed from Fig. 5, Mn has the highest concentration at PNR25 (168.95 mg kg −1 ) and then the next highest at PNR21 (150.2 mg kg −1 ). This indicates that concentration of Mn is high at downstream sampling pints (PNR17-PNR25). The total concentration of heavy metals gradually increases as the water flows PNR1 to PNR25. There is no significant difference found in the concentrations of heavy metals within the upstream (PNR1-PNR8) river area and within the midstream (PNR9-PNR16), but there is a significant difference within the downstream (PNR17-PNR25) due to discharge of sewage from households, leather industry, and transport.

Confirmation of presence of heavy metals by SEM-EDS.
Studied heavy metals were found in all river sand samples mainly as small particles (< 50 µm). These particles were frequently identified inside aggregates as shown in Fig. 6a. In the view point of SEM/EDS, heavy metals (< 1 wt%) are detectable since they are concentrated in a structure 60   www.nature.com/scientificreports/ and pathways of heavy metals. The obtained results of Pearson correlation analysis for heavy metals were given in Table 6. According to Rakesh and Raju 61 , high correlation coefficient (near + 1 or − 1) means a positive correlation between two variables, and its concentration around zero means no relationship between them at a significant level of 0.05%, it can be strongly correlated, if r > 0.7, whereas r values between 0.4 and 0.7 shows moderate correlation between two different variables. Pearson correlation analysis was performed between the heavy metals and given Table 7. The obtained result reveals that there are no strong positive correlations were reordered. But a moderate correlation (r > 0.438) was found between As-Cr-Ni-Mn and Hg-Pb-Zn, indicate that the general contamination sources of these metals were primarily discharge of effluents from domestic and industry [62][63][64] . A very weak correlation was observed between Cu and other studied metals at p < 0.01. This indicates that Cu was derived in part from natural source (local soil or rock) 63 . Zn showed poor correlation with all other metals except Hg (0.438), which may be due to the influence of transport activities near to the river area 65,66 .
Principal component analysis. Principal component analysis (PCA) identifies the potential sources of heavy metal contamination. As part of this procedure, correlation matrixes are prepared between heavy metal variables, PCs are extracted and possible rotation is performed to reach a final solution with simpler PCs. In principal component analysis, the PC1 tends to be more general, representing the most important common part of the variables analyzed. This PC1 is the best summary of the linear relationship exhibited by the data. The PC2 is independent of the first one (orthogonal), considering only the residual variance not included in the PC1, and so successively for the other axes. In this study it was decided to retain two factors for interpretation, accounting for approximately 36.59% of the total variance. To obtain more reliable information about the relationships between the heavy metals, principal component analysis was performed using varimax rotation method using Kaiser Normalization and extracted data loadings are given in Table 8. The results of PCA indicated that all the heavy metals are well represented by two components 64 . The principal components with eigenvalues greater than 1 were considered to be relevant 32 , which explains approximately 36.59% of the total variance for the data. Components with factor loadings above 0.75, between 0.5 and 0.75, and between 0.3 and 0.5 were considered to be strong, moderate and weak, respectively 33 . As shown in Table 8, PC1 included As, Cr, Ni, Mn; PC2 included  Cluster analysis. According to Kannel et al. 68 , the cluster analysis is a multivariate statistical technique and commonly used in several environmental studies to identify groups or clusters of same variables based on similarities. CA, which involves the evaluation of proximity matrix of squared Euclidean distance along with an agglomeration schedule for clustering similar variables. This method is regarded as very efficient and yields clearly structured and relatively stable clusters 69 . In the present work, the cluster analysis was performed using the heavy metal data set and presented in a two-dimensional dendrogram plot as shown in Fig. 7. This dendrogram contains two clusters. Cluster I consists As, Pb, Hg, Ni, Zn and Cr. This first cluster group of metals (As, Pb, Cr, Ni, Hg, and Zn) shows the high similarities which imply that these heavy metals were mainly derived anthropogenic activities such as industrials and transport activities. Cluster II consist only Cu which indicate that Cu is derived from weathering of parent rock in the study area. These results are good agreement with PCA and Pearson correlation analysis.

Conclusion
Ponnai river sand samples were characterized by different spectroscopic techniques to determine the presence of minerals and heavy metals. From the results of FT-IR, it is concluded that the minerals like quartz, feldspar, gibbsite and calcite are major constituent and kaolinite is the minor constituent in the samples. The presence of these minerals has also confirmed by XRD technique. This XRD treatment of samples shows that presence of accessory minerals such as zircon, aragonite, magnetite and kyanite in the samples. The identified peaks of FT-IR and XRD for minerals indicate that sample quality is not affected by anthropogenic activities. Further, among the determined heavy metals manganese (Mn) was the most abundant and lead (Pb) was the least abundant heavy metal found in study area. The total concentration of heavy metals gradually increases as the water flows from PNR1 to PNR25. The maximum concentration of Cr found in the sample at PNR25 is 31.47 mg kg −1 which is slightly higher than the toxicity reference value which is 26 mg kg −1 . This may be due to contamination of samples by effluents from leather industries located near to the study area. Using SEM/EDS, heavy metals (< 1 wt%) are detected SEM/EDS results confirms the presence of Zn, Cr, Pb, Ni, Cu, Mn, As and Hg in the sand samples. The result of multivariate statistical methods reveals that studied metals As, Cr, Ni, Mn, Hg, Pb were deposited in samples due to discharge of effluents from domestic and industry while Zn was derived from transport activities. In addition to that, Cu was derived in samples due to weathering of local soil or rocks in the study area. Hence, the results of this study indicate that that quality of the sand samples is altered by both natural and anthropogenic activities daily and needs continuous monitoring to establish the pollution level of the study area.