Introduction

The valley of Kashmir is home to innumerable and pristine freshwater lakes situated at different altitudes with varied depths, sizes and shapes (Kaul et al. 1980; Zutshi et al. 1980). The water from these lakes is widely used for various purposes including drinking, agriculture and horticulture, and tourism purposes by the local settlers (Sarah et al. 2011; Yousuf et al. 2015). As a result of these direct influences, most of the Himalayan lakes exhibit eutrophication and thus are under greater threat due to high sedimentation rates, sediment and water pollution, especially due to human interferences (Sheikh et al. 2014; Rashid et al. 2013, 2014; Sarkar et al. 2016; Lone et al. 2017). The human impact on the lakes has not only declined the water quality, but have also made a significant impact on the sediment chemistry and lake flora and fauna. In addition, during the recent years, sewage and domestic effluent inputs and proliferation of local population, runoff from agricultural land and the residual insecticides and pesticides from the paddy fields and orchard plantations have also accelerated the process of eutrophication in these lakes. This has led to excessive growth of unwanted vegetation within the lake waters, which is the case for most of the Himalayan lakes (Rashid et al. 2013).

The natural and anthropogenic processes occurring along the lake catchments primarily govern both the water and sediment chemistry of the lake basins (Tarras-Wahlberg et al. 2002). Further, the sediment chemistry of the lakes is governed by a complex combination of biological, physical and chemical processes that take place within the lake waters and this is also in response to the sediment–water chemistry (McManus et al. 2015). Weathering of rocks both by physical and chemical processes release sediment detritus and soil particles that are later transported and deposited through the catchment watershed into the lake basins (Kastratovic et al. 2016). Sediments that are distinctly modified during the transportation and subsequent deposition hold signatures of local catchment inputs with high local representation including natural and anthropogenic influences (Chapman 1992; Blumberg and Schutt 2004).

The major source of organic and inorganic nutrients in lake sediments is often controlled by the deposition of particulate detritus from natural and anthropogenic sources (Meyers and Ishiwatari 1993; Holmer and Storkholm 2001). However, limited studies have been carried out on the relative contribution of OM from the catchment areas, its distribution pattern and origin and source of organic carbon in the lake systems (Hilton 1985). Thus the geochemistry of lake sediments is a collective function of catchment lithology, weathering type and intensity and erosional and depositional processes occurring within the lake basins (Minyuk et al. 2007; Khan et al. 2012). Currently, geochemical survey of the lake surface sediments is being increasingly applied to assess the environmental impact, sediment provenance and weathering intensity, etc., in the lake systems (Tarras-Wahlberg et al. 2002; Meyers 2003; Boyle et al. 2004; Yao and Xue 2015). Detailed study on the physico-chemical parameters of lake surface sediments helps us to understand the controlling factors responsible for the distribution pattern and geochemistry of the eroded detritus sediments deposited within the lakes (Zhang et al. 2014; Chandrajith et al. 2008).

Anchar Lake is one of the important freshwater urban lakes, located in the Kashmir Valley, and the lake serves as a vital resource of freshwater for the local population. In recent decades, this aquatic ecosystem has altered significantly in size and come  under tremendous pressure due to anthropogenic loading, ingressing mainly from the catchment areas in the form of domestic and agricultural waste (Ahanger et al. 2012a, b). The lake waters and its sediment chemistry have deteriorated considerably, thereby affecting the aquatic life and the health of the lake (Bhat et al. 2013). This has also increased the rate and speed of eutrophication process around the lake margins. The lake is now under stress both in terms of its hydrology and sediment load and its chemistry (Kango et al. 1987). This is also owing to its close proximity to the main Srinagar city (Farooq and Muslim 2014), that contributes anthropogenic inputs in the form different point and non-point sources.

A comprehensive study regarding the assessment of sediment geochemistry, trace metal pollution, spatial distribution of OM and sources of anthropogenic influences into the Anchar Lake is lacking up to date. Hence, it is important to assess the spatial distribution of various physico-chemical parameters combined with surface sediment geochemistry and environmental risk assessment to improve our understanding of the present-day sedimentation pattern in the Anchar Lake. The primary goals of this study were to: (1) provide information concerning the geochemistry and OM sources controlled by climate-driven lake–catchment interactions and (2) examine the potential environmental risk using pollution indices such as enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF) and pollution load index (PLI). These findings will improve our understanding of fundamental lake processes and thus form an important base for the interpretation of the past sedimentary record for this region by analysing sedimentary lake core profiles.

Study area

The Kashmir Valley, an intermontane basin, is bounded by the Pir-Panjal thrust on the southwest and the Zanskar thrust on the northeast forming a Nappe zone. The valley is oriented NW to SE, running parallel to both the thrust blocks on either side (Ahmad and Chandra 2013; Alam et al. 2015). The valley is abound with numerous deep, freshwater lakes with fluvial, glacio-fluvial and lacustrine nature of origin (Babeesh et al. 2017a, b).

Anchar Lake is located nearly 25 km northwest of the main Srinagar city (Fig. 1) and lies between 34°20′N latitude and 74°82′E longitude with an average elevation of ~ 1583 mts amsl. The mean depth of lake is ~ 4 metres. The catchment of the lake spatially covers an area of about 5.8 km2 with an open drainage type fed by a network of channels including the local catchment watersheds, open springs and the Sindh River (Fig. 2). The Sindh River constituting one of the largest basins in the Kashmir Himalayas drains major part of its water into the lake. Further, a number of channels from the agricultural fields, sewage from human settlements and surface runoff directly drain into the lake throughout the year. The excess water of the Anchar Lake drains into the Jhelum River through the Shalabaugh wetland (Ganaie et al. 2015; Khanday et al. 2016).

Fig. 1
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DEM map of study area showing location of Anchar Lake

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Fig. 2
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Watershed map of the study area with Anchar Lake

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The lake catchment is surrounded by diverse lithological formations, which mainly include the Panjal traps, the Triassic limestones, the Karewa deposits and the recent alluvium (Fig. 3). Panjal traps (volcanic rocks) are of Permian age dominated by the basalts. The Triassic limestones consist of the carbonates and dolomites. The 2000-m thick Plio-Pleistocene Karewa sediments are glacial-fluvio-lacustrine deposits composed of gravels, silt, marls, lignite and glacial moraines associated with varved clays. (Agrawal et al. 1985, 1989; Singh 1982). The catchment of the lake is also surrounded by various land-use land-cover patterns, including built-up, parks, orchards and agricultural fields (Fig. 4). A major portion of the lake is presently under the floating garden cover including the aquatic vegetation and macrophytes and this is due to high anthropogenic influences. The area experiences a temperate climate with warm summers and intense cold winters characterised by four seasons, spring, summer, autumn and winter. Most of the precipitation falling around the area is due to the western disturbances (WD) (80%) mainly during the winter season and owing to local topography. The average annual rainfall is 650–710 mm with an average annual temperature of 13 °C (Dar et al. 2014).

Fig. 3
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Lithology map of the Anchar Lake catchment

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Fig. 4
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Land use/land cover (a) and Google earth map (b) of the Anchar Lake catchment

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Materials and methods

In this study, lake surface sediments (upper 0–10 cm) were collected in July 2015 at regular intervals under a water column of 2–6-m depths. Twenty-four (24) lake sediment samples were collected with the help of a Van Veen grab sampler (Fig. 5). Further, the samples were tightly packed in polyethylene zip lock covers, dried in open air and later in the oven at the 35 °C. For further laboratory analyses, the samples were homogenised using an agate mortar.

Fig. 5
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Map of the study area showing sample stations

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The sediment textural analysis of the lake surface sediment samples was carried out to understand the sand, silt and clay content and its spatial distribution (Ingram 1970). Before obtaining the grain size measurement, the sediment samples were pre-treated with H2O2 and HCl to decrease the influence of OM and carbonates. A small pinch of sodium hexametaphosphate (NaPO3)6 was also added in order to fully disperse the finer-size particles.

For the geochemical analyses, all the representative samples were analysed for the major oxides and trace elements using VP-320 X-ray fluorescence (XRF) spectrometry at the Department of Geology, Savitribai Phule Pune University, Pune. For the XRF analyses, the seddiment samples were powdered and later allowed to pass through the mesh sieve (ASTM-230) size. The above procedure involved an analytical error of ± 0.5% for major oxides and < 1 ppm for the trace elements. Carbon-to-nitrogen (C/N) ratio and total organic carbon (TOC) were analysed to infer the source of OM using (CHNSO-2000) elemental analyser available at the Department of Geology, Anna University, Chennai. For this analyses the samples were powdered using an agate mortar and later sieved through ASTM-230 sieve. Further, 10–12 mg sample was filled in a tin capsule and positioned in the auto-sampler tray for analyses. Blank samples (tin capsules) and standard BBOT (2,5-Bis (5-tert-butyl-2-benzo-oxazol-2-yl) thiophene) with known elemental composition were used for accurate data assurance. The analytical precision of this method, expressed in terms of standard deviation, ranges from 3 to 5%. For calculating TOC, the samples were acidified with a 1 M solution of hydrochloric acid to remove inorganic carbon (IC) (Hedges and Stern 1984). Later, the samples were washed using distilled water and allowed to dry in oven for 40 °C and further analysed using the CHNSO-2000 analyser at combustion temperature of 950 °C. The relative OM content was determined as percent loss during ignition (LOI). Samples were placed in a furnace at temperatures of 550 and 950 °C for over 1 h, and the LOI was calculated from the measured loss in weight following Heiri et al. (2001).

Statistical analyses

Multi-variate statistical analysis was carried out using SPSS software version 21 of windows. Principal component analysis (PCA) was performed to assess the continuity and/or overlap of clusters and homogeneity in the studied parameters. Correlation matrix was applied to check the significant relationship (positive and negative) between the studied parameters. The triangular plot was prepared using the tri-plot software and represents the relative percentage of sand, silt and clay in the sediment samples. The spatial distribution of the physico-chemical parameters was interpolated through inverse distance weighted (IDW) algorithm available in the geo-statistical analytical tool of Arc GIS 10.3. Cluster analytical (CA) results were displayed using the dendrogram, which evaluates the degree of similarity between the different physical and chemical parameters (Cesari 2007).

Results

Sediment grain size and spatial distribution pattern

The relative abundance of sand, silt and clay content inferred for the lake sediments are presented in the trilinear diagram for sediment nomenclature (Fig. 6). The sediment grain size was found to be (silty + clay) mud dominated except one sample that fall under sandy clay domain in the ternary diagram. The sand class was very low, ranging from 0.39% to 30.18%. The grain size content of clay and silt varies from 19.45% to 79.68% and 19.76% to 79.05%, respectively. The clay and silt fraction revealed almost the similar trend in grain size concentration with a mean value of 51.87% and 44.97%, respectively. However, the sand fraction showed the lowest average value of 3.16%, however one station showed a very high value of sand (ANG-24 = 30.18%).

Fig. 6
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Ternary plot for sand, silt and clay distribution of lake surface sediments (Shepard 1954)

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The lake sediment samples showed a near positive link between the lake water depth (bathymetry) and sediment grain size. The sediment spatial distribution maps (Fig. 7a–c) revealed the highest percentage of fine grain sediments (clay and silt) along the central portions of the lake. The high content of sand is observed along the north-eastern edges of the lake and along catchment stream inlets (Fig. 7a). The spatial distribution of silt exhibited a high concentration of 79.68% along the south-eastern edges of the lake and lowest content along the north-western portions (Fig. 7b). Elevated clay content with maximum values of 79.05% (Fig. 7c) was observed along the central and south-western flanks of the lake. In general, all the surface samples were considerably low in the coarser sand fraction.

Fig. 7
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Spatial distribution map for a sand, b silt, c clay, d organic matter, e CaCO3 and f TOC in lake sediments

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OM and calcium carbonate (CaCO3) content

The concentration of OM and CaCO3 is presented in Table 1. Mainly terrestrial runoff contributes OM and CaCO3 and organisms present in the water column of the basin. The concentration of both OM and CaCO3 varies largely both between and within the lakes (Dean 2006). This disparity is due to the differences in water chemistry, which in turn is controlled by the presence and type of bedrock geology in the catchment, climate, lake flora and fauna and anthropogenic influences along the catchments. The overall concentration of OM (Fig. 7d) and CaCO3 (Fig. 7e) in surface sediments varies along the four transects. The OM varies between 3.88% and 17.22% with mean value of 9.95%. In addition, the CaCO3 percentage varies between 6.25% and 15.22% having an average value of 9.78%. The spatial distribution diagram showed high percentage of OM along the south and south-western flanks of the lake. However, the concentration of CaCO3 was predominantly high along the north-eastern fringe and along the central portions of the lake.

Table 1 Sand, silt, clay, OM, CaCO3, C/N and TOC concentration in surface sediments of Anchar Lake
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C/N ratio and TOC content

The C/N ratio of lake sediments reveals the relative contribution of terrestrial, anthropogenic, phytogenic and phycogenic carbon and nitrogen supplied to the accumulation of sediments from diverse sources (Kaushal and Binford 1999; Meyers 1994; Meyers and Ishiwatari 1993; Meyers and Teranes 2001; Mahapatra et al. 2011; Sampei and Matsumoto 2001). Lake algae bear a C/N ratio that varies from 4 to 10, whereas for terrestrial vegetation the ratio is always > 20. The Anchar Lake surface sediments revealed a wide variation in C/N ratio at different sampling stations and also from the margins to deeper and central part of the lake. The C/N ratio exhibited a low value of 5.49 and a higher value of 27.27 with an average of 12.32. The TOC revealed a minimum value of 1.8% (ANG-12) and a higher content of 10.16% (ANG-23) with an average of 4.48% (Fig. 7f).

Sediment chemistry (major and trace element)

The sediment chemistry of the lake is a function of the catchment flux, climate, rock-mineral weathering and physico-chemical characteristics of the detritus material from the watershed (Basavaiah et al. 2014). The amount of in situ organic and inorganic matter incorporated into the sediments also governs this proportion (Lottermoser et al. 1997; Jin et al. 2006a, b). The major oxides in decreasing order of average percentages can be summarised as SiO2 > CaO > Al2O3 > Fe2O3 > MgO > K2O > Na2O > TiO2 > P2O5 > MnO, respectively. From the geochemistry data, it is observed that the enrichment of SiO2 varies from 40.14% (ANG-24) to 50.62% (ANG-5), CaO ranges from 10.46% (ANG-05) to 20.04% (ANG-24), Al2O3 ranges from 10.66% (ANG-05) to 16.08% (ANG-24), Fe2O3 ranges from 5.25% (ANG-24) to 7.87% (ANG-6), MgO ranges from 2.92% (ANG-17) to 4.12% (ANG-6), K2O ranges from 1.88% (ANG-24) to 3.67% (ANG-05), Na2O ranges from 0.62% (ANG-23) to 1.68% (ANG-05). TiO2 ranges from 0.75% (ANG-23) to 0.93% (ANG-05), P2O5 ranges from 0.14% (ANG-06) to 0.42% (ANG-17), and MnO ranges from 0.07% (ANG-19) to 0.12% (ANG-7) and were present in minor concentrations.

The chemical variation in the major elements in lake sediments were plotted on variation diagrams against Al2O3 and further compared with upper continental crust (UCC values) (Taylor and McLennan l985). In a plot against Al2O3 (Fig. 8), all other major oxides show a positive correlation except CaO and P2O5 which showed a negative trend with Al2O3. This suggests CaO and P2O5 in the lake sediments have caused relative depletion of other elements and are derived primarily from authigenic sources probably in situ vegetation. The geochemistry data of Anchar Lake sediments when compared with UCC values (Fig. 9a) revealed enrichment of CaO, MgO and TiO2 concentrations and depletion in SiO2, Na2O and P2O5 data. Further, MnO, K2O, Al2O3 and Fe2O3 values show a similar trend. This suggests that the lake sediment geochemistry is controlled by weathering intensity and the dilution effect of CaO. The relatively high content of CaO and lower concentration of SiO2 may be due to carbonate fraction and its dilution effect, respectively.

Fig. 8
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Variation diagrams of major elements plotted against Al2O3 for the Anchar Lake sediments

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Fig. 9
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Sediment normalised diagrams for a major oxides and b trace elements of Anchar Lake sediments

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The relative enrichment or depletion of trace elements in sediments has been extensively used to infer source changes in sediments in response to weathering, sedimentation and anthropogenic inputs. Weathering, provenance, diagenesis, anthropogenic inputs, sediment sorting and the aqueous geochemistry of the individual elements mainly control trace element concentrations in sediments (Das and Haake 2003). The distribution of various trace elements showed enrichment of S followed by Cl, Sr, Zn, Rb, V, Cr, Cu, Ni, Pb and Co in the lake surface sediments. When compared with UCC values (Fig. 9b), the trace elements reflect relatively high concentrations of S, Cl, Cr and Cu values, moderately high in Pb and Rb and low in Nb and Ba contents. The high concentration of S and Cl is related to human and anthropogenic interferences.

Environmental risk assessment

The geochemical composition of lake sediments is a reliable marker for inferring the anthropogenic pollution, as the geochemistry is mainly controlled by the physico-chemical processes operating near the sediment–water boundary during depositional processes (Sheela et al. 2012). The evaluation of enriched and unsafe anthropogenic inputs in the form of trace metals is vital for extensive lake mitigation and management (Dung et al. 2013). To evaluate the extent of anthropogenic impacts and the level of contamination, numerous environmental indices (e.g. the geo-accumulation index (Igeo), enrichment factor (EF), contamination factor (CF), pollution load index (PLI) and statistical tools are employed globally (Tang et al. 2014; Wang et al. 2014; Li et al. 2013; Iqbal et al. 2016; Yao et al. 2006; Suresh et al. 2012; Xiao et al.2014; Gopal et al. 2016; Kasilingam et al. 2016). The pollution load index for the studied sediments varies from 1.32 to 3.10 with a mean value of 2.17. The average values for the geo-accumulation index include Cr (0.59), Co (0.25), Ni (0.92), Cu (1.52), Zn (0.70), Pb (0.04), Fe (0.14) and Mn (− 0.12). In the enrichment factor index, Cu, Ni and Zn showed considerable enrichment in the sediments.

Geo-accumulation index (Igeo) was initially defined by Muller (1979) to assess the lake sediment contamination by comparing the studied heavy metal contents with the background crustal average elemental concentrations. The geo-accumulation index (Igeo) enrichment for the studied metals was quantified using the below formula:

$${\text{Igeo}} = \frac{\text{Cn}}{{1.5{\text{Bn}}}}$$

where (Cn) is the measured concentration of studied element (n) and (Bn) is the geochemical average background value of crustal metal(n) (Wedepohl 1995). The factor 1.5 is introduced to minimise the effect of probable variation in the background values, governed by geogenic or anthropogenic influences. The Igeo index is divided into six classes that represent sediment quality as: 0 (practically no pollution), 0–1 (no pollution to moderately polluted), 1–2 (intermediately polluted), 2–3 (moderately to significantly polluted), 3–4 (strongly polluted), 4–5 (strongly to extremely polluted) and > 5 (extremely polluted). The average Igeo of the elements in the surface sediment is 1.52 for Cu; 0.92 for Ni; 0.59 for Cr; 0.70 for Zn; 0.25 for Co; 0.14 for Fe; 0.04 for Pb and − 0.12 for Mn. The observed Igeo value for Mn (Fig. 10a) was found lower than 0, indicating practically no pollution. However, Igeo index of Pb, Fe, Co, Cr and Zn showed uncontaminated to moderately contamination. Igeo for Cu ranges from 0.08 to 2.96 with an average of 1.52 indicating moderately to strongly contaminated sediments.

Fig. 10
figure 10

Stock plot for a geo-accumulation index and b enrichment factor for Anchar Lake sediments

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Enrichment factor (EF)

The evaluation of enrichment factor (EF) widely helps us to determine the extent of anthropogenically sourced trace element pollution in diverse environments using a normalised element "r" to minimise the variations governed by the sediments. Various trace metals including Fe, Al, K are applied in the form of normalised elements. The EF was calculated using by the formula:

$${\text{EF}} = \frac{{\left( {\text{Metal/Al}} \right)_{\text{sample}} }}{{\left( {\text{Metal/Al}} \right)_{\text{background}} }}$$

The earth crustal average values are drawn from Wedepohl (1995) which correspond to the average composition of the surficial rocks. In this study, the EF enrichment was interpreted following Sakan et al. (2009). EF < 1 indicates rock sources with no enrichment, EF 1–3 slight enrichment, EF = 3–5, intermediate enrichment, EF = 5–10, moderate to severe enrichment, EF = 10–25, significant enrichment, EF = 25–50, very severe enrichment and EF > 50 extremely strong enrichment. The average enrichment of trace metals is 5.27 for Cu; 3.25 for Ni; 2.83 for Zn; 2.58 for Cr; 2.09 for Co; 1.75 for Pb and 1.56 for Mn. The surface sediments reveal higher values of Pb, Cu, Zn, Cr, Co, Ni and Fe. EF technique (Fig. 10b) sighifyed moderate to severe enrichment for Cu; moderate enrichment for Ni and Zn and minor enrichment of Co, Pb and Mn in the sediments.

Contamination factor (CF)

The mechanism and deposition pattern of contaminants in the sediments is strongly governed by the nature of the pollutant, and the in situ physico-chemical conditions that cause dissolution and accumulation (Sheela et al. 2012). The CF acts as a vital indicator of the sediment contamination, to study and evaluate the lake environment pollution using a single parameter. Contamination factor (CF) is computed by dividing the concentration of each studied metal by the earth’s crustal value (Hakanson 1980). The CF was calculated as:

$${\text{CF}} = \frac{{{\text{C}}_{\text{sample}} }}{{{\text{C}}_{\text{crust}} }}$$

In CF, the concentration levels are differentiated based on their intensity on a scale varying from 1 to 6 as < 1 indicates low contamination, 1–3 moderate contamination, 3–6 strong contamination and > 6 significantly strong contamination. The trace element contamination concentration (Table 2) for Anchar Lake reveals that the sediments are low to moderately contaminated by trace metals.

Table 2 Metal contamination factors (CFs) and pollution load index (PLI) for selected metals at various sampling sites
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Pollution load index

The PLI was initially proposed by Tomlinson et al. (1980) to evaluate pollution intensity in sediments. PLI allows a comparison of pollution level between the various stations and over a temporal scale. In this study, the PLI was calculated as suggested by Tomlinson et al. (1980). According to Singh et al. (2003), PLI values vary from 0 to 10. The PLI level of 0 indicates background concentration, PLI 1–4 as polluted condition, while PLI > 4 signifies significant pollution (Tomlinson et al. 1980; Iqbal et al. 2016). PLI is calculated as the nth root of the (n) number of contaminating factors multiplied together and computed as:

$${\text{PLI}} = \left( {{\text{CF}}1 \times {\text{CF}}2 \times {\text{CF}}3 \times \cdots \times {\text{CFn}}} \right)^{1/n}$$

where n represents the number of elements and CF signifies the contamination factor.

The PL index for the studied sediments fluctuates from 1.32 to 3.10 with an average value of 2.17. Higher PLI value was found in case of sampling stations designated as ANG-6, 7 and 8. Overall, the PLI (Fig. 11) revealed that all the samples fall under moderately polluted class.

Fig. 11
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Pollution load index (PLI) for different sampling stations of Anchar Lake sediments

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Discussion

The texture analysis of the lake surface sediment reveals that all the samples exhibit clayey to silty texture except one falling under the sand category. These results clearly demonstrate that the lake environment is favourable for the accumulation of fine sediment fractions and the sediment texture is mainly controlled by lake bathymetry and drainage inlets. Higher sand content with a tongue-like feature along the northernmost fringe and along the shallow water column, particularly at station ANG-24, is found due to the Sindh River inlet in the lake which brings in a lot of coarser sediments from the upper catchment. Higher clay content in the central portion reflects the higher bathymetry of the lake basin and the relatively stagnant depositional environment, which allows finer sediments to settle. Based on Shepard (1954) classification using the ternary diagram, the sediment samples of the present study are classified as clayey, silty clay and sandy in nature.

Aquatic flora, fauna and vegetation growing around the lake are the primary suppliers for the organic material initially supplied to the sedimentary basins. The amount and type of lake biota reflect the ecology and environmental conditions prevalent around the lake (Meyers and Lallier-Verges 1999). The high OM content (Avg. = 9.95) indicates high concentration of detritus materials (terrigenous) and other flora and fauna (autochthonous) occurring within the lake. The high percentage of OM is primarily attributed to the algae and lake flora. However, a significant contribution is also from the terrigenous or the agricultural and horticultural practices presently being carried out by the local population in the catchment areas (Lone et al. 2017). The higher content of OM along the south and south-western areas is probably owing to the predominance of aquatic macrophytes (floating gardens), agricultural fields and anthropogenic inputs.

The high concentration of CaCO3 (15.22%) found along the northern fringe is attributed to the large influx of weathered material brought by the Sindh River and then depositing at the river–lake water interface. The primary source of CaCO3 along the central portions of the lake is probably due to the in situ organic shells and allogenic carbonate sediments delivered to the lake. This observation is also supported by the positive correlation (r = 0.55) of CaO with the CaCO3 suggesting their similar sources. Variation diagram for OM and CaCO3 shows negative correlation suggesting their different source of input into the lake. The low correlation between OM and CaCO3 is attributed to the disparity in texture, bathymetry and contributing sources. The similar distribution of OM and finer sediments probably points to a fixation between organic components and finer sediments. TOC content is a primary parameter for assessing the extent of OM and is mainly controlled by the in situ (aquatic flora and fauna) processes and contributions from exogenous vegetation. The high TOC value in the surface sediments indicates that the lake is organically polluted. Higher concentration of TOC in the north-eastern fringes as indicated by spatial distribution map points to the riverine input from the lake catchments and later deposited along the lake margins. The average TOC content of Anchar Lake sediments (4.48%) is significantly elevated when compared to the Wular Lake (2.33%) (Shah et al. 2017). However, the TOC content was found to be lower as suggested for the Manasbal Lake (8.33%) bottom sediments (Babeesh et al. 2017b). This also points to the higher organic activity prevailing in the Anchar Lake in comparison with Wular Lake.

The C/N ratio of lake sediments is an indicator of proportion of allochthonous and autochthonous OM supplied to the lake surface sediments from diverse sources (Meyers 1994; Meyers and Ishiwatari 1993; Meyers and Teranes 2001). The average C/N ratio of 12.32 indicates that the lake is governed by the mixture of terrestrial and in situ sources for accumulation of OM. This elevated ratio in the C/N is owing to the increased aquatic vegetation and wastewater discharges from the nearby watershed and direct inflow of sewage from the local settlements. The enhanced values of C/N are significantly controlled and contributed by the increased use of land classes along the lake catchment for urban and agricultural activities during the recent years. The average C/N ratio of Anchar Lake sediments (12.32) is consistent with that of Wular lake sediments (12.07) (Shah et al. 2017). However, compared to Manasbal Lake (Avg. = 15.48) (Lone et al. 2017) these sediments exhibit a relatively lower C/N values.

Environmental risk assessment

Environmental risk assessment indices (Igeo, CF, PLI, EF) supplemented by statistical methods were applied to assess the present environmental and trophic status of the lake ecosystem. Metal pollution in lake sediments is well known by the human interference and partly by weathering input from bedrock lithology (Xiao et al. 2014).The environmental risk assessment of trace metals suggests low to moderate contamination of the lake sediments. This is mainly attributed to their incorporation by anthropogenic influences mainly from agriculture practices, urban domestic sewage and land runoff, which is in strong agreement with the sampling stations. The samples collected close to the agricultural and urban land-cover sites show high pollution level index compared to the samples taken towards the central and distal portions. Some of the metals such as Cu, Ni and Zn show elevated values in the sediments either with the OM or finer fraction associated with anthropogenically active stations. Further, the lake surface sediments show mixed origin of OM (in situ and terrestrial), suggesting contribution from both anthropogenic and natural sources. However, due to the non-existence of major industries and metal mines in the catchment area, the agricultural inputs, domestic effluents and untreated sewage discharge are major source for the increase in heavy metal pollution in the lake sediments. Similarly, Sheikh et al. (2014) and Lone et al. (2017) found that the sediment of the Wular and Manasbal lakes fall in no to moderately polluted classes, respectively. They attributed this low to moderate pollution to the anthropogenic inputs due to the absence of industrial and mining activities in the catchment and nearer to the lake margins. Further, the complex ecological and land system changes occurring rapidly along the lake catchments are the primary reasons responsible for the deteriorating health of the lakes. This also has adversely influenced the erosion and other earth surface processes in and around the lake catchment (Badar and Romshoo 2008; Romshoo and Muslim 2011; Badar et al. 2013; Rashid et al. 2013; Khanday et al. 2016).

The geochemical composition of the lake sediments depend not only on the natural processes such as the catchment erosion, but also on the anthropogenic inputs, including the use of agrochemicals, deforestation and waste disposal and dumping (Chandrajith et al. 2008). The lake geochemistry summarised data reveal that SiO2, CaO and Al2O3 are the most abundant major oxides and constitute nearly 70% of the total oxide composition. The high CaO concentration is due to both biogenic and lithogenic (terrigenous) material delivered to the sediments through various processes and due to weathered calcium-rich sediments transported from dominant catchment limestone lithology (Sheikh et al. 2014). Part of the calcium is also incorporated through the shell fragments from gastropods thriving abundantly in the upper water column. The overall concentration of CaO seems to be controlled by concentration of CaCO3 and coarser sand particles in the lake sediments as indicated by the positive correlation of CaO with CaCO3 and sand (r = 0.55 and r = 0.54). Low concentrations of SiO2, Al2O3 and Na2O in the surface sediments is mainly attributed to dilution effects by CaO. Elevated concentrations of S, Cl, V and P2O5 signify anthropogenic influences, such as use of various types of agro-fertilisers in the catchment agricultural fields. This is in consistent with the geochemical characterisation of Dal Lake sediments, which reveal differential natural and human impact in the form of sewage from surrounding habitation, agricultural fields, houseboats, hotels, parks (Jeelani and Shah 2006). In order to assess the source and intensity of weathering of the catchment rocks, major oxide and trace element data of the lake surface sediments were compared with the average upper continental crust (UCC) values in the form of normalised multi-element pattern. The negative anomalies of the SiO2, Al2O3, Na2O, K2O and enriched values of CaO indicate significant dilution effect, weak to intermediate weathering of the source rocks and significant contribution from limestone lithology (Rashid et al. 2014; Lone et al. 2017). However, Al2O3 and Fe2O3 show almost a similar trend. With the exception of CaO and P2O5, the other oxides show positive correlation and broadly follow the trend of Al2O3, indicating their strong association with micaceous and/or clay minerals. This suggests that most oxides are present as a result of weathering of alumina-silicate-rich rocks. The rock units present in the lake catchment in the Himalayan ranges are frequently exposed and subsequently weathered/eroded due to the rise of Himalaya in phases (Das and Haake 2003).

Among the trace elements, Rb, Sr and Y are generally similar to UCC data, whereas S, Cl and Cu with the exception of few samples show enriched values with respect to the UCC values. Zr, Pb and Nb show a slightly depleted trend. Enrichment of Cr, Cu, Zn, V, Ni, and Co in the lake can be directly attributed to several anthropogenic sources and partly bedrock lithology. Sedimentary rocks always vary (mafic or felsic) according to the nature and composition of the protolith rocks. The sediment geochemistry has long been used to investigate the provenance and the tectonic setting of the clastic sediments using certain discrimination diagrams (Bhatia 1983; Bhatia and Crook 1986; Roser and Korsch 1986; Floyd and Leveridge 1987; McLennan and Taylor 1991; Graver and Scott 1995). TiO2 versus Al2O3 and TiO2 versus Zr plots are useful to infer the provenance of lake sediments. The provenance plots for the Anchar Lake sediments (Fig. 12a, b) indicate that all the sediment samples fall along the basalt-granite line indicating a mixed source of sediments varying in composition from mafic to felsic rocks.

Fig. 12
figure 12

Scatter plots of a Al2O3 versus TiO2, b Zr versus TiO2 for inferring provenance of lake sediment

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The Anchar Lake, located within the intermountain setting, receives large amount of sediments from the nearby surrounding catchments, which consists of diverse lithological formations suggesting a variety of provenance with variable geological settings. The catchment watershed of the Anchar Lake is characterised by varied geological formations ranging in age from Precambrian to recent (Fig. 3). The exposed lithological units of the area consists of the Panjal Volcanic Series (Panjal trap and the Agglomeratic slates), granites and metamorphic schists, Quartzites, Triassic limestone and recent Alluvium (Raza et al. 1978; Thakur and Rawat 1992; Dar et al. 2014; Bali et al. 2016; Ahmad et al. 2017). These rock types form the important source of weathered sediments that are redeposited into the lake through various waterways including the Sindh River and other catchment watershed ephemeral streams. The unique climate of the valley (cold winters) and the steep slopes enhance the physical weathering of the exposed surfaces resulting in increased erosion and subsequent deposition of sediments into the Anchar Lake (Rashid et al. 2014).

Multi-elemental statistics

The multi-elemental statistical techniques offer a better understanding of the geochemical processes taking place within the natural limnic systems. In the present study, PCA and Pearson correlation matrix were applied to explain the variance of interrelated variables with a smaller set of independent variables. Table 3 shows the Pearson correlation matrix obtained for the sediments in which most elements display both significant positive and negative variations. Strong positive correlations occur between SiO2 and Al2O3 (r = 0.90), SiO2 and Fe2O3 (r = 0.86), and SiO2 and K2O (r = 0.85) SiO2 with TiO2 (r = 0.81). Conversely, strong negative correlations were observed for SiO2 with CaCO3 (r = − 0.57), CaO with Cr (r = − 0.78), CaO with Ni (r = − 0.80), MgO with P2O5 (r = − 0.81). Al2O3 shows a significant positive correlation with most of the major oxides, indicating a detritus source, and its association with clay and/or micaceous minerals. CaO negatively correlates with Pb, Rb, Zn, Cu, Ni, Cr, Cl and all other major oxides signifying its geogenic source. Sr correlates positively with TOC (r = 0.71), whereas Rb shows positive correlation with clay and most of the major oxides except for CaO and P2O5. The correlation coefficient of CaCO3 with CaO (r = 0.55) indicates that most of the Ca in the sediments is derived from carbonate-rich source rocks. CaO shows a negative correlation with all the other oxides indicating the dilution effect. A significant positive correlation of S, Cl, V, Cu, Ni, and Zn with each other in the surface sediments indicates their anthropogenic inputs in addition to their detritus origin. Phosphorous and Sulphur are typically associated with lacustrine OM (Wetzel 2001).

Table 3 Pearson correlation matrix for understanding the sediment quality parameters of the Anchar Lake (r > 0.6 is significant at 95% confidence level; n = 24)
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The principal component plot for various parameters in rotated matrix (Fig. 13a) analysis followed using varimax normalisation was applied to extract the variables. The significant positive relationship shared between silt and TOC indicates that they serve as a major sink for OM accumulation. The close affinity of S with P2O5 is probably due to their similar source from the nearby agricultural fields. The close association of Cu, Zn, Ni, Cl, Co, S and Cr is due to their similar origin by anthropogenic influences. Close association of major oxides, V and clay in the surface sediments seems to be controlled by weathering of detrital source rocks. The enrichment of trace metals along anthropogenically active stations and their association with OM also points to human sources.

Fig. 13
figure 13

Principal component plot (rotated matrix) (a) and dendrogram (b) showing association of variables for Anchar Lake sediments

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The cluster analyses were applied to verify similarities among the various physico-chemical parameters and to find out similar groups which were mutually correlated within the sediment dataset. The dendrogram (Fig. 13b) indicates two major stable clusters/associations for all the studied parameters. Cluster 1 reveals that sulphur has a positive relationship with Cl signifying strong anthropogenic source into the lake. However, cluster 2 reveals a significant relationship between Sr with other parameters indicating their geogenic sources. Cu, Ni, Zn and Pb were moderately enriched in the studied sediments indicating that these metals were probably governed by anthropogenic sources. The occurrence of various agricultural, horticultural and other land-use sites located along the catchment areas and the use of various types of crop fertilisers, pesticides and cattle slurry ingressed in the lake aid in the anthropogenic pollution of the lake sediments. The repeated application of fertilisers and pesticides in the catchment agriculture and horticulture fields is responsible for the accumulation of Cd, S, As, Pb, Hg, Ni, P, and V in the lake surface sediments coupled with surface runoff processes. Similarly, Co, Mn and Cr are probably accumulated due to sewage sludge and domestic solid wastes sourced from the catchment areas, tourist picnic parks and boating activities.

Further, we have compared the trace metal concentration of Anchar Lake sediments with other reported national and international lake pollution studies (Table 4). The concentration of Mn was lower than the geochemical background values but higher than the reported studies. Similarly, Cu and Zn levels were higher than the standard background values. Ni, Fe and Mn content in the Anchar Lake is higher when compared to concentrations reported from other Himalayan lakes. However, Wular Lake has a slightly higher concentration of Ni, Fe and Mn levels when compared to the Anchar Lake trace metal data. The trace metal data of the Anchar Lake were found relatively higher than the other published reports and seems to have gradually increased since the past few decades in the Himalayan lakes particularly Anchar and Wular Lakes (Table 4). This gradual increase is owing to the progressive influence of anthropogenic and natural sources such as increased human activities, untreated sewage dumping and weathering of catchment rocks (Badar et al. 2013; Sheikh et al. 2014).

Table 4 A comparison of the investigated trace metals levels (µg/g−1) with other national and international studies
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Conclusions

The present study is a maiden attempt to examine the source and influence of various anthropogenic pollutants and trace metals that control and alter sediment chemistry of the Anchar Lake . Environmental changes along the lake catchment area since the last few decades have left important geochemical imprints in the lake surface sediments in terms of anthropogenic influences. This study provides detailed data for understanding the major processes (physical and chemical) that control recent sediment deposition within the Anchar Lake.

The textural analyses of the lake floor sediments indicate that most of the samples are clayey silt to silty clay with only one sample falling in sandy–clay–silt domain. Clay was found to be enriched along central and deeper portions of the lake. The OM, CaCO3 and C/N ratio results reveal that the sediments are organically rich and the provenance of OM is composed of mixed sources and origin (allochthonous and autochthonous). The major oxide geochemistry reflects a higher concentration of CaO, MgO and TiO2 compared to the UCC values. The significant enrichment of CaO in silica-poor samples indicates the calcium dilution effect. In the provenance diagrams, all the analysed sediment samples fall along the basalt–granite line indicating mixed sources ranging in composition from mafic to felsic rocks. Environmental assessment indices suggest that the surface sediments are low to moderately contaminated with Cu, Ni, Zn, Co and Cr concentrations. The concentration of other trace metals is relatively low. Further, higher trace metal pollution was found along the sampling stations that receive abundant anthropogenic inputs including the expanded urbanisation zones, agriculture and horticulture sites. These findings offer useful information regarding the sediment quality within the lake. Further, it identifies the surface sediments polluted with respect to trace elements such as S, Cl, Cu, Zn, Ni and Pb that are a direct threat to the health of the lake life (flora and fauna) and thus demands continued monitoring and mitigation efforts.