Introduction

Investigating the compositional properties of rocks, sediments and soils using mineral magnetic measurements is nowadays a routine form of analysis (Thompson and Oldfield 1986; Walden et al. 1999; Maher and Thompson 1999; Oldfield 1999a, b). The technique has been applied to various depositional environments (e.g. Arkell et al. 1983; Oldfield et al. 1985, 1999; White et al. 1997; Walden et al. 1995, 1997; Schmidt et al. 1999; Wheeler et al.1999). Many have also explored the relationship between mineral magnetic measurements and chemical/physical properties of sediments and soils (Oldfield et al. 1985; Oldfield and Yu 1994; Clifton et al. 1997, 1999; Chan et al. 1998; Petrovsky et al. 1998; Xie et al. 1999, 2000; Booth 2002). These investigations have revealed mineral magnetic measurements to be a suitable tool for determining sediment provenance (Oldfield and Yu 1994; Booth 2002; Gawali et al. 2010), sediment transport pathways (Lepland and Stevens 1996), which also serves as a proxy for geochemical, radioactivity, organic matter content and particle size data (Bonnett et al. 1988; Oldfield et al. 1993; Hutchinson and Prandle 1994; Clifton et al. 1997, 1999; Xie et al. 1999, 2000; Zhang et al. 2001). The environmental magnetic method has been successfully utilized for coastal investigations that include sediment sources and transport pathways, and determining contamination of heavy metals in estuaries and tidal flats (Oldfield et al. 1985; Lees and Pethick 1995, Zhang et al. 2001). Environmental magnetic methods (magnetic fingerprinting and magnetic inclusions) are extensively used to resolve various coastal research problems including investigating particulate pollution, to identify and characterize the sediment sources and to examine sediment dynamics in coastal environments (Maher et al. 2008, 2009; Maher and Kinnersely 2010, 2011; Maher and Hatfield 2009).

Recently environmental magnetic methods have been used to map the distribution of heavy minerals and to assess their degree of sorting (Cioppa et al. 2010). Studies by Hatfield et al. (2010) used environmental magnetics to identify and monitor areas of erosion and deposition on a coastal foreland. Gallaway et al. (2012) investigated the mechanism of magnetic mineral transport and highlighted the role of burial mechanism in magnetite enrichment in swash zones. However, the processes controlling magnetic mineral enrichment and transport in coastal depositional environments are not yet well enough understood to make detailed predictions about their distribution patterns and to draw deductions on the governing local hydrodynamic conditions. This paper presents magnetic and geochemical analyses of beach sediments along Vengurla and Aravali beaches of Sindhudurg district, Maharashtra. The purpose of this investigation is to critically examine the potential of applying mineral magnetic parameters as a proxy for heavy mineral concentration.

Study area

The two beaches, Vengurla and Aravali, stretch for more than 8 km in length and are located in the Sindhudurg district, the southernmost part of the Maharashtra state (Fig. 1). These beaches are microtidal and are under the influence of semi-diurnal tides. They are part of the Konkan Coast, which has morphological features distinct from those of the rest of the Indian coast (Chandramohan et al. 1992). The entire coastal stretch of Konkan was tectonically active during the Miocene–Pliocene period. Since then, exogenetic processes are seen to be dominating (Tandale 1993). The study area has Vengurla Beach (Vengurla-Kepadevi, Stations 1–4; width 30–150 m) and Aravali Beach (Mochemad-Kerwada, Stations 5–9; width 50–185 m). The rainfall ranges between 300 and 470 cm/year, and wave height reaches up to 1.0 m, with an average wave period of 5 to 6 s. The predominant wave activity is of plunging type with multiple breakers.

Fig. 1
figure 1

a Location map and Geology (modified after Deendar 2003) of the study area. REDI is the location of iron ore deposit

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The geology of the area (Fig. 1) consists of banded hematite quartzite, varieties of schist, and granitic rocks (Deendar 2003). Vengurla has a moderate to bold relief with hills and deep valleys. It has a coastline on its western side with a NNW–SSE trend. The coastline to the north of Vengurla is rocky, but it is not so in the south. River Karli flows from east to west and borders the northern part. River Talvada flows from north-east to south-west and joins the sea at Mochemad. River Redi, which marks the southern border of Vengurla, has a north–south flow on the eastern side and abruptly changes to an east–west direction near Shiroda to join the sea. The general trend of the major rivers in the area is from east to west, where they join the Arabian Sea. Two more rivers join the sea at the Vengurla port hill, situated on the northern and southern sides of the hill. These rivers have a major east–west trend. The important rock units in the region are banded hematite quartzite, quartzite, and schist (amphibolite and garnet), as well as granitic rocks. The area is structurally disturbed and influences the geology and the drainage pattern to a large extent (Deendar 2003). Faulting is a major factor influencing the deformation and rock alterations, facilitating the formation of residual ore deposits and iron ore containing rocks in the area. The sustainable iron ore deposit of Redi, thus formed, has supported large-scale mining operations over a long period, with workable reserves of about 48 metric tons (Hiremath 2003).

Materials and methods

The upper 3–4 cm of surface sediment samples were collected from the beaches seasonally premonsoon (PreM; May 2003), during monsoon (M) (July 2003), and postmonsoon (PM; November 2003) at nine stations (3–4 samples from each station) along Vengulra and Aravali beaches of the study area (Fig. 1). The samples were collected in a polythene bag. In the laboratory, sample were dried at 40 °C, weighed and packed tightly into 8 cm3 plastic bottles before the measurements. Initial, low-frequency, mass-specific magnetic susceptibility (χ lf) was measured using MFK1-FA Kappa bridge (AGICO). The percentage frequency dependence susceptibility χ fd % is calculated using the formula χ fd % = (χ lf − χ hf)/χ lf × 100. Anhysteric remanent magnetization (ARM) was measured after demagnetization in an alternate field (AF) field of 100mT, inducing Direct Current biasing field of 0.05 mT using Molspin AF demagnetizer and was measured on the Molspin magnetometer. Isothermal remanent magnetization (IRM) acquisition curves in steps from 10 to 1000 mT; backfield application to the saturation isothermal remanent magnetization (SIRM) and evaluation of the remanent coercive force (Hcr) were performed using Molspin pulse magnetizer. After a sample has acquired an IRM it is often possible to (partially) demagnetize the sample by exposing it to a magnetic field of reversed direction. Such a partial demagnetization can yield information about the ease of remanence acquisition, or the coercivity of a sample. The results are expressed as S ratio (S 300 = IRM−300/SIRM) and hard isothermal remanent magnetization (HIRM = SIRM-IRM−300, being IRM−300 the acquired IRM at a backfield of 300 mT). In order to identify magnetic mineralogy, temperature dependent magnetic susceptibility (χ–T curves) using AGICO KLY-4S Kappabridge attached to CS-3/CSL furnaces and SIRM thermal demagnetization measurements using a MMTD-80 thermal demagnetizer (Magnetic Measurements Ltd.) were carried out on 10 samples.

Geochemical analysis for some samples was performed using X-ray fluorescence (XRF) for determining elemental concentrations. For sample preparation 8 gm of dried sediment was ground, of which 4 g was used for geochemical analysis using Spectro XEPOS XRF spectrometer (AMETEK) by the Turboquant-Powders method (http://www.ametek.com). For analysis, the standard MCA calibration sample from Fluxana (http://www.fluxana.com) was used. The whole set of data (environmental magnetic and geochemical data) in this study was analyzed by multivariate statistical methods, including correlation matrix. All the data processing for statistical analysis was done with the statistical software SPSS for Windows (Ver. 16).

Results and discussion

Environmental magnetic analysis

Magnetic concentration parameters χlf, SIRM and ARM are sensitive to changes in the concentration of magnetic minerals (e.g. Anderson and Rippey 1988; Lanci et al. 1999; Geiss et al. 2004). Superparamagnetic (SP) particles record high χlf and relatively high values for multidomain (MD) grains (Thompson and Oldfield 1986). ARM is more sensitive than SIRM to single domain (SD) grains (Turner 1997). The total χ of a sample is the sum of the ferrimagnetic, paramagnetic or diamagnetic contributions, whereas SIRM and ARM are independent of paramagnetic minerals. SIRM is highly dependent on the make-up of different types of magnetic minerals (e.g., magnetite has measurements two orders of magnitude higher than hematite). Therefore, all three parameters should be studied simultaneously to infer changes in magnetic concentration.

Mineral magnetic data for PreM, M, and PM are presented in Fig. 2. The χlf values show considerable variation PreM (0.02–2.05 × 10−7 m3/kg), during M (0.02–2.17 × 10−7 m3/kg) and PM (0.12–6.03 × 10−7 m3/kg) exhibiting low concentration of magnetic minerals at all stations, except at 8 and 9 stations where a sudden rise is observed PreM, during M and PM (Fig. 2a). Samples from PreM, M and PM of stations 1 and 2 indicate high ARM values (Fig. 2c, 1.5 × 10−5, 1.20–1.74 × 10−5 and 0.94–1.57 × 10−5 Am3/kg). Samples from stations 3, 4, 5 and 6 contain moderate and low to moderate ARM (PreM-0.28–0.58 × 10−5, M-0.17–0.86 × 10−5 and PM-0.19–0.99 × 10−5 Am3/kg) values. Stations 7, 8 and 9 exhibit variable concentration of stable SD magnetic minerals with moderate to very high ARM values in all three seasons. SIRM values for PreM, M and PM samples from stations 1–7 indicate very low to low concentrations of all remanence-carrying magnetic minerals (Fig. 2b). By contrast, stations 8 and 9 show SIRM concentration to be moderate PreM (581 and 564 × 10−5 Am3/kg), low to moderate in M (80 and 996 × 10−5 Am3/kg) and low to moderate PM (31 and 690 × 10−5 Am3/kg).

Fig. 2
figure 2

a–i Plot of χ lf, SIRM, ARM,ARM/χlf, ARM/SIRM, SIRM/χ lf, χ fd %, S ratio and HIRM vs. sampling stations

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Changes in magnetic mineralogy can be determined by using IRM acquisition curves, H cr, χ–T curves, SIRM thermal demagnetization, S ratio and Hard IRM. S ratio is a dimensionless parameter that discriminates content of ferrimagnetic with respect to antiferromagentic minerals; values close to 1 correspond to the predominance of ferrimagnetic minerals. Magnetic minerals in sediments can also be identified as soft and hard fractions. The hard fraction has high coercivity and can be used to estimate the total concentration of canted antiferromagnetic minerals (hematite; Oldfield and Richardson 1990). The variation of S ratio (Fig. 2h) is from 0.4 to 0.8, 0.2 to 0.8 and from 0.3 to 0.8 for PreM, M and PM with an average value of 0.5. Hard IRM varies from 20 to 3767 × 10−5 Am3/kg PreM, 10 to 8401 × 10−5 Am3/kg during M and from 22 to 7498 × 10−5 Am3/kgPM. Figure 2i shows that there is a lower concentration of HIRM up to station 8, whereas at station 9 higher concentrations exist. This gives an idea that soft magnetic minerals are present up to station 8 and a subordinate hard component at station 9. Figure 3a–f shows typical curves for IRM acquisition, back field and χ–T curves. For most samples, <95 % of saturation is achieved at ~200 mT, indicating a predominantly low coercivity mineral contribution (Fig. 3a).This is supported by Hcr values ranging from 30 to 60 mT (Fig. 3b). All the samples from stations present a curie temperature from 580 to 620 °C suggesting the dominance of magnetite and maghemite minerals. At stations 3 and 9, there is a small decrease of χ between 250 and 350 °C and increasing thereon with a peak at around 450 °C, suggesting titanomagnetite.

Fig. 3
figure 3

Typical examples of a acquisition of isothermal remanent magnetization; b backfield demagnetization and c–f temperature dependence of magnetic susceptibility (χ–T curves)

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Samples collected at Station 1, M and PreM display SIRM decrease at unblocking temperature (Tb) of about 300, 400 and 675 °C (Fig. 4a). This could be due to titanomagnetite and hematite. Samples collected at station 2 during the M and PM campaigns display a spectra of decrease in SIRM, with temperature reaching a T b of about 300, 580, and 680 °C (Fig. 4b), indicating titanomagnetite, magnetite and hematite, respectively. The SIRM thermal demagnetization of samples collected at Station 3, PM and PreM display a distinct change in behavior from 200 to 400 °C (Fig. 4c). A drop in SIRM occurs in the PM sample at 250 °C, after which a gradual decrease is seen to about 675 °C. This could be due to titanomagnetite and hematite. The PreM sample has one magnetic mineral phase with (T b) of about 400 °C, denoting the presence of titanomagnetite, and the other is magnetite with a T b of about 580 °C. On demagnetization, the sample collected in M season at Station 5 shows a T b of around 450 °C and 675 °C (Fig. 4d), denoting the presence of titanomagnetite and hematite, respectively. The sample collected at Station 9, during M season is seen to display two phases of magnetic mineralogy (Fig. 4e), one with a T b at 420 °C (titanomagnetite) and the other with a Tb that lies between 580 and 600 °C. This could be magnetite. The PM sample at Station 9, displays a T b of 580 °C, i.e., magnetite (Fig. 4e). Overall the magnetic mineralogy of the sediments is variable in Titanomagnetite, magnetite and hematite.

Fig. 4
figure 4

Thermal demagnetization of selected samples for PreM, M and PM. Open circles, squares and diamonds represent M, PM and PreM, respectively

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Changes in magnetic grain size and the interparametric ratios of ARM/χ lf, ARM/SIRM and SIRM/χ lf can be used for grain size interpretation. The ARM/χ lf ratio can be used to distinguish particle size of sediments in general and magnetic minerals in particular (Banerjee et al. 1981). Low ratios indicate coarse grain sizes, whereas high values suggest fine magnetic grain sizes. At stations 1, 2, 3, 4 and 6, ARM/χ lf values (Fig. 2d) show increasing trend except at station 5 and then decreasing trend at 7, 8 and 9 is seen PreM. In M, initially there is a decreasing, then increasing and again a decreasing trend. PM, there is a increasing and decreasing trend. The ARM/SIRM ratio is the best estimator of changes in magnetic grain size because neither of the two parameters depends on paramagnetic minerals (Turner 1997). The ARM/SIRM (Fig. 2e) values for PreM and M are seen to fluctuate up to station 5 and then exhibit decreasing trend. There is an increasing trend up to station 4 and then decreasing trend PM (Fig. 2e). The decreasing trend of ARM/SIRM values towards Aravali stations signifies increase in grain size (coarser) as compared to finer at Vengurla stations. The SIRM/χlf displays high values where smaller, SD grains are present (Peters and Dekkers 2003; Heider et al. 1996). However values >70 kA/m are considered to be an indicator of the iron sulfide greigite (Fassbinder and Stanjek 1994; Blanchet et al. 2009; Roberts et al. 2011). SIRM/χ lf varies from 11 to 43, 6–45 and 4.6–17.8 kA/m PreM, M and PM, respectively (Fig. 2f). χ fd %, which is an indicator of SP grains, can also be used as a proxy for grain size. Values of χ fd % <4.0 % indicates virtually no SP and dominant MD and SSD grains while χ fd % >10.0 % reflects high content of SP grains (Dearing 1999). χfd % is seen to vary from 0.03 to 2.6 % with an average value of 1.27 % PreM; 0.05 to 8.2 % with an average of 3.9 % in M and 0.09–7.0 % with an average of 4.7 % PM (Fig. 2g). Mean values of χ fd % are lesser than 10 % and, therefore, indicate that SP minerals are not dominant in these samples. All the above referred grain size parameters suggest these beach sediments are dominated by SD and MD minerals.

Descriptive statistics and inter-parametric correlations for beach sediments are presented in Tables 1 and 2. The mass-specific susceptibility (χ lf), ARM and SIRM show notable increase PM, during M and PreM, respectively. The mean values of ARM/SIRM, S ratio and HIRM do not show any significant change in PreM, M and PM periods. ARM/χ lf values for PM are relatively high suggesting presence of coarser and larger grain size sediments relative to PreM samples. χ fd % mean values during PreM, M and PM fall in the range of SD and MD. The inter-parametric correlations (Table 2) indicate significant positive correlation (supported by P values at cut off level 0.05) between χ lf and ARM PreM; and good correlation in M and PM. χ lf also show good correlation with SIRM and HIRM preM during M and PM.

Table 1 Descriptive statistics of various magnetic parameters
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Table 2 Correlation coefficient values of mineral magnetic parameters
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Geochemical analysis

Figure 5 depicts the concentration of abundant (Fe, Mg and Al) and heavy metals (Cu, Cr, N and Zn). The concentration ranges of abundant elements Al, Fe, and Mg are 1.2–2.1, 0.77–18.88, and 0.20–0.60 % PreM; 0.70–3.36,0.18–0.72 and 1.31–2.16 % for M and 0.77–17.68, 0.27–1.05 and 1.33–2.25 % PM, respectively (Fig. 5a, c and e). As can be deciphered from Fig. 5a, c and e, Fe is more PreM and PM for station 8 and 9 of Aravali beach. The concentrations of heavy metals in beach sediments are Cu: 4.1–18.6 ppm (PreM), 3.5–8.4 (M) and 3.2–15.1 ppm (PM); Cr: 21.9–286.6 ppm (PreM), 25.3–100.2 ppm (M) and 17.6–310.7 (PM); Ni: 5.8–20.7 ppm (PreM), 5.2–10.2 ppm (M) and 5.6–17.8 ppm (PM); Zn: 6.2–37.9 ppm (PreM), 5.3–16.9 (M) and 5.6–132.8 ppm (PM) (Fig. 5b, d and f). Cr is more dominant in stations 8 and 9 PreM and PM periods than M.

Fig. 5
figure 5

Variation of Heavy metal concentration Iron (Fe), magnesium (Mg), Aluminum (Al), Copper (Cu), Chromium (Cr), Nickel (Ni) and Zink (Zn) at different stations for PreM, M and PM. a, b Fe, Mg, Al, Cu, Cr, Ni and Zn for PreM; c, d Fe, Mg, Al, Cu, Cr, Ni and Zn for M and e, f Fe, Mg, Al, Cu, Cr, Ni and Zn for PM

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Correlation analysis was carried out to identify relationship, if any, that exists between magnetic parameters and heavy metal concentration. The concentrations of four heavy metals (Cu, Cr, Ni and Zn) are plotted against χlf, SIRM and HIRM for three seasons (Figs. 6, 7), where correlation coefficients between the heavy metal concentrations and magnetic susceptibility are also given. Table 3 shows Pearson’s correlation coefficient values (r) between mineral magnetic parameters and heavy metals for PreM, M and PM periods. Significant correlations (supported by P values at cut off level 0.05) exist between χlf, SIRM, HIRM and heavy metal concentration PreM and PM (Figs. 6a–h, 7e–h). However, each of these relationships is absent in M. No significant correlation between any of the mineral magnetic and heavy metal concentration is seen during M. ARM shows good correlation with heavy metal concentration only PM. S ratio shows a strong negative correlation with heavy metal concentration PreM and PM (Fig. 7a–d).

Fig. 6
figure 6

Variation of Copper (Cu), Chromium (Cr), Nickel (Ni) and Zink (Zn) versus magnetic mineral concentration parameters: magnetic susceptibility (χ) and saturation isothermal remanent magnetization (SIRM). a–d Cu, Cr, Ni and Zn versus χ lf; e–h Cu, Cr, Ni and Zn versus SIRM. Squares, open circles and filled triangles represent PreM, M and PM, respectively. NS-not significant

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Fig. 7
figure 7

Variation of Copper (Cu), Chromium (Cr), Nickel (Ni) and Zink (Zn) versus magnetic mineral concentration parameters: S ratio and Hard isothermal remanent magnetization (HIRM). a–d Cu, Cr, Ni and Zn versus S ratio; e–h Cu, Cr, Ni and Zn versus HIRM.. Squares, open circles and filled triangles represent PreM, M and PM, respectively. NS-not significant

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Table 3 Mineral magnetic and geochemical correlation coefficient values (N = 27)
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The Sindhudurg beaches, just like other Konkan beaches, receive sediments from a number of sources. The bedrock is broken into smaller fragments by water, wind and other climatic processes operative through physical comminution and chemical weathering. Hydrodynamic processes are an important source of placer formations across the beaches (Kurian et al. 2000) along with alongshore and cross-shore movement of sediments (Chandrasekar et al. 2003). Li et al. (2002) consider sediment transport processes in the swash zone are important to beach morphology and shoreline stability. Heavy mineral accumulation depends on source and is controlled by shoreline configuration and seasonal wave climate (Chandrasekar et al. 2005). Strong winnowing action of waves is responsible for heavy mineral concentration rather than the selective transport through alongshore currents (Hegde et al. 2006). The Vengurla and Aravali beaches are exposed to variable environmental conditions which are reflected in the detrital pattern of accumulation and/or erosion. Though they seem to share similar geochemical realm, the heavy metal as well as abundant element characteristics sometimes are seen to be at variance with each other. The heavy metal concentration shares significant correlation with magnetic parameters PreM and PM. This correlation is completely lost during M. S ratio, on the other hand, shows a strong negative correlation with heavy metals PreM and PM. Overall the Aravali beach has more concentration of magnetic minerals than the Vengurla beach. The strong association of heavy metals with magnetic mineral concentration indicates their interrelationship. Ti and Fe are reckoned to have been released from the banded hematite formations. The hinterland is likely having some deposits of chromite. Our results underline the presence of Cr in the beach samples, especially towards the end of Aravali beach. Gujar et al. (2010) studied heavy mineral placers from Vijaydurg to Redi and found opaques to contain ilmenite (43.07 % in areas of significant concentration and 11.02 % in areas of localized concentration), magnetite (58.15 and 8.72 %) and chromite (up to 10.09 %). The metamorphic rocks of the hinterland contribute substantially to the sands of the beaches, apart from the Deccan traps. The significance of temperature dependent susceptibility and SIRM measurements backs our contention regarding the presence of both magnetite and hematite along the Vengurla and Aravali beaches. The pathways for antiferromagnetic minerals coming from the hinterland are obvious. The metamorphic rocks in the vicinity of these two beaches are brought down in remnants through wear and tear during the transit. The iron ore mines are also found to be active around the Redi area. The ore material spills during the transportation adding the antiferromagnetic material to these beaches. But, the presence of magnetite and titano magnetite can be attributed to the longshore currents prevailing along these beaches, which bring the eroded particles from the Deccan traps.

The Vengurla–Aravali beaches are perceived to receive their detrital input from the surrounding region. The hinterland is seen to be dominated by hematitic mineral assemblage. The present mineral magnetic study provides a strong support for this, which brings out the domination of antiferromagnetic minerals. It must also be noted that oxidation of magnetite and Ti-magnetite can result in the formation of maghemites (Barron and Torrent 2002) by the process called maghemitization (Dunlop and Ozdemir 1997). The small size of authigenic magnetite crystals can be oxidized in periods when the soil is aerated (van der Marel 1951; Schwertmann and Heinemann 1959; Murad and Schwertmann 1993) to form maghemite. Maghemite can form from magnetite because of their structural similarities (Fasiska 1967). Maghemite is relatively abundant in well-aerated tropical soils containing high hematite/goethite ratio (Fontes and Weed 1991; da Costa 1996), which is in appreciable abundance around Vengurla–Aravali beaches. Maghemitization is a widespread process reported from different environments and in natural samples magnetic evidence for maghemite with a thermal stability around 597 °C has been reported from basaltic rocks (Gee and Nakanishi 1995; Chévrier et al. 2006). The high heavy metal concentration and Fe preM and PM at Aravali beach reveals that source is from south of Aravali beach in which sediment transport, delivery and flux play a major role in this process. Iron ore deposits are transported from near Redi beach which is situated to the south of Aravali beach (Gawali et al. 2010). Iron ore in the Redi and surrounding areas is associated with banded ferruginous quartzites and Precambrian ferruginous phyllites. The iron ore consists essentially of hematite and partly of magnetite, limonite, and goethite, which occur as reefs and lentoid bodies. This further strengthens our results regarding source of the sediments. Our studies also bring out the interrelationship of heavy metals and magnetic parameters. Thus, the geology and geomorphology of the area plays a big role in accumulation of sands at these beaches, which are reworked by the prevalent wind and current regime.

The beach environment all over the world is under stress and being polluted by heavy metals. The fast and inexpensive magnetic measurements can be used to screen heavy metal hotspots for further detailed analyses. Magnetic parameters can thus act as proxy for heavy metals.

Conclusions

The Aravali and Vengurla beach sediments are dominated by magnetite, maghemite, titanomagnetite and hematite magnetic minerals which contain SD and MD grains. Strong correlation is evident between heavy metal concentration and magnetic concentration dependent parameters (χ, SIRM and HIRM) preM and PM. This correlation ceases to exist in M. Magnetic concentration dependent parameters are observed to covary with heavy metals, suggesting magnetic mineral and heavy metal input to beach sediments is from same source, especially from south of Aravali beach. The prevalence of heavy metals at Aravali is governed by geomorphological changes in the coastal tract. Significant correlation that exists between heavy metals and magnetic parameters has a huge potential in aiding in magnetic screening/monitoring of heavy metal pollution in coastal sediments in a rapid and cost effective manner.