Environmental Magnetism and Application in the Continental Shelf Sediments of India R

Environmental magnetism and application in the continental shelf sediments of India R. Alagarsamy

To cite this version:

R. Alagarsamy. Environmental magnetism and application in the continental shelf sediments of India. Marine Environmental Research, Elsevier, 2009, 68 (2), pp.49. 10.1016/j.marenvres.2009.04.003. hal-00563074

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Environmental magnetism and application in the continental shelf sediments of India

R. Alagarsamy

PII: S0141-1136(09)00037-3 DOI: 10.1016/j.marenvres.2009.04.003 Reference: MERE 3330

To appear in: Marine Environmental Research

Received Date: 11 January 2008 Revised Date: 27 February 2009 Accepted Date: 13 April 2009

Please cite this article as: Alagarsamy, R., Environmental magnetism and application in the continental shelf sediments of India, Marine Environmental Research (2009), doi: 10.1016/j.marenvres.2009.04.003

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1 Environmental magnetism and application in the continental shelf sediments of India

2 R. Alagarsamy1*

3 National Institute of Oceanography, Council of Scientific and Industrial Research

4 (CSIR), Donapaula, Goa-403004, India

5 Abstract

6 Mineral magnetic and geochemical analyses were carried out on surface

7 sediments from the continental shelf of India. The purpose of this study is to examine

8 the environmental assessment of heavy metal concentrations and its impact in the

9 coastal environment using magnetic techniques and to gain an understanding on the

10 factors controlling metal concentrations and distributions in the east and west coast of

11 India. The strong relationships between Anhysteretic Remanent Magnetization (χARM)

12 and heavy metals can be explained by the role of iron oxides controlling metal

13 concentrations, though the link is also reinforced by the strong tendency of χARM to be

14 associated with the finer particle sizes. Higher values of magnetic susceptibility,

15 IRM20 mT and SIRM are associated with the east coast shelf sediments suggest the

16 presence of high ferrimagnetic content, which can be derived from the weathering

17 products of the Deccan Basalts. χARM can be used as a normalizer for particle size

18 effects in the way that Aluminium (Al) is often used. The relationship between

19 magnetic parameters and heavy metal concentrations (Fe, Cu, Cr and Ni) showed a

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20 strong positive correlation in the east coast sediments, much less so in the case of the

21 west coast. This finding suggests that the simple, rapid and non-destructive magnetic

22 measurement can be used as an indicator for the heavy metal contamination and

23 proxies for the measurement of heavy metals content in the coastal environment.

24 Key words: Mineral Magnetic; Heavy metal; Magnetic method; Sediment;

25 Continental shelf; India.

26 *Corresponding Address: Earth Dynamic System Research Center, National Cheng

27 Kung University, Tainan, Taiwan 70101, Republic of China

28 email: [email protected]

29 Telephone: +886-6-2757575-65432-219; Fax: +886-6-27402851

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30 1. Introduction

31 Magnetic measurements are being used as a powerful tool for the assessment of

32 heavy metal contamination in soils and sediments and in the investigation of the

33 compositional properties of rocks, sediments and soils (Thompson and Oldfield, 1986;

34 Walden et al., 1999; Maher and Thompson, 1999). Magnetic minerals in soils are

35 derived either from the parent rocks (lithogenic origin), pedogenesis or as a result of

36 anthropogenic activities. Susceptibility measurements play an important role for

37 monitoring environmental pollution in the case of minor contributions of the first two

38 sources to the magnetic properties of soils. Accumulation of anthropogenic

39 ferrimagnetic particles, originating during high temperature combustion of fossil fuels

40 (e.g. Vassilev 1992; Dekkers and Pietersen 1992), results in significant enhancement of

41 topsoil magnetic susceptibility. Therefore, under favourable circumstances, magnetic

42 properties of soils and sediments can be used for the assessment of imissions, both from

43 local pollution sources and on a wider regional scale. Already a pioneering work of

44 Thompson and Oldfield (1986) reported that soils near urban areas and industrial zones

45 have an increased magnetic susceptibility, due to the deposition of magnetic particles,

46 such as, dusts of the metallurgical industries and fly ashes of the coal combustion.

47 Magnetic measurements are relatively simple and non-destructive and due to high

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48 sensitivity and speed are often applied for detecting industrial pollution. Because there

49 is a need for fast and cost effective screening and monitoring tools for industrial

50 pollution, magnetic methods have drawn the attention of modern researchers as an

51 approximate tool, to detect and characterize environmental pollution (e.g., Dearing et

52 al., 1996; Petrovský and Ellwood 1999; Hoffmann et al., 1999; Magiera and Strzyszcz

53 2000; Petrovský et al., 2001; Hanesch and Scholger 2002; Veneva et al., 2004, and

54 others). This work has been used to identify the sources of industrial pollution

55 (Scoullos et al., 1979; Hunt et al., 1984; Matzka and Maher 1999; Petrovský et al.,

56 2000, 2001; Klose et al., 2001; Knab et al., 2001; Lecoanet et al., 2001; Hanesch and

57 Scholger 2002; Muxworthy et al., 2002; Hanesch et al., 2003; Kapicka et al., 2003;

58 Moreno et al., 2003; Desenfant et al., 2004; Jordanova et al., 2004; Spiteri et al., 2005)

59 and to characterize various depositional environments (e.g. Arkell et al., 1983; Oldfield

60 et al., 1985, 1999; White et al., 1997; Walden et al., 1995, 1997; Schmidt et al., 1999;

61 Wheeler et al., 1999). The relationship between mineral magnetic measurements and

62 chemical/physical properties of sediments and soils have been established in several

63 studies (Oldfield et al., 1985; Oldfield and Yu, 1994; Clifton et al., 1997, 1999; Chan et

64 al., 1998; Petrovský et al., 1998; Xie et al., 1999, 2000; Booth, 2002). Mineral magnetic

65 measurements were used as a tool for determining sediment provenance (Oldfield and

66 Yu, 1994), transport pathways (Lepland and Stevens, 1996), as a proxy for

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67 geochemical, radioactivity, organic matter content and particle size data (Bonnett et al.,

68 1988; Oldfield et al., 1993; Hutchinson and Prandle, 1994; Clifton et al., 1997, 1999;

69 Xie et al., 1999, 2000; Zhang et al., 2001; Booth et al., 2005). Magnetic measurements

70 as a proxy for industrial contamination have also been employed in India (Goddu et al.,

71 2004). Hence, the present study is undertaken in order to examine the environmental

72 assessment of heavy metal concentrations and its impact in the coastal environment

73 using magnetic techniques and to gain an understanding on the factors controlling

74 metal concentrations and distributions along the east and west coast of India.

75 2. Materials and methods

76 2.1. Study Area

77 2.1.1. Geographic setting

78 The subcontinent of India, geographically covers tropical to subtropical

79 climatic zones and exhibits strong seasonality from warm humid to arid and cold arid

80 conditions. The climate of the subcontinent is dominated by Monsoon systems; this

81 region remains humid during summer and dry during winter. It has basically two main

82 seasons, the wet and dry, instead of four seasons. The gradient of climatic zones is

83 largely affected by the sharp altitudinal changes in the northern half where the

84 Himalaya forms the major relief. The Indian subcontinent experiences mostly a

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85 tropical monsoon climate, with significant seasonal variations in rainfall and

86 temperature. The Himalayas, acting as a barrier to the cold northerly winds from

87 Central Asia, maintain the pattern of the Indian Ocean monsoon circulation. The Thar

88 Desert allows oceanic atmospheric circulation and sediment dust-aerosol influx deep

89 into the continent. The southwest (SW) monsoon is divided into Arabian Sea Branch

90 of the SW Monsoon and Bay of Bengal Branch of the SW Monsoon. The Arabian Sea

91 branch extends the low-pressure area over the Thar Desert in Rajasthan whereas the

92 Bay of Bengal branch results in turbulence in the region due to the rapid altitudinal

93 differences and narrowing orography. The Arabian Sea branch is roughly three times

94 stronger than the Bay of Bengal branch. This branch of the monsoon moves

95 northwards along the Western Ghats giving rain to the coastal areas west of the

96 Western Ghats and the eastern parts of the Western Ghats do not receive much rain

97 from this monsoon. The climate-induced sedimentation processes take place in

98 large-scale during strong monsoons as cyclones occur in the coastal region (Sangode

99 et al., 2007).

100 2.1.1.2. East Coast-Geology-Climate

101 The outer limit of the eastern continental shelf of India lies at ~200 m (Fig. 1)

102 and the inner shelf and the continental slope are covered by clastic sediments (Rao,

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103 1985). The outer shelf is covered by calcareous relict sediments and off the river

104 mouths the shelf is covered by fine-grained terrigenous sediments. The shelf at the

105 mouths of the rivers receives a large part of its sediment from the rivers Ganges,

106 Brahmaputra and Mahanadi in the north, Godavari and Krishna in the central region,

107 all forming fertile, heavily populated deltas. Sediment from the rivers has made the

108 bay a shallow sea, and the waters have reduced the salinity of surface waters along the

109 shore. Sediment input and annual discharge is less from the smaller rivers such as

110 Pennar and Cauvery in the south (Rao, 1979; Rao, 1985).

111 The presence of the Deccan Basalt is observed along the tributaries of the

112 Godavari and discharged into the Bay of Bengal from the east coast of the Peninsular

113 India through the Mahanadi, Godavari and Cauvery river systems (Sangode et al.,

114 2007). Deccan Province also covers the studied stations in the east coast of India

115 (Raman et al., 1995).

116 2.1.1.3. West Coast-Geology-Climate

117 The Indus river is the largest source of sediments in the Arabian Sea, which

118 extend outward to a distance of ~1000-1500 km (Rao and Rao, 1995). It

119 predominantly drains the Precambrian metamorphic rocks of Himalayas and to a

120 lesser extent the semi arid and arid soils of West Pakistan and NW India (Krishnan,

121 1968). Deccan Trap basalts (Fig.1) are the predominant rock types cropping out in the

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122 Saurashtra and the drainage basins of the Narmada and Tapti rivers, which annually

123 discharge ~ 60x106 tonnes sediment through the Gulf of Cambay (Rao, 1975) where a

124 semi-arid climate prevails. The Western Ghats are composed of basalts between

125 Mumbai and Goa, and Precambrian granites, gneisses, schists and charnockites

126 between Goa and Cochin (Krishnan, 1968). The Ghats are located on the coast

127 between Goa and Bhatkal but are 50-80 km from the coast south of Bhatkal. The

128 source of sediments in rivers originating in the western part of India is derived the

129 black “cotton soils”, covering the Deccan Traps, a large percentage of which is

130 mainly composed of montmorillonite. The drainage area in the upper reaches of these

131 rivers is a montmorillonite-rich, zone but in the lower reaches, they drain through

132 Precambrian formations that contain kaolinite rich soils that are of secondary

133 significance in the shelf sediments derived from the Godavari and Krishna rivers (Rao,

134 1991). It is well established that source rock compositions and weathering

135 mechanisms basically control the distinct geochemical compositions of sediments in

136 the east and west coast of India (Alagarsamy and Zhang, 2005).

137 2.2. Sampling and Analysis

138 Surface sediments from the east and west coasts of India were collected using

139 a Van Veen Grab/Snapper during several cruises of the R.V. Gaveshani (130, 139 and

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140 143) (Fig. 1; Table 1). Subsamples were collected from the uppermost layer of the

141 sediment taking care to minimize contamination. Samples were frozen after collection

142 and later thawed, dried at 50-60ºC in an oven and disaggregated in an agate mortar

143 before chemical treatment for total metal analysis. For element analysis, the known

144 quantity (~0.2 g) of the powdered samples were digested with a mixture of

145 concentrated HF–HNO3–HClO4 for the total metal content (Zhang and Liu, 2002).

146 Solutions were analyzed for Al, Ca, K, Fe, Cr, Cu and Ni using ICP-AES (Model:

147 PE-2000).

148 Accuracy of the analytical methods was monitored by analysing standard

149 reference materials (GSD-9) with study samples in every batch of analysis. Further

150 checks were made through repeated analyses of the standard reference materials by

151 ICP-AES (Model: PE-2000). Precision of the analyses was checked by triplicate

152 analysis of standard materials. Calculated coefficients of variation were within ±

153 ~1-8% for the different elements except K and within ± 4 for K of the certified ones

154 (NIM-G).

155 Magnetic susceptibility measurements were carried out on sub samples which

156 were dried at 40°C and disaggregated. The magnetic susceptibility (χ, mass-specific),

157 'saturation' isothermal remanent magnetization (SIRM) after application of 1T, and

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158 then back field remanence measurements at -20 mT, -40 mT, -100 mT and -300 mT

159 were measured on all samples. Both low (0.47 kHz) and high (4.7 kHz) frequency

160 susceptibility (χlf and χhf respectively) were determined using a Bartington MS2 dual

161 frequency susceptibility meter. Frequency dependent susceptibility (χfd) was

162 calculated from the expression χfd (%) = [(χlf −χhf)/χlf] x100. Anhysteretic remanent

163 magnetization (ARM), here expressed as susceptibility of ARM (χARM) was measured

164 after demagnetion in an AF field of 100 mT inducing a DC biasing field of 0.04 mT

165 using a Molspin AF demagnetizer and Minispin pulse magnetizer and measured on

166 the Molspin Minispin magnetometer (Walden et al., 1997). Isothermal Remanent

167 Magnetization (IRM) is the remanent magnetization acquired by a sample after

168 exposure to, and removal from, a steady (DC) magnetic field. IRM depends on the

169 strength of the field applied, which is often denoted by a subscript. It is also a function

170 of the magnetic mineralogy and grain size. The maximum remanence that can be

171 produced in a sample is called Saturation Isothermal Remanent Magnetization

172 (SIRM). IRM is often used as an indicator for the presence of ferrimagnetic minerals,

173 but antiferromagnetic minerals, such as hematite and goethite also contribute to IRM

174 measurements where field in excess of 100 mT are used.

175 After a sample has acquired an IRM it is often possible to (partially)

176 demagnetize the sample by exposing it to a magnetic field of reversed direction. Such

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177 a partial demagnetization can yield information about the ease of remanence

178 acquisition, or the coercivity of a sample. The results are expressed as an S-ratio, for

179 example,

180 S100=IRM−100/SIRM and S300=IRM−300/SIRM

181 where IRM−100, -300 denotes an IRM acquired in a reversed field of 100 mT and 300

182 mT respectively after SIRM acquisition. S-ratios can be used to gain information

183 about the magnetic mineralogy (Bloemendal et al., 1992). Hard IRM (HIRM), broadly

184 indicative of antiferromagnetic minerals, mainly hematite, was defined as HIRM =

185 0.5 x (SIRM+IRM-300 mT).

186 3. Results and discussion

187 The parameters sensitive to fine-grained magnetic minerals (χARM and χfd), the

188 ratio of χARM to χlf and χARM/χfd are discussed in this study. The concentration-sensitive

189 magnetic parameters such as magnetic susceptibility (Table 2) and its concentration

190 related parameters (Fig. 2a-c); Magnetic susceptibility vs. IRM20 mT (Fig. 3); χARM/χlf

191 vs. χARM/χfd (Fig. 4); Fe, Cr, Cu and Ni vs. Al (Fig. 5 a-d); Fe, Cr, Cu; Ni vs. χARM (Fig.

192 6a-d); Fe, Cr, Cu and Ni normalized by χARM vs. Al normalization (Fig. 7) and

193 Magnetic susceptibility vs. EF (metal) (Fig. 8) are presented along the east and west

194 coast of India.

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195 3.1. Variation of heavy metals - influence of particle size

196 Heavy metals vary significantly in the shelf sediment of east and west coast of

197 India, especially in the case of Cr, Ni and Cu (Table 3). This variability could be due

198 to particle size effects or anthropogenic influences. Finer sediments with abundant

199 clay minerals, iron/manganese oxides as well as organic matter tend to show higher

200 metal concentrations (Rae, 1997). In our study, the heavy metals (Fe, Cr, Cu and Ni)

201 showed a more significant coefficient of determination with magnetic susceptibility in

202 the east coast sediment than the west (Table 4a,b). It is observed that higher

203 concentration of ferrimagnetic minerals are found in the east coast sediments and low

204 concentration in the west coast of India. The comparable or even stronger dependence

205 of heavy metals on Fe (Table 4a,b) is in accordance with the fact that iron and

206 manganese oxides, which are commonly in close association with clay minerals, have

207 strong absorptive capacity for metals (Kersten and Smedes, 2002).

208 The higher χfd (%) in the east coast showed that the stronger presence of super

209 paramagnetic grains in these samples than in the west. Magnetic quotients are often

210 useful indicators of the properties of certain minerals and domain states (Lu and Bai,

211 2006). F300 (IRM300 mT/SIRM) and S300 (IRM-300 mT/SIRM) can be used as

212 approximate indication of relative importance of canted antiferrimagnetic components

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213 and to distinguish ferrimagnetic component from antiferrimagnetic components

214 respectively.

215 3.2. Remanent magnetic parameters

216 IRM20 mT is expected to be approximately proportional to the multidomain

217 (MD) grain content of ferrimagnetic components (magnetite). Hard IRM

218 (SIRM–IRM300 mT) can be used as a rough guide to canted antiferrimagnetic

219 (hematite) components in a sample. High values of IRM20 mT and SIRM can be

220 sometimes attributed to a greater accumulation of anthropogenic magnetic minerals

221 (Lu and Bai, 2006). There is a highly significant and much stronger correlation

222 between magnetic susceptibility and IRM20 mT for east coast shelf sediment of India

223 (Fig. 3; Table 4a) than the west (Table 4b). This suggests that the magnetic

224 susceptibility of east coast sediment contains mainly from MD ferrimagnetic minerals

225 derived from basalts. Magnetic susceptibility and other environmental parameters of

226 the tributaries of the Godavari river sediment revealed the presence of high

227 ferrimagnetic content as it is composed of weathering products of the Deccan Basalt

228 (Sangode et al., 2007).

229 3.3. Magnetic parameters as proxies of particle size

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230 χARM is sensitive to single domain (SD, 0.03–0.07 µm) ferrimagnetic

231 minerals (Maher, 1988), while χfd indicates the presence of fine viscous grains near

232 the SP-SD boundary (Maher, 1988; Oldfield and Yu, 1994). As SP and SD grains are

233 of sub-micrometer size, if they occur as individual particles, it is expected that they

234 would reside mainly in the finest fraction of sediments (e.g. <2 µm). The

235 inter-parameter ratios χARM/SIRM and χARM/χ reflect variations in the ferrimagnetic

236 grain size, with values peaking in the SD range (Maher, 1988) and are used as grain

237 size indicator of magnetic minerals (Banerjee et al., 1981; Maher, 1988). It is shown

238 that similar correlations exist with χARM as to particle size (Zhang et al., 2007). χ is

239 considered as a reflection of magnetic mineral concentration (Thompson and Oldfield,

240 1986).

241 The differences in ferromagnetic grain is explained by using the plot of

242 χARM/χlf vs. χARM/χfd (Fig. 4). It is clearly indicating that finer grains present in the east

243 coast of India showed correlation (R2=0.76) than the west. The west coast sediments

244 showed a weaker χfd values and hence the ratio indicated that it contains coarser

245 magnetic grains.

246 3.4. Variability of heavy metals - Magnetic normalization

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247 A normalizer must covary in proportion with the natural concentration of the

248 metals of interest (Roussiez et al., 2005). A more significant coefficient of

2 249 determination (R = 0.91-0.98) between χARM and different heavy metals (Cu, Ni, Cr

250 and Fe) was observed in the east coast sediments than those along the west coast of

251 India (Table 4a,b). χARM/SIRM demonstrates strong coefficient of determination with

252 heavy metals (Cu, Ni, Cr and Fe) in the east and the same is observed only Cu and Cr

253 in the west coast sediment of India (Table 4a, b).

254 Compared to the geochemical normalizer Al, χARM displays equally or even

255 better coefficients of determination with heavy metals (Table 4a,b; Fig. 5a-d; Fig.

256 6a-d). It is also evident from the previous study that the relationships of heavy metal

257 levels with χARM are quite similar to those with the <16 µm fractions (Zhang et al.,

258 2007). The preferred association of heavy metals with fine-grained sediments is

259 actually related to sediment mineralogy, since clay minerals and iron oxides, the

260 important carrier phases of heavy metals, are dominantly associated with finer

261 fraction of sediments (Rae, 1997). This relationship may therefore reflect the

262 important role of iron oxides in controlling heavy metal concentrations in addition to

263 aluminosilicate minerals. This is also reflected in the strong correlations between

264 heavy metals and iron (Table 4a) in the case of east coast sediment samples. Taking

265 into account the high capacity for remanent magnetization of magnetite, magnetic

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266 parameters based on remanent value will be dominated by the magnetite signal

267 (Walden et al., 1997). Although magnetite is a minor component in iron oxide

268 assemblages, fine-grained magnetite may be proportional to the major iron oxides

269 (Zhang et al., 2001). These findings reinforce evidence from the study of Irish Sea

270 coastal sediments (Oldfield et al., 1993; Clifton et al., 1999) that χARM can be used as

271 proxy for finer fraction of sediments and applied to the normalization of heavy metal

272 concentrations for particle size effects.

273 Currently, Al is the most frequently used geochemical normalizer in estuarine

274 and coastal sediments (Kersten and Smedes, 2002). Fig. 7a-d demonstrates that

275 normalization of heavy metals by χARM is positively correlated with normalization by

276 means of Al. The weaker relationship for Fe/Al versus Fe/χARM and Ni/Al versus

277 Ni/χARM is evident from the dissimilarity in scatter plots of Ni against these two

278 normalizers (Fig. 7a-d; Table 4a,b). This similar relationship between Ni and χARM

279 and the <16 µm particle size content is observed and suggested that Ni, χARM may be

280 the preferred normalizer in the study area (Zhang et al., 2007). Overall, in order to

281 compare the degree of pollution at different sites, normalization by χARM will give

282 comparable results with that by Al, at least for metal Cu. In order to compare the

283 degree of pollution at different sites, χARM normalization results are comparable with

284 that by Al. Furthermore, analysis of Al requires a total digestion employing HF to

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285 destroy silicates within the sediment. As HF digestion would dissolve heavy metals

286 contained in silicates, which are considered to be not related with anthropogenic

287 source (often referred to as the ‘residual’ phase), a partial digestion with minimal

288 extraction of ‘residual’ forms is considered to be able to provide more meaningful

289 environmental information in pollution studies (Sutherland et al., 2004). In this case,

290 if Al is to be used as a normalizer, heavy metal and Al determination will require two

291 separate digestion procedures. Compared to geochemical analysis, the measurement

292 of χARM is relatively straightforward, rapid and therefore cost effective (Walden et al.,

293 1997). There may be a limit to the potential of this method in studies of sediment

294 cores, due to the possibility of reductive diagenetic alteration of fine-grained magnetic

295 minerals under reducing conditions (Walden et al., 1997), and hence the reliability of

296 χARM. However, it is unlikely that surface sediments under oxidizing conditions would

297 be seriously influenced. Therefore χARM normalization can play an important role in

298 similar coastal environments. In the case of the Irish Sea sediments, it is known that

299 the high ARM values linked to the clay fraction are the result of bacterial

300 magnetosomes growing in situ (Oldfield and Yu, 1994), This could be the case here

301 and it is evidenced from the relationship with heavy metals and its enrichment factor

302 in the east coast of India than the west. Hence, there is the possibility that greigite

303 could be present in areas where brackish rather than truly marine salinities prevail.

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304 The presence of greigite and biogenic magnetite needs to be confirmed from

305 GRM/RRM and SEM/TEM studies.

306 3.5. Enrichment Factor-Influence of grain size and mineralogy

307 Here, as a common practice, Al is used as a normalizer, upon which enrichment

308 factor (EF) were calculated. The following equation was used to estimate the EF of

309 metals from each sediment station using Al as a normalizer to correct for differences in

310 sediments grain size and mineralogy:

311 EF= (Me/Al) sample/ (Me/Al) crustal average

312 where (Me/Al)sample and (Me/Al) crustal average value are, respectively, the metal

313 concentration (µg/g dw) in relation to Al levels (% dw) in sediment samples, and crustal

314 average values taken from Taylor and McLennan (1995).

315 It is observed that there is a good correlation between magnetic properties (e.g.

316 magnetic susceptibility χ) and enrichment factor (EF of metals) in the east coast of

317 India rather than the west suggesting the enrichment of ferrimagnetic mineral (Fig.

318 8a-d). χ is generally regarded as a reflection of magnetic mineral concentration

319 (Thompson and Oldfield, 1986). χ vs. EF for metals showed R2 values of Fe (0.55;

320 0.36), Cr (0.78; 0.17), Cu (0.78; 0.04) and Ni (0.34; 0.02) for the east coast of India and

321 the west coast respectively.

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322 In the Figs. 5,6,7,8, the four data points of the east coast (Station nos. 3024,

323 3025, 3026, 3248 and 3249) lie close to the data points of the west coast. The clay

324 mineral smectite mostly derived from the Deccan Trap basalts (Deccan Trap

325 Province), occurs in both the region as this the Deccan Province also covers this

326 stations in the east coast (Raman et al., 1995; Rao and Rao, 1995). One of the end

327 products of chemical weathering of Deccan Traps is smectites, which may be trapping

328 K and thereby contributing to its limited mobility (Das and Krishnaswami, 2006). The

329 major element chemistry of the clay fraction in sediment is controlled by the variation

330 of K, Al, Mg, Si and Fe content. The K content of clayey sediments is primarily

331 bound to clay minerals, potassium feldspar and mica. K is found to be less in the

332 basalt. K is mainly held in illite and feldspar, therefore the variation in the K/Al ratio

333 may reflect the variations in illite and feldspar present in the sediments. The ratios

334 also reflect the mixing of a ‘primary’ end member composed of illite derived from

335 micas (K2O/Al2O3=0.33-0.45) and a secondary end member chlorite (K2O/Al2O3=0).

336 A higher potassium content relative to Al in sediments implies greater feldspar

337 contribution. K/Al values ranged from 0.11 to 0.16 in the above stations compared to

338 the rest in the east coast of India and Ca/Al varied from 2.79 to 11.79 in the above

339 mentioned stations in the east coast compared to the rest of the stations and its

340 similarity with the west coast samples.

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341 4. Conclusions

342 The magnetic properties used in this study for both normalization and for

343 assessment of the degree of contamination from the samples of east and west coast of

344 India showed a distinct variation in this coastal environment. The presence of finer

345 ferromagnetic grain and enrichment of ferrimagnetic minerals are observed in the east

346 than the west coast of India. Higher values of magnetic susceptibility, IRM20 mT and

347 SIRM are associated with the east coast shelf sediments suggest the presence of high

348 ferrimagnetic content, which can be derived from the weathering products of the

349 Deccan Basalts.

350 In shelf sediments from the east and west coast of India, the magnetic

351 parameter χARM, which is sensitive to single domain (SD, 0.03–0.07 µm)

352 ferrimagnetic minerals, displays more significant relationships with the heavy metals

353 in the east coast sediments than the west. A comparison with Al, which is frequently

354 used to compensate for particle size effects in pollution studies, indicates that in these

355 samples χARM is an efficient normalizer for comparison of the degree of pollution at

356 different sites. The strong association between χARM and heavy metals can be

357 explained by the role of particle size and iron oxides in controlling metal

358 concentrations. Furthermore, the rapid and non-destructive nature of mineral magnetic

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359 measurements means χARM can be easily determined, compared to other approaches

360 such as the analysis of Al. When heavy metals are analyzed using a partial digestion

361 method, which can provide more meaningful environmental information related to

362 total pollutant determination, this magnetic approach will prove especially useful for

363 environmental quality assessment. Magnetic parameters, χ, χARM, IRM20 mT and SIRM,

364 have shown that more significant relationship with the concentration of Fe, Cr, Cu

365 and Ni in the east coast of India than the west. Linear relationship between magnetic

366 mineral concentration-related parameters and the concentration of Fe, Cr, Cu and Ni

367 makes it possible to use magnetic technique as a simple, rapid, and non-destructive

368 tool for the assessment of heavy metals contamination in the shelf region. Although

369 the co-variations presented in this study are rather convincing, it is clear that such

370 approach rely on a careful calibration of the proxy i.e. directly comparing heavy metal

371 concentrations and magnetic parameters for a selected set of samples. The calibration

372 will obviously be site specific and must therefore be adapted to each study site.

373 Acknowledgments

374 The author wish to express their sincere gratitude to the Chinese Ministry of

375 Education and SKLEC/ECNU, Shanghai for financial support. I thank Professor J.

376 Zhang, Drs. W. Zhang and X. Han for their help in the sample analysis and discussion.

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377 The author is also thankful to Drs. S.W.A. Naqvi, E. Desa, former Director, NIO,

378 Satish R. Shetye, Director, NIO and CSIR, New Delhi for their kind permission during

379 the period of work. His Holiness Sri Kanchi Sankaracharya Swami’s and Sri Sri

380 Ravishankar’s grace to complete this work is also highly appreciated. Frank Oldfield,

381 Q. Liu, J. Bloemendal and two anonymous reviewers are thanked for the constructive

382 reviews and suggestions. This is NIO contribution No.

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599 Table 1. Sample details and locations

600 Table 2. Magnetic susceptibility in the surface sediments of east and west coast of India

601 Table 3. Metal concentrations (in µg g-1 except Al and Fe in %) in the surface sediments

602 of east and west coast of India

603 Table 4a. Coefficient of determination (R2) for metals, potential geochemical and

604 magnetic particle size proxies in surface sediments of east coast of India (R2 > 0.25,

605 p < 0.05, n = 14, bold figures are significant)

606 Table 4b. Coefficient of determination (R2) for metals, potential geochemical and

607 magnetic particle size proxies in surface sediments of west coast of India (R2> 0.40,

608 p < 0.05, n = 8, bold figures are significant)

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609 Legend to Figures.

610 Fig. 1. Location of sediment sampling stations in the coastal region of India.

611 ∆-RVG 130; - RVG 143; o-RVG 139 represent the sampling sites of different

612 cruises.

613 Fig. 2 (a-d). Plot of magnetic susceptibility and its concentration related parameters

614 along the east and west coast of India.

615 Fig. 3. Plot of Magnetic susceptibility vs. IRM20 mT along the east and west coast of

616 India.

617 Fig. 4. Plot of χARM/χlf vs. χARM/χfd along the east and west coast of India.

618 Fig. 5 (a-d). Plot of Fe, Cr, Cu and Ni vs. Al along the east and west coast of India.

619 Fig. 6 (a-d). Plot of Fe, Cr, Cu and Ni vs. χARM along the east and west coast of India.

620 Fig. 7 (a-d). Plot of Fe, Cr, Cu, Ni normalized by χARM vs. Al normalization along the

621 east and west coast of India.

622 Fig. 8 (a-d). Plot of magnetic susceptibility (χ) vs. EF (metal) along the east and west

623 coast of India.

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624

625 Fig. 1.

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626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663

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664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 Fig. 2 (a-c).

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679

680 681 682 683 684 685 686 687 688 689 690 691

692

693 Fig. 3.

694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712

713 Fig. 4.

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714

715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741

742

743

744

745

746 Fig. 5 (a-d).

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747

748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774

775

776

777

778 Fig. 6 (a-d).

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779

780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 Fig. 7 (a-d).

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813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840

841

842

843

844

845 Fig. 8 (a-d).

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Table 1

East Coast

Sr. No Station No. Latitude (N) Longitude (E) Depth (m)

RVG 130 ()

1) 3012 16° 26' 81° 31.0' 40

2) 3013 16° 11' 81° 47.5' 55

3) 3014 15° 54.5' 81° 14.6' 50

4) 3017 15° 39.5' 81° 00' 48

5) 3018 15° 22.8' 81° 33.1' 52

6) 3019 15° 0.3' 80° 16.7' 52

7) 3024 12° 00' 80° 05.6' 49

8) 3025 12° 00' 80° 04.2' 50

9) 3026 11° 00' 80° 02.5' 52

RVG 143 ()

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10) 3242 16°31' 82° 30.7' 700

11) 3244 15° 39.1' 81° 06.4' 660

12) 3245 14° 54.1' 80° 20.5' 80

13) 3248 13° 30.5' 80° 31.7' 120

14) 3249 13° 7.8' 80° 32.6' 120

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Table 1 continued.

West Coast

RVG 139 (o)

15) 3164 10° 02.5' 76° 00.0' 35

16) 3165 10° 01.0' 75° 40.1' 90

17) 3170 10° 58.2' 75°18.7' 73

18) 3171 10° 58.2' 75° 39.0' 33

19) 3173 12° 00' 74° 40.0' 75

20) 3180 12° 59' 74° 20.9' 60

21) 3182 14° 00' 74° 19.0' 40

22) 3184 14° 00' 73° 40.0' 78

23) 3191 14° 59.7' 73° 20.0' 75

24) 3192 14° 59.6' 73° 40.0' 49

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Table 2

East Coast- Station No. Low Frequency Average Mean 3012 0 1626 1627 1 1626 1626 1626 1590 1 1573 1572 2 1572 1570 1571 2 1575 1576 3 1573 1573 1573

3013 3 475 475 1 472 474 473 463.5 3 470 471 2 467 469 468 4 453 453 3 449 450 449.5

3014 4 2685 2685 5 2681 2680 2680.5 2699.17 4 2733 2733 5 2729 2728 2728.5 5 2694 2695 7 2689 2688 2688.5

3017 7 813 813 6 806 807 806.5 806.67 6 812 812 6 806 806 806 6 814 815 8 808 807 807.5

3018 8 1727 1728 6 1719 1722 1720.5 1722 7 1730 1731 6 1723 1725 1724 10 1731 1730 8 1721 1722 1721.5

3019 8 390 397 8 382 389 385.5 388.5 8 400 399 9 392 390 391 8 398 398 10 390 388 389

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3024 10 249 251 10 239 241 240 239.83 10 250 250 10 240 240 240 10 249 250 10 239 240 239.5

3025 10 315 315 9 305 306 305.5 305.33 9 314 314 9 305 305 305 9 315 315 10 306 305 305.5

3026 10 144 144 10 134 134 134 134.83 10 145 146 11 135 135 135 11 148 147 13 137 134 135.5

3242 13 1359 1360 13 1346 1347 1346.5 1347.33 13 1360 1360 12 1347 1348 1347.5 12 1361 1360 13 1349 1347 1348

3244 13 1609 1608 13 1596 1595 1595.5 1596.83 12 1609 1610 13 1597 1597 1597 12 1611 1611 14 1599 1597 1598

3245 14 1444 1444 14 1430 1430 1430 1431 14 1445 1445 14 1431 1431 1431 14 1446 1447 15 1432 1432 1432

3248 15 248 248 15 233 233 233 233.83 15 249 249 14 234 235 234.5 14 248 248 14 234 234 234

3249 14 66 66 13 52 53 52.5 52

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13 64 65 13 51 52 51.5 13 65 66 14 52 52 52

3164 14 59 59 13 45 46 45.5 44.83 13 58 58 13 45 45 45 13 57 57 13 44 44 44

3165 13 117 117 14 104 103 103.5 103.5 14 117 117 14 103 103 103 14 118 118 14 104 104 104

3170 14 87 87 14 73 73 73 72.33 14 86 86 14 72 72 72 14 86 86 14 72 72 72

3171 14 34 34 13 20 21 20.5 20.83 13 35 34 14 22 20 21 14 35 35 14 21 21 21

3173 14 55 55 14 41 41 41 40.83 14 54 55 13 40 42 41 13 53 55 14 40 41 40.5

3180 14 58 58 14 44 44 44 44.17 14 57 58 14 43 44 43.5 14 59 60 15 45 45 45

3182 15 74 74 15 59 59 59 59.33 15 74 73 14 59 59 59 14 74 74 14 60 60 60

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3184 14 47 47 14 33 33 33 33.33 14 48 48 16 34 32 33 16 49 50 15 33 35 34

3191 15 66 66 15 51 51 51 51 16 66 66 16 50 50 50 16 68 68 16 52 52 52

3192 16 143 144 17 127 127 127 127 17 143 143 16 126 127 126.5 16 144 144 17 128 127 127.5

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East Coast High frequency Average Mean Station No 3012 0 1518 1518 4 1518 1514 1516 1516.17 2 1518 1519 5 1516 1514 1515 2 1520 1522 5 1518 1517 1517.5

3013 1 437 436 0 436 436 436 436.5 0 437 438 3 437 435 436 3 441 443 6 438 437 437.5

3014 6 2690 2691 10 2684 2681 2682.5 2649.5 10 2640 2643 16 2630 2627 2628.5 16 2654 2656 19 2638 2637 2637.5

3017 19 774 774 15 755 759 757 757.67 15 772 772 13 757 759 758 13 770 771 12 757 759 758

3018 12 1675 1676 9 1663 1667 1665 1666.67 9 1678 1680 12 1669 1668 1668.5 12 1679 1680 14 1667 1666 1666.5

3019 14 394 394 14 380 380 380 376 14 388 390 16 374 374 374 16 389 391 16 373 375 374

3024 16 250 250 14 234 236 235 234.67 14 247 248 12 233 236 234.5

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12 246 247 12 234 235 234.5

3025 12 309 308 12 297 296 296.5 297.17 12 308 310 12 296 298 297 12 311 312 15 299 297 298

3026 15 142 142 11 127 131 129 129.33 11 139 139 8 128 131 129.5 8 137 137 7 129 130 129.5

3242 7 1289 1288 4 1282 1284 1283 1284.33 4 1287 1288 2 1283 1286 1284.5 2 1288 1290 5 1286 1285 1285.5

3244 5 1547 1546 1 1542 1545 1543.5 1544 1 1546 1548 5 1545 1543 1544 5 1550 1551 7 1545 1544 1544.5

3245 7 1392 1392 6 1385 1386 1385.5 1379.5 6 1382 1383 6 1376 1377 1376.5 6 1382 1383 6 1376 1377 1376.5

3248 6 230 230 5 224 225 224.5 223.67 5 228 227 3 223 224 223.5 3 226 227 4 223 223 223

3249 4 53 52 0 49 52 50.5 51.17 0 50 51 -1 50 52 51 -1 50 51 -2 51 53 52

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West coast 3164 -2 43 42 -3 45 45 45 45.17 -3 40 40 -7 43 47 45 -7 39 40 -5 46 45 45.5

3165 -5 96 95 -4 101 99 100 99.67 -4 95 96 -3 99 99 99 -3 98 100 1 101 99 100

3170 1 71 71 -3 70 74 72 72.33 -3 69 69 -4 72 73 72.5 -4 68 69 -4 72 73 72.5

3171 -4 15 15 -7 19 22 20.5 21 -7 17 18 -2 24 20 22 -2 19 20 0 21 20 20.5

3173 0 42 42 -2 42 44 43 43.5 -2 43 44 1 45 43 44 1 45 46 3 44 43 43.5

3180 3 50 50 4 47 46 46.5 46.83 4 52 52 6 48 46 47 6 53 55 8 47 47 47

3182 8 67 67 3 59 64 61.5 61.67 3 66 67 5 63 62 62.5 5 66 67 6 61 61 61

ACCEPTED MANUSCRIPT

3184 6 39 40 5 33 35 34 35 5 41 43 7 36 36 36 7 42 43 8 35 35 35

3191 8 60 61 10 52 51 51.5 51.67 10 61 61 9 51 52 51.5 9 62 64 13 53 51 52

3192 13 140 141 15 127 126 126.5 126.33 15 142 145 20 127 125 126 20 148 150 25 128 125 126.5

ACCEPTED MANUSCRIPT

Table 3

Metals East coast West coast

Min Median Max Min Median Max

Al 2.25 6.77 11.68 0.7 2.26 4.45

Fe 1.67 (0.64) 5.27 (1.11) 8.58 (1.48) 1.13 (0.39) 2.20 (1.64) 3.43 (2.36)

Cr 3.71 (0.07) 70.4 (0.74) 133.22 (1.40) N.D 5.97 (0.30) 19.39 (0.74)

Ni 7.8 (0.38) 56.39 (0.87) 81.76 (1.17) 0.32 (0.01) 10.45 (0.65) 16.88 (1.68)

Cu 10.63 (0.37) 78.27 (1.55) 140.14 (2.67) 3.82 (0.13) 12.89 (1.10) 26.31(2.58)

N.D- Not detectable

ACCEPTED MANUSCRIPT

Table 4a

χARM/

Cu Ni Cr Fe χARM SIRM χfd (%)  SIRM IRM20 mT Cu/Al Ni/Al Cr/Al Fe/Al Cu/ARM Ni/ARM Cr/ARM Cu 1 Ni 0.90 1 Cr 0.99 0.93 1 Fe 0.97 0.89 0.94 1

χARM 0.96 0.91 0.94 0.98 1 χARM/ SIRM 0.79 0.74 0.80 0.67 0.70 1

χfd (%) 0.10 0.17 0.09 0.15 0.18 0.00 1  0.81 0.64 0.81 0.75 0.71 0.70 0.00 1 SIRM 0.85 0.73 0.85 0.77 0.72 0.82 0.00 0.92 1

IRM20 mT 0.85 0.65 0.84 0.79 0.75 0.70 0.01 0.99 0.91 1 Cu/Al 0.87 0.81 0.91 0.76 0.77 0.84 0.05 0.78 0.83 0.79 1 Ni/Al 0.43 0.58 0.49 0.35 0.39 0.48 0.12 0.34 0.42 0.31 0.65 1 Cr/Al 0.88 0.86 0.93 0.78 0.79 0.82 0.05 0.78 0.83 0.77 0.98 0.67 1 Fe/Al 0.52 0.42 0.56 0.42 0.41 0.48 0.02 0.55 0.54 0.54 0.78 0.54 0.71 1

Cu/χARM 0.89 0.81 0.91 0.79 0.78 0.88 0.04 0.73 0.83 0.75 0.95 0.52 0.92 0.70 1 Ni/χARM 0.01 0.02 0.00 0.01 0.01 0.01 0.03 0.01 0.00 0.03 0.00 0.18 0.00 0.02 0.01 1 Cr/χARM 0.91 0.90 0.95 0.81 0.82 0.87 0.07 0.73 0.82 0.73 0.96 0.64 0.98 0.65 0.96 0.00 1 Fe/χARM 0.41 0.47 0.42 0.34 0.47 0.51 0.11 0.25 0.27 0.27 0.44 0.49 0.44 0.14 0.35 0.00 0.45

ACCEPTED MANUSCRIPT

Table 4b

χARM/

Cu Ni Cr Fe χARM SIRM χfd (%)  SIRM IRM20 mT Cu/Al Ni/Al Cr/Al Fe/Al Cu/χARM Ni/χARM Cr/χARM Cu 1 - Ni 0.28 1 - Cr 1.00 0.28 1 - Fe 0.15 0.36 0.15 1 -

χARM 0.14 0.36 0.14 0.01 1 -

χARM/ SIRM 0.45 0.02 0.45 0.07 0.01 1 -

χfd (%) ------ 0.37 0.05 0.37 0.20 0.06 0.70 - 1 SIRM 0.39 0.02 0.39 0.07 0.05 0.77 - 0.95 1

IRM20 mT 0.47 0.01 0.47 0.04 0.00 0.37 - 0.35 0.50 1 Cu/Al 0.14 0.05 0.14 0.26 0.00 0.31 - 0.02 0.11 0.47 1 Ni/Al 0.00 0.02 0.00 0.26 0.10 0.04 - 0.03 0.00 0.07 0.45 1 Cr/Al 0.53 0.00 0.53 0.03 0.01 0.52 - 0.17 0.31 0.69 0.81 0.16 1 Fe/Al 0.04 0.01 0.04 0.00 0.13 0.28 - 0.03 0.08 0.13 0.53 0.26 0.41 1

Cu/χARM 0.77 0.46 0.77 0.12 0.54 0.16 - 0.08 0.08 0.14 0.04 0.00 0.24 0.01 1

Ni/χARM 0.36 0.73 0.36 0.16 0.76 0.01 - 0.00 0.00 0.00 0.01 0.01 0.01 0.06 0.78 1

Cr/χARM 0.87 0.44 0.87 0.16 0.40 0.25 - 0.16 0.15 0.21 0.05 0.00 0.31 0.00 0.98 0.68 1

Fe/χARM 0.36 0.60 0.36 0.53 0.34 0.02 - 0.02 0.00 0.02 0.10 0.07 0.00 0.06 0.66 0.78 0.62