Magnetic Susceptibility and Statistical Modeling for the Assessment of Toxic Heavy Metals in Surface Sediments of Ennore Port, East Coast of Tamil Nadu, India

Magnetic Susceptibility and Statistical Modeling for the Assessment of Toxic Heavy Metals in Surface Sediments of Ennore Port, East Coast of Tamil Nadu, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Magnetic Susceptibility and Statistical Modeling for the Assessment of Toxic Heavy Metals in Surface Sediments of Ennore Port, East Coast of Tamil Nadu, India D. Rajendiran, N. Harikrishnan, N. R. Sheela, K. Veeramuthu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8257448/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The investigation of heavy metals in the Ennore ecosystem is crucial for assessing the magnitude and distribution of pollution in the region. In the present study, the concentrations of major and trace elements, including Mg, Al, Si, K, Ca, Ti, Fe, V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Ba, La, and Pb, were determined in twenty-six sediment samples. The abundance of heavy metals in the sediments followed the decreasing order: Si > Al > Fe > Ca > Ti > K > Mg > Mn > Ba > V > Cr > Zn > La > Ni > Pb > Co > As > Cd > Cu. The average concentrations of all analyzed metals were found to be below the global crustal average. Owing to their strong affinity for forming metallic bonds with ferromagnetic materials, these metals significantly influence the magnetic properties of sediments, particularly magnetic susceptibility. Consequently, low-frequency (χlf), high-frequency (χhf), and frequency dependent (χfd) magnetic susceptibility measurements were carried out using an MS2B dual-frequency susceptibility meter. Furthermore, multivariate statistical techniques, including Pearson correlation, factor analysis, and cluster analysis, were employed to elucidate the relationships between heavy metal concentrations and magnetic susceptibility parameters, with the objective of evaluating anthropogenic influences on the sediments. The results demonstrate that magnetic susceptibility measurements provide a rapid, cost effective, non destructive, and reliable proxy for identifying and assessing sources of heavy metal contamination in sedimentary environments. Sediments Pollution Sources Magnetic Susceptibility Heavy Metals Statistical Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1.0. Introduction Heavy metal contamination of sediments represents one of the most critical global environmental issues, posing substantial risks to ecosystems and human health. In recent years, the accumulation of heavy metals in coastal environments has intensified due to increasing anthropogenic activities, including industrial effluent discharge, tourism, and fishing operations (Dang et al., 2002 ). Sediments function as major sinks for heavy metals through the formation of stable complexes with organic matter, carbonates, and iron (Fe) and manganese (Mn) oxides (Duzzin et al., 1988 ). These metal fractions exhibit varying degrees of mobility, toxicity, and chemical reactivity, influencing their environmental behavior and bioavailability. Consequently, a comprehensive understanding of the geochemical speciation of metals is essential for accurately assessing sediment contamination and evaluating the potential ecological and human health risks associated with heavy metal exposure (Yalcin et al., 2016). Magnetic susceptibility is a measure of the concentration of iron-bearing components in sediments and is widely used to characterize sediment types and quantify the abundance of iron-bearing minerals (Canbay, 2010 ). It represents a rapid, sensitive, and cost-effective analytical approach, providing an important parameter for applications in mineralogical and granulometric studies (Canbay, 2010 ). In recent years, magnetic susceptibility measurements have been extensively employed to identify and trace sources of heavy metal pollution. Techniques based on the magnetic properties of sediments have been widely adopted by environmental scientists and have demonstrated considerable success in pollution assessment studies. Numerous investigations have established magnetic susceptibility as a reliable indicator for identifying sources of heavy metal contamination (Thompson et al., 1986 ; Hay et al., 1997 ; Strzyszcz et al., 1998; Durza, 1999 ; Lecoanet et al., 2001 ; Knab et al., 2001 ; Kapicka et al., 2003; Hanesch and Scholger, 2005 ; Lu et al., 2007 ). This relationship is primarily attributed to anthropogenic emissions, which commonly contain fine, strongly magnetic particles that are often enriched with heavy metals, particularly in soils and sediments of industrialized regions (Canbay, 2010 ). Magnetic measurements have proven to be an effective tool for assessing the transport and distribution of pollutants through atmospheric and aquatic pathways (Scholger, 1998 ; Petrovský et al., 2000). The application of magnetic parameters as proxies for heavy-metal contamination is based on the close genetic and geochemical association between heavy metals and magnetic mineral phases. Numerous environmental magnetism studies have consistently reported strong correlations between heavy-metal concentrations and magnetic properties of soils and sediments (Hanesch et al., 2001 ; Jordanova et al., 2004 ; Schmidt et al., 2005 ; Spiteri et al., 2005 ). Coastal populations are particularly vulnerable to heavy-metal exposure through direct contact with beach sediments and the inadvertent ingestion of seawater (Yalcin et al., 2016). The present study area includes several environmentally sensitive hotspots, such as tourist destinations, active fishing zones, and nearby industrial regions, which together increase the potential risk of metal contamination. In this context, the objectives of the present study are to: (i) quantify the concentrations of heavy metals and measure the magnetic susceptibility of surface sediments collected from Ennore Port along the east coast of Tamil Nadu; (ii) characterize the magnetic mineralogical properties of the sediments; (iii) evaluate the relationships between heavy-metal concentrations and magnetic susceptibility using multivariate statistical techniques; and (iv) identify the potential sources of heavy-metal pollution. 2.0. Geology of the Study Area Ennore hosts the Ennore Port, officially designated as Kamarajar Port Limited, which functions as a key hub for regional trade and commercial activities. The port handles a diverse range of cargo, including containers, coal, petroleum products, and general merchandise. Strategically situated along the eastern coast of the Indian peninsula in the Bay of Bengal, Ennore Port lies approximately 2.5 km north of the Ennore Creek (Pandian et al., 2002). Owing to its intensive maritime operations, the port plays a pivotal role in supporting industrial growth and maritime trade within the Chennai metropolitan region and its adjoining areas. The Ennore region and its environs accommodate numerous industrial establishments, such as thermal power stations, petroleum refineries, chemical processing units, and other manufacturing industries. While these activities significantly contribute to regional economic development, they also exert considerable pressure on the surrounding environment, leading to concerns related to pollution and ecological degradation of coastal systems. Similar to other rapidly industrializing and urbanizing coastal zones, Ennore experiences multiple environmental stresses, including contamination of air, water, and sediments, habitat alteration, and declining biodiversity. In response, various mitigation measures, environmental regulations, and conservation strategies have been initiated to manage and reduce these impacts. Despite the extent of industrial development, Ennore retains substantial ecological importance, supported by coastal ecosystems such as mangroves, estuaries, and tidal flats. These habitats provide essential ecological services, including biodiversity support, shoreline protection, enhancement of water quality, and improved coastal resilience. Overall, Ennore represents a complex landscape characterized by the coexistence of industrial, commercial, and residential activities, coupled with significant environmental and socioeconomic challenges. Sustainable management and integrated planning are therefore essential to balance development with environmental protection. The geological characteristics of the study area are summarized in Table 1 , while Fig. 1 illustrates the location map of the study area. 3.0. Materials and Methods 3.1. Sample Collection and Preparation Figure 1 illustrates the locations of the sediment sampling sites, extending from 13°13′18.50″ N, 80°20′10.10″ E to 13°15′13.33″ N, 80°20′17.43″ E, as detailed in Table 1 . A total of 26 sediment samples were obtained during the pre-monsoon period from three distinct environmental settings within the Ennore region, namely the sea (S), beach (B), and creek (C) zones along the east coast of Tamil Nadu. A Peterson grab sampler was used to carry out the sampling. This study used a flame atomic absorption spectrometer (AAS; iCE 3000, Thermo Scientific, USA) to determine the concentrations of heavy metals. The principle of optical radiation absorption by free atoms in the gaseous phase was used to carry out both qualitative and quantitative analyses. Three to five standard solutions, prepared by appropriate dilution of a 1000 ppm certified stock solution, were used to establish calibration curves for each metal. Hollow cathode lamps corresponding to Pb, As, Hg, Cu, Zn, Cr, Ni, and Mn served as radiation sources, with an air-acetylene mixture being employed as fuel. The mean values were reported as the final concentrations after analyzing all samples and standards in replicates. Table 1 Latitude and Longitude of the Study Area of Ennore, East Coast of Tamilnadu, India S. No Sample Latitude Longitude 1 S1 13°14'46.02'' 80°20'31.56'' 2 S2 13°14'44.83'' 80°20'48.04'' 3 S3 13°14'44.09'' 80°21'6.35'' 4 S4 13°14'1.40'' 80°20'20.71'' 5 S5 13°14'1.32'' 80°20'39.85'' 6 S6 13°14'1.20'' 80°20'56.76'' 7 S7 13°13'18.50'' 80°20'10.10'' 8 S8 13°13'19.04'' 80°20'29.96'' 9 S9 13°13'19.54'' 80°20'46.77'' 10 B1 13°15'13.33'' 80°20'17.43'' 11 B2 13°14'49.77'' 80°20'8.12'' 12 B4 13°14'0.75'' 80°19'46.26'' 13 B5 13°15'12.59'' 80°20'21.18'' 14 B6 13°14'49.51'' 80°20'10.55'' 15 B7 13°14'0.79'' 80°19'48.15'' 16 B8 13°15'12.01'' 80°20'25.27'' 17 B9 13°14'49.51'' 80°20'13.40'' 18 C1 13°13'58.55'' 80°19'38.62'' 19 C2 13°13'50.50'' 80°19'22.59'' 20 C4 13°13'26.04'' 80°18'53.16'' 21 C5 13°13'9.64'' 80°18'42.67'' 22 C9 13°13'2.15'' 80°18'52.99'' 23 C10 13°13'59.38'' 80°18'55.29'' 24 C11 13°14'17.55'' 80°18'56.60'' 25 C12 13°14'35.42'' 80°18'55.95'' 26 C13 13°14'54.22'' 80°18'51.95'' The Ennore coastal region was studied for the spatial variation of natural radionuclides from terrestrial to marine environments by collecting sediment samples. Sampling was carried out at three distinct zones: the nearshore marine region at a distance of 10 m parallel to the shoreline and at a water depth of 4 m (nine samples), the beach zone (eight samples), and the creek region (nine samples). After being collected, the samples were placed in clean polyethylene bags, labelled appropriately for their respective sampling locations, and transported to the laboratory. After air drying the samples in ambient conditions, coarse materials like shell fragments and stone debris were manually removed. 3.2. Atomic Absorption Spectrometry (AAS) Analysis The flame atomic absorption spectrometer (AAS; iCE 3000, Thermo Scientific, USA) was used to determine the concentrations of heavy metals in this study. Qualitative and quantitative analyses were performed utilizing the principle of optical radiation absorption by free atoms in the gaseous phase. Three to five standard solutions prepared with appropriate dilution of a 1000 ppm certified stock solution were used to establish calibration curves for each metal. Hollow cathode lamps corresponding to Pb, As, Hg, Cu, Zn, Cr, Ni, and Mn served as radiation sources, with an air-acetylene mixture being employed as fuel. The mean values were reported as the final concentrations after analyzing all samples and standards in replicates. 3.3. Magnetic Susceptibility (χ) Measurements To prevent any potential chemical changes and minimize the impact of moisture, the collected samples were air-dried at ambient temperature in the laboratory. Using established procedures (Zhang et al., 2011 ), extraneous materials like glass fragments, plant remains, refuse, and coarse lithic particles were removed from the dried samples after sieving through a 1 mm mesh. After processing, the samples were transferred to clean plastic containers and preserved for future analysis. The sieved material was packed into 10 ml plastic sample holders and analyzed at room temperature for magnetic susceptibility measurements. The use of an MS2B dual-frequency magnetic susceptibility meter, interfaced with a computer running Multisus2 software, resulted in measurements at both low (0.0.465 kHz) and high (4.65 kHz) operating frequencies. The sensitivity setting of 1.0 was used for all analyses. The mean value was calculated to ensure analytical reliability by taking five replicate measurements at each frequency for each sample. In natural sediment samples, the grain-size distribution is generally continuous and nearly uniform, making magnetic susceptibility a trustworthy indicator of relative variations in the concentration of pedogenic fine-grained magnetic minerals (Liu et al., 2005 ). The standard expression proposed by Dearing ( 1999 ) was used to determine frequency-dependent magnetic susceptibility (χfd). \(\:{\chi\:}_{fd\:}\left(\%\right)=\left[\frac{{\chi\:}_{lf\:-\:}{\chi\:}_{hf\:}}{{\chi\:}_{lf\:}}\right]\times\:\) 100 ------------- (1) 3.4. Multivariate Statistical Analysis Multivariate statistical techniques namely Pearson’s correlation analysis, factor analysis, and hierarchical cluster analysis were applied to examine the interrelationships between heavy metal concentrations and magnetic susceptibility parameters in sediment samples. These methods were employed to identify potential source contributions and to discriminate between lithogenic and anthropogenic inputs of heavy metals. All statistical analyses were performed using SPSS software (Statistical Package for the Social Sciences, version 16.0), and the statistical significance was evaluated at the predefined confidence level. 4.0. Results and Discussions 4.1. Heavy Metal Concentration in Sediments The distribution of heavy metals in surface sediments of coastal environments such as Ennore is governed by a combination of anthropogenic and natural factors. Industrial operations, port and shipping activities, rapid urban expansion, and associated land-based inputs play a significant role in controlling metal accumulation in these sediments. Heavy metals enter the sedimentary system through multiple pathways, including industrial effluents, atmospheric fallout, urban surface runoff, and the weathering and transport of contaminated terrestrial materials. Systematic evaluation of metal concentrations in surface sediments is therefore essential for understanding sediment quality, identifying pollution sources, and assessing potential ecological and human health risks. The measured concentrations of the investigated elements in the sediment samples from the study area are summarized in Table 2 . The concentration (mg kg − 1 ) varies as follows: 298 to 5987, 11478 to 37432, 115987 to 224978, 2985 to 9850, 3792 to 23176, 540 to 49434, 3597 to 56502, 22.37 to 691, 11.5 to 198.29, 69.10 to 1227.61, 1.40 to 19.95, 11.48 to 38.63, BDL to 3.60, 11.04 to 87.99, 1.8 to 9.9, 1.1 to 11.2, 142.3 to 426.8, BDL to 214.7 and BDL to 30.7 for Mg, Al, Si, K, Ca, Ti, Fe, V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Ba, La and Pb respectively. Among the analyzed elements, aluminium (Al), iron (Fe), calcium (Ca), magnesium (Mg), and silicon (Si) were identified as the dominant constituents in the sediment samples. The mean concentration of metals followed the descending order: Si > Al > Fe > Ca > Ti > K > Mg > Mn > Ba > V > Cr > Zn > La > Ni > Pb > Co > As > Cd > Cu across the study area. Notably, sampling locations S2, S9, B4, B7, B9, C4, and C9 exhibited comparatively elevated levels of V, Cr, Co, Ni, and Zn relative to other sites. These enhanced concentrations can be attributed to intense anthropogenic influences, including harbor-related activities, industrial operations, urban effluent discharge, and dredging practices. The observed trends are consistent with reports from comparable coastal and industrialized environments documented in earlier studies (Nath et al., 1989 ; Millward and Moore, 1982 ; Santhiya et al., 2011 ). Elevated concentrations of Ti, Fe, V, Cr, Mn, As, and Pb were observed at sampling locations S1, S2, S9, B2, B7, B8, B9, C4, C5, C9, and C11. These enrichments are likely associated with intensive anthropogenic activities linked to Ennore Port, which manages diverse cargo types such as coal, petroleum products, and general merchandise. Operational activities including dredging, vessel movement, and cargo loading and unloading can disrupt sediment strata and promote the resuspension and redistribution of metal contaminants, thereby enhancing their accumulation in bottom sediments. Occasional events, such as ballast water discharge, oil spill incidents, and accidental leakage of hazardous substances from ships, may further contribute to the input of heavy metals into the coastal environment. Table 2 Heavy Metal Concentration of the Sediment Samples along the Study Area of Ennore, East Coast of Tamilnadu, India Element Mg Al Si K Ca Ti Fe V Cr Mn Co Ni Cu Zn As Cd Ba La Pb Unit ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm ppm S1 2822 ± 19.3 22895 ± 215.40 196697 ± 174.5 4568 ± 14.6 23176 ± 19.5 10689 ± 84.2 30771 ± 128.4 264.56 ± 17.6 113.33 ± 10.2 741.38 ± 44.1 12.95 ± 2.1 34.30 ± 1.6 1.69 ± 0.6 67.67 ± 6.5 6.8 ± 0.6 4.5 ± 0.4 182.3 ± 14.5 30.0 ± 2.8 17.8 ± 1.7 S2 4851 ± 28.1 30132 ± 101.0 146205 ± 132.9 4950 ± 21.0 20679 ± 14.6 18739 ± 44.6 40889 ± 355.5 312.87 ± 20.6 145.77 ± 10.5 871.09 ± 49.5 16.35 ± 1.4 33.23 ± 2.6 3.60 ± 0.9 69.94 ± 5.9 8.4 ± 0.7 1.9 ± 0.1 146.0 ± 13.2 49.2 ± 0.7 24.5 ± 2.1 S3 840.0 ± 8.1 21112 ± 111.5 178446 ± 267.4 2985 ± 11.4 10057 ± 74.1 2357 ± 9.4 12807 ± 110.9 98.94 ± 7.6 64.52 ± 4.1 249.11 ± 13.5 6.01 ± 0.9 13.21 ± 1.4 1.29 ± 0.1 29.78 ± 2.4 4.2 ± 0.1 2.7 ± 0.2 246.7 ± 18.5 17.1 ± 0.9 8.1 ± 0.9 S4 1508 ± 17.0 12066 ± 98.01 130139 ± 124.9 5192 ± 8.54 13628 ± 55.9 3976 ± 12.7 13937 ± 124.6 78.38 ± 4.8 75.33 ± 1.6 264.74 ± 14.9 3.71 ± 0.1 27.59 ± 2.6 BDL 31.00 ± 3.1 3.7 ± 0.4 3.4 ± 0.3 226.1 ± 18.4 9.4 ± 0.4 7.1 ± 0.7 S5 805.0 ± 7.1 24954 ± 156.2 179547 ± 155.5 7786 ± 14.9 14363 ± 99.7 731 ± 7.1 8298 ± 78.9 37.85 ± 1.9 41.85 ± 2.0 159.81 ± 8.7 9.10 ± 1.0 26.84 ± 1.9 2.46 ± 0.1 23.47 ± 2.9 3.8 ± 0.1 2.1 ± 0.2 318.2 ± 19.5 1.9 ± 0.1 9.8 ± 0.8 S6 1842 ± 16.2 22728 ± 124.5 203630 ± 194.8 9850 ± 13.2 10586 ± 84.6 924 ± 6.8 6873 ± 66.5 23.12 ± 1.5 29.32 ± 1.1 138.35 ± 10.6 1.40 ± 0.8 19.67 ± 0.7 2.34 ± 0.3 39.023.7 5.9 ± 0.6 1.9 ± 0.1 426.8 ± 32.8 BDL 4.6 ± 0.3 S7 1935 ± 14.9 21075 ± 164.3 177947 ± 154.7 7047 ± 19.4 8403 ± 41.0 1683 ± 14.6 9120 ± 17.9 41.01 ± 3.1 67.16 ± 3.8 165.61 ± 14.6 6.42 ± 0.6 22.16 ± 1.1 1.98 ± 0.1 26.08 ± 2.1 5.0 ± 0.4 3.8 ± 0.2 312.5 ± 19.4 3.9 ± 0.3 7.5 ± 0.9 S8 3355 ± 11.4 29875 ± 109.1 134994 ± 249.8 4059 ± 9.84 17169 ± 13.4 3769 ± 147.0 17881 ± 15.9 96.6 ± 4.6 102.3 ± 7.4 132.3 ± 9.7 7.54 ± 1.0 28.1 ± 2.6 BDL 39.8 ± 3.4 4.5 ± 0.4 5.4 ± 0.4 290.4 ± 21.6 19.0 ± 1.1 6.0 ± 0.4 S9 4518 ± 35.2 25067 ± 100.6 139370 ± 176.2 5932 ± 24.6 10027 ± 97.5 8714 ± 74.6 24994 ± 21.4 191.9 ± 11.2 148.1 ± 13.4 108.1 ± 8.9 7.31 ± 0.9 31.4 ± 2.0 2.65 ± 0.2 46.0 ± 4.1 7.0 ± 0.7 2.9 ± 0.2 234.0 ± 19.4 9.0 ± 0.9 8.4 ± 0.6 B1 895.0 ± 4.6 18546 ± 98.44 175149 ± 144.0 7615 ± 12.8 9643 ± 47.5 2139 ± 18.4 9534 ± 68.7 77.11 ± 6.2 41.38 ± 3.2 190.26 ± 9.1 7.38 ± 1.3 18.86 ± 1.1 1.05 ± 0.5 27.54 ± 2.2 4.9 ± 0.3 4.5 ± 0.4 298.4 ± 16.4 BDL 3.4 ± 0.1 B2 844.0 ± 7.1 11478 ± 87.02 204961 ± 185.7 6302 ± 10.3 7939 ± 68.1 2390 ± 11.6 8458 ± 74.1 48.9 ± 1.2 29.3 ± 1.6 177.1 ± 12.6 4.8 ± 0.8 12.8 ± 1.0 2.47 ± 0.2 21.0 ± 2.9 3.7 ± 0.2 3.1 ± 0.2 316.1 ± 28.4 28.1 ± 2.4 1.9 ± 0.1 B4 1942 ± 11.0 19254 ± 144.3 194328 ± 165.9 4484 ± 8.65 7970 ± 44.2 49434 ± 141.6 56502 ± 447.6 691.0 ± 12.9 198.3 ± 11.6 1227.6 ± 98.6 19.95 ± 1.6 27.4 ± 1.6 1.09 ± 0.1 87.99 ± 7.4 9.9 ± 0.9 2.9 ± 0.1 190.2 ± 14.6 214.7 ± 19.5 30.7 ± 2.9 B5 985.0 ± 6.5 20147 ± 121.3 201876 ± 199.5 6844 ± 18.7 3792 ± 17.9 1520 ± 107.6 3637 ± 288.6 22.4 ± 2.9 28.5 ± 1.9 72.1 ± 4.4 5.1 ± 0.6 19.2 ± 1.9 BDL 11.04 ± 1.0 5.0 ± 0.4 3.1 ± 0.2 420.9 ± 35.1 BDL 1.9 ± 0.1 B6 2447 ± 15.1 21654 ± 210.6 149657 ± 136.4 7369 ± 25.9 6806 ± 41.7 1116 ± 84.6 7520 ± 84.6 26.37 ± 1.9 28.2 ± 1.1 128.05 ± 10.6 2.88 ± 0.8 11.48 ± 1.0 BDL 19.16 ± 1.2 3.8 ± 0.3 11.2 ± 1.1 335.4 ± 28.1 BDL 1.4 ± 0.1 B7 1974 ± 3.9 14785 ± 144.6 115987 ± 98.60 5084 ± 17.4 17809 ± 147.2 13464 ± 119.5 22269 ± 199.6 198.71 ± 10.6 129.00 ± 10.4 798.16 ± 47.9 18.51 ± 1.1 38.63 ± 3.6 2.04 ± 0.2 63.31 ± 6.4 3.5 ± 0.2 4.4 ± 0.4 210.0 ± 19.4 49.0 ± 4.6 18.0 ± 1.1 B8 889.0 ± 3.4 17362 ± 173.1 224978 ± 178.5 4478 ± 11.4 20176 ± 155.0 11589 ± 105.4 33441 ± 299.5 214.56 ± 17.6 113.33 ± 9.8 777.38 ± 68.4 16.95 ± 0.9 37.30 ± 2.9 1.9 ± 0.1 61.67 ± 5.8 6.8 ± 0.5 1.1 ± 0.1 162.3 ± 14.6 32.0 ± 3.1 21.8 ± 1.9 B9 1748 ± 1.6 21568 ± 241.6 224879 ± 199.1 4350 ± 32.9 19679 ± 146.1 18539 ± 174.6 40189 ± 400.9 330.87 ± 12.5 144.77 ± 12.4 899.09 ± 74.6 17.35 ± 1.6 31.23 ± 2.4 3.60 ± 0.3 69.94 ± 5.7 9.4 ± 0.9 1.2 ± 0.1 196.0 ± 12.6 50.2 ± 4.9 15.5 ± 1.1 C1 298.0 ± 4.4 13132 ± 128.7 209618 ± 199.5 7100 ± 10.8 4582 ± 10.3 540 ± 6.8 3597 ± 294.5 22.4 ± 0.8 11.5 ± 5.6 69.10 ± 5.9 2.10 ± 0.4 14.2 ± 1.6 BDL 12.0 ± 1.3 5.0 ± 0.4 2.4 ± 0.2 401.9 ± 39.4 BDL - C2 1166 ± 6.1 18966 ± 176.1 191935 ± 270.6 7769 ± 21.7 7396 ± 14.2 1206 ± 11.9 5510 ± 51.6 24.37 ± 1.8 22.21 ± 1.9 112.05 ± 9.8 2.88 ± 0.6 14.48 ± 1.1 2.11 ± 0.2 18.16 ± 1.4 3.8 ± 0.3 9.3 ± 1.1 275.4 ± 24.6 BDL 2.4 ± 0.2 C4 5987 ± 1.1 30793 ± 298.4 163332 ± 145.9 5144 ± 15.4 20909 ± 147.0 14964 ± 164.2 36169 ± 311.0 224.71 ± 14.5 187.00 ± 15.8 748.16 ± 16.5 11.51 ± 1.1 31.63 ± 2.4 1.11 ± 0.1 61.31 ± 5.9 4.5 ± 0.4 4.1 ± 0.6 219.0 ± 18.4 45.0 ± 3.8 19.0 ± 1.5 C5 2945 ± 2.4 26995 ± 256.3 134697 ± 122.2 4668 ± 9.87 20976 ± 133.7 11989 ± 113.7 34071 ± 314.9 211.56 ± 11.0 111.33 ± 9.4 741.38 ± 14.6 10.95 ± 1.6 34.30 ± 2.8 1.98 ± 0.1 64.67 ± 5.1 1.8 ± 0.1 1.5 ± 0.1 142.3 ± 11.6 29.0 ± 1.9 14.6 ± 1.3 C9 4974 ± 6.4 37432 ± 341.2 151205 ± 149.6 4750 ± 14.9 21579 ± 211.2 18939 ± 163.5 40389 ± 394.6 309.87 ± 21.5 154.77 ± 9.7 871.09 ± 61.5 13.35 ± 1.1 31.23 ± 2.9 3.60 ± 0.4 61.94 ± 2.9 6.4 ± 0.5 2.5 ± 0.2 166.0 ± 14.6 55.2 ± 4.9 22.7 ± 2.0 C10 879.0 ± 11 22112 ± 198.4 149446 ± 110.0 6185 ± 11.0 11957 ± 97.8 3157 ± 32.8 12807 ± 1118.4 61.94 ± 4.6 53.52 ± 3.5 241.11 ± 10.9 7.01 ± 0.6 24.21 ± 2.1 2.01 ± 0.3 29.78 ± 4.6 4.2 ± 0.4 4.9 ± 0.4 254.7 ± 19.4 21.6 ± 2.1 8.7 ± 0.9 C11 1598 ± 5.7 18966 ± 166.6 127139 ± 99.21 5692 ± 19.5 11528 ± 54.6 3676 ± 29.4 12937 ± 99.7 98.38 ± 8.4 51.33 ± 2.8 261.74 ± 18.8 5.61 ± 0.7 22.59 ± 2.0 3.15 ± 0.4 28.00 ± 2.2 3.7 ± 0.3 3.5 ± 0.3 234.1 ± 19.4 7.9 ± 0.7 5.9 ± 0.1 C12 802.0 ± 9.0 23454 ± 199.2 177547 ± 127.9 7986 ± 10.1 10963 ± 77.9 921 ± 7.9 8288 ± 74.6 30.85 ± 1.0 42.85 ± 1.6 147.81 ± 10.4 2.10 ± 0.6 24.84 ± 1.9 BDL 21.47 ± 2.7 5.8 ± 0.5 2.2 ± 0.2 309.2 ± 24.6 BDL 6.7 ± 0.6 C13 1801 ± 4.4 22828 ± 211.5 190630 ± 146.3 9050 ± 18.5 11986 ± 78.1 794 ± 6.6 6793 ± 51.2 27.12 ± 1.1 29.32 ± 1.1 122.35 ± 9.5 3.40 ± 0.9 22.67 ± 1.1 1.65 ± 0.2 39.02 ± 1.6 6.6 ± 0.4 1.7 ± 0.1 401.8 ± 38.4 2.5 ± 0.2 7.1 ± 0.7 Average 2101 21899 172090 6047 13222 7998 19526 144.86 83.24 400.58 8.56 25.14 1.68 41.19 5.3 3.55 266 26 10.6 Maximum 5987 37432 224978 9850 23176 49434 56502 691.00 198.29 1227.61 19.95 38.63 3.60 87.99 9.9 11.2 426.8 214.7 30.7 Minimum 298 11478 115987 2985 3792 540 3597 22.37 11.5 69.10 1.40 11.48 BDL 11.04 1.8 1.1 142.3 BDL BDL Moreover, the rapid expansion of nearby industrial infrastructures such as thermal power plants, oil refineries, and manufacturing industries can introduce metal pollutants through wastewater effluents, atmospheric deposition, and improper industrial waste management practices. In addition, the release of untreated or partially treated municipal and industrial sewage represents a significant pathway for heavy metal loading in coastal waters and sediments (Dai et al., 2007 ; Cheng and Hu, 2010 ; Hosono et al., 2011 ; Mendiguchia et al., 2006 ; Basaran et al., 2009 ). Figure 2 illustrates the spatial distribution and concentration levels of heavy metals within the study area. 4.2. Distribution of Magnetic Susceptibility (χ) in Sediments Table 3 summarizes the results of the magnetic measurements, including low-frequency \(\:{\chi\:}_{lf}\) and high frequency \(\:{\chi\:}_{hf}\) magnetic susceptibilities. The \(\:{\chi\:}_{lf}\) values range from 1.2145×10 − 8 m 3 kg − 1 to 57.7217×10 − 8 m 3 kg − 1 , with a mean value of 8.6295×10 − 8 m 3 kg − 1 . Variations in \(\:{\chi\:}_{lf}\:\) are primarily controlled by local geological conditions, sedimentary processes, and the contribution of anthropogenic inputs. Previous studies have demonstrated that the magnetic susceptibility of natural, non-polluted sediments is governed by five key factors: parent material, climate, geomorphology, vegetation, and time (Dearing et al., 1996 ; Lu et al., 2007 ). The high frequency magnetic susceptibility ( \(\:{\chi\:}_{hf}\) ) values range from 1.1039×10 − 8 m 3 kg − 1 to 55.7733×10 − 8 m 3 kg − 1 , with an average of 7.5163×10 − 8 m 3 kg − 1 , exhibiting a trend comparable to that observed for \(\:{\chi\:}_{hf}\) . Table 3 Sample ID with Low Frequency Susceptibility (lf) ( ×10 − 8 m 3 kg − 1 ), High Frequency Susceptibility (hf) ( ×10 − 8 m 3 kg − 1 ) and Frequency Dependent Susceptibility (fd) % S. No Location ID Low-Frequency Mass Depended Susceptibility (lf) (×10 − 8 m 3 kg − 1 ) High-Frequency Mass Depended Susceptibility (hf) (×10 − 8 m 3 kg − 1 ) Frequency Dependent Susceptibility (fd) % 1 S1 7.8738 6.6578 15.4436 2 S2 1.5996 1.5890 0.6627 3 S3 35.0048 34.4855 1.4835 4 S4 11.9887 11.3836 5.0473 5 S5 21.7731 20.3737 6.4272 6 S6 1.8659 1.3986 25.0442 7 S7 14.9841 11.1779 25.4016 8 S8 9.2712 8.3332 10.1174 9 S9 3.9235 1.8192 53.6332 10 B1 1.6871 1.3696 18.8193 11 B2 9.9921 9.7706 2.2168 12 B4 1.2145 0.8134 33.0259 13 B5 4.7743 3.3419 30.0023 14 B6 6.8852 6.7885 1.4045 15 B7 3.0568 2.6605 12.9645 16 B8 4.7312 2.4737 47.7152 17 B9 3.9865 1.3659 65.7369 18 C1 1.9811 1.1339 42.7641 19 C2 57.7217 55.7733 3.3755 20 C4 2.0923 1.1719 43.9899 21 C5 1.9687 1.3115 33.3824 22 C9 2.0687 1.7778 14.0620 23 C10 1.9809 1.1039 44.2728 24 C11 7.3217 4.7733 34.8061 25 C12 2.2322 1.1792 47.1732 26 C13 2.3871 1.3968 41.4855 Average 8.6295 7.5163 25.4022 According to the classification proposed by Gautam et al., (2004), sediments are categorized as normal magnetic ( \(\:{\chi\:}_{lf}\) 100×10 − 8 m 3 kg − 1 ). Based on this classification, samples S1, S2, S6, S8, S9, B1, B2, B4, B5, B6, B7, B8, B9, C1, C4, C5, C9, C10, C11, C12, and C13 fall within the normal magnetic category, whereas samples S3, S4, S5, S7, and C2 are classified as moderately magnetic. None of the analyzed samples exhibit highly magnetic characteristics. Approximately 80.7% of the sediment samples exhibited moderate magnetic properties, while 19.3% displayed normal levels, and none were found to be highly magnetic. This distribution indicates a notable presence of ferrimagnetic minerals within the sediments. The observed variation in low-frequency magnetic susceptibility ( \(\:{\chi\:}_{lf}\) ) values suggests that the relatively high measurements cannot be solely explained by local geology. Therefore, the elevated magnetic susceptibility is likely influenced by anthropogenic inputs of diverse origins and other human activities. As illustrated in Figs. 3 and 4 , \(\:{\chi\:}_{lf}\) values are consistently higher than high-frequency susceptibility ( \(\:{\chi\:}_{hf}\) ) values. At high frequencies, the relaxation time of superparamagnetic grains is shorter than the measurement duration, rendering their contribution negligible. In contrast, low-frequency measurements capture the susceptibility of all grains, including those with short relaxation times, such as fine super paramagnetic particles. Consequently, while the grain volume in the sediment remains constant, magnetic susceptibility is higher at low frequencies than at high frequencies (Kanu et al., 2013 ). 4.3. Frequency Dependent Susceptibility ( \(\:{\chi\:}_{fd}\) ) Frequency dependent susceptibility ( \(\:{\chi\:}_{fd}\) ) is widely used as an analytical tool to examine variations in the magnetic grain size distribution within sediment samples (Kanu et al., 2013 ). when \(\:{\chi\:}_{fd}\:\) values are low, the magnetic properties of the samples tend to be dominated by coarse multi domain (MD) particles rather than by superparamagnetic (SP) grains. According to Dearing's model for \(\:{\chi\:}_{fd}\) (Dearing, 1999 ), sediments with \(\:{\chi\:}_{fd}\) values between 0% and 2% are typically characterized by coarse MD grains. Sediments with \(\:{\chi\:}_{fd}\) values ranging from 2% to 10% contain a mix of SP and single-domain (SD) grains, while those exceeding 10% are primarily composed of SP ferrimagnetic grains. In this study, all three types of magnetic assemblages were observed. Samples S2, S3, and B6, showing \(\:{\chi\:}_{fd}\) values from 0% to 2%, are dominated by coarse MD grains. On the other hand, samples S3, S4, S5, B2, B6, and C2, with \(\:{\chi\:}_{fd}\) values between 2% and 10%, represent an intermediate group, indicating a blend of SP and SD grains. Finally, samples S1, S6, S7, S8, S9, B1, B4, B5, B7, B8, B9, C1, C4, C5, C9, C10, C11, C12, and C13, which exhibit \(\:{\chi\:}_{fd}\) values greater than 10%, are dominated by SP ferrimagnetic grains. The spatial variation of \(\:{\chi\:}_{fd}\) across the different sampling sites is presented in Fig. 5 . 4.4. Pearson Correlation Analysis The correlation between magnetic parameters and heavy metal contents indicated a clear association between ferrimagnetic oxides and heavy metals (Dong et al., 2014 ). In previous studies, the relationship between magnetic susceptibility and heavy metal concentrations has typically been evaluated using Pearson correlation analysis. In the present study, although the correlation between magnetic susceptibility and heavy metals was significant at the 1% level for all samples, the relationships between magnetic particles and pollutants remain complex and vary across different industrial processes (Bandaru et al., 2016 ). Notably, the correlation coefficients (r²) between magnetic susceptibility and heavy metal concentrations in this study did not exceed 0.50, suggesting that the relationship diminishes in sediment samples with low magnetic susceptibility. The detailed correlation coefficients between heavy metals and magnetic susceptibility are presented in Table 4 . Table 4 Correlation Matrix between Heavy Metals and Magnetic Parameters in Sediment Samples along the Study Area of Ennore, East Coast of Tamilnadu, India Correlations Variables Mg Al Si K Ca Ti Fe V Cr Mn Co Ni Cu Zn As Cd Ba La Pb LF HF FD Mg 1 Al 0.756 1 Si -0.437 -0.271 1 K -0.330 -0.166 0.239 1 Ca 0.570 0.530 -0.223 -0.533 1 Ti 0.388 0.212 0.017 -0.519 0.339 1 Fe 0.587 0.418 -0.060 -0.669 0.655 0.907 1 V 0.422 0.253 0.016 -0.598 0.432 0.984 0.946 1 Cr 0.710 0.459 -0.217 -0.682 0.647 0.832 0.938 0.867 1 Mn 0.432 0.272 -0.003 -0.623 0.670 0.881 0.940 0.904 0.845 1 Co 0.361 0.227 -0.021 -0.647 0.648 0.808 0.873 0.843 0.831 0.920 1 Ni 0.502 0.372 -0.279 -0.480 0.828 0.504 0.707 0.561 0.759 0.694 0.757 1 Cu 0.297 0.298 -0.016 -0.135 0.429 0.249 0.392 0.307 0.326 0.389 0.428 0.342 1 Zn 0.558 0.374 -0.069 -0.574 0.731 0.842 0.944 0.887 0.896 0.930 0.878 0.784 0.423 1 As 0.216 0.196 0.432 -0.139 0.166 0.631 0.588 0.660 0.505 0.504 0.501 0.303 0.282 0.567 1 Cd 0.018 -0.079 -0.252 0.146 -0.302 -0.217 -0.299 -0.251 -0.250 -0.285 -0.290 -0.416 -0.323 -0.335 -0.361 1 Ba -0.463 -0.290 0.334 0.780 -0.729 -0.615 -0.810 -0.691 -0.772 -0.783 -0.755 -0.702 -0.445 -0.751 -0.206 0.136 1 La 0.226 0.083 0.061 -0.469 0.181 0.962 0.791 0.923 0.709 0.785 0.714 0.345 0.138 0.728 0.557 -0.161 -0.489 1 Pb 0.501 0.395 -0.056 -0.578 0.679 0.865 0.930 0.888 0.876 0.943 0.904 0.760 0.400 0.923 0.558 -0.349 -0.769 0.772 1 LF -0.271 -0.155 0.112 0.024 -0.249 -0.291 -0.331 -0.288 -0.319 -0.309 -0.280 -0.408 0.022 -0.366 -0.315 0.420 0.052 -0.228 -0.294 1 HF -0.264 -0.151 0.097 0.022 -0.249 -0.284 -0.329 -0.284 -0.321 -0.303 -0.284 -0.422 0.005 -0.363 -0.327 0.433 0.053 -0.216 -0.290 0.997 1 FD -0.002 0.034 0.280 0.043 0.060 0.171 0.219 0.180 0.226 0.141 0.137 0.296 0.071 0.210 0.385 -0.439 -0.014 0.092 0.129 -0.468 -0.510 1 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). 4.5. Factor Analysis Principal Component Analysis (PCA) is an effective statistical tool for elucidating the relationships between magnetic minerals and heavy metals in sediments (Zhu et al., 2012 ; Wang et al., 2018 ). Table 5 Rotated Factor Loadings of Heavy Metals and Magnetic Parameters in Sediments Samples along the Study Area of Ennore, East Coast of Tamilnadu, India Variables Factor 1 Factor 2 Mg 0.695 -0.114 Al 0.486 -0.047 Si -0.494 0.869 K -0.665 -0.103 Ca 0.641 0.099 Ti 0.806 0.482 Fe 0.894 0.438 V 0.831 0.494 Cr 0.910 0.266 Mn 0.823 0.464 Co 0.770 0.414 Ni 0.723 0.086 Cu 0.334 0.167 Zn 0.851 0.403 As 0.331 0.686 Cd -0.138 -0.365 Ba -0.828 -0.084 La 0.695 0.472 Pb 0.842 0.413 LF -0.336 -0.061 HF -0.326 -0.073 FD 0.052 0.351 % of variance explained 43.81 14.76 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 3 iterations. As presented in Table 5 , the analysis extracted three principal components, collectively explaining 58.57% of the total variance. The first principal component (PC1) accounted for 43.81% of the variance and exhibited strong loadings for Ti, Fe, V, Cr, Mn, Co, Ni, Zn, and Pb. The second principal component (PC2) explained 14.76% of the variance, primarily associated with FD%, Si, and As. The analysis indicated that LF and HF components did not correlate with heavy metal concentrations. The observed spatial variations in heavy metal levels are likely influenced by a combination of natural geological processes and anthropogenic activities. Figure 6 illustrates the rotated factor loadings of heavy metals and magnetic parameters, highlighting these relationships. 4.6. Cluster Analysis Cluster analysis is a multivariate technique used to classify variables into distinct groups based on similarities across a set of measured parameters, ensuring that closely related subjects are grouped together (Harikrishnan et al., 2018 ). In the present study, magnetic mineral and heavy metal variables were grouped into two clusters using the average linkage method (Chandrasekaran et al., 2021 ). The clustering results are illustrated in the dendrogram shown in Fig. 7 . Cluster I is primarily associated with the concentrations of heavy metals and magnetic minerals, whereas Cluster II is dominated solely by Si. These results indicate that the distribution of magnetic minerals in the study area is strongly influenced by the presence of heavy metals. 5.0. Conclusion Sediments from the Ennore region along the east coast of Tamil Nadu, India underwent systematic analysis to determine the concentrations of heavy metals and magnetic susceptibility measurements ( \(\:{\chi\:}_{lf}\) , \(\:{\chi\:}_{hf}\) , \(\:{\chi\:}_{fd}\) ). The detected heavy metals were found in the following descending order: Mn > Ba > V > Cr > Zn > La > Ni > Pb > Co > As > Cd > Cu > Al > Fe > Ca > Ti > K > Mg. Magnetic susceptibility analysis indicated that the average low-frequency ( \(\:{\chi\:}_{lf}\) ) and high frequency ( \(\:{\chi\:}_{hf}\) ) susceptibilities were 8.6295×10⁻⁸m³kg⁻¹ and 7.5163×10⁻⁸m³kg⁻¹, respectively. It should be noted that sample C2 had a higher \(\:{\chi\:}_{lf}\) (52.7217×10⁻⁸m³kg⁻¹) and \(\:{\chi\:}_{hf}\) (55.7733×10⁻⁸m³kg⁻¹), which indicates that there is a slight dominance of ferromagnetic minerals in the sediments. The frequency dependent susceptibility ( \(\:{\chi\:}_{lf}\) %) further indicates the presence of super paramagnetic or single domain magnetic minerals. The heavy metals found in the sediments were primarily derived from anthropogenic activities, such as the discharge of treated or untreated sewage, industrial effluents, and harbor or shipping related operations. Overall, this study demonstrates that magnetic susceptibility serves as an effective tool for identifying hotspots of heavy metal contamination and tracing their pollution sources. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding Declaration The authors declare that no funding or financial support was received from any agency, institution, or organization for the conduct of this research, preparation of the manuscript, or publication of the article. Author Contribution Rajendiran Dhanapal: Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization, Harikrishnan Nallaiyan: Writing – review & editing, Resources, Methodology, Conceptualization, Sheela N R: Resources, Methodology, Conceptualization, Veeramuthu Kasinathan: Writing – review & editing, Validation, Supervision, Resources, Methodology, Conceptualization, Project administration. Acknowledgement The authors express their gratitude to the Coastal Environmental Engineering Division (CEE) of the National Institute of Ocean Technology (NIOT), NIOT Campus, Pallikaranai, Chennai – 600100, Tamil Nadu, India, for their valuable collaboration, insights, and contributions. Data Availability The data that support the findings of this study are openly available. References Bandaru, V.L., Gawali, P.B., Hanamgond, P.T., and Kannan, D., 2016. 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Yalcina, F., Nyamsarib, D.G., Paksub, E., Yalcinb, M.G., 2016. Statistical Assessment of Heavy Metal Distribution and Contamination of Beach Sands of Antalya-Turkey: an Approach to the Multivariate Analysis Techniques, Filomat, 30 (4), 945–952. DOI: 10.2298/FIL1604945Y . Zhang, C., Qiao, Q., Piper, J.D.A., Huang, B., 2011. Assessment of heavy metal pollution from a Fe-smelting plant in urba river sediments using environmental magnetic and geochemical methods. Environ. Pollut. 159, 3057–3070. DOI: 10.1016/j.envpol.2011.04.006 . Zhu, Z., Han, Z., Bi, X., and Yang, W. (2012). The relationship between magnetic parameters and heavy metal contents of indoor dust in e-waste recycling impacted area, Southeast China. Sci. Total Environ. 433, 302–308. DOI: 10.1016/j.scitotenv.2012.06.067 . Additional Declarations No competing interests reported. Supplementary Files RajaMHHighlights.docx RajaMHDeclarationofcompetinginterest.docx RajaMHCRediTauthorshipcontributionstatement.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8257448","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558864659,"identity":"13a3d864-8ff9-478e-a46f-729424309aad","order_by":0,"name":"D. Rajendiran","email":"","orcid":"","institution":"Shanmuga Industries Arts and Science College","correspondingAuthor":false,"prefix":"","firstName":"D.","middleName":"","lastName":"Rajendiran","suffix":""},{"id":558864660,"identity":"868a3a10-2750-4b6d-9364-6a4cf076cc2f","order_by":1,"name":"N. Harikrishnan","email":"data:image/png;base64,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","orcid":"","institution":"Sri Venkateswara College of Engineering","correspondingAuthor":true,"prefix":"","firstName":"N.","middleName":"","lastName":"Harikrishnan","suffix":""},{"id":558864662,"identity":"decd1d68-a1bb-4f31-b9b6-f1ef33abaf3f","order_by":2,"name":"N. R. Sheela","email":"","orcid":"","institution":"Sri Venkateswara College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"N.","middleName":"R.","lastName":"Sheela","suffix":""},{"id":558864665,"identity":"a145959f-c2e0-47c3-b286-86b5baf5d895","order_by":3,"name":"K. Veeramuthu","email":"","orcid":"","institution":"Thiru Kolanjiappar Government Arts College","correspondingAuthor":false,"prefix":"","firstName":"K.","middleName":"","lastName":"Veeramuthu","suffix":""}],"badges":[],"createdAt":"2025-12-02 07:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8257448/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8257448/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103356622,"identity":"61dfca7d-3e9c-4c10-b0af-3ef9a5f778d3","added_by":"auto","created_at":"2026-02-24 18:41:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99929,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation Map of the Study Area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1LocationMapoftheStudyArea.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8257448/v1/25eaf3f57eb6035f7bcd7e74.jpg"},{"id":103509696,"identity":"dd6fd882-714f-4a84-8674-618955d938e3","added_by":"auto","created_at":"2026-02-26 14:00:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63425,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeavy Metal Concentration Levels of Sediments in the Study Area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2HeavyMetalConcentrationLevelsofSedimentsintheStudyArea.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8257448/v1/52cb9a2178d1120180275fea.jpg"},{"id":103356623,"identity":"2de5a445-d13b-4269-973c-e3028435eb41","added_by":"auto","created_at":"2026-02-24 18:41:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation Vs Low Frequency Susceptibility (10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-8\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003em\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003ekg\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3LocationVsLowFrequencySusceptibility108m3kg1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8257448/v1/be093f68f767bcd3c94fefbe.jpg"},{"id":103356629,"identity":"79563c99-230a-417f-bb55-d7e3ea58a751","added_by":"auto","created_at":"2026-02-24 18:41:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation Vs High Frequency Susceptibility (10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-8\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003em\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003ekg\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-1\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4LocationVsHighFrequencySusceptibility108m3kg1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8257448/v1/c447641d82ba535893990c56.jpg"},{"id":103507118,"identity":"3c76c325-cbfb-434b-88f9-2f75a24211d9","added_by":"auto","created_at":"2026-02-26 13:40:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":81906,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation Vs Frequency Dependent Susceptibility (FD) %\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5LocationVsFrequencyDependentSusceptibilityFD.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8257448/v1/da44eb3a272c22f20325eae6.jpg"},{"id":103507493,"identity":"070ef963-1a0d-4f68-ba79-2ca00a914752","added_by":"auto","created_at":"2026-02-26 13:41:35","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":27838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRotated Factor Analysis of Heavy Metals and Magnetic Parameters\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6RotatedFactorAnalysisofHeavyMetalsandMagneticParameters.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8257448/v1/b9fcff410555aa482d735ebf.jpg"},{"id":103356631,"identity":"56ada6ad-b739-4395-9917-83f6b02dc877","added_by":"auto","created_at":"2026-02-24 18:41:18","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":27298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDentogram Shows the Cluster Analysis of Heavy Metals and Magnetic Parameters\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7DentogramshowstheClusterAnalysisofHeavyMetalsandMagneticParameters.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8257448/v1/b82d3d21beceb6042f05eede.jpg"},{"id":105904772,"identity":"8a49f88a-66b7-412a-aa50-d4d7433f00fd","added_by":"auto","created_at":"2026-04-01 10:10:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2493419,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8257448/v1/ca202799-a8d1-4e6b-99e6-29cf964045c0.pdf"},{"id":103506729,"identity":"168630c3-dcb3-4f58-a464-78707eb597b9","added_by":"auto","created_at":"2026-02-26 13:39:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13573,"visible":true,"origin":"","legend":"","description":"","filename":"RajaMHHighlights.docx","url":"https://assets-eu.researchsquare.com/files/rs-8257448/v1/a3b6e262a68cdcde142a15ec.docx"},{"id":103506917,"identity":"830bd63c-7b6f-4f58-acb2-61bf0d405aca","added_by":"auto","created_at":"2026-02-26 13:39:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29497,"visible":true,"origin":"","legend":"","description":"","filename":"RajaMHDeclarationofcompetinginterest.docx","url":"https://assets-eu.researchsquare.com/files/rs-8257448/v1/64c4b7a8791b43759153397f.docx"},{"id":103356625,"identity":"a025a5c1-aa9f-44ff-9473-7d1b1f169ae8","added_by":"auto","created_at":"2026-02-24 18:41:18","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11821,"visible":true,"origin":"","legend":"","description":"","filename":"RajaMHCRediTauthorshipcontributionstatement.docx","url":"https://assets-eu.researchsquare.com/files/rs-8257448/v1/b131d276bd531902f8680773.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Magnetic Susceptibility and Statistical Modeling for the Assessment of Toxic Heavy Metals in Surface Sediments of Ennore Port, East Coast of Tamil Nadu, India","fulltext":[{"header":"1.0. Introduction","content":"\u003cp\u003eHeavy metal contamination of sediments represents one of the most critical global environmental issues, posing substantial risks to ecosystems and human health. In recent years, the accumulation of heavy metals in coastal environments has intensified due to increasing anthropogenic activities, including industrial effluent discharge, tourism, and fishing operations (Dang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Sediments function as major sinks for heavy metals through the formation of stable complexes with organic matter, carbonates, and iron (Fe) and manganese (Mn) oxides (Duzzin et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). These metal fractions exhibit varying degrees of mobility, toxicity, and chemical reactivity, influencing their environmental behavior and bioavailability. Consequently, a comprehensive understanding of the geochemical speciation of metals is essential for accurately assessing sediment contamination and evaluating the potential ecological and human health risks associated with heavy metal exposure (Yalcin et al., 2016).\u003c/p\u003e \u003cp\u003eMagnetic susceptibility is a measure of the concentration of iron-bearing components in sediments and is widely used to characterize sediment types and quantify the abundance of iron-bearing minerals (Canbay, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It represents a rapid, sensitive, and cost-effective analytical approach, providing an important parameter for applications in mineralogical and granulometric studies (Canbay, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In recent years, magnetic susceptibility measurements have been extensively employed to identify and trace sources of heavy metal pollution. Techniques based on the magnetic properties of sediments have been widely adopted by environmental scientists and have demonstrated considerable success in pollution assessment studies. Numerous investigations have established magnetic susceptibility as a reliable indicator for identifying sources of heavy metal contamination (Thompson et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Hay et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Strzyszcz et al., 1998; Durza, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Lecoanet et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Knab et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Kapicka et al., 2003; Hanesch and Scholger, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This relationship is primarily attributed to anthropogenic emissions, which commonly contain fine, strongly magnetic particles that are often enriched with heavy metals, particularly in soils and sediments of industrialized regions (Canbay, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMagnetic measurements have proven to be an effective tool for assessing the transport and distribution of pollutants through atmospheric and aquatic pathways (Scholger, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Petrovsk\u0026yacute; et al., 2000). The application of magnetic parameters as proxies for heavy-metal contamination is based on the close genetic and geochemical association between heavy metals and magnetic mineral phases. Numerous environmental magnetism studies have consistently reported strong correlations between heavy-metal concentrations and magnetic properties of soils and sediments (Hanesch et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Jordanova et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Schmidt et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Spiteri et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoastal populations are particularly vulnerable to heavy-metal exposure through direct contact with beach sediments and the inadvertent ingestion of seawater (Yalcin et al., 2016). The present study area includes several environmentally sensitive hotspots, such as tourist destinations, active fishing zones, and nearby industrial regions, which together increase the potential risk of metal contamination. In this context, the objectives of the present study are to: (i) quantify the concentrations of heavy metals and measure the magnetic susceptibility of surface sediments collected from Ennore Port along the east coast of Tamil Nadu; (ii) characterize the magnetic mineralogical properties of the sediments; (iii) evaluate the relationships between heavy-metal concentrations and magnetic susceptibility using multivariate statistical techniques; and (iv) identify the potential sources of heavy-metal pollution.\u003c/p\u003e"},{"header":"2.0. Geology of the Study Area","content":"\u003cp\u003eEnnore hosts the Ennore Port, officially designated as Kamarajar Port Limited, which functions as a key hub for regional trade and commercial activities. The port handles a diverse range of cargo, including containers, coal, petroleum products, and general merchandise. Strategically situated along the eastern coast of the Indian peninsula in the Bay of Bengal, Ennore Port lies approximately 2.5 km north of the Ennore Creek (Pandian et al., 2002). Owing to its intensive maritime operations, the port plays a pivotal role in supporting industrial growth and maritime trade within the Chennai metropolitan region and its adjoining areas.\u003c/p\u003e \u003cp\u003eThe Ennore region and its environs accommodate numerous industrial establishments, such as thermal power stations, petroleum refineries, chemical processing units, and other manufacturing industries. While these activities significantly contribute to regional economic development, they also exert considerable pressure on the surrounding environment, leading to concerns related to pollution and ecological degradation of coastal systems. Similar to other rapidly industrializing and urbanizing coastal zones, Ennore experiences multiple environmental stresses, including contamination of air, water, and sediments, habitat alteration, and declining biodiversity. In response, various mitigation measures, environmental regulations, and conservation strategies have been initiated to manage and reduce these impacts.\u003c/p\u003e \u003cp\u003eDespite the extent of industrial development, Ennore retains substantial ecological importance, supported by coastal ecosystems such as mangroves, estuaries, and tidal flats. These habitats provide essential ecological services, including biodiversity support, shoreline protection, enhancement of water quality, and improved coastal resilience. Overall, Ennore represents a complex landscape characterized by the coexistence of industrial, commercial, and residential activities, coupled with significant environmental and socioeconomic challenges. Sustainable management and integrated planning are therefore essential to balance development with environmental protection. The geological characteristics of the study area are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, while Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the location map of the study area.\u003c/p\u003e"},{"header":"3.0. Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Sample Collection and Preparation\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the locations of the sediment sampling sites, extending from 13\u0026deg;13\u0026prime;18.50\u0026Prime; N, 80\u0026deg;20\u0026prime;10.10\u0026Prime; E to 13\u0026deg;15\u0026prime;13.33\u0026Prime; N, 80\u0026deg;20\u0026prime;17.43\u0026Prime; E, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 26 sediment samples were obtained during the pre-monsoon period from three distinct environmental settings within the Ennore region, namely the sea (S), beach (B), and creek (C) zones along the east coast of Tamil Nadu. A Peterson grab sampler was used to carry out the sampling.\u003c/p\u003e \u003cp\u003eThis study used a flame atomic absorption spectrometer (AAS; iCE 3000, Thermo Scientific, USA) to determine the concentrations of heavy metals. The principle of optical radiation absorption by free atoms in the gaseous phase was used to carry out both qualitative and quantitative analyses. Three to five standard solutions, prepared by appropriate dilution of a 1000 ppm certified stock solution, were used to establish calibration curves for each metal. Hollow cathode lamps corresponding to Pb, As, Hg, Cu, Zn, Cr, Ni, and Mn served as radiation sources, with an air-acetylene mixture being employed as fuel. The mean values were reported as the final concentrations after analyzing all samples and standards in replicates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLatitude and Longitude of the Study Area of Ennore, East Coast of Tamilnadu, India\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'46.02''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'31.56''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'44.83''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'48.04''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'44.09''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;21'6.35''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'1.40''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'20.71''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'1.32''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'39.85''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'1.20''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'56.76''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;13'18.50''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'10.10''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;13'19.04''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'29.96''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;13'19.54''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'46.77''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;15'13.33''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'17.43''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'49.77''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'8.12''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'0.75''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;19'46.26''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;15'12.59''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'21.18''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'49.51''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'10.55''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'0.79''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;19'48.15''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;15'12.01''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'25.27''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'49.51''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;20'13.40''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;13'58.55''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;19'38.62''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;13'50.50''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;19'22.59''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;13'26.04''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;18'53.16''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;13'9.64''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;18'42.67''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;13'2.15''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;18'52.99''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;13'59.38''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;18'55.29''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'17.55''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;18'56.60''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'35.42''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;18'55.95''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u0026deg;14'54.22''\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;18'51.95''\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Ennore coastal region was studied for the spatial variation of natural radionuclides from terrestrial to marine environments by collecting sediment samples. Sampling was carried out at three distinct zones: the nearshore marine region at a distance of 10 m parallel to the shoreline and at a water depth of 4 m (nine samples), the beach zone (eight samples), and the creek region (nine samples). After being collected, the samples were placed in clean polyethylene bags, labelled appropriately for their respective sampling locations, and transported to the laboratory. After air drying the samples in ambient conditions, coarse materials like shell fragments and stone debris were manually removed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Atomic Absorption Spectrometry (AAS) Analysis\u003c/h2\u003e \u003cp\u003eThe flame atomic absorption spectrometer (AAS; iCE 3000, Thermo Scientific, USA) was used to determine the concentrations of heavy metals in this study. Qualitative and quantitative analyses were performed utilizing the principle of optical radiation absorption by free atoms in the gaseous phase. Three to five standard solutions prepared with appropriate dilution of a 1000 ppm certified stock solution were used to establish calibration curves for each metal. Hollow cathode lamps corresponding to Pb, As, Hg, Cu, Zn, Cr, Ni, and Mn served as radiation sources, with an air-acetylene mixture being employed as fuel. The mean values were reported as the final concentrations after analyzing all samples and standards in replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Magnetic Susceptibility (χ) Measurements\u003c/h2\u003e \u003cp\u003eTo prevent any potential chemical changes and minimize the impact of moisture, the collected samples were air-dried at ambient temperature in the laboratory. Using established procedures (Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), extraneous materials like glass fragments, plant remains, refuse, and coarse lithic particles were removed from the dried samples after sieving through a 1 mm mesh. After processing, the samples were transferred to clean plastic containers and preserved for future analysis. The sieved material was packed into 10 ml plastic sample holders and analyzed at room temperature for magnetic susceptibility measurements. The use of an MS2B dual-frequency magnetic susceptibility meter, interfaced with a computer running Multisus2 software, resulted in measurements at both low (0.0.465 kHz) and high (4.65 kHz) operating frequencies.\u003c/p\u003e \u003cp\u003eThe sensitivity setting of 1.0 was used for all analyses. The mean value was calculated to ensure analytical reliability by taking five replicate measurements at each frequency for each sample. In natural sediment samples, the grain-size distribution is generally continuous and nearly uniform, making magnetic susceptibility a trustworthy indicator of relative variations in the concentration of pedogenic fine-grained magnetic minerals (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The standard expression proposed by Dearing (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) was used to determine frequency-dependent magnetic susceptibility (χfd).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd\\:}\\left(\\%\\right)=\\left[\\frac{{\\chi\\:}_{lf\\:-\\:}{\\chi\\:}_{hf\\:}}{{\\chi\\:}_{lf\\:}}\\right]\\times\\:\\)\u003c/span\u003e \u003c/span\u003e 100 ------------- (1)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Multivariate Statistical Analysis\u003c/h2\u003e \u003cp\u003eMultivariate statistical techniques namely Pearson\u0026rsquo;s correlation analysis, factor analysis, and hierarchical cluster analysis were applied to examine the interrelationships between heavy metal concentrations and magnetic susceptibility parameters in sediment samples. These methods were employed to identify potential source contributions and to discriminate between lithogenic and anthropogenic inputs of heavy metals. All statistical analyses were performed using SPSS software (Statistical Package for the Social Sciences, version 16.0), and the statistical significance was evaluated at the predefined confidence level.\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0. Results and Discussions","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Heavy Metal Concentration in Sediments\u003c/h2\u003e \u003cp\u003eThe distribution of heavy metals in surface sediments of coastal environments such as Ennore is governed by a combination of anthropogenic and natural factors. Industrial operations, port and shipping activities, rapid urban expansion, and associated land-based inputs play a significant role in controlling metal accumulation in these sediments. Heavy metals enter the sedimentary system through multiple pathways, including industrial effluents, atmospheric fallout, urban surface runoff, and the weathering and transport of contaminated terrestrial materials. Systematic evaluation of metal concentrations in surface sediments is therefore essential for understanding sediment quality, identifying pollution sources, and assessing potential ecological and human health risks. The measured concentrations of the investigated elements in the sediment samples from the study area are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The concentration (mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) varies as follows: 298 to 5987, 11478 to 37432, 115987 to 224978, 2985 to 9850, 3792 to 23176, 540 to 49434, 3597 to 56502, 22.37 to 691, 11.5 to 198.29, 69.10 to 1227.61, 1.40 to 19.95, 11.48 to 38.63, BDL to 3.60, 11.04 to 87.99, 1.8 to 9.9, 1.1 to 11.2, 142.3 to 426.8, BDL to 214.7 and BDL to 30.7 for Mg, Al, Si, K, Ca, Ti, Fe, V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Ba, La and Pb respectively. Among the analyzed elements, aluminium (Al), iron (Fe), calcium (Ca), magnesium (Mg), and silicon (Si) were identified as the dominant constituents in the sediment samples. The mean concentration of metals followed the descending order: Si\u0026thinsp;\u0026gt;\u0026thinsp;Al\u0026thinsp;\u0026gt;\u0026thinsp;Fe\u0026thinsp;\u0026gt;\u0026thinsp;Ca\u0026thinsp;\u0026gt;\u0026thinsp;Ti\u0026thinsp;\u0026gt;\u0026thinsp;K\u0026thinsp;\u0026gt;\u0026thinsp;Mg\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Ba\u0026thinsp;\u0026gt;\u0026thinsp;V\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;La\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Co\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Cu across the study area. Notably, sampling locations S2, S9, B4, B7, B9, C4, and C9 exhibited comparatively elevated levels of V, Cr, Co, Ni, and Zn relative to other sites. These enhanced concentrations can be attributed to intense anthropogenic influences, including harbor-related activities, industrial operations, urban effluent discharge, and dredging practices. The observed trends are consistent with reports from comparable coastal and industrialized environments documented in earlier studies (Nath et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Millward and Moore, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Santhiya et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eElevated concentrations of Ti, Fe, V, Cr, Mn, As, and Pb were observed at sampling locations S1, S2, S9, B2, B7, B8, B9, C4, C5, C9, and C11. These enrichments are likely associated with intensive anthropogenic activities linked to Ennore Port, which manages diverse cargo types such as coal, petroleum products, and general merchandise. Operational activities including dredging, vessel movement, and cargo loading and unloading can disrupt sediment strata and promote the resuspension and redistribution of metal contaminants, thereby enhancing their accumulation in bottom sediments. Occasional events, such as ballast water discharge, oil spill incidents, and accidental leakage of hazardous substances from ships, may further contribute to the input of heavy metals into the coastal environment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeavy Metal Concentration of the Sediment Samples along the Study Area of Ennore, East Coast of Tamilnadu, India\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e13222\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e7998\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e19526\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e144.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e83.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e400.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e8.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e25.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e1.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e41.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e5.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e3.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e266\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e10.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5987\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e37432\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e224978\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e9850\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e23176\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e49434\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e56502\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e691.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e198.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1227.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e19.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e38.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e3.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e87.99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e9.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e11.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e426.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e214.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cb\u003e30.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e298\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11478\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e115987\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2985\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3792\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e540\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3597\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e22.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e11.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e69.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e1.40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e11.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eBDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e11.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e1.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e1.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e142.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003eBDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003eBDL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, the rapid expansion of nearby industrial infrastructures such as thermal power plants, oil refineries, and manufacturing industries can introduce metal pollutants through wastewater effluents, atmospheric deposition, and improper industrial waste management practices. In addition, the release of untreated or partially treated municipal and industrial sewage represents a significant pathway for heavy metal loading in coastal waters and sediments (Dai et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cheng and Hu, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hosono et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mendiguchia et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Basaran et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the spatial distribution and concentration levels of heavy metals within the study area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Distribution of Magnetic Susceptibility (χ) in Sediments\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the results of the magnetic measurements, including low-frequency \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\)\u003c/span\u003e\u003c/span\u003e and high frequency \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{hf}\\)\u003c/span\u003e\u003c/span\u003e magnetic susceptibilities. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\)\u003c/span\u003e\u003c/span\u003e values range from 1.2145\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to 57.7217\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a mean value of 8.6295\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Variations in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\:\\)\u003c/span\u003e\u003c/span\u003eare primarily controlled by local geological conditions, sedimentary processes, and the contribution of anthropogenic inputs. Previous studies have demonstrated that the magnetic susceptibility of natural, non-polluted sediments is governed by five key factors: parent material, climate, geomorphology, vegetation, and time (Dearing et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The high frequency magnetic susceptibility (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{hf}\\)\u003c/span\u003e\u003c/span\u003e) values range from 1.1039\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to 55.7733\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an average of 7.5163\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, exhibiting a trend comparable to that observed for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{hf}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSample ID with Low Frequency Susceptibility (lf) (\u003c/b\u003e\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003cb\u003e), High Frequency Susceptibility (hf) (\u003c/b\u003e\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003cb\u003e) and Frequency Dependent Susceptibility (fd) %\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocation ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-Frequency Mass Depended Susceptibility (lf) (\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh-Frequency Mass Depended Susceptibility (hf) (\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrequency Dependent Susceptibility (fd) %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.6578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.4436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.4855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.3836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.0473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.7731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.3737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.4272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.0442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.9841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.1779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.4016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.1174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.6332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.8193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.9921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.7706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.0259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.0023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.9645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.7152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.7369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.7641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.7217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.7733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.3755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.9899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.3824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.0620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.2728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.8061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.1732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.4855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.6295\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e7.5163\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e25.4022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the classification proposed by Gautam et al., (2004), sediments are categorized as normal magnetic (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\)\u003c/span\u003e\u003c/span\u003e\u0026lt;10\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), moderately magnetic (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\)\u003c/span\u003e\u003c/span\u003e=10\u0026ndash; 100\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and highly magnetic (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\)\u003c/span\u003e\u003c/span\u003e\u0026gt;100\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003em\u003csup\u003e3\u003c/sup\u003ekg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Based on this classification, samples S1, S2, S6, S8, S9, B1, B2, B4, B5, B6, B7, B8, B9, C1, C4, C5, C9, C10, C11, C12, and C13 fall within the normal magnetic category, whereas samples S3, S4, S5, S7, and C2 are classified as moderately magnetic. None of the analyzed samples exhibit highly magnetic characteristics.\u003c/p\u003e \u003cp\u003eApproximately 80.7% of the sediment samples exhibited moderate magnetic properties, while 19.3% displayed normal levels, and none were found to be highly magnetic. This distribution indicates a notable presence of ferrimagnetic minerals within the sediments. The observed variation in low-frequency magnetic susceptibility (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\)\u003c/span\u003e\u003c/span\u003e) values suggests that the relatively high measurements cannot be solely explained by local geology. Therefore, the elevated magnetic susceptibility is likely influenced by anthropogenic inputs of diverse origins and other human activities. As illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\)\u003c/span\u003e\u003c/span\u003e values are consistently higher than high-frequency susceptibility (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{hf}\\)\u003c/span\u003e\u003c/span\u003e) values. At high frequencies, the relaxation time of superparamagnetic grains is shorter than the measurement duration, rendering their contribution negligible. In contrast, low-frequency measurements capture the susceptibility of all grains, including those with short relaxation times, such as fine super paramagnetic particles. Consequently, while the grain volume in the sediment remains constant, magnetic susceptibility is higher at low frequencies than at high frequencies (Kanu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Frequency Dependent Susceptibility (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003eFrequency dependent susceptibility (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd}\\)\u003c/span\u003e\u003c/span\u003e) is widely used as an analytical tool to examine variations in the magnetic grain size distribution within sediment samples (Kanu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ewhen \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd}\\:\\)\u003c/span\u003e\u003c/span\u003evalues are low, the magnetic properties of the samples tend to be dominated by coarse multi domain (MD) particles rather than by superparamagnetic (SP) grains. According to Dearing's model for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd}\\)\u003c/span\u003e\u003c/span\u003e (Dearing, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), sediments with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd}\\)\u003c/span\u003e\u003c/span\u003e values between 0% and 2% are typically characterized by coarse MD grains. Sediments with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd}\\)\u003c/span\u003e\u003c/span\u003e values ranging from 2% to 10% contain a mix of SP and single-domain (SD) grains, while those exceeding 10% are primarily composed of SP ferrimagnetic grains. In this study, all three types of magnetic assemblages were observed. Samples S2, S3, and B6, showing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd}\\)\u003c/span\u003e\u003c/span\u003e values from 0% to 2%, are dominated by coarse MD grains.\u003c/p\u003e \u003cp\u003eOn the other hand, samples S3, S4, S5, B2, B6, and C2, with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd}\\)\u003c/span\u003e\u003c/span\u003e values between 2% and 10%, represent an intermediate group, indicating a blend of SP and SD grains. Finally, samples S1, S6, S7, S8, S9, B1, B4, B5, B7, B8, B9, C1, C4, C5, C9, C10, C11, C12, and C13, which exhibit \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd}\\)\u003c/span\u003e\u003c/span\u003e values greater than 10%, are dominated by SP ferrimagnetic grains. The spatial variation of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd}\\)\u003c/span\u003e\u003c/span\u003e across the different sampling sites is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Pearson Correlation Analysis\u003c/h2\u003e \u003cp\u003eThe correlation between magnetic parameters and heavy metal contents indicated a clear association between ferrimagnetic oxides and heavy metals (Dong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In previous studies, the relationship between magnetic susceptibility and heavy metal concentrations has typically been evaluated using Pearson correlation analysis. In the present study, although the correlation between magnetic susceptibility and heavy metals was significant at the 1% level for all samples, the relationships between magnetic particles and pollutants remain complex and vary across different industrial processes (Bandaru et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Notably, the correlation coefficients (r\u0026sup2;) between magnetic susceptibility and heavy metal concentrations in this study did not exceed 0.50, suggesting that the relationship diminishes in sediment samples with low magnetic susceptibility. The detailed correlation coefficients between heavy metals and magnetic susceptibility are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Matrix between Heavy Metals and Magnetic Parameters in Sediment Samples along the Study Area of Ennore, East Coast of Tamilnadu, India\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"23\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"23\" nameend=\"c23\" namest=\"c1\"\u003e \u003cp\u003eCorrelations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003eLa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c20\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c21\"\u003e \u003cp\u003eLF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c22\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c23\"\u003e \u003cp\u003eFD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.533\u003c/p\u003e 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colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e 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align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e 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colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e 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colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e 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align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" 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align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e-0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e-0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e-0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e-0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e-0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e-0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e-0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e-0.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c21\"\u003e \u003cp\u003e-0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e-0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c23\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"23\" nameend=\"c23\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"23\"\u003e\u003cb\u003e*. Correlation is significant at the 0.05 level (2-tailed).\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Factor Analysis\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis (PCA) is an effective statistical tool for elucidating the relationships between magnetic minerals and heavy metals in sediments (Zhu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRotated Factor Loadings of Heavy Metals and Magnetic Parameters in Sediments Samples along the Study Area of Ennore, East Coast of Tamilnadu, India\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e% of variance\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eexplained\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e43.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e14.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtraction Method: Principal Component Analysis.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eRotation Method: Varimax with Kaiser Normalization.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ea. Rotation converged in 3 iterations.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the analysis extracted three principal components, collectively explaining 58.57% of the total variance. The first principal component (PC1) accounted for 43.81% of the variance and exhibited strong loadings for Ti, Fe, V, Cr, Mn, Co, Ni, Zn, and Pb. The second principal component (PC2) explained 14.76% of the variance, primarily associated with FD%, Si, and As.\u003c/p\u003e \u003cp\u003eThe analysis indicated that LF and HF components did not correlate with heavy metal concentrations. The observed spatial variations in heavy metal levels are likely influenced by a combination of natural geological processes and anthropogenic activities. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the rotated factor loadings of heavy metals and magnetic parameters, highlighting these relationships.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Cluster Analysis\u003c/h2\u003e \u003cp\u003eCluster analysis is a multivariate technique used to classify variables into distinct groups based on similarities across a set of measured parameters, ensuring that closely related subjects are grouped together (Harikrishnan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the present study, magnetic mineral and heavy metal variables were grouped into two clusters using the average linkage method (Chandrasekaran et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The clustering results are illustrated in the dendrogram shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Cluster I is primarily associated with the concentrations of heavy metals and magnetic minerals, whereas Cluster II is dominated solely by Si. These results indicate that the distribution of magnetic minerals in the study area is strongly influenced by the presence of heavy metals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5.0. Conclusion","content":"\u003cp\u003eSediments from the Ennore region along the east coast of Tamil Nadu, India underwent systematic analysis to determine the concentrations of heavy metals and magnetic susceptibility measurements (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{hf}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{fd}\\)\u003c/span\u003e\u003c/span\u003e). The detected heavy metals were found in the following descending order: Mn\u0026thinsp;\u0026gt;\u0026thinsp;Ba\u0026thinsp;\u0026gt;\u0026thinsp;V\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;La\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Co\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Al\u0026thinsp;\u0026gt;\u0026thinsp;Fe\u0026thinsp;\u0026gt;\u0026thinsp;Ca\u0026thinsp;\u0026gt;\u0026thinsp;Ti\u0026thinsp;\u0026gt;\u0026thinsp;K\u0026thinsp;\u0026gt;\u0026thinsp;Mg. Magnetic susceptibility analysis indicated that the average low-frequency (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\)\u003c/span\u003e\u003c/span\u003e) and high frequency (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{hf}\\)\u003c/span\u003e\u003c/span\u003e) susceptibilities were 8.6295\u0026times;10⁻⁸m\u0026sup3;kg⁻\u0026sup1; and 7.5163\u0026times;10⁻⁸m\u0026sup3;kg⁻\u0026sup1;, respectively. It should be noted that sample C2 had a higher \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\)\u003c/span\u003e\u003c/span\u003e (52.7217\u0026times;10⁻⁸m\u0026sup3;kg⁻\u0026sup1;) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{hf}\\)\u003c/span\u003e\u003c/span\u003e (55.7733\u0026times;10⁻⁸m\u0026sup3;kg⁻\u0026sup1;), which indicates that there is a slight dominance of ferromagnetic minerals in the sediments. The frequency dependent susceptibility (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}_{lf}\\)\u003c/span\u003e\u003c/span\u003e%) further indicates the presence of super paramagnetic or single domain magnetic minerals. The heavy metals found in the sediments were primarily derived from anthropogenic activities, such as the discharge of treated or untreated sewage, industrial effluents, and harbor or shipping related operations. Overall, this study demonstrates that magnetic susceptibility serves as an effective tool for identifying hotspots of heavy metal contamination and tracing their pollution sources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eDeclaration\u003c/p\u003e \u003cp\u003eThe authors declare that no funding or financial support was received from any agency, institution, or organization for the conduct of this research, preparation of the manuscript, or publication of the article.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRajendiran Dhanapal: Writing \u0026ndash; original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Conceptualization, Harikrishnan Nallaiyan: Writing \u0026ndash; review \u0026amp; editing, Resources, Methodology, Conceptualization, Sheela N R: Resources, Methodology, Conceptualization, Veeramuthu Kasinathan: Writing \u0026ndash; review \u0026amp; editing, Validation, Supervision, Resources, Methodology, Conceptualization, Project administration.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors express their gratitude to the Coastal Environmental Engineering Division (CEE) of the National Institute of Ocean Technology (NIOT), NIOT Campus, Pallikaranai, Chennai \u0026ndash; 600100, Tamil Nadu, India, for their valuable collaboration, insights, and contributions.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are openly available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBandaru, V.L., Gawali, P.B., Hanamgond, P.T., and Kannan, D., 2016. 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DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.scitotenv.2012.06.067\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2012.06.067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sediments, Pollution Sources, Magnetic Susceptibility, Heavy Metals, Statistical Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8257448/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8257448/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe investigation of heavy metals in the Ennore ecosystem is crucial for assessing the magnitude and distribution of pollution in the region. In the present study, the concentrations of major and trace elements, including Mg, Al, Si, K, Ca, Ti, Fe, V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Ba, La, and Pb, were determined in twenty-six sediment samples. The abundance of heavy metals in the sediments followed the decreasing order: Si\u0026thinsp;\u0026gt;\u0026thinsp;Al\u0026thinsp;\u0026gt;\u0026thinsp;Fe\u0026thinsp;\u0026gt;\u0026thinsp;Ca\u0026thinsp;\u0026gt;\u0026thinsp;Ti\u0026thinsp;\u0026gt;\u0026thinsp;K\u0026thinsp;\u0026gt;\u0026thinsp;Mg\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Ba\u0026thinsp;\u0026gt;\u0026thinsp;V\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;La\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Co\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Cu. The average concentrations of all analyzed metals were found to be below the global crustal average. Owing to their strong affinity for forming metallic bonds with ferromagnetic materials, these metals significantly influence the magnetic properties of sediments, particularly magnetic susceptibility. Consequently, low-frequency (χlf), high-frequency (χhf), and frequency dependent (χfd) magnetic susceptibility measurements were carried out using an MS2B dual-frequency susceptibility meter. Furthermore, multivariate statistical techniques, including Pearson correlation, factor analysis, and cluster analysis, were employed to elucidate the relationships between heavy metal concentrations and magnetic susceptibility parameters, with the objective of evaluating anthropogenic influences on the sediments. The results demonstrate that magnetic susceptibility measurements provide a rapid, cost effective, non destructive, and reliable proxy for identifying and assessing sources of heavy metal contamination in sedimentary environments.\u003c/p\u003e","manuscriptTitle":"Magnetic Susceptibility and Statistical Modeling for the Assessment of Toxic Heavy Metals in Surface Sediments of Ennore Port, East Coast of Tamil Nadu, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 18:41:13","doi":"10.21203/rs.3.rs-8257448/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce0ec8d3-54bc-471f-b5a8-92b88501dd06","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-31T12:57:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 18:41:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8257448","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8257448","identity":"rs-8257448","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}