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Assessment of Metal Contamination with Seasonal Variation in Sediments of Narmada River (India) Using Pollution Indices

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01 November 2023

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02 November 2023

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Abstract
This study aimed to assess the presence of metal contamination in the sediments of the Narmada River by considering seasonal variations. In this context, samples were gathered from six stations along the river in 2021–22. To assess the various metals including Li, Na, Mg, Al, Ca, Fe, Cu, Zn, Co, and Ni by utilizing ICP-MS. Sediment contamination was assessed by utilizing indices like contamination factor (CF), pollution load index (PLI), geo-accumulation factor (Igeo), and enrichment factor (EF). The observed order of metals concentration revealed that metals Fe>Al>Mg>Ca>Na>Cu>Zn>Co>Ni>Li. The seasonal variation reveals metals Al, Fe, Cu, Zn, and Ni exhibit the highest concentrations during the monsoon and metals Li and Co, the highest concentration in the post-monsoon, while metals Na, Mg, and Ca, exhibit their peak concentration during the pre-monsoon. The findings from CF, PLI, Igeo, and EF indicate that there were low to moderate contaminations, metal pollution exists, uncontaminated to moderate contaminations, and minor enrichment. The cluster analysis revealed the source of contamination was anthropogenic, marine, and fertilizer activities. Additionally, Correlation matrix was employed to establish connections between metals. The findings of this research are to understand the metal contamination and potential carcinogenicity in the sediment of Narmada River.
Keywords: 
Subject: Environmental and Earth Sciences  -   Environmental Science

1. Introduction

In recent years, the escalating presence of metal contamination in aquatic ecosystems has raised significant concerns due to its toxicity, pervasiveness, and accumulation. This issue has garnered substantial attention and awareness, underscoring the growing need for immediate action. The main sources of metal contamination in river sediments are untreated domestic wastewater, agricultural fertilizer and pesticides, industrial discharge, and natural activities [1,2,3]. The aqueous phase and river sediments get a distribution of metal that is released into a river system throughout its conveyance by point or nonpoint sources [4]. Due to the combined effects of adsorption, hydrolysis, co-precipitation, and fine particles, most unbound metal ions do not persist within the solution and instead accumulate in large quantities in sediments [5]. When harmful and persistent chemical pollutants with slow decomposition rates are discharged into the river water, sediments act as a top pollutant key store for such contaminants. River sediments serve as habitats for diverse aquatic animals and plants, playing a crucial ecological role in the aquatic environment while also serving as reservoirs for pollutants. This can lead to the accumulation of metal residues, potentially posing health risks if these contaminants enter the human food chain. In aquatic systems, Sediments serve the dual role of potential secondary sources and transporters of pollutants. The retention of metals in river sediments has the capacity to perpetual future pollutant. Hence, it is imperative to conduct comprehensive research on sediment quality to assess the potential threat posed by metal contamination and the associated carcinogenic risks [6,7,8,9,10,11]. Sediment contamination assessment studies have been utilized globally during the past few decades [6,11,12,13,14,15,16,17,18]. Metals are categorized based on their source of origin into three primary groups: anthropogenic metals (Li, Cu, Zn, Co, Ni), crustal metals (Al and Fe), and marine-related metals (Na, Ca, Mg) [19].
Correlation and Cluster analysis have gained widespread recognition as valuable tools for identifying, classifying, and understanding the relationships among sources of pollutants [2,16,20].
Narmada River is an important river in India and This was the first in-depth study on metal. The report was finished using routine sampling over two years at six sample stations. ICP-MS was applied for the determination of metal concentration and Method 3050B was applied for calculation. Hence, the main aim of this research is to investigate the concentration of metal with seasonal variation in the sediment of the Narmada River.

2. Materials and Methods

2.1. Study area

The Narmada River ranks as the fifth-longest river in the Indian subcontinent and it is called the lifeline of Madhya Pradesh. Over time, numerous small towns and large cities have flourished along their banks. Unfortunately, due to various human activities, such as the discharge of domestic, dairy industrial waste and natural drainage, the river’s water quality has deteriorated significantly. Therefore, it is essential to analyse the quality of sediments. For this, six sampling stations were selected in such a way as to cover Jabalpur city (Figure 1). The first sampling station is Jamtaraghat which is close to the Pariyat tributary and dairy industries. The second, third, and fourth sampling stations are Gwarighat, Tilwaraghat, and Bhedaghat. These are close to urban areas. The fifth and sixth sampling stations are Ghugharaghat and Parmatghat. They are close to agricultural and rural areas. The distance between the first four sampling stations is 5 km each and fourth to fifth and fifth to sixth are 10 km and 15 km respectively. As such a stretch of 40 km has been taken for this study.

2.2. Sample Collection and Instrumental Analysis

Samples of sediment were gathered from sampling sites over four distinct phases: firstly, May (Pre-monsoon); secondly, July (monsoon); thirdly September (Post- monsoon) and fourthly January (Winter) during the year 2021-22. The samples were collected from six stations of four composite samples each weighing approximately 200 g. After sampling, samples were immediately enclosed within uncontaminated polyethylene bags and transported to the laboratory, where they were stored at a temperature of 4˚C. In preparation for analysis, the samples underwent an air-drying process. Subsequently, plant fragments and stones were eliminated by sieving the dried sample through a 2 mm mesh. The analytical procedure was followed to EPA protocol 3050B, involving acid digestion for sediments, sludges, and soils. This method utilizes a combination of hydrogen peroxide and nitric acid for complete metal digestion. The next step involved utilizing ICP-MS (Inductively Coupled Plasma-Mass Spectrometry) to ascertain the concentration of metals within the sediment samples.

2.3. Analysis of Sediment Contamination

In the explication of geochemical data, the selection of background value is crucial. Many authors have utilized the average shale value as a reference background value [13,16,21]. However, due to the absence of data regarding background values for the Narmada River sediment and the enclosed area under investigation. Consequently, this study adopts a meticulous approach and computes background values by averaging the metal concentrations in uncontaminated sediments within the study region [1,5,20]

2.3.1. Contamination factor (CF)

CF is expressed as CF = (Sediment Metal Concentration) / (Background value of the metal).
Following the classification by Hakanson (1980), CF values are interpreted as follows: CF < 1 indicates low contamination; 1< CF < 3 suggests moderate contamination; 3 < CF < 6 implies considerable contamination.

2.3.2. Pollution load Index (PLI)

PLI is expressed as: PLI = (Product of contamination) 1/n
When PLI exceeds 1, it signifies the presence of metal pollution. Conversely, If PLI is less than 1, there is no evidence of metal pollution.

2.3.2. Geo-accumulation index (Igeo)

Igeo is determined using the formula Igeo = Log2 C n 1.5 B n
Cn and Bn represent measured concentration and geochemical background values. Constant 1.5 is applied to account for possible variations in the background value. Igeo values correspond to different contamination classes: Class 0 (Igeo ≤ 0) indicates Uncontaminated status; Class 1 (0 < Igeo < 1) signifies uncontaminated to moderately contaminated; Class 2 (1 < Igeo < 2) represents moderate contaminated; Class 3 (2 < Igeo < 3) denotes moderately to heavily contaminated.

2.3.3. Enrichment factor (EF)

The EF is defined as: EF = [(Metal/Fe) sample] / [(Metal/Fe) background]
Fe serves as the reference element for geochemical normalization., The interpretation of EF values is as follows: EF<1 implies no enrichment; < 3 indicates minor enrichment; 3-5 suggests moderate enrichment; 5-10 implies moderately severe enrichment; 10-25 signifies severe enrichment.

3. Results and Discussion

3.1. Metal contamination

The findings concerning metal concentrations (Table 1) reveal a range across different stations and seasons. Li concentration ranged between 0.034 to 0.077 mg kg-1, with station third exhibiting the highest concentration during the post-monsoon season and station six having the lowest concentration during the monsoon season. Na concentrations ranged between 16.426 to 32.383 mg kg-1, with station first registering the highest concentration during the pre-monsoon season and station six recording the lowest concentration during the monsoon season. Similarly, Mg concentrations varied from 77.459 to 167.286 mg kg-1, with station fourth having the highest pre-monsoon concentration and station six the lowest during the monsoon. Al concentration spanned from 229.937 to 479.472 mg kg-1, with the first station exhibiting the greatest concentration during monsoon season, while station six had the lowest concentration during the pre-monsoon season. Ca concentrations ranged between 13.231 to 39.912 mg kg-1, with station first registering the highest concentration during the pre-monsoon season and station fifth recording the lowest concentration during the monsoon season. Fe concentrations varied between 309.682 to 1133.822 mg kg-1, with station first exhibiting the highest concentration in the monsoon season and station six having the lowest concentration in the pre-monsoon season. Cu concentrations spanned from 0.842 to 2.965 mg kg-1, with the third station having the highest during the monsoon and station six displaying the lowest during the pre-monsoon. Zn concentrations varied between 0.686 and 2.09 mg kg-1, with station third exhibiting the highest concentration during the monsoon and station fifth recording the lowest before the monsoon. Co concentrations spanned from 0.548 to 1.387 mg kg-1, peaking at station third in the post-monsoon and being lowest at station six during the monsoon. Ni concentrations spanned from 0.323 to 0.953 mg kg-1, with station third displaying the highest concentration during the monsoon and station fifth recording the lowest during the pre-monsoon.
During the monsoon season, the interaction between agricultural runoff and river water led to an increase in the concentrations of Cu, Zn, and Ni due to their presence in fertilizers and pesticides.
In the post-monsoon season, higher human activities contributed to elevated concentrations of Li and Co. The pre-monsoon period witnessed increased concentrations of Na, Mg, and Ca due to higher evaporation rates during summer.
The spatial distribution patterns indicated that the station first exhibited higher concentrations of Na, Al, Ca, and Fe owing to geological and marine influences. Station fourth had elevated Mg concentrations due to the presence of marble rock sections. Human and agricultural activities lead to increased concentrations of Li, Cu, Zn, Co, and Ni at station third. Notably, the concentration hierarchy was Fe > Al > Mg > Ca > Na > Cu > Zn > Co > Ni > Li.
Regarding seasonal variations, the distribution of the metals followed the order of monsoon > post-monsoon> winter > pre-monsoon for Al, Fe, Cu, Zn, and Ni. Conversely, Li and Co showed a distribution pattern of post-monsoon > winter > pre-monsoon > monsoon, while Na, Mg, and Ca demonstrated a distribution sequence of pre-monsoon > post-monsoon > winter > monsoon. Two of the analysed metals, Co and Ni, possess carcinogenic potential. However, their concentrations remained below the acceptable limit, indicating no cancer risk.

3.2. Analysis of Pollution Indices

In Table 2, The average value of CF was 1.306, ranging between 0.833 and 2.172. The lowest CF value was observed at station sixth for metal Mg and the highest CF value was recorded at station third during the monsoon season for metal Cu. The findings of the CF indicate that there was low contamination to moderate contamination.
Regarding the PLI, its average value was 1.296, with a range spanning from 1.012 to 1.531. The lowest PLI value was detected at the sixth station, while the highest was observed at the third station. The PLI results indicate a spectrum from no metal pollution to the presence of pollution.
In Table 3, the average Igeo value was -0.229, ranging from -0.849 to 0.534. The lowest Igeo value was observed at station sixth for metal Mg, while the highest was found at the third station for Cu. These findings suggest a range of uncontaminated to moderate classes of metals.
In Table 4, the average value of EF was 1.062. ranging from 0.704 to 1.506. The lowest EF value was detected at station first for metal Zn, and the highest was recorded at station third for metal Cu. The EF results indicate a range from no enrichment to minor enrichment.
A comparison of the metal concentration in sediment from the Narmada River and other selected rivers is shown in Table 5. The maximum concentration of the Narmada River was very low to comparisons to other selected rivers for Fe, Al, Cu, Zn, Co, and Ni. Additionally, concentrations of Li, Na, and Ca were not measured in the other selected rivers. Furthermore, the concentration of metals in the Narmada River sediment remained below permissible limits.

3.3. Correlation coefficient

A correlation matrix was applied to establish the relationship regarding the common source among the metals in the sediment of the Narmada River (Bhuyan et al., 2019a; Islam et al., 2015; Varol, 2011).The Person correlation coefficient values are provided in Table 6. Notably, the calculated correlation coefficient indicates robust linear associations between certain metal pairs. For instance, a notably strong linear relationship was observed between Na and Mg (0.927), Na and Ca (.982), Al and Fe (0.998) as well as Li and Co (0.876), Cu and Zn (0.986), Cu and Ni (0.998), Zn and Ni (0.979). These Correlation outcomes offer insights into the common sources of these metals. The finding suggests that marine metals, lithogenic sources, and anthropogenic activities are likely caused by the presence of these metals in the sediment.

3.4. Cluster analysis (CA)

CA was employed to identify similar characteristic sources of contamination among the various parameters through the use of a dendrogram [40,41,42,43] shown in Figure 2. From the outcome of the dendrogram; three clusters have found; Cluster 1 encompasses Li and Co; these parameters are attributed to anthropogenic activities. Cluster 2 comprises Na, Ca, and Mg, originating from marine metals activities. Finally, Cluster 3 includes Al, Zn, Fe, Cu, and Ni, stemming from agricultural fertilizers and natural earth sources.

4. Conclusions

The main aim of this investigation was to assess the presence and concentrations of various metals (Li, Na, Mg, Al, Ca, Fe, Cu, Zn, Co, and Ni) within the sedimentary deposits of the Narmada River. The concentration of these metals was within the permissible limit. The observed trends in metal distribution throughout the different seasons exhibited distinguishable patterns. Notably, during the monsoon season, there was an elevated concertation of metals Al, Fe, Cu, Zn, and Ni, with post-monsoon, winter, and summer seasons following in descending order of concentration. Conversely, metals like Na, Mg, and Ca demonstrated higher concentrations during the pre-monsoon period, succeeded by winter, post-monsoon, and monsoon seasons. Moreover, metals Li and Co exhibited greater concentrations in the post-monsoon season, succeeded by winter, pre-monsoon, and monsoon. Upon assessment of Co and Ni concentrations, it was determined that the sediment samples did not pose a carcinogenic risk. Pollution indices, when applied to the results, indicated that the sediment quality ranged from unpolluted to minimally polluted. The sources of contamination were identified as being associated with human activities, natural crustal processes, and marine-derived metal inputs. Notably, this study holds significance as the first of its kind focused on the Narmada River. However, it’s important to acknowledge that the study was conducted using a limited number of samples. The finding of this research establishes a foundational dataset for prospective studies and contributes valuable insights to the formulation of strategies for effective river basin management.

Author Contributions

Conceptualization, acquisition of data, preparation of Graphs and Tables by First author (Dal Chand Rahi), Analysis and interpretation of data by Second author (Rajeev Chandak), Drafting and analysis by Third author (Amit Vishwakarma) and final approval of manuscript by all authors.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data Availability Statement

Data may be shared upon the request.

Acknowledgments

The author is grateful for the support given by the Principal, Jabalpur Engineering College, Jabalpur M.P. for the support given for conducting the study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study area and sampling Point Location along the Narmada River.
Figure 1. Study area and sampling Point Location along the Narmada River.
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Figure 2. Dendrogram showing clustering of the analysed parameters.
Figure 2. Dendrogram showing clustering of the analysed parameters.
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Table 1. Concentration of various metals at all studies stations in the sediments of Narmada River.
Table 1. Concentration of various metals at all studies stations in the sediments of Narmada River.
S. No. Seasons Metal Concentration (mg kg-1)
Li Na Mg Al Ca Fe Cu Zn Co Ni
S-1 Pre- Monsoon 0.048 32.383 140.741 341.852 39.912 739.951 1.103 0.693 1.151 0.456
Monsoon 0.039 21.962 108.641 479.472 25.873 1133.822 2.263 1.457 0.965 0.796
Post- Monsoon 0.056 26.218 128.862 386.637 32.872 989.549 1.492 0.879 1.262 0.573
Winter 0.051 28.983 133.264 352.916 33.648 762.161 1.166 0.795 1.183 0.475
Average value 0.049 27.387 127.877 390.219 33.076 906.371 1.506 0.956 1.140 0.575
S-2 Pre- Monsoon 0.057 31.752 142.467 332.852 36.647 718.246 1.410 0.728 1.172 0.509
Monsoon 0.043 21.484 113.646 463.173 21.527 1076.161 2.353 1.691 0.976 0.892
Post- Monsoon 0.069 25.017 135.757 372.374 30.982 972.076 1.583 1.05 1.326 0.616
Winter 0.061 28.674 137.442 345.751 31.473 732.807 1.506 0.899 1.273 0.568
Average value 0.058 26.732 132.328 378.538 30.157 874.823 1.713 1.092 1.187 0.646
S-3 Pre- Monsoon 0.068 29.738 152.262 313.786 34.342 703.972 1.924 1.085 1.176 0.535
Monsoon 0.052 20.141 122.528 443.284 19.638 1099.291 2.965 2.09 0.983 0.953
Post- Monsoon 0.077 23.41 144.742 357.438 28.347 956.752 2.179 1.383 1.387 0.692
Winter 0.071 27.374 146.472 330.264 29.793 716.992 1.968 1.126 1.281 0.572
Average value 0.067 25.166 141.501 361.193 28.030 869.252 2.259 1.421 1.207 0.688
S-4 Pre- Monsoon 0.065 25.895 167.286 287.732 32.842 581.862 1.472 0.998 1.130 0.390
Monsoon 0.050 18.462 137.866 425.683 16.749 982.653 2.526 1.922 0.951 0.614
Post- Monsoon 0.072 22.173 158.742 352.693 26.476 754.528 1.843 1.294 1.295 0.501
Winter 0.067 24.021 161.573 302.744 27.638 590.372 1.511 1.094 1.247 0.414
Average value 0.064 22.638 156.367 342.213 25.926 727.354 1.838 1.327 1.156 0.480
S-5 Pre- Monsoon 0.047 22.946 104.451 233.739 25.852 415.795 0.969 0.686 0.779 0.323
Monsoon 0.036 16.894 89.741 364.153 13.231 911.658 1.972 1.556 0.597 0.593
Post- Monsoon 0.054 18.688 96.065 312.753 18.887 666.926 1.262 0.828 0.852 0.390
Winter 0.050 20.458 100.231 247.025 20.236 432.897 0.993 0.718 0.811 0.349
Average value 0.047 19.747 97.622 289.418 19.552 606.819 1.299 0.947 0.760 0.414
S-6 Pre- Monsoon 0.046 22.863 94.052 229.937 26.998 309.682 0.842 0.706 0.727 0.332
Monsoon 0.034 16.426 77.459 358.583 15.098 799.854 1.882 1.562 0.548 0.598
Post- Monsoon 0.053 18.219 84.362 309.482 19.931 628.874 1.212 0.922 0.896 0.396
Winter 0.050 20.316 89.984 242.219 22.626 327.215 0.869 0.714 0.844 0.354
Average value 0.046 19.456 86.464 285.055 21.163 516.406 1.201 0.976 0.754 0.420
Table 2. Contamination factor and Pollution load index of all studies stations in Sediments of Narmada River.
Table 2. Contamination factor and Pollution load index of all studies stations in Sediments of Narmada River.
S. No. Contamination factor PLI
Li Na Mg Al Ca Fe Cu Zn Co Ni
S-1 1.244 1.330 1.232 1.356 1.696 1.504 1.448 1.059 1.508 1.459 1.373
S-2 1.474 1.298 1.274 1.316 1.546 1.452 1.647 1.209 1.570 1.640 1.435
S-3 1.718 1.222 1.363 1.255 1.437 1.442 2.172 1.574 1.596 1.746 1.531
S-4 1.628 1.099 1.506 1.189 1.329 1.207 1.767 1.470 1.529 1.218 1.379
S-5 1.199 0.959 0.940 1.006 1.002 1.007 1.249 1.049 1.005 1.050 1.043
S-6 1.173 0.945 0.833 0.991 1.085 0.857 1.155 1.081 0.997 1.066 1.012
Ave. Value 1.406 1.142 1.191 1.185 1.349 1.245 1.573 1.240 1.368 1.363 1.296
Max. Value 1.718 1.330 1.506 1.356 1.696 1.504 2.172 1.574 1.596 1.746 1.531
Min. Value 1.173 0.945 0.833 0.991 1.002 0.857 1.155 1.049 0.997 1.050 1.012
Table 3. Geo-accumulation index (Igeo) of all studies stations in the Sediments of Narmada River.
Table 3. Geo-accumulation index (Igeo) of all studies stations in the Sediments of Narmada River.
S. No. Geo-accumulation index
Li Na Mg Al Ca Fe Cu Zn Co Ni
S-1 -0.270 -0.174 -0.284 -0.145 0.177 0.004 -0.051 -0.503 0.008 -0.040
S-2 -0.025 -0.208 -0.235 -0.189 0.044 -0.047 0.135 -0.311 0.066 0.129
S-3 0.196 -0.296 -0.138 -0.257 -0.062 -0.057 0.534 0.069 0.090 0.219
S-4 0.118 -0.448 0.006 -0.335 -0.175 -0.314 0.237 -0.030 0.027 -0.301
S-5 -0.323 -0.645 -0.674 -0.577 -0.582 -0.575 -0.264 -0.516 -0.578 -0.514
S-6 -0.355 -0.667 -0.849 -0.598 -0.467 -0.808 -0.377 -0.473 -0.589 -0.493
Ave. Value -0.110 -0.406 -0.362 -0.350 -0.178 -0.300 0.036 -0.294 -0.163 -0.167
Max. Value 0.196 -0.174 0.006 -0.145 0.177 0.004 0.534 0.069 0.090 0.219
Min. Value -0.355 -0.667 -0.849 -0.598 -0.582 -0.808 -0.377 -0.516 -0.589 -0.514
Table 4. Enrichment factor (EF) of all studies stations in Sediments of Narmada River.
Table 4. Enrichment factor (EF) of all studies stations in Sediments of Narmada River.
S. No. Enrichment factor
Li Na Mg Al Ca Fe Cu Zn Co Ni
S-1 0.827 0.884 0.819 0.902 1.127 1.000 0.963 0.704 1.003 0.970
S-2 1.016 0.894 0.878 0.906 1.065 1.000 1.135 0.833 1.081 1.130
S-3 1.191 0.847 0.945 0.870 0.996 1.000 1.506 1.091 1.107 1.211
S-4 1.349 0.911 1.248 0.985 1.101 1.000 1.464 1.218 1.267 1.009
S-5 1.191 0.952 0.934 0.999 0.995 1.000 1.240 1.042 0.998 1.043
S-6 1.369 1.103 0.972 1.156 1.266 1.000 1.348 1.261 1.164 1.244
Ave. Value 1.157 0.932 0.966 0.970 1.092 1.000 1.276 1.025 1.103 1.101
Max. Value 1.369 1.103 1.248 1.156 1.266 1.000 1.506 1.261 1.267 1.244
Min. Value 0.827 0.847 0.819 0.870 0.995 1.000 0.963 0.704 0.998 0.970
Table 5. Metal content comparison with selected other rivers.
Table 5. Metal content comparison with selected other rivers.
Name of River Maximum Concentration (mg kg-1) References
Li Na Mg Al Ca Fe Cu Zn Co Ni
Narmada River 0.08 32.38 167.29 479.47 39.91 1133.82 2.97 2.09 1.39 0.95 This study
Huafei River - - - - - - 866.90 4206.97 - 101.35 [22]
NileRiver - - - - - 50814 49.52 166.56 29.85 62.74 [23]
Weihe River - - - - - - 69.34 143.64 - 62.38 [7]
Old Brahmaputra - - - 90000 - - 6.20 52.7 4.10 12.8 [2]
Yinma River - - - - - - 23.80 151.15 - 25.06 [24]
Swarnamukhi River - - - - - 23296 108.3 40.93 7.22 6 [10]
Zarrin-Gol River - - 785.96 2923.86 - 13751.04 - 32.68 8.79 12.39 [25]
Liaohe River - - - - - - 17.82 50.24 - 17.73 [26]
Halda River - - - 9316.83 - - 5.90 79.58 4.92 15.97 [27]
River Ganga - - - - - 31988.6 29.8 67.8 - 26.7 [28]
Brisbane River - - - - - - 29 106.6 - 15.3 [29]
Le’an River - - - - - - 1428.4 1280.11 - 44.36 [8]
Meghna River - - - - - - - 79.02 - 76.1 [30]
Arvind River - - - - - - 22.5 - 26.81 64.5 [31]
Jialu River - - - - - - 107.61 210 80.26 [32]
Langat River - - - - - - 14.84 74.70 - 8.25 [33]
Pearl River - - - - - - - 189.49 - - [34]
Euphrates, River - - - - - 2249.5 18.9 48.0 - 67.1 [12]
Kabini River - - - - - 1855.7 161.03 191 - 280.32 [35]
Tigris River - - - - - - 5075.6 2396.6 389.8 288.0 [20]
River Po - - - - - - 90.1 645 - 161 [36]
Gediz river - - - - - 18233 56 72 25 51 [37]
Tinto River - - - - - - 2700 5280 42 36 [38]
Gomti River - - - - - 19305.5 245.33 343.47 - 76.08 [39]
Shing Mun River - - - 114 - - 1.66 2.2 - - [18]
Table 6. Correlation matrix of metals in sediments.
Table 6. Correlation matrix of metals in sediments.
Li Na Mg Al Ca Fe Cu Zn Co Ni
Li 1
Na 0.620 1
Mg 0.366 0.927 1
Al -0.955 -0.584 -0.262 1
Ca 0.697 0.982 0.842 -0.702 1
Fe -0.969 -0.573 -0.259 0.998 -0.687 1
Cu -0.923 -0.416 -0.073 0.981 -0.552 0.982 1
Zn -0.956 -0.563 -0.240 1.000 -0.683 0.999 0.986 1
Co 0.876 0.824 0.725 -0.736 0.812 -0.756 -0.631 -0.729 1
Ni -0.900 -0.389 -0.038 0.974 -0.533 0.973 0.998 0.979 -0.589 1
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