Ecological Risk Assessment of Heavy Metal in the Bottom Sediment of River Surma, using Monte Carlo Simulation and Multivariate Statistical Analysis.

Arup Acharjee Shahjalal University of Science and Technology Zia Ahmed (  [email protected] ) Shahjalal University of Science and Technology Raul Alam BRAC University Md. Saur Rahman Bangladesh Atomic Energy Commission Zhixiao Xie Florida Atlantic University

Research Article

Keywords: Heavy metals, Ecological risk, Surma river, Monte Carlo simulation, Multivariate statistics, Hakanson risk index

Posted Date: May 17th, 2021

DOI: https://doi.org/10.21203/rs.3.rs-499511/v1

License:   This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Ecological risk assessment of heavy metal in the bottom sediment of River Surma, Bangladesh using Monte 2 Carlo simulation and multivariate statistical analysis.

3 1Arup Acharjee, 1*Zia Ahmed, 2Rafiul Alam, Md. Safiur Rahman3 Zhixiao Xie4

4 1 Department of Geography and Environment, Shahjalal University of Science & Technology

5 2 BRAC James P Grant School of Public Health, BRAC University

6 3 Chemistry Division, Atomic Energy Centre (AECD), Bangladesh Atomic Energy Commission, Dhaka- 7 1207, Bangladesh

8 5Department of Geosciences, Florida Atlantic University, Boca Raton, Florida, USA 9

10 *Corresponding author email: [email protected] 11

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13 Abstract

14 The study aims to achieve an accurate assessment of 7 heavy metals, including Cu, Fe, Mn, Zn, Ni, Pb and Cd. Data 15 were collected from 15 sampling stations and analyzed by atomic absorption spectrophotometry. The potential 16 detrimental effects of these heavy metals were evaluated by Hakanson risk index. Hakanson risk index (RI) and 17 Monte Carlo Simulation (MCS) and the ecological risk were articulated as probability distribution of Risk Index 18 values instead of single point values to estimate the uncertainties in the risk evaluation process. The results showed 19 that the levels of SEF (sediment enrichment factor) in the sediments were in the following order: 20 Ni>Pb>Cd>Mn>Cu>Zn. Geo-accumulation index (Igeo) values showed no pollution for most of the metals except 21 Ni, which portrayed the sediment was moderately contaminated and may have adverse effects on the ecology of the 22 river. RI values from Hakanson Risk Index showed each individual station are at low ecological risks throughout the 23 study area. According to the Monte Carlo simulation results and the traditional Hakanson risk index, the cumulative 24 probability of Risk Index (RI) values is less than 150, which, according to the Hakanson risk index, represents low 25 ecological risk. From the sensitivity analysis, the comparatively highest contribution to variance can be ranked as 26 Cd>Pb>Ni>Zn>Cu. The outputs of this study could be used to assess ecological risk near urban area river sediment 27 and help predict how the disposal of domestic sewage will affect the riverine ecosystem.

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29 Keywords Heavy metals, Ecological risk, Surma river, Monte Carlo simulation, Multivariate statistics, Hakanson 30 risk index.

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35 1. Introduction

36 In developing countries, heavy metal contamination in river water and sediment is a matter of dire concern. 37 (Silambarasan et al., 2012). The general ways these heavy metals reach river bodies are via weathering, erosion of 38 rocks, and an array of anthropogenic sources. The sources of contamination are found to be generally, occurred from 39 industrial and agricultural activities, surface runoff, and sewage disposal (Barakat et al., 2012). The sources of the 40 contamination can be either point or non-point in nature (Shazili et al., 2006). River sediment can be used to 41 measure the pollution levels in natural waters as it serves as one of the vital environmental indicators (Islam et al., 42 2015). Though soil pollution is occurred by a diverse variety of heavy metals, some of them (Cu, Ni, Cd, Zn, Cr, and 43 Pb) are more significant because of their distinct toxicity (Karaca et al., 2010). Iron, Zinc has been reported to be 44 biologically important for human beings and their diet and medicinal preparations, but in contrast to these metals, 45 Hg, Cd, and Pb have no biological significance to humans of any sort and ingestion of which can be harmful owing 46 to the high toxicity (Duruibe et al., 2007). The level of harm done to the riverine ecosystems by the waste discharges 47 from anthropogenic and industrial sources can be measured by conducting a thorough inspection of pollution 48 attributable to the heavy metals in the river sediment. (Saleem et al.,2015). Disposed urban wastes, untreated 49 industry effluents, agrochemicals in the most adjacent water bodies are the most contributing factors to the heavy 50 metal pollution scenario in Bangladesh (Islam et al., 2015). Heavy metals are dangerous over critical limits despite 51 being essential micronutrients for floral and faunal lifeforms; examples of such metals include Fe, Mn, Co, Zn. 52 (Moore et al., 2009). A Myriad of diseases is caused by exposure to heavy metals and other physiological 53 complications, including inhibition of development, renal failure, genetic mutation, disruptive effect on intelligence 54 and behavior (Saha et al., 2011).

55 In Bangladesh, the Surma river forms the important Surma- system, which is the longest river system 56 in the country. , on the edge of river Surma, in the north-eastern city of Bangladesh. It is located at 24°53’ N 57 latitude and 91°53’ E longitude with an estimated population of 0.6 million and population growth rate of 4% per 58 annum (Rahman et al., 2000) in contrast with the annual growth rate of 2.01% in Bangladesh (Ahmed et al., 1994). 59 Excessive production of waste materials is a general outcome of population growth in a city. On a typical day, the 60 city produces approximately 215 tons of waste product (Rahman et al, 2011). Generally, industrial effluents and 61 municipal wastewaters are enriched with high levels of heavy metals such as As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Zn 62 (Larsen et al., 1975). The study river is a recipient of an excessive amount of domestic waste, industrial effluents 63 through municipal sewage outlets. Non-point sources include urban runoff and agricultural runoff supposedly 64 carrying heavy metals into the river. Sediments are unavoidable constituent elements in a riverine environment 65 where they provide living organisms with sustenance as well as work as a natural sink for hazardous chemicals 66 (Gomez-Ariza et al. 1999). However, the accumulated hazardous chemicals in the sediment continue to pose a threat 67 to ecological and biological entities even though the contaminants are seized from being released from different

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68 sources. (Lasheen and Ammar 2009). Risk assessment methods should be applied for a correct understanding of 69 heavy metal contamination, its management, and pollution monitoring. (Qu et al. 2015). However, Risk assessment 70 is a complex process that intrinsically allows a degree of uncertainty. (Duke and Taggart 2000). The uncertainty can 71 be attributed to these factors: lack of accurate understanding, data scarcity, and variability, which is a common 72 feature of the environmental domain and dynamics. (Chen et al. 2011, 2015; Wang 2010; Yu et al. 2013). 73 Hakanson's risk index naturally aims at achieving a definite estimation of risk by integrating average and worst-case 74 point values of risk. (Bai et al.2011; Su et al. 2011).

75 There has been a little scientific investigation on heavy metal contamination in the bottom sediment of important 76 rivers of Bangladesh,whereas more concentration was given on river water quality. To the best of our knowledge, 77 this research work involving the assessment of ecological risk with a view to eradicating the uncertainty principle is 78 the first scientific assessment of ecological risk in river-bed sediment in Bangladesh through the coupled application 79 of Monte Carlo Simulation (MCS) and Hakanson Risk index (RI). In addition, the findings of the study will provide 80 a significant contribution to the formulation of policies related to river pollution and will help in taking apposite 81 initiatives for the management of domestic sewage disposal from the urban complex settlements. The principal 82 objectives of this study are to assess the contamination of the bottom sediment using multiple pollution indicators 83 and estimate ecological risks due to the heavy metals using the concerted approach of traditional ecological risk 84 index and relatively new Monte Carlo Simulation technique.

85 2. Materials and Methods

86 2.1 Study area

87 The study was conducted on the Surma River, which forms the longest river system, the Surma-Meghna river 88 system (669 km) in Bangladesh, and flows through the north-eastern city of Sylhet. The study river originates from 89 the Shillong hills in Meghalaya, India.). Our study river starts from its source, which is the slopes of the Naga- 90 Manipur catchment area and is known as river Barak. The river gets divided into two distinct branches at Cachar in 91 Assam district in India. The northern branch is known as Surma, which entered Bangladesh through 92 and the southern branch is Kushiara. Both of these distributary rivers meet at Madna at the lower segment of the 93 river courses. (Haroun Er Rashid, Geography of Bangladesh). The river segment covering the metropolitan 94 encroachment limits (From Tukerbazar Ghat to Kushi Ghat) was selected as the study area owing to the major 95 industrial activity, agricultural activity, and urban land use are increasing in this region. The study area is, as shown 96 in figure 1 located at Sylhet Metropolitan stretch, which is approximately within the latitudes 24.910225°N 97 24.874900°N and longitudes 91.823333333°E 91.90277778°E. The local names of the sampling locations are given 98 in a supplementary file with their geographical locations. A pilot survey was conducted in the area before sample 99 collection. A total of 15 sampling locations were selected and shown in Figure 1.

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101 Fig. 1 Study area and sampling stations

102 2.2Sample Collection and Preparation

103 Fifteen sediment samples were collected on 17th March 2019 from the selected sampling locations. Sediment 104 samples were collected from a depth of 0-30 cm with a PVC corer and transferred in polyethylene bags immediately. 105 All geographical coordinates were taken with a handheld GPS device. Before the sampling procedure, the 106 polyethylene bags were cleansed with diluted 10% HNO3 acid solution and distilled water (Manoj et al., 2012; 107 Rabee et al., 2011). Samples were brought to Soil Resource Development Institute (SRDI), Sylhet, Bangladesh. The 108 sediment samples were air-dried in a dry, dust-free room at room temperature. The samples were grounded after 109 discarding the plant roots and inorganic debris and sieved with a 2 mm sieve.

110 2.3 Heavy metal analysis

111 2.3.1 Reagents and sample digestion

112 All standard solutions, reagents along with acids and chemicals are provided by Merck and Fluka. All used chemical 113 substances were of 99.99% purity level.

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114 2.3.1.1 Soil extraction and determination of Fe, Mn, Cu and Zn

115 10 gm of soil was taken into a 1.25 ml dry polyethylene bottle. 20 ml of DTPA solution was added with a pipette. 116 The solution was shaken continuously for exactly 2 hours on a horizontal shaker and filtered immediately after 117 shaking through Whatman no. 42 filter paper into a conical flask. The contents of metals in DTPA extract of soil 118 were determined by AAS using appropriate cathode lamps. Direct reading of Copper and Zinc were taken from 119 AAS. For Iron (Fe) and Manganese (Mn), the reading was taken after the solution is diluted further. The solution is 120 diluted by taking 5 ml of the solution added with 45 ml distilled water. (Petersen, L., 2002)

121 2.3.1.2 Soil extraction and determination of Ni, Pb, Cd

122 2 gm of soil sample was weighed into a 50 ml crucible, to which 10 ml concentrated HNO3 was added. The mixture 123 was kept for 30-45 minutes for oxidation. After cooling, 2.5 ml of HCLO4 (Perchloric Acid) of 70% strength was 124 added and the mixture was reheated until the digest was clear. Then, the sample is filtered using Whatman no. 42 125 filter paper. Upon adding distilled water, the mixture was shifted to a volumetric flask ready to be analyzed by AAS. 126 (Petersen, L., 2002)

127 2.3.2 Analytical technique and quality assurance

128 All of the soil matrixes were analyzed for Fe, Mn, Cu, Zn, Ni, Pb, and Cd by atomic absorption spectrophotometer 129 (Model Shimadzu AA 7000 series). AAS conditions for analytical measurement were tabulated in Table 2 of 130 Supplementary files. Glassware and all containers used were purified with 20% HNO3 acid and de-ionized water 131 and air-dried before usage. The quality of the data obtained from analyzed elements through AAS is thoroughly 132 maintained. The calibration curves were maintained linear for all elements to be studied, after which the 133 performance of the calibrated system was checked. The analytical procedure was checked using a Reference Soil 134 sample provided by SRDI, Sylhet.

135 3. Result and discussion

136 3.1 Heavy metal in sediments

137 Heavy metal concentrations in river soil determined by AAS are tabulated in Table 3. The mean concentrations were 138 found- 850 mg/kg for Ca; 197.64 mg/kg for Mg; 2.68 mg/kg for Cu;6.12 mg/kg for Zn; 291.1 mg/kg for Fe; 88.03 139 mg/kg for Mn, 11.73 mg/kg for Pb; 0.06 mg/kg for Cd; 92.34mg/kg for Ni.The results indicate that nearly all of the 140 studied metals failed to exceed the background values given by (J. Martin and M. Meybeck, 1979). This suggests 141 that the investigated area being enriched with a low quantity of metal content in a massive volume of sediment. 142 (Herut et al., 1993). The metal concentrations in the study area were found to be in following order: Fe > Mn> Ni > 143 Pb > Zn > Cu > Cd.The total findings of the heavy metal from collected samples are given below in table 1.

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144 145 Table 1Heavy Metal Concentration in the bottom sediment of Surma river with descriptive statistics

Sample Stations Heavy Metals (in mg/kg)

Cu Zn Fe Mn Pb Cd Ni

S1 3.05 2.490 238.00 207.30 17.00 0.03 113.90 0 0 0 0 0 0

S2 2.20 4.710 389.00 102.10 12.20 0.06 73.000 0 0 0 0 0

S3 2.55 8.340 295.00 58.500 11.30 0.02 65.560 0 0 0 6

S4 2.79 3.300 359.00 28.500 13.90 0.01 112.66 0 0 0 5 0

S5 3.45 7.080 363.00 208.50 16.30 0.10 108.05 0 0 0 0 0 0

S6 2.55 3.360 297.00 62.700 1.080 0.01 92.310 0 0 5

S7 5.05 8.100 400.00 85.800 27.75 0.15 130.64 0 0 0 0 0

S8 4.35 9.700 312.00 99.400 23.73 0.35 159.40

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0 0 0 0 0

S9 3.95 17.56 325.00 223.45 41.23 0.29 172.50 0 0 0 0 0 0 0

S10 8.52 19.80 397.00 303.49 38.77 0.09 189.62 0 0 0 0 0 0 0

S11 6.65 17.90 418.00 209.60 35.81 0.04 165.56 0 0 0 0 0 0 0

S12 2.65 11.49 170.00 74.700 1.550 0.07 86.540 0 0 0 9

S13 3.93 15.65 270.00 4.200 17.40 0.16 78.670 0 0 0 0 5

S14 2.04 2.350 260.00 107.40 15.30 0.06 109.32 0 0 0 0 4 0

S15 1.59 2.430 270.00 26.400 11.30 0.01 83.430 0 0 0 7

Mean 3.68 8.951 317.53 120.13 18.97 0.09 116.07 8 3 6 5 9 7

Standard Deviation 1.86 6.196 70.224 88.473 12.27 0.10 39.248 7 8 1

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Minimum 1.59 2.350 170.00 4.200 1.080 0.01 65.560 0 0 5

Maximum 8.52 19.80 418.00 303.49 41.23 0.35 189.62 0 0 0 0 0 0 0

Surface rock average (Martin and Meybeck, 32 127 35900 750 16 0.2 49 1979)

WHO (2004) 1.5 123 NA NA NA 6 20

USEPA (1999) 16 110 30 30 40 0.6 16

146 A comparative scenario of heavy metal pollution in other major rivers around the world along the studied river is 147 given below in Table ⁱ

148 Table 2 Comparison of metals in sediment with other studies around the globe

River/Date of Pb Cd Zn Ni Fe Mn Cu Reference Sampling/Cou ntry Euphates,1997 19.5 0.08 30 125 - 450 - (Kassim et al., , Iraq 1997) Tigris, 1993, 17.9-30.6 0.1- 8.3- 105.4- - 451.3- 17.4- (Al-Juboury, Iraq 1.7 47.1 125.5 565.6 28.9 2009) Cauvery 2007- 4.3 1.3 93.1 27.7 11144 176.3 11.2 (Raju et al., 2009, India 2012) Surma River, 11.73 0.06 6.12 92.34 291.1 88.03 2.68 Present study 2019, Bangladesh Yangtze,2005, 49.19 0.98 230.9 41.86 - - 60.03 (Wang et al., China 2011) World 230.75 1.4 303 102.1 57405.9 975.3 122.9 J. Martin and

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Average M. Meybeck (1979) , 59.99 0.61 117.15 25.67 - 483.44 - (Rahman et al., 2014, 2014) Bangladesh

149 3.2 Assessment of sediment quality

150 Values from background levels (continental shale value or crustal abundance of different elements) can be used as a 151 reference to measure the increase in concentration levels (Christophoridis et al., 2009). It is measured in contrast to 152 the values from pre-industrial levels. (Turekian and Wedepohl,1961). Due to the unavailability of the background 153 values for this study area directed this study to utilize the world rock surface values for the assessment of pollution 154 indices (Mohammed et al., 2012). Following pollution, indices were applied to obtain a satisfactory relative ranking 155 of samples- Contamination Factor (CF), Contamination Degree (CD), Modified Degree of Contamination (MCD), 156 Enrichment Factor (EF), Pollution Load Index (PLI), Geo-Accumulation Index (IGeo)

157 3.2.1 Contamination Factor (CF)

158 Contamination Factor (CF) and Contamination Degree (CD) together are considered primary indicators of metal 159 pollution status of the subjected soil or sediment. (Manoj et al., 2012). The CF can be obtained for each of the 160 sampling locations by dividing the metal concentrations in sediment by the background concentration values of the 161 respective metals. The CF is the result of dividing the metal concentration in the sediment by the concentration of 162 background value of the respective metal. (Varol, 2011). (Hakanson, 1980) proposed the following equation to 163 calculate CF:

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165 CF= Cm Sample/Cm Background…………………………….(1)

166 Where Cm Sample is the metal concentration derived from river sediment, and Cm Background is the standard metal 167 concentration value equal to the world surface rock average given by Martin, J., & Meybeck, M. (1979). 168 Contamination factor are graded into four classes: CF < 1 = low contamination; 1 ≤ CF ≤ 3 = Moderate 169 Contamination; 3 ≤ CF ≤ 6 = Considerable Contamination; CF ≥ 6 = Very High Contamination. From figure 2, it is 170 evident that there exists a distinct variation among CF values regarding all subject elements. The CF values for 171 Nickel in sample stations S8, S9, S10, S11are within the category of 3 ≤ CF ≤ 6as to state the moderate 172 contamination. 1 ≤ CF ≤ 3, which means there is moderate contamination of Nickel in all the river sediment 173 stations. Moreover, Lead (Pb) also shows some variations in terms of CF values. At sample stations, S1, S5, S7, S8, 174 S9, S10, S11, S13, there is moderate contamination of Lead (Pb) as CF values are in the category 1 ≤ CF ≤ 3. Cd in

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175 stations S8 and S9 portrays moderate contamination. All the other metals show low contamination as the CF values 176 do not exceed value 1.

Contamination Factor 4.500 4.000 3.500 3.000 2.500 2.000 1.500 1.000 0.500 0.000 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15

Cu Zn Fe Mn Pb Cd Ni 177

178 Fig. 2 Contamination Factor of Heavy Metals at all sample stations

179 3.2.2 Contamination Degree (CD)

180 …………………………..(2)

퐶푑 = ∑(퐂퐅) CD and MCD

9 8 7 6 5 4 3 2 1 0 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15

CONTAMINATION DEGREE (CD) Modified Contamination Degree (MCD) 181

182 Fig. 3 CD and MCD values of the Heavy Metal of the river sediment

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183 The study area falls into class 2 as 6 ≤ CD ≤ 12 for sample stations S7, S8, S9, S10, S11 which demonstrates 184 moderate contamination degree. For the rest of the river, there is a low degree of contamination as the CD values do 185 not exceed value 6. The highest contamination degree is found at station 9 and lowest at station 6. 186 3.2.3 Modified Contamination Degree, MCD

187 (Abrahim and Parker, 2008)had given a more simplified method of measuring contamination degree previously 188 given by (Hakanson 1980). The formula is given below-

189 mCD = ∑(CF)/n………………………………………(3)

190 where n= number of analyzed elements

191 CF= Contamination Factor

192 In table 3, the categories of MCD are shown used to describe and classify modified contamination degree. For the 193 classification and description of the modified degree of contamination (MCD) in sediment, the following gradations 194 are proposed: MCd < 1.5 is nil to a very low degree of contamination; 1.5 ≤ MCD < 2 is a low degree of 195 contamination; 2 ≤ MCd < 4 is a moderate degree of contamination; 4 ≤ MCd < 8 is a high degree of contamination; 196 8 ≤ MCD < 16 is a very high degree of contamination; 16 ≤ MCd < 32 is an extremely high degree of 197 contamination; MCd ≤ 32 is an ultra-high degree of contamination.” (Abrahim and Parker, 2008). In the present 198 study, the MCD values of all sample stations MCD are below 1.5 which indicates nil to a very low degree of 199 contamination in the respective sample stations. The MCD values are shown in thefigure 3.

200 Table 3Modified Contamination degree categories (Abrahim and Parker, 2008)

MCD Categories Levels of Contamination

MCD < 1.5 Nil to very low degree of contamination

1.5 ≤MCD < 2 Low degree of contamination

2 ≤MCD < 4 Moderate degree of contamination

4 ≤MCD < 8 High degree of contamination

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8 ≤ MCD < 16 Very high degree of contamination

16 ≤ MCD < 32 Extremely high degree of contamination

MCD ≥ 32 Ultra-high degree of contamination

201 3.2.4 Enrichment Factor (EF)

202 S. Sinex and G. Helz (1981) proposed the Enrichment factor as an indicator to quantify the anthropogenic 203 contribution to any change in the metal concentration in the sediment (H. Feng et al., 2004). The enrichment factor 204 for the metals can be calculated by the following equation-

205 EF= (Me/Fe) sample/(Me/Fe) background ………………………………….(4)

206 Where, (Me/Fe) sample is the ratio of subjected metal and Fe of the sediment from sampling location and on the 207 other hand, (Me/Fe) background denotes the environmental background value of Metal-Fe ratio. Values of metal 208 concentrations of surface world rocks were chosen as reference owing to the lack of background values of pre- 209 industrial times (J. Martin and M. Meybeck,1979). Iron was elected as the suitable element for normalization 210 between the two sets of values from both Metal-Fe ratio of sample & backgrounds used previously by (V. Tippie, 211 1984; Salah et al., 2012). Enrichment factor is graded in 5 classes, EF values less than 2 indicates deficiency to 212 minimum enrichment; moderate enrichment expressed by EF ranging from 2 ≤ EF < 5; values ranging from 5 ≤ 213 EF < 20 indicates significant enrichment; metal enrichment is very high when EF values fall between 20 ≤ EF < 40 214 and lastly EF ≥ 40 indicates extremely high enrichment. In the present study, the highest enrichment factor is found 215 for Nickel in the “extremely high enrichment” category as all the values are above 40 which can be observed in 216 figure 4. The study area is also abundant with Pb and Cd with high enrichment. The maximum EF values for Pb and 217 Cd reach up to 284.65 and 201.36, respectively.

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Enrichment Factor

400

300

200

100 Pb

0 Cu S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15

Cu Zn Fe Mn Pb Cd Ni 218

219 Fig.4Metal enrichment of all heavy metals in River Surma

220 3.2.5 Pollution Load Index (PLI)

221 Pollution load index is a frequently used method for estimating the quality and toxicity of sediment proposed by 222 (Tomlinson et al.,1980). The PLI of a particular site is generally estimated by calculating the nth root of the product 223 of multiplying n-numbered CF values for all investigated elements. The following equation was used for the 224 determination of PLI-

(1/n) 225 PLI= (CF1×CF2×CF3×…×CFn) ……………………………………(5)

226 Where CF denotes the contamination factor and n is the considered number of metals. There are three discrete 227 categories for pollution measurement with this index. Perfect pollution status is indicative of no pollution when the 228 PLI values are 0, 2nd category indicative of baseline degree of pollution when the PLI values are equal or less than 1 229 and third category (when PLI greater than 1) designates progressive decline in terms of pollution of the sites. The 230 PLI values for respective sampling locations are shown in figure 5

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Pollution Load Index (PLI) 0.400

0.350

0.300

0.250

0.200

0.150

0.100

0.050

0.000 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 231

232 Fig.5 Pollution Load Index values of sampling sites at Surma River

233 3.2.6 Geo-accumulation Index (Igeo)

234 Geo-accumulation index is a method of estimating the enrichment of metal concentration abovebackground values 235 proposed by G. Muller, (1981). The equation used to determine Igeo values is-

236 Igeo = Log2 [Cm Sample/ (1.5 × CmBackground)]………………………………..(6)

237 here, Cm Sample is the concentration of a particular element in the sample, and Cm Background is the geochemical 238 background value of the metal. The values from world rock surface average given by J. martin and M. Meybeck, 239 (1979) is used as reference background value (Salah et al., 2012). Geo-accumulation index produces results in 7 240 classes of purity. These classes are portrayed in table 4. From the achieved results, it is evident that for most of the 241 sites and metals, the Igeo values remained below 0, depicting uncontaminated sediments, whereas Nickel (Ni) and 242 Cadmium (Pb) showed some deviance from the trend and fall in class 1. Geo-Accumulation Index values of the 243 heavy metals in river sediment is shown in the following Table 4.

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Geo-Accumulation Index 2

0 0 2 4 6 8 10 12 14 16

-2

-4

-6

-8

-10

Geo-Accumulation Index Cu Geo-Accumulation Index Zn Geo-Accumulation Index Fe Geo-Accumulation Index Mn Geo-Accumulation Index Pb Geo-Accumulation Index Cd Geo-Accumulation Index Ni 244

245 Fig.6 Igeo variation in the sediment of River Surma 246 Table 4Geo-Accumulation Index categories (Bhuiyon et al., 2010).

Igeo Class Igeo Values Contamination profile

Class 0 Igeo < 0 uncontaminated sediments

Class I 0 < Igeo < 1 uncontaminated to moderately contaminated sediments

Class II 1 < Igeo < 2 moderately contaminated sediments

Class III 2 < Igeo <3 moderately to highly contaminated sediments

Class IV 3 < Igeo < 4 highly contaminated sediments

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Class V 4 < Igeo < 5 highly to extremely contaminated sediments

Class VI Igeo > 5 extremely contaminated sediments

247 Figure 6 shows that the bottom sediment of the river is almost uncontaminated for the metals subject to cause soil 248 contamination at most of the sites. While Nickel contributed a moderate amount of sediment contamination at 249 stations S8, S9, S10, S11, the rest of the study area remained uncontaminated to moderately contaminated for the 250 rest of the stations, which may contribute to future sediment toxicity. Similar to Nickel, Igeo values for Lead (Pb) 251 depicted noticeable variation. Positive values at stations S7, S9, S10, S11 suggest extant moderate contamination. 252 Altogether, the river bed sediment is present in the “Class 0” meaning uncontaminated to moderately contaminated 253 bottom sediment for a greater number of metals except for Nickel and Lead.

254 3.3 Pearson Correlation Matrix

255 Pearson’s correlation (PC) was calculated for examined metal elements to investigate if there is any correspondence 256 among the elements. Pearson Correlation matrix corroborates inter-metal characteristics in terms of origin and 257 behavior along their paths of transport. (Suresh et al., 2011). The results were tabulated below in table 5.

258 Table 5 Pearson’s Correlation Matrix of heavy metals

Cu Zn Fe Mn Pb Cd Ni Cu 1 Zn 0.78*** 1 Fe 0.58** 0.29 1 Mn 0.66*** 0.48* 0.39 1 Pb 0.78*** 0.71*** 0.6** 0.69*** 1 Cd 0.26 0.45* 0.06 0.15 0.49* 1 Ni 0.8*** 0.61** 0.48* 0.74*** 0.87*** 0.5* 1

259 * is significant at0.05

261 Existing metal concentrations in the bottom sediment of river Surma stipulate the concurrent levels of correlation 262 with each other at significant levels of p ≤ 0.05 and p < 0.01 (table 5). In the present study, Cu, Zn, Ni, and Pb 263 showed significant correlation coefficients only to indicate they have common sources of origin and could be 264 dominated by an exclusive factor. Cu displayed soaring levels of a positive relationship with Zn, Mn, and Pb and in

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265 a moderate degree with Fe. The correlation matrix demonstrates that Cu has a low level of relationship with Cd 266 which points to the possibility of different origin of these elements. Ni is found to be corresponding and 267 intercorrelated with Cu, Mn and Pb significantly (at p< 0.01 levels) and moderately with Zn (at 0.01 ≤ p ≤ 0.05 268 levels) which is associated with common sources of input of heavy metal to the river from municipal waste, 269 agricultural runoff, and industrial sewage. Poor correlations between Fe and Cd could be resulting from the 270 differential sources of origin where Fe has a natural origin and Cd has anthropogenic origins. Copper and Cadmium 271 also deviate from the possibility of being originated from undifferentiated sources and this difference can be 272 ascribed to the copiousness of Cd in common anthropogenic sources such as industrial effluents and municipal 273 waste on the other hand, the original sources of copper can be attributed to agricultural runoffs. Zn, Cu, and Cd also 274 possibly have origins in natural fluvial sediment.

275 Evaluation of Potential ecological risk index (PERI)

276 Hakanson (1980, 1988) proposed the PERI method to evaluate the environmental characteristics due to heavy metal 277 contamination in fluvial sediments. (Gandhi et al., 2017) to evaluate concurrent pollution levels and the 278 environmental response to the pollution. The equations employed to determine the ecological risk of a certain area is

i 279 RI= ∑ (Er )………………… (7)

i = i 280 Er Tr × CF………………...(8)

281 Here,

282 RI= Risk factor or summation of all individual potential ecological risk factors contributed by each meal element.

i 283 Er = Factor of Potential Ecological Risk,

284 CF= Contamination factor,

285 According to Hakanson (1980), elements such as Ni, Cd, Pb, Zn, and Cu have toxic response factors of 5, 30, 5, 1, 286 and 5 respectively. As per Hakanson's (1980)’s suggestion, Eri and RI are two terms to be multiplied together for 287 calculating ecological risk where. According to this approach, the potential ecological risk is minimal when Eri< 40; 288 moderate level of potential ecological risk occurs when values range from 40 ≤ Eri ≤ 80; 80 ≤ Eri ≤ 160 portrays a 289 considerable level of potential ecological risk; 160 ≤ Eri ≤ 320 depicts the staggering level of potential ecological 290 risk whereas Eri> 320 is construed as a very high ecological risk. While a total ecological risk (RI) value below150 291 points towards a low ecological risk; 150< RI <300 suggests a moderate degree of ecological risk; a considerable 292 level of ecological risk is generally designated by RI values between 300 to 600 is and ultimately RI>600 tends to 293 portray a very high ecological risk of the study area.

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294 Table 6 Potential Ecological risk factor and Risk Index for heavy metals

i Potential Ecological Risk Factor (Er )

Sampling Sites Cu Zn Pb Cd Ni RI S1 0.48 0.02 5.31 4.5 11.62 21.930 S2 0.34 0.04 3.81 9 7.45 20.640 S3 0.4 0.07 3.53 3.9 6.69 14.590 S4 0.44 0.03 4.34 2.25 11.5 18.560 S5 0.54 0.06 5.09 15 11.03 31.720 S6 0.4 0.03 0.34 2.25 9.42 12.440 S7 0.79 0.06 8.67 22.5 13.33 45.350 S8 0.68 0.08 7.42 52.5 16.27 76.950 S9 0.62 0.14 12.88 43.5 17.6 74.740 S10 1.33 0.16 12.12 13.5 19.35 46.460 S11 1.04 0.14 11.19 6 16.89 35.260 S12 0.41 0.09 0.48 11.85 8.83 21.660 S13 0.61 0.12 5.44 24.75 8.03 38.950 S14 0.32 0.02 4.78 9.6 11.16 25.880 S15 0.25 0.02 3.53 2.55 8.51 14.860

i 295 According to table 6, the Er values of Pb, Cd, Cu, Ni, Zn in all sampling sites stipulated values predominantly lower 296 than 40 as to specify low levels of potential ecological risk from the subject metals except for Cd in sampling sites i 297 S8 and S9. Moderate ecological risks are observed for Cd in these two sites where Er values are 52.5 and 43.5, 298 respectively. All of the sample stations can be categorized with low ecological risk levels as the Risk Index (RI) 299 values are less than 150 in each station.

300 Monte Carlo Simulation

301 Generally, Monte Carlo Simulation is performed to elucidate the uncertainty issue, which is intrinsic to the 302 calculation of potential ecological risk using absolute point values of metal concentration. In this method, a suitable 303 dataset is developed, which agrees with a particular probability distribution (Li et al., 2019). The elemental 304 concentrations of the river sediment acted as the primary dataset for finding apposite probability distribution and 305 simulation of RI. According to the Kolmogorov-Smirnov test, the most suitable fitting was demonstrated by the 306 Log-normal probability distribution function, while other notable density functions with poor fitting included log-

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307 logistic, BetaPERT, Weibull, Gamma, Max-extreme density functions. 10000 Monte Carlo iterations were carried 308 out employing the software CrystalBall (Oracle Corporation). Repeated calculation produced probability distribution 309 for the Hakanson Risk index. The output distribution for RI followed a log-normal distribution.

310 311 Fig. 7 Probability and Cumulative probability of Ecological Risk factor of Ni

312 313 Fig. 8 Probability and Cumulative probability of Ecological Risk factor of Pb

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314 315 Fig.9 Probability and Cumulative probability of Ecological Risk factor of Cd

316 317 Fig.10 Probability and Cumulative probability of Ecological Risk factor of Zn

20

318 319 Fig.11 Probability and Cumulative probability of Ecological Risk factor of Cu

320 321 Fig.12 Probability and Cumulative probability of Risk Index (RI) Value

i 322 The results from the Monte Carlo simulation produced probabilistic ecological risk values (Er ) for heavy metals.

i 323 Copper (Cu) indicates 100% of the probability to fall under the Er value of 40 which indicates low ecological risk 324 shown in figure 7. Lead (Pb) exhibited a probability of 98.25% to fall in the low-risk category and 1.51% for 325 moderate potential ecological risk (Figure 8). Cadmium (Cd) portrayed 92.04% of the probability for the low-risk 326 category and 6.22% in moderate ecological risk and 1.49% probability of considerable potential ecological risk 327 shown in figure 9. Zinc (Zn) and Copper (Cu) both metals have depicted low-risk potential ecological risk

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328 probabilities as shown in figure 10 and figure 11, respectively. Finally, in figure-12, 100% of the cumulative 329 probability of Risk Index (RI) values is less than 150, which, according to Hakanson, risk index is representative of 330 low ecological risk. From the sensitivity analysis, it is evident that 67.3% of RI is contributed by Cd, followed by Pb 331 with 2.4% trailing by Ni with 10.3% to variance. 332 Principal Component Analysis: 333 PCA was applied to determine the factor responsible for deteriorating the surface water quality. It signifies the 334 association between components and variables. Eigenvalue greater than one was considered to define the 335 components. As a result, two principal components were found whose eigenvalues were greater than one. The figure 336 shows that the scree plot reaches a sharp decline after getting an eigenvalue one. PC1 has an eigenvalue of 4.40, and 337 PC2 has 1.04. Moreover, Pb, Ni, and Cu were found to have higher PC1 values respectively, compared to other 338 parameters. On the other hand, Cd and Fe have lower PC1 values. Besides, PC2 dominated with a higher range of 339 negative values. Fe, Mn, and Cu have negative PC2 values, while Cd has a higher positive PC2 value. However, 340 PC1 and PC2 explain 63% and 78% cumulative variance, whereas these two components have 71% and 25% total 341 variance as per Figure 13.

342

343 Fig.13 Scree plot of PCA

344

345

346

347

348

349

350

22

351 Table 7: Principal component analysis

352

353 Variables PC1 PC2

354 Cu 0.43 -0.16

355 Zn 0.38 0.23

356 Fe 0.29 -0.52

357 Mn 0.37 -0.24

358 Pb 0.45 0.03

359 Cd 0.23 0.77 360 Ni 0.44 0.06 361 Eigenvalue 4.40 1.04 362 363 Cumulative variance (%) 63.00 78.00

364 Total variance (%) 71.00 25.00 365 366 Agglomerated hierarchical cluster analysis sorted sampling stations according to their magnitude. It clustered the 367 sampling sites using the dendrogram approach. Four clusters were found to have identical characteristics each. 368 Figure 14 elaborately depicts that S5, S9, S11, and S10 have different features within their cluster. Besides, S1, S8, 369 S12, and S14 have comparatively lower values within the cluster. It denotes the cluster of sampling stations has

23

370 lower pollution. Cluster analysis implies the degree of pollution over the sampling stations.

371

372

373 Fig.14 Cluster Analysis

374 4. Conclusion

375 The study demonstrated the concentration of several heavy metals in the river bottom sediment and systematically 376 examines the ecological risk by employing PERI and Monte Carlo simulation. However, the result of the present 377 investigation indicates that the comprehensive ecological risk posited by heavy metals in Surma River does not 378 exceed the lowest limits of the Hakanson RI index for all heavy metals. This joint approach secures the lessening of 379 the problems of underestimation and overestimation regarding estimation of ecological risks. Analyses of heavy 380 metal with Hakanson RI index and Monte Carlo simulation in the urban river sediment are very significant for 381 monitoring and management of river pollution in the developing world because from the urbanized and densely 382 populated cities huge amount of domestic sewage directly discharge in the river. The study provides useful tools for 383 future study combined with land use land cover change, public health issues, and other ecological parameter helps 384 the decision-maker for the formulation of rules and guidelines about the sustainable management of domestic 385 sewage disposal and aid in minimizing negative impacts on riverine organisms and environment. This study 386 suggests that proper focus should be employed on monitoring the point sources of metals entering the river water 387 from nearby cities and also on the reduction of urban domestic sewage discharge and industrial effluent.

388 Ethical approval

389 This study received ethical approval from the head and all the other board members from Dept. of Geography and 390 Environment, Shahjalal University of Science and Technology, Sylhet, Bangladesh.

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391 Consent to Participate

392 Not applicable

393 Consent to Publish

394 Not applicable

395 Data availability statements

396 The datasets generated during the current study are available from the corresponding author on reasonable request.

397 Conflict of interest

398 The authors proclaim that there is no conflict of interests. This study primarily focuses on the heavy metal contents 399 and total ecological risk in river Surma. The research had been completely devoid of association with any other 400 people, group or organizations except mentioned in acknowledgment and had not received any financial support 401 from any source.

402 Funding

403 This research does not receive any kind of funding.

404 Authors’ contribution

405 AA conceptualized and contributed to the preliminary concept of the study under the supervision of ZA. AA 406 collected the primary data and completed all investigations and formal analysis. Subsequently, RA did the statistical 407 analysis. AA drafted the initial version. ZA and RA completed all necessary revisions and editing in the manuscript. 408 Meanwhile, AA completed the final draft, and MSR and ZX gave intellectual input to the manuscript, Finally ZA 409 revising it critically. Finally, all the authors approved this final version.

410 Acknowledgments

411 The authors would be delighted to express gratitude to the authority of the Soil Resource Development Institute 412 Laboratory (SRDI), Sylhet, Bangladesh, for provisioning expert suggestions and laboratory aptitude crucial to the 413 fulfillment of this study. The authors are deeply ingratiated to express cordial gratitude to the staff members of SRDI 414 facility for the avid support and supervision throughout the sample analysis process.

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Figures

Figure 1

Study area and sampling stations Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Figure 2

Contamination Factor of Heavy Metals at all sample stations Figure 3

CD and MCD values of the Heavy Metal of the river sediment Figure 4

Metal enrichment of all heavy metals in River Surma Figure 5

Pollution Load Index values of sampling sites at Surma River Figure 6

Igeo variation in the sediment of River Surma

Figure 7 Probability and Cumulative probability of Ecological Risk factor of Ni

Figure 8

Probability and Cumulative probability of Ecological Risk factor of Pb

Figure 9 Probability and Cumulative probability of Ecological Risk factor of Cd

Figure 10

Probability and Cumulative probability of Ecological Risk factor of Zn

Figure 11 Probability and Cumulative probability of Ecological Risk factor of Cu

Figure 12

Probability and Cumulative probability of Risk Index (RI) Value Figure 13

Scree plot of PCA

Figure 14

Cluster Analysis