Ecological Risk Assessment of Heavy Metal in the Bottom Sediment of River Surma, Bangladesh Using Monte Carlo Simulation and Multivariate Statistical Analysis
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Ecological Risk Assessment of Heavy Metal in the Bottom Sediment of River Surma, Bangladesh 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 12 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. 28 29 Keywords Heavy metals, Ecological risk, Surma river, Monte Carlo simulation, Multivariate statistics, Hakanson 30 risk index. 31 32 1 33 34 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-Meghna river system, which is the longest river system 56 in the country. Sylhet, 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 2 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 Sylhet district 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.