Heavy Metal Distributions in Water of the Aras River, Ardabil, Iran

Nasehi, Fatemeh; Hassani, Ali; Monavvari, Masoud; Karbassi, Abdoreza; Khorasani, Nematollah; Imani, Aliakbar

doi:10.4236/jwarp.2012.42009

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…A type of statistical analysis often applied in pollution studies is principal component and factor analysis, used for temporal and spatial analysis (e.g., Lambrakis et al, 2004;Zhang et al, 2012). In this study, we apply the statistical technique of cluster analysis, which has been found to be applicable in data analysis of seasonal and spatial patterns (Nasehi et al, 2012). Despite its wide use, statistical analysis has yet to be fully utilized as a cost-saving device, and we believe that with carefully planned methodology, complicated monitoring may be possible with less funds.…”

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confidence: 99%

Reducing Monitoring Costs in Industrially Contaminated Rivers: Cluster and Regression Analysis Approach

et al. 2014

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Monitoring contamination in river water is an expensive procedure, particularly for developing countries where pollution is a significant problem. This study was conducted to provide a pollution monitoring strategy that reduces the cost of laboratory analysis. The new monitoring strategy was designed as a result of cluster and regression analysis on field data collected from an industrially influenced river. Pollution sources in the study site were coal mining, metallurgy, chemical industry, and metropolitan sewage. This river resembles those in other areas of the world, including developing countries where environmental monitoring is financially constrained. Data were collected on variability of contaminant concentrations during four seasons at the same points on tributaries of the river. The variables described in the study are pH, electrical conductivity, inorganic ions, trace elements, and selected organic pollutants. These variables were divided into groups using cluster analysis. These groups were then tested using regression models to identify how the behavior of one variable changes in relation to another. It was found that up to 86.8% of variability of one parameter could be determined by another in the dataset. We adopted 60, 65, and 70% determination levels () for accepting a regression model. As a result, monitoring could be reduced by 15 (60% level) and 10 variables (65 and 70%) out of 43, which comprises 35 and 23% of the monitored variable total. Cost reduction would be most effective if trace elements or organic pollutants were excluded from monitoring because these are the constituents most expensive to analyze.

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confidence: 99%

Reducing Monitoring Costs in Industrially Contaminated Rivers: Cluster and Regression Analysis Approach

et al. 2014

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“…Hence about 60% of the South East Nigerians which live in rural areas continuously use water contaminated with all kinds of germs, heavy metals, and other pollutants enough to cause various diseases. The study of heavy metals contamination and their adverse effect on living organisms in aquatic environment and human beings have been widely studied all over the world (Singh et al, 2005;Nabi et al, 2007;Kimete et al, 2010;Nagehi, 2012). South Eastern Nigeria has a current population projection of 19,904,478 and cover an area of 28,821.56 km 2 belonging to the forest savanna mosaic zone of Nigeria (NPC, 2006).…”

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confidence: 99%

Health risk assessment in relation to heavy metals in water sources in rural regions of South East Nigeria

Nwachukwu¹,

Janefrances²,

S³

et al. 2014

Int. J. Phys. Sci.

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“…Unfortunately, no comprehensive research has been conducted on the heavy metals in the bodies of organisms living in the Aras River except for the study by Nasehi et al (2012Nasehi et al ( -2013 who investigated the concentrations of heavy metals in carp muscles and did not detect any cadmium or lead deposition in this sh species. However, in the present research, the amounts of both of these metals at station 2, which showed the highest quantities of these two metals deposited in this sh species among all the stations, exceeded the international standard in all seasons.…”

Section: Health Risk Assessmentmentioning

confidence: 99%

Trophic transfer, bioaccumulation, and health risk assessment of heavy metals in Aras River: case study—Amphipoda- zander – human

Dehghani

Aminjan

Dehghani

2022

Environ Sci Pollut Res

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This research investigates and traces heavy metals (As, Pb, Cd and Cu) in a model of the aqueous food chain in the Aras River, located in northwestern of Iran. The selected model includes the zander (Sander lucioperca L.) and crustacean species known as amphipods (Gammarus spp.) that belong to the food chain of this sh. A total of 172 samples (70 sh and 102 Amphipoda) were collected randomly from the stations and analyzed for heavy metals using an Atomic Absorption Spectrophotometry (AAS). The results showed that the accumulation of heavy metals in both Taxon are in the order of As > Pb > Cd > Cu and concentrations of heavy metals in sh body are higher than Gammarus spp. in every stations in different seasons. Specimens of station (1) due to industrial activities indicates the highest heavy metal content. The concentrations of As, Pb, and Cd exceedes the permissible limits. There is a close correlation between the concentration of heavy metals in the amphipod and zander. The target hazard quotient (THQ), Total target hazard quotient (TTHQ) and Carcinogenic Risk (CR) were calculated to assess human health risk. The average of THQ for As (1.43) exceedes the international standards and will endangere the consumers of this sh species. The TTHQ for heavy metals has been estimated highr than 1. and at all stations, the value of CR Cd > 1×10 − 3 , which reveals the degree of evidence of carcinogenicity of this metal in all parts of the Aras River. Therefore, according to results, effective controls and regular monitoring should be established in this regions

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Heavy Metal Distributions in Water of the Aras River, Ardabil, Iran

Cited by 11 publications

References 13 publications

Reducing Monitoring Costs in Industrially Contaminated Rivers: Cluster and Regression Analysis Approach

Reducing Monitoring Costs in Industrially Contaminated Rivers: Cluster and Regression Analysis Approach

Health risk assessment in relation to heavy metals in water sources in rural regions of South East Nigeria

Trophic transfer, bioaccumulation, and health risk assessment of heavy metals in Aras River: case study—Amphipoda- zander – human

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