Artificial Neural Networks,Different Flow Direction,Loess,Multiple Linear Regression

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POURKHABBAZ H.R.; Hedayatzadeh f.; CHERAGHI M.

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Journal: ECOPERSIA Year:2018 | Volume:6 | Issue:1 Page(s): 55-66

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Information Journal Paper

Title

Artificial Neural Networks,Different Flow Direction,Loess,Multiple Linear Regression

Author(s)

POURKHABBAZ H.R. | Hedayatzadeh f. | CHERAGHI M. | Issue Writer Certificate

Keywords

AlgaeQ1

Heavy MetalsQ3

Karun RiverQ1

SedimentQ3

Water TreatmentQ1

Abstract

Aims Karun River, which is the largest river in Iran, represents a unique ecosystem. However, increased anthropogenic activities result in the formation of this river is seriously affected by a large range of pollutants especially the heavy metal pollutants which may be toxic to human and aquatic fauna. Therefore, there is a need for continuous monitoring of pollution levels in the river. Materials & Methods In this study, water, Sediment, and Algae samples were collected from six different stations along the course of the river in September 2015 to investigate the quality of Karun’ s River in terms of Heavy Metals (Pb, Zn, Cr, and Cd) at the basin of drinking Water Treatment in Ahwaz and Mollasani cities. After drying and digestion of samples, heavy metal concentrations were determined using an atomic absorption spectrophotometry (Perkin Elmer-Analyst 300). Findings The highest concentration of trace metals was found in Sediment samples with Zn having the highest mean concentration values in all stations. The heavy metal concentrations in the downstream indicated an increase in the pollution load due to the flow of water from upstream to downstream of the river resulted in the movement and accumulation of all contaminants to the river in the downstream; hence, there was the highest concentration of metals in basin of the Kut Abdollah treatment (downstream) and the lowest in Mollasani (upstream). Conclusion Comparison of the concentration of metals in the Sediments with some universal standards including EPA3050 and the criterion of Sediments quality standard from NOAA and Canadian Environment Agency showed that the concentration of chromium and cadmium in stations was higher than the allowable limit of EPA3050 standards and some environmental standards of Canada among all metals. Since Algae samples have been able to accumulate a significant amount of Heavy Metals, therefore, these are suitable bio-indicators to determine the concentration of Heavy Metals in this aquatic ecosystems.

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APA: Copy

POURKHABBAZ, H.R., Hedayatzadeh, f., & CHERAGHI, M.. (2018). Artificial Neural Networks,Different Flow Direction,Loess,Multiple Linear Regression. ECOPERSIA, 6(1 ), 55-66. SID. https://sid.ir/paper/377135/en

Vancouver: Copy

POURKHABBAZ H.R., Hedayatzadeh f., CHERAGHI M.. Artificial Neural Networks,Different Flow Direction,Loess,Multiple Linear Regression. ECOPERSIA[Internet]. 2018;6(1 ):55-66. Available from: https://sid.ir/paper/377135/en

IEEE: Copy

H.R. POURKHABBAZ, f. Hedayatzadeh, and M. CHERAGHI, “Artificial Neural Networks,Different Flow Direction,Loess,Multiple Linear Regression,” ECOPERSIA, vol. 6, no. 1 , pp. 55–66, 2018, [Online]. Available: https://sid.ir/paper/377135/en

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