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Environmental Geochemical Assessment of Heavy Metals in Soil and Sediment of (Shatt-Al-Hilla) Babil Governorate, Central Iraq
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Heavy metals concentration in the soils and sediments has increased worldwide during the last century as a result of the rapid increase in population which combined by an increase in human activity as agriculture,  industrial and many other activities. Ten soil and three river sediment samples were collected from 10 main sampling stations at Shatt Al-Hilla River from Sada area to Dora Bridge in Babylon province. The chemical analysis of the sedimentation sample in the laboratory included pH calculation, electrical conductivity (EC) (Cation Exchange Capacity (CEC), organic matter (OM), and heavy metals as (Mn, Ni, Cr, Zn, Cu, Co, Pb, Cd, As and Fe). Indirect geochemical background (IGB) of heavy metal was calculated by the iterative 2 standard deviations (SD) method.

The results of enrichment factor for heavy metals of the soil and sediment show that the all heavy metals in the studied samples were within Ef<2 indicate to depletion to minimal enrichment (i.e. no or minimal pollution). While Cd in the sample (5), Co in the sample (7), Cr in the sample (8) and Mn in the sample (1S) are within 2 ≤ EF < 5 indicate to Moderate enrichment. Contamination factor (Cf) for heavy metals of soil and sediment show that the all heavy metals in the studied samples were within Cf < 1 -Low contamination. While Sample "2" (Cd, Fe), sample "3" (Ni, Cr, Pb, and Cd), sample "4" (Mn, Fe) and sample "8" (Cr) are within 1 ≤ Cf < 3 moderate contamination.

Pollution load index result of all the soil and sediment samples are less than one indicate that  "no pollution" are present, except sample 3 where the PLI value higher than 1 indicates the samples have been "polluted ". The modified degree of contamination (mCd) data indicate nil to a low degree of contamination for all of the soil and sediments samples.

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Publication Date
Mon Jun 05 2023
Journal Name
Journal Of Economics And Administrative Sciences
Fuzzy Bridge Regression Model Estimating via Simulation
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      The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin

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