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Assessment and zoning of environmental hazard of heavy metals using the Nemerow integrated pollution index in the vineyards of Malayer city

Yari A. Ali, Varvani J., Zare R.

Acta Geophysica

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2021

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Vol. 69, no. 1

149--159

EN

The purpose of the present study was to evaluate and zoning the environmental hazards of heavy metals in the soil using the Nemerow integrated pollution index in vineyards (vineyards) of Malayer city. In this study, after consecutive visits to the study area and reviewing the map of the area, sampling operations were performed in January 2019. Soil sampling was performed based on a systematic random method. A total of 286 samples were collected from topsoil (depth 0 to 20 cm). Of these, 157 samples were located in agricultural lands (gardens and agricultural land) and 129 samples in natural areas (pastures and barren lands). The samples were analyzed by high-resolution continuum source atomic absorption spectrometry (HR-CS AAS) and using the fame method. The average concentrations of Cu, Mn, Zn, Cr, As, Ni and Hg are 87.86, 597, 109, 67.12, 47.1, 84.32, and 0.344 mg/kg, respectively. These values are lower than the NIPI index values for these metals. The results showed that there is a positive and signifcant correlation between Cu and Mn, Cu and zinc, Cu, and As as well as As and Ni. There is also a signifcant negative correlation between Cu and Hg, As and the electrical conductivity (EC) as well as As and Mn. At present, these soils are not at serious risk of soil contamination with heavy metals.

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A performance evaluation of neuro‑fuzzy and regression methods in estimation of sediment load of selective rivers

Varvani J., Khaleghi M. R.

Acta Geophysica

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2019

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Vol. 67, no. 1

205--214

EN

Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. Also, the average value of the AI of MSRCs is 1.15 which is an acceptable amount of the other considered various methods.

3

Simulation of Relationship between River Discharge and Sediment Yield in the Semi-arid River Watersheds

Khaleghi M. R., Varvani J.

Acta Geophysica

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2018

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Vol. 66, no. 1

109--119

EN

Complex and variable nature of the river sediment yield caused many problems in estimating the long-term sediment yield and problems input into the reservoirs. Sediment Rating Curves (SRCs) are generally used to estimate the suspended sediment load of the rivers and drainage watersheds. Since the regression equations of the SRCs are obtained by logarithmic retransformation and have a little independent variable in this equation, they also overestimate or underestimate the true sediment load of the rivers. To evaluate the bias correction factors in Kalshor and Kashafroud watersheds, seven hydrometric stations of this region with suitable upstream watershed and spatial distribution were selected. Investigation of the accuracy index (ratio of estimated sediment yield to observed sediment yield) and the precision index of different bias correction factors of FAO, Quasi-Maximum Likelihood Estimator (QMLE), Smearing, and Minimum-Variance Unbiased Estimator (MVUE) with LSD test showed that FAO coefficient increases the estimated error in all of the stations. Application of MVUE in linear and mean load rating curves has not statistically meaningful effects. QMLE and smearing factors increased the estimated error in mean load rating curve, but that does not have any effect on linear rating curve estimation.