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Zn(60.04%) >Pb(36.63%) > Cu(30.32%) > Ni(14.84%) and Cu(13.68 mg/kg) > Zn(10.41 mg/kg) > Ni(6.58 mg/kg) >Mn(5.96 mg/kg) >Pb(0.146 mg/kg). In the present study, Cu shows maximum adsorption capacity between all studied metals. Based on results, the concentration of NO3 decrease with an increase in salinity in the area of study. The concentration of total dissolved organic carbon (DOC) in the fresh river water was about 1.92 mg/L that increased to 22.34 mg/L at a salinity of 3‰. Such an increase is indication of a marine origin in the estuarine zone. Cluster analysis shows that Mn, salinity, DOC and NaClO joined together with high similarity coefficient indicating flocculation of Mn is governed by NaClO, salinity and DOC. In the present study, pH doesn’t play any role on flocculation and adsorption processes of studied metals (Figure 1). Also, based on cluster, it can be inferred that adsorption rate of Mn, Zn and Cu is controlled by NO3. According to the chemical sequential exraction it can be noted that approximately 25% of the total heavy metals (Cu, Ni, Zn, Mn) contents were in the form of sulfide ions. Figure 1- Dendrogram of cluster analysis for metals and other physic-chemical characteristics of Karganrud River and Caspian Sea water Conclusion In this study, flocculation, adsorption and desorption processes of copper, zinc, nickel, lead and manganese during mixing of Karganrud River water with Caspian Sea water at a wide variety of salinities from 0.5 to 3 ppt were investigated. The highest percentage of flocculation observed for manganese in comparison with copper, zinc, nickel andlead. Also, Pb showed desorption behavior from suspended particulate matter during estuarine mixing. It can be clearly seen that the Maximum adsorption capacity belongs to Cu compared with other studied metals. Among studied physicochemical parameters of mixing samples, DOC shows a linearity increasing behavior toward salinity. Based on the cluster analysisthe flocculation process of Zn, Cu and to lower extent Ni is controlled by NO3. On the other hand, the flocculation process of Mn is mainly controlled by NaClO. According to the chemical partitioning study it should be noted that about 63% of concentration of adsorbed Cu found in carbonate fractions. Generally, the highest percent of metal contents found in sulfide and carbonate compounds. The flocculation and adsorption rate of studied metals showed that overall colloidal metal pollution loads can significantly be reduced by various percentiles at different salinity regimes. This not only states the importance of these processes in natural self-purification of estuarine ecosystems, but also shows the ecological importance of the estuarine process. Future investigations should focus on the role of seawater in the treatment of trace metals during industrial wastewater purification.]]>
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10) is usable for Discriminant analysis. The stepwise Discriminant function analysis was employed to select the optimum composite fingerprinting. The comparison of different mean showed different was significant for Ag and Zn between of groups. Table1. Various steps of import elements to model Cumulative % Sig Wilks Lambda Element Step 100.00 0.004** 0.208 Zn 1 100.00 0.000** 0.091 Ag 2 ** Significantly in the 0.01 level The results of table 1 showed to added each element was unchanged Cumulative percentage but wilks Lambda was declined and Significant level was better therefore was increased separation ability between groups. The power of detection function is evaluated with results ofthe audit functioncanonical (Table 2). Table2. Results ofthe audit functioncanonical Function Eigenvalues Percentageof variance % cumulative of variance Canonical correlationcoefficient 1 1.42 100.00 100.00 0.77 Table(3) b oflinear regressioncoefficientsoffunctionsis presented. Table3.Auditfunctionscanonicalcoefficients Tracer elements Function Zn 0.919 Ag -0.849 In finally Discriminant function was defined according to Canonic Discriminant Function Coefficients (equation 4). F1 = 0.919 Zn – 0.849 Ag (4) To determine the roleof each of theresourcesfallingdust using theresults of thedetection function is in the function average concentrationof heavy metalsinthe monthwasin the function.The results are showed most likelybelongingtodust is associated to Sebkha in the six months.Therefore most contribution of falling dust of originsuburbanarea is Sebkha in Yazd – Ardakan plain. The best result was obtained of scenario with two groups including Sebkha - Kalut & Yardang and Hill - Glacis Epandage Plain. Therefore were defined discriminate analysis based on the scenario. The sources contribution in sediment production The according to mixed multivariate model was obtained sources contribution 99.9 and 0.1 percent respectively. Therefore major contribution of falling dust is related to Sebkha and Kalut & Yardang. The results of minimizing the sum of the squares of the residuals are indicative the best portion for falling dust sources. The results showed portion of groups for production of falling dust are 100 and 0 percent respectively. These results almost are corresponded with results of mixed multivariate model. The assessments of this model showed percent of the relative error are between 0.0001-3.41 for all samples. The coefficient of performance model variable is between 0.71 – 0.99 for samples. Conclusions Most occurrences of severe sand storms and wind with speeds that is more than 100 km/h are mainly severe in February to June and it events sometimes the black storms and thick clouds of dusts in Yazd Province, so it selected winter and spring seasons for research. The investigation of low relative error and high coefficient of performance model is indicating the accuracy and performance of model. The results of this model are in agreement with field observation completely. The high sensitive of Sebkha and Kalut against the wind and fine soil in this area are indicating major role this area in production of falling dust. The results of investing wind erosion in faces of Yazd – Ardakan plain is showed Sebkha and Kalut – Yardang among other of faces are the highestshare in production of falling dust because Sebkha are Crustofclay–salt therefore due tohighsalinity andsodiumishighly sensitivetoerosion and The soilof thislandisa sensitive andhighly susceptible to erosion. The Neogene hills are thehigherresistance againstwind erosion because they cover ispebblesand rubble. The researchin case of wind erosion in Yazd – Ardakan plain showed area involving Sebkha and Kalut despite the slight area than other area is highest proportion in wind erosion and production of dust. ]]>
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