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The aim of this study was to measure the level of Arsenic, Cadmium, Nickel and Mercury in gill, muscle and hepatopancrease tissue of Aras dam Astacus leptoductylus and compare of them concentration in different body tissues. For this purpose, 271 samples of Astacus leptoductylus were randomly collected in Aras dam from December 1 to December 31, 2017 and transferred to the laboratory. After that tissue samples processed for injection in atomic absorption for apparatus for measuring the level of Arsenic, Cadmium, Nickel and Mercury. For measuring the protein, ash and moisture content of the samples AOAC method was conducted. The results showed that Arsenic, Cadmium, Nickel and Mercury were present in the Astacus leptoductylus samples and the highest level of these metals was in the muscle and the lowest in the gills. The highest level of metals existing in Astacus leptoductylus muscle was related to Cadmium (0.338± 79 0.79 ppb), and also Nickel (0.285±0.066 ppb) was in the second rank. The level of Arsenic and Mercury in muscle were ranked third and fourth. However, it was found that the total moisture content of the gills was higher than muscle and hepatopancrease tissue. On the other hand, the results of correlation between metals of different tissues of Astacus leptoductylus showed that the correlation between Cadmium and Nickel was higher than Arsenic and Nickel. The level of total protein and ash in Astacus leptoductylus muscles was higher than other tissues. The present study showed that analyzed Arsenic, Cadmium, Nickel and Mercury level were in the muscle, gill, and hepatopancrease tissue samples of the Aras dam A. leptoductylus in detectable concentrations, and Nickel and Mercury in the samples were lower than the WHO standard. As a result, the levels of metals in the Astacus leptoductylus samples collected from Aras dam were safe and reliable and therefore there is no problem.

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Microorganisms play an important role in formation of mines. In this research, the bacteria inhabiting in Aq-Darreh Takab gold mine were isolated and compared with agricultural soils. The isolates were characterized using 16S rDNA sequencing and the homology searches were performed using BlastN, EzTaxon, and RDP Classifier web tools. Resistance of the isolates was also investigated against arsenic and silver in the presence and absence of 3.5 ppm gold. Although the control soil showed a wide variety of bacterial diversity (43 isolates belonging to 13 genera), only 17 isolates belonging to 11 genera were isolated from mine soils including Acinetobacter, Agrobacterium, Comamonas, Deinococcus, Listeria, Microbacterium, Micrococcus, Pseudomonas, Rhizobium, Roseomonas and Staphylococcus. Among the isolates, A. radiobacter, D. ficus, M. antarcticus, M. luteus, R. radiobacter and R. selenitidurans were able to tolerate different amounts of arsenic and silver in the presence of gold, among which A. radiobacter and D. ficus showed the highest resistance in such a way that they grew in the presence of 50 ppm arsenic, 50 ppm silver, and 3.5 ppm gold. Our results showed that bacterial diversity in soils containing gold, silver and arsenic metals is less than agricultural soils. It was also found that the bacterial diversity in gold mines depends on the amount of gold and the amount and type of associated elements. Due to high resistance of two endogenous bacterial species to arsenic and silver, A. radiobacter and D. ficus, have also the potential for industrial purposes in environments contaminated with these metals.
 

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Lots of ecosystems including soil and water in the world is contaminated by the arsenic every year. The emission of arsenic (As) to the surface and groundwater by human activities such as mining, agricultural and industrial activities is considered a global threat to the ecosystem and human health. Arsenite and arsenate are the two dominant arsenic species in contaminated soils that are highly toxic to the human health and ecosystems. Thus, the As elimination from aqueous solution is considered as crucial issue. Among the different removal methods, adsorption is the low cost, and high efficient technique for the As elimination from aqueous phase. In the adsorption process, the adsorbent type is the one of the main factors of successful removal process. Application of nano-adsrobent may lead to produce less secondary waste in the adsorption process. Moreover, bimetal nano-adsorbent due to the some properties including increasing As removal in the early time was selected as adsorbent to remove As from aqueous solution. Many researches believe that Jacobsite nanoparticles (MnFe₂O₄) are an effective absorbent for the removal of organic and inorganic materials. Due to the special properties of nanoparticles such as high reactivity, Jacobsite nanoparticles were selected for the adsorption of arsenic from water and prepared based on co-precipitation method. The prepared nanoparticles were characterized through the X-ray fluorescence (XRF), X-ray diffraction XRD, scanning electron microscopy methods (SEM), and pH_pzc. According to the XRD, the obtained peaks for the synthesized adsorbent were followed by the previous researches indicated the production of Jacobsite. Based on the XRF, the Mn-oxide and Fe-oxide was 27.8% and 65.5%, respectively. Overall, results of XRD and XRF proved that the synthesized nanoparticles were Jacobsite. Moreover, based on the Fe-SEM, the nanoparticle size was less than 100 nm with mean size of 33.8 nm. Moreover, the he pH of zero point of the nanoparticle (pH_pzc) of the synthesized adsorbent was 7.2.  In the presnet study, Response Surface Methodology (RSM) was used to model and optimize the adsorption process of arsenic from solution with Jacobsite nanoparticles. Four factors of pH (3 to 11), concentration of arsenic in solution (1000 to 4000 μg/l), amount of nanoparticles (1 to 5 g/l) and time (15 to 195 min) were selected as independent factors affecting the adsorption efficiency of arsenic. The central composite design (CCD) was used to design of the experiment and optimize the model parameters. Variance analysis indicated that prediction of adsorption of arsenic from the nano-adsorbent by the CCD model was well performed (p <0.0001) with the high accuracy (R2 of 96.24%). The results showed that the effect of four factors pH, nanoparticles, initial arsenic concentration and time was significant. According to the optimization objectives, the results showed that the optimum pH, amount of nanoparticles, time and initial concentration of arsenic were 3, 2 g / l, 48 min and 3250 μg/l, respectively. The arsenic removal from the solution at optimum values ​​calculated for the factors was estimated to be 79.7%. However, 94.77% of As was removed in the adsorption experiments.