University of GuilanCaspian Journal of Environmental Sciences1735-303318320200701Correlation between serum concentration of diazinon pesticide and breast cancer incidence in Mazandaran Province, northern Iran197204413210.22124/cjes.2020.4132ENGhazalehKhalili TanhaDepartment of Basic Sciences, Sari Agricultural Sciences and Natural Resources University, Sari, IranAliBarzegarDepartment of Basic Sciences, Sari Agricultural Sciences and Natural Resources University, Sari, IranMohammadShokrzadehDepartment of Toxicology, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, IranNovinNikbakhshDepartment of Surgery, School of Medicine, Babol University of Medical Sciences, Babol, IranZarbakhtAnsariDepartment of Animal Sciences, Sari Agricultural Sciences and Natural Resources University, Sari, IranJournal Article20200702Growing evidences have revealed a positive association between organophosphorus pesticides and diseases like cancer. Located in northern Iran, Mazandaran Province is a region with the overuse of agricultural pesticides. Due to the extensive use of the pesticides like diazinon in rice paddies of Mazandaran for the control of Chilo suppressalis, and high incidence rate of cancer in this province, we analysed and compared diazinon in the serum samples of the breast-cancer patients with the healthy volunteers. This cross-sectional case-control study inludes 10 breast-cancer patients and 10 healthy controls. Diazinon was extracted with a mixture of acetone and diethyl ether (1:1 v/v) in acidic medium and the residue was analyzed by GC-MS instrument. Results showed the presence of 0.151 ppm diazinon in the serum of just one healthy subject. In conclusion, despite excessive use of diazinon in Mazandaran Province, no association was found between serum level of diazinon and the incidence of breast cancer.https://cjes.guilan.ac.ir/article_4132_ae922098a8d380618020b9310787fade.pdfUniversity of GuilanCaspian Journal of Environmental Sciences1735-303318320200701Soil geochemistry study of the listvenite area of Ayvacik (Çanakkale, Turkey)205215413310.22124/cjes.2020.4133ENAlaaddinVuralDepartment of Geological Engineering, Faculty of Engineering and Natural Sciences, Gümüşhane University, Gümüşhane, Türkiye0000-0002-0446-828XDoğanAydalDepartment of Geological Engineering, Faculty of Engineering, Ankara University, Ankara, TürkiyeJournal Article20200702Objective of this study is to use soil geochemistry surveys using statistical methods for the exploration of possibly gold mineralization related to Alakeçi listvenite area (Bayramiç, Çanakkale, Turkey). In the scope of soil geochemistry, about 350 soil samples were analyzed and evaluated for nine elements including gold. Element concentrations for Cu, Pb, Zn, As, Sb, Mo, Ni, Co and Au (all element except Au in ppm, Au in ppb) were 10-265, 10-90, 15-545, 5-800, 2-60, 4-8, 10-6300, 15-1255 and 40-300 respectively. The gold concentrations were comparatively detected in low value than important ore deposit and exhibited no correlation with other elements (except Mo). However, positive correlation was observed between Cu and Zn; Sb and As; Mo with As and Sb; Au and Mo; Ni and Co. In literature, Cu, Zn, As, Sb and Mo were widely used as pathfinder elements for exploration of gold mineralization. So, these elements were accepted as pathfinders in this study and their distribution maps were prepared. The element distribution maps for pathfinder elements and gold displayed remarkable zoning in the central to southeast site of the area connected with tectonic lines and lithological borders. So, in conclusion, this part in the area needs to be investigated in detailed with geophysics and drilling methods for blind gold mineralization.https://cjes.guilan.ac.ir/article_4133_b7d51632fb70cf24b5eedd0b1a9ce92b.pdfUniversity of GuilanCaspian Journal of Environmental Sciences1735-303318320200701Modeling the species distribution of Caucasian pit viper (Gloydius halys caucasicus) (Viperidae: Crotalinae) under the influence of climate change217226413410.22124/cjes.2020.4134ENAijamalGhelichy SalakhDepartment of Biology, Faculty of Sciences, Golestan University, Gorgan, IranHaji GholiKamiDepartment of Biology, Faculty of Sciences, Golestan University, Gorgan, IranMehdiRajabizadehDepartment of Biodiversity, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, IranJournal Article20200702During recent years, the effects of climate change on various biological and ecological aspects of the species have been discussed in several litterateurs. The aim of this study was to reveal the effect of climate change on the extent and suitability of the <em>Gloydius halys caucasicus</em> habitats at present time and future. So that, 50 presence points and 19 bioclimatic variables were used. To compare the effects of global warming and climate change over the extent andsuitability of habitats, future bioclimatic variables were used in two mild climate change (PCR2.6) and severe (PCR8.5) scenarios in species distribution modeling. The results show that due to warming process of the planet and its growing trend in the future, the extent of suitable habitats of Caucasian pit viper is declining. Due to the fact that most of the suitable habitats of Caucasian pit viper are outside the protected areas, comprehensive studies are needed to plan and introduce new protected areasin future.https://cjes.guilan.ac.ir/article_4134_0d6ab707c839b73d54a633b44ce33eb5.pdfUniversity of GuilanCaspian Journal of Environmental Sciences1735-303318320200701Coastal solid waste prediction by applying machine learning approaches (Case study: Noor, Mazandaran Province, Iran)227236413510.22124/cjes.2020.4135ENBanafshehKavyanifarDepartment of Environmental Science and Engineering, Faculty of Natural Resources, University of Guilan, Guilan, IranBabakTavakoliDepartment of Environmental Science and Engineering, Faculty of Natural Resources, University of Guilan, Guilan, IranJavadTorkamanDepartment of Forestry, Faculty of Natural Resources, University of Guilan, Guilan, IranAbolfazlMohammad TaheriDepartment of Environmental Science and Engineering, University of Tehran, Hamedan Governorate, Hamedan, IranAliAhmadi OrkomiDepartment of Environmental Science and Engineering, Faculty of Natural Resources, University of Guilan, Guilan, IranJournal Article20200702Nowadays, intelligent systems are used as innovative tools in different environmental issues. However, the prediction of short-term waste, unlike the long-term scale, is less developed due to more uncertainties and the difficulty in determining measurable independent parameters. In this study, two types of artificial neural networks (MLP and RBF) and two decision tree algorithms (CHAID and CART) have been used as effective tools for short-term forecasting of total waste production in coastal areas of Noor in Mazandaran Province, Iran. So that, average temperature, daily rainfall, sunny hours, maximum relative humidity and maximum wind speed were determined as the most important independent parameters, while the amount of waste produced in the coastal areas of Noor was considered as the dependent variable. Wastes from the coastal areas were gathered and their weights were analysed during 12 months from July 2017 through June 2018. Samplings were carried out twice a week, three weeks of a month and 12 months of a year, overall 72 times a year. The required meteorological data was gathered from the meteorological station in Noor. Then the sensitivity analysis was performed to check the independency of the major independent parameters. Thereafter, the mentioned machine learning approaches were applied to predict the short-term total waste production in IBM SPSS Modeler version 18 environment. In the applied models, 60% of data were used in training the model and the other 40% were used for model evaluation. The results indicated that the CHAID tree algorithm exhibits a better performance in predicting total solid waste production compared to CART, MLP and RBF models. The mean absolute error and the correlation coefficient (R) of CHAID algorithm was 0.067 and 0.828, respectively.https://cjes.guilan.ac.ir/article_4135_824cff517ac82c8515342e506047670b.pdfUniversity of GuilanCaspian Journal of Environmental Sciences1735-303318320200701Monitoring and prediction of drought using TIBI fuzzy index in Iran237250413610.22124/cjes.2020.4136ENBehrouzSobhaniDepartment of Physical Geography, Climatology, Faculty of Literature and Humanities, University of Mohaghegh Ardabili, Ardabil, IranVahidSafarianzengirDepartment of Physical Geography, Climatology, Faculty of Literature and Humanities, University of Mohaghegh Ardabili, Ardabil, IranJournal Article20200702The drought phenomenon is not specific to the region, affecting different parts of the world. One of these areas is Iran in Southwest Asia, suffering from this phenomenon in recent years. The purpose of this study was to model, analyze and predict the drought in Iran. So that, climatic parameters (precipitation, temperature, sunshine, minimum relative humidity and wind speed) were used at 30 stations during 29 years (1990-2018). For modelling the TIBI fuzzy index, at first, four indicators (SET, SPI, SEB, and MCZI) were been fuzzy in MATLAB software. Then the indices were compared and the TOPSIS model was used for prioritizing areas involved drought, followed by employing ANFIS adaptive artificial neural network model for predicting drought. Results showed that the new fuzzy index TIBI for classifying drought reflected four above indicators with high accuracy. Of these five climatic parameters used in this study, the temperature and precipitation exhibited the most impact on the fluctuation of drought severity. The severity of drought was more based on 6-month scale modelling than on 12-month one. The highest rate of drought occurrence was found at the Bandar Abbas station with 24.30% on a 12-month scale, and while the lowest was at the Shahrekord station with 0.36% on a six-month scale. Based on ANFIS model and TIBI fuzzy index, Bandar Abbas, Bushehr and Zahedan stations were more encountered ones to drought due to the TIBI index of 0.62, 0.96 and 0.97 respectively. According to the results in both 6- and 12-month scales, the southern regions of Iran were more severely affected by drought, which requires suitable water management in these areas.<br /> <strong> </strong>https://cjes.guilan.ac.ir/article_4136_b94b7840f81def1f0940c7aae7aed543.pdfUniversity of GuilanCaspian Journal of Environmental Sciences1735-303318320200701Estimation of pollution load to Anzali Wetland using remote sensing technique251264413710.22124/cjes.2020.4137ENMaryamNavabianDepartment of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran0000-0001-7254-4312MajidVazifehdostDepartment of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, IranMehdiEsmaeili VarakiDepartment of Water Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, IranJournal Article20200702To control pollution sources and prioritize the reduction strategies of pollution, estimating the pollution load and contributing to prevent entering the different pollution sources to the water resources is very important. To estimate the pollution load, detailed quantitative and qualitative information of pollutant is needed. However, ground-based measurement is very costly and time consuming. Instead, approaches based on remotely sensing techniques exhibits very high potential in determining the water quality parameters over an extensive area in a short time period. The aim of this study was to evaluate the possibility of applying remotely sensing data and hydrometric station data to estimate the pollution load entering Anzali Wetland. So that, the quality parameters including nitrate concentration, total dissolved solids, total suspended solids and orthophosphate in the entrance point of three important rivers leading to Anzali Wetland (Bahmbar, Siahdarvishan, and Pirbazar Rivers) was measured at the time of satellites overpassing over the period of November 2011 through August 2012. Pre-processing practices including radiometric and geometric corrections were performed on the Landsat images. Then multi-variable equations were derived for estimating the water quality parameters based on ground truth data and spectral reflections in the range of visible to middle infrared. After validating the accuracy of water quality parameters derived from Landsat satellite images (by statistical indices), the contamination loads of nitrate, total dissolved solids, total suspended solids and orthophosphate entering the wetland were estimated. To assay the contamination load of each parameter, the river discharges were multiplied by the concentrations of these parameters which derived from satellite images in the period of April 2012 through July 2013. The highest pollution load occurred during this period at the entrance of Siahdarvishan River into Anzali Wetland. Comparison of pollution load of nitrate, orthophosphate, total suspended solids and total dissolved solids derived from satellite images during the study period revealed that the Pirbazar and Bahmbar rivers discharged the most loads of pollution to Anzali Wetland respectively.https://cjes.guilan.ac.ir/article_4137_dcd469681de9b1182c0583c11e8ee1e9.pdfUniversity of GuilanCaspian Journal of Environmental Sciences1735-303318320200701Impact of silver nanoparticles on hepatic enzymes and thyroid hormones in striped catfish, Pangasianodon hypophthalmus (Pisces: Pangasiidae)265275413810.22124/cjes.2020.4138ENSalehGhazanfariDepartment of Fisheries and Environmental Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, IranRuhollahRahimiDepartment of Fisheries and Environmental Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, IranRasoolZamani-AhmadmahmoodiDepartment of Fisheries and Environmental Sciences, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord, IranAliMomeninejadShahid Momeni Nejad Ornamental Fish Education and Research Center, Qom, IranAmirrezaAbed-ElmdoustDepartment of Fisheries Sciences, Faculty of Natural Resources, University of Tehran, Karaj, IranJournal Article20200702Nanotechnology is the exploitation of physical, chemical, and biological characteristics of the particles with less than 100 nanometers in size. The most of the produced nanoparticles (56%) are composed of silver. The high consumption of these materials in industry and household products has led to their frequent release in aquatic ecosystems. The median lethal concentration (LC<sub>50</sub>) and the impact of silver nanoparticles on liver enzymes (ALP, LDH, AST, ALT) and thyroid hormones (T4 and T3) in <em>Pangasius hypophthalamus </em>wereinvestigated in the present study in three steps: At first, OECD (The Organization for Economic Cooperation and Development) protocols were used to determine the fatal levels of the silver nanoparticles (Ag NPs) in striped catfish. Second, semi-lethal concentration was found as 37.32 µg L<sup>-1</sup> via regression test. In the last step, 168 fish received 0, 3.37, 7.46, 18.66 µg l<sup>-1</sup> Ag NPs with three replicate. Six fish were randomly selected after 14 days from each replicate. Whole fish body extraction was used to measure the liver enzymes and thyroid hormones. The results suggested that due to the lower LC<sub>50</sub> of Ag NPs in striped catfish, this species is more susceptible compared to various other fish species. Exposure to the silver nanoparticles with different concentrations significantly increased the levels of liver enzymes (ALP, LDH, AST, ALT) and also significantly decreased the T3, but no effect on T4.https://cjes.guilan.ac.ir/article_4138_5ca9efb152ab6389567adcdaa1d29fb1.pdfUniversity of GuilanCaspian Journal of Environmental Sciences1735-303318320200701Rangelands production modeling using an artificial neural network (ANN) and geographic information system (GIS) in Baladeh rangelands, North Iran277290413910.22124/cjes.2020.4139ENGhasem AliDianati TilakiDepartment of Range Management, Faculty of Natural Resources, Tarbiat Modares University, Noor, IranMaryamAhmadi JolandanDepartment of Range Management, Faculty of Natural Resources, Tarbiat Modares University, Noor, IranVahidGholamiDepartment of Range and Watershed Management and Dept. of Water Eng. and Environment, Faculty of Natural Resources, University of Guilan, Sowmeh Sara, IranJournal Article20200702Rangelands production measurement is time-consuming and expensive. Therefore, models are often employed to simulate rangelands conditions as a supplement. Artificial neural network (ANN) is widely used for modeling in environmental studies, yet it cannot preset its results in the form of a map or geo-referenced data. We used ANN to estimate the spatial distribution of rangelands production, then a geographic information system (GIS) was applied as a pre-processing and post-processing framework in rangelands production modeling. The ANN was trained (R<sub>sqr </sub>= 0.95, MSE = 0.02) and tested using data from the Baladeh rangelands located in the northern part of Iran. Rangelands production was simulated using a multi-layer perceptron (MLP) network. We estimated rangelands production (using many plots and field studies) as the network output, along with the influencing factors in the production (vegetation, climatic, topographic, edaphic and human factors) as the inputs. After modeling and model optimizing in ANN, the model test was performed (R<sub>sqr</sub>=0.8, MSE=0.3). Furthermore, the studied area was divided with the pixels 100×100 m (raster format) in the GIS medium. Then, the digital layers of the network inputs were combined and a raster layer was prepared including the network inputs values and geographic coordinate. The values of pixels (network inputs) were imported in ANN (NeuroSolutions software). Rangelands production was simulated using the validated optimum network in the sites without production measurements. In the next step, the results of ANN simulation were imported in the GIS medium, then rangelands production map was prepared based on the estimated results of ANN. The results indicated that integrating ANN and GIS exhibits high accuracy and performance in rangelands production estimation. Hence, the prepared rangelands production map can be used for planning and managing the rangelands.https://cjes.guilan.ac.ir/article_4139_2791d388e7db0f7271201db64d484b5c.pdf