01 آذر 1403
مهدي بي باك

مهدی بی باک

مرتبه علمی: استادیار
نشانی: پژوهشکده خلیج فارس - گروه شیلات و زیست شناسی دریا
تحصیلات: دکترای تخصصی / شیلات
تلفن: 0
دانشکده: پژوهشکده خلیج فارس

مشخصات پژوهش

عنوان Predicting the Trace Element Levels in Caspian Kutum (Rutilus kutum) from South of the Caspian Sea Based on Locality, Season and Fish Tissue
نوع پژوهش مقالات در نشریات
کلیدواژه‌ها
Akaike information criterion . Caspian Sea . Guilan Province . Trace elements
مجله BIOLOGICAL TRACE ELEMENT RESEARCH
شناسه DOI https://doi.org/10.1007/s12011-021-02622-4
پژوهشگران محمد فروهر (نفر اول) ، مسعود ستاری (نفر دوم) ، جاوید ایمان پور نمین (نفر سوم) ، مهدی بی باک (نفر چهارم)

چکیده

Elements are the shared result of the erosion of rocks in the catchment area and human activities. Nutritional habits, ecological needs, heavy metal concentrations in water and sediment, duration of fishing in the aquatic environment, fishing season, and physicochemical properties of water (salinity, pH, hardness, and temperature) are among the effective factors in the accumulation of heavy metals in various fish organs. In this study, 150 specimens of Rutilus kutum were collected from the southern shores of the Caspian Sea including Astara, Anzali, and Kiashahr in Guilan Province, Farahabad in Mazandaran Province, and Bandar Torkaman in Golestan Province from December 2018 through October 2019. It is possible to predict the metal concentrations using the variables such as fish tissue, sampling region, and season. Akaike information criterion (AIC) was used to select the best regression model. We used fish muscle tissue and Anzali sampling site which were considered reference variables in the regression model. For some elements, a better model is obtained by considering all variables (AIC criterion is its lowest value). The best model obtained for Cu, Mn, and Si was only with region (as a variable). The best model obtained for Sn and Sr only concerns the region and tissue variables. The best model obtained for Sb only related to tissue variable. Using these models, environmental monitoring becomes easier and cheaper. We suggest further studies to be carried out in the shortest possible time along with the least laboratory cost.