azadeh atabati

One of the most important problems in today's world, which is accompanied by industrial development and population growth, is the pollution of the environment, especially water sources, with toxic and dangerous heavy metals. In this study, the synthesis and application of activated gamma alumina filament granules in the removal of chromium and lead from water in a continuous fixed-bed reactor with an upward flow have been investigated. For the preparation of active gamma alumina, thermal method is used, and furfuran binder as well as phenolic resin and polyvinyl alcohol are used to form it and it is converted into filaments by an extruder. Agilent 240 AA atomic absorption spectrometer was used to measure the concentration metal ions and Thomas model was used to predict the behavior of the column. The results showed that the percentage of removal at the column height of 20 cm is at its maximum (70.7% for chromium and 94% for lead). It was found that by increasing the initial concentration of the pollutant and increasing the flow rate, the failure time decreases. The highest adsorption capacity of activated gamma alumina reactor for chromium is 20.7 mg/g and for lead 1.31 mg/g (column height 20 cm, flow rate 4.5 mL/min, concentration 10 mg/L, and pH equal to 3 for Chromium and pH equal to 7 for lead) were obtained. Formed activated gamma alumina is an effective adsorbent for adsorbing heavy metals, especially chromium and lead, due to its optimal adsorption, ease of preparation, and reasonable price.

Today, the use of raster data from climate databases such as WorldClim is one of the reliable data sources that have been used instead of the point data of weather stations. However, the use of these climate databases with low spatial resolution has generated limitations for many studies related to biological and ecological studies. This study aims to investigate the relationship between air temperature and land surface temperature and then reproduce the land surface temperature with the high spatial resolution for downscaling of annual average air temperature using two widely used products, namely,  annual average air temperature from the WorldClim database and annual average day and night temperature from MOD11A2 v061 MODIS sensor. In this study, firstly, the performance of machine learning models including random forest, artificial neural network, elastic network regression, and support vector machine for downscaling of MOD11A2 v061 product from 1 km to 250 meters was assessed. For this purpose, continuous and discrete predictor variables including height above sea level, latitude, vegetation cover, soil texture, slope direction, and land cover were used. Then, WorldClim's annual average air temperature was downscaled from 1 km to 250 meters using the land surface temperature with a 3rd-degree polynomial regression model.  Also, the weather data of seven synoptic stations were used to check the validity of the downscaled product. The results of the Taylor diagram represented that the random forest model has the best performance for the downscaled land surface temperatures with a root mean square error of 0.54 degrees Celsius. Also, the 3rd-degree polynomial regression model has a lower relative error rate in producing downscaled air temperature. The value of the root mean square error of the results for the uncorrected and corrected downscaled air temperature was 1.32 and 1.21 degrees Celsius, respectively, which did not show a significant difference at the 0.05 level according to the paired t-test. The findings of this research show that the downscaling of the mean annual air temperature of WorldClim has the required validity.

Heat-humidity stress is one of the most important environmental factors affecting the development of the fetus and the health of pregnant maternal., Studies in this area have been limited. The aim of this study was to investigate the effects of short-term exposure to heat-humidity stress in the form of THI on fetal growth and development. For this purpose, animal models were used...  pregnant NMRI mice that were exposed to heat-humidity stress in the chamber during the second period of pregnancy (3, 5, 8, 10 and 15 minutes) daily for short periods Placement courses (GD 14.5-17) is designed in accordance with the growth and development of the human embryo. Exposure to thermal-humidity stresses in the second period (GD6.5-14) during of pregnancy shows an increase in maternal mortality and fetal weight loss, crown-to-rump length (CLR) and placental weight in comparison with the control group. Also, with increasing thresholds of stress at different thresholds of the index, embryonic abnormality (Exohatics, Facial Deformity, C-Shaped Body, Ear structure defects, Hemangioma and Limbic Adhesion) have increased. T-test of two independent samples and ANOVA confirmed the above-mentioned results at the level (p-value<0.01). The results of this study can be effective in the development of care programs, treatment and education of pregnant mothers, and to monitor the thermal stresses and design of warning systems.

Khorasan Razavi province has the potential for growing and producing rapeseed because of favorable environmental conditions, so that the northern and central cities of province have high potential for cultivation of rapeseed. Modeling the correct relationship between environmental conditions and yields is a critical step to find how crop-planting choices in different regions of Iran.Spatial modeling in GIS is one of the most important strategies that can provide a basis for measuring environmental factors and land suitability for the cultivation of a particular product by combining statistical methods and spatial data. In this research, the link between water, soil and meteorological factors and yields modeled during the growing season in sample farms.

In this research, the position of 24 sample fields of rapeseed farming was recorded by Global Positioning System (GPS) and then actual yield was calculated. To explore how the environmental conditions and yields relationship has changed over space, we used ten environmental parameters influencing rapeseed productions yield, including elevation, slope, aspect, EC and pH groundwater resources, mean air temperature, incoming solar radiation, potential evapotranspiration, wind exposition index, Soil texture during the growing season. The values of each independent variables were extracted into samples by nearest neighbor method. Then, after normalizing the variables and taking into account the range of numbers, the samples were divided into two subsets: training (60%, 14 farms) and the testing dataset (40%, 10 farms) randomly. Two methods of nonparametric K of the nearest neighbor and random forest were then used to estimate rapeseed yield over the study area.

The results of mean absolute error percentage in the methods used showed that K is the nearest neighbor with 26% error and random forest with 11% error. The results of Nash–Sutcliffe efficiency index for validation data set represent the value of 0.65 for K nearest neighbor and 0.82 for random forest method. In general, the results indicate that the random forest method has a lesser error than the K nearest neighbor method in estimating the yield of rapeseed productions for the study area.

Based on the results of this research, it can be concluded that among the variables used, two variables of wind supply index and average temperature had the most effect on the yield of rapeseed in comparison with other variables. Also, according to the final map, it was determined that suitable areas for rapeseed cultivation over Sabzevar region are located in the northern and northwestern regions. Low yield in the central regions of this part is mainly due to the excessive salinity of water and gypsum formations. Crop yield is a result of combination of genetic factors and also environmental conditions of the cultivation, which we emphasized on the environmental factors in this study.

Fires in natural areas are one of the factors decreasing forested area of northern Iran. In this study, forest and rangelands susceptibility to fire were analyzed using data-driven methods over Mazandaran Province. Fourteen important environmental and anthropogenic parameters influencing forest and rangelands susceptibility to fire were used to model probability of fire susceptibility. Binary logistic regression and artificial neural network, as two well-known data driven methods was then used to evaluate environmental and anthropogenic performance on landfire and map of forest fire susceptibility estimates were prepared in GIS environment. The area under the successive rate curve (AUSC) showed that ANN method modeled forest fire susceptibility with an accuracy of around 88% and BLR with 85%. 21.6% of the total area of Mazandaran province is located in areas with high and very high susceptibility levels of forest and rangeland fire. Overall, ANN method showed promising results to estimate landfire susceptibility. The forestry and rangelands fire susceptibility map presented in this study can be used as a basic map of the strategic planing in Mazandaran province to reduce probability fire damages.

The aim of this study was to identify similar areas of physiography and climatic potential of walnut in Baft city. Logistic regression and discriminant analysis were used for the purpose of classifying of study area using a set of predictor variables to find the suitable areas to grow walnut. For this purpose, environmental conditions affecting walnut trees used included Annual Mean Temperature, Annual Precipitation, Precipitation of Driest Month, Precipitation of Wettest Month, Elevation, Slope Gradient and Aspect. 70% of the data, as training data and the remaining 30%, are used as model validation data. Using probit logistic regression and discriminant analysis, the effect of variables used on walnut in Baft County was calculated and then map of walnut was calculated. The results showed effect of physiographic and climatic variables on walnut. The results of validation data set in this study showed that probit logistic regression with 75% and discriminant analysis with 86% able to accurately explain the favorable areas for walnut cultivation. Suitable physiographic areas and climatic potentials for planting walnut trees are located in the central, northern, and northwestern regions of Baft area.

Gills of fish in direct contact with the water environment and hence is vulnerable to aquatic pollutants. In this study, 20 adult fish, Cynoglossus bilineatus, were caught at two different sites in Hormozgan. Province, namely Bandar Kong and Bandar Abbas in summer and gill histology were studied. The histological changes in gills were detected microscopically. Histopathologial changes in gill were classified and histopathological alternation index (HAI) was calculated separately for each station. HAI value in Bandar Kong represents slight alteration. In contrast, we observed severe and irreversible changes in gill tissue in Bandar Abbas. Meanwhile, various histopathological abnormalities were observed in gills of fish specimens. In the gill filaments, cell proliferation, lamellar cell hyperplasia, lamellar fusion, lifting of the respiratory epithelium, and the presence of aneurysmal areas were observed. Because Bandar Abbas city is near to waterfront and oil refineries, effects of environmental stress factors such as environmental pollution are more intense. Gill tissue is sensitive to pollutants and so can be used as a biomarker for monitoring the environment. For this reason, the use of histopathological analysis of gill fish, particularly fish Demersal, in application to evaluating water quality can be used as a tool for more accurate evaluation of environmental quality in the region.

The toxic impact of copper sulfate in lethal and sublethal concentrations was investigated on gill of grass carp, Ctenopharynogodon idella. In this investigation, 27 specimens were used as control group and three treatment groups were treated with 2.5 and 5mg/l of copper sulfate. Gill samples were collected from each treatment after 96hr and lesions were analyzed by light microscopy. In histopathological study of the gill tissues, hyperplasia was clearly obvious in treatment specimens. In all of the treated groups, heavy gill-mucus response was observed, which indicate a direct relation with high concentrations. Also, in histological study of the gill, epithelial cells faced to hyperplasia, which increased with high copper densities. Primary lamellar cells wrinkling and also changing in formation were observed in chloride cells. This lesion enhanced in higher densities and in concentrations of 2.5 and 5mg/l, primary and secondary lamellar epithelial cells were degenerated.

The most important indicator of climate in a region is vegetation community. Climate can be different over large areas، because of changes in vegetation community. Vegetation influences weather and climate in its surrounding areas mostly by way of evapotranspiration and albedo so they play a role in the earth’s energy balance. These effects on the earth’s energy balance are taken through air temperature، relative humidity، rainfall، solar radiation and cloud cover on their own micro-weather (Neilson، 1986، Small and Kurc، 2003 and Weiss، et al 2004). Disasters such as drought، flood، forest fire and among them can occur whenever the global energy balance become outside of normal range. Remote sensing data provide valuable information for vegetation studies because it presents a quick look evaluation of the vegetation conditions. Normalized Difference Vegetation Index (NDVI)، Land Surface Temperature (LST) and land surface albedo are key biophysical variables for studying land surface processes and surface-atmosphere interactions. These variabels can be calculated by transforming raw satellite data. The aim of this paper was to study relationship between NDVI، LST and land surface albedo due to the impact of vegetation on surface temperature and albedo and identify dryness status in the northern Iran.

Study area is located in the northern part of Iran which covers with dense vegetation such as Hyrcanian forests in the north and sparse vegetation in the southern part of the Alborz Mountain range. This area is an alteration bio-climate region lying between the humid climate in the north and arid and semi-arid climate in the south. The methodology used in this study consisted of remotely sensed data processes. The remotely sensed data processes involved data acquisition of Moderate Resolution Imaging Spectroradiometer (MODIS)، data pre-processing (i. e. atmospheric correction، geometric correction، and data masking)، and data processing (i. e. derivation of vegetation، biophysical indices such as NDVI، LST and land surface albedo variables). In this study، MODIS data was selected because the MODIS sensor has several benefits compared to other instruments for example MODIS has a wide swath of 2330 km that covered the entire study area. The MODIS dataset had a variety of products that each contained different levels of data processing. In this study، Level 1B dated July، 2010 (MOD021KM and MYD021KM calibrated and geolocated) was used to derive NDVI and surface albedo، and retrieving LST. This level 1B collection contains calibrated and geolocated radiances at-aperture for all 36 MODIS spectral bands at 1km resolution. In this study، Simplified Model for Atmospheric Correction (SMAC) was used for atmospheric correction of MODIS data. Absorbtion in visible light (solar radiation) occurs in live green vegetation because of photosynthesis. Scattering (reflectance) of solar energy in the near infrared occurs at the same time. This difference in absorption and reflectance lead us to NDVI. NDVI is an vegitation index which measures this difference to show vegetation density and condition. NDVI value ranges between -1 and +1. The values close to zero means no green vegetation and close to +1 (0. 8 - 0. 9) represents the highest density of green vegetation. LST is an important parameter in determining the earth radiation budget and heat and moisture flow between the surface and the atmosphere and temperature strongly influence vegetation processes. Thermal bands of MODIS data (band 31 and 32) converted to radiance then converted to brightness temperature using plank law for calculating surface temperature. Albedo is also an important bio-physical indicator of reflecting land surface energy distribution and balance. In the process of broadband albedo retrieval، an empirical regression was used for MODIS data (Liang et al.، 2002). Finally، regression and geostatistical approach (e. g. CoKriging) was used in this study to estimate LST and surface albedo using NDVI of MODIS data.

In this study three criteria such as mean absolute error، root mean square error and mean absolute percentage error have been used to measure the differences between the values estimated for LST and Albedo using regression and CoKriging geostatistical method. The results obtained in this research indicate that the geostatistical method of cokriging has good potential to estimate LST and surface albedo using normalized difference vegetation index. The results of the study show that changes in vegetation cover alter the LST and surface albedo، leading to a local temperature change. Plants and forest have a very low albedo and absorb a large amount of energy. The relationship between normalized difference vegetation index and LST and surface albedo equations were then used to found the surface dryness condition in the study area. The result of 3D feature space of Albedo-NDVI-LST spectral shows that it has a suitable index for extracting drought information. The study revealed that coastal and forested northern slopes of the Alborz Mountain are identified with high normalized difference vegetation value (0/85)، minimum surface temperature (23° C) and albedo (7%). The southern part of Alborz Mountain and the central Iran experiences low normalized difference vegetation value (-0/09)، high surface temperature of 45 ° C and high surface albedo (38%).

In this study، the relationship between NDVI and LST and surface albedo was analyzed to estimate LST and surface albedo derived by regression and CoKriging methods. It has been recognized that there is a strong correlation between albedo، LST and NDVI. By comparison of the regression relationship among LST، Albedo and NDVI، results exhibit that LST and Albedo are negative correlations with NDVI. In this paper، a 3D feature space of Albedo-NDVI-LST spectral is analyzed for monitoring surface dryness condition. The result represents that surface albedo nad LST is affected by the change of vegetation. This 3D feature space is reliable index to show surface dryness status for soil and plant cover. it is recommended that 3D feature space of Albedo-NDVI-LST spectral can monitor to the surface dryness condition and also it is easy to operate for quick surface dryness assessment. Studies are underway to incorporate other variables in surface dryness condition.

The use of liver histopathology in various fish species as a biomarker in marine monitoring programs to assess the biological effect of exposure to contaminants is well established although the Euryglossa orientalis has repeatedly been recommended as an appropriate bioindicator for monitoring purposes in the Persian Gulf, information on histopathological liver alterations of this species is scarce. In order to obtain an overview, a total of 80 flounder, collected in the period between winter 2011 and summer 2012 from 7 sampling sites distributed over north of the Persian gulf, were histologically analyzed. Liver lesions were recorded and classified according to established liver lesion categories. A high prevalence of non-specific and early toxicopathic non-neoplastic hepatic lesions was recorded. In contrast, pre-neoplastic lesions were observed only in some cases and neoplastic changes were absent. Indices of liver tissue damage in Euryglossa orientalis Eskeleh Nafty Majidieh, Bushehr and Bandar Abbas significantly higher than other stations (p<0.05). The results revealed that the observed alterations may indicate effects of environmental stressors including contaminants. The results of the present study, thus, support the application of Euryglossa orientalis liver histopathology in environmental monitoring.