فصلنامه محیط شناسی
سال چهل و ششم شماره 2 (پیاپی 94، تابستان 1399)

IntroductionUrbanization especially in big cities of developing and developed countries has major impacts on climate change by producing greenhouse gas and increasing average temperature, and thus creating urban heat islands(UHI). The unplanned urban development is one of the main factor, which is responsible for making such circumstances. Lack of enough attention to preserving natural and green infrastructure is one of the factors causes city warming and urban heat islands challenges that are important issues in urban environmental planning nowadays. Urban heat island consists of air temperature and surface temperature. Studies show that land cover planning and management can control surface temperature. The relationship between increasing the green spaces as an important element of the green infrastructure and decreasing surface temperature is already has been studied. Regarding the literature has been reviewed in this paper, the purpose of this study is to investigate and clarify the detailed relationship between the characteristics of spatial patterns of urban green spaces and their influences on surface temperature. Spatial composition and spatial configuration are two main elements of spatial patterns of urban green areas. Classification of green land cover based on Local Climate Zone (LCZ) help to discover the detailed relationship between each patterns’ components and the classified green spaces. The case under study is the city of Tehran, which has witnessed certain changes in relation to the development of built-up areas (both in form of planned and unplanned developments), reduction of green spaces and their spatial patterns, as well as rising average temperature. Materials & MethodsIn the process of investigating the relationship between urban spatial patterns of greenspaces in city of Tehran and land surface temperature, different methods and techniques are applied. The greenspace classification map of the city of Tehran was produced with the help of Landsat 8 satellite (2019) and LCZ method of land use classification, which divides green areas into 4 classes as follows: A; heavily wooded landscape of deciduous and/or evergreen trees. Land cover mostly pervious(low plants). Zone function is natural forest, tree cultivation, or urban park.B; Lightly wooded landscape of deciduous and/or evergreen trees. Land cover mostly pervious(low plants). Zone function is natural forest, tree cultivation, or urban park.C; Open arrangement of bushes, shrubs, and short, woody trees. Land cover mostly pervious (bare soil or sand). Zone function is natural scrubland or agriculture.D; Featureless landscape of grass or herbaceous plants/crops. Few or no trees. Zone function is natural grassland, agriculture, or urban park.Kappa coefficient and overall accuracy of this map was 0.8706, 88.172 percent, which confirms its accuracy. The next step was selecting landscape metrics. Based on the aim of the study and the reviewed literature spatial composition and spatial configuration are selected as two main elements of spatial patterns of urban green areas. The relationship between land cover patterns and surface temperature is analyzed and discussed by using Pearson and Pearson Partial correlation method. Discussion of ResultsThe result of Pearson correlation analysis showed that there is a significant and negative correlation between spatial composition of A, B and D land cover classes with surface temperature. The highest negative correlation belongs to class B (scattered trees) and the lowest belongs to class A (dense trees). In contrast to these negative correlations, the correlation coefficient of class C with surface temperature is positive and significant. The result of Pearson correlation analysis regarding spatial configuration showed that the average size of each green space class has a continuous and significant negative relationship with the surface temperature, though, the size of these correlations varies in different classes. The correlation also showed that besides size and significance, the direction of green marginal density of each class also differs. It should be noted that the surface area of green space classes (as a composition metric) has a great impact on the results so that the correct and clear correlation of configuration metrics with temperature could not be distinguished. This issue was resolved by using Partial Pearson correlation coefficient and controlling the effect of Class Area metric. As a result, the relationship between configuration metrics and LST changed significantly. Before controlling the Class Area metric, almost all metrics were correlated with LST, however, the new detailed findings showed that only the Mean Size of Patches in A and D classes and Edge Density in B and C classes had a significant relationship with surface temperature.The study shows that spatial composition of green spaces in Tehran in relation to the Class area of classes A, B and D had a negative and inverse relationship with surface temperature. Class B, located in the east and west of Tehran, has the highest negative correlation. Class A in the east and center of the city with the lowest surface area and its scattered distribution pattern in comparison to other classes has the least negative correlation with surface temperature (95% confidence level). Class D, located mostly in the south and west of the city, has a negative relationship between Class area and temperature at the 99% confidence level. The correlation of spatial composition of class C in the northern half of the city is not like the other three classes and indicates a positive and significant relationship with surface temperature due to the presence of shrubs and grasslands with low density, scattered shrubs, and soil. Regarding the partial Pearson correlation of spatial configuration metrics, the Mean Patch size of Class A at 99% confidence level shows a negative and significant relationship with temperature, but due to its subdivision and uneven distribution of green space in this class, the effect of this class in the reduction of temperature is not significant. The Mean Patch size of Class D has a significant negative relationship with surface temperature at 95% confidence level, although its cooling effect is not considerable.Both Edge Densities of classes B and C at 95% confidence level had a significant positive correlation with surface temperature, but as trees did not exist in a dominant and dense manner to cause shading and temperature adjustment in these type of greenspace classes, a positive correlation between the Edge Density of them and surface temperature is occurred. Conclusion This paper has demonstrated the relationship between urban heat islands and spatial patterns of green spaces in Tehran city. The literature based study showed the scope of the problem explaining that urban green spaces contribute to mitigate climate change impacts through decreasing the surface temperature. The spatial form and pattern of urban green spaces have different effect on surface temperature as indicated in several studies. Importantly, planners and designers, need more detailed studies to take into account the relation between effects of spatial composition and configuration of different classification of plants and their effects on urban surface temperature. In this research, greenspaces patterns was studied using Local Climate Zone(LCZ) method and correlation of spatial pattern (composition and configuration) of each of LCZ green classes with the surface temperature were provided. The results of the analysis of the spatial composition of these classes showed that tree canopy greenspaces in both cases of high / low density and low plants has a reducing effect on temperature, but low-density grasslands and scattered shrubs with soil cover, has a positive relationship with temperature. More detailed results on the spatial configuration shows that only the mean patch size in dense tree areas and low plants has a significant negative correlation with temperature. But Edge density of scattered trees and open arrangement of bushes had a significant positive relationship with temperature. Thus urban green space planning and management, through determining the type, composition, and configuration of existing patterns and their improvements based on their effect on the reduction of surface temperature will help to decrease urban heat island impacts.

Introduction Wetlands ecosystems play an important role in the ecosystem in arid and semiarid regions. Drying these ecosystems can be caused by human and natural factors; Meteorological droughts are caused by natural factors such as precipitation and temperature rise and evaporation, but in the event of hydrological drought, despite the above factors, human factors in water management play a key role. Climate change and ongoing droughts with rising temperatures and evaporation and declining humidity and runoff in catchments and by human intervention through the construction of dams and large irrigation networks, the entry of agricultural wastewater, industrial and urban pollution And the oil ecosystem has made it difficult for wetlands.The trend of climate change and the occurrence of continuous droughts with increasing temperature and evaporation and decreasing humidity and runoff in catchments, especially in the catchment area of wetlands located in arid and semi-arid regions; Like Shadegan, and due to human intervention, ecosystems have caused wetlands problems and caused them irreparable damage. The purpose of this article is to monitor the wetland surface area of the wetland over a period of 30 years. The relationship between the occurrence of hydrological drought and the water area of the wetland and the calculation of the drainage water entering the wetland and its relationship with the water area of the wetland is one of the objectives of this article. The results of this paper will help water resource management decision makers to provide both wetland water supply from the Jarahi River freshwater source and integrated sugarcane drainage potential with respect to its treatment as an opportunity to save the wetland as an ecosystem. Live help with multiple functions.Materials and Methods . In order to investigate the relationship between hydrological drought and its role on Shadegan wetland changes, Streamflow Drought Index (SDI) was used. For Jarahi basin with 10 stations that have a 30-year period. Hydrological drought characteristics including the frequency of continuity and magnitude were calculated and analyzed for decades for the catchment area of the wetland. Landsat TM and ETM + and OLI satellite images were used in the years 1988 to 2017 in June. Three stages of preprocessing, processing, and post-processing on images are carried out and to categorization use of Supervised vector machine (SVM). The images were classified into three classes of water, vegetation, and no cover or soil In order to evaluate the classification accuracy of classified images, two indicators, total accuracy and kappa were calculated. Changes in Discharge The Shadegan hydrometric station was calculated as the last entry point of the Surgical River to the wetland and other water sources entering to Shadegan wetland, whose discharge was measurable and available, such as sugar cane Drainage water And their values were compared with changes in wetland area water. Results and Discussion Naturally, the most important source of water for wetlands is seasonal and permanent rivers and runoff from the watersheds leading to them. The frequency of occurrence, persistence and magnitude of drought in the watershed area of the wetland leads to a decrease in freshwater inflow to the wetland. Meteorological drought, especially in the last decade, and the development of irrigation networks and the construction of dams have been the main reasons for the occurrence of hydrological drought in the watershed, resulting changes in the water level of Shadegan wetland. The frequency of hydrological droughts of the decade in the watershed of Shadegan wetland has been increasing and in the last decade the drought storm has reached 8 to 9 event. The persistence of hydrological droughts in the basin has an increasing trend, although there is no continuity in the first decade, but in the second decade in most stations the continuity is three years and in the third period the drought is 8 years. At Shadegan station, which is the last water entry station to the lagoon, the continuity has reached 9 years. The study of the large size of the hydrological drought of the decade in the watershed of Shadegan in the first and second decades was low, but in the third decade the magnitude of the drought reached more than 8, which indicates the persistence and pervasiveness of the drought in the third decade.The trend of 30-year changes in the water area of Shadegan Wetland is increasing. Prior to the arrival of the drainage system due to sugarcane projects, the highest water area of the wetland was 22.4%, which was due to the conditions of the watershed related to the wetland, which faced severe wet conditions. In 2005, the wetland's water area reached its maximum value during the 30-year period under surveillance; in the previous year, 240 million m3 of Drainage Water entered the wetland from sugarcane projects, and this year 266 million m3 of Drainage Water entered the wetland. In addition, mild wetland conditions have prevailed in the watershed this year. Therefore, the water area of the wetland in the early years was subject to precipitation conditions in the wetland watershed and since the year that sugarcane drainage entered the water of the wetland, it has been subject to precipitation conditions and the volume of incoming drainage water from sugarcane projects.Conclusions The results showed that although the frequency of drought persistence and magnitude increased in recent decades compared with other decades, the area of wetland water has increased trend. The area of the wetland in the last decade has not been consistent with the discharge exit from the last hydro meteorological station of Shadegan, but has been associated with the total amount of water entering the sugar cane and discharge Shadegan hydro meteorological station. Therefore, sugarcane Drainage water, regardless of its quality, has played a key role in the recovery of the Shadegan wetland as a living ecosystem in recent decades, due to the severity and severity of drought.

IntroductionDue to increase of water exploitation and drought, the need for water resources has risen in past decades. Numerous regions around the world are under threat of environmental crisis, as a result of climate change. Declination in the amount of precipitation can be led to various subsequences, such as significant reduction in the level of ground and surface water, e.g., lakes. Through the development of satellite imagery systems, it is possible to monitor and evaluate changes in rainfall, groundwater level, surface water area, and level.Numerous studies have been conducted to observe and evaluate climate change after the launch of Gravity Recovery and Climate Experiment (GRACE) satellite mission. GRACE dataset has been used widely to determine water storage variations over the world as well as Iran. This satellite data has been used for various purposes including ground and surface water monitoring. Employing this dataset beside precipitation and satellite altimetry data have been used for observing changes in watersheds and lakes in numerous studies. Modelling and predicting environmental and climate changes are always an important task. Gathering several remote sensing data and predicting them would be helpful mostly for disaster management and also decision making.Therefore, it is possible to observe and evaluate variation in rainfall, groundwater level, surface water area, and level. In this study, Urmia Lake and its watershed changes were monitored using various satellite data such as TRMM, GLDAS, GRACE, MODIS. Moreover, machine-learning based methods were developed to predict the lake surface changes.Materials & MethodsTo monitor Urmia lake changes, several data were used to survey variation in precipitation, ground and surface water storage, lake water level, and area in 183 months from April 2002 to June 2017. Sufficient temporal resolution of the data is an essential factor in monitoring of changes through the time. Accordingly, for monitoring the overall change of the Urmia lake, we prefer a satellite data with at least monthly temporal resolution. Therefore, overall variations of the lake and its corresponding basin were modeled using these data with adequate temporal resolution. Tropical Rainfall Measuring Mission (TRMM) is an international collaboration which aims to observe rainfall for environmental studies. TRMM data provides precipitation in various temporal and spatial resolutions. In this study, TRMM-3b43 level 3 monthly data, with 0.25 degree spatial resolution estimates rainfall in Urmia lake basin, including 83 pixels in each time step. The GLDAS hydrological model consists of various variables (e.g., soil temperature, soil moisture, precipitation, etc.). In this study, The GLDAS data with 1 degree spatial resolution provides terrestrial water storage (TWS) by integrating soil moisture (kg m-2), snow water equivalent (kg m-2), and canopy water storage (kg m-2). Three types of monthly GLDAS model data (MOS, VIC, and NOAH) were hired for this purpose. GRACE is a joint missions between Germany and the USA, giving information about mass changes within Earth. The level 2 (RL05) data was of GRACE was used to monitor TWSA, which was computed from spherical harmonics using methods developed by Wahr and Swanson. In addition, a 300 km Gaussian filter was applied to reduce high frequency noises. The investigated Global Reservoirs and Lakes Monitor (G-REALM) dataset including Jason-1, Jason-2/OSTM, and Jason-3 altimeters was employed to survey Water Level (WL) variation of Urmia lake. In order to monitor lake extent changes during the 17 years, MODIS atmospheric corrected product MOD09Q1 version 6 data, with 250 meters spatial and 8-day temporal resolution was used through Google Earth Engine. The product provides surface spectral reflectance of bands 1 and 2, which is the composite of 8 products with the absence of clouds, cloud shadow, and aerosol loading. Although, the Normalized Difference Water Index (NDWI) is a common method to separate water from land and it also had the best result on Landsat data, Normalized Difference Vegetation Index (NDVI) performs transcendent distinguishing between water and land while using MODIS data and also in the specific case of Urmia lake. Therefore, in this study, the NDVI index was chosen as an appropriate index to separate water and non-water. To determine lake area, firstly, water region was detected. Then, area of water extent was computed as lake area.For modeling the lake's area variation, machine learning based methods were investigated. As a time-series prediction problem, a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM) networks were constructed using TRMM rainfall, GLDAS, GRACE TWS, and altimeter WL as inputs (predictors) of the models, and lake's area as Target. About 80% of data was assigned to training, 10% to validation, and the same portion to test. A feedforward MLP including one hidden layer and 5 neurons and a Recurrent LSTM network with same hidden layer and 10 neurons, were obtained. In order to evaluate network's performance, Root Mean Square Error (RMSE) was used. In addition, the delay parameter of 12 months or one year was chosen for estimating future variations. Results & DiscussionExcept seasonal changes, amount of monthly rainfall during the mentioned period experienced a significant decrease from 2004 to 2008, and then it fluctuates to 2017. The changes in precipitation rate can affect other parameters considerably. As a result, water mass variation obtained from GLDAS data, falls from 2003 to 2008, and after that, similarly to rainfall variation, it fluctuates. However, TWSA computed by GRACE data, after reduction to 2008 and rise to 2010, behaved otherwise, and it went down steadily to 2017. Urmia lake WL declined during the whole period. This decrement was intensified from 2006 to 2010, after that it halted gradually to 2017 as consequence of increase in rainfall rate. Area of the lake decreased from 2004 to 2015, also it faced an extreme fall in 2008. Next, to 2017 the area increased slightly. Due to a decade drought of Urmia lake, it was in critical circumstance. Consequently, estimating future variation of the lake is necessary. Instead of using physical models or assessing the impact of each parameter on the surface of the lake directly and indirectly, which are complicated tasks, a machine-learning based method is hired. Disregarding the exact relation between factors, this learning based method can determine and model changes. By using two of the most common ANN based methods including MLP and LSTM, variation of the lake during that period was modeled. MLP and LSTM models reached overall RMSE (for normalized data) of 0.0586 and 0.0511, respectively, which indicates reliability of both models for predicting lake area changes, however LSTM network performed superior specially over test data (RMSE of 0.0487). In addition, to predict Urmia lake's further changes and assess LSTM model capabilities comprehensively, 4 networks were constructed to predict lake area of next 3, 6, 9, and 12 months. Accordingly, result demonstrates LSTM abilities for predicting upcoming year variation of the lake with RMSE of 0.0882 (better than prediction for 6 and 9 months).ConclusionVariation in each part of environment and climate (such as rainfall, TWS, WL and area of lakes) affects others. Therefore, it is possible to monitor and model these relations between the parameters. In this study, two ANN methods of MLP and LSTM were investigated to model Urmia lake surface area which the LSTM model performed transcendent. Moreover, LSTM method provides a model which is able to predict the lake area of next 12 months with a high accuracy. In order to improve the network’s accuracy, it is suggested to increase the number of data and parameters, which are used as network input. It would help the network to implement the training stage with a higher capability to recognize diverse situations properly.

The climate becomes warmer than ever before, as evidenced by numerous observations and modeling that has in turn created a warming climate in cities. Therefore, various climate change projects and attention to heatwaves in recent centuries have been considered and the increase in population and its activities in various fields have caused problems such as heatwaves around the world. In addition, changing the pattern of occurrence of these changes and their unpredictability has led to an increase in the number of them in cities and to reduce concerns and adapt to these risks in various aspects of urban life, especially human health, to create sustainable urban forms. This phenomenon has caused the death of thousands of people in the world, and one of the important reasons for this is the flaw of the cities to deal with the increase in heatwaves. While different changes and global warming can affect urban areas, urban areas can also exacerbate these changes. Rising urbanization rates and people's desire to live in cities have led to higher urban temperatures than their surroundings. Studies show that a city with a population of one million people has experienced an increase in temperature between 1 and 12 degrees. This increase in temperature is due to the structure of cities, which has many negative effects and consequences for cities, so urban environments should be planned and designed in such a way as to improve the health of individuals and thus the presence in public spaces of cities. Urban forms such as urban canyons and vegetation at the pedestrian level are among the factors affecting the reduction of urban temperatures in urban areas and neighborhoods, the impact of these factors on the outdoor thermal comfort of humans by few research in Iran has been considered. A review of the research background of the subject shows that no serious attention has been paid to the native urban forms of Iran as a factor for reducing urban heat and promoting human health; However, local urban planning helps architects and planners to address urban problems by identifying needs in indoor and outdoor environments to provide the most effective way to reduce the severity of outdoor environments using all design elements. Given that there is still, no place to pay attention to how urban forms change in order to reduce the effects of climate change and outdoor thermal comfort professionally, so the present study focuses on the impact of urban forms on informal settlements and old textures on outdoor thermal comfort. The ambient air temperature and the creation of outdoor thermal comfort in the city of Mashhad, which is one of the cities that experience the most urban forms of informal settlements. The present article, firstly, examines the research done in the thematic field of the article and then in the next part, the theoretical framework is extracted by using articles, dissertations, and books. In the third part of this framework, using simulation in Envi-met software and PET index output in Rayman software and analysis in Leonardo to identify the effects of urban forms on air the temperature in microclimate and outdoor thermal comfort using physiological equivalent temperature index (PET). Studies conducted in the summer and on August 26, 2019, have been selected due to the high heat of the sun and its effect on creating outdoor thermal comfort in open urban spaces. Materials and Methods,Due to the nature of the subject, the research method is applied and based on two parts. In the first part, the documentary method is used to formulate the theoretical framework. Thus, by referring to articles, treatises, and books by taking notes, the information needed to understand the thematic literature as well as studies conducted in the field of research has been collected and then using descriptive and analytical methods to prepare and compile the conceptual framework of research. In the next part of the research, the case study was studied and the maps were extracted using GIS. The method of survey and harvesting of climatic information from the Mashhad Climate Organization has been used to collect climatic data. In order to identify the effects of the influential components of the urban form, the Envi-met 4 and Leonardo software collections as well as Rayman have been used. In this study, most simulations were performed in summer (August) and based on data from Mashhad Meteorological Station. This simulation was selected on August 17, 2019, and at sunrise and sunset between 6:00 AM and 8:00 PM local time due to the high temperature of the sun and its effect on ambient temperature and surfaces in open urban spaces. This simulation lasted for 156 hours for three urban forms.Discussion of Results The present study investigates the outdoor thermal comfort in informal settlements in Mashhad. Various studies have shown that lowering the ambient temperature in summer can reduce environmental thermal stress and thus improve outdoor thermal comfort. The results in this paper suggest that as the height-to-width (H / W) ratio increases, the sun's access to the environment decreases, and the amount of shading on the surfaces increases resulting in a decrease in ambient temperature. Therefore, it can be said that the height-to-width (H / W) ratio is inversely related to the ambient temperature, and urban form factors such as the height-to-width ratio (H / W) and its shading play an important role in reducing the ambient temperature. On the other hand, the simulations performed to show the temperature difference, so that the difference between the mean indexes (PET) during the hours of thermal stress are 0.68 ° C, 2.53 ° C and 3.27 ° C. The heat stress of the Hojjat fabric is greater than that of the other two fabric and there are more hours in the absence of outdoor thermal comfort, which indicates the temperature difference in all three fabric; but the same the difference stems from different environmental parameters in the three urban forms. One of these parameters is the coverage of outdoor surfaces and materials used in the outer shell of buildings, which is indicated by the Tmrt average temperature index as an important parameter in creating thermal equilibrium in the environment. Studies show that the average temperature index of radiant temperature (Tmrt) is directly related to the PET index and the higher the average temperature index of radiant temperature, the higher the PET index, and vice versa, therefore, the high average radiant temperature in the texture of Hojjat compared to the texture of the agent and the Ghale of the building in the peak hours of heat shows the high rate of heat reflection to the environment in the texture of Hojjat compared to the other two textures. Higher average radiant temperature (Tmrt) in the texture of Hojjat means reflecting more heat than urban levels and can be closely related to the materials used in this texture and the amount of sun access and thus the effect of height to width ratio (H / W) on the ambient temperature. And have levels. Due to the color and texture of the brick, it absorbs less heat and gradually releases the hidden heat and its heat exchange with the environment during the night hours when the air cools down. On the other hand, the temperature a difference of 0.68 C in the first and last hours of simulation in the operating tissue shows that the use of brick materials can reduce the heat exchange with the environment and thus reduce the ambient temperature.

IntroductionThere are assessing the impact of environmental change to internalize the externality. Climate change is the environmental change that needs to evaluate the impact on various sectors in the economy. Climate change refers to the long-term variability in the behavioral pattern of the average climate measures of an area in the absence of a change in the general climate of the region. Climate change occurs when changes in Earth's climate system result in new weather patterns that remain in place for an extended period of time. Agriculture is a climate-sensitive sector, and climate-smart agriculture is the way forward to increase agricultural productivity in a sustainable manner. Based on the reanalyzed index of global land-ocean temperature prepared by National Aeronautics and Space Administration combined land and ocean skin temperature represents warming approximately to 1.35 °C between 1880 and 2018. The climate change fact is intensive among the Middle East countries and especially Iran. According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, climate change (CC) will amplify existing stresses on agricultural systems, particularly those in Asia for several reasons. Given the inevitability of the effects of climate change on the agricultural sector, the assessment of its effects can be a basis for compliance with the uncertain future conditions. Some of the research has investigated the potential impacts of climate change on agriculture crops across different geographical locations. Regarding the importance of wheat in Iran‘s food security, The purpose of this research is to assess the effects of climate change on wheat crops in Iran. In this way, Partial goals are including:1-Determination of irrigated land rent in Iran. 2-Investigation the impact of climate variable on irrigated land rent in Iran3-Determination of climate change impacts on irrigated wheat lands rent in IranMaterials and MethodBecause of the potential for global warming, there are widespread concerns about the impact of changing climate upon the productivity of land in farming and other sectors. The needed statistics and information were collected using secondary data. The main wheat production province in Iran are West Azarbaijan, East Azarbaijan, Ardebil, Esfahan, Ilam, Razavi Khorasan, Khuzestan, Sistan and Balouchestan, Fars, Qazvin, Kordestan, Kerman, Kermanshah, Golestan, Lorestan, Markazi and Hamedan Province. About 70 percent of total wheat production is related to irrigated wheat and 30 percent is related to rain-fed wheat. In recent year, land area of irrigated wheat in recent year is about 2.2 hectar in Iran. Also, The yield of irrigated wheat is about 3.5 Ton per hectar in Iran. To accomplish the research objectives, using the panel data and Ricardian approach, the final effects of climate change on wheat rent in major provinces of the country wheat production during 2000-2015 were investigated (relation 1 to 4).In relation (1) to (4), where the Quantity of production, P is is the market price of the crop, L land, and Pl is the agricultural land value. In order to predict the effect of climatic variables on the rent in the future of climate scenarios (A1, B1, and AB) was used. Climate change scenarios or socioeconomic scenarios are projections of future greenhouse gas emissions used by analysts to assess future vulnerability to climate change. Producing scenarios requires estimates of future population levels, economic activity, and the structure of governance, social values, and patterns of technological change.The climate change scenarios for Iran are published by the Intergovernmental Panel on Climate Change (IPCC). Change in precipitation and Temperature will happen to 2100. Temperature and precipitation respectively increased and decreased. Based on Climate change scenarios in Iran between 2025 to 2100, change in Temperature will happen one degree centigrade in 2025, 1.7-degree centigrade in 2050, 2.3 degrees centigrade in 2075 and 3 degrees centigrade in 2100. Also, precipitation will decrease by 0.9 % in precipitation in 2025, 1.3% in 2050, 1.4% in 2075 and 2.5% in 2100.Also, Iran will experience an increase of 3 °C in mean temperatures and a 2.5% decline in precipitation in the next Century. Also, Iran by total greenhouse gas emissions nearly 616,741 million tons of CO2 is the first responsible country to climate change in the Middle East, and seventh in the world. Results and discussion & ConclusionsThe results showed that the first and second-degree coefficients of variables indicate the effects of U or reverse U on net agricultural income, which indicates that the average precipitation of spring season up to 91.5 mm leads to an increase in agricultural land rent and after this, the spot decreases with increasing precipitation. The results of the fall season were similar to those of spring season precipitation, i.e., rainfall increase up to 112.2 mm in this season, will increase the rents, but if rainfall is higher than this, the rent will decrease. The results of the fixed effect by using Generalized least squares (GLS) show that the fixed effect is the best model for investigating climate change impacts on irrigated wheat land rent in Iran. F-test is applied to select between the pooled model and panel model. Also, the Hausman test is applied to select between the random effect model and the fixed-effect model. The quantity of F and Hausman test is respectively 3.14 and 49.58. the result indicates that the panel model is better than the pool model. Also, the fixed-effect model is better than the random effect model to evaluate the climate variable on agricultural land rent in Iran. The quantity of JB shows that the model has normality. However, we applied GLS to resolve the heteroskedasticity in the fixed-effect model.Log-lin is the best form of this model. First, 72 proposed variables including Temperature and precipitation in spring, fall, and interaction between variable, latitude, above mean sea level and quantity of inputs Were estimated by stepwise regression. Then, based on the fixed-effect model, Workforce, Seed, spring temperature, fall precipitation, and total precipitation are significant and R2 is equal to 65 percent.When Spring precipitation increases, first agricultural land rent increases and then decreases. Also, When fall precipitation increases, first agricultural land rent increases and then decreases. Both fall precipitation and Spring precipitation have a two-degree effect on agricultural land rent in Iran. Besides, by one millimeter increasing in spring precipitation agricultural land rent increases 0.05 percent. The negative-coefficient Squared spring precipitation indicates after the maximum spot rent decreases. Aso, the seed coefficient shows that by one Kilogram per hectare increasing in wheat seed, agricultural land rent increases by 0.01 percent. The Workforce indicates when one labor increases, agricultural land rent increases by 0.04 percent. The results of the final effect of climate change on agricultural land rent using scenarios of climate change in the future years showed that climate change has a significant negative impact on wheat yield rents and will lead to lower product rents in the future, So that the climate change will decrease the rent by 1.35,4.51 and 10.41 percent in 2025, 2100 and 2050, respectively.As a result, it could be mentioned that consideration of the effects of climate change on food security and farmers needed can decrease the negative effects of climate change

IntroductionThe application of kriging in the field of environment is focused on four main sections: mapping of precipitation, quantitative and qualitative status of groundwater, quantitative and qualitative evaluation of surface water and spatial forecasting of air quality (Hanefi Bayraktar,2005; Nas, 2009; Chen et al., 2016; Sharma et al., 2017; Yang et al., 2018).Different types of kriging have been developed, but the most popular of these is ordinary kriging (OK). The most important disadvantages of OK is that it assumes the mean of modelled variables to be constant and the prediction is only based on the spatial structure of the studied points. In addition, the effect of important parameters does not take into account the estimation result, and in some cases the predicted values by OK may be out of the studied range (Mukhopadhyay et al., 2017; Montero and Matthew, 2015). To overcome these problems, the universal kriging and blind kriging have been developed. Blind kriging (BK) is a more complete version of universal kriging and is based on Bayesian variable selection technique which is complicated and takes a lot of time to identify the unknown mean function.In this research, due to the complexity of the Bayesian computation, we will combine the regression technique with blind kriging as the unknown mean function is defined by variable selection techniques that are used in linear regression analysis such as forward selection, backward elimination, and step-wise regression. The residuals at the known points are calculated from the difference of the observed values and the values of the selected function. Then the residual mean at the unknown point is solved by OK method.Tajan River is one of the most important rivers in Mazandaran province. This permanent river is about 140 kilometers long and originates from the mountainous area on the northern slopes of Alborz Range. Tajan River runs to the plains carrying the water from various tributaries in the mountains and then drains into the Caspian Sea. Dodangeh, Lajim, Chahardangeh and Zarem rivers are the most important branches of it. The area of Tajan River watershed in the Aldehil region (before entering the Caspian Sea) is 4700 square kilometers. There are different land uses including agriculture, aquaculture, dam construction and industrial activities around the river. The necessity to perform a systematic study of the river water quality is, therefore, a need and of prime importance. The objective of this study is to evaluate water quality parameters in different sites on Tajan River. This study also attempts to propose a modified kriging method, in which the unknown mean function is defined by using linear regression in order to simplify the computations of blind kriging. The proposed method and ordinary kriging were used to model the spatial variability of heavy metals and water quality index and their results were compared. This case study may be counted as an initial effort to study the spatial variability of water quality parameters, which may have many practical implicationsMaterial and methodsThe water sampling took place in spring of the year 2018. Sampling sites were selected based on natural conditions and accessibility to Tajan River by taking into account natural and human impacts, including river sub-branches, changes in the polluting sources such as agricultural lands, residential centers, existing industries, etc. Sampling sites were named 1 to 10 from downstream (near the Caspian Sea) to upstream (the Shahidrajae Dam). Water samples were collected by Nansen bottles and transferred into 1-liter bottles previously cleaned by nitric acid (0.1 N). The temperature, pH, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), redox potential (Eh), and turbidity (Turb) of each water sample were directly measured at the sampling points. The biochemical oxygen demand (BOD5) was determined by the Winkler Azide method and chemical oxygen demand (COD) by the dichromate reflux method. Phosphate (PO4-P) and Nitrate nitrogen (NO3-N) were analyzed by spectrophotometric method, and fecal coliform (Fcoli) were measured by multiple tube method.For measuring metals, each water sample was filtered through Whatman filter (0.45 μm) and about 5 mL of HNO3 (0.1 N) was added to the samples (until pH<2). Then, the samples were stored in the refrigerator at a temperature below 2 °C until being transferred to the laboratory. The measured elements included Arsenic (As), Barium (Ba), Calcium (Ca), Cobalt (Co), Chromium (Cr), Copper (Cu), Iron (Fe), Nickel (Ni), Lead (Pb) and Thorium (Th). Metal measurements were done by inductively coupled plasma optical emission spectrometry (ICP-OES). Analysis of the samples was done based on the instructions recommended by (APHA, 2005).All mathematical and statistical computations were made using EXCEL 2016, SPSS 22 and ArcGIS 4.10.1.The proposed method and ordinary kriging method were applied to interpole and predict IRWQI, Fe, Ni, Co, Th, Ba, As, Pb and Cr and their results were compared. Results and discussionsIRWQI had low values in midstream (sites 5, 6 and 7) near Sari City and its value increases by moving away from the city to show an improvement in the water quality status. This is due to the effect of anthropogenic pollutants in Sari city which are negatively associated with the water quality index. The average range of pH was from 7.6 to 8.11. pH values of water samples indicated that it was slightly alkaline. The relatively higher pH in water are probably due to the presence of pollution and eutrophication status. The mean values for Eh ranged from 142 mV to 201 mV. The water of Tajan River (excepting in site 1 near the sea) is non-saline.The mean function for each variable (IRWQI, Fe, Ni, Co, Th, Ba, As, Pb and Cr) was created by multiple linear regression. The coefficient of determination (R2) and adjusted R2 were used to check regression model adequacy. t-Test and residual analysis were used in testing the regression coefficients verifying of the applicability of the regression model. The coefficient of determination (R2) ,Coefficient of Variance (C.V.) and Root Mean Square Error (RMSE) were used to evaluate the results of the ordinary kriging and proposed method. The proposed method showed 8.8 percent improvement for Cr, 34 for Co, 56 for Pb, 62 for As, 44 for Ba and 169 for IRWQI. In addition, both methods for prediction of thorium and nickel were almost identical. While ordinary kriging performance was good in predicting iron and better than the proposed kriging, because for parameters whose correlation is strong with distance and spatial distribution, ordinary kriging method can work well in modeling them. the mean concentration of elements in the water followed the following pattern: Ca> Fe > Ba > Ni > As > Cr > Cu >Th> Co > pb. The mean concentration of elements (Co, Cr, Ni, Cu, pb and Th) demonstrate a similar pattern with a decreasing trend from the upstream to the downstream. This will strongly show a similar process and origin. while the mean concentration of Ba and As was increased at various sites from the upstream to the downstream.

Introduction There are several ways to monitor macroinvetebrate communities as a biological indicator of river health. One of these methods is a comprehensive method in Australia, the SIGNAL Index (average level of the number of invertebrate streams, SIGNAL) that assesses the degree of susceptibility to contamination for all major species of invertebrates in Australia. Based on the species at each station, the high sensitivity of inanimate invertebrates is used to calculate the water quality rating of streams or other water bodies. Also, the use of the EQR index, which is a multi-criteria indicator, 18 ecological factors from macroinvetebrate, evaluates the ecology of the river. The EQR is the latest multi-criteria indicator for water ecological assessment, first used in the Vietnam River in 2015. This study was conducted with the aim of identifying the macroinvetebrate and also in order to evaluate the efficiency of multi-criteria indicators for determining the biological health of Madarsoo river water, in Golestan forest using macroinvetebrate in large quantities and EQR index.Materials and methodsThis research was carried out in 2018 from 3 seasons of spring, autumn and winter (no sampling in summer due to reduced Dubai and in some parts of the river without water) in the upper part of the river of Golestan forest area to the end of the strait in 4 stations. Sampling was performed using a sampler (30 × 30 cm). The Biological SIGNAL Index was set to assess water health in Australia. The index measures water quality from 1 (pollution-resistant) to 10 (pollution-sensitive) and gives each family a score between zero and ten based on its susceptibility to pollution. In the evaluation method, using a macroinvetebrate, many parameters and taxon richness are combined with the index of species resistant.The Multi-Indicator Index (MMIF) describes the status of an ecosystem by several basic indices. Each of these variables offers a different combination of ecosystem quality and is evaluated in one indicator. Composite indices were first used for fish communities and later for other index groups such as the macroinvetebrate. The Ecological Quality Ratio Index (EQR) is one of the most recent multivariate indicators in 2014, which evaluates the ecological integrity of a river based on 18 macroinvetebrate ecological parameters.Discussion of ResultsRiver in the Golestan forest area were sampled, identified and counted. The macroinvetebrate of the Madarsoo River is given in Table 5. The most common of the unidentified organisms were Chironomidae (255, 32.9%) and after Caenidae (178, 22.97%) and Baetidae (118, 15.23%) of the order Ephemeroptera. The most diverse groups identified were Diptera (37.5%) and Ephemeroptera (18.75%), respectively. The larvae of aquatic insects accounted for the largest population of invertebrates. macroinvetebrate were available in all seasons, with only Decapoda (Station 1) and Physidae (Station 2) being observed in the fall. The highest frequency was recorded at station 1 (35%) and 2 (25%) and the lowest frequency was recorded at station 4 (19%). The study of macroinvetebrate abundance in 4 stations from Madarsoo River among the study seasons showed that in autumn (48%) the highest abundance and in winter (21%) the lowest abundance in this river.This river has the largest number of low quality water pollution stations. The results of the SIGNAL index show that most stations are on less pollution class and only stations 4 are on class b in all seasons. The highest value of this index was observed in station 1 (1.5) in spring and the lowest in station 4 (3.1) in winter. The SIGNAL 2 index also showed that only the station 1 in the study seasons is higher than 4 and is in the fourth a. However, the value of the index in other stations is less than 4 and according to the number of species, this station is in a quarter b. The lowest value of SIGNAL 2 (3.11) was observed at station 3 in winter.The results of the MMIF composite index show that the ecological situation and the level of pollution in the mother river in the spring are in better condition. In general, 3 qualitative class (good, medium and bad) of this index were observed in Madarsoo River in 2018. Stations 1 and 2 were on the good class in the spring, stations 1 and 2 were in the fall, and stations 1 were in the middle class during the winter, and the other stations were on the bad class. Station 1 was on the good quality class and station 4 was on the bad quality class EQR. The highest value of this index is 0.9 in station 1 and the lowest value is 0.24 of station 4.ConclusionsHigher average SIGNAL rating than Stations 1 and 2 compared to a lower score on Stations 3 and 4 indicates that more infected species such as Baetidae and Heptageniidae live in natural environments. This indicator suggests that susceptible species such as Trichoptera and Ephemeroptera can also live in areas exposed to relative organic pollution with suitable environmental conditions.The EQR index describes Station 1 as a control station with good quality. Station 2 was also described as of good quality, with recent natural or human activity causing reversible changes at the station. Station 3 is of medium quality and that often human activities disrupt some of the ecological relationships of living societies. Station 4 is also on a poor quality floor, which needs to be rebuilt and planned to reduce the number of works immediately.Nowadays, aquatic organisms are used as biological indices to assess the quality of ecological water. Therefore, we used multimetrices indicators, including MMI, to assess the water quality of the Madarsoo River. Unfortunately, based on the indicators studied, some stations are in poor quality. In particular, downstream stations are affected by human activities and land use change. These results are important for local river managers studied, as well as other rivers in northern Iran that are under the same land use stress. Monitoring and evaluation tools for water resources management are usually more effective if they are based on a clear understanding of the mechanisms that lead to the presence or absence of species in the environment. The results showed that the SIGNAL and EQR indicators are suitable for assessing river health by macroinvetebrate.

AbstractAmong surface water resources, wetlands have special importance in providing habitat for various plant and animal species due to their ecological roles. Wetlands are swampy areas, reservoirs, and natural and man-made ponds that have static or flowing water, fresh or saline, permanent or temporary. One of the most important but little known traits of wetlands is to improve surface water quality. These systems can provide effective treatment for a variety of contaminants in the water, hence they are known as "natural water purifiers". Removal of pollutants occurs by the effect of simultaneous operation of physical, chemical, and biological processes including deposition, filtration, chemical reduction, adsorption, biodegradation, photo-oxidation, consumption by animals and plants, etc. The mechanisms and interdependencies between the ecological components of the wetland are complex and many of them are not yet fully understood.With the development of technology and the use of computers in engineering processes, the use of computer models to simulate ecological processes in natural ecosystems has become very common during the last years. Therefore, making an appropriate hydrodynamic model of the water body with the ability to simulate the affecting processes for the fate and transmission of the pollutant, has become a technical necessity.So far, several field studies and computer simulations have been reported to evaluate the efficiency of the wetlands to improve water quality under the predicted load of pollution entering the wetlands. The experience of using mathematical models for the simulation of the wetlands has shown the high ability of these models for the simulation of the complex ecological processes. So the computer models have seriously been considered as a modern tool for the management of wetlands and improving its purification efficiency. In the current study, the experiences of Babol city in the province of Mazandaran for the planning of a city wetland i.e. GoleNilofar wetland, to the common space is reported. The hydraulic retention times in wetland different basins are about 20 to 60 days. The inflow to the wetland is not changing along the seasons and was measured about 0.153 m3/s in spring, 0.157 m3/s in summer, 0.273 in fall and 0.217 m3/s in winterIn this study, the computer model of the GoleNilofar wetland is developed using Hydrodynamic and ECOLab modules of the MIKE3 software. The FM hydrodynamic model is a basic numerical model for the flow simulation in MIKE3 that can be used in all water bodies i.e. the wetlands, rivers, bays, coastal waters, and open oceans. This model can simulate the flow unsteady three-dimensional features in the conditions of density changes in the environment. The model can simulate the impacts of external forces including meteorological and tidal parameters. The EcoLab module of MIKE software was developed as a modeling tool to investigate the effects of natural aeration, sunlight photooxidation, and sedimentation together with the plants and bacterial uptakes on the fate of organic matter and purification capability of the different water bodies. In the simulations, only BOD (Biochemical Oxygen Demand) time variations and spatial changes in the wetland were investigated. The amount of oxygen needed by microorganisms for the oxidation of degradable materials within 5 days is called BOD5 i.e. 5-day biological oxygen demand. Biological oxygen demand is one of the most important indicators of water pollution. Water contamination is caused by external material in a suspended or dissolved form that changes the physical, chemical, and biological properties of water.The BOD of the inflow was measured by sampling the incoming current in different seasons along 1398 (2019-2020). The BOD together with the discharge flow rate, temperature, and density of the water are modeling inputs that are required for the simulation. Here in this paper, the results of the wetland ecological simulations have been reported for the season of the spring. This season is selected as it is the beginning of a growing season in the wetland. To better investigate the distribution of pollutants and the changes in the flow properties, the water body of the wetland is assumed to be stationary and the concentration of BOD is considered equal to zero at the beginning of the simulation. So the inflow to the wetland (to the pond 1) was measured 0.153 m3/s and the BOD of incoming water was measured 5.5 mg/l, both of which did not change significantly during the spring. The time interval of 3 hours and the number of steps of 735, equivalent to one whole season is introduced to the model. Finally, the simulation results at the end of spring were compared with the observed values from the field sampling at the beginning of summer.As a result of hydrodynamic modeling when the wind speed is at its maximum during the season, i.e. 9 m/s, the flow velocity in the wetland is also at its maximum. For wind speeds of 9 m/s, the velocity of the surface flow was above 0.37 m/s, which due to the closed boundaries of the environment, lead to deep current and material conduction to water depth. Ecological modeling exhibited that due to the chemical and biological processes, as well as the long retention time of the pollution in the wetland, distancing from the entrance, the BOD decreases clearly. According to the discharge flow rate and volume of each pond, the retention time in the first pond is about 18 days, in the second pond 24 days, and the third pond 73 days. The results also showed that the amount of BOD entering the wetland (5.5 mg/l) at the end of spring and early summer reaches 4.7 mg/l at output 1 and about 3 mg/l at output 2. The field measurements of BOD at the beginning of summer in both outlets showed the values of 4 and 3.4 mg/l, respectively. Comparison of the modeling results with our field observation at the end of this season exhibited that the model can predict the BOD concentration with 80% accuracy without adjusting the coefficients and only using the values reported for similar conditions in previous studies. In this work, an ecological model has developed using the ECOLab module of MIKE3 for the Gole-Niloufar wetland which is an urban wetland in the city of Babol, north of Iran. The cpability of the wetland to improve the quality of incoming water has been discussed. This waterbody is a valuable natural resource in the region in terms of entertainment and recreation and has been used to supply water to rice farms downstream. In the developed model, the wetland is simulated during the spring for 93 days with 735 of 3-hour time steps in which the actual data of the inflow and BOD have been utilized. The BOD parameter was selected as an indicator of contamination to the organic matters and the process of transfer, diffusion, and decomposition were investigated by hydrodynamics modeling of the flow and the simulation of the BOD degradation in the wetland. The calculated values were compared with the field measurements at the end of the season and the accuracy of the model was investigated. A comparative study of the results with the field data exhibited that the model can predict the degradation of BOD concentration in the ponds. The results of this study showed that due to the high retention time, low flow rate, and the natural rehabilitation and purification, this wetland can reduce pollution to a desirable level. It has also been observed that the water quality of the wetland depends on the physical, chemical, and biological processes of wetland beside the properties of the incoming water. So improving the wetland performances from this perspective can ensure the safe use of water downstream.

This paper studies thermal behavior of dominant residential structure patterns in the city of “Tabriz” that has cold and dry climate. With this regard, thermal behavior of three typical residential structure patterns, including: traditional courtyard, row house, and high-rise building is studied to determine the most sustainable structure pattern that is capable of achieving the optimal energy consumption in Tabriz environment. For this purpose, it is necessary to study the thermal behavior of each type of structure patterns from different aspects. Therefore, the influences of several architectural features on energy consumption are investigated to provide designers, constructors, and consumers with useful measures for residential construction in the city of Tabriz. Thermal behavior of each type of structure patterns is simulated using “Ecotect” software. The simulation results are then analyzed using “EnergyPlus” software. Based on the results, in the city of Tabriz, the amount of energy required for heating residential structures under cold weather conditions are three times more than the amount needed for cooling them during hot season. Amongst all three types of dominant residential structure patterns in the city of Tabriz, the high-rise building pattern provides the best heating performance due to proper insulation and also for the maximum use of sunlight. On the other hand, the traditional courtyard pattern provides the best cooling performance due to the minimum heat exchange of its outer walls. The results also suggest that insulation with impact of 0.41 is the most significant variable parameter for residential constructions in Tabriz environment.Materials and MethodsIn this research thermal behavior of three dominant residential structure patterns, including: traditional courtyard, row house, and high-rise building is studied to determine the most sustainable structure pattern that is capable of achieving the optimal energy consumption in Tabriz environment. For this purpose, the thermal behavior of each type of structure patterns is carefully studied from different aspects. With this regard, the influences of several architectural design parameters on energy consumption are investigated to provide designers, constructors, and consumers with useful measures for residential construction in the city of Tabriz. In this research, form of the structure and age of the building are selected as two independent parameters according to national thermal standards. In addition, four variable parameters namely: orientation, construction materials, type of overlay, and insulation are also measured. Effects of these parameters on energy consumption are studied for all three types of structure patterns considered in this research. Thermal behavior of each type of structure patterns is simulated using “Ecotect” software. The simulation results are then analyzed using “EnergyPlus” software. In addition, for validation of the results obtained through software simulation, a number of consumers’ bills related to each structure pattern are collected as field impressions and comparison of energy consumption is conducted.In order to determine the importance of each architectural feature for each type of structure, the criteria are weighted using AHP 8 (Analytic Hierarchy Process) method. Hence, variable parameters together with independent parameters are organized in a matrix (shown in Table 5), and then multiple criteria are weighted using AHP method. Calculation of multiple weighted criteria results in precedence of parameters. Thus, the importance of each parameter in each pattern is determined. Discussion of ResultsFirst, all three models are simulated and the amount of annual energy consumption for heating and cooling for each month is measured and the results are presented as bar charts accordingly. The results in Figure 7 show that the highest level of energy required for heating is during November to April, which is due to the cold climate of Tabriz.This amount exists on average and with a significant difference between a high-rise building (B.M.3) with a row house (B.M.2) and a courtyard house (B.M.1). Thus, considering the amount of energy required for heating regarded to square meters of the building, with a high difference, the high-rise pattern with lowest amount achieved the most optimal level of energy consumption. Among the other two patterns, the amount of energy consumption for heating in a courtyard house is slightly higher than in a row house. According to the results, the percentage of total amount of energy required for heating for each pattern is 43.2% for B.M.1, 42.8% for B.M.2, and 14% for B.M.3, respectively.On the other hand, the results in Figure 8 show that the highest level of energy required for cooling is during January to September, which is due to the short summer season in Tabriz. Based on the obtained results, the traditional courtyard pattern has the advantage of proper climate design in that slight amount of energy is required for cooling. On the other hand, the high-rise building requires the highest amount of energy for cooling. Yet, this pattern provides the best heating performance due to proper insulation and also for the maximum use of sunlight. According to the results, the percentage of total amount of energy required for heating for each pattern is 21.8%for B.M.1, 34.7%for B.M.2, and 43.5% for B.M.3, respectively.Based on calculations of the weighted parameters, significant architectural features are insulation (0.41), type of opening (0.32), construction materials (0.23), and orientation (0.04), respectively. In fact, consideration of these measures at the early stages of designing residential structures leads to optimal energy consumption in Tabriz environment. ConclusionsIn this paper, thermal behavior of three dominant residential structure patterns in Tabriz housing is studied to find out how different architectural features affect energy performance of residential structures. Overall, the outcomes of this study can be summarized as below:1. The effect of independent parameters on the amount of energy required for heating and cooling is presented in Table 6. 2. Numerical figures indicate that the amount of energy required for heating residential structures under cold weather conditions are three times more than the amount needed for cooling them during hot season.3. Based on this study, the high-rise building pattern provides the best heating performance due to appropriate insulation and also for the maximum use of sunlight, However, this type of structure has low performance in term of energy required for cooling, which is due to large openings in front design of this type of structure.4. According to the analytical data, the row house pattern (the urban block model of 60% density) fails to compete with other residential structure patterns in terms of energy performance.5. Generally, due to the cool weather condition in Tabriz during May to October, there is no need for any cooling or heating equipment for residential structures, and the weather conditions comply with the thermal comfort situation.6. Simulations confirm the importance of selecting appropriate form of the structure as well as correct direction of the structure considering the maximum absorption of direct sunlight. Appropriate selection of these parameters improves the energy performance in terms of heating residential structures under cold weather condition in Tabriz.7. Calculations performed using AHP method, determines that architectural features including insulation, type of opening, construction materials, and orientation have great impact on thermal behavior of residential structures in Tabriz housing. In fact, consideration of these influential features at the early stages of designing residential structures leads to optimal energy consumption in Tabriz environment.

IntroductionIran is one of the countries that, due to its geographical location, is facing a lot of natural disasters that affect many countries and causes a lot of economic and human losses every year. In recent years, Iran has significantly exposed to floods. Because human activity has concentrated in flood-prone areas, which are often the right places to live and economic activity, there is a probability of being a lot of damage. It has caused financial and human losses. Reports of relief and crisis response from the United Nations inferred that floods should be considered one of the most severe natural disasters. The goal of the prevention of floods damage is to improve the quality of life by reducing human and public losses, both economic and environmental.Flood management actions can have divided into two groups: structural actions and management actions. Structural actions include the physical activities [1] for buildings and facilities to deal with floods, such as actions improving the route of the river, construction of reservoir dams, and longitudinal coastal embankments. These actions are the hardest part of dealing with floods. Management actions include a variety of precautionary actions to reduce flood damage, including land-use control and warning systems of the flood. Such actions constitute the Software aspect of confrontation in floods. These actions should have taken in three areas: flood prevention, response and reconstruction, and improvement of the damaged regions. As mentioned earlier, one of the management actions is flood warning systems to estimate the damages. In this research, have been tried a cost-effective solution to identify and evaluate and damage estimates floods created and provided to using in Flood warning systems.Methodology and ImplicationPart of the Caspian Sea has selected as a suitable study area due to the presence of pure water bodies. Images of the Landsat 8 satellite, the OLI sensor, have been used as the data source to prevent the impact of various sensors. All images selected are cloudless to reduce cloud impact. To minimize time processing, a clipping of images has considered. Some of the images were to validating this purpose method. The resolution of Landsat images (30 m) is vast for identifying small pieces with mixed pixels. For the increasing spatial resolution of images, the IHS image fusion algorithm has used with the panchromatic image.Due to the spectral behavior of water in different bands, NIR, SWIR, and Green bands were recognized and used. March 2019 has considered due to the floods around the Caspian Sea. The study area was selected part of the Caspian Sea border, around Kiashahr near Lahijan. In the first step, to improve the accuracy of the final results, the three selective bands were combined with a panchromatic band that has twice resolution (15 m) of the above bands.In the next step, small areas in the deeper part of the sea that do not have cloud cover were used as the standard reflectance of water and to calculate the degree of classification error. The vector angle values of the band and the water reflectance standard value its (such as SAM method) and the distance their values were used to create the map. Probability water in each pixel, its reflectance proximity to the standard reflectance of water in the same band, will be between zero and one.After creating a probabilistic map of the existence of water, this map enters the optimization algorithm as a relatively simple classification. According to the goal of implementing an optimization algorithm that is detecting and extracting the water range from images, creating a map of the probability of water can be an excellent initial solution for better implementation of the algorithm. In the optimization algorithm, before the implementation of such algorithms, the objective function should be defined, and it used to the optimizing problem. When its value is more valuable in this problem, that is Larger value. In this research, a means of maximized value is more probability of water. Function and particle swarm algorithm coefficients have determined from the beginning of the algorithm implementation. c1, c2, φ1, φ2, and w, in the PSO algorithm structure, and k1, k2, and k3 in the objective function are coefficients whose values are determined. In the following, Relationship 1 is The function of calculating the probability map of water, Relationship 2 is the objective function [2], Relationships 3, and 4 are a function of the particle swarm algorithm [4,5].At each stage of implementation, the status of pixels was compared with the best solution of the objective function, if it is better than the best solution up to replace. In addition to each pixel, It will have saved the objective function calculated for the whole range. If the response was better than the optimal state of the global solution, it replaced. In this way, the answers have compared with the most optimal solution Due to defining conditions for the algorithm. Finally, after 500 repetitions, the algorithm ends. Figure 1 is a visual comparison of the proposed method and methods of SVM and k-means in the study area.By studying and checking the optimization algorithms, the particle swarm algorithm as a collective intelligence algorithm that takes effects of the neighborhood [5], According to the water behavior and The process of creating floods, will be advantageous. This algorithm was selected using an objective function that would cover the essential issues and considering the water probability in the points and the impact of the neighbors. To improving the usability research optimization algorithm, a relatively right initial solution was created by the probabilistic maps of the presence of water in the pixels and using spectral behavior of water and spectral reflection in the used bands.ConclusionFinally, the performance of the proposed algorithm was visually and statistically compared with several other classification methods such as SVM and k-means. The Overall Accuracy and Kappa Coefficient values calculated and compared for statistical comparison. The OA value of 98.93% for the proposed algorithm, 98.39% for SVM and 96.73% for k-means, and KC 95.6%, 91.2% and 67.8% to the proposed research algorithm, SVM and k-means. As a result, The proposed algorithm found to be useful and appropriate in this problem. Figure 2 is a statistical comparison chart of the proposed method and methods of SVM and k-means.Future WorkFor future research, other techniques can be used on fusing images and compared with the used method. On the other, using radar images causes increasing accuracy and eliminating cloud effects. Using Modis satellite imagery due to its wide range of spectrum can better distinguish the components of pixels. The using meteorological satellite images to improve the time series of studies, and the quickly monitoring and predicting floods can have a good effect. And using different methods of optimization and comparison with the proposed method in this research to improve the identification and monitoring and pathology of crises such as floods, can be beneficial. Using the time series of images will also be very appropriate and efficient.

The Performance of modified pumice by TEPA in Adsorption of CO2 in Process IndustriesIntroductionThe overuse of fossil fuels to supply the fast-growing population of the earth with their needed energy, as well as advanced technologies and industrial development have led to the emission of great amount of greenhouse gases. From among the greenhouse gases, CO2 is of particular importance and accounts for around 60% of the effects of global warming. The best long-term solution to reduce the amount of released CO2 is through its adsorption and sedimentation. As the adsorption stage in carbon capture and storage (CCS) technology is the most expensive phase (70-90 percent of the total costs), conducting research into solid adsorbents and increasing their CO2 adsorption capacity seems reasonable. As a result, adsorbents made of natural and eco-friendly materials, which are economical and do not necessitate the use of complicated synthesis processes are of considerable importance. In order to fulfill such a goal, this study, for the first time, examined the CO2 adsorption capacity of raw (natural) pumice as a green adsorbent. A considerable body of previous research has focused different applications of pumice since 1995. The majority of the studies were related to the removal of pollutants in water and wastewater treatment. After an exhaustive review of the literature, it seems that the available body of research is void of any findings regarding the use of pumice modified with Tetraethylenepentamine (TEPA) as a CO2 adsorbent. Having large contact surface, high porosity (90% on average), and –OH group, this igneous rock seems a suitable choice for the adsorption process. The performance of the adsorbent could be improved if functional groups with high affinity to adsorb CO2 is added to it. Highly porous solids and amine groups can make a very suitable compound to achieve high adsorption rates. According to the recent studies on the selective adsorption of CO2 by amine compounds, TEPA enjoyed the highest adsorption, and therefore was selected in this study as the added substance to pumice.Materials and MethodsIn this study, a new method was used to modify the pumice taken from Maragheh mine. In this method, 0.01 moles of 2-(3,4-epoxycyclohexyl)ethyltrimethoxysilane (2.88 grams) to increase adhesion, and 0.01 moles of tetraethylenepentamine (1.89 grams) were mixed in a 50cc beaker containing 10 milliliters of isopropylamine with oxirene ring. The product was used as the modifying agent and was added to powdered pumice (pumicite) at the mass percentage of 6%. This involved adding 10 milliliters of the solution of water: ethanol (1:10 volume fraction) to 10 grams of the powder and the modifying agent (6%) was added to the beaker while being stirred. The content of the beaker was mixed with 0.01% ammonia solution for 1 hour at 60 degrees Celsius. The sediment was poured on filter paper, rinsed three times with 60% ethanol, and left in the oven for four hours at 60 degrees Celsius to completely dry.First, the CO2 adsorption capacity of raw pumice and then that of pumice modified with 6% TEPA were measured using the BELSORP-max instrument. Then, the Ideal Adsorption Solution Theory equations were calculated. The analytical equation of spreading pressure is presented based on Toth isotherm: The selectivity of CO2 to N2 is calculated using the following formula:The adsorbent performance indicator (API) is calculated using the material balance equation for the three parameters of adsorption capacity, selectivity, and adsorption enthalpy.According to the following equations, the shares of physical and chemical adsorption on the total amount of adsorption (of the adsorbate on the selected adsorbent) can be calculated.ResultsThe results of the XRF test revealed SiO2 and Al2O3 to be the main constituents of pumice. In the XRD results of pumice (from Maragheh) crystal phase was seen when . According to the FT-IR results, in this sample features of SiO4 group was observed at 1033 cm-1, 1037 cm-1, 1048 cm-1, 461 cm-1, and 780 cm-1 wavelengths. The morphology of the sample pumice examined using scanning electron microscope (SEM) demonstrated that in the sample, the amorphous structure of lamella is split into uneven phases and bonds which shows evenly spread pores are extruded in nature. According to the results, the CO2 adsorption capacity of pumice from Maragheh was around 0.230 mmol/g. This figure for the modified pumice was around 0.510 mmol/g, which is twice as much as that of raw (natural) pumice. Increasing the temperature affected the CO2 adsorption capacity negatively and at 298K, 328K, and 348K, the adsorption capacity was calculated to be around 0.510 mmol/g, 0.402 mmol/g, and 0.357 mmol/g, respectively. The values of reduced spreading pressure were measured as molar fractions of the adsorbed CO2 on 6% TEPA modified pumice at 298K and different CO2 concentrations of 5, 15, 25, and 35 percent by volume, and were 0.2, 0.4, 0.5, and 0.6, respectively. Consequently, the adsorbent’s selectivity of CO2 molecules compared to N2 is possible to estimate. The results reflecting the CO2 working capacity after the alteration of the concentration of CO2 revealed that the higher the concentration of CO2 is, the better the modified pumice adsorbent performs. The selectivity of CO2 on modified pumice showed that if the CO2 concentration (partial pressure) rises, the rate of adsorption decreases .This point is justified because molecules of CO2 have high affinity for the sites with more adsorption energy in comparison with N2 molecules. Moreover, when the pressure increases and high-energy sites get full, CO2 and N2 molecules compete to sit on the sites with lower energy (which are of less value in terms of selectivity).When the volume percentages of CO2 were 35 and 25 (which is the common case in cement industry), the rates of selectivity were 2.79 and 3, respectively. When the concentration of CO2 was 15 percent by volume (the common case at coal power plants), the amount of selectivity was equal to 3.76. This amount with CO2 at 5 percent by volume (common in combined cycle and gas turbine power plants) was 4.75.Discussion and ConclusionIn this study, the experimental results of CO2 adsorption capacity of raw pumice and amine-modified pumice were compared. The natural (raw) pumice demonstrated the rate of CO2 adsorption of 0.230 mmol/g. There was a considerable increase in the amount of CO2 adsorption capacity when pumice was modified using 6% TEPA content (0.510 mmol/g), which showed the adsorbents better performance next to the amine compound. This point has already been proved in several other studies on adsorbents. Upon alterations of the temperature, the adsorption capacity at 298K, 328K, and 348K was higher than that of raw pumice at 298K. Additionally, the highest rate of CO2 adsorption in the modified sample was observed at 298K, which signals that a lower temperature is more favorable for 6% TEPA-modified pumice. The investigation of the effect of concentration of CO2 on the adsorption capacity and API of modified pumice in process units revealed that the lower the concentration of CO2, the better the performance of the adsorbent. In addition, the thermodynamical parameters proved that the process of CO2 adsorption on modified pumice was of the physical adsorption kind and was both exothermic and spontaneous. Despite the lower capacity of CO2 adsorption for pumice in comparison with other synthesized adsorbents, the low cost of production of pumice when compared to other adsorbents, along with its accessibility due to the large number of mines in the country, makes its commercial use justified.