فصلنامه جغرافیا و پایداری محیط
پیاپی 50 (بهار 1403)

Vulnerability context is one of the basic concepts of sustainable livelihood framework. The vulnerability contexts, including shocks, trends, and seasonal variability affect the rangeland users’ livelihoods. This study aims to assess the vulnerability contexts of rangeland users in three villages of Jalal Abad, Haji Abad, and Nahzatabad in Najafabad city. A structured questionnaire was used to measure the vulnerability contexts. From the statistical population of 68 people, 58 people were selected using Cochran's formula. The results showed that about 80% of the respondents evaluated the changes in vulnerable areas as high and 19% as very high. The results of measuring the attitude of the rangeland users of the three studied villages showed the shocks including air pollution and drying of plants and economic shocks with an average of 3.21 and 3.17, trends consisted of inflation and price and rangeland degradation with an average of 3.92 and 3.80 and the seasonality including drought, temperature increase and rapid melting of snow and frost and heat exhaustion of plants with an average of 3.69 and 3.68 had the greatest impact on livelihood vulnerability. Since vulnerability contexts are not one or two, but several factors of vulnerability may occur at the same time, this study suggests policymakers employ a multifaceted approach and a set of adaptive strategies in dealing with vulnerability contexts. In addition, since individuals, households, and communities affected by vulnerability contexts due to the spatial distribution in the geography of the land may not experience risks and vulnerabilities in the same way, future studies can show how different individuals, groups, households, and communities are exposed. In the face of vulnerability, including environmental hazards, and natural disasters.

Livelihood is abilities, assets, and activities that are needed for life and survival. Livelihood, as a means of better understanding the quality of life and well-being, is one a new approach in line with the goals of environmental sustainable management. Livelihood thinking introduces the basic resources through which, people can improve their lives. The main components of the sustainable livelihood framework include livelihood capital (1), vulnerability contexts (2), policies, institutions and processes (3), livelihood strategies (4), and outcomes (5), which have been the subject of various studies. Vulnerability contexts are one of the main components in the sustainable livelihood framework, which directly and indirectly affect livelihood capital. The vulnerability contexts are related to the external environment. The external environment or vulnerability contexts include shocks (i.e. livestock and human diseases and plant and animal pests, environmental hazards such as floods or earthquakes, and economic shocks), trends (i.e. population, biodiversity, migration), and seasonality (i.e. drought, flash floods, rains). One of the areas in which, the livelihoods of individuals and human groups are affected by vulnerability are rangeland resources. They have been reported to be vulnerable to the livelihoods of rangeland users depending on them; at the same time, the vulnerable contexts also affect the sustainability of livelihoods and rangeland resources. This study intends to specifically assess three main criteria and sub-criteria related to the vulnerable contexts of rangeland users in the central part of Najafabad County.

This research measures the criteria related to the vulnerability contexts of livelihoods of rangeland users in three villages of Jalalabad, Hajiabad, and Nahzatabad in the central part of Najafabad County. The statistical population was 68 rangeland users; According to Cochran's formula, 58 users were selected as a statistical sample. To measure the criteria of vulnerability contexts, three main criteria of shock, trend, and seasonality were taken into consideration. Prioritizing and extracting items for measuring the criteria of vulnerability contexts, according to the literature review, in the form of a preliminary questionnaire, was assessed and reviewed by an expert panel. Finally, by analyzing the views of the expert panel and prioritizing the examined items, 5 items in the final questionnaire and 15 items, total in the form of a five-point Likert scale (1-5) including very little (1), little (2), somewhat decreased (3), increased (4) and very increased (5) were investigated. The content validity of the questionnaires was confirmed by using the opinions of the expert panel and its reliability was confirmed by calculating Cronbach's alpha coefficient. After completing the questionnaire and recording the information in the Excel spreadsheet, data analysis was done using SPSS software, and graphs were drawn in the Excel environment. To prioritize and classify the items of vulnerability contexts in the three investigated dimensions, the difference in the standard deviation from the mean or the ISDM standard was used by using the sum of the points.

The results showed that 79.31% of the respondents with the highest frequency evaluated the vulnerability contexts (shock, trend, and seasonality) as high, 18.97% as very high, and 1.72% as moderate during the last 20 years, which affected their livelihood. According to Friedman's test, "drying of plants (rangeland and forest)" and "economic" shocks with an average rating of 3.21 and 3.17, and the shock of "natural disasters and unforeseen events (storms and fires, floods and loss of land)" with an average rating of 2.66, respectively, had the greatest and least impact on livelihoods in the last 20 years. It can be stated that the drying of rangeland plants is an objective and tangible issue due to the decrease in rainfall and extreme weather changes. Regarding dust, the pollution caused by this phenomenon is observed in most parts of Iran, and Najafabad is no exception. According to the Friedman test, the trends of "inflation" and "land degradation" with an average rank of 3.92 and 3.80, and the trend of "immigration of family members to other regions" with the lowest average rank of 1.90 have the highest and lowest impact had on livelihood, respectively. It can be said that according to the current conditions, the state economy dependent on oil, existing sanctions, global inflation, and high prices, are obvious and outstanding issues. According to Friedman's test, the items of seasonality "freezing and heat exhaustion of plants" and "increasing temperature and rapid melting of snow" with average rank values of 3.70 and 3.69 had the highest impact on the rangeland users’ livelihoods. "Occurrence of destructive and sudden floods" with the lowest average rating of 1.51 had the lowest impact on rangeland users. It can be acknowledged that the drought and the increase in temperature largely lead to people's lack of proper productivity of environmental facilities and potentials; including lack of water for various activities and low productivity of crops and many other activities that are directly related to people's livelihood.

Shocks, trends, and seasonality are among the factors and vulnerability contexts, each of which can have negative effects on the livelihood of rangeland users; so the cumulative and combined effects of these vulnerability criteria can permanently affect environmental resources, including rangelands. In the studied area, economic shocks, inflation and high prices, drought, and drying of plants affected the livelihood of rangeland users more than in other cases. In this regard, the study suggests adopting multifaceted approaches and adaptation strategies to deal with vulnerable contexts. Future studies can examine how individuals, groups, households, and communities face different areas of vulnerability, including environmental hazards, natural disasters, and other social and economic vulnerabilities. This helps the policymakers and planners to use a specific strategy in facing risks and crises in every situation, place, and local community and to take basic measures in line with the sustainable management of environmental resources, especially sustainable rangeland management.

Wetlands are one of the most important ecosystems and living areas in the world, one of the most obvious beauty and masterpieces of creation. As an essential part of the global ecosystem, these wetlands have played an important role in preventing or reducing the intensity of floods, feeding underground aquifers, providing a unique habitat for plants and animals, maintaining water quality, agricultural production, fisheries, saving floods, and controlling soil erosion. The current research aims to investigate the changes in water surface and water area as well as the changes in the land cover of Horul Azim Wetland for the statistical period of 2013 to 2022 using Landsat8 satellite images. In this study, maps of water areas and land cover were made using Landsat8 image integration techniques and by applying the AWEI spectral index and maximum likelihood algorithm in ENVI5.3, ArcGIS software. By checking the accuracy of the results of satellite image processing and classification (2013 Kappa coefficient equal to 95% and overall accuracy 96%, year 2022 Kappa coefficient equal to 90% and overall accuracy 92%) It was found that the supervised image classification, the maximum likelihood algorithm for the studied area is close to ground reality and has acceptable accuracy. Also, the maps related to the monitoring of changes in the water area of Horul Azim wetland (AWEI index) showed that the size of the wetland has been decreasing in the studied years, so in 2013 the area of the water area of the wetland was equal to 336 square kilometers. which has decreased to 147 square kilometers in 2022. The results of the classification of images in the studied years also indicate the decrease of water and plant covers and the increase of barren lands and salt marshes in the studied periods. There have been many influential factors, including environmental and human factors, on this process of changes in the studied periods. 

Wetlands are considered one of the most obvious beauties of nature, the most useful and at the same time the most challenging part of nature's ecosystems. These vital and diverse habitats are among life-giving systems that have absolutely no substitute. Recently, it has been estimated that global wetlands occupy about 6.2 to 6.7 percent of the earth's surface. With more than 251 large and small wetlands, Iran is of special importance in Southwest Asia due to its geographical location. Horul Azim Lagoon has changed its area and water level in the past decades due to various reasons. Considering the ecological importance of this wetland for the country, it is necessary to monitor and evaluate the changes in this wetland and study the consequences of these changes. The study of land cover changes has a very long history and coincides with the beginning of human life. Thus, after the formation of societies, the first humans began to change the cover of unused land to suitable land for agriculture and animal husbandry. Considering the importance and necessity of monitoring the change of Horul Azim wetland. We intend to use satellite images by applying the technique of integrating Landsat 8 satellite images during the period from 2013 to 2022. By applying the maximum likelihood classification method and AWEI spectral index, let's investigate the changing process of land cover and water areas of Horul Azim lagoon.

In this research, Landsat 8 multi-temporal satellite images (OLI meter) have been used. In total, 2 images were obtained from the US Geological Survey website and were used in the analysis of the research steps that will be described below. In total, 2 images were obtained from the US Geological Survey website and were used in the analysis of the research steps that will be described below. In total, 2 images were obtained from the US Geological Survey website and were used in the analysis of the research steps that will be described below. Radiometric (atmospheric) corrections were done for all the used images. In this study, to highlight the images and extract more information from the images from the false color combination, Landsat 8 and (5-4-3 near-infrared bands, red and green). To use different applications, the Gram, Schmidt and Pansharpring methods showed better sharpness of the features of the study area than other methods, so the combination of images was applied with this method. To identify the blue zone of the investigated area from the AWEI spectral index and to investigate land cover changes, the maximum likelihood classification method has been used.

In the present study, using ENVI 5.3 software in band calculations Spectral index, AWEI was selected and calculated for the years 2013 and 2022 to extract the water area of Horul Azim lagoon. the results showed that; the AWEI index has values greater than zero (positive values) indicating the water zone and smaller than zero (negative values) indicating the non-water zone (vegetation and soil). After performing the classification process, it is necessary to calculate the accuracy of this classification. To ensure the accuracy of the classification, the classification accuracy was evaluated, for this purpose, the Kappa coefficient and overall accuracy were calculated using the ground control points for each class separately in ENVI 5.3 software. The result of the accuracy evaluation is shown in the error matrix table. According to the available data, the spatial resolution of the images, and the researcher's knowledge, 4 educational classes, including water cover, vegetation cover, barren land, and saline land, have been selected for each image separately. The results obtained from the maximum likelihood classification method in the ENVI5.3 software environment were changed to vector format and the result was transferred to the GIS software environment in shape file format and land cover classes was determined.

The results of monitoring changes in the water area of Horul Azim lagoon (AWEI index) showed that the size of the wetland has been decreasing in the studied years it was also determined by checking the accuracy of the results of processing and classification of satellite images that the classification of images in a supervised manner, the maximum likelihood algorithm for the studied area is close to the ground realities and has acceptable accuracy. The results of the classification of images in the investigated years also indicate a decrease in water and vegetation cover and an increase in barren and salt marshlands in the investigated periods. There have been many influential factors, including environmental and human factors, on this process of changes in the investigated period, which include these cases: Successive droughts and the occurrence of harmful changes in the climate have caused changes in environmental conditions such as high salinity and inappropriate temperature. This means that significant changes in the ecosystem have reduced the number of aquatic species, animals, and plants, as well as people, which has led to a decrease in diversity indicators and an increase in dominance. The increase in pollution was caused by the entry of industrial, agricultural, and urban effluents into the rivers leading to the wetland. In addition, the construction of roads and embankments, in addition to cutting off the water connection of parts of the wetland, has turned them into salt marshes and dry land. The construction of the Karkheh dam and a result of not respecting the water rights of the lagoon, human encroachments, and occupations, including road construction, Oil activities, and encroachment on wetland lands have caused some parts of the wetland to dry up completely As a result of the fragmentation of the wetland and the increase of dry and salinity spots, the drying of the wetland is spreading. This trend states that the increase in human interventions will lead to an increase in the destruction process.

Water stress is a major environmental issue and has a significant impact on the sustainability of urban areas worldwide. This study examines the changes in land surface temperature (LST) and their relationship with variations in vegetation cover during wet and dry periods in Zayandeh Rood. To accomplish this, Landsat TM, ETM, and OLI satellite images from three dry years (2001, 2009, and 2018) and three wet years (2005, 2006, and 2020) were used. The research findings indicate that, in 2006, when water was flowing in the river bed, vegetation cover decreased from 36% (201 km2) to 23% (km2) in 2018, when the river bed had no water. Over the last two decades, the Earth's surface temperature has increased. The highest average temperature was observed in 2018, at 40/4 degrees Celsius, and the maximum temperature in the dry years of 2001, 2009, and 2018 was higher than in the wet years of 2005, 2006, and 2020. The highest density of heat islands was observed in regions 2, 4, 5, 6, 7, 14, and east of region 15, which is concentrated on barren lands and then on urban areas. In the dry year of 2009, the total area of temperature classes 42-49 increased by approximately 12% compared to the dry year of 2001 and also increased by about 25% in 2018. In contrast, it was zero in the wet years of 2005 and 2006, and in 2020, it decreased by around 34% compared to 2018. Furthermore, urban development, which has grown by 7/3% over the past two decades, has contributed to the reduction of vegetation and intensified the urban heat island effect. Examining the average temperature changes at different distances from the Zayande Rood indicated that the temperature increased by about 1 degree Celsius with the distance from the river.

Climate change refers to unusual shifts in the earth's atmospheric climate that have far-reaching consequences across the globe. These shifts have led to an increase in the average temperature of the earth's surface, which has created numerous challenges for human security worldwide. Climate change has a significant impact on the hydrological cycle, causing changes in water resources, and leading to an increase in the frequency and intensity of droughts and floods. Water stress, one of the major environmental problems, has significant effects on the sustainability of urban areas across the world. The city of Isfahan has experienced many challenges, such as a rise in temperature, as a result of climatic changes and the drying up of the Zaynde-Rood River. Therefore, examining temporal and spatial changes in land use and their effects on the earth's surface temperature provides a clear picture of these changes. This analysis presents the behavior of the data before and after the drying of the Zayandeh Rood.

For this research, we used 6 Landsat satellite images during the summer season, as it has the maximum vegetation cover. We obtained the images of TM, ETM, and OLI sensors of the Landsat satellite from the Google Earth Engine system (https://code.earthengine.google.com). We calculated and extracted the LST of each image to investigate temperature changes in different areas of the city. We calculated the changes in vegetation area during the dry years of 2001, 2009, and 2018, and the wet years of 2005, 2006, and 2020, and their effects on the surface temperature using the NDVI index. We also calculated the rate of urban growth and expansion using the NDBI index. All the calculations, graphs, and analyses were done using ArcGIS Pro and Excel.

The catchment area of Zayandeh Rood has experienced drought in recent years, causing the water flow of Zayandeh Rood to stop flowing continuously in the metropolis of Isfahan. This has led to significant changes in the natural vegetation and planting in terms of land area. The complete interruption of the river water flow has affected the amount of temperature, evaporation, and transpiration of plants in the Isfahan metropolis, which has subsequently affected the extent of vegetation cover. Research shows that in 2006, when water was flowing in the riverbed, the vegetation area decreased from 36% (201 km2) to 23% (126 km2) in 2018, when the riverbed was devoid of water. This was due to the expansion of residential areas and the change of agricultural uses to residential. Over the last two decades, the temperature of the earth's surface has increased, with the highest average temperature observed in 2018 at 40.4 degrees Celsius. The maximum temperature in the dry years of 2001, 2009, and 2018 was higher than the wet years of 2005, 2006, and 2020. The highest density of heat islands was observed in regions 2, 4, 5, 6, 7, 14, and east of region 15, which is concentrated on barren lands. The total area of the temperature classes 42-49 in the dry year of 2009 increased by about 12% compared to the dry year of 2001 and also in 2018 by about 25%, while in the wet years of 2005 and 2006, it was zero. In 2020, compared to 2018, it decreased by about 34%, and in 2020 compared to 2009 and 2018, it increased by about 11% on average, indicating an increase in temperature in dry years and a decrease in temperature in the wet years. Urban development has increased by 3.7% over the past two decades, which has contributed to reducing vegetation and intensifying the urban heat island effect. Examining the average temperature changes at different distances from the Zayande Rood showed that the temperature increased by about 1 degree Celsius with the distance from the river.

The analysis of NDVI data revealed that vegetation significantly decreased during dry years compared to wet years. The area of weak vegetation increased by 3%, indicating that water stress led to a shift towards vegetation types that require less water. The examination of the earth's surface temperature also showed a clear difference between dry and wet years. During dry years, LST values were generally higher than in wet years, with an average temperature of 40.4 degrees Celsius in 2018. In contrast, wet years showed lower LST values, with an average temperature of 31.4°C in 2006. The distribution of LST was also different between dry and wet years, with dry years characterized by a wider range of LST values, indicating the urban heat island effect. Water stress emerged as a crucial factor affecting vegetation and LST. Additionally, urban development, which has increased by 3.7 percent over the past two decades, has contributed to reducing vegetation and intensifying the urban heat island effect.

The area under study (Tehran gas line 3) is one of the most important and sensitive areas in terms of threats to gas supply due to the instability of domains and tectonic activities. In this research, to achieve the research objectives, different data from different sources and different criteria were used. For example, geology, height, distance from the slope fault, slope direction, distance from the river, land use, soil, distance from the road, precipitation, land cover, and height were used. Risk assessment using five fuzzy models, network analysis, Fuzzy network, multilayer perceptron, and random forest method were analyzed. Because the traditional methods of risk assessment are based on mathematical functions and need more knowledge of experts and are less practical, intelligent systems were used which, in addition to being easy to use and analyzing relationships, provided more appropriate results. The results of the investigations showed the comparison of landslide and earthquake risk with different models shows that the risk of landslide is higher with the RF model. Still, with the use of the ANP model, the studied area shows relatively higher risk. In the Fuzzy model, the highest percentage belongs to the low-risk class and the lowest percentage belongs to the medium-risk class. In the fuzzy-ANP model, the relatively high-risk class shows the highest percentage and the high-risk class shows the lowest amount in the range. In the MLP model, most of the study range has medium risk and the high-risk class range has the lowest amount in the range. Therefore, in the RF model, the highest percentage belongs to the relatively high-risk class and the lowest percentage belongs to the low-risk class. According to the evaluations made from the results of the ANP model, the systematic error (MBE) of this model is -0.20336 and the absolute error of the model is 0.209895. The RMSE error was 0.131107. According to the evaluations of the results of the Fuzzy model, the systematic error (MBE) of this model is -0.23687 and the absolute error of the model is 0.25511. The RMSE error rate was 0.162122.

Risk assessment in threats that have a spatial aspect and are related to the characteristics of the environment and the pipeline bed is considered essential. Due to the nature place of risk, it seems that it is possible to establish a connection between the process of risk estimation and the geographic information system, and by using different models, the high-risk areas can be specified. According to the issues, in this research, the process of environmental risk estimation has been investigated with a geographic information system and using hybrid-fuzzy algorithms. After positive results, by assessing the risk of gas pipelines, valuable information such as risky components can be determined and a suitable reaction and strategy can be used to reduce and even eliminate it. To achieve the goal, the appropriate technique has been used in the research. which can assess the existing risks more accurately and reliably. Also, planners and managers should act with a wider horizon and a lower risk factor toward the optimal management of gas transmission lines.

In this research, five models were analyzed for risk assessment. These models were fuzzy analysis, network analysis, fuzzy network, multi-layer perceptron, and random forest method. Given that the traditional methods of risk assessment are based on They are mathematical functions and lack the knowledge of practical meter experts, intelligent systems were used which, in addition to easy use and relationship analysis, provided more appropriate results. In this research, the network analysis method was used. The Multilayer Perceptron (MLP) model was also used in this research. This method requires less investigation in estimation or statistical methods to analyze the accuracy of data. The most important advantages of MLP are high learning potential, robustness against noise, non-linearity, parallelism, error tolerance, and high capabilities in task generalization. The overall goal of this model is to find a system to minimize the total error for the relevant training data by The training algorithm. The networks between these layers are with different weight values in the interval [1 and -1]. The result of input values, weighted values, and bias values are obtained from equation (1)based on the aggregated performance.
Equation (1)                                               
n shows the total number of input points, I_(i) the input variable, β_j the bias value, ω_ij the connection weight.
Random forest model (RF) was another model used in this research. In the structure of the RF method, the importance of the variables was determined in the model and the variables that have a greater role in each tree and the final model were identified. It was considered from the bag and these data played the role of experimental data to evaluate that tree. Based on this model, the most weight is given to the rainfall criterion and the least to the soil criterion. The measured values were used in the test stage. To validate the model, the statistical indices of root mean square error (RMSE), mean error of exploitation (MBE), and mean absolute error (MAE) were used. The relations of these indices are in the form of equations 2 to 4:
Equation(2)                 RMSE =       Equation (3) MBE =                Equation(4) MAE =                
In the above relationships, O_i and P_i are the observed and estimated values at time i, and t is the number of days.

In this study, it was tried by using different models, while the level of risk is checked, a comparison is also made between the models. In the scope of the study, The comparison of landslide and earthquake risk with different models shows that the risk of landslide and earthquake is higher based on the RF and ANP models. The analyses conducted by other researchers show that the MLP model and the ANES model show better results. The results show the difference between the output values of the model and the values. The result of smart tracking is as target points for model evaluation. The MBE error shows whether the model error is generally positive or negative, that is, whether the model estimates the data more than the real values or less than the real values. This error includes any systematic errors in the design, collection, analysis, interpretation, and dissemination of data that lead to incorrect estimates. This error is one of the systematic errors in that the increase of sample points does not affect the reduction or increase of the error. The systematic error (MBE) of this model is 0.002812.

In the ANP model, parts of the pipeline based on earthquake criteria are 0.363 km long with a low vulnerability is 20.317 km. Also, 258.60 km is with relatively high vulnerability and 29.062 km is with high vulnerability. Based on the results of the review of landslide criteria of the ANP model, 0 % of the area is in the low-risk class, 17.28% is in the risk class average, 73.14% in the relatively high class, and 9.58% in high-risk class. According to the landslide criterion using the ANP 008 model, 19.19 km of the studied area with medium vulnerability, 80.454 km with relatively high vulnerability, and 10.538 km with high vulnerability. According to the landslide index results of the MLP model, 9.78% of the area is in the low-risk class, 47.17% in the medium-risk class, 36.95% in the It is relatively high and 6.10% is in the high-risk class. According to these results, most of the region is in the class with relatively high risk. Based on the results of the MLP model earthquake criteria, it was found that 5.093 km with low vulnerability, 29.48 km with moderate vulnerability, 713.7 km 42 km with relatively high vulnerability, and 32.714 km with high vulnerability. Therefore, according to the results, it can be said that in areas with high risk, it is necessary to use higher class pipes, periodic studies and investigation of physical factors and an environment that is effective in causing damage should be done to reduce their vulnerability.

Today, air pollution has tightened the air for the residents of metropolises. Air pollution is one of the most harmful pollutants for the environment especially for humans. In this study, based on daily data, concurrent and widespread air pollution in five selected metropolises in the northern half of Iran, including Tehran, Mashhad, Tabriz, Isfahan, and Rasht, was investigated to design and analyze synoptic patterns related to the atmospheric ridges affecting them. For this purpose, the beginning, peak, and end days of concurrent and multi-day pollutants (pollution wave) were identified and for each of them, the effective atmospheric ridge position was determined. The first results showed that 107 air pollution waves with simultaneous characteristics occurred during the period 2017 to 1400. Of these, 14 waves have been widespread. Synoptic patterns showed that when the same height curve of 5825 geopotential meters, along with other curves in the northwest of the African continent, formed a subtropical atmospheric ridge, the widespread air pollution in the westernmost metropolis of Iran, Tabriz began. When this ridge reaches Iran, air pollution covers four of the five selected metropolises of Iran, and then, as the ridge reaches Pakistan, the air conditions face a clean state and a wave of pollution is coming to an end. In total, the East-ward movement of the ridge axis during the three days of the beginning, peak, and end of air pollution was evident on three territories including Iraq, Iran, and Pakistan, respectively. These three situations confirmed the coordinated behavior of the atmospheric pattern of the ridge with concurrent and widespread air pollution for residents of five metropolises of Tabriz, Rasht, Tehran, Isfahan, and Mashhad.

 Today, we are witnessing population increase, the growth of the techniques, the rapid expansion of cities, and the manipulation of the earth's ecosystems by humans. These have caused air pollution, especially in the metropolis, to become an important bottleneck for the population living and active in them. The atmosphere is a huge system of dynamic and interrelated components.  These two characteristics have so far caused natural pollutants such as particles and ash from volcanoes and human pollutants such as aerosols and soot in urban and industrial environments during the upward and downward movements of the atmosphere and mechanisms such as precipitation have been cleared from the atmosphere or have the opportunity to spread and disperse gradually. What has become a serious hazard today is the indiscriminate increase of pollutants and their concentration in industrial areas, including in metropolises.

This study was conducted with an environmental approach to circulating. To design and analyze synoptic patterns of concurrent and widespread air pollution in five selected metropolises of Iran, including Tabriz, Rasht, Tehran, Isfahan, and Mashhad. The daily air pollution data for six pollutants from air pollution monitoring stations were collected from the Iranian meteorological organization. These six pollutants include carbon monoxide, ozone, nitrogen dioxide, sulfur dioxide to PM10, and PM2.5 aerosols between 2017 and 2021. Calculation of Air Pollution Measurement (AQI) for each of the six pollutants and based on valid air quality indicators, the polluted days were retrieved in each of the mentioned metropolises. Determination of concurrent and continuous days of air pollution from sequential and asynchronous days provided the possibility of determination of concurrent and widespread waves of air pollution. Widespread and simultaneous waves of air pollution were defined as waves that have four characteristics:
There should be a report of pollution from one of the metropolises and generally the westernmost metropolis of Tabriz.
During the following days, other metropolises in the center and east of the country have been involved in pollution.
During one day of the continuous days of pollution, the highest number of metropolises (generally out of 5 metropolises of 4 ones) have shown pollution.
During the following days, the frequency of contaminated metropolises should be reduced to zero.
To achieve the aim of the study, the daily weather maps from the middle level of the atmosphere were studied for the days of air pollution waves.
 By accessing daily air maps at sea level and the middle level of the atmosphere, the dominant location of pressure systems including effective ridges was drawn on base maps in the form of synoptic patterns for the beginning, peak, and end days of widespread air pollution. The purpose of this positioning was to match the eastward shift of southern ridges along the geographical orbits during three simultaneous conditions in the pollution index values for selected metropolises of Iran. These three conditions included the positioning of the effective ridge during the beginning, peak, and then end of the registration of contamination values.

The first results showed that the most polluted months for the five selected metropolises were December, January, and February, respectively. Meanwhile, the pollution behavior of the two metropolises of Tehran and Isfahan was closer to each other than other cities and had two peaks, one summer and one winter. The longest wave of pollution lasted 17 days and the shortest wave lasted 5 days of a total of 107 monitored pollution waves, 14 were of widespread characteristics. The prevalence of air pollution wave in this study was defined as air pollution in at least four cities out of five metropolitan areas at the same time. The design synoptic patterns showed that when the curve of 5825 geopotential-meters height is seen from the set of effective ridge curves in northwest Africa, we can expect the beginning of widespread air pollution in Tabriz metropolis and then the eastern cities up to Mashhad. Positioning patterns showed that with the reach of the ridge on Hejaz land, pollution became widespread and since then, the pollution wave in the east of the country has subsided.  Also, the ridges in southern latitudes had a longer range (more orbital elongation) than the types monitored in northern latitudes. The orbital elongation of the ridges in the southern half of Iran shows that the cities of the southern part of Iran have a hidden and alarming potential for the occurrence of durable pollutants in the event of expansion of industries and sources of urban and industrial pollutants.

The co-occurrence of widespread air pollution in selected metropolises of Iran with the apparent shift of subtropical ridge toward the east from Africa indicates the high potential of Iran's atmosphere, especially in the southern regions for increasing traffic and industrial pollutants in urban space. In other words, when the synoptic aspect dominates in a climate hazard, planners should be more concerned with preventive and long-term measures than curative and short-term ones. It means the widespread air pollution of Iranian metropolises has a synoptic aspect and relating to the nature of Iran's climate, which has atmospheric stability for most days of the year, increasing access to clean energy is essential.

Accurate assessment of forest above-ground biomass is essential for sustainable forest management. Estimation of forest biomass is necessary for studies such as estimation of greenhouse gases, carbon stored in forest resources and climate change models. Also, the forest biomass represents the production rate per unit area. The optical image data of Sentinel-2 satellite was used to estimate the above-ground biomass of the forest in the area of 285 hectares of the forests in Ilam province. 124 square-shaped sample plots with a 20×20 m dimension were located on the ground using a cluster method. Some characteristics of a total of 508 trees (both single stems and coppice forms), including the major and minor crown diameters were measured within each sample plot. Depending on whether the trees are single stem and multi-stem clumps, suitable allometric equations were used to calculate the above-ground biomass based on the measured characteristics. Finally, the total above-ground biomass was calculated for all trees in each sample plot. In order to estimate the above-ground biomass, MSI sensor images of Sentinel 2 satellite were used at the level of L2A corrections. Using spectral ratios, vegetation indices were calculated. In the next step, the corresponding spectral values of the sample plots were extracted from the main bands, and vegetation indices. A random forest regression model was used to estimate forest above-ground biomass. 70% of the samples were used for training the model, and the models were validated using the remaining 30% of the data. The results with R2=0.80 and RMSE=28.70 t/ha showed the acceptable performance of model for estimating the above-ground biomass of the forest. By using the Gini statistic, it was shown that RVI, GNDVI, NDVI, and DVI vegetatuin inices played a larger role in the estimation of biomass.

 Accurate assessment of forest above-ground biomass is essential for sustainable forest management. Estimation of forest biomass is necessary for studies such as the estimation of greenhouse gases, carbon stored in forest resources, and climate change models. Also, the forest biomass represents the production rate per unit area. Estimating forest biomass through direct measurements and cutting and weighing trees in the forests provides an accurate estimate of biomass, but it is a destructive, difficult, and time-consuming method. Therefore, the use of remote sensing methods is very important in the estimation of biomass.

The optical image data of the Sentinel-2 satellite was used to estimate the forest above-ground biomass in the area of 285 hectares of the forests in Ilam province. 124 square-shaped sample plots with a 20×20 m dimension were located on the ground using a cluster sampling strategy. Some characteristics of a total of 508 trees (both single stems and coppice forms), including the major and minor crown diameters were measured within each sample plot. Depending on whether the trees are single-stem or multi-stem clumps, suitable allometric equations were used to calculate the above-ground biomass based on the measured characteristics. Finally, the total above-ground biomass was calculated for all trees in each sample plot. In order to estimate the above-ground biomass, MSI sensor images of the Sentinel 2 satellite were used at the level of L2A corrections. Using spectral ratios, vegetation indices were calculated. In the next step, the corresponding spectral values of the sample plots were extracted from the original bands and vegetation indices. The correlation coefficient between the values of the original bands and vegetation indices with the amount of biomass calculated from the allometric equations in the sample plots was investigated. A random forest regression model was used to estimate forest above-ground biomass. 70% of the samples were used for training the model, and the models were validated using the remaining 30% of the data.

 The results of the descriptive statistics of above-ground forest biomass measured in 120 sample plots which were calculated using allometric equations showed that the lowest biomass in the sample plots is 0.61 and the highest is 268.88 tons per hectare. The average above-ground biomass per tree was measured as 657.6 and 231.2 kg in the single and multi-stemmed trees, respectively. The results of the correlation analysis of biomass with the investigated variables showed that among the main bands of the sensor, the red wavelength has the highest correlation (0.402) with biomass due to the high chlorophyll absorption of green plants in this wavelength. Among the vegetation indices investigated in the research, RVI and NDVI indices have the highest correlation with the forest above-ground biomass with a correlation coefficient of 0.529 and 0.525, respectively. The results of random forest regression analysis to estimate the forest above-ground biomass with R2=0.80, RMSE=28.70 t/ha show the acceptable performance of the model for estimating the above-ground biomass of the forest. Since in this research, the amount of forest above-ground biomass of the sample plots is calculated based on allometric equations in a part of Zagros forests; but these equations are not exactly related to the studied area, part of the model error can be due to this reason. By using the Gini statistic, it was shown that RVI, GNDVI, NDVI, and DVI vegetation indices played a larger role in the estimation of biomass. RVI, NDVI, and DVI indices are calculated using red and near-infrared bands, and since they are influenced by the photosynthetic activity of plants, they are very important in estimating the amount of biomass. GNDVI, which is calculated using green and near-infrared bands, is an indicator of the level of greenness or photosynthetic activity of the plant and is highly sensitive to changes in the chlorophyll content of plants.

 The results of forest above-ground biomass estimation using Sentinel 2 satellite images and random forest regression method showed that using the non-parametric method of the random forest regression model, which performs a large number of uncorrelated models; it has an acceptable ability to estimate forest biomass. Also, the findings showed that vegetation indices are more important in the process of forest above-ground biomass estimation model than Sentinel 2 original bands. The findings of the present research provide the possibility for the managers of Zagros forests to estimate the forest above-ground biomass and provide the basis for sustainable forest management strategies.

Many arid and semi-arid regions are also exposed to the risk of dust storms due to their fragile environmental stability. This environmental hazard has caused displacement of surface soil, damage to agricultural lands and vegetation and has affected the lives of people, especially vulnerable groups, close to critical centers and distant areas. The purpose of this research is to evaluate and analyze the vulnerability of social, economic and environmental criteria caused by dust storms in Kerman province. In this regard, social and economic criteria, including population density, gender structure, rural-to-urban population ratio, and rainfed agricultural lands have been considered. Also, the erodibility of geomorphological landforms, poor pastures and bare lands has been identified as an environmental criterion. The results of horizontal visibility (from weather stations) dispersion analysis show that there is a minimum average horizontal visibility in the center of the province. The amount of exposure to dust in Kerman province is between 0.73 and 8.7 meters, so the cities of Baft, Bardsir and Raber are the most exposed to this phenomenon. The results of ranking the Cocoso model showed that the cities of Kerman, Bam, Erzuye and Qalaganj are the most sensitive to dust. The results of the vulnerability of the dust phenomenon in Kerman province showed that all the cities of this province are not the same vulnerability to the dust phenomenon. The vulnerability of the dust phenomenon in Kerman province was obtained in 5 categories from very low to very high. 86% of Kerman province area is very high and high vulnerable to dust phenomenon. The results of this research can provide a basis for the development of dust storm reduction studies in order to reduce the vulnerability caused by dust storms in Kerman province for managers and decision makers.

Rising greenhouse gases due to human and natural activity have caused climate change and global warming. Consequently, these changes have exacerbated natural events such as dust activity, droughts, floods, and other natural phenomena. Climate change has a remarkable impact on the earth's hydrological cycle, surface and groundwater quality, and vegetation, and changes in these parameters play a vital role in the spread of dust storms. Many scientists have conducted research related to spatial and temporal distribution, the number of dusty phenomena with emphasis on climatic conditions, transmission routes, chemical components, and numerical simulation. Power, water, road, and other important infrastructure failures might occur as a result of sand and dust storms which can interrupt the provision of vital and critical services for the community. All these impacts can affect the sustainability and resilience of infrastructure and small and big businesses. Spatial and seasonal variations of sand-dust events and their relation to atmospheric conditions and vegetation cover in semi-arid regions of central Iran by using the Ridge Regression (RR) method and seasonal variations of precipitation, surface winds speed, air temperature, and Enhanced Vegetation Index (EVI) with Dust Storm Index (DSI) for two different periods (2001–2008 and 2009–2016) showed that the activity of sand-dust storms in the second period was greater than the first period, especially in the border region of Iran and Turkmenistan. Although many studies have been conducted on the phenomenon of dust and its relationship with vegetation, wind speed, and the origin of fine dust in some parts of the study area, no research has been done on the estimation of the vulnerability of dust storms in Kerman province. Therefore, in the current research, the amount of exposure caused by dust storms in Kerman province will be estimated by using horizontal visibility data. Then we will assess and analyze the sensitivity and vulnerability of the social, economic, and environmental criteria of Kerman province to dust storms in recent years.

The data has been used in this research such as 1. the minimum, maximum, and average horizontal visibility data (m) of meteorological stations from 2009 to 2018. 2. Precipitation in 10 years (2009-2018). 3. Vegetation 1:100000. 4. Geomorphology 1:100000. 5. Geology 1:100000. 6. Digital Elevation Model (DEM) 12.5 m PALSAR radar sensor. To prioritize each of the social, economic, and environmental criteria, we will use the Cocoso model. Finally, using the weighted linear combination method, we will estimate the vulnerability caused by dust storms. The method of estimating dust storm vulnerability in Kerman province is described below.
The combined compromise solution (CoCoSo) is a multi-criteria decision-making method that was initially proposed by Yazdani et al (2018). This method is used to rank the sensitivity of the cities of Kerman province based on overlapping criteria. This method is an integration of simple additive weighting (SAW) and weighted product models (WPM). There are 5 steps to solve a CoCoSo decision problem. The first step is to form a decision matrix, the second step is to normalize the decision matrix, the third step is to calculate SAW and WPM, the fourth step: compute aggregation strategies, and the fifth step: determine the final score and rank the options.
Vulnerability to SDS is defined as a function of exposure, sensitivity, and adaptive capacity components. Exposure refers to the nature and degree to which elements of a system are at risk of a natural or human-induced hazard; Sensitivity is another concept related to vulnerability, defined as the degree to which a system is modified or affected by hazard stimuli; and while exposure and sensitivity determine the scale and nature of likely impacts caused by hazards/perturbations, the adaptive capacity of system quantifies its ability to cope with, manage, recover from, and adapt to the potential adverse impacts of hazardous phenomenon.

The amount of exposure to dust storms in the center of Kerman province is very high and it decreases with the distance from the center of Kerman province and reaches 7.8 meters. So, in the cities of Baft, Bardsir and Raber, they have the least exposure to dust storms. The sensitivity to dust storms is generally calculated from the integration of social, economic, and environmental criteria. Social and economic criteria include residential areas (rural and urban), population density, gender ratio (male-to-female ratio), rural-to-urban population ratio, and rainfed lands. Also, the environmental criteria that are sensitive to dust storms include bare lands, poor pastures, and erodible lands. The results of the vulnerability map in Kerman province show that there are 5 levels of vulnerability in the study area, including very low, low, medium, high, and very high. The area is very large and large, with an area of 8636470 and 6545769 hectares, respectively, in the cities of Raver, Kerman, Bam, Narmashir, Jiroft, Baft, Sirjan, Shahrbabak, Rafsanjan, Bardsir, Raber, Zarand and Kohbanan. The cities of Anar, Anbarabad, Faryab, South Rudbar, and Manojan are located in the zone with high vulnerability. In general, about 95% of Kerman province is in the medium, high, and very high vulnerability category.

Nowadays, due to the effects of dust storms on human societies, many researches have been conducted on dust storms in different parts of the world. The research carried out by (Pouyan et al., 2018) showed that the cities of Regan, Fahraj, Bam, South Kerman, and Qalaganj were identified as dust sources and Menasha fine particles with high and very high class. On the other hand, the wind causes the movement of dust and leaves its effects on the neighboring cities. Therefore, it is in line with the present research. Therefore, there is a need for more planning and studies of decision-makers in the field of reducing the effects of dust in Kerman province.