نشریه جغرافیا و مخاطرات محیطی
پیاپی 7 (پاییز 1392)

Piping is a subsurface form of erosion which involves the removal of subsurface soils in pipe-like erosional channels to a free or escape exit. Although it is one of the coolest and rarest Erosion phenomena that can be formed in any weather conditions، but generally it develops in geological formation with low infiltration capacity and high soluble minerals. Piping materials are commonly highly erodible. However the situation is not fully understood، especially in terms of geomorphology. Compared with surface soil erosion by water، subsurface erosion (piping) is generally less studied and harder to quantify. However، wherever piping occurs، it is often a significant or even the main sediment source (Verachtert et. al.، 2011). This erosion process might occur at any crack that exists in the earth structure، due to differential settlements، seismic movements، tension stresses، or holes caused by dry roots or gnawing animals (rabbits، rats، and etcetera). The erosion starts at any point where the seepage water discharges and works toward the reservoir، gradually enlarging the seepage channel. Field scientists studying badland processes in Mediterranean and Semi-arid climates require assurances that the material in which gullies are presented is not dispersive (Faulkner، 2010). Piping materials are commonly highly erodible. Factors that are involved in Piping formation may include several categories. They are divided to bedrock، soil، climatic and biological factors. Material properties have a significant effect on erosion. When soils are dispersive، piping is very frequently a significant geomorphological agent. To fully interpret badland form and function in rapidly erosion Mediterranean and semi-arid climatic contexts، therefore، field scientists embarking upon any investigation into the gully processes involved require assurances that the material in which the gullies are presented is not dispersive (Faulkner، 2010). Piping can be responsible for accelerated subsurface sediment transport. Subsurface piping pathways can extend for some distance as either continuous features or as a system of interconnected features that form extensive، branched networks. As the pipe enlarges، flow in the conduit becomes increasingly concentrated and turbulent. Collapse of these subsurface conduits and networks can lead to the development of karst-like (“pseudokarst”) topography (ZIEMER، 1992:188، after Halliday، 1960) and the development of gully networks (ZIEMER، 1992:188، after Higgins، 1984; Swanson et al.، 1989). ‘‘Internal erosion’’ is similar to backwards erosion piping in that tractive forces remove soil particles. However، internal erosion is due to flow along pre-existing openings such as cracks in cohesive material or voids along a soil-structure contact. By this definition، internal erosion is not due to the dynamics of intergranular flow and the hydraulics of the problem is quite different than for backwards erosion (Lane، 1934). Soil piping is an important، but little studied. Piping must be understood in order to design for environmentally sound land use. Research that has been done، are stressed mainly on surface erosion and the dynamics of water in the gully. Recent studies indicate that (1) gully erosion represents an important sediment source in a range of environments and (2) gullies are effective links for transferring runoff and sediment from uplands to valley bottoms and permanent channels where they aggravate off site effects of water erosion (Poesen et. al. 2003:51). The study of erosion forms in marls demonstrated that is a drift relationship between erosion forms and some soil chemical properties and includes different sort of erosion (Esmaeilzadeh، 2002). North of Oman Sea is located in the Makran region، this zone is composed of seabed sediments، sediments of surface waters and ophiolites. During Eocene to Oligocene، South Makran coverd by the high thickness of flysch type sediments (Alaee Taleghani، 2003). Badland hills are the major part of Makran Flysch formation at Sirik and Jask County; the main geomorphologic forms of these foothills، is Piping (Fig 1). The aim of this study is to find out Piping erosion in Makran Flysch formation is affected from which of sediment characteristics at Sirik and Jask County. Study Area: The study area is located at 25o38’-26o23’N 57o4’-58o7’E، in Sirik and Jask County، Hormozgan Province، south of Iran at the north of Oman Sea (Fig 2). In general the region under research could be assumed a dry land with very low rain. From geological aspect، this area affected by Makran region general construction and mainly composed of shale، marl and sandstone layers. The formation of this region began on Tertiary (Neogene) and has been continued in the Quaternary period. The geological structure of this area is mainly east-westward and it is located in Coastal Makran sub division that mainly composed of marl. In this region abundance of blinded holes has led to Piping erosion on green marl sediments in Makran flysch formation. Piping erosion has more extensive in areas where marls sediment thickness increases (Fig 3). Research data were including sediment characteristics such as lime and gypsum percentages، calcium، magnesium، sodium and potassium levels، pH، Ec، soil texture and type of clay minerals. Maps of geology and topography، Satellite Imagery، Aerial Photos، laboratory equipment and computer software such as ILWIS، ArcGIS and Minitab also were used as a tools. The methods can be divided into several sections: A) Geomorphologic studies: Distribution map of Badland hills with and without Piping erosion was determined by using of available documents and expedition results. B) Choosing the sediment sampling sites: With the help of networking، samples sites (20 controls and 30 samples)، were identified in landforms map. Then، given the limits of costs، 4-5 points were randomly assigned in a manner that could be statistically processed. C) Sampling: Sediment sampling was conducted by referring to the field. Before sampling، in order to mitigate the effects of climatic factors، 15 cm of the surface layer were taken aside. D) Laboratory works: Sediment samples were transferred to the laboratory and characteristics such as lime and gypsum percentages، calcium، magnesium، sodium and potassium levels، pH، Ec، soil texture and type of clay minerals were identified. E) Statistical analysis: Laboratory results analyzed with appropriate statistical test data in Minitab program. Sediment characteristics of controls and samples such as lime and gypsum percentages، calcium، magnesium، sodium and potassium levels، pH، Ece and soil texture were obtained from the laboratory results. These characteristics were statistically analyzed and compared. According to 1. Piping erosion is related to pH، Ece، clay، silt and sand percentage، lime percentage and calcium، sodium and potassium ions، at the significant level of %1. 2. Piping erosion is related to gypsum percentage and magnesium ion at the significant level of %5. 3. The electrical conductivity (Ece)، silt and sand percentages، lime and gypsum percentages، magnesium، calcium، sodium and potassium ions، are directly related to Piping. 4. Soil pH، soil saturation (sp) and clay percentages، are inversely related to Piping. Among sedimentological characteristics which studied in Makran flysch formation، the electrical conductivity of soil، silt، lime and gypsum percentages، also magnesium، calcium، sodium and potassium ions have been led to Sensitivity and soil acidity (pH)، soil saturation percentage (SP) and clay percentage have been led to resistance from Piping. The present study in the Sirik and Jask county area، Hormozgan province، Iran، shows that electrical conductivity، clay، silt and sand percentage، also calcium، potassium، sodium ions and lime percentage، ordinarily are the most affective factors on Piping erosion. In the next ranks، it affected by gypsum percentage and magnesium ion content. Among these factors، electrical conductivity، silt، lime and gypsum percentage، also magnesium، calcium، sodium and potassium ions have been led to Sensitivity and soil acidity (pH)، soil saturation percentage (SP) and clay percentage have been led to resistance from Piping.

Malaria is a global public health challenge، with about one million deaths each year and a further 250 million new cases of malaria diagnosed annually. Interestingly، malaria have been distributed disproportionately such that the poorest countries in sub-Saharan Africa bearing about 85% of the burden of malaria morbidity and mortality in the world. However، more than half of the populations of the Eastern Mediterranean Region are at potential risk of contracting malaria. The Islamic Republic of Iran is one of the countries located in the Eastern Mediterranean Region with low malaria endemicity، with some regions having a reported API ranged from 0. 14 to 8. 74 per 1، 000. The south-eastern areas of Iran، including Sistan & Baluchestan (S&B)، Hormozgan and the tropical part of Kerman provinces accounting for around 95% of all malaria cases in the country (Raiesi، 2011). Malaria is basically an Italian word which means bad weather and is meant a kind of disease that is with fever، which is the result of bad weather and swampy areas. Most outbreaks and spread of diseases such as malaria، in addition to economic، social and cultural issues are affected by environmental and geographical factors. Malaria is a Infectious Diseases which is caused by celled parasites that infect human body through an anopheles mosquito. In human body the parasites grow to their next stage of development and multiply. Malaria is Caused and transmitted by Anopheles Mosquito. Distribution and ecology of Anopheles Mosquito is strongly affected by temperature، humidity، evaporation، sweating and rain fall factors. The studies show that، proper temperature range for a malaria outbreak in southeastern Iran، according to the insect species، is 25 to 35 degrees Celsius. Humidity is another factor affecting the growth and spread of mosquitoes. Proper relative humidity range for a malaria outbreak in southeastern Iran، according to the species of insects، is 50 to 80 percent. Spawning and early release of Anopheles occurs in water that appears in the form of larvae. Since flying distance of mosquitoes is limited and spawning is necessary to be done in ponds، there are numerous insects around the places where there are water. Flying distance of mosquito species found in South and South-East of Iran is considered 2Km so the water levels within 2 km of an area is appropriate for the presence of Anopheles mosquitoes (Ahmadian Marj،2008). People such as Ramage (1962) with studies on tropical cyclone، concluded in West Pakistan as a result of severe warming of drought surface some energy transferred to the middle classes atmosphere and heat load down. Pishaorti (1965) on the maps of air detect rain resulted from atmospheric turmoil and found that the turmoil monsoon in the summer associated with the monsoon of low pressure. Gilchrist (1977) claimed that during the summer monsoon، a strong field of thermal difference comes when it changes is from the ocean to the land. Huque (1977) knew Southeast Asia''s monsoon as the result of combining the effects of dynamic turbulences of seasonal changes in the position and density belt high pressure outside tropic in two hemispheres and the confrontation of the thermodynamic between the continents and the oceans. RAO (1987) indicated effect of reaction temperature between the water surface temperature and nearby air of the Arabian Sea and know the development of the monsoon caused by thermal reaction above. Research is on several aspects of the malaria، the past and present situation of malaria in Iran examined by the Edrissian (2006). Reviews of a particular type of release of the malaria in the province of Sistan and Baluchistan by Salehi and colleagues (2008). Schumann (2011) reviewed global climate change and infectious diseases and the resulted if global climate change continue، it is likely that a range of deadly diseases such as malaria spread or shift. Hoshvar (1986) in his book “introduction to medical geography of Iran” assigned chapter to malaria and familiarity with it. Geographic information system GIS designed to control and manage the disease malaria in kahnuj town by Zamani et al. Reviews of new prospects in the malaria control conducted by shikhany، et al (2003). Najar Salighe (2006) explored the mechanisms of rain in the South East of the country. In addition، he concluded in this area the influence of the moisture is from path A، B and C. In path C، moisture is in the lower and median atmosphere، with low-pressure cycle monsoon takes place، causes the sever cloudburst in hot zone. Ahmadian Marj (2008) presented a good algorithm to determine the areas with the potential outbreak of malaria using satellite images. The research indicated that a weather condition is effective directly on the amount of growth and development of the mosquito Anopheles and ultimately malaria outbreaks. Study Area: The study area includes beach areas in Sistan and Baluchestan area which is located in 26 and 30 in the north and 61:40 in the east which is a high danger area for malaria disease. In this study، we have tried to investigate the relationship between extended seasonal systems and Malaria disease in Chabahar province (Pourkermani and Zomorodian،1987).

Annual data on malaria incidence during (2010-1991) Chabahar and data about rainfall and humidity for the June، July، August and September months were gathered through the weather station of Chabahar، by weather agency of Sistan and Baluchistan province. The data of 850 hPa geopotential height and sea level data ranging from 0 to 80 and 0 to 120 North East were also studied. Those were considered as the upper atmosphere s data obtained from the United States National Oceanic and Atmospheric site (www. cdc. noaa. gov).

In the first phase، we in the Jun، July، august and September، found days with humidity over 60% which there were 1858 days with humidity greater than 60 percent in this period، then the sum of relative humidity above 60 percent per year (wet of the day) and the statistics of patients were calculated. The patient was given the same amount of years of relative humidity and illness statistics were increased compared with the previous year. During the course of the disease process charting data (2010-1991) were reviewed. Statistics show that with the increase of the humidity، the percent of people with the disease also increase. The peripheral circulation method is used in this survey. In other words، the early years، simultaneously with high annual moisture and high rated of infected people with malaria were identified. Then the days with an average humidity of over 60% were obtained and the maps of geopotential height and sea level pressure related to these days were classified and their flow patterns were identified. For classification، all maps were standardized then standard matrix of 648 × 1617was based for Euclidian distance calculations. As before categorizing there is no idea about the number of groups، cluster analysis to categorize groups seems feasible. In this case، all points are compared to each other to reveal their similarity. And then all of them are clustered together according to similarity degree of the pressure points that show a pattern is detected. The four main patterns of sea level pressure and geopotential height patterns were obtained. By mapping software، Grads، the map of blowing humidity for the day with the highest precipitation and humidity was drawn.

Generally، malaria situation in terms of cases reported during (1991-2010) showed a decreasing trend. In the city of Chabahar the disease has been declining since about 10،555 in 1991 to 1،148 in 2010 so. There is a positive and meaningful correlation between the annual incidence of the disease and annual humidity at 0. 05 Alpha level in Chabahar. The maps of pressure patterns revealed that the entire period of (1991-2010) rainfall events in the region have circulating levels of 850 hPa pattern 3 and circulating pattern of sea level type C. The above mentioned patterns showed that the summer rains in south east of Iran are associated with forming and western expansion of Indian low - pressure monsoon and also frequency of their occurrence in the Arabian Sea region. In all the years that statistics of disease has increased over the previous year، the study area is dominated by a strong low pressure system centered on Pakistan and some parts of it are spread to East and West. In all models، three core units، one located on the Ganges Valley، the other on Pakistan and there is often a low pressure on the Persian Gulf (fig. 9، 10(. In most of the maps there are three Low pressure cores which one id dominated most of the Ganges Valley، the other in Pakistan and there is often a third core with little pressure on the Persian Gulf. The monsoon systems in the region and its climate implication provides the rare conditions which is necessary for the growth and spread of mosquitoes، these conditions includes lowering the temperature of the earth Vabgyry Post marshy lands followed Widespread Larval abundance in the area، resulting in increased carrier is provided. Stats increase in malaria cases in the years that a strong monsoon system has since confirmed this.

Hot days are considered as one of the manifestations of extreme temperature. Hot days are very important atmospheric events in terms of losing water resources، large demand for water and energy، and its effect on human comfort. Accordingly، these events could have physical، economic and social consequences. As Bonsal et al (2001) has stated this atmospheric event and the related atmospheric systems might emerge and occur during every month. These kinds of temperature anomalies especially in large scales are in relation with given synoptic systems. Although many investigations have been carried out on synoptic analyses of hot days around the world، it seems they have been neglected in Iran. For example Nasrallah et al (2004) studied hot waves in warm season over Kuwait during 1958-2000. They assume northward transfer of subtropical jet stream and a ridge emergence in 500 hp are synchronized with hot days. In Iran the studies primarily underlined hot days consequences. For example Brati and Mosavi (2007) studied hot days trend and Farjzadeh and Darand (2010) investigated the relationship between hot days and mortality rate. Yazdan Panah and Alizadeh (2012) had an investigation into probability occurrence of hot days based on Markov chain model. Study Area: In this study، atmospheric conditions during the most pervasive hot day over Iran were investigated; in addition، a new method for hot day identification was applied. To this end، Iran and also an area between 10W to 120E and 0 to 80N were taken as our field study. In the present study، in order to investigate synoptic of the most pervasive hot days in Iran، the circulation to environment approach was utilized (Masodian 2005). Accordingly، the following databases were used: 1-The maximum of daily mean database with 15*15 kilometer resolution during 1963-2009 was used. This database was obtained from 664 synoptic and climatology stations using Krigging interpolation method. Therefore، the database contains 17166*7187 dimensions and every pixel on each day map has its own mean of temperature value. According to the definition by «The Join World Meteorological Organization Commission for Climatology”، a hot day is a day when the temperature of each pixel in the country and each day is more than 90th percentile of a given pixel and a given day. Therefore، 366 maps of hot day threshold have been created. These maps were hot days criterions. Country experienced the most pervasive hot day in 2004/3/7. In this day، virtually 96. 7% of the country experienced temperature which was above the defined threshold temperature. 2- The atmospheric database for the day with the most pervasive hot and also for the period of 1963-2009 which was obtained from NCEP/NCAR contains sea level pressure (SLP)، meridian and zonal components of wind، temperature and 500 hp geopotantal heights. Synoptic and dynamic analyses were carried out in order to investigate atmospheric situation synchronized with hot day: - Synoptic analyses of SLP and 500 hp were analyzed to reveal the pressure and the height of atmosphere and their anomalies in 2004/3/7 and also they were compared with those in 1963-2009 mean. - Dynamic analyses were considered jet stream، advection and front genesis function. The jet stream was investigated in four levels (300،400،500 and 600 hp). Finally، in order to analyze the dynamic – thermodynamic relations of atmosphere، the relationship of vorticity and temperature in aforementioned level has been estimated based on Pearson correlation coefficient. The maximum temperature in (2004/3/7) occurred in southeast of the country specified from 39 to 40 degree centigrade، while the minimum temperature specified from 0 to 3 degree centigrade has happened in a small area at north east of Eastern Azerbaijan. There are only small distinct areas that cover 3. 3% of the country (Northwest، Caspian coast and in Northeast of the country) where the positive anomalies have not occurred. Generally، most parts of the country experienced positive anomalies and there are also areas in Kerman،Yazd، Semnan And Khorasan Razavi characterized by up to 16 degree centigrade positive anomalies. Sea level pressure showed two high pressure systems were located in east and west of Iran، meanwhile the polar low came from southward to north of Iran. Therefore، a dramatic pressure gradient in between caused a massive hot advection toward Iran. The pressures anomalies in all over Iran were positive and up to 5 hp were negative through northern part and in small area of Oman sea coast. In the 500hp level، a two center cut off system was observed on Russia and Iran is located in front of Mediterranean-Red sea trough. This pattern moved air masses from lowest latitude، northeast Africa، toward Iran. Also a ridge on Iran caused a dramatic height gradient due to its adjacency with northern cut off. This condition caused a negative anomaly pattern all over Iran. The anomalies are -550 to -300 meters. Investigating westerly''s jet stream، it revealed that jet stream came to 600 hp in southwest-northeast orientation which was covered northwest of Iran to northeast of Kazakhstan. A vast area of Iran was located in southern mouth quarter in which cyclonic convergent air and dropping pressure level were naturally expectable. Calculating front genesis function، it is clear that there was front genesis potential all over Iran. All situations mentioned tend to have warm advection in all atmospheric levels toward Iran although there are some reigns characterized by cold advection in which the temperature anomalies are still positive. The correlation between lowest levels of atmosphere and uppers indicated a strong positive correlation. This indicates a thick atmospheric layer warming limiting heat transfer. The pervasive hot day was due to difference in pressure pattern causing a deep atmospheric front and a high and deep jet stream. There are other patterns such as negative anomalies in atmospheric height، warm advection. Warm convergence in all atmospheric levels tends to decrees spatial temperature difference. Consequently، it caused difficult situation for heat vacation. It is obvious that surveying hot days necessitate thermodynamic as well as dynamic consideration.

Probabilistic risk analysis is a systematic approach capable of bringing multiple expertizes and fields of sciences together for a comprehensive analysis of performance of engineering systems. Moreover، risk analysis is a managerial tool in hands of the disaster managers for decision making considering different methods to examine reactions to probable risks and vulnerabilities. At international policies level، the term “multi-hazards risk analysis” was first introduced in UN permanent development plan (UNEP) in 1992. The document calls for “complete multi-hazard research” as a part of man settlement programming and management in hazards prone regions (UNEP 1992). The term multi-hazards risk analysis was used again in Johannesburg program for “integrated protecting and managing the natural resource base of economic and social development” (UN، 2002). Then، the Hyogo Framework of Action (UN-ISDR 2005) adopted this aspect and suggests an ‘‘integrated، multi-hazard approach for disaster risk reduction into policies، planning and programming related to sustainable development، relief، rehabilitation، and recovery activities in post-disaster and post-conflict situations in disaster- prone countries.” (UN-ISDR،2005). Moreover، the term multi-hazards was used in the strategy plan for reducing national disaster in the USA (FEMA، 1995)، devised to attenuate risk of national disasters effects and concentration on multi-hazards on design and structure of buildings. Multi-hazards analysis as defined by Delmonaco et al. (2006) is “implementation of methodologies and approaches aimed at assessing and mapping the potential occurrence of different types of natural hazards in a given area”. Multi-hazard risk survey and evaluation was subject of a study by Xing et al. (2008). After dealing with probabilities، they obtained risk of hazard (assets and infrastructures) quantitatively. Javanbarg and his colleagues surveyed analysis of multi-hazards in binary networks. In their analyses، they considered the infrastructure components as nodes and links، and failure and simultaneous effects of multi-hazards considered as failures on links and nodes were assessed. Finally they obtained failure risk of a binary network. (Javanbarget al. 2009). Schemidt et al. (2012) proposed a framework for multi risk modeling (earthquake، volcano، flood، wind، and tsunami) and developed RiskScape software designed to calculate multi-hazard risks. The software was written in JAVA with some limitations. Considering necessity of accurate information of regions and districts for preventing and managing and urban programming، it is essential to conduct studies for controlling and identifying hazards and threats in Tehran. To this end، we need to survey and study different districts، maneuverability of the city and analyze vulnerabilities. Cleary، the results are helpful in strengthening crisis management system before the event. Study Area: District No. 20 (Shahr-e-Ray) is located in far south of Tehran city with an area of 23km2، inside the main body of the city and circled in an area of 153km2. Traces of life in the district reach back to 6000 years and the region is home to many historical elements and religious centers including Abdolazim’s tomb; these centers make the region distinguishable among the other regions. Old neighborhoods are one of the outstanding features of the region. Shahr-e-Ray is one of the oldest cities of Iran. The district is threatened by Ray Fault and other faults under Tehran city and positioned in Sorkh-e-Hesar downstream. Taking into account hazards such as flood of 2001 and the historical earthquake of Shahr-e-Ray، it is vital to conduct surveys and studies aimed to reduce vulnerability and manage the hazards. As pictured in Figure 1، the method of the study includes flood and earthquake hazard analysis modules، flood and earthquake damages analysis modules، and multi-hazard risks analysis module. What follows is a brief introduction to each module. Different scenarios based on the status of hazards occurrence status and their consequences are explained in follow table. A 7 magnitude earthquake and a flood with return period of 100 years are considered. Considering the scenarios of this table and using the explained methodology the damage probability for residential buildings of the region is calculated. Then the multi-hazars risk of damage is analyzed based on the probability of scenarios، the probability of damage states and the number of structures in each block. The resultant risk of “flood and not earthquake”، “earthquake and not flood”، and “earthquake and flood” were calculated for different damage states. Inputting the results in GIS، Mutli-Hazards risk maps were generated. The results for scenarios 2 and 10 are pictured in figures 11 and 12. Different scenariosDamage state Common cause Event Flood Earthquake ScenarioSlight (DS1) CCE2 No Yes 1Moderate (DS2) CCE2 No Yes 2Extensive (DS3) CCE2 No Yes 3Complete (DS4) CCE2 No Yes 4Slight (DS1) CCE3 Yes No 5Moderate (DS2) CCE3 Yes No 6Severe (DS3) CCE3 Yes No 7Collapse (DS4) CCE3 Yes No 8Slight (DS1) CCE4 Yes Yes 9Moderate (DS2) CCE4 Yes Yes 10Extensive (DS3) CCE4 Yes Yes 11Complete (DS4) CCE4 Yes Yes 12 A methodology and an algorithm for quantitative analyzing of Multi-hazard risk of damage of residential buildings were introduced and used. The method may be used alone for assessing risk of assets regarding a single hazard. However، it is a comprehensive analysis procedure that takes multi-hazards into account (consecutive hazards in particular) and assets risks resulting in more realistic quantitative results. There are cases where some of the hazards are missed in urban planning and programming or it is not easy to calculate the result of implement preventive measures against some of the hazards. The proposed method introduces a framework to address this challenge. Multi-hazard risks maps answer this issue as they deal with all hazards in one place and depict the effects and dependencies of the possible hazards. Such maps can be dealt as bases for risk management and integrated disaster management. Although، in case of multi-hazard analysis the risk is reduced up to 15% and 2% with respect to earthquake single hazard or flood single hazard، respectively (because simultaneous incident of flood and earthquake is very rare (0. 39 * 0. 22))، damages in the case of multi-hazards are shifted to the extensive and heavy states for all structures and 33 percent increase in number of complete damages is expected. Moreover، based on multi-hazards at complete damage state، the number of damaged masonry، steel، and concrete structures increases in a fold of 1. 25، 1. 26، and 1. 5، respectively.

Pollution can be defined as an undesirablechange in the physical، chemical or biologicalcharacteristics of the air، water or land that canaffect health، survival or activities of humans orother organisms. Air pollution is the biggest environmental problem in whole of the world that industrialization and increasing in number of city cause to increasing of its intensity. Tehran، one of the heavily-populated capitals and air pollution is known one of the environment problems for Tehran citizens during past recent century so this city today''s is known one the pollutant city in the world. Some factors such as Tehran topography and climatology، population growth (more than 10 million person)، increase in the number of motor vehicles (more than two million vehicles) as well as industrial expansion، cause to intensify of air pollution in this city. Also this city is a greatest industrial city in Iran andthere are some Power plant، Refinery and chemical plants in it. In fact the important reason for Tehran air pollution is irregular consumption fuel fossil particular gasoline. Particulate matter is considered one of the main sources of air pollution problems in Tehran. The emitted particles in the air for a while and then slowly deposited on exposed surfaces such as tree leaves. Heavy metal studies are necessary to evaluate air contamination. The problem of environmental pollution due to toxic metals has begun to cause concern now in most major metropolitan cities. The toxic heavy metals entering the ecosystem may lead to geo-accumulation، bio-accumulation and bio-magnifications. Heavy metals like Fe، Cu، Zn، Ni and other trace elements are important for proper functioning of biological systems and their deficiency or excess could lead to a number of disorders. The road side tree leaves accumulate more particulate matter. Tree leaves are capable to absorption air pollution، so using plant for air pollution monitoring is known as an effective method. Biomonitoring is one of inexpensive and simple method to concentration of heavy metals and air quality investigation. In this method living organism can be used to obtain environmental data and its quality. Morus alba has large and flatten leaves surface that can accumulate large amount of precipitated atmospheric particles and have monotonous distribution in Tehran city، so this species are used to air pollution studies. Therefore trees have natural Bio filter role in pollutant cities. Traffic pollution has toxic material such as Pb، Cu، Cd that are so harmful for health of peoples. The aim of this investigation is the ability of Morus albatree leavesto deposition of pollution such as heavy metals As، Al، Cr، Co، Fe، Cu، Hg، Mn، Ni، Zn. At final will deal with to mapping air pollution using pollutant elements that deposited on Morus albatree leaves. Study Area: The metropolis of Tehran is the capital city of the province and of Iran also it is the biggest city of Iran. The northern parts which are adjacent to Alborz Mountains are clearly colder and more moderate than southern parts. The prevailing wind in western part of the city is generally from the west. It covers on area of 18،909 square kilometers and is located to the north of the central plateau of Iran. Tehran city has been divided in to 22 sectors and each sector has online digital air pollution monitoring display. The concentration of air pollution is mapped daily and the sampling sites were chosen on the base of these maps. The number of sampling sites in a sector is determined on the bases of the degree of air pollution in that sector، i. e. more pollution sector، more number of sampling sites in it. In this research for air measurement Morus alba that has a homogeneous spread in the Tehran city were chosen. Then heavy metals such as Al، As، Fe، Co، Cr، Cu، Mn، Ni، Pb، Zn were measured in the all of the 22 regions of Tehran. For this aim Sampling was carried out during a fifteen-day period. The highest pollution in Tehran city occur in autumn، sampling was done in September 2010. First of all، using Tehran''s controlling air quality administration maps، high، low and medium air pollution regions was recognized. For this idea100 sample points were selected in whole of the Tehran region. As far as possible the sampling sites were distributed over an area in and around the of Tehran city. The Morus alba leaves species were chosen for this research. This species is distributed evenly across the whole of this city. Then tree leaves of the aforesaid species were collected from the Tehran and from around of highways and streets. Sampling was confined to branches، facing road. Sampling were done in shiny days and at a height of 1–1. 5 m above ground. In order to ensure the leaves were of similar-sized، leaves with 10-15 cm length were collected. Samples were put in pocket-size sealable plastic bags and all leaf samples were refrigerated at 5°C. The totals of 100 sampling sites were selected in urban administration of Tehran city. Three packages of leaves sample were collected at each sampling site، and total samples were 300. Specimen preparation include: room temperature drying at closed system and powdering before measurement. After sample preparation include: room temperature drying and powdering، sample were digested، then using ICP set amount of heavy metals were measured in leaves. Heavy metals were measurements: Destruction procedures based on the use of a combination of HNO3 and H2O2 are also commonly used for leaf plant analysis. Samples (1g) were weighed into 100 ml Pyrex beakers and treated with 10 ml concentrated HNO3 (ultrapure 65%). The beaker was covered with a watch-glass and the suspension was heated up to 130 ◦C for 1 h. A total amount of 4ml 20% H2O2 was added in aliquots of 1 ml. After cooling، the suspension was filtered in a 50-ml volumetric flask and diluted to the mark (Laing et al.، 2003). Then heavy metal concentrations were determinate by ICP method at geological survey of Iran. Finally using GIS software، all of results were put to mapping air pollution. One of the systems which have appeared lately is Geospatial Information System (GIS). GIS is not only a system for creating، managing and analyzing graphic and attribute data، but also is a decision supporting system. Results showed that most amount of the heavy metal centralized at central، south and east south of Tehran that include regions such as 9، 10، 11، 12، 14، 15، 16 and 17. Because of high traffic of vehicles، industrial workshops، international airport of Mehrabad and several military center in region 9 there is a high pollution than other regions. Region 12 is in the center of Tehran. Great market of Tehran is in this region and cause to high traffic. Also، Shoush and Enghelab square have high pollution. Results of this research showed that high concentration of Cu in Morus alba was 123. 162 mg/kg and for Ni is 12. 85mg/kg. Because of nickel gasoline abrasion in motor vehicle this element product and then disperse in air. Also، between different elements، Al and Fe had maximum pollution، so minimum value in Fe was seen in district 3 but it was higher than standard value. Either، there is a high correlation between some elements such as Al، Cu، Fe، Ni and Zn. This correlation index is refer to how the presence of an element can be used for estimation of another elements. This means that high correlation confirm significant relation between two or more elements. It can be so useful because only one element measurement can show presence of another elements percentage. High concentration of elements (Al، Cu، Fe، Ni and Zn) is a serious problem for human health in this city. Tree leaves can be effective to measure amount of air pollution in pollutant cities، also some plant such as Morus alba with expanded leave seem to be suitable for air pollution monitoring because of high surface area in tree leaves are capable to deposited more amount of air pollution materials. 6. ConclusionThis paper pay to investigation of heavy metal concentration on 22 Tehran´s sectors using Morus alba tree leaves. For this aim Morus albatree with flat leaves were chosen. Results showed that the heavy mineral analysis of deposited particles on Morus alba leaves، has high concentration of Fe، Cu، Al، Ni، Zn، Mn، Pb). Leaves with large surface areas per unit of weight such as Morus alba leaves، have favorable surface properties for pollutant materials deposition. Between different elements، Fe and Al has highest pollution. At the end، high pollution were seen in central، south and south east of Tehran and some factors such as Tehran''s topography conditions، climate element such as wind، population and motor vehicles increasing، industrial concentration have important role in Tehran air pollution. Also، most of these elements are in severe pollution condition and in most cases measured concentration show higher amount of standard values that can be a serious threat for human health.

Air pollution caused by changes in the quantity and quality of atmospheric gases. This phenomenon is due to the increased use of fossil fuels in urban areas. Status of natural environment of the city and the characteristics of the weather elements and phenomena cause congestion، transport and displacement of pollutants، particularly in the central areas of a city. Tehran have Known as a one of the largest city in the world by population. It usually meets critical air quality condition special in central part of the city. In this context، for careful analysis of the physical and natural factors in mitigation or aggravation of air pollution، the micro-scale numerical modeling methods based on a laboratory model of airflow (CFDs) are used. The usefulness of this method is being the quantitative effect of each of the urban fabric and climatic factors in small-scale on spatial and temporal resolution. However، this method can be largely resolves this problem. To evaluate the effects of urban spaces on air pollution in micro-scale، the components such as street width and orientation، mass-produced building model، land use، and green space patterns and classes against atmospheric elements including local prevailing wind direction and intensity، temperature and humidity fluctuations have been discussed. In spite of the fact that numerical modeling can offset most part of micro-scale data deficiencies. Study Area: The study pathway starting from Tehran Pars neighborhood to Azadi Square located in eastern and the west Tehran، respectively. It embraces different pollutant sources resulted from congested traffic and placing of numerous shopping، business and public centers. Since، central Main Street of Tehran with a length of 19km was chosen to be the study path of present research. In order to conduct an accurate study on air pollution، interaction between man-made features and natural environment، in particular atmosphere should be examined. However، because of limitations on data achieving and processing in micro-scale، it is partially neglected. Whereas micro-scale studies have been shown high relation between urban spaces and atmospheric elements in increase or decrease of air pollution intensity. In this study a comprehensive library research، local sampling، and statistical methods complete it. In the second، three most congested points includes; Azadi Square (west Tehran)، Valiasr cross (mid-city intersection) and TehranPars 3-way Junction (east Tehran) selected to be interpreted information from traffic films of morning (High-traffic) and noon (low-traffic) on July 26th2011. The research continued with the statistical analysis of Calculated CO emissions and daily weather data recorded by the closest weather station to sampling points (Mehrabad synoptic station) and subsequently، defining them as ENVI-met model inputs to simulate air pollutants emissions. The simulation output provides statistics on the output and input variables for each three cross sections of the pathway. According the simulations outputs for Azadi Sq. shows that the most air pollutants concentration is on emission lines sources، especially at crowded streets in the east edge of the Square. The lowest amounts are visible in open and green spaces as well as passages between the sub-blocks. In Azadi Square except the east edge that is characterized dense building blocks، all around the square have no the major and important obstacle down wind. As a result، despite the high level of daily traffic، the density of pollutants around Azadi Square is low. Valiasr cross is another indicator that recognized by high dense structure، high traffic jam، and a medium park located in the southeast corner. On comparison two other sections، Valiasr cross is the most polluted by CO. the most concentration is shown on the center of cross and east-west streets. Because of katabatic and anabatic airflow along the north-south streets، Pollutants are dispersed into surrounding streets. Condition in Tehran Pars as an end of the study pathway has different conditions in the north and south parts. Northern half characterized by dense building blocks and in contrast the southern half that recognized with open areas. The most important physical features of the area include two passenger terminals، relatively open texture، especially in the southern edge; the traffic is lighter than the other two passages. Therefore، Valiasr cross is the most pollutant section of the study pathway in comparison Azadi sq and Tehran-pars. In addition، the highest amount of CO pollution is visible in the early morning depend on colder condition. Results of simulation outputs show that the maximum concentration of pollutants، as expected، formed in dense urban areas، especially during the early hours of the day. Whereas، the lowest values were observed on wide passageways such as west side of Azadi Square and south side of Tehran Pars3-way Junction، green spaces and areas far from the hotspots of emission during mid-day. In addition، the more increase in high، the lower level of CO emissions was simulated.

United Nations statistics show that floods and storms have a greater number of casualties and cause more damage to communities compared to other natural disasters. Flood occurs due to the specific climate and topography conditions and especially after rainfalls with high intensity and duration. In Iran، investigation on direct flood damage at recent 50 years showed that its rate increased over than 250 percent. Flood predictions are made by processing hydrometric data، and the most commonly used parameters appliedto such processes are those of peak flow and flood volume. However، the necessary statistics are often incomplete due to a lack of meteorological stations so it is important to develop a process for making predictions using indirect techniques for flood discharge estimatesThis can be done with the Creager formula that applies empirical equations for estimating maximum instantaneous flow in catchment areas with no statistics (Equation1): (1) Where; Q: the instantaneous peak flow (cubic feet per second)، A: area (square miles)، and C is the regional coefficient of Creager''sformula which its maximum value is 200، the whole world will cover floods observed (Mahdavi، 166:2007). Mahdavi et al. (2005) and Jamali et al. (2006) were obtained peak discharge data with the return period of 2 - 100 years using 10 peak discharge empirical formula in the main basin of Iran. According to these researches the Morfi model was suitable for low return period and the Kramer formula had better estimation at high return period but the Creager formula had a terrible error at high return periods. Seidali et al. (2010) studied on Creager coefficient range in Yazd province and their results showed that these coefficients were from 0. 014 to 20. 95 according to different returning periods. In Japan، Ohnishi et al. (2004) estimated peak flood at some basin with maximum one square kilometer but they mentioned that these formula have some error at the basin which are larger than 1178 square kilometers. Central Iran basin، is a part of the areas where has the greatest crisis in touch with hydrometric stations and the missing flood data. This lack is the major challenges in the research and development programs of water resources. In this study، we tried to among of 80 statistical distributions find the best distribution using flood frequency analysis and estimate the peak discharge with different return periods. Also in Hinks and Dedja (2002)، research which has done at 5 earth fill dams in Grate Britain، the average Creager coefficient was calculated about 36 and its amount trough the 500 – 10000 years of return period was suggested between 13. 5 and 25 for the studied region. The main objective of this method is to use estimates to compensate for lack of hydrometric data on return period flood discharges. This study attempts to make analyses of flood frequency by estimating peak discharge with different return periods. And then to evaluate and make calibrations of regional coefficients for the central Iran basin in order to enable consideration of various different return periods. Study Area: The area under evaluation was the central part of Iran، an area of 823946 km2. The area was classified as an arid and semi-arid climate، which applies to about 50. 75% of the country’s land. In this type of climate zone، rainfall conditions make areas susceptible to short-term and instant flooding. Meteorological data is restricted because there are inadequate numbers of gauging stations because they could only supply long-term statistics. On the other hand these hydrometric stations have a heterogeneous distribution so that most of these stations are located in semi-arid regions and only a few stations are located in arid and ultra-arid regions. So، high climate and geographic variety of these areas causes heterogeneous area (Hayatzadeh، 2009). Methodology and Firstly، data were collected from gauging stations and reservoir dams in the area. Reservoir dams make some jump in the data so we omitted these data. Some of the stations were excluded from the study due to a lack of sufficient data or because of closure. Stations with a high deficit in data relating to short-term records of instantaneous discharges were eliminated and other stations along with more than 25 years’ of statistics، from 1965 to 2011 were used for the next stage of the study. In the following، out-layer’s data test was applied using the Groboz-Back equation and data were examined by Makoos’s equation for accuracy and validity، and determined as adequate; thus 29 gauging stations were selected. Continues data with appropriate length is very important in flood studies. However، in most catchments located in arid and semi-arid regions data inadequacy and also gaps in the existing data series is a common problem. Therefore، so far several methods have been presented for reconstruction of missing data series. In this research it has been tried to evaluate the efficiency of some new methods in reconstruction of instantaneous peak flow data in arid and semi-arid regions of Iran. For these 29 selected stations reconstruct the missing data series of peak discharge by using adaptive neuro-fuzzy inference network method. To flood frequency analysis using statistical distributions for process and estimate the maximum discharge with different return periods، is a common method (Hadiyan et al.، 2010). Then statistics for individual stations using the software of Easy fit and Mathematician over 80 different continuous distributions were fitted and using maximum likelihood (MLE) and moment of methods (MOM)، the most appropriate statistical distribution was determined. Thus for peak discharge frequency analysis، using the Easy fit and Mathematic software''s of fitting over 80 continuous statistical distributions، using maximum likelihood (MLE) and method of moments (MOM) methods was selected suitable Statistical distributions. The final step was to determine the maximum flow rate at each station، the amount of discharge with different return periods was given in the Creager formula and from the area relating to each station، the regional coefficient was determined with the Creager formula 2، 5، 10، 20، 25 and 50 year’ return period. In order to data validation in each station we used to root mean square error (RMSE)، coefficient of determination (R2)، mean absolute error (MAE)، mean absolute percentage error (MAPE) and Efficiency Coefficient Nash- Sutcliffe (ENS) were used. (Yang et al.، 2009; Akhavan et al.، 2011) In data reconstruction of missing data neuro-fuzzy had the lowest error and the genetic algorithm (GA) had the most error at all of the stations. But using T test there were no difference between the methods at 5 % significant. The mean adequacy level of all stations was taken from across the whole area; adequacy of data was determined at about 74. 08%. According to the Groboz-Backequations، instantaneous discharge data relating to Sulanand Esfarjan and Delichaystationswere each given a number for out-layer data. Analysis of outputs by Easy Fit showed that stations in 17 of the statistical distribution were classified under best fit. According to the distribution of choices in each station، instantaneous discharge with return periods of 2، 5، 10،25 and 50 years، respectively. The area of each station was specified and estimates were made for return periods associated with the regional coefficient determined by the Creager’s formula (Table 1). Also there was no significant relationship between peak discharge and the related upstream drainage area in the studied region. Table1: Creager''s coefficient for the peak discharge with different return periods in central parts of Iran Sub basin Number of stations Range of Creager''s coefficient2 5 10 25 50Jazmorian 1 2. 87 5. 71 8. 13 11. 81 15. 08Gavkhoni 3 -0. 050. 03 -0. 920. 12 -1. 37x0. 20 -1. 840. 44 -2. 10. 83Yazd-Kerman 3 -0. 590. 13 -1. 420. 36 -2. 390. 65 -4. 481. 45 -7. 072. 1Salt Lake 14 -1. 360. 03 -2. 70. 08 -3. 370. 77 -6. 110. 23 -10. 970. 34Kavir plain 7 -0. 720. 10 -1. 670. 25 -2. 740. 35 -4. 950. 50 -7. 550. 6Bakhtegan 1 2. 28 5. 29 7. 57 10. 59 12. 87Sum 29 -2. 870. 03 -5. 710. 08 -8. 130. 2 -11. 80. 23 -15. 080. 6 The results showed that regional coefficient of the Creager formula had low values in the region of central Iran. With a return period of 50 years in the Daman station located on Jazmorian areas in the southeast of the central area، the maximum value of this ratio was determined as15. 08. In Sulan station located in the Salt Lake sub-basin in the Northwest of the center، the lowest amount equal to 0. 34. (Table 1) It is commonly believed that an increase in area، increases the maximum flow rate، but no relationship was determined between peak discharge and upstream areas of each station. According to the Creager''s formula structure، it is likely that the situation in terms of the model is presented for the first time، a variety of climatic، tectonic and morphological rule in central Iran، is the fading parameter area. The results showed predictions for generalized instantaneous discharge in terms of area parameter in relation to the other stations، the creation of a gross error in the estimation of flood discharge in areas without gauging stations. According to the negative coefficient of Nash - Sutcliffe for Sarabhende، Salehabad، Mondarjan، Daman، Jiroftu، Gharyatolarab and Bonkoo stations accuracy of the Creager model for estimating instantaneous discharge was determined as sufficient. Furthermore، MAPE statistics revealed that the mean absolute error of the Creager method in central Iran was 54. 85%.

Salinity is a highly important problem in arid and semi- arid region. In Iran، about 235،000 km 2 (or 14. 2% of the total area of the country) area is salt-affected، which is equivalent to about 50% of irrigated lands in Iran (Pazira 1999). Irrigating of these lands causes to transfer salts to the area of root growth and thus increases the osmotic pressure and reduces the absorption of the nutrient elements and product. Some of researchers believe that this process is one of the disasters that threats development of human societies. Esfahan province is exposed to the danger of salinity. Esfahan province is located in the central arid region of Iran. Of 105،000 km 2 total area، an area of 5000 km 2 is used for crop and fruit production. Soil and water salinity is the major limitation to achieve optimum crop yields. However، the classical soil survey methods of field sampling، laboratory analysis and interpolation of these field data for mapping، especially in large areas، are relatively expensive and time-consuming but to get informed about distribution of salinity in soil is very important for recognizing critical threshold، planning، management، operation of source، and suitable distribution of water to correct the soil saline. Soil chemical properties commonly have spatial dependence at regional scale (Yost et al.، 1982). Regional assessment of soil properties requires evaluation of their spatial distribution. In recent years، environmental scientists have come to appreciate the merits of geostatistics and kriging for investigating and mapping Soil chemical properties in un-sampled areas. There are a large number of reports to natural resource distributions. Geostatistic method such as Ordinary Kriging is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. Many geostatistical tools were originally developed as a practical means to describe spatial patterns and interpolate values for locations where samples were not taken. Three functions are used in geostatistics for describing the spatial or the temporal correlation of observations: these are the correlogram، the covariance and the semivariogram (hasani pak، 1999). The last is also more simply called variogram. The following parameters are often used to describe variograms: nugget: The height of the jump of the semivariogram at the discontinuity at the origin، sill: Limit of the variogram tending to infinity lag distances and range: The distance in which the difference of the variogram from the sill becomes negligible. In applied geostatistics the empirical variograms are often approximated by model function ensuring validity (Chiles and Delfiner 1999). Some important models are (Chiles and Delfiner، 1999; Cressie، 1993): The exponential variogram model، the spherical variogram model and The Gaussian variogram model. In assessments of some variables، spatial correlation structure is the same in all directions، or isotropic. In this case the variogram depend only on the magnitude of the lag vector، h = h، and not the direction، and the empirical variogram can be computed by pooling data pairs separated by the appropriate distances، regardless of direction. Such a variogram is described as omnidirectional. In many cases، however، a property shows different autocorrelation structures in different directions، and an anisotropic variogram model should be developed to reflect these differences. The most commonly employed model for anisotropy is geometric anisotropy، with the variogram reaching the same sill in all directions، but at different ranges. However some Geostatistical methods such as kriging need valid variograms. Cross validation is used to find the best model among the competitors. “Cross Validation” allows us to compare estimated and true values using the information available in our sample data set (Houlding، 2000). Study area: This research was conducted in Isfahan province (Fig1). It is located in the center of Iran in a predominantly arid or semiarid climate condition and is about 6800 km2 around Zayandehroud River. Mean annual precipitation and temperature are 120 mm and 14. 5 Co and Annual evapotranspiration is 1500 mm. The soils are classified as Aridisols. The area covers different land uses including agricultural، industrial، urban and uncultivated lands. In this study، soil sampling strategy was random stratify. In this method، the region was stratified in to regular- sized grid cells of 4 × 4 km and within each cell a sampling location was chosen randomly. A total of 255 soil samples (0-20 cm) were collected (Fig 2). At each sampling point the coordinates were obtained using a portable GPS and its land use was recorded. After calculation، 46. 5% of the sampling locations occurred in agricultural lands، 43. 5% in uncultivated lands and 10% in industrial and urban area (Fig2). Soil samples were air dried and ground to pass through a 2 mm sieve، Electrical conductivity was measured in a 1:2. 5 soil-water ratio suspension. Statistics including mean، variance، maximum، minimum، coefficient of variation (CV) and comparison average EC in different land use were calculated. The results of factor analysis were used to calculate the autocorrelation value between observed points and produce a minimum unbiased variance estimate. This variance is calculated as a function of variogram model. Variogram is calculated using the relative location of the samples (Soderstrom، 1998). The experimental variogram is calculated for several lag distances this is then generally fitted to a theoretical model and the parameters in suitable model are then used in the kriging procedure (Mohamadi، 2007). The next step is cross validation of the prediction models. The cross-validation technique is used to choose the best variogram model among candidate models and to select the search radius and lag distance that minimizes the kriging variance. Cross-validation is achieved by eliminating information، generally one observation at a time، estimating the value at that location with the remaining data and then computing the difference between the actual and estimated value for each data location. To compare different interpolation techniques، we examined the difference between the known data and the predicted data using the Mean Square Reduced Error (MSE). Correlation coefficient (Pearson) computed between real and estimated data with ordinary kriging. Distribution map of EC was produced using the ordinary kriging and use from maximum 16 point and minimum 3 point in estimation. The descriptive statistical parameters were calculated with Microsoft EXELE and SPSS (version 11). Maps were produce with Surfer (version 8) and ILWIS (version 3. 0) and geostatistics analyses were carried out with VARIOWIN and WINGSLIB. The average EC was 6. 9 dSm-1 with range of 1-74 dSm-1 in Isfahan surface soils (Table 1). EC values don’t follow a normal distribution and had a strongly skewed distribution (Fig 4a) therefore these values were transformed to logarithm and the log-transformed data fit an approximately normal distribution (Fig 4b). The average EC between different land uses was compared by using one-way ANOVA. The results showed that there is a significant difference in the soil salinity between uncultivated and agricultural area، but there is no significant difference between uncultivated and urban-industrial area. Soil salinity mean in the uncultivated area is at least three times higher than other land uses (Fig 3). The study of the spatial variability of EC is begun with the computation of directional variograms for EC in different directions. The best variograms was fitted in directions of 45 degrees، and a spherical theoretical covariance model fitted on variogram (Fig 6). The next step، cross validation of the prediction models computed with MSE and Pearson coefficient. MSE was minimum and Pearson coefficient was high (77%) and this parameter is identifier. Therefore valid variogram parameters (sill= 21000، rang= 0. 6 and nugget effect =0. 127) use for kriging and mapping (Table 2). According to the map of salt distribution in the top soil of study area (Fig 7)، all lands are saline (Ec> 4dS. m-1) but critical accumulation of salt is in the eastern region، especial Segzi plain. Segzi plain is located in the Eastern part of Isfahan province in the center of Iran and is about 40 kms from Isfahan center. The climate of the area according to the Gowsen method is found to be dry and semi-desert، respectively (Mojiri et al.، 2011). In Segzi plain are gypsum mines and wind can transport particles of chalk and sand that cause erosion and air pollution in Esfahan. Rainfall in east of Esfahan in comparison with center and west are lower and temperature is higher so there are more salt in compare to central and west parts. Moreover comparison of maps (salt distribution and land use) delineate، agricultural lands have lower salinity. Because irrigation and leaching are continued and transfer salt to deeper part. Moisture regime in study area is aridic. In this regime evapotranspiration is high and suction gradient transfer salinity solution to soil surface (Richards، 1954). The important result of this research was obtain concentration map of salt with this hope that this survey can be effective step in chose best decision in management of salinity lands to intention improvement and reformation this land. According result of this research، to be near with Lut deserts، low average rainfall، high annual temperature and high evaporation in eastern parts than western parts، are the most important parameters in accumulation of salt in this part of study area.