نشریه جغرافیا و مخاطرات محیطی
پیاپی 14 (تابستان 1394)

1. Dust storm is a natural phenomenon that occurs frequently in the arid and semi-arid regions all over the world (Alijani, 1997). Dust can affect soil fertility, forests, rivers, lakes, and marine ecosystems around the world (McTainsh et al., 2007). Therefore, soil erosion can lead to the loss of the minerals and organic matter of topsoil. Some elements of dust have also an indirect effect on absorption of other elements (Reynolds et al. 2001). It is estimated that each year 2000 Mt dust is emitted into the atmosphere, 75% of which is deposited to the land and 25% to the ocean (Shao et al., 2011). The morphology and elemental composition of the particles can change alone the transportation in reaction to gasses and other particles in the atmosphere (Wang et al., 2007). Identification of the physical properties and chemical composition of dust aerosols is important to determine aerosol sources, mixing processes and transport pathways (Rashki et al., 2013). Chemical analysis of airborne dust can also characterize major and trace elements of airborne dust which is important for quantitative climate modeling, in understanding possible effects on human health, precipitation, ocean biogeochemistry and weathering phenomena (Goudie & Middleton, 2006)
2. Study Area: Shahdad is a region located in west of Lut Desert and South East Kerman province, in centre of Iran. Low precipitation, high temperature, salinity (Alavipanah, 2002) and sever winds are characteristic of this region. Shahdad with a maximum temperature of 71 ° C is one of the hottest regions in the earth (Alavi Panah, 2002, Mildrexler et al., 2006, Ehsani et al., 2008) this region with extensive wind erosion, and intense dust storms, causes adverse effects in regional air quality and human health. To mitigate the impact of these phenomena, it is vital to ascertain the chemical characteristics of airborne and soil dust
3. Material and This paper examines for the first time, the chemical properties of dust over Shahdad region by collecting dust samples at five stations established at 5 villages close to Lut desert downwind of dust source region, from spring 2013 to September 2013. Furthermore, soil samples were collected from topsoil (0-5 cm depth) at several locations in upwind areas. The data was analyzed to investigate the chemical characteristics of dust, relevance of inferred sources. X-ray Fluorescence (XRF) analysis of airborne and soil dust samples have been produced to show Chemical properties of dust and characterize major and trace elements.
4. Major-element and ion-chemistry analyses provide estimates of mineral components, which themselves may be hazardous to human health and ecosystems and can act as carriers of other toxic substances. X-ray Fluorescence (XRF) analyses of all the samples indicate that the most important oxide compositions of the airborne and soil dust are Silicon dioxide (SiO2) in quartz minerals, Calcium oxide (CaO) in calcite minerals and substantial Aluminum oxide (Al2O3) that exhibiting similar percentages for all samples. Chemical analysis of dust samples showed that the main element of dust in the Shahdad is SiO2 (49.35%), which is close to the average of this element in southeastern Iran( 47%) (Rashki et al., 2013) and more than Southwest Iran with (38%) (Zarasvandi et al., 2011). The global average SiO2 is 59.9%. Therefore, amount of Quartz in Iran is lower than Quartz the earth.
Major elements in the dust and soil samples in these analysis are: SiO2 (33.33-55.59%), LOI (12.68-26.31%), CaO (10.12-18.76%), Al2O3 (7.97-10.45%), a small amount of Fe2O3 (3.04-4.34%), Na2O (2.04-13.91%) , MgO (0-3.03%) and K2O (0.85-1.37%), as well as small amounts.

1. As strongly accepted among climatologists, climate change is a result of human activities. There is disagreement about how to define climate change, because of tremendous difference of climate changes in scale, intensity and occurrence time in various regions. Recently there have been several observation analyses involving daily and extreme daily temperature trend and variability in regional and global scale. Analyses In global scale (Caesar & Alexander, 2005; Alexander et al., 2006; Brown, 2008) show an increasing trend in the lower end of the maximum and minimum temperature distributions with regional differences. For instance, in south America, analyses showed a general warming trend in the region (Aguialar et al., 2005) also analysis results indicate no consistent change in the indices based on daily maximum temperature while significant trends were found in the indices based on daily minimum temperature (Vincent et al., 2005). Therefore in this paper, using gridded daily temperature data (Mean, maximum, minimum), we have analyzed the trend of the area which is affected by climate warming in Iran.
2. Study Area : Iran is a country located on North latitude to, East longitude, to, on subtropical region. The minimum and maximum of Iran's temperature is and .respectively.
3. Material and Analyzing extreme indices, we used the temperature data from Asfezari’s national gridded database which dates from 23.5.1961 to 31.12.2004.
The temperature database, were collected from 664 climate and synoptic stations using Kriging interpolation method. In this database a matrix of 7187×1599 is defined for each climatic variable in which rows represent spatial cells and columns represent time (per day). The coordinate system of the database is Lambort conformal conic and the dimensions of each grid is 15×15 sq. meter.
1. In this research, warming is defined as the ratio of temperature rise in the long-term regional average. Since in this study gridded data were used, long-term average of each grid is considered as the regional norm. 2. In the second step the number of the grids with a higher amount than the long-term average in the same grid and the same day was recorded and the area percentage of each grid was calculated.
3. Finally the output trend of the second step was calculated.
4. In this study, spatial behavior of temperature analyzed using minimum, maximum and mean temperatures that each represents a different structure of a region’s climate. The minimum temperature results from the output of the solar radiation and daytime temperature results from the input of solar radiation. On the other hand, green gases such as vapor and carbon dioxide are the basic effective elements in a balance of the output of the solar radiation, whereas the balance of the input of solar radiation depends on atmosphere radiation characteristics such as atmospheric transparency.
Therefore, the minimum and maximum temperatures are affected by different factors and may have different trends. The mean temperature of each region can be the representation of the changing structure and provides features of temperature behavior, according to time and location and gives a background of the region. Therefore, due to temperature rise in the current century, and due to development of the regions with rising temperature and also temperature importance as a basic element in climate system, it is important to know in what velocity the temperature is rising and what areas it affects in each period. The results show the areas that their minimum temperature increase are more than the areas that their maximum temperature increases. The time series of AI also shows the mean of this index increases in more than 50 percent of Iran in recent years.
Therefore, sorting temperature rising value and AI increasing trend adds to the importance of the subject. The 54% increase of AI is an alarm for ecosystems and natural resources in a country like Iran whose climate structure adds to its sensitivity. According to previous studies (Masoodian, Loarie et al 2009) such changes affect mountain areas less and therefore future programmers should pay special attention to such areas.
3. The results show the areas that their minimum temperature increases are more than the areas that their maximum temperature increases. The time series of AI also shows the mean of this index increases in more than 50 percent of Iran in recent years. Minimum and maximum temperatures play a decisive role in different features of man’s life, such as agriculture, social and fundamental issues and are considered as irritable thresholds for all man’s activity which is a reason why it is of such importance to governors and programmers. In studies performed on climate changes, it is obvious that plant and animal habitats are affected more than that of humans. According to studies, it seems that Iran’s climate is tending towards a warmer climate with less precipitation. In studies performed on climate change, it is obvious that plant and animal habitats are affected more than that of humans. The 54% increase of AI is an alarm for ecosystems and natural resources in a country like Iran whose climate structure adds to its sensitivity. Moreover, an important feature of management against climate change is to mark vast, vulnerable areas such as arid and semi-arid regions and deserts, which constitute an extensive part of Iran. Due to the high sensitivity of such areas, protection of their ecosystem is of great importance.

In recent years, Desert Dust frequency has increased in the Global system. Due to lack of vegetation in the regions prone to dust, the earth’s surface air becomes warm enough in these regions, moves upward and causes a cold downward circular air flow when it collides with upper tropospheric winds. When these winds strongly hit the earth’s surface, they create dust storms (Xuan et al, 2004). Many studies from all around the world have been conducted on the source regions of this environmental phenomenon (Kai & Gao, 2007). The studies on Asian dust storms with sea sources during 2000-2002 showed 64% of storms originate from China seas and become more severe from west to east and carry dust on the land. Wang, Stein, Draxler, Rosa & Zhang (2011) studied sand and dust storms in 2008 and defined North Africa, Middle East, Mongolia, and north-west China with high frequency of dust phenomenon, using HYSPLIT Model. Shamsipour and Safar-Rad (2012), analyzed the July 2009 dust in the west of Iran. In the chosen sample, the Zagros hillsides contain the highest amount of pollutants. Also, the results indicate that the location of Naveh, upper-level divergence of 500 and forming a thermal low-pressure system on earth’s surface play the main role in flowing the dust toward Iran.  Study area includes the Mashhad metropolis which is located in the northeastern part of Iran. As the second most populated metropolis in Iran and due to its vicinity to northeast deserts and strong winds of summer, Mashhad is threatened by this environmental hazard. Therefore, to be aware of this disaster, knowing about its trend and source locations are essential for the study. The studies on particulates and dust in Mashhad are based on analysis of synoptic or atmospheric circulation patterns or statistical methods using air quality monitoring equipment or weather stations. In addition, no study has clearly demonstrated the source location of dust in Mashhad. Because of the necessity of air pollutant observation and control in metropolises and dissipation or lack of weather stations, it is necessary to use other data sources to check the air quality such as remote sensing data and satellite imagery. Therefore, the aim of this study is to use HYSPLIT model outputs by analyzing MODIS satellite images in combination with synoptic methods and tracing the dust with retrograde method.  The study materials include meteorological records relating to 06 dust code, synoptic maps and MODIS satellite images in dusty days. The data of the first group was obtained from Khorasan Razavi Meteorological Organization for the period of 2009-2013 and Mashhad synoptic station at intervals of 8 hours. The second group includes gridded climate data with 2.5×2.5° longitude and latitude taken from NCEP/NCAR website. The synoptic maps of dusty days in Gradis setting in 25-55° north latitude and 35-85° east longitude were drawn and analyzed. The third group includes calibrated level-1 MODIS atmospheric product with 1 km resolution. Totally, 16 concurrent images of dusty days were obtained between March 3, 2009 and October 30, 2013 from GODDARD NASA website. The fourth group includes the average daily data of PM10 from air quality monitoring stations in 2011which were used in the same year in order to assess the validity of AOD daily data of MODIS. The fifth group is the HYSPLIT Lagrangian output model with retrograde method for tracing the dust source of Mashhad. In this research, we used HYSPLIT model to locate the path and source of dust that reach Mashhad outside the station. The model input data were extracted from NCEP/Reanalysis data. Also, the flow measurement method was isobaric lines with a 6-hour time step and altitudes of 50 and 1000m above sea level. The present research is an analytic-descriptive case study with a quantitative method for data analysis and uses descriptive statistics and spatial analysis. Based on meteorological records, we have first analyzed temporal trend of Dust phenomenon in hourly, monthly, seasonally and annually durations. Then we documented spatial distribution of Dust on urban areas by MODIS images which is obtained from NASA website. These images were processed by two indicators: 1- Brightness Temperature Index (BTD31,32) which is suggested by Ackerman (1997) and, 2- Normalized Difference Dust Index (NDDI). BTD measures between 11-12 micrometer and MNDVI parameter were used to separate the desert arid regions and threshold temperature of 290° Kelvin in the band 32 for separating cloudy from dusty regions.  The observation hours on July 3rd, 6th, 9th, June 12th, March 12th and 15th had the highest average dust. In contrast, January, February and December had the lowest dusty hours. July, June, March and August had the highest and January, February and December had the lowest monthly dusty days during the above-mentioned period. Also, the highest number of dusts in this period occurred in 2009.  In this analysis, about 69% of PM10 dependent variable changes are defined by the MODIS AOD 550nm independent variable which shows a fine correlation between the two validated parameters. The implementation of the index algorithm led to clarification of dust on the image bands in four calculation steps. According to the models mentioned here, the dust local algorithm in Mashhad shows that the northwest, west and south parts of this city were covered with dust. Therefore, it seems the districts 1, 8, 9, 10, 11, 12 located in these regions experience more dust compared with other districts. Also, the analyses show that compared with city center, outskirts of Mashhad are subject to more dust pollution. In order to study the synoptic conditions in dusty days, the synoptic maps concurrent with images at 12 pm local time were analyzed. In dusty days, the middle level of high-pressure atmosphere with negative Omega dominates Iran which leads to dust due to lack of humidity. Considering the path and speed of the wind at the earth’s surface and domination of flows in a north-south pattern, it seems the dust moves from its focal points towards north-east of Iran and Mashhad. Generally, according to the dusty days map we can say that the main and closest source of Mashhad dusts lies to the east of the region and Afghanistan and east of Turkmenistan.  As soon as the firs dust was reported, the path of particles was examined since then back to 24 hours. The model’s output maps show 3 main paths which transfer the dust to Mashhad. In most cases with Turkmenistan and Central Asia as the source, the main path of dust was from north-east to south-west mostly during the warm seasons of the year. The second path is from north-west to south-east in cold seasons. The third path is from south-west to north-east which has a low frequency with the cold seasons domination. According to meteorological dust records of Mashhad synoptic station, the maximum and minimum frequencies of daily dusts occur at morning and midnight hours respectively in local time in most seasons during the statistical period of the study. In monthly view, most of Mashhad dusts occur in July, June, August, and March, respectively. The minimum number of monthly records without dust in December occur during the cold period of the year. The BTD index properties was able to clarify the regions covered with dust on satellite images in Mashhad. It also provided a synoptic analysis of dusty days to identify the dust and find the way by which it is transferred. It was specified from a dusty days sample with a synoptic approach that Afghanistan and part of Turkmenistan located in the north-east of Mashhad are the main focal points of dust that reach Mashhad. The HYSPLIT model maps output overlap synoptic maps of dusty days. Generally, during the period of study, north-west, west and south of Mashhad were covered with dust. The districts in these regions experience more dust conditions compared with other Mashhad districts.

In general, Garmesh wind in Rasht province increases the temperature by 9°C and reduces relative humidity by 47%. The direction of prevailing wind during the Garmesh wind is Southern and its average speed increases by 2 m/s to 5 m/s. The highest frequency of Garmesh wind occurs at 09 and 12 UTC in December and January. The occurrence of this phenomenon substantially increases the horizontal visibility, so the sky would be clear in more than 40% of the cases.
1.

Wind is one of the atmospheric elements which has a great impact on the region and local climate. Wind transfers air from one area to another. Thus, wind is very important in the regional climate study. Garmesh is a hot and dry wind that often blows from the north of Alborz Mountains in the cold season. The main reason for the creation of Garmesh wind is the simultaneous presence of high pressure system or its ridge on Iranian plateau and its association with low pressure system on the south of Caspian SeaGarmesh wind that is warm and dry is a well-known wind for the people in Gilan provience. This wind blows from the northern slope of Alborz Mountains and flows from Iranian plateau to the northern slopes of Alborz Mountains. The wind can last from several hours to several days. Relative humidity suddenly reduces and air temperature increases during the Garmesh wind. In many cases, the wind mostly covers the southern part of the Caspian Sea. According to Gilan Governor Documents, financial losses due to the Garmesh wind and fire forest was about 8.71478 million Rials in the period of 1381 to 1388.
2. Material and

Synoptic weather station of Rasht is located in the south of the Caspian Sea and north of Alborz Mountains in Gilan province. Its height is about 8 meters above the sea level. Atmospheric data including wind, pressure, cloudiness, air temperature and relative humidity were extracted in the period of 1982-2010. Then Visual Basic program was used to decode the data. Finally, these data were analyzed using statistical software such as Excel.
3.

The frequency of days with Garmesh wind during the statistical period (1982-2010) was 479 days. Mean kjnhgftr6yikmn bvds3 annual days with Garmesh wind is 16.5 days per year. In some cases, Garmesh wind blows for just one hour but in other cases it last for several days. This phenomenon is more frequent in the evenings times. The highest frequency of the Garmesh wind occurs in January, February and December. In more than 75% of cases the direction of this wind is southern. Under normal conditions 60% of times in the year, wind is calm in the Rasht station and more than 91% of cases, the wind speed is less than 3.6 m/s, but in more than 69% of the cases, the Garmesh wind speed is between 2.1 and 8.8 m/s.
Normally, the relative humidity in Rasht station is 82% but during Garmesh wind it decreases to 34%. With the onset of Garmesh wind, temperature increases and this increment continues until the end of Garmesh wind period. In some cases in the other regions in the world, during Garmesh wind, temperature increases or decreases by 20°C in less than three hours. But in Rasht station it increases more than 9°C. In most cases of Garmesh wind occurrences, temperature varies between 15 to 25°C, and in 92% of Garmesh wind occurrences, it varies between 10 and 30°C. The most important cause of Garmesh wind in Rasht is the presence of high pressure system or high pressure system tongue in Iranian plateau and low pressure system on the south of Caspian Sea. In more than 94% of cases mean monthly pressure (QFF) increases and varies between 1000 to 1020 hPa during Garmesh winds.
Usually, there are low values for horizontal visibility along with low pressure system, but at the beginning of Garmesh wind, horizontal visibility increases and then at the end of this phenomenon horizontal visibility decreases. In 23% of cases during the Garmesh wind, the sky is clear and in 43% of cases the sky is slightly cloudy (2.8 or less). In 13% of cases, Altocumulus cloud type 4 has been seen with Garmesh wind phenomenon.
4.

The results showed a jump in the number of hydro-climatic variables such as temperature, relative humidity, horizontal visibility, wind direction and wind velocity when Garmesh wind occurs in Rasht. The mean No. of days with Garmesh wind is 16.5 days during the year. In more than 91% of cases the wind speed is less than 3.6 m/s, but during Garmesh wind, the wind speed in 69% of cases is between 2.1 and 8.8 m/s. Normally the average relative humidity is 82% in Rasht, but during Garmesh wind, it decreases to 34%. In some cases, in less than 3 hours during Garmesh wind, temperature increases by 20 degrees Celsius or decreases on the contrary. Normally low horizontal visibility is along with low pressure system. At the start of Garmesh wind, horizontal visibility decreases and at the end of it, horizontal visibility increases.

1. During the past decade, many efforts for investigating the relationship among air pollution and meteorological factors have been undertaken and some statistical methods have been proposed. Population increase and technological advancement were among the factors affecting creation and extension of air pollution in big cities, forcing large cities all over the world to contemplate measures to curb and prevent the spread of pollutants. Air pollution is emblematic of a wide range of impacts on biological, physical and economic systems. In recent years, air quality has been debated as a pivotal factor in materialization of the quality of life in urban areas, specifically in densely populated and industrial areas. In the present research, in order to evaluate air pollution in the city of Mashhad, eleven air-monitoring stations were used and several parameters for air pollution were studied. The main purpose of the research is to use a model which can establish a link between weather and pollutant factors as well as to identify climate factors affecting air pollution in the city of Mashhad and predict the density of pollutant gases using neural network and linear regression.
2. Study Area: Mashhad Metropolis, the capital of Khorasan Razavi province spanning over a land area of 204 square kilometers, located in northeast of Iran at a longitude of 59 degrees and 15 minutes and a latitude of 35 degrees and 43 minutes up to 38 degrees and 8 minutes in the vicinity of Abrood, Kashafrood, is flanked by Hezarmasjed and Binalood mountains. It has a cold and arid climate and has its own specific climate and regional specifications. The preponderance of the city has a cold and air climate, with some cold and semi-arid regions and a small cold and wet section situated in the loftiest heights of Hezarmasjed and Binalood mountains. Overall, the city of Mashhad has a changing climate, inclining towards cold and arid.
3. Material and Data and information regarding pollutants in the city of Mashhad for a one-year period was obtained from Khorasan Razavi’s environment organization. The data is collected from eleven air-monitoring stations on an hourly basis. The average for each day has been used as the data for that day. Then meteorological data, including wind speed; wind direction; rain; air pressure; relative humidity; average temperature and maximum and minimum temperature were obtained from meteorological organization. After collecting the required data and information, first the average for the data concerning air pollutants, gathered on an hourly basis, was calculated. Then using ArcGis, climate parameters and pollution information were merged, using merge button, to form a coherent daily output. The next step involved using inverse distance weighting interpolation, which is one of the methods used in meteorological and geographical studies. In this method, for the purpose of prediction in locations where data is not collected, the data gathered in the vicinity of the intended location is used. In the next stage, the correlation among meteorological data and air pollutants was calculated. Finally, Neuorosolutions was used in order to predict non-linear factors and teach the neural network.
4. The findings of the study reveal that the potential neural network, compared to regression and multilayer perceptron, was able to establish a link between meteorological parameters and the density of pollutants (NO-O3-CO-SO2) in the city of Mashhad. The aim of the present study is to use a model which can establish a link between climate factors and air pollutants and identify the climate factors which affect air pollution. The result of sensitivity analysis graph indicates that among the climate factors affecting the pollutant of CO, relative humidity at the hour (12:30) and the direction of wind are the most influential. It was also revealed that relative humidity at the hour (6:30) and maximum absolute temperature are the climate factors affecting the density of sulphur dioxide. As for the graphs depicted in the study, it was revealed that the potential neural network, compared to the other two models, was able to establish a more logical relation among air pollutants and climate factors. In the potential neural network for carbon monoxide, the Mean Square Error (MSE) was calculated to be 66.61 and in regression model it was 76.92. Similarly, the Mean Absolute Error (MAE) was 3.005 and 3.12 respectively. In the neural network model for sulphur dioxide, MSE was calculated to be 14.12 and MAE was 2.15; while in the regression model these measurements were 14.003, -0.18 and 2.13 respectively.

1. While human habitation was formed as concentrated and stable communities, air pollution had become perceptible on human life. By passing of time and the improvement of thriving and particularly by the industrial revolution commencement, air pollution gradually has converted into an international issue, principally in the present times (Ghanbari and Azizi, 1388: 16). Whereas air pollution is one of the most significant risks to human; especially in metropolises, the investigation of its impacts is one of the primary priorities in climate researches. Meteorology cannot connive at atmospheric pollution. Nowadays, pollutant concentrations are developed threateningly by the urbanization growth and the improvement of living standards and industrial development. Human adds to the increase in air pollutants by their activities and make survival difficult for themselves and other organisms. Besides, they have applied changes in weather patterns of Planet Earth and environment and cause disorders in them. Meteorological conditions and physical and dynamic features alterations of atmosphere play a critical part on air pollution levels. On the issue of the dispersion and transport of air pollution, the most significant causing factors include the winds of earth surface and low levels of the atmosphere as well as the vertical temperature gradients, which are determining elements in the upward movements and the pollutants vertical distribution in the atmosphere (Ranjbar and Mohammadian, 1389: 112). With regard to the fact that the main goal of the synoptic studies is to explain the key interactions between the air-sphere and the surface environment (Yarnal, 1385: 1); in this paper, the determination and identification of influential synoptic patterns on the pollutant days in the categorization of environmental to circulation, as well as the quantity and procedure of the annual sulfur dioxide, carbon monoxide and particulate matter polluters were investigated in Shiraz city.
2. Study Area : Shiraz is located in the south of Iran and, is located in latitude 29 degrees, 36 minutes north and 52 degrees of 33 minutes of east longitude. It is built in a green plain at the foot of Zagros Mountains 1500 meters, (4900 feet) above sea level. Fifth most population city of Iran and the capital of Fars Province, in 2009 the population of city was 1455073.
3. Material and For the investigation of urban pollutants situation, data and statistics of air pollution were achieved from the Environmental Protection Organization of Fars Province to perform this research; and according to the air quality standards Table the standard level of pollutants were indicated. Through EXCEL software application, polluted days were filtered and drew out. With regard to the fact that the main goal of this research is to identify the influential synoptic patterns on the quantity of particulate matter and carbon monoxide pollutants as well as polluted days; as a result, hourly and daily pressure data of open seas level (standard), the height of levels 500 and 850 hPa, the temperature of level 1000 as well as the Omega of 850-hPa of the considered days (polluted days) were obtained from the website of the National Center of Environmental Prediction and National Center of Meteorological Research (NCEP / NCAR). They were mapped and analyzed in the GrADS software environment. In addition, the PSI pollution standards index was applied to indicate the amount of pollutants.
4. In the sum of 2247 days in the staff measurement station for particulate matter and carbon monoxide pollutants, 410 and 152 days were diagnosed as polluted, respectively. In addition, the investigations demonstrated that the greatest level of the particulate matter pollutants are related to July, which is 216/30(µg/m3) and the carbon monoxide pollutants highest level are related to September, which is 6/41(PPM). With regard to the fact that the Synoptic Systems of air-sphere set the boundary layer, in high-pressure system the inversion layer surface is pulled down and as a result, the boundary layer depth is decreased. This is owing to the downward subsidence of the air of free air-sphere (FA) and non-penetrating of it into the boundary layer which is caused by the existence of the strong covering inversion layer. Consequently, air pollutants are confined in shallow boundary layer and lead to air stability as well as the air pollution occurrence (Falah Qalhary, 1389: 3). According to the index of PSI pollution standards, the numbers which are higher than (PSI > 100) are assumed as polluted day. In conformity with the coherence and continuity of polluted days, among 410 polluted days for particulate matter pollutants, four summer patterns were detected and among 152 polluted days for carbon monoxide pollutants, one winter pattern was detected.
5. The existence of a stack height (anticyclonic) at 500 hPa level was observed in the synoptic maps during the investigation period in all four summer patterns. The anticyclonic position in model (A) includes the extent of the Red Sea, higher than the latitude of 20 degrees north to the center of Iran. The Arabian Peninsula center is the location of Anticyclone center. In model (B), the anticyclone with closed contour of 5875 geopotential meters is located in southeast of Iran and southern coasts of Oman. However in pattern (C) the existence of deep ascending is observed and includes the anticyclone position of the Arabian Peninsula to the west coast of the Red Sea. In model (D), the anticyclonic center with the closed contour of 5910 geopotential is located in Saudi Arabia, on the subtropical high pressure position. Generally, at the level of 500 mg, the variations of the anticyclone position between synoptic patterns in warm period, three models are placed in Saudi Arabia. Therefore, model (B) is the only model which is located on the east of Iran. While with the absence of these conditions in the cold period, the state of full-orbital is predominant. At the level of 850 hPa, the cyclonic conditions are predominant. The organization of pressure patterns in sea level is associated with the predominance of low pressure condition. The contour which is isotherms of 40 ° C is the witness for the thermal nature of this system. Whereas in winter patterns, contrary to the summer patterns, at the level of 850 hPa, the spatial organization of the anticyclonic is predominant. Furthermore, the existence of tab system pressure at sea level is the evidence for the complete coordination of the full height system location at high levels of air-sphere and pressure system at sea level. By shoaling the boundary layer, this factor will cause the confinement of pollutants close to the earth surface and the growth of pollutants concentration, in the anticyclonic conditions during the cool season.

1. Early frost in the autumn and late spring frost are the most common disasters related to the huge agricultural damages in West Azerbaijan province. Among 39 natural disasters in the world, 7 dominant disasters occur in this region and the frost has the first rank regarding the damage to the public sector and insurance industry in recent years. Based on several reports, early spring frost damage could reach to 25-100% of total production among orchard trees including almond, apricot and walnut. Considering the irregular and unscientific development of orchards in the past, therefor, determining of suitable sites (low frost risk) for crop production based on GIS is necessary. Probability and the risk of causing damage due to temperature differ depending on the time and year as well as plant sensitivity to temperatures below zero. Numerous studies have been done in the country and abroad to determine the probability of occurrence of frost. However, most of these studies have failed to consider the frost threshold temperature at which the plants are damaged. Determining the most probable occurrence of frost with a hypothetical risk in province of west Azerbaijan, which is also known as an agricultural poles in Iran is economically important by applying of the latest tools and geo-statistical methods and preparing spatial patterns of the frost dominated sites in the region are the main objectives of this paper.
2. Study Area: The study area is located in northwestern Iran, western Azerbaijan Province (latitude 44°02' to 47°32' E and 354°58' to 39°46' N). The province has an area of 37,600 square kilometers (without the Urmia Lake) and limited on the north by Turkey, Armenia and the Republic of Nakhichevan, on the west by Turkey and Iraq, and on the south by East Azerbaijan and Kurdistan provinces.
3. Material and In the previous studies related to frosts, minimum daily temperature records of 34 synoptic, evaporation and climatology stations of the province were collected. The critical damage temperature (Tc) from year to-year were extracted from data bank as 0 °C for very sensitive plants, -2 for sensitive plants and -4 °C for approximately resistant plants. Probability and frequency of spring and autumn time series of frost occurred dates were determined with fitting Gumble and non-extreme distributions to the data. A basic program was prepared for fitting normal, log normal, Pearson Type III, log Pearson, and Gumble distributions. Frequency analysis was applied to the data for all 34 stations. Probabilities were calculated using a Gumble extreme value function. To avoid too many frost maps, only 2 seasons, 3 critical temperature thresholds and 10 probability classes, and one common frequency formula were adopted.
IDW (inverse distance weighted), Kriging and Co-Kriging interpolation methods are used for spatial prediction with Geostatistical wizard in ARCGIS10. The IDW is referred to as a deterministic interpolation method because it is directly based on the surrounding measured values or on specified mathematical formulas that determine the smoothness of the resulting surface. However, Kriging, which is based on statistical models, assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. The Kriging tool fits a mathematical function to a specified number of points, or all points within a specified radius, to determine the output value for each location. The best spatial prediction method was selected based on cross validation approach with RMSE criteria. The cross validation is based on removing one data location and then predicting the associated data using the data at the rest of the locations and Root Mean Square Error, indicates how closely the model predicts the measured values.
4. Based on the results of the interpolation methods, Co-Kriging method showed better results in 67% of cases. In the west Azerbaijan, Co-kriging is dominant with height. The fitting of height, represents a rippling effect of mountains, hills and the effects microclimates. Frost occurrence generally increases by increasing of the height and temperature gradients increase the intensity of the frost. The latitude also could be related to the direction of air masses and frost flow. Higher coefficients (irregular occurrence of frost at a given date) are seen at mountainous area such as north-west and west of Urmia Lake and Tekab and fluctuations of frost are evident in the form of coefficient of variation in the most of maps. Early autumn frosts in the Makoo and Tekab regions occurred much earlier and are rarely seen in the region of Sardasht and the central region and Nazloo from16 October to 15 November. Late spring frost has the same process and lasts from 24 March to 25 February. Coefficient of variations indicating regular or irregular data happened during years. The lower coefficient of variation on the map indicating close frost occurrence time at that point over the years and therefore, the occurrence of the frost could precisely be forecasted. Final frost map of province with 75% probability of occurrence as presented in this paper means that for example among 20 years of history 15 years of frost occurred at this date. Obviously, higher probability levels mean lower risk and vice versa. According to the provided maps, the coefficient of variations was high ( 0.07-0.09) in central parts as well as in Khoy, Piranshahr, Sardasht and Tekab regions indicating that frost occurs in irregular patterns. In contrast, lower coefficient of variations (0.06 – 0.045) in northern parts of province and regions surrounded by Urmia Lake indicate a regular frost occurrence at springtime.
5. Preparation of frost maps in every area has a particular importance in reducing the risk of frost damage. In the present study, for mapping the risk of frost in West Azerbaijan Province -Iran, the minimum temperature at different stations were recorded and date of the frost occurrence in the fall and spring for three classes were extracted. Gumble distribution as a dominant regional distribution is proposed for the distribution of frequency analysis. Co-Kriging interpolation method is the dominant method for producing maps related to the frost. Optimized models posing half-angle, resulted in significant improvement in the fit of the model. Frost damage was increased further to the west and mountains regions. By comparing maps a clear trend between the high potential frost and cold masses was detected. These maps can be used for a better field selection for orchards, the development of greenhouses and areas prone to industrial development.

1. Considering the importance of Lake Urmia in the weather of northwest of Iran, the present study, using dynamic modelling and simulation, investigates the consequences of the drying up of Lake Urmia on the climate parameter of downfall.
2. Study Area: Lake Urima, considered the biggest pond in western Asia, with a surface area of 5 thousand and 822 square kilometers, is the second biggest lake in Iran, after the Caspian Sea, and the second highly saturated saline lake in the world. The drainage basin of Lake Urmia constitutes nearly 3 percent of the entire area of Iran. In recent years, climate issues are considered to be the main culprit in water crisis engulfing northwest of Iran. In some studies, climate is estimated to have been the cause of environmental crisis in Lake Urmia somewhere between 60 to 65 percent. Studies indicate that the aggregate seasonal and annual rainfall and snowfall in northwest of Iran is declining. It has, also, been shown that the sharp decline in rainfall and snowfall throughout the year in northwest of Iran, especially in winter, is the main reason for the decline, on an annual basis, in this period (Charbgu, 2011).
3. Material and In the study, for simulation using RegCM, it was paired the Lake’s model and was executed with a resolution of 10 kilometers for a three-year period (2001-2003). For the initial input of the model, digitized data were used. Lateral boundary condition data were obtained from the reanalyzed data with 2.5 degrees of horizontal separation, supplied by National Center for Environmental Prediction (NCEP) /National Center for Atmospheric Research (NCAR). The data, gathered in six-hour intervals, includes geopotential height, parallel (u) and meridian (v) components of wind, temperature, relative humidity, surface pressure and vertical speed. Data regarding the initial conditions include the surface temperature of the sea, gathered weekly, with a spatial horizontal separation of one degree, provided by National Oceanic and Atmospheric Administration (NOAA). The data for surface specification includes the topographical data gathered by National Cartographic Information Center in the USA, with a horizontal separation of 30 seconds, and land cover data together with the data regarding soil humidity and texture, with a spatial separation of 30 seconds. In order to investigate the influence of Lake Urmia on the amount of downfall in north-west of Iran, the paired model of the lake was executed first using the real condition and then by replacing the surface of Lake Urmia with a semi-desert and salt marsh. In order to investigate the importance of Lake Urmia’s existence and its effect on region’s climate for a three-year period (2001-2003), Regional Climate model (RegCM 4.3) with Grell parameterization in initial settings (with the lake) and simulated settings (without the lake) was executed. Since the aggregate daily or hourly rainfall or snowfall in a region forms its annual and seasonal amount, and on the other hand, depicting the pattern of rainfall or snowfall on a smaller scale is of importance for studying drought and wet year cycles, daily rainfall or snowfall was analyzed using Grell parameterization. In this parameterization, two ascending and descending major convective streams for clouds are considered. In fact, it is one of the cogent parameterizations for calculating rainfall and snowfall.
4. The map reveals that in case Lake Urmia is dried up, the eastern section of the lake will experience a more dramatic decline in the amount of downfalls. As can be seen, of the three kernels of decline in downfall, one is located in proximity to the Shabestar, Tabriz and Marand counties, the second kernel is in the middle of the eastern section of the lake, in Azarsharh county, and finally the third kernel is discernable in south-west of the lake, in Maragheh county. The greatest decrease in downfall, in simulated settings, occurs in the northeastern kernel, above Shabestar, Marand and Tabriz, which is much higher compared to other kernels, to the extent that in case of the lake’s disappearance, the entirety of western and northern sections of East Azerbaijan province will experience a decrease in the amount of downfall. In other regions, too, the amount of downfall will gradually become zero. Finally, it can be said that downfall during winter, in simulated settings, has decreased and the decrease of downfall in spring is much more severe and extensive, in comparison with other seasons.
5 The map indicates that in case the lake disappears, Jolfa, Maraghe, Tabriz, Shabestar, Azarsharh, and Marand counties will experience a decrease in the amount of downfall. This decrease will have dire consequences for the provinces of East and West Azerbaijan, during the rainy season of spring. These consequences are much more salient for East Azerbaijan. The extent of the decrease in downfall around the lake, in case of its disappearance, will impact all the counties around it, and similar to the pattern of spring, will affect East Azerbaijan province more than before. With regards to the maps obtained through Grell parameterization, it can be said that this parameterization is capable of simulating the amount of downfall in northwest of Iran. The results yielded by Grell parameterization were validated by the data gathered through observation, and the percentage of error and skewness were analyzed using the data gathered by stations. After analyzing the aggregate seasonal three-year downfall (2001-2003), the outputs of the model in initial settings and after eliminating the lake, and their comparison with the real amount of downfall in Lake Urmia, revealed that RegCM4.3, in connection with the lake’s model, is capable of simulating the amount of snowfall and rainfall. Although the model has simulated more downfall than real situation (especially in spring), its conformity with the downfall recorded in stations of Tabriz, Jolfa, and Maraghe is satisfactory and in fact, RegCM has been able to perfectly simulate the pattern of monthly downfall with a high correlation of 0.9. The average percentage of error and skewness of downfall, compared to the observed amounts in synoptic weather stations in Iran for the months of the year 2003, were calculated using relations 1 and 2, and it was revealed that simulation for the amount of north-west downfall using Grell parameterization, has the highest amount of skewness in spring (85 millimeters) and lowest amount of skewness in summer (-2 millimeters). Model’s high amount of skewness in spring is indicative of a higher-than-real estimation compared to the actual data for northwest region. The pie chart, too, depicts model’s skewness and its ability to simulate downfall in the region, to the extent that winter, with a skewness of 15%, spring with 58%, autumn with 26% and finally summer with a 1% were simulated. It merits a mention that the reason for model’s low degree of simulation in summer is that; generally, downfall in this season is minimal (3%). The findings reveal that in case Lake Urima dries up, the amount of downfall in the eastern half of the lake and especially in East Azerbaijan will decrease, posing serious challenges to north-west of Iran.