مجله مدیریت مخاطرات محیطی
سال دوم شماره 1 (بهار 1394)

In this research, for determining the synoptic patterns of dust storm occurrence in northwest and northeast of Iran; four types of data were used including: Hourly data of dust phenomena and horizontal visibility for 30 meteorological stations in northwest and northeast of Iran. Six-hourly global data analysis from NCEP/NCAR reanalysis, including air temperature, geo-potential height, U-wind and V-wind components, relative humidity, soil moisture and omega from 1000hPa to 500hPa, were used for the preparation of maps and identify the synoptic patterns by using GRADS software In order to identify the source of dust generation, tracing and simulating the path of dust particles, HYSPLIT model Lagrangian approach of backward trajectory was used. And to detect dust, the unreal images of METEOSAT-9 second generation for EUMETSAT satellite were used. Synoptic studies have shown that low and high pressure, and the vertical motion of air are the main causes of dust storms in Iran. The circulation of the atmosphere during dust storm, shows that a low pressure cell has been stretched from Pakistan to the south of Iran and from there to the deserts in Iraq, Syria.This condition causes the formation of cyclonic circulation in the East of Syria and west of Iraq on 31 July. Wind speed Increasing, cyclonic circulation, dry soil and lack of coverage, provided the conditions for removing the soil particles. Due to the extended ground surface's low pressure and weakening of the stable conditions of low level of troposphere, the approaching of trough has led to the beginning of a dust storm in Iraq deserts on jul31 and this unstable condition providing enough power to carry the soil particles away from its origin. Besides that, the stable location of ridge on Iran made anticyclone circulation in the wind blowing and it cause that the dust storm cycle the northern part of Iran and enter from northeast in to the country.

Air pollution is one of the most important environmental hazards in Tehran metropolitan that with understanding and cognition of factors which affecting pollution can reduce adverce effects of it. In order to reduce these effects, accurate studies on the basis of effective factors on pollutant is essential. The goal of this research is to analysis variation of air quality index(AQI) in relation to atmospheric conditions and traffic in Tehran. The Pearson correlation coefficient and regression analysis were used in order to compute the relationship between air quality indices(average and maximum) with traffic and meteorological variables. This study was done during 60 days (November,6th to January 5th) for the 2010 to 2012 years. The results revealed that atmospheric unstability index (ki-index) has the most impact on air pollution variations. Multiple linear regression  models for the year 2011 was the most accurate model to estimate maximum air quality index with the least relative deviation, RD (RD of -0.05 for first model and -0.1 for second one). An important achievement of this study was indirect correlation between number of vehicle and air quality index which is not happened to the reality. It seems that pollutant production in two cases including car’s movement or stop are quite different from each other and should be investigated using other methods.

The drought has been known as a complex and perilous phenomenon at the whole of the world especially in Iran. Determining and predicting its severity can be effective at managing the hazards due to it. To determine the drought severity, the indices have been used that can be divided into two broad categories of meteorological (M) and remotely-sensed (RS) indices. The most important M index has been the standardized perception index (SPI), and the common RS indices have been those extracted from the vegetation index (NDVI) and land surface temperature (LST) index. For modeling time series behavior of these indices and also predicting their future values, the machine learning methods can indicate the high efficiency. This paper also aims to evaluate the performance of four important machine learning methods, i.e. neural network (NN), support vector regression (SVR), least squares support vector machine (LSSVM) and also an adaptive neuro fuzzy inference system (ANFIS) for modeling the M and RS indices of Eastern district of Isfahan during 2000 to 2014 and predicting their values at 2015 and 2016. The data used in this paper are the NDVI and LST time series of MODIS, and the rainfall time series of TRMM satellite of study area. At first, the vegetation condition index (VCI) and temperature vegetation index (TVX) have been built by NDVI and LST and 12-month SPI has been built by rainfall data. Next, the time series behavior of three these indices has been modeled by four aforementioned methods that according to the results, SVR has a highest efficiency and NN has a lowest efficiency among these methods. The speed performance of LSSVM and then ANFIS have been higher than the other methods. Finally by designing a fuzzy inference system (FIS), the drought severity at spring and summer of 2000 to 2016 has been monitored that the results have shown the normality of the spring in all years except 2000 and 2011 and severe drought in the summer in all years except for the four years 2000, 2010, 2011 and 2014. In fact, this research has aimed to present a strategy for modeling drought behavior and predicting and monitoring it at future using machine learning methods and the remotely-sensed and meteorological time series data and fusing them in a FIS system.

Geomorphological risks are One of the major landslide. Identification of risk areas susceptible to landslides is one of fundamental steps in the management of natural resources and reduction of the risks .Kashtr with an area 26 square kilometers south-west of Kurdistan Province is located on the eastern edge of the mountains Shahoo. To zoning first sliding zones was examined with field visit، that about 28 sliding zones were identified and after processing in GIS application came as a layer. With underlying landslides distribution map landslide weighting parameters took place، including geology، slope and direction of the slope، elevation، soil texture، land use، soil erosion، distance from fault، waterways density، and distance from road. Logistic regression model was run in SPSS and the results showed that the influencing factors on landslides occurrence in the region are distance from the fault، geology، soil erosion، direction of the slope، etc. Finally study area in terms of Sensitivity to the risk of landslides was classified into 5 classes، based on that: 2/6 has a very high risk، 6/4 high risk، 3/19 average risk، 7.9 low risk، and finally 5/6 has very low risk.