فاطمه ارسلانی

In large and industrial cities, thousands of heavy metal particles are released into the environment [2]. These heavy metal particles are released by attaching to dust particles on a large scale [3]. It is important to study heavy metal concentrations in the dust falling due to the threats for human health [1, 4, 5]. Dust originates from both natural and human sources. Studies have been conducted to investigate the sources of heavy metal emissions in street dust with cluster analysis [CA] and principal component analysis [PCA] methods. The metropolis of Tehran has a population of over eight million people, that facing a severe air pollution problem. Therefore, the purpose of the present study was to identify the factors of increasing the heavy metal concentrations (Al, As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Se, Si, V, Zn) in the dust falling of Tehran city.

Dust fallout samples were collected with using Marble Dust Collector (MDCO) from 28 different locations across the city of Tehran, during winter of 2017. XRF analysis used to identify and determine the concentration of heavy metals. The Principal Component Analysis (PCA) method were summarized 15 heavy elements studied into three factors. The kriging interpolation method was used to determine the role of each factor in the study area. In the next step, the extracted factors were grouped based on factor scores, by statistical method of cluster analysis.

The results of the Principal Component Analysis (PCA) showed the existence of three factors: natural, human and combination of natural and human factors which increasing the concentration of heavy metals in the dust of Tehran metropolis.
Factor 1: Aluminum, chromium, iron, magnesium, manganese, nickel, silicon, and vanadium have the same emission source. These factors are not affected by human activities but have natural origin. Therefore, this category was called the natural factor.
Factor 2: Arsenic, cadmium, copper, lead, and selenium have the same emission source. Probably, human factors have role in increasing these elements in the dust falling.  Therefore, this category was called the human factor.
Factor 3: Cobalt and zinc have the same emission source. This factor is combination of both natural and human factors.
Based on the extracted factors and with using method of Cluster Analysis (CA), the stations in Tehran were classified into four clusters as follows:The First group: Forest and Range Organization of IRAN, Dadman Blvd, Pole Gisha, Arash Blvd, Majidiyeh Shomali, Janatabad, Shahre Ziba, Pole Kan, Motahhari, Narmak, Hashemi, Tehransar. These stations are located in the northern and western regions of Tehran.
The second group: Afsaria station is located in the southeast of Tehran. This station has the highest concentration of heavy metal in dust falling.
The Third group: Velenjak station is located in north of Tehran. The heavy metal concentrations in dust falling are higher than other groups in human resources.
The Fourth group: Enghelab, Darvaze Dolat, Imam Hossein Square, Nawab, Si-e-tir, Komeil Gharbi, Tehranno, Piroozi, Nazi Abad and Shahr-e-rey are located in this cluster. These stations are located in the central, eastern and southern regions of Tehran. The concentration of heavy metals in these regions are high.

Heavy metals in dust are an important component of urban pollution. The harmful effects of heavy elements on human health have been proven in different ways. Considering the population of Tehran metropolis, the results of this research are very important for developing management approaches to create a healthier environment. The results showed that the pollution of dust falling into heavy metal with human origin in the northern and western regions of Tehran is less than other regions and in the southeast of Tehran is more than other regions. results indicated that population density, traffic and traffic volume, industries and mines, topographic pattern, vegetation pattern, prevailing wind pattern and air stability are effective in the concentration of heavy elements in Tehran dust falling.

Introduction :

Dusts are referred to as aerosol particles that are made up of different sources of land and humanization, and after which time, again, they fall on the surface due to their size and density (Salman Zadeh et al., 2012). This phenomenon can damage infrastructures, telecommunications and agricultural products and affect transport through reduced visibility and cause a lot of economic damage. (Song et al 2007., Cao et al 2016). The purpose of this study is measuring and spatial analysis of the city of Tehran in a one-year statistical period. 

Materials and methods:

 In this research, we used the laboratory method to measure falling dust, collecting dust using Marble Dust Collector. For this purpose, the falling dust was collected using a Marble Dust Collector in 28 stations in Tehran during the statistical period. In order to analyze the spatial distribution of dust, dust was collected from 28 dust collecting stations, and Tehran PM10 data taken from the air quality control company, the number of construction urban under construction in Tehran were obtained from Tehran Municipality Organization, mean maximum wind speed parameters, average relative humidity,days of rainfall above 5 mm,the average temperature of Tehran taken from the country's meteorological organization in the one-year statistical period (1/10/96 - 30/9/97) to enter the Arc Map10.5 environment and preparing the desired layers Were prepared. Statistical analysis of the data showed that Dust collected, pm10 and the number of running construction projects have regional (trend) behavior. Therefore, Universal trend is better suited. Due to the high preconditions for stagnation use of Universal trend In the area with fewer meteorological stations (Chitgar, Geophysics, Mehrabad and Shemiran) Universal trend is not applicable, Therefore, the IDW method was selected for climatic parameters. Also, the vegetation cover and factories file Shapes were taken and by analyzing Euclidean distance of each of these complications in GIS for Tehran, there impact on the dust in each area were considered. Then all the layers were weighed to determine the weights using the Reclassify tool. Then, using Expert Choice software, we compared all the layers two by two till estimate the value of each layer relative to the other layer. We multiplied the values obtained at each level. We transferred all layers to the Fuzzy Overlay tool. And draw up the final map of the spatial analysis of falling dust in Tehran city using the Gama 0.9 function. Also, daily speed and wind direction data were received from the Meteorological Organization of the country during the one-year statistical period, (30/9/97- 1/10/96). And with the help of the WRPLOT software for statistical analysis and the location of the wind, the windrose was drawn. Results and discussion The results of computations performed on the data obtained from the collecting of falling dust in Tehran showed that the weight of falling dust in the winter of 1396 is 18943.5 tons, in the spring of 1397 it is equivalent to 27119.5 tons, in the summer of 1397 it is equivalent to 17111.2 tons and in the fall of 1397 it is equivalent to 23002.3 tons. Also, the results showed that the highest falling dust was collected in spring, autumn, winter and summer, respectively. The spatial analysis map of Tehran's falling dust is a combination of 9 layers, based on the weight assigned to each layer. The results showed that the highest amount of dust in the winter of 1396 was found in west of Tehran. We had the lowest amount of falling dust in the north and northeast (regions 1 and 4). In the spring, summer and autumn of 1397, the halo of the most falling dust was displaced slightly eastward and settled in the southwest. The lowest amount of dusts in these seasons was located in the north and northeast. The halo with the lowest amount of dust falling has expanded further in the autumn than spring and summer.

 Conclusion:

 The results of this study showed that the spatial distribution of falling dust varies in different seasons. Which shows that the source of falling dust in the city of Tehran is not uniform throughout the year. Field precise surveys have shown that the increase in falling dust in different parts of Tehran is directly related to urban construction. So that in the statistical year of the study, construction and subsequent falling dust has been less in eastern Tehran than its west and this increase is also associated with pm10. The largest amount of pm10 was reported from the west and southwest, which simultaneously collected the highest amount of falling dust. The highest density of factories and the lowest vegetation density are in these areas. The climatic factors also contributed to these conditions. So that, It was reported that the highest number of rainy days to exceed 5 mm was reported in north and north east of Tehran, where the lowest amount of dust was collected. And the highest average temperature in different seasons is reported from Southwest of Tehran, which has the highest amount of falling dust in spring, summer and autumn. But in winter climate conditions were slightly different from other seasons. So that, the highest relative humidity reported in other seasons from the West has been reported from north and northeast this season. The dust collected in winter is higher in the west than in the southwest. But the average maximum wind speed, which is in the west and south, is in the winter, spring and autumn to the west and southwest Which is from sand quarts of Quds, Shahriar, and Malard cities, especially the sandy-sand dune areas and abandoned agricultural land in Baharestan, Islam-Shahr and Robat-Karim, then dust from these areas enters Tehran west. In addition, the wind disperses the dusts generated by construction around the city. In the summer, in addition to the west and southwest, there is wind for north and south east. The northern wind comes from Shemiranat, bringing fresh air to the north and north-east of Tehran. The southeast wind passes from the Pakdasht sand and cement factories in Tehran and the abandoned agricultural land of Varamin and contains dust. The low wind speed in these areas gives more time to hangs off more particles. And of course, climatic conditions with the lowest relative humidity, the highest temperature, and a lack of rainfall above 5 mm also help to pollute the southern part of Tehran. Keywords Spatial analysis, Sediment trap, Tehran City, falling dust

In this study parameters of daily and monthly rainfall، minimum، average and maximum temperatures، relative humidity، number of sunny hours and number of freezing days in the towns of Kashmar and Ghaen were analyzed for a statistical period of 20 agricultural years (1992-2011). Several traditional methods of climate classification were used to compare climate conditions of Ghaen and Kashmar towns by climate determining software. Then effective meteorological indicators on saffron cultivation were selected. Results of climate classification by traditional methods showed that these towns are located in the same climate class. Although the climate was similar، but studies showed that saffron quality in color، odor and taste in Ghaen was higher than Kashmar. The Maximum temperature and relative humidity were the same in both towns. Relative humidity in both towns was the same، especially in October and November which coincided with the beginning of saffron flowering time. Minimum temperature and sunny hours made climate conditions of Ghaen at more favorable for saffron because of mountainous nature. Sunny hours in Ghaen during flowering season was more than Kashmar. Finally، a slight difference in three climate parameters، i. e. rainfall during summer، temperature and sunny hours cannot be the only reason for lower quality of saffron in Kashmar in comparison to Ghaen and saffron quality in Kashmar could be highly raised by other factors such as better planning and management.

Climate signals are large-scale models of abnormalities in circulation and pressure and spread over wide geographical area. These signals are very important in translating the climate behaviour. In this research the relationship between precipitation and the climate signals (AO, NAO, SOI, and ENSO) in the Central Iranian zone, has been studied. The signals data are acquired from the NCEP Data Centre; also the aggregate data of the monthly precipitation are obtained from the Automation Centre in Iran Meteorological Organisation. Monthly data gathered through a 30-year statistical period (between 1978 to 2008). Finally, by exploiting Artificial Neural Network method, the simulation models for 0,3 and 6 months intervals were created. The results indicated that among the investigated signals, signal ENSO has a meaningful impact on precipitation in NINO1.2 and NINO3 zones, and the 3 and 6 month delay has strengthened the correlation coefficient of the ENSO index in zones NINO1.2 and NINO3 in relation to precipitation in the studied stations. The 6-month delay has resulted in negative correlation coefficient between ENSO index in NINO1.2 and NINO3 zones. According to the presented models, ENSO signal in NINO1.2 and NINO3 zones combined with other effective parameters could regard as a precipitation forecast model. Other climate signals do not have a meaningful impact of precipitation in the stations under study.