javad bodagh jamali

This study was conducted with the aim of providing a remotely sensed water quality index in Assaluyeh port using remote sensing technology. so, according to the region conditions, studying of scientific resources and access to satellite data, the parameters of heavy metals, dissolved ions, SST, chlorophyll-a and pH were selected. Then, by reviewing sources, the product MYD091km, MYD021km, MOD021km, MOD091km and level2 images of chlorophyll-a and SST of MODIS sensor were used after preprocessing operations. Also In-situ data were collected Simultaneously with the capture of satellite images in August 2014. Then, the relationships between the water quality parameters and MODIS data, with (R2) from 0.59 to 0.94 and (RMSE) from 0.07 to 0.1 were obtained. Next the images of the MODIS sensor from 2015 to 2017 were prepared and the models were applied to them, then the layers were standardized by fuzzy logic. Also time series of SST data from 2003 to 2017 were prepared and for each month the average pixel values were calculated and based on this, from 2015 to 2017, the variation of this parameter was standardized. Finally, an effective index for assessing the quality of coastal waters was provided by time series of satellite images and the waters of Assaluyeh port were zoned. The results showed that the water quality in 2015 and 2016 has shifted from poor to very poor status in 2017. Based on the results, with the development of a proposed index, in future studies a continuous assessment of environmental monitoring is possible.

Tehran metropolis, with an area of 750 km2, a population of more than 8 million people, and about 4 million vehicles is associated with the problem of air pollution. A thorough study of the spatial distribution of pollutants such as CO and NO2 in Tehran is significant for identifying the risks, probabilities, and risks of these contaminants. Therefore, mathematical and computational methods such as the confidence level method can be useful. The main goals of this research were to investigate the changes in air pollution levels in terms of CO and NO2 concentration, study the radius of impacts of fixed pollution stations, and calculate the level of reliability by investigating the probability of air pollution and the map of the risk of air pollution in different parts of the urban area of Tehran.

In this study, Tehran's air pollution data in October, November, and December 2017 was used in spatial modeling. Using geostatistics and indicator kriging methods, data were analyzed and maps of the distribution of pollution concentration, and also two-dual maps (0 and 1) of the probability of pollution and risk of pollution in Tehran's for the study period were produced by ArcGIS Software.

The resulting maps showed the highest NO2 emissions areas (Ghaem Park, Razi Park, and the municipality of district 16) and areas with the least risk of NO2 pollution (Shahid Beheshti University, Pasdaran, Science, and Technology University, and Shad Abad). Moreover, the highest CO emission areas were the municipality of districts 11, 15, and 16, Ray station, Sharif University, Fatah Square, Health Park, and Razi Park). Aghdasyeh station, Shahid Beheshti University, municipality of district 2, Rose Park, Science and Technology University, Golbargh, Shad Abad, and Masoudieh had the lowest CO emissions.

The indicator kriging was a useful method for assessing the risk of contamination by providing a possibility map. The hazardous maps produced in this study were useful tools for identifying areas with CO and NO2 contaminations. The results of this study can play an effective role in urban management decisions by correctly identifying the amount of air pollution in an appropriate spatial distribution.

Nowadays, oil spill to the seas and oceans is one of the most critical factors in the sea-based pollution in the world. On-time recognition and prevention of oil spills to the marine environment and decreasing of its harmful effects on sea ecosystems are necessary. Remote sensing technology is suitable for early recognition of oil pollution from the air because of the data-collecting in various ranges of Electromagnetism and a broad view of the region. In this study, after evaluation of the scientific sources and the comparison of various sensors and about the time and place of the oil pollution, the images of the second level of MODIS sensor, from 2005 to 2015, have been used. So that a new method for monitoring of the oil spill has been developed. Moreover, the suggested method has focused on enhancment of sensor data. In this study, in order to enhance the bands 1 or 2 oil spill of MODIS sensor, the mean long-term statistical parameters and standard deviation of time-series data have been applied. Therefore, the undesirable data sets for each pixel have been deleted through the initiative process. Then, the mean and the long-term standard deviation of the data have been acquired from the long-term data. Moreover, finally, the value of each pixel has been standardized by utilizing those parameters. The oil spill event has been simulated by this method. Afterward, the normal frequency distribution method was applied to screen the oil patches, and the polluted pixels have been screened by applying  the appropriate threshold. In this way, the oil pollution happened in 2007, and 2010 have been screened by the designed model, and their accuracy has been compared to the real land map. The results of the map for each oil spill event have been acquired as follows: 96% total accuracy and 0.95 Kapa co-efficient for August 2007, 95% total accuracy and 0.92 Kapa co-efficient for July 2010. Finally, the overall results of the study have shown that the designed model has enough accuracy in recognition and screening of oil spills events.

Remote sensing technology is nowadays used in studies of aerosols and dusts in the atmosphere and because of large extent of covered area this technology has become most efficient in the study of different climatic, atmospheric and hydrological processes. This technology provides images with extensive cover and high frequency at different times and as such it can be used for monitoring of aerosols and dust particles. There are various sensors to detect aerosols and dust and in this article we covered some sensors including MODIS, SEVIRI, OMI, POLDER-P, MISR, MERIS and AVHRR. The results showed that MODIS is a powerful sensor for detecting and monitoring dust because of its high spectral resolution. The geostationary SEVIRI images give us the possibility of continuous dust monitoring. The OMI sensor can be used in both cloudy and cloudless conditions to detect aerosols. POLDER-P is suitable for detecting fine particles and MISR for early detection of dust storms. MERIS also has global measurements, and AVHRR has a global daily coverage and high spatial resolution. We conclude that each of the sensors has different capabilities in the identification of dust and aerosols and depending on the purpose of the study, one or a mix of them can be employed.

The release of toxic, hazardous and fire hazardous substances from storage tanks in process and chemical industries has always been one of the hazards of working people, residents around these industries, and the environment. This study was done with the aim of modeling and evaluating the consequences of benzene leakage in the coke production unit of Isfahan Steel Company.

In order to observe ethical standards in research, research information was obtained with written permission from Isfahan Steel Company. In the present study, in order to modeling and investigating the release of benzene, at first cycle of the process in the coking plant, and then Existing hazards were identified by performing a risk assessment using the FMEA method. The consequence evaluation in a process unit consists of selecting a scenario, specifying the scenario's specifications, modeling the consequences of the scenarios and finally analyzing the results. For this purpose, the software ALOHA version 5.4.7 has been used for modeling the outcome and evaluation of benzene leakage.

The results of simulations show that the most serious risk factor for personnel is the concentration of benzene in the environment. And due to the control room being 72 meters from the corresponding reservoir, up to 169 meters of the tank the concentration of benzene vapors reaches 800 ppm. Hence, people who are at this distance will not be able to escape during an incident. Also, the contour lines of simulating the pool fire resulting from the benzene release, shows that in the leakage scenarios with a diameter of 5 mm, 25 mm and 100 mm, distances less than 10, 14 and 51 m influenced with fire, respectively.

Despite the errors in the results of mathematical modeling and the explanation of possible scenarios, simulation of benzene leakage and its consequences, can be used in the formulation of preventive strategies and emergency planning in the coking plant of Isfahan Steel Company.

Mulching on the ground surface is one of the common approaches for fixing the sandy dunes and controlling dust. Despite the frequent use of oil mulch, due to its heavy costs and environmental problems, researches are focused on producing new environmentally friendly mulches. In this research, different treatments of cement, lime, wind sands and steel-slag, as cement-slag mulch, have been used to fix the sand dunes samples prepared from Robat-Karim region. Steel-slag is added to the mulch composition because of the benefits of waste reduction from environment and replacement of a part of the cement in the mulch composition because of Steel-slag pozzolanic property. To reduce the number of tests and find the optimal composition of the mulch, statistical mixture design approach was used. Furthermore, the effect of different percentages of the ingredients on shear, compression and impact strength and moisture content of cement-slag treatments were investigated. Results show that the addition of steel-slag increases the compressive and shear strength as well as the ability to maintain soil moisture in the proposed mulch. Finally, developing the mathematical model of the experiments, six different objective functions were considered for optimizing the composition of the proposed cement-slage mulch. As a result combination of 68.731% sand, 27% cement, 2.979% steel-slag and 1.3% lime were the best mulch considering all the objective functions. To validate the results of the optimization, six optimal compositions were re-constructed and the tested again. Obtained results showed an acceptable consistency between the predicted and tested values.

In this paper, in order to detection of cloud types over Iran, NOAA satellite data and of cloud recognition Algorithm of International Satellite Cloud Climatology Project (ISCCP) have been applied. Firstly, monthly mean of optical cloud depth and cloud top pressure have been derived from AVHRR sensor related to NOAA satellite during (1981-2010) for 12 months (January -December). Secondly, coverage of 9 cloud types including Cumulus (Cu), Altocumulus(Ac), Altostratus(As), Stratocumulus(Sc), Deep Convection(Cb), Stratus(St), Nimbostratus(Ns), Cirrus(Ci), and Cirrostratus(Cs) have been identified over Iran using ISCCP Algorithm. The method is innovative and has been used for the first time in Iran. The remarkable results of cloud detection in different months for Iran showed that maximum of frequency percent of Cu clouds occur in Oct., Nov., Dec., Jan., and Feb. over southern and Southeast of coast of Iran. As clouds have high frequency over all months but its distribution is different in various months. Frequency of As clouds is more than 50% over vast area and its maximum exceeds than 80% in all months. Sc clouds also have similar distribution in Autumn and winter seasons including Sep., Oct., Nov., Dec., Jan., and Feb. In contrast, frequency of St clouds are very low for all months except July which the Frequency is 5%-25% in Southwest of Caspian Sea. Percent of frequency of Cs clouds have high difference in different months. The most frequency of this cloud occurs in Jan., Feb., and Oct. In these months the maximum of frequency exceeds more than 50%.

Dust phenomenon is of natural processes in arid and semi-arid regions of the world which damages various parts very much annually. South West of Iran is exposed to this phenomenon continuously due to being in the arid and semi-arid belt of the world. In this research to determine the dust corridors, One of high dust concentration events (19 June 2012) was chosen and dust detection method of DUST RGB was applied on the MODIS images of the day when dust happened and a day before and after that. and on the basis of DUST RGB time-series images of dust, the corridors of this event was determined and this images compared and validated with DUST RGB products of METEOSAT satellite (sensor of SEVIRI). Also, in order to complete the analysis, numerical HYSPLIT model to determine dust corridors was used. Finally three main dust corridors consisting of North West – South East, South – North and West – East to the South West of Iran were specified. The results of this study demonstrated that MODIS images can identify the dust and its corridors on the condition that the visual range of images are appropriate and if they are not cloudy. but in order to determine the corridors exactly and complete the analysis, other kinds of information such as numerical meteorology models beside remote sensing data

This paper is aimed at climatological study of low level clouds in four seasons and accordance to the long-term pattern for operational cloud seeding over Iran. Clouds are observed in different shapes and heights in the atmosphere. Regarding the classification of clouds there are various ideas. Based on the Howard classification 10 types of clouds are classified in 4 groups based on the height of cloud base from surface includes low with height of less than 6500 ft, middle with height of 6500-16000 ft, high with 16000-18000 ft, and vertical development. Cloud climatology is main part of weather modification program for cloud seeding site selection. In this study, frequency of occurrences of low level cloud including Stratocumulus (Sc.), Stratus (St), Cumulus (Cu) occurrence have been collected from 44 synoptic stations of Iranian Meteorological Organization (IRIMO) for 30 years (1981-2010) for day time including 06, 09, 12, and 15 UTC and CFSR[1] data with 0.5 degree resolution have been used for deriving of long term seasonal pattern. Seasonal mean of geopotential height, temperature, horizontal wind, vertical wind at 500 hPa, surface pressure and precipitable water (PW) in (500-1000) hPa layer have been derived. Investigations have been carried out based on location of province and watershed basins. The most important investigating results show that low level clouds occurrence with less than %30, have not been observed in selected stations in cold season including autumn and winter . The Northwest, North, and Northeast provinces and basins have high (>%70) low level clouds occurrence in all months which have suitable condition for operational cloud seeding with respect to this index. Low clouds occurrences in warm season including spring and summer show that southern part of Iran have low (<%30) to medium (%30-%70) but were high (>%70) for other stations which detail information of Southern provinces are as following; provinces including Hormozgan , Bushehr, Sistan-Baluchestan and Khuzestan, Kerman, Fars , and South Khorasan are between %30 -%70. Maximum of low cloud occurrence belongs to spring, autumn, winter, and summer respectively. Maximum of cloudiness belongs to March and April in spring. The percent of low level cloud occurrence in southern basins in most months were low (<%30) which achieved very low score for operational cloud seeding with respect to this index. Long term atmospheric pattern shows suitable condition for amplification of cloudiness in clod seasons which accompanied with falling of cold weather Siberian high pressure and amplification of the warm Southern flow provide suitable condition for enhancement of humidity specially for west part of country. Formation of thermal low pressure in summer season, coverage of level cloud and precipitable water reduces over region. In cold seasons (roughly autumn and winter) maximum of low cloud occurs at Bandar- Anzali station in Gilan Province with max of 93.03% and Ramsar station in Mazandaran province with max of 94.34% which both occur in October. In the warm season (roughly spring and summer) is related to Ramsar station with Max of 99.10% and Noshahr station with 98.72 % which both occur in September. The minimum of low cloud occurrence is related to Zabol station in Sistan- Baluchestan province with 32.88% and Iranshahr station in Sistan- Baluchestan province with 37.93% in clod season which both occur in October. In warm season Bushehr station in Bushehr province and Abadan station in Khuzestan province have 15.79% and 16.34% respectively which both occur in September. Due to location of Caspian Sea in North of Iran and Alborz Mountain in southern part of it, therefore the most low level clouds occur in moist and rainy climate.
Long term surface pressure pattern show formation of suitable condition for amplification of cloudiness amount in cold season which with falling of cold weather in North and Northwest associated with Siberian high pressure and amplification of warm and humid weather provide suitable condition for increasing of humidity specially in Midwest of country. Formation of thermal low pressure at the surface causes decreasing of coverage of low cloud and precipitable water over the region in summer.
Although application of satellite data for investigating of clouds is prevalent due to their expand coverage, but the accuracy and importance of surface observation is not deniable. However, in order to achieve more accurate mapping of low level cloud over Iran, due to the shortage of number of synoptic stations with long term observation using satellite data is necessary.

Dust event is one of the atmospheric events of the world arid and semi-arid areas that had a significant increase in recent years and negative effects in different parts. In this study used MODIS data to identify and select the best algorithm for dust detection. For this purpose, three dust events of South West of Iran detected in 2012 using five different algorithms of dust detection including Ackerman BTD, Miller, dust index, TIIDI and DUST RGB methods, and methods compared. Studies show that methods of Ackerman BTD, Dust index, and Miller need to threshold regulation for each dust event; for this reason, suitable threshold was determined for each dust event using histogram method and dust identified. In addition, TIIDI method could separate dust phenomenon from other complications on the surface of the earth but as well could not identify dust on water. In DUST RGB method as well dust identified from other complication. In addition results of images classification and accuracy assessment showed that in all three dust events, DUST RGB method has maximum total accuracy among of other methods. Therefore, based on the results of matrix error and accuracy assessment, DUST RGB method was chosen as the best algorithm for dust detection.

Dust particles are important atmospheric aerosol compounds. The particles are resulting performance of strong winds at the soil surface desert areas. Sources of dust are 2 types: 1- Natural Resources 2- Human Resources. Iran is located in the desert belt which this problem cause increased the frequency of dust storms, especially in South East (Sistan) and South West. China Meteorological Administration Center classifies storms based on particles type, visibility and speed storms to 4 kind: Floating Dust, Blowing Dust, Sand/Dust Storm and Sever Sand/Dust Storm. In general, the effects of dust storms in 7 of Environment (particles into remote areas, the effect of dust particles on the material, climate, oceans and deserts), public health and health (increase of respiratory diseases , cardiovascular problems, digestive, eye, skin, reduced hearing, infections, reduced life expectancy and premature death, etc.), economic (unemployment, road accidents, damage to communication lines, air, land, sea, increase water turbidity in water utilities, creating uncertainty for all economic activities, etc.), Agriculture and Livestock (negative effect on the growth of plants and animals, reduced productivity and diversification, intensification of plant and animal pests and diseases, rising costs maintenance of livestock, etc.), socio-cultural (poverty and the loss of local jobs, destruction of subcultures, rural migration to the cities, closure of educational premises, industrial units, services, etc.) and military-security (disabling weapons, food and beverage contamination, the threat of sensitive electronics and power transmission systems, and reduce the useful life sitting on warehouse equipment, logistics cargo weight gain, etc.) can be evaluated. One way to identify, evaluate and forecast dust storm modeling. Dust cycle consists of 3 parts, dust emissions, dust and subsidence transfer dust that can be simulated by models.
In this study using the WRF_Chem model with FNL[1] input data and GOCART schema, sever dust storm in Sistan region was simulated to date 14 & 15 July 2011. Satellite images of the event was received by the MODIS sensor. Dust concentration data was received from the Department of Environment. The dust storm code, minimum visibility data and maximum wind speed data was received from the, Meteorological Organization.
The results of the simulation for dust concentration which peak amount of dust was for 21Z14July2011 and 03Z15 July 2011. Model output showed maximum wind speed 20 m/s with North to South direction in the study area. The model predicts maximum dust concentration for the latitude 31 degree North and longitude 54 degree East to 66 degree East (Within the study area). MODIS sensor images showed clearly the sever dust storm. Simulated time series in Figure 3-1 Changes in dust concentration during the event show in the Sistan region. As can be seen from the peak of the concentration of dust in 21 hours on 14 July (350 micrograms per cubic meter) and 03 hours on 15 July (425 micrograms per cubic meter) 2011 was created. Model simulation and satellite images indicated which the Sistan region, especially dry bed of Hamoun wetland in East of Iran was main source of sand and dust storm. Also, based on the model output blowing wind direction from North to South on Iran which converging these currents in East Iran caused by strong winds in the lower levels (According to the meteorological data), arise dust, increasing the dust concentration (According to Department of Environment data), increasing the dust and being transferred to the Southern regions, especially Oman sea. To identify the source of the sand and dust storm, the path of the particle and anticipated this event cant actions and warned to stop and reduce effects its. . Simulation of dust particles in the resolution of 10 and 30 kilometers, the plains of Sistan in Iran's East region as the main source screen. The findings suggest that compliance with the maximum concentration limits on known sources of particles (especially Sistan plain dry bed of plain wetlands) is. Check drawings wear rate showed that the source of dust in the Sistan region, particularly the high potential of our wetlands dry bed of soil erosion in wind activity 120 days during the hot and dry conditions, and silt and clay up to thousands of kilometers away from their source transfers. Vector lines on maps wear rate, indicative of converging flow north-south and severe dust storms in history is this. It is better than models forecast dust events and rapid alert

Iran location in the desert belt is accompanied with frequency increasing the sand and dust storms, especially in the eastern and southern areas and created adverse environmental effects. One of ways to study this event is dust simulation and forecasting. Aim of this study, Weather Research & Forecasting-Chemistry coupled model (WRF-Chem.3.6.1) is used to simulate, forecast and design alerting system for sand and dust storm events (East of Iran). In addition, dust concentration data that were collected by environmental protection organization (EPO), wind speed and direction data from the Meteorological Organization and MODIS images are used to investigate transport path of storms and have better forecast and right time alerting. True color images by software ERDAS IMAGIN queries to identify dust source. Results showed that the main dust and sand emission source in Sistan is dry bed of Hamoun wetland. Also during the storms that are investigated in this study, transport of dust clouds are observed to southern part of Iran up to Oman sea because of converging currents north-south winds on Eastern part of Iran (especially in spring and summer) that create strong winds in lower levels of atmosphere. This wind are responsible of increasing dust emission. WRF-Chem model had reasonable estimation related to spetial and temporal scale in the study area. Due to use of global forecasted data as model input data, it was expected to observe bias in concentration estimation from real situation. The model was run for 30 km spatial resolution and results revealed storm formation in Sisitan affected by local geographical properties especially topography features.