فصلنامه محیط شناسی
سال چهل و هفتم شماره 3 (پیاپی 99، پاییز 1400)

Accelerating urban expansion is increasingly challenging the sustainable use of land, since modeling urban growth is important in order to adapt to balanced development. Among all the models for urban simulation, dynamic models based on cellular automata have significant usage in urban modeling because of its applications in different places and times. One of the most widely used spatial models based on cellular automata is the SLEUTH model, which in recent years has improved its accuracy and efficiency for performing calculations, and today it is widely considered in predicting the development trend of different cities in the world.This study was carried out with the aim of simulating the future urban expansion of Birjand Metropolitan from 2020 to 2050 using Cellular Automata (CA) methodology in the SLEUTH modeling considering two scenarios: historical and environmental growth.

Study areaBirjand Metropolitan is the capital of South Khorasan province. The area of Birjand is 14.265 km2 and located between 53', 32° N and 59', 12° E in the northeastern margin of The Lut Desert, which is surrounded by mountains. In the last decade, and especially after the division of Khorasan province into three provinces of North Khorasan, South Khorasan and Khorasan Razavi, Birjand Metropolitan as center of South Khorasan province faced to socio-economic and political changes that led to rapid urban growth and physical and functional changes.SLEUTH modelIn this research, modeling the expansion of Birjand has been considered using cellular automata by SLEUTH method. SLEUTH name is derived from the names of input layers: Slope, Land use, Excluded areas, Urbanization, Transportation and Hillshade. The main attribute of SLEUTH is that it be calibrated on the base of the region changes in the past and therefore reasonably predicts the future. SLEUTH starts with the oldest data (first year of control) and generates growth cycles. Each growth cycle is assumed to represent one year. A run is the set of growth cycles from the first control year to the final year. Considering the comparison the simulated image with the real image in the control years, evaluation indexes are generated.SLEUTH modeling performed in four steps:1- Data gatheringAll layers (Slope, Land use, Excluded areas, Urbanization, Transportation and Hillshade) were georeferenced to the same geographical references system (UTM-40N) and pixel size 30*30. Slope and Hillshade layers extracted from DEM. Land use and Urbanization provided from classification of landsat_TM images 1990, 2000, 2001, 2020 years. These images were belonged to row and path 159 and 37, respectively and classification was performed using ‎Support Vector Machine (SVM) algorithm. In preparing the road layer for four mentioned years, in addition to using satellite images, the existing maps and Google Earth were used for updating. Two Excluded areas layer in this study were prepared according to two scenarios: historical growth and environmental growth. In the historical growth scenario, roads and cities and in the environmental growth scenario, vegetation and high slopes (slops higher than 30%) were considered as Excluded areas.2- Confirmation of the correct execution of the modelThe SLEUTH model was downloaded from the Gigalapolis project from the website: http://www.ncgia.ucsb.edu/projects, and the required simulator in the Windows (Cygwin) on the PC was installed. To ensure the correct execution of the model, SLEUTH was performed by experimental data and data from the study area. Surveying the results and outputs of these performances confirms accuracy of execution.3- The calibration stepCalibration is one of the most important steps in simulating urban growth using SLEUTH. In the calibration step, based on the historical data, the best set for the five global parameters/coefficients (Diffusion, Bread, Spread, Slope and Road Gravity) is extracted. These coefficients, which indicate the contribution of factors to the expansion of the study area, vary from zero to 100 and according to the Brute Force method in four stages, which consist of coarse, fine, final and average, are obtained based on cell size, search range and Monte Carlo execution number. In each forward stage the range of search become narrow and number of Monte Carlo execution become more. ‎Optimum Sleuth Metric (OSM) and Leesalee metrics in each stage were used to determining the calibration coefficients for the next stage. These metrics show the overall precision of the simulation too.4- Prediction the Birjand growthFour kinds of growth simulate by SLEUTH consist of spontaneous, spread, organic and road influenced. This growth rules and the coefficients obtained from previous step constitute the transfer rules in the Cellular Automata of SLEUTH model for prediction. Output image of prediction was reclassified to 1: urbanization probability> 80% and 0: urbanization probability< 80%.

In this study, using the SLEUTH model, the expansion of Birjand city was predicted according to historical and environmental growth scenarios from 2020 until 2050. During the study period, the growth of the urbanization is quite evident and it is mainly happening in the northeast and southwest of the city. One scenario of this research was based on the historical growth and the past trend, which means that all the drivers that have caused the growth of the city in the past will continue in the future. In the environmental growth scenario, urban growth will not located in vegetation cover and slopes above 30%. The largest increase will be happened around roads in the historical growth scenario and in the vicinity of urban in the environmental scenario. The Calibration results showed that there was different coefficients for scenarios. High diffusion in historical growth scenario (87) rather than environmental growth scenario (20) denoted that probability of a new urban center by 2050 is high and spontaneous growth/outlying growth will occur by 2050 but, in the environmental growth scenario the growth will has a more coherent pattern. Bread coefficients in historical growth scenario (73) show the probability of filling around the new urban points by 2050 is high in the future, but in environmental growth scenario (4) does not support new points for urban expansion. Spread coefficient in environmental growth scenario (32) was more than historical growth scenario (20) that means probability of edge (organic) growth in environmental growth scenario is more than historical growth scenario in the future. Slope Gravity in both scenarios was very low (1) so slope isn’t the control factor in this area. The main reason for low rule of slope is the uniformity of the slope and the scarcity of high slopes in the study area. Road Gravity in historical growth scenario (22) and in environmental growth scenario (37) that shows there are high eventuality of linear growth type in both scenario and road-influenced growth is significant for the future growth.The simulation results showed that urban expansion in the historical and environmental growth scenarios would be 2201.85 and 2150.91 hectares, respectively. Although the extent of the urbanization area is close to each other, the probable places for urbanization are more compact with organic pattern in the environmental scenario and more scattered in the historical scenario. Therefore urban expansion in environmental scenario has lower influence on surrounding environment rather than historical scenario and is more close to sustainable development.The results can provide useful information for the decisions of land managers and municipalities in the direction of sustainable urban development.

The new political division of Khorasan Province had significant changes on urban growth of Birjand that turn it to metropolitan. According to simulation of urban growth increasing the area of Birjand city is inevitable in both historical and environmental scenarios. A comparison of the two scenarios denoted that in the historical growth scenario, the urban growth rate is higher, the vegetation destruction and spontaneous settlements is maximal. The findings of this study can help policy makers and managers in formulating informed urban planning strategies to have the least destructive effect on the environment in the future.

Disposal of wastes in landfills is the last step considered in a solid waste management plan. Landfilling is the final fate of all unwanted products which are considered insignificant from the producer's point of view. In other words, landfilling is a mandatory component in the structure of the waste management hierarchy. The current waste landfill of Mahabad, due to its proximity to rural areas as well as Maskan-e-Mehr apartments with impressive dwellers, an average distance of 620 meters from waterways, being located in a fault adjacency, and completing 70% of its capacity, cannot be considered as a good option for the future development, to handle wastes generated in Mahabad city in the next 20 years. In addition, the short distance from the current landfill to Mahabad Dam Lake, caused the accumulation of a significant number of large birds (mainly storks) to invade the current waste landfill and feed on food scraps. This unprecedented phenomenon has made animal wildlife extremely vulnerable. Therefore, finding a suitable place for burying solid waste in Mahabad is essential.The process of selecting a suitable place for burying municipal waste depends on various technical, economic, social, cultural, and environmental factors, and in most cases, urban communities face a lack of space to determine the appropriate location. Therefore, identifying regional constraints, assessing the extent of damage to affected communities, investigating possible damage to the environment, and anticipating practical actions to reduce the damage are part of the requirements of the process of locating, designing, implementing, and operating landfills.The tools available in the Geographic Information System (GIS) can be used to solve the problems and complexities of the process of determining the appropriate location for the construction of a waste landfill. In the location process using GIS, only technical factors are considered, however, a suitable place for waste landfill, in addition to technical criteria, must also satisfy social, cultural, economic, and environmental criteria. To study these factors and also to create compatibility and balance between the activities of a project and the surrounding environment, the proposed method can be used to assess the environmental impact.The matrix technique is one of the most popular methods used in most construction projects to assess environmental impacts. The Rapid Impact Assessment Matrix (RIAM) is one of the most widely used matrices developed by Pastakia and Jensen. This matrix has become an ideal process to provide a clear and fast assessment of the environmental impact of a construction project due to its ability to integrate all components and parameters of the environment. Another matrix that has been considered by many researchers to assess the environmental impact of landfills due to its simple operating system and multi-criteria assessment, is the Leopold matrix. One of the main advantages of this matrix is the summation of the negative and positive effects of a project in two stages of construction and operation.The main purpose of this paper is to provide a comprehensive model for site selection and assessing the environmental impact of waste landfills by GIS software and RIAM and Leopold matrix methods. Furthermore, by setting the same input parameters for the two mentioned matrices, an attempt has been made to examine the reasons for the similarities/differences of the obtained results in detail.

In the first step, by overlaying the existing information layers (16 layers in the elimination phase and 13 layers in the phase of identifying susceptible areas) using GIS software, a suitable place for the construction of a landfill in Mahabad city has been determined. Then, by reviewing the technical literature, 21 parameters were selected to assess the environmental effects by two matrix methods, RIAM and Iranian Leopold. Finally, the results obtained from these two methods are presented and compared. It should be noted that in the Leopold method, unlike the RIAM matrix, which presents the project assessment in general, two phases of construction and operation are considered. Therefore, to analyze the causes of differences/similarities in the results of the two methods, The RIAM matrix, like the Leopold matrix, was examined in two phases of construction and operation. Also, the same input parameters were defined for the two mentioned matrices.

After overlaying information layers in two elimination and identifying the susceptible area phases on the GIS space, as well as taking over the area for 20 years (35 hectares), a suitable place for burying waste of Mahabad city was obtained. The results of the RIAM matrix in the construction phase showed that the most negative effects of the selected site for construction of the landfill occurred in Physico-chemical, biological and socio-economic environments. Furthermore, the most positive effects occurred in the socio-economic environment. So that the most negative effect in the construction phase is related to the greenhouse gas production parameter, and the employment parameter has the most positive effect. The results of the Leopold matrix in the construction phase showed that the most negative effects belong to Physico-chemical and biological environments, and the most positive effects belong to the socio-economic environment. The parameters of adverse effects on traffic flow, noise pollution, dust production, greenhouse gas emission, and threat to animal habitats have the most negative effects, and employment has the most positive effects. The results of the RIAM matrix in the operation phase show the Physico-chemical, biological and socio-economic environments with the most negative effects and the cultural and socio-economic environments with the most positive effects. The parameters of greenhouse gas emission, soil pollution, dust production, unpleasant odors, increase in carriers, and population and migration have the largest share of negative effects and the parameters of health indicators and employment have the largest share of positive effects of this phase. The results of the Leopold matrix in the operation phase also show the Physico-chemical environment with the most negative effects and the biological environment with the most positive effects. The parameters of unpleasant odors, greenhouse gas emission, noise pollution, adverse effects on traffic flow, land use, and surrounding land acquisition costs have the most negative effects and the parameters of employment, soil erosion, and plant habitats have the most positive effects.The results obtained from both methods indicate that the design by making the necessary improvements, especially in the parameters that have received significant negative points in both methods (dust production, unpleasant odors, greenhouse gas emission, noise pollution, animal habitats) is applicable.

Most of the natural parameters considered in both methods have similar scores, but in some of the parameters, the results obtained from the two methods were different, (such as the parameters of plant habitats and soil erosion) or had significant differences (such as the parameters of plant habitats, soil erosion and health indicators in the operation phase). The reason for these differences and the inconsistency of the two methods can be explained by the fact that in the RIAM matrix the impact radius (importance of the effect), magnitude and intensity of the effect, stability, reversibility, and accumulation of the effect on the parameters are investigated but in Leopold matrix, only intensity and importance of the effect on the determined micro-activities on natural parameters is investigated. In other words, the Leopold matrix has a reasoned structure compared to the RIAM matrix due to the consideration of different micro-activities for each parameter, but the way the scores identified based on the parameters in the RIAM matrix are better than the Leopold matrix due to considering the stability, reversibility, and aggregation.

Metropolitans are increasingly facing the problem of air pollution due to the widespread presence of vehicles. Air pollution at the street level is a challenging issue of urban sustainable development. In addition to its sources of production, air pollution deals with a large number of factors such as urban morphology and ventilation, and urban wind. The latter can be considered as an important one since the long-term stability of air in an urban area can quickly stabilize pollutants and increase their volume in urban space. In addition, urban morphology can play a role in transfer pollution from one place to another by creating specified paths for wind.Thus, triple relationships are created between urban morphology, air flow and air pollution. Urban morphology as an independent variable directly affects the accumulation and dispersion of pollutants (as a dependent variable) and indirectly affects the air flow.In recent years, computational fluid dynamics (CFD) has been employed for assessment of a wide variety of variables and indices including: wind angle with respect to the street canyon, aspect ratio of the streets, the average height, different heights, street continuity ratio and street spatial closure ratio, neighborhood form (rectangular and square), length of the urban canyon, size of neighborhood, street architecture (roof configuration), degree of enclosure, plot ratio or floor area ratio (FAR).This study is intended to prove the existence or non-existence of a relationship between air quality (CO pollutants) and mineralization index in the neighborhood and open space index in the street canyon in Tehran (where the wind is perpendicular to the main street) with the help of CFD, which is known as a more reliable than statistical studies, due to better computational accuracy.-

The CFD simulations have been performed using Ansys Fluent. The validation of the all CFD settings (including mesh arrangement and turbulence model etc.) is based on experimental analysis (wind tunnel -reduced scale (. The case study is located in the residential areas of Tehran, Iran. The GIS software and satellite images have been applied to select the case study. The dimensions of the neighborhood are 300 m wide, 300 m long, and 16 m high. The street width equals 12m. In the models, tetrahedral meshing for the inner region and hexahedral meshing for the outer region have been used (Hybrid mesh). The aspect ratio equal to 1.1 in inner region and is 1.15 in the whole geometry. The number of cells in the F1, F2, F3, and F4 is 7.3, 7.4, 7.4 and 7.5 million cells respectively for the simulation of one half of the geometry.The turbulence is simulated using RANS models, which are formed based on the temporal averaging of parameters. Due to high speed, low computational cost and acceptable accuracy of RANS models, RANS equations have been used in this research. Among the RANS models, the Realizable k-epsilon turbulence model has been selected, which has achieved better in validation part. The model is three-dimensional, isothermal, steady, and incompressible. Carbon monoxide is considered as the pollutant which is injection from two lines source (with 5cm wide and 40cm high) along the main street. The pollutant emission modeling method is the species transfer model (mixed-species).- Discussion of

Based on the CFD output, the maximum velocity at the pedestrian height in F1, F2, F3and F4 respectively equals 4.27, 5.31, 5.31, and 5.35 (m/s), which has been created in the corners of windward blocks. In the other forms except for F1 (it lacks an East-West street), the maximum velocity is blown at the entrances of the streets which are parallel wind.By increasing the OS index in F1, F2, and F3 (0, 0.04, 0.27), the mean velocity at the main street increases (0.73, 0.75, 0.78), but in F4, where the index equals F3, we see a decrease in velocity (0.59) due to the difference in the shape and size of the open space in the neighborhood. The longer length of this space in F4 has minimized the canalization effect of the west-east street and consequently the wind velocity in the middle of the open space (where the main street passes).With the decrease of the MI index, the average velocity in the whole domain decreases. But F1 is exception. although it has the highest index, it also has the lowest velocity, which is due to the lack of East-the West street in this form.Based on the maximum mass fraction, F4 is the worst form (0.0136). After that, F1, F3, and F2 are in the next ranks in terms of CO mass fraction with 0.0116, 0.0104, and 0.0103, respectively. The concentration of pollutants in all forms can be seen in the vicinity of the leeward wall. In F1, the accumulation of pollutants is in the middle of the street, in F2, it is inclined to the intersection, in F3, it is inclined in the vicinity of the open space, and in F4, it is in the middle of the enclosed sections of the street. Considering the average mass fraction at the height of the pedestrian in the main street and comparing it between the forms, it should be said that the F3 has the best conditions. It is 10% less than F4, 20% less than F2, and 30% lower than F1.Based on the OS index, it can be said that with the increase of the index, the amount of pollutant in the main street decreases and there is a negative correlation between them. But in F4, due to the lower wind velocity, the amount of pollutants is slightly higher than the F3.The street roof (16 meters) in the F1, F2, F3, and F4 has the highest amount of pollutant respectively and their mass fraction average equals to 0.00066, 0.00052, 0.00041, and 0.00036. So, increasing in the OS index and decreasing in MI index (F1 to F4) cause a reduction in vertical ventilation (by the street roof) as well as an increase in horizontal ventilation (through lateral openings).The amount of CO mass fraction in the longitudinal profile in the sidewalk axis in the main street (near the western wall), in F1 at the beginning and end of the street is the minimum and in the center of the street, this amount has reached its maximum value of 0.0072. In F2, at the intersection of the East-West street and the main street, CO mass fraction is drastically reduced to zero. In the F3 and F4 at the open space, the amount of co is very small. Based on the graph and contour outputs, F1 has the worst form and F4 has the best form. The average mass fraction in F4, F3, and F2 is 56.52%, 65.22%, and 82.61% of F1, respectively.-

Findings show that in the forms in which wind direction is perpendicular to the street, ventilation is mostly done through the street's roof and by increasing the open space index and decreasing the mineralization index, the vertical ventilation decreases, and the horizontal ventilation via lateral openings increases. On the other hand, increasing the figures of the open space index, leads to a decrease in the amount of mass fraction at pedestrian height (2 meters) in the main street. Thus, a negative correlation is reported between them. In addition, also the results show a relationship between the increase in both wind velocity and ventilation rate with the decrease in the amount of CO, but the relationship could not be considered a direct relationship. The reason is that the ventilation is not only by horizontal movement of pollutants, but there are other vertical and turbulent flows too which causes ventilation. Finally, regarding the mineralization and open space indices, the third form is evaluated as the most suitable form, which should be considered in the future developments of Tehran.

Investigating the relationship between macroeconomic variables and variables of waste generation is of great importance in urban management planning with a waste disposal reduction approach. Therefore, in this study, using linear regression method and using data from 56 months (March 2014 to October 2018) related to five independent economic variables and four dependent variables of waste generation in Tehran, four models were developed. In this study, the statistical relationship between independent economic variables and dependent variables of waste generation at the level of 90% confidence has also been investigated. The results showed that 74% of the changes in the tonnage of construction and demolition waste using the economic variable of the price index of goods and consumer services and 69% of the changes of the tonnage of recycable waste separated at the source by the two economic variables of the price index of goods and services and US dollar price announced by Central bank can be described. On the other hand, by using the variable price of the Euro currency announced by the Central Bank, 35.6% of the changes in the tonnage of sweeping & green waste in Tehran can be predicted. It is worth noting that only 21.4% of the changes in the tonnage of municipal mixed waste can be described by the economic variable of the US dollar price in the market.

In this study, relevant data of independent economic variables and dependent waste generation variables during 56 months were analyzed. The five independent economic variables considered in this study were as follow: 1.consumer price index (X1), 2. US Dollar currency price announced by central bank of Iran (CBI) (X2), 3. Euro currency price, announced by CBI (X3), 4. US Dollar currency open market price (X4) and 5. Euro currency price, open market price (X5). Also, four dependent waste generation variables of Tehran included in this study were: amounts of mixed MSW transported to transfer stations (Y1), sweeping & green waste (Y2), and source separated recyclable waste (Y3) and construction and demolition waste tonnage (Y4) collected from Tehran. Then, Shapiro-Wilk test was used to check the normality of data distribution received from Tehran waste management organization (TWMO). According to the results of similar studies in the field of the prediction of MSW generation regarding to different variables impacts, the backward removal method and the following equation used to develop a multiple linear regression model:Yi=β0+β1X1+β2X2+β3X3+β4X4+β5X5+ϵWhere Yi is the dependent variable, β0 is the intercept, X1 to X7 are independent variables, β1 to β7 are regression parameter and ϵ is residuals. On the other hand, by examining the dependent variables data over time using R software, it was determined that the data may also have a significant relationship with time. So the modified model is presented as follows:Yi=β0+β1X1+β2X2+β3X3+β4X4+β5X5+β6T1+β7T2+ϵWhere T1 is auxiliary variable of time and T2 is square of T1.

Regard to the outputs of R and SPSS software, it was found that the data of dependent variables considered in this study follow the normal distribution. Then, using the backward elimination method in multiple linear regression, the developed model for each of the dependent variables was presented as follows:MAE MARE RMSE R2 Adjusted Developed models Dependentvariables156.5 0.023 259.2 0.214 Y1=641.560-2.883 × 10-18 X 44 + 11.473 T1 Y151.3 0.056 85.5 0.356 Y2=-743.398 + 0.093X3 -1.35× e-6 X34 Y236.3 0.033 56.5 0.69 Y3 =1647.268 – 0.0004X1X2 + 32.554T1 Y30.058 0.0053 0.084 0.74 Ln(Y4) = 7.181 + 0.060X1 + 0.00015X12 – 0.001T2 Ln(Y4).

In this study, linear regression method and 56 months data (April 2014 to November 2016) related to five independent economic variables 1- Price Consumer Price Index 2- US dollar price announced by the Central Bank 3- US dollar price in the market 4- Euro currency price announced by the Central Bank and 5- Euro currency price in the market and four dependent variables of waste production 1- Tonnage of mixed urban waste 2- Tonnage of sweeping & green waste 3- Tonnage of recycable waste separated at souce and 4- Tonnage of construction and demolition waste in Tehran were used to fit four new models. Based on the results, 74% of the changes in the tonnage of construction and demolition waste in Tehran can be described using the economic variable of Consumer Price Index. The low values of error criteria in this regard indicate the high power of this model in predicting changes in the dependent variable of tonnage of construction and demolition waste in Tehran using the values of the independent economic variable Consumer Price Index. 69% of the changes in the production tonnage of recycable waste separated at source can also be described by the economic variables of Consumer Price Index and the price of the US dollar announced by the Central Bank. Also, using the Euro currency price variable announced by the Central Bank, 35.6% of the changes in the tonnage of sweeping & green waste production in Tehran can be predicted. However, only 21.4% of the changes in the tonnage of municipal mixed waste production can be described by the economic variable of the US dollar price in the market.Also, at the 90% confidence level, the two response variables of municipal mixed waste tonnage transported to intermediate transfer stations (Y1) and recycable waste tonnage separated at source (Y3) have shown a statistically significant relationship with time (T1). On the other hand, the response variable of sweeping & green waste tonnage in Tehran entering Aradkooh disposal site (Y2) have shown a statistically significant relationship with the independent variable price of Euro announced by the Central Bank (X3) .also the response variable of construction and demolition waste tonnage (LnY4) have shown a statistically significant relationship with time variable T2 and independent economic variable of Consumer Price Index (X1) at the 90% confidence level.

In recent years, dust storms have been increased health problems in Abadan and Khorramshahr. The purpose of this study was to investigate the origin of dust storms in Southwestern Iran from December 2018 to January 2020 using bio-aerosols and studied the effects of environmental parameters on bacterial concentrations by sampling soil of Hoor-Al-Azim and Shadegan wetlands as probable sources. A sampling of bio-aerosols and particulate matters was performed using Quick take30 sampler and environmental particle meter AEROCET531S, respectively. The images of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and HYSPLIT model tracked the dust mass entering the air of Abadan and Khorramshahr. After the cultivation and isolation of bacteria from soil and air samples, their identification was conducted by the 16S rRNA gene sequencing method. Based on the results, Bacillus zhangzhouensis, Bacillus aerius, Bacillus subtilis, Paenibacillus, Bacillus mojavensis, Lysinibacillus macrolides were common bacteria identified in both the soil of Hoor-Al-Azim and Shadegan wetlands and the air of Abadan and Khorramshahr. Dust storms with domestic origins had more affect than foreign dust origin on bacteria concentration of Abadan and Khorramshahr. These results showed that dust storms could play an essential role in transmitting bacteria from their sources to remote locations. Bacillus bacteria (36genus) was known as the most common bacteria in Abadan and Khorramshahr air on dusty and non-dusty days due to their gram-positive (92%) and sporulating properties.

One of the phenomena that have caused air pollution in recent years and have had adverse environmental and health consequences is the dust storm. Iran is affected continuously by local dust systems due to its geographical location and location on arid and semi-arid belts. Most of Iran's dust activity comes from high-pressure intrusions from southern Iraq and northern Saudi Arabia. Drought, reduced rainfall, and relative humidity have caused to dry up some wetlands, lakes, and deserts in Iraq and Syria, which are strongly correlated with dust production areas. One of the essential functions of a wetland is to prevent dust storms. Vast volumes of dust from dry land and deserts carry biological agents at great distances. Dust storms increase the concentration of (PM 2.5, PM10) and opportunistic pathogens on a large scale, thus affecting the population and downstream ecosystems of the dust stream and increasing a wide range of diseases. Bio-aerosols are airborne particles containing bacteria, fungi, viruses, protozoa, algae, plant pollen and microorganisms that originate from natural and artificial sources. Their natural source; Soil, lakes, oceans, animals, humans (sneezing, coughing, and other activities), are plants and dust particles that absorb bio-aerosol on their surfaces. Several artificial sources that originate from bio-aerosols include wastewater treatment, fermentation processes, and agricultural activities that disperse the soil. Studies in Iran on bio-aerosols have been primarily on indoor environments and based on morphological methods. Few studies in outdoor environments, mainly wetlands, have used molecular approaches to study bio-aerosols. Our studies, for the first time, using molecular techniques show the similarity between the bacteria in the soil of Hoor-Al-Azim and Shadegan wetlands with the bacteria in the air of Abadan and Khorramshahr. A variety of approaches for dust storm monitoring have been proposed and evaluated. Remote sensing, compared with other procedures, is becoming one of the most popular methods to detect dust storms at large scales due to its ability of efficient global coverage. Sensors installed on satellites detect different types of Earth's surface radiation that are effective in monitoring, and identifying the origin of dust, obtaining the required parameters for dust modeling and obtaining quantitative dust-related relationships such as optical depth particle size. Therefore, in this study, remote sensing was used to determine the source of dust. Also, the HYSPLIT model was used to identify the origin and trace the entry of dust into the air of Abadan and Khorramshahr.

2.1 Detection of Abadan and Khorramshahr Air DustIn the present study, satellite information, Khuzestan Environment Department, and Abadan Meteorological Station were used to determine the dust days of Abadan and Khorramshahr. Daily Images of the Terra and Aqua satellites were downloaded from http://ladsweb.modaps.eosdis.nasa.gov/search and reviewed with classic ENVI software and MCTK plugin used for pre-processing (geometric correction, radiometric, atmospheric) images. 2-2. Sampling stations and sample collectionSampling of surface soil carried out in Hoor Al-Azim (31º33ʹ44ʺN, 47º39ʹ38ʺE) and Shadegan (30º38ʹ58ʺN, 48º39ʹ52ʺE) wetland randomly. The sampling sites were selected to measure airborne particulate matter, bacteria, moisture, temperature, and ultraviolet radiation under USEPA standards. According to these standards, the Abadan College of Medical Sciences (ACMS), Khorramshahr Fire Department (KFD), Khorramshahr Fisheries and Aquatic Office (KFAO), Farzanegan School Abadan (FSA) and Eight Station (ES) were selected as sampling sites for ten days.2.3. Morphological and microscopic identificationBacteria isolated from the surface soil of Hoor AL-Azim and Shadegan Wetlands were serially diluted. Samples collected from the air of the study area were incubated on the Nutrient agar medium for 24 to 72 h. Different colonies grew on the nutrient agar medium. Bacterial concentrations were also evaluated according to Colony (CFU/m3) and morphological characteristics. Gram staining was used for the microscopic study of the desired isolates.2.4. DNA ExtractionThe phenol/chloroform method was used as the DNA extraction method in this study.

3.1. Results of the MODIS Satellite ImagesAccording to the data obtained from satellite information, the General Department of Environment of Khuzestan Province and Abadan Meteorological Station dates of 2019/5/8, 2019/5/19, and 2019/6/13 were identified as dusty days. The results of dust detection by MODIS image showed that the BTD (23-31)>5.5 threshold had a high ability to detect dust, compared to other thresholds used. Therefore, the BTD index was capable of detecting dust, but varied from image to image due to differences in cloud properties, reflecting surface, changes in dust mass characteristics (height and mineral structure particle). The results of HYSPLIT model showed that the air masses originated from Syria and Iraq (on the day with the northern wind), Saudi Arabia (on the day with the southeastern wind), and Syria (on the day northwest wind). The results obtained from the present study showed that the images of MODIS satellite and HYSPLIT model can complement to each other and are very suitable to tracking the movement of dust mass entering the aquatic and terrestrial ecosystems and the bacteria transmitted with them.3.2 Comparison of sampling stationsThe results showed that the highest mean concentration 127.94 CFU/m3 of airborne bacteria was observed in (ACMS) station and lowest mean concentration 30.98 CFU/m3 of airborne bacteria was observed in (FSA) station. According to the result of ANOVA, there was a significant difference between the mean of stations (p-value <0.05). The significance level of ANOVA is less than 0.05 and indicates differences between groups. The results of T-test analysis showed that there was a significant difference between bacterial concentration in the (KFD) compared to the two (ES) and (ACMS) (p-value <0.05). The average (KFD) was significantly lower than the two groups of (ES) and (ACMS). The mean bacterial density in (KFAO) was significantly lower than the average bacterial concentration in the two stations of (ES) and (ACMS) (p-value<0.05). The mean bacterial concentration in (FSA) was significantly lower than the average bacterial concentration in the Eight station (ES) and (ACMS) (P<0.05). In summary, the results showed that the mean bacterial concentration in the two (ACMS) and (ES) was no difference, but was significantly higher than the mean of the other three stations. Several factors contributed to the increase of bacterial concentration in the (ACMS) Abadan College of Medical Sciences. The occurrence of local dust at the sampling time may be the most critical factor in increasing bacterial concentration at the station. Also, the (ACMS) is one of the educational sites, and due to its proximity to Abadan International Airport, it resulted in increased bacterial concentration at the station compared to other stations

Wetlands and gulfs are peculiar ecosystems comprising of both land and water habitats. This, gives rise to a rich biological diversity which seldom occur elsewhere on the planet. Unhappily, must of our wetlands countrywide have undergone massive negative changes during the recent years losing much of their surface and depth. These changes have been the result of multiple causes such as climate changes, decreased rainfall in line with increased evaporation, human activities such as dam construction. Our coastal wetlands have been affected by sea water level changes. Gorgan Gulf is the sole gulf in Iran that has been located in southern part of the Caspean Sea. It has been registered as biosphere reserve in Ramsar convention. The only permanent way that connects Gorgan Gulf to Caspian water is Ashoursdeh-Bandar Torkaman; a way with approximate width of 2500 meters and depth of at most 3.3 meters when the Caspian water level rises in northeast parts of the Gulf. Caspian water level may vary as a result of one or a combination of multiple factors such as: climate change, tectonic processes and human activities. The effect of each one is not the same as the other factors. Needless to say, that any alteration in Caspian level shall exert direct effect on Anzali wetland and river systems resulting in manipulation of morphology and animal life and even economy of the coastal areas. Furthermore, since wetlands are usually affected by seas and rivers, any change in sea and river dynamics shall definitely affect . In this study, different layers of Gorgan Gulf current pattern for one year with inclusion of Caspian level changes using Mike 21 has been simulated and analyzed.

Mike 21 hydrodynamic module was used for the purpose of studying Caspian current pattern. This module is the most basic model of Mike 21, and other modules of this model are dependent on the outcome of this module. This module is able to simulate water level changes and currents in two dimensions; therefore, it has the capability to study full details of a current in different spots. This model is helpful in studying the water level and current patterns of seas, rivers, gulfs and coastal areas which are affected by wind and tides. This module is also used to simulate combinational effects of such phenomena. It can model inconstant currents with inclusion of bed changes, mass changes and tide changes.

This model is able to solve the three-dimensional incompressible Navier-Stokes equations with the inclusion of Boussinesq approximation and hydrostatic pressure, using unstructured mesh. Unstructured meshes are better than rectangular structured meshes for the purpose of covering complicated borders such as coastal lines and islands borders. Horizontal unstructured mesh is a collection of 20 layers, perpendicular in direction, for sigma and z level system which uses 10 sigma layers for the distance between surface level up to -40 m, and 10 sigma layers from -40 to sea bed and 10 layers with fixed thickness (z level) 4-150. We use finite volume as our numerical methodology to solve equations. This study covers the whole Caspian Sea. We use unstructured mesh to simulate currents. Mesh dimensions vary from 0.25 degrees in northern part to 0.01 degree in some spots of southern part. To layer the model vertically, sigma and z level system was applied. In the implemented current model, time step was fixed between 0.01 to 60 seconds. Results demonstrate that Caspian currents are often counter-clockwise, which had previously been reported by some researchers as well. Western current is north-south and southern current (parallel to coast) is east-west, and eastern current is south-north. Current rate starts to fade in bottom layers, and current direction tends towards right due to Ekman transport. It can be seen in all seasons of the year. Furthermore, a current when is parallel to coast may be affected much more from topography. Sometimes currents may be deviated towards deep water as a result of being affected by bed. This can be observed in bottom layer more than elsewhere. Results indicated that overall Caspian current in western border more intensified in autumns, while weaker in eastern border. However, in winters, big storms take place and overall current becomes intensified in southern part of the Caspian Sea. In summers, which weather is often calm, currents are weak in central part of Caspian Sea, yet intensified in eastern border. It is evident that in Gorgan Gulf and surrounding area, where coast has low slop, wind pattern and topography of currents is weak and current rate begins to slow down in central and western parts. In southeast part of Caspian Sea, current rate and current direction reach to the least levels possible in various layers as a result of low depth and weak rate of currents. In this part, current layering cannot be observed. Effect of Water Level on Surface Area of Gorgan Gulf To study the effect of water level changes to Gorgan Gulf morphology, we study the effect of past year decrease of water level on Gorgan Gulf. We calculated Gorgan Gulf surface area for various years along with water levels. We found that water level has a nearly 15 cm of decrease; therefore we assumed a trend of 5 cm decrease in water level for Gorgan Gulf for the future. We extracted Caspian water level from the Anzali station records available. Given the low depth of Gorgan Gulf as well as rather low slope in most parts of the Gulf, it can be anticipated that if water level continues to decrease, most part of gulf area and particularly gulf inlet (Chapgholi channel and Ashouradeh channel which connect Gorgan Gulf to Caspian Sea) shall undergo remarkable morphological alterations.

Generally, it can be suggested that the current has had a similar pattern during the first three months (Gregorian calendar) which occurs when current rate has the utmost rate in western spots of middle Caspian, yet the current has less intensity in western part. In these three seasons, current rate is very low and near to zero in various layers surrounding Gorgan Gulf. In June, July and August, it is observed that current starts to dominate in a direction parallel to the coast of Gorgan Gulf. This domination is evident in cold months as well. However, its intensity starts to fade when warm months approach. It is noteworthy this occurs in July as a result of both the coastal-parallel current and the effect of north-east current in eastern border. Results indicate that the current in eastern border as well as the current risen parallel to southern coast have immense effect on current pattern of Gorgan Gulf. When the direction of the eastern border current is towards south-north, the intensity of the current inside the gulf starts to decrease, but when this direction is towards north-south, the current starts to dominate the gulf evidently. Also, when the current that is risen parallel to southern coast has noticeable intensity, part of this current penetrates to the gulf causing the gulf water to circulate. The coincidence of the eastern border current in north-south direction and the current risen parallel to southern cost exerts the highest effect on water circulation in Gorgan Gulf giving rise to the highest current rate in the gulf.