mohammadreza dalalian

In addition to natural disasters such as floods, which cause great damage to the environment and human societies, the phenomenon of dust also causes irreparable damage to urban environments, transportation systems, respiratory systems, etc. Identifying Potential Dust Source Areas is considered the first step to control and prevent this phenomenon. Various researches have been conducted to identify dust sources, but most studies have relied on small-scale images. This research aims to use medium-scale satellite images to identify local areas prone to dust production. Google Earth Engine was used to analyze factors influencing dust generation, including slope, Digital Elevation Model (DEM), land use, soil salinity, Normalized Difference Vegetation Index (NDVI), soil moisture, wind speed, precipitation, and Land Surface Temperature (LST). Analytical Hierarchy Process (AHP) was used to assign weights to these elements. The resulting weights were: soil moisture (0.264), NDVI (0.208), wind speed (0.153), precipitation (0.107), land use/soil salinity (0.081), LST (0.064), DEM (0.024), and slope (0.020). The inconsistency index (0.015) indicated a high degree of consistency between the assigned weights, which is below the acceptable threshold (1). Due to the lack of ground-based air quality measurements, the Aerosol Optical Depth product (a satellite-derived measurement of airborne particles) was used to validate the resulting dust source maps. The final map showed that the potential for dust generation decreased closer to the heights of Sahand and increased closer to the lake.

Soil contamination due to heavy metals is a global environmental issue. One vital aspect for understanding the impact of a contaminant in porous media is to describe their transport behavior using appropriate models. The governing equations for solute transport in soil consist of the convection–dispersion equation (CDE) and the mobile–immobile model (MIM). Mathematical models are usually used to evaluate solute transport in porous media. The first model used to express the transport of solutes and pollutants in porous media is CDE it provides acceptable and satisfactory results in homogeneous soils in laboratory tests. Hydrus-1D is a modeling environment for simulating water, heat, and solute movement in one-dimensional variably saturated media. Sensitivity analyses and model identification are standard approaches in modeling applications to investigate the relative importance of model components that control the system’s behavior. The sensitivity analysis is applied to identify the parameters that influence the model performance most. The sensitivity analysis is defined as the rate of variation in the model outputs due to changes in the input parameters. This study is a fundamental practice for analyzing the behavior of a model under different conditions of an application. The sensitivity analysis could be a practical and powerful tool for investigating the role and importance of model components, such as parameters and forcing data on the model responses.

The loamy soil samples were collected in both disturbed and undisturbed forms from a farm in the Qaramalek area with appropriate humidity located in western Tabriz, Iran, at 38º 5' 59.89'' north and 45º 12' 38.57'' east. To determine and present breakthrough curves, concentration values are required throughout the laboratory columns at different times. To simulate the CDE model, Hydrus software was used. Solute transport parameters such as diffusion coefficient (D), distribution coefficient (Kd), and dispersion coefficient (β) were estimated using soil hydraulic parameters and data related to the metal concentration of cadmium, nickel, and zinc by an inverse modeling method. A sensitivity analysis was carried out for the identification of the most influential factors on the model output. This method examines the impact of input data on a given model and its actual conditions. In line with this purpose, in each run, one input data was changed to a value equal to Positive and negative five to 15%, and the other input data was kept constant. To identify the effect of the input parameters of a given model on its output, the sensitivity analysis for the Hydrus model was utilized. The parameters of hydrodynamic dispersion coefficient (D), distribution coefficient (Kd), and spreading parameter (β) were changed between five to 15 %. Sensitivity analysis was carried out on cadmium, nickel, and zinc metals with densities equal to 50, 100, and 150 mg.l-1 in two disturbed and undisturbed soils.

Examining the breakthrough curves of cadmium in disturbed and undisturbed soils shows that the fitted curves using the Hydrus model and the measured curve almost coincide with each other, which is more obvious in disturbed soils. It should be noted that the model fits better in the disturbed soil than in the undisturbed soil. This may be due to the disruption of the structure the increase in the contact surface of the particles in the disturbed soil and the presence of heterogeneity in the undisturbed soil column. The simulation results show the transport of heavy metals (Zn, Ni, Cd) and Hydrus output have the highest and the lowest sensitivity to dispersion coefficient β and diffusion coefficient (D), respectively. In general, the impact of input parameters can be reported as follows: spreading parameter (β) > distribution coefficient (Kd) > dispersion coefficient (D). Therefore, it can be observed that D has a negligible effect on the model results; and consequently, measurement errors can be ignored.

Sensitivity analysis is used to analyze model behavior under different conditions. This analysis is used to investigate the relative importance of model components that control the system’s behavior. In this research, the transfer of hydraulic parameters of heavy metals Cd, Ni, and Zn in disturbed and undisturbed loam soil columns with initial concentrations of 50, 100, and 150 mg.l-1 was performed under the simulation of the Hydrus-1D model. The comparison of the simulated BTCs of the Hydrus-1D model and the measured data indicates a high agreement between the simulation curves and the measured data. Solute transport parameters such as hydrodynamic dispersion coefficient (D), distribution coefficient (Kd), and spreading parameter (β) were estimated using soil hydraulic parameters and data related to Cd, Ni, and Zn metal concentration by inverse modeling method. Based on the results of sensitivity analysis, the spreading parameter (β) and hydrodynamic dispersion coefficient (D) had the highest and lowest sensitivity, respectively. In other words, due to the significant effect of β changes on the output values of the model, this parameter should be measured more accurately and on the other hand, the measurement errors of parameter D can be ignored. The degree of sensitivity of the parameters was independent of the initial concentration of the elements.

The contamination of agricultural lands, especially orchard soils with heavy metals, caused by long-term agricultural practices (overuse of fertilizers and pesticides), has become a high-priority topic for soil, food, and human health. This research was conducted with the aim of investigating the pollution and ecological risk of heavy metals in different types of soil in the apple orchards of Urmia Plain. The studied soils were classified in the Incepitsols order and the subgroups: Typic Haploxerepts (TH), Typic Endoaquepts (TE), Typic Calcixerepts (TC), Fluventic Haploxerepts (FH) and Aquic Calcixerepts (AC). The Nemerow Pollution Index (PIN) and Ecological Risk (ER) were calculated. Most heavy metals had the highest concentration in the Ap horizon. The results showed that the concentration of all studied heavy metals are lower than the permissible limit of pollution. The highest average concentration of the Zn and Cu were in the TH soil (73.1 and 21.87 mg kg-1, respectively). The difference of the concentration of Pb was significant only in the FH and there was no significant difference in other soil types. The highest average concentration of the Ni and Cd was observed in the TH soil (30.86 and 1.19 mg kg-1, respectively) and the lowest in the TE soil (25.96 and 0.8 mg kg-1, respectively). The PIN order of the studied heavy metals was as Cd>Zn>Pb>Cu>Ni, showed that the risk of Cd for the soil contamination was higher than other metals. Multivariate analysis showed that the main sources of Mn and Cu are from natural processes, while Cd, Pb and Ni were derived from the both natural and anthropogenic factors.

Contamination of soil, water, and air with heavy metals is an environmental issue and of particular importance due to its toxicity, persistence, and mobility in soil. For a better understanding of the environmental behavior and risks associated with heavy metals, modeling their fate and transport is prioritized as an efficient tool. In the present research, the transport of the heavy metals cadmium (Cd), nickel (Ni), and zinc (Zn) in two disturbed and undisturbed loamy soils with the initial concentrations of 50, 100, and 150 mg/L was simulated using CXTFIT and Hydrus-1D as analytical and numerical models, respectively. The results showed that both the models can simulate the transport of heavy metals and describe the BreakThrough Curves (BTCs) with a coefficient of determination (r2) of higher than 0.9 and a root mean square error (RMSE) of less than 0.06. However, the analytical CXTFIT model showed a better fit to the BTCs compared to the numerical Hydrus model. Also, the models showed better efficiency in the disturbed soil than undisturbed soil. With the increase in the concentration of all the three heavy metals, the retardation factor (R) decreased and indicated a trend as Zn>Ni>Cd. The hydrodynamic dispersion coefficient (D) in the undisturbed soil was estimated to be higher than in the disturbed soil and followed the trend of Cd≥Zn≥Ni. The evaluation of the results showed that despite the better performance of the CXTFIT model, there is no major difference between the two models.

Evaluating and determining the minimum data set to evaluate soil quality is very important, valuable in terms of cost, and time save. The aims of this study were to assess the quality index of soils under successive, and long-term cultivation of wheat by a valid model (Integrated quality index= IQI), determine the minimum data set to evaluate the quality of soils in this region, and evaluate the relationship between soil quality index and wheat yield components. For these purposes, 18 physical, chemical, and fertility properties of the surface soil belonging to 40 soil profiles were determined in the croplands from Piranshahr region of West Azerbaijan province. The soils were classified in the three order of Mollisols, Inceptisols, and Vertisols. Total variables affecting soil quality (TDS) were determined, and minimum effective data (MDS) were estimated using principal component analysis (PCA). The soil quality index was divided into five classes according to the growth of the plant using the interpolation technique. Based on the results of PCA, five properties of organic carbon, clay, sodium adsorption ratio, calcium carbonate equivalent and silt had a specific value greater than one and were selected as MDS indicators. The highest values of IQITDS, and IQIMDS were observed in the Inceptisols, followed by Mollisols and Vertisols, respectively. A significant relationship (p <0.05) was found between wheat yield components (grain yield, and biological yield of wheat) with IQITDS and IQIMDS models, which showed a positive, and significant effect of soil quality index on wheat yield components. Moreover, the IQITDS model was more accurate than the IQIMDS model in predicting the performance of wheat components due to its higher R2. However, due to the positive, and significant correlation between IQITDS and IQIMDS (R2 =0.9(, the IQIMDS model is an economic and better model for evaluating the quality of soils in the region.

The chemical analysis of dust particles is essential to assess the potential impacts of dust on climate, environment, soil, and health. The objective of this study is to compare the different chemical compositions of dust particles that are collected at different heights above the soil surface and the eroded soils around Lake Urmia. To trap the dust particles, the BSNE samplers were used. 14 poles were installed inside 3 ha. area and 4 samplers were installed on each pole at 0.15, 0.5, 1, and 2 m heights above the soil surface. Chemical properties such as %T.N.V, %OC, ECe, pH, and SAR of collected particles were determined. The results of variance analysis and mean comparison illustrated that there was no significant difference between the eroded soil and the particles sampled from 15 cm height among all the investigated chemical parameters. It proves that the source of the moving particles at > 0.15 m is different from the eroded soil. By elevating height above the soil surface increased, the %T.N.V, ECe, and pH decreased but the SAR and %OC increased. There was a strong negative and significant correlation between the monthly rainfall and the ECe, %T.N.V, and SAR, except for the %OC. The correlation between the speed of the strongest wind and the ECe, %OC, and SAR was positive and remarkable (P≤0.01). The pH was the only parameter that was independent of all meteorological parameters in this research. Furthermore, the SAR was the most sensitive factor to the meteorological parameters.

This study was conducted to evaluate the interaction effects of potassium nitrate and arbuscular mycorrhiza on the growth and quantitative characteristics of G. glabra in the greenhouse condition.

Methods & Materials:

 In order to evaluate the effects of KNO3 and Arbuscular mycorrhiza (Glomus mossea) on the growth and quantitative characters of G. glabra an experiment as factorial based on the completely randomized design with three replications was carried out. The mycorrhiza treatments included: noninoculated (Control), 25, 50 and 100 g per pot and potassium nitrate had three levels: 0 (control), 10, 20 mmol L-1. Some characters such as plant height stem diameter, leaf area, foliage and root fresh and dry weight, leaf relative water content, chlorophyll a and b and carotenoid content were assessed.

Based on the results of this study the interaction of potassium nitrate and mycorrhiza affected the investigated characters and treatment combination of 20 mM potassium nitrate with 100 g per pot mycorrhiza increased plant height (67%), stem diameter (48.5%), foliage fresh weight (48.7%) foliage dry weight (54.9%), root fresh (69.1%) and root dry weight (64%) in relation to control. Also leaf area (41%) leaf relative water content (6.25%) chlorophyll a (28%), b (38%) and carotenoid (41%) contents increased significantly in comparison with control.

Simultaneous application of potassium nitrate and mycorrhiza, significantly affected the all of evaluated characters and improved all of them.

Plants that can accumulate metals to exceptionally high concentrations in their shoots are so-called hyper-accumulators. To further quantify potential interactions between Ni and Cu, the Alyssuem murale grown in soils with factorial additions of NiSO4·6 H2O (0, 50, 250, 500 and 750 mg kg-1 Ni = Ni-T0, 50, 250 and 750) and/or CuSO4·H2O (0, 50, 250 and 500 mg kg-1 Cu = Cu-T0, 50, 250 and 500) salts were investigated. The experiments were carried out in pots in a greenhouse under controlled temperature, light conditions and ambient humidity. The test plants for biomass production were harvested three times; 30, 60 and 100 days after germination. Ni and Cu concentrations in the digests were determined by flame atomic absorption. The results showed that by each different levels of Ni, the maximum amount of absorbed Ni was achieved at 50 mg kg-1 Cu concentration. Also, with elevation of Cu concentration, Ni uptake decreased. These results indicated that the Ni-T750   and Cu-T50 at the third time period had the maximum average of 1585 µg kg-1 and was significantly different from the other treatments. The statistical analysis indicated that by the increased Ni levels from zero to 50 mg kg-1 in soil, the performance of the plant dry matter was significantly declined at the all Cu levels. In addition, by the Cu-T50   in the soil, the dry matter amount at the all Ni levels were higher than that of Cu-T0, although the differences were not significant (p < 0.05).

Landslide is one of the natural hazards that lead to a lot of human and financial losses. Researchers on the subject of landslide susceptibility are investigating the possibility of landslides with respect to topographic and geo-environmental conditions, and the obtained information is critical in landslide risk management Preparation of landslide sensitive points is an essential tool for assessing landslide risk and is very useful in better planning and management of these areas. In this research, models based on artificial intelligence and two statistical variables in determining landslide sensitive points in West Azerbaijan province have been studied and compared.

Methods based on artificial intelligence and two statistical variables were used to prepare landslide-sensitive points in the province of West Azerbaijan, which is located in northwestern Iran. This study was conducted in four stages. The first stage: the study of landslides in the studied region based on the database of the Forests, Rangelands and Watershed Organization of Iran (FRWO) and the identification of 110 landslides through field surveys, interpretation of aerial photographs and Google Earth satellite images, the second stage: data collecting and creating a spatial databases of effective factors, the third stage: applying the Frequency Ratio (FR), Shannon Entropy (SE), Bagging (BA), Random Forest (RF) and hybrid model (RF-BA) and stage four: methods validating using the system performance curve (ROC). Based on field surveys and similar studies, 12 factors affecting landslide occurrence including altitude, slope angle, slope direction, distance from fault, distance from river, distance from road, drainage density, road density, rainfall, soil, land use and lithology were identified. In the field survey, 110 landslides were identified in West Azerbaijan. 70 percent of the data were randomly selected and used for modeling and 30 percent of the data were used for validation.

In terms of geographical directions, the southern direction with a weight of 1.49 had the greatest impact on the occurrence of landslides in the province. The least weight was related to flat areas where no landslide occurred. The results of slope factor showed that the middle slopes had the greatest effect on the occurrence of landslides, so that in low slopes due to low gravity, less landslides occur and too much slopes were related to mountainous areas that were covered with rocks and there was very thin soil that is not suitable for landslide. The study of land use factor showed that 48 percent of landslides occured in agricultural areas. The results showed that most of the landslides occurred near rivers and faults. Also, in some areas, the closest distances to the road had the greatest risk of landslide

The results of this study showed that the artificial intelligence models (RF and the combined model RF-BA) had the higher efficiency than the statistical models (FR and SE). The accuracy of the combined models was higher than the single models. The ROC curve results showed the accuracy of 0.92, 0.91, 0.89 and 0.88 with RF-BA, RF, FR and SE models, respectively.

The main reason for soil loss in arid and semiarid regions is wind erosion. About 40 percent of lands have potential to wind erosion. In Iran due to the drying of a large area of the Lake Urmia and creating the salt marshes and abundant salt plains, it has recently been one of the main problem in this region. Due to the location of this lake, it will become one of the main sources of dust and wind erosion in northwestern of Iran. Determination of the wind erosion flux and its components will be useful for choosing appropriate solutions for this problem. Reliable field measurement of the amount of soil loss will not be possible if there is no satisfactory equipment. Several wind erosion samplers have been developed so far. But the most recommendable one is the BSNE (Big Spring Number Eight) sampler that was made and used by Donald Fryrear to gather the suspended particles.

In order to trap the suspension and saltation particles from the wind erosion, 14 poles with four BSNE samplers installed on each of them at height of 0.15, 0.5, 1 and 2 m above the soil surface were installed in a circular pattern with a 100 m radius on a land area of 3.14 ha. Also, in order to trap the creeping particles, 14 sediment traps which the opening of them was in the same level with ground were installed at a radial distance of 20 cm from each BSNE pole. For 6 months (from March - August 2017) the eroded particles were collected and weighed. Then the suspension (Isus), saltation (Isal) and creeping (Ic) fluxes were calculated.Meteorological information was obtained from Khosroshahr Weather Agency during the research period, which was at the closest distance from the study area. In order to investigate the effect of sampling time on suspension, saltation and creeping fluxes, a completely randomized experiment was designed with three replications. Duncan's test was used to compare the means. Statistical analysis was performed by SPSS software and charts drawn by EXCEL software. In order to determine the vertical profile of the suspension flux, the data obtained from the BSNE samplers at four heights (0.15, 0.5, 1 and 2 m ) was used.

Findings of this study showed that the effect of sampling time was significant on Ic, Isal and Isus (p≤0.01) and on total flux (It)(p≤0.05). The results of mean comparing showed that the highest of It was obtained in March and July (2.091 and 2.0153 ton.h-1.mon-1, respectively) due to the high speed of strongest wind (16 and 18 m.s-1, respectively) and lack of plant cover in March and dry soil surface in July (Monthly rainfall was zero). The lowest of It was obtained in April (0.1007 ton.h-1.mon-1) due to high monthly rainfall (78.3 mm) and high moisture of soil surface. Totally, the wind erosion in the study area was 11.028 ton.h-1.y-1. The Isus decreased with increasing the height of soil surface and the power equation was the best one to explain these profile.

There was a negative correlation between Ic (wind erosion section), Isus and It fluxes with monthly rainfall and positive correlation with speed of strongest wind. The mean It was maximum in March and July and minimum in April. The results showed that a large part of the particles were moving at a height of less than 15 cm above the soil surface.

Destruction of soil structure and reduction of soil organic matter are major problems of cultivated soils which result from improper tillage operations, excessive consumption of chemical fertilizers and low consumption of organic and green fertilizers. One method for maintaining sustainable agriculture is to add organic and inorganic amenders. By producing resistant aggregates, organic matters improve soil structure and enhance soil permeability, FC moisture and water availability capacity. Furthermore, through enhancing organisms’ activities, especially earthworms, organic matters improve soil hydraulic conductivity and reduce bulk density. Organic matters may be added to soil through different way, however, the effect of each one on the soil’s physical properties is different. Chicken feather (CF) is readily available through henhouses and slaughterhouses, however, significant amounts of CF are destroyed by burning and burying them. Potassium Humate (PH) is a potassium salt from humic acid. Humic acid is extracted from various natural sources such as humus, peat, lignite and coal. Vermicompost (VC) is a compost which is produced by a non-thermal process. The impact of CF on different soil properties has not been studied yet. Accordingly, we investigated the impact of adding differing weight percentages of three types of amenders (PH, CF and VC) on the physical properties of soil under wheat cultivation at different moisture levels.

The experiment was conducted in factorial form based on randomized complete block design with 27 treatments in three replications. The first factor included the above-mentioned amenders; the second factor included three weight levels of these amenders (0%, 2.5% and 5%); the third factor included three moisture levels (0.5FC, 0.7FC and 0.9FC). The amenders were uniformly mixed with the soil up to the depth of 10 cm; then, wheat seeds were planted and moisture treatments were carried out during the growth period (from late April 2016 to September 2016). The soil moisture of the plots was controlled during the experiment period using the gravimetric method. For investigating the changes in the soil’s physical properties, samples (disturbed and undisturbed) were taken from the plots before and after the experiment. The following physical parameters were measured: bulk density (BD), soil moisture in field capacity (FC), permanent wilting point (PWP), wet aggregate stability (WAS), saturated hydraulic conductivity (KS), penetration resistance (PR), retention curve slope at inflection point (Si), mean weight diameter of aggregates (MWD) and mass-size fractal dimension of aggregates (Dm). Statistical analysis was done by SPSS software and means were compared via Duncan test. Tables and graphs were generated by Excel software.

Variance analysis and means comparison indicated that using amenders reduced bulk density for 89%. Reduced bulk density was caused by high keratin (91%) in CF, high porosity and the production of coarse pores in soil. On the other hand, VC with many pores led to increased aggregation and reduced bulk density.
Results revealed that consuming CF increased soil moisture to field capacity (FC) (87%). CF had more significant impacts on increasing FC at high moisture levels. Thanks to its keratin structure, feather operates like a sponge which enhances soil porosity; hence, it absorbs more moisture and improves FC. Furthermore, results indicated that increasing the amounts of amenders led to increased soil moisture in PWP (91%). By increasing the amount of amenders in soil, aggregation and soil porosity increased which led to enhanced PWP.
Large amounts of CF, PH and soil moisture (0.9FC) resulted in 3.7 times enhancement of Ks. CF led to the production of large soil pores and reduced soil density which resulted in improved soil structure and increased Ks. Thanks to its adhesion properties, PH increased Ks.
Increasing the amount of amenders and the level of soil moisture in all three types of organic matters (especially CF) caused the 2.5 times enhancement of WAS.
The results revealed that increasing soil moisture and amenders led to reduced Si (101%). Given all three types of amenders, PH had the highest impact on the reduction of Si. Moreover, soil penetration resistance (PR) was reduced as a function of increasing the soil moisture level.
Contrary to the expectation, MWD was reduced as a result of increasing amenders. Furthermore, it was found that, given little soil moisture, increasing the amount of amenders resulted in increased Dm; however, given high soil moisture, increasing the amount of amenders led to decreased Dm. Thus, it should be noted that adding amenders improved the stability of aggregates over long time periods and at high soil moisture levels.

One major strategy for improving soil physical and chemical properties is using modifiers, especially organic matters. In this study, we investigated the impact of chicken feather on physical properties of soil and compared its effect with those of potassium humate and vermicompost under different levels of soil moisture and wheat cultivation.
The results indicated that consuming amenders resulted in reduced Bd but increased FC, PWP, Ks and WAS.
In other words, it improved physical properties of soil. Moreover, Si decreased as a result of increasing soil moisture and organic matters. Among the three types of amenders, potassium humate had the highest impact on reducing Si. PR was reduced as a function of increasing soil moisture. However, increasing organic matter led to decreased MWD. Furthermore, it was unexpectedly found that, given low soil moisture, Dm increased as a result of increasing the organic matters weight. Nevertheless, in high levels of soil moisture, Dm ​​decreased as a function of increasing organic matter. Thanks to positive impacts of organic matters (especially CF which is cheaper and more accessible than other amenders) on soil’s physical properties, they are highly recommended for soil improvement. Regarding future studies, investigation of the effect of these amenders on soil chemical properties under different soil textures is suggested