فهرست مطالب ahmad landi

Dust is created by wind erosion. In nature, based on the size of the particles, the movement of soil particles due to wind is carried out in three ways. Rolling, jumping and suspension. In the wind ranges that usually occur in nature, proportional to the diameter of the soil particles, these three modes of particle motion are somewhat separable. Particles smaller than 0.1 mm in diameter are usually suspended, soil particles with a diameter of 0.05 to 0.5 mm usually bounce, and soil particles with a diameter of 0.5 to 2 mm move in a crawling (rolling) manner. In the wind tunnel tests, which were conducted on the soil samples collected from the dust centers in the southeast of Ahvaz. It showed that at the reference point, when the wind blows, a lot of dust is formed in front of the soil mass.The obtained results show that the maximum amount of dust is formed in the first 150 seconds. The maximum amount of dust produced occurs in the first 85 seconds, which is about 7 times more than the dust after the first 150 seconds.

The present research performed to estimate soil texture using visible near-infrared spectrometry in Semirom, Isfahan. A total number of 200 soil samples (0-10 cm) were collected from the Semirom area (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan. The samples were air dried and passed through a 2 mm sieve, and soil particles percentage was determined in the laboratory using hydrometry method. Reflectance spectra of all samples were measured using an ASD field spectrometer. Different pre-processing methods i.e., First Derivatives and Savitzky-Golay Filter, Multiplicative Scatter Correction and Standard Normal Variable were applied and performed on spectral data. The Partial Least Squares Regression, Support Vector Machine Regression and Artificial Neural Network models were used to estimate soil texture. The best result was obtained for Silt estimation, with excellent values of RPD >2, R2 =0.98 and RMSE=1.08 using Artificial Neural Network model with MSC pre-processing technique. The results indicated the desirable capability of Artificial Neural Network model with MSC and SNV pre-processing techniques in estimating the Clay (RPD >2, R2=0.94 and RMSE=1.21) and Sand (RPD >2, R2=0.84 and RMSE=6.24) contents of the soils, respectively. In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil particles percentage and showed its potential for substituting laboratory analyses.

Soil organic carbon (SOC) is one of the most important components of soil physical and chemical properties that prevented soil decay and destruction. The objective of the present study is the evaluation of SOC changes using the remote sensing technique compared with field methods at central Zagros forests in Khoozestan province in Iran over the past 2 decades.

 The soil samples were collected randomly from the soil surface (0-10 cm depth) to estimate the SOC concentrations in the laboratory. Analysis of digital data by using Operational Land Imager (OLI) of satellite Landsat 8 and Enhanced Thematic Mapper (ETM+) sensor of satellite Landsat 7 images in 2016 (the sampling year) was done to estimate surface organic carbon levels of soil. The main objective was to establish soil organic carbon relation with landsat different bands ratios and also herbal and moisture indexes such as NDVI, SAVI, BSCI, NDMI and NSMI corresponding to the SOC values obtained from soil samples of the forest areas in the central Zagros mountain, and for that purpose these data were evaluated using different linear regression methods.

The best fit model of stepwise regression method showed R2 value of 0.435 for landsat 8 and R2 value of 0.501 for landsat 7 and finally based on these results, evaluation of SOC changes occurred in previous years.

Results show the significant relationship between soil organic carbon and the reflectance in the Visible, Near-Infrared and Short-wave Infrared part of the spectrum.

Soils and waters are frequently subject to contamination by inorganic elements including As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, and Zn, mainly due to anthropogenic activities, such as mining, incineration of wastes, drilling of petroleum shaft and agricultural practices (i.e., pesticides and sewage sludge application). Phytoremediation employs the use of plants to degrade, remediate and stabilize various environmental contaminants in soil, water and air. Biochar (BC) can be produced from a wide range of organic wastes via pyrolysis. It has great potential as an amendment for phytoremediation but its effects depend on the type of feedstock it derives from. This study was conducted to identify the effects of biochar addition and phytoremediation on As, Cr, and Pb concentrations in a soils polluted by oil drilling wastes.

The current study was carried out to examine the heavy metal immobilizing effect of biochar produced from sugar cane waste and subsequent heavy metal uptake by Sorghum, Atriplex sp. and prosopis farcta. Sugar cane wastage biochar was incorporated into four application rates (0, 0.5, 1 and 2 % (w/w)) and soil biochar mixtures were examined for an incubation period of 2 months (so that the contaminated material could be distributed evenly in the soil particles). Then pot trials were provided for cultivation of Sorghum, Atriplex sp and Prosopis farcta so that for each plant, a row of 12 pots and a total of 36 pots for the three under-study plants.

The results showed, average of Cr, As and pb concentration   in contaminated soil is 56, 53.4 and 582 ppm. Average of Cr, As and pb concentration   in contaminated soil is Amendment with BC is 30, 21.5 and 224. Average of Cr, As and pb concentration   in contaminated soil is Amendment with Sorghum is 22, 7.2 and 43, Amendment with Atriplex sp is 16, 15.3 and 141, Amendment with prosopis farcta is 18, 19.9 and 192 ppm.

Effective treatment of Cr was prosopis farcta and 1 % (w/w) biochar, Sorghum and 2 % (w/w) biochar for As and pb. Although heavy metal-contaminated soils can be reclaimed effectively by application of biochar, further research is needed to explore its long-term environmental and economic aspect to gain maximum benefits from this novel material

Soils at the earth crust could be considered as the result of interaction among five soil-forming factors. The strength and weakness of each of these factors causes formation of different soils with different properties and horizons. Previous studies have reported that Iranian agricultural soils show different levels of zinc deficiency. Although the literature review confirms many studies in this regard, the number of studies compared the distribution of the chemical forms of micronutrients and especially zinc after the application of chemical fertilizers in the soils of Khuzestan especially in the common diagnostic horizons of these soils, is limited. Therefore, the purposes of this study were two-fold: (1) to measure and determine the chemical forms of zinc in selected diagnostic horizons (Mollic, Calcic and Salic) of Khuzestan Province, (2) to identify the contribution of some physical and chemical properties of these soils on availability of zinc.

In order to carry out this research, Mollic and Calcic diagnostic horizons were collected from the Karun 3 watershed in the east of Khuzestan province, with thermic and mesic temperature and xeric moisture regimes. However, Salic diagnostic horizon was sampled from the route between Ahvaz to Abadan in the southwest of the province, with hyperthermic temperature regime and an aridic moisture regime. After being transferred to the laboratory, the samples were first air-dried, and after passing through a 2-mm sieve and before carrying out the fractionation and speciation experiments, their physical and chemical characteristics including electrical conductivity (EC), pH, particle size distribution, organic C content, calcium carbonate equivalent, cation exchange capacity (CEC), specific surface area (SSA), and total and DTPA extractable Zn were determined employing common standard procedures (Table 2). Different forms of zinc in the solid phase of the Mollic, Calcic, and Salic soils were extracted using the method described by Tessier et al. (1979). In brief, 5 components including water soluble, exchangeable, carbonates, iron and manganese oxide, organic and residual forms were determined. The concentration of zinc in the extracts obtained from different stages of successive soil extraction was determined using an atomic absorption device. In order to estimate the accuracy of the extraction method described above, the recovery percentage was calculated. To do this, one gram of a certified soil from the state of Montana (ANIST 2711A) was used and the recovery percentage of zinc in our study was 94.5%. In order to determine the chemical forms of zinc in the solution phase of the soils, 100 g of each soil sample was saturated with double distilled water, and then extracted using a vacuum pump and Buchner funnel. The extracts were passed through filter paper and centrifuged at 2500 rpm for 8 minutes, then the values of pH, electrical conductivity, dissolved organic carbon, and dissolved cations and were determined using common laboratory methods described earlier (Table 1). These parameters were considered as input in Visual MINTEQ software to predict zinc species in soil solution (all free and complexed organic and mineral species). The data analysis was conducted considering a factorial design based on a completely randomized design through SAS software (v9.1). The comparison of means was also done using the Tukey’s test at the 5% probability level. Graphs were also drawn using Origin software (v2018).

Considering the critical level of zinc deficiency in the soil (1 mg/kg), all the soils are deficient in terms of available zinc. The studied soils are among alkaline and calcareous soils, but they did not show any differences from each other in terms of soil texture. It is noteworthy that the mollic horizon has a greater cation exchange capacity and specific surface area than the other two soils due to the higher amount of organic matter and the greater abundance of clay contents. The results of zinc fractionation in the studied soils showed that organic, carbonate and oxide forms in the Mollic and Calcic horizons, and carbonate, oxide and exchangable forms in the Salic horizon were the dominant forms of Zn which affects its supply to the plants. The results also showed that Zn2+, Zn(OH)2(aq), Zn-DOM (aq), and Zn (OH)+ species were the dominant species of Zn in soil solution of all diagnostic horizons. The result of Zn mobility factor calculations with the addition of 10 mg/kg Zn(SO4)2 showed lower values of mobility factor in the mollic diagnostic horizon than those of the calcic and salic.

Dust is one of the most important environmental pollutants, containing particulate matter and heavy metals. The aims of this study were to determine the pollution level and to identify the distribution pattern of heavy metals in dust samples collected around the Hoveyzeh - Khorramshahr dust center. Thirty points from different urban and rural areas were taken using marble sediment traps to examine the heavy metals contents of the dust particles. The results showed that the concentrations of heavy metals in dust particles follows the order Mn > Zn >Cr > Ni > Pb > Cu > Co. Also, the highest values of enrichment factor were found in the southern parts of the study area, where the oil fields activities are predominant. The lowest concentrations of heavy metals were also observed in rural areas. In most of the stations, both values of enrichment factor and geo-accumulation index were higher in dust samples compared to those of soil samples. The values of geo- accumulation index for Hg, Pb, and Mn elements in dust particles and soil samples were in the range of medium to high pollution (2 ≥ I geo ˂ 3), indicating the impact of various human resources on distribution of these elements. The result of cluster analysis is fully consistent with the results of Principal Component Analysis (PCA), indicating several sources deriving from human activities including oil, industrial and traffic on heavy metals distribution in dust samples.

Biochar is a recalcitrant soil amendment that is being used to carbon sequestrates and also enhances soil fertility. This research was done to study the effect of corn and sugarcane biochar as organic amendment on some soil biological properties and CO2 emission.
Material and

Corn and sugarcane residues were air dried and then pyrolised in handmade furnace. Biochar was added to the soil according to the treatments (0, 1 and 2 percent). The samples were stored 100 days at 25oC and their moisture content was kept at 60% of field capacity. After this period microbial respiration by titration with hydrochloric acid, microbial biomass carbon by fumigation-extraction method, ammonium emission by titration with sulfuric acid and nitrification by colorimetric method was measured. Then microbial and metabolic quotient and carbon sequestration were calculated.

The results showed significant effect of biochar on microbial respiration, microbial biomass carbon, microbial and metabolic quotient, ammonium emission and carbon sequestration (P<0/001), but that effect was not significant on soil nitrification. The highest amount of microbial respiration (27/31 mg CO2-C/100g soil), microbial biomass carbon (37/05 mg Cmic/100g soil) and carbon sequestration (13/67 mg/kg) was obtained in soil treated with 2 percent of sugarcane biochar and the highest ammonia emission (18/8 % N) was the most in the samples containing one percent of corn biochar.
Discussion and

The results showed that sugrecane biochar had more effect on carbon-dependence biological properties of soil due to more carbon content.

Threshold friction speed is an important factor for assessing the soil erodibility, but its measurement is time consuming and costly. Estimating threshold friction velocity by use of soil reflectance increases operating speed and reduces cost. The aim of this study was to compare the efficiency and accuracy of partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN) regression models in estimating the threshold friction velocity in dust-prone areas of Khuzestan Province. For this purpose, 91 soil samples were collected from the central and southern parts of dust-prone areas of the province and the threshold friction velocity was determined using wind tunnels. Then, the reflectance spectra of soil samples were obtained with a spectrometer. Pre-processing methods were performed on the main spectrum and modeling was performed using, PLSR, SVR and ANN models. The results showed that the threshold friction velocity in the region was 9.7 m/s and the minimum was 5.25 m/s.  Also, the threshold friction velocity was significantly (p<0.05) correlated with dissolved sodium (r= -0.58) and sodium adsorption ratio (R= -0.48). The ANN model had the best estimation accuracy in the second derivative preprocessing (PRD = 2.52) and the SVR model had the lowest estimation accuracy in the main spectrum (PRD = 0.56). Finally, the key wavelength of the threshold friction velocity was in the range of 1850 and 1930 nm. Because of the soil reflectance correlation with threshold friction velocity (r=0.76), the spectroscopy method can be used to assess the soil erodibility in areas prone to dust production.

This research conducted various experiments using a wind tunnel on soil samples from the most critical parts of dust sources in southeastern of Ahvaz. The main goal of this study is to show the effect of moisture on the movement of soil particles in the dust centers. The laboratory work was carried out in two parts: a: Addition of water to completely disturbed soil surface (wetting in the bag) b: Addition of water to soil layers of samples wetting that was named pseudo undisturbed soil. The results showed that dust generation under natural conditions (dry soils) in a laboratory wind tunnel will be 53 times more than the pseudo undisturbed soils with moisture content of 2-4% in volume. Moreover, soils with moisture contents of 5-8 and 10-12% do not differ significantly from those with 2-4% moisture content in preventing dust generation. If soil surface is disturbed after it is provided with adequate moisture, not only will dust generation not decrease compared to the case when the soil surface was dry but it will also increase by 25 percent. The practical point of this study is that in the study area, increasing soil moisture between 2 to 4% can greatly reduce the formation of dust.

The preferential flow is enhanced by the nature of the soil structure and by providing a direct and rapid flow for transferring pollutants, leads to pollution of groundwater. Therefore, the purpose of this study was to investigate and compare some quantitative and qualitative preferential flow indices in different soil structures in Lorestan province. The study was conducted on un distributed soil columns with three different structures namely: (Granular, Blocky, and Massive) and a distributed soil with 3 replicate in a completely randomized design. In order to the plot breakthrough curve in each soil structure, injections of bromide at a concentration of 50 mg/L were carried out using a tension infiltrometer device under tension of 15-millimeter to soil columns. The breakthrough curve of different structures was plotted and accordingly the quantitative parameters of the preferential flow such as the mean time of breakthrough curve ( ) and the skewness index of the breakthrough curve (S) were calculated. For the study the paths of preferential flow, the infiltration of the dye tracer and image processing in different soil columns were performed. The results showed that soil structure had a significant effect on preferential flow occurrence in different soils. The comparison of breakthrough curve shape for different structures, showed the occurrence of preferential flow in blocky and granular soils and non-occurrence of this phenomenon in massive and disturbed soils. Also, the index of mean breakthrough time (0.2 and 0.39) and the skewness of breakthrough curve (1.17 and 1.45), in blocky and granular structures respectively confirmed the occurrence of preferential flow in these soils. The results of image processing showed that the infiltration depth of the tracer material in blocky structure was 29, 62 and 70% higher than granular, massive and disturbed structures, which demonstrated the role of type and enhancement of structure on occurrence preferential flow.

Considering that Khuzestan province has a large area of land susceptible to dust generation, novel approaches such as Hyperspectral images could be used in the determination of soil characteristics. One of the main challenges in using hyperspectral images for evaluation of soil properties in these areas is the presence of some compounds such as gypsum which may lead to some errors in estimating other soil properties. This research has mainly been conducted to determine the soil-gypsum key wavelength in the dust center of Khuzestan province. To achieve this goal, at first the original soil spectrum was preprocessed using FieldSpec3 setup via five methods including the Savitzky-Golay filter, the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the Standard Normal Variant (SNV) and the Continuum Removal method (CR). Two Multivariate regression models including Partial Least Squares Regression (PLSR) and Support Vector Machine (SVR) were used and compared in the estimation performance of soil gypsum. The results showed that the SVR model had better performance rather than the PLSR model in estimating soil gypsum. Also, in the SVR model, the continuum removal method (R2cal=0.93, RMSEcal=2.47, RPDcal=3.71) and the main spectra (R2cal=0.76, RMSEcal=6.32, RPDcal=1.59) had the best and weakest performance in estimating soil gypsum, respectively. It is noteworthy that the continuum removal method (R2val=0.88, RMSEval=3.57, RPDval=2.49) and the original spectrum (R2val=0.52, RMSEval=7.81, RPDval=1.12) in the validation group showed the best and the weakest performance, respectively. In the present study, wavelength ranges around 1450, 1550, 1700, 2100, 2200, 2400 nm which had the highest level of correlation with soil-gypsum content, was obtained as the key wavelengths of the soil in sensitive areas to dust production.

Nowadays spectroscopy is used for estimating soil properties. The main objective of this research was to estimate some soil properties of Susceptible Areas of Dust Production using visible and near infrared. Therefore 142 soil sample of Susceptible Areas of Dust Production were Collected and analyzed. Equivalent calcium carbonate, gypsum, organic carbon and nitrogen of soil sample were measured and linear regression models of PCR and PLSR were used to estimate these properties .Three methods of reflection of the main spectra and pre-process methods of first derivative and second derivative were compared in two regression models of PCR and PLSR. The results showed that the PLSR method is more accurate than the PCR model for estimating soil properties. The PLSR model in the pre-processing second derivative with noise reduction, showed the highest accuracy for equivalent calcium carbonate, organic carbon and total nitrogen with the coefficient of determination as 0.95, 0.92 and 0.81, respectively. For gypsum, the highest accuracy in the first derivative with the coefficient of determination was 0.87. The results of this research revealed the use of spectroscopy in estimating soil properties of dust production-prone areas in Khuzestan province have an appropriate accuracy; and due to the extent of these areas and the speed of operation and cheapness of this method, it can be used to predict the amount of soil properties in these areas.

Soil organic carbon is an important factor in determining the global carbon cycle and global climate regulation. Soil is also the input/output source of carbon to the atmosphere which is depended on the land use. For this purpose, the objective of this study was to compare different methods of data mining in predicting soil organic carbon storage in irrigated, mixed cultivation (irrigated and rainfed), pasture and palm trees lands in some parts of Behbahan city in southwestern of Iran. Soil sampling from depths of 0-30 and 30-60 cm was carried out using conditional Latin hypercube square method. Organic carbon content of the soil samples was determined by Walky-Black method. Bulk density of the soils was determined using paraffin method. The auxiliary parameters used in this study included territory components, OLI sensor image data from landsat 8 and land use map. The results showed that the SAVI, NDVI, NDSI, salinity, carbonate, gypsum and clay indices have the highest correlation with the soil organic carbon stock values. The results also showed that the random forest (RF) (R2= 0.983, RMSE=2.32) was the best model to predict soil organic carbon storage followed by artificial neural network model (R2= 0.887, RMSE= 4.257) and Support Vector Regression Machine model (SVR) (R2 = 0.707, RMSE=7.344).

In recent years, due to the lack of surface coating and low soil resistance to wind erosion, the large area of Khuzestan province is sensitive to dust production. Among the soil characteristics, the organic matter by collecting soil particles, has important role in soil resistance to wind erosion and dust production. Since these areas are so wide, the use of traditional methods of soil analysis is really costly and time consuming. The spectroscopy approach, due to the advantage of speed and easy movement, can reduce the cost and time of measurement. The aim of this study is to investigate the spectral behavior of soil organic carbon in central and southern regions of Khuzestan province by using tow multivariate regression, Support Vector Regression) SVR( and neural network (PLS-ANN), and key wavelength determination of soil organic matter in these areas.

In this research, the study area was divided into 2 km square grids and systematic and random sampling Methods were performed. The soil organic matter in samples was measured in the laboratory. The Reflectance spectra of soil samples were determined using FildSpect setup in dark room. And spectral measurements were carried out with three types of detectors in range of visible to near infrared (3500-2500 nm). To eliminate the noise in normal reflectance spectra, the main spectra were preprocessed by four methods, including the first derivative with the Savitzky-Golay filter (FD-SG), the second derivative with the Savitzky-Golay filter (SD-SG), the standard normal variant method (SNV) and the continuum removed method (CR). Next, the performance of SVR and PLS-ANN models in main spectra preprocessed method were compared.

The results showed that the PLS-ANN model had better accuracy compared to SVR model in estimating organic carbon. In SVR models, the continuum removal method (CR) had the best performance (R2CAL=0.84, RMSECAL=0.06 and RPDCAL=1.82) and the main Spectra had the worst performance (R2CAL=0.74, RMSECAL=0.14 and RPDCAL=1.66). In PLS-ANN models, the best performance belonged to the second derivative (SD-SG), (R2CAL=0.92, RMSECAL=0.05 and RPDCAL=2.34) and the worst performance was related to the first derivative (FD-SG), (R2CAL=0.80, RMSECAL=0.1 and RPDCAL=1.86).

In this study, the preprocessing methods improved the overall accuracy of SVR and PLS-ANN models compared to the main spectrum. According to the second derivative method, in PLS-ANN witch had the best accuracy in estimating soil organic carbon, the Wavelength ranges around 800, 1800 and 2000 nm were identified as the key wavelength of the organic carbon in sensitive centers to dust production.

Wind erosion and its dust are serious danger in erodible soil degradation, environment, air pollution and human health. It is an important problem in Iran and especially in Khuzestan province. In the current research, the effect of Sporosarcina pasteurii bacteria in Tryptic Soy Broth (TSB) and sugarcane molasse as a culture media on wind erosion was investigated. The sandy soil sample was obtained from the Hamidyeh area in the Azadegan plain of Khuzestan province and the effect of time and culture media on the test was considered. The treatments were conducted with sugarcane molasse (10%) and TSB as a culture media and urea (2%) and calcium chloride as an injection solution. The experiment was carried out with the basis of completely randomized design with three replications. After 7, 15 and 30 days of injection, calcium carbonate content, penetration resistant and erodibility by wind tunnel were considered. Results showed that the highest concentrations of calcium carbonate were observed under 10% molasses treated with bacteria, which increased the percentage of calcium carbonate by 52%. The highest resistance to penetration was related to treatment 10% molasses application, which resulted in an increase of 1000 times the penetration resistance. The penetration resistance and the percentage of calcium carbonate accumulated in the soil showed a significant increase at the 5% level by LSD test, over time. The investigation of erodibility for selected samples was performed in wind tunnel with an average wind speed of 16 m/s. The amount of erosion in the sandy soil was decreased from 52% to zero, which indicates a 100% reduction in erosion. The results of this study indicate the significant role of molasses in reducing the erodibility of sandy soil and increasing the penetration strength. Thus, it can be suggested as a method to stabilize the sand.