M. Moeinfar; M.H. Rasouli Sadaghiani; M. Barin; F. Asadzadeh
Abstract
Introduction: Dust is one of the most important destructive phenomena in the world, that annually causing damage to human health and the environment. This issue ranks after two major challenges of climate change and water scarcity as the third most important challenge facing the world in the ...
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Introduction: Dust is one of the most important destructive phenomena in the world, that annually causing damage to human health and the environment. This issue ranks after two major challenges of climate change and water scarcity as the third most important challenge facing the world in the 21st century that is considered. Microbial-induced calcite precipitation (MICP) is a relatively green and sustainable soil improvement technique. It utilizes biochemical process that exists naturally in soil to improve engineering properties of soils. The calcite precipitation process is uplifted by the mean of injecting higher concentration of urease positive bacteria and reagents into the soil. In this process, the enzyme present in the bacteria hydrolyzes the urea in the environment and through reacting with the calcium ion, leads in the deposition of calcium carbonate. The main objective of this study is isolation native ureolytic bacteria from different soil of around Urmia Lake and then, the evaluation their efficiency in the MICP for stabilization of sandy soils and reduce windy erosion.
Materials and Methods: In order to isolate ureolytic bacteria, 25 soil samples were taken from different land use in West Azarbaijan province, Iran. To increase the number of ureolytic bacteria in soil samples were used from the enrichment solution and then ureolytic bacteria were isolated and purified. These isolates were subjected to various biochemical tests, as well as the growth curve and urease activity were determined. In order to investigate the potential for soil improvement, a factorial experiment was conducted based on a completely randomized design with two factors including microbial treatment in eight levels (including five isolated bacteria (U3, U8, U16, U35 and U40) and Bacillus pasteurii (as control Positive), non-bacterial and non-cementation (as control negative) and non-microbial but with cementation solution treatments) and another factor including different concentrations of calcium chloride solution with urea at three levels (0.1, 0.5 and 1 molar), in three replications. After injection of cementation solution and bacterial solution to soil, penetration resistance and windy erosion rates in sandy soil were assessed
Results and Discussion: In study, overall 45 isolates of the bacteria were isolated and purified. Among of 44 isolates, five bacterial isolates (U3, U8, U16, U35 and U40) had the highest urease activity. The growth curve of bacterial isolates showed that the highest urease activity and microbial population were in the time period of 13 to 16 hours after microbial culture, which it is represents the best time use bacterial solution in the MICP process. According to the results of soil improvement tests, the amount of soil erosion in the MICP treatment at a wind speed of 25 m/s was zero and the rate of penetration resistance was averaged over 13 MPa, which has a very impressive impact on MICP in controlling wind erosion, especially at high speeds of wind. The results showed that U3 and U16 isolates had the highest amount of urea hydrolysis and also U16 and U3 had the lowest and the highest tolerance to salinity, respectively. The results of the wind tunnel showed that the wind erosion threshold in negative control samples (non-bacterial and non-cementation) were 9.4 m/s and for MICP samples (including five isolated bacteria and Bacillus pasteurii ) were much higher than the wind tunnel speed in the wind tunnel machine in Urmia university (25 m/s). The maximum penetration resistance (13.5 MPa) was obtained in the sample treated with U3 isolate and 1 molar calcium chloride, but negative control treatments (non-bacterial and non-cementation) as well non-microbial but with cementation solution treatments were 0 and 97.0 MPa, respectively.
Conclusion: The amount of soil wind erosion was zero in MICP treatment with the wind tunnel speed 25 m/s that indicates very important effects MICP to control wind erosion of sandy soils to compare control treatments (non-bacterial and non-cementation and non-microbial but with cementation solution) in high wind speeds. The application of MICP treatment in the soil, in addition to increasing its wind erosion resistance, also increased penetration resistance in the soil. Increasing the penetration resistance of MICP treatments (including five isolated and Bacillus pasteurii) can be due to the activity of bacterial isolates, chemical interactions, and the formation of calcium carbonate precipitation into soil cavities, which causes to form a hard layer in soil. Also, obtained resistance by using isolated bacteria indicates that there are many unknown microorganisms that can carry out MICP better than Bacillus pasteurii and probably they will be better compatible and establish because they are native.
farrokh asadzadeh; Kamal Khosraviaqdam; Nafiseh Yaghmaeian Mahabadi; Hassan Ramezanpour
Abstract
Introduction: Soil texture is the average size of soil particles which depends on the relative proportion of sand, silt and clay contents. Soil texture is one of the most important features used by soil and environmental scientists to describe soils. Soil texture directly affects the soil porosity, which ...
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Introduction: Soil texture is the average size of soil particles which depends on the relative proportion of sand, silt and clay contents. Soil texture is one of the most important features used by soil and environmental scientists to describe soils. Soil texture directly affects the soil porosity, which in turn, determines water-retention and flow characteristics, nutrient-holding capacity, internal drainage, sorption characteristics and long-term soil fertility. High-resolution soil maps are essential for land-use planning and other activities related to forestry, agriculture and environment protection. Given the soil texture roles in controlling the soil functions, it is necessary to understand the spatial distribution of this feature in regional scale. As soil texture is a staticproperty, regional scale soil texture maps can thus help environmental scientists to predict different soil-related processes. The objective of this study was to develop a soil textural class map using Terra satellite MODIS sensor images.
Material and Methods: To achieve this goal, the digital elevation model SRTM radar of the studied area for soil samples from different altitudes and slopes was prepared in foursen consecutive 30 meters time frame. The nearest neighbor method with an error of less than 0.5 pixels was used and the elevation layers were mosaicked and transmitted to the UTM ZON-38 coordinate system and GIS Ready Became. The normalized difference vegetation index of bands 1 and 2 of the matrix was obtained to isolate the reflection of the electromagnetic spectrum of vegetation and soil. This final mosaicked digital elevation model was then divided into different altitudes to accurately evaluate the surface texture. The 60 spatial points were selected to estimate the texture of surface soil in thestudied area with systematic randomized sampling. In the current study, soil texture was determined forthe air-dried samplesusing hydrometer. The SWIR bands of MODIS with resolution of 500 meters were selected for sampling dates. After corrections, DN values of the bands for sampling points were extracted. The Pearson correlation coefficient and step wise regression techniques were used to establish proper relationships between the DN values of the SWIR bands and the soil particles. Kriging and cokriging methods were also employed to create a spatially distributed map of the soil textural classes.
Results and Discussion: The results showed that there is a close correlation between the SWIR bands of the terra satellite and the MODIS sensor with band 3, and using this auxiliary variable significantly reduces the estimation error. The best model for fitting semivariogram for clay, silt and sand contents were spherical, spherical and exponential models and the best fitting Cross-semivariogram for clay, silt and sand contents were spherical, exponential and exponential models, respectively. The highest and lowest error estimation was, respectively, related to sand and clay content. The maximum and minimum decrease of estimation error by the auxiliary variables was found for sand and clay content, respectively. The nugget/sill ratio of the kriging semivariograms was greater than 25%for sand and claycontentand lower than 25%for sand and silt content. This indicates that sand and silt contents had a strong spatial dependency, and clay content hada moderate spatial dependency. These ratios for cokriging cross-semivariograms of sand, silt and clay contentsware less than 25%. The interpolation of estimated soil texture was also determined using the cokriging method with 70% of the soil texture measured in the laboratory.
Conclusions: Our results indicated thatcokriging method estimated the soil particles more accurately as compared with linear multi-variable stepwise regression and kriging methods. Application of cokriging method also reduces the number of sampling points and the estimation error of soil texture zoning. Therefore, cokriging method seems to be better suited in impact assessments for data-scarceregions such as Iran.
mohsen barin; Ehsan Ehsan-Malahat; Farrokh Asadzadeh
Abstract
Introduction: Soil is a complex and dynamic biological system, and it still is difficult to determine the composition of microbial communities in soil. Most soil microorganisms are dormant, so their rate of respiration is low. However, their respiration can be stimulated by adding an easily decomposable ...
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Introduction: Soil is a complex and dynamic biological system, and it still is difficult to determine the composition of microbial communities in soil. Most soil microorganisms are dormant, so their rate of respiration is low. However, their respiration can be stimulated by adding an easily decomposable substrate. Also, by adding a simple organic matter, respiration may rapidly increase to a maximum and remains at a constant rate for more than 4 h. Glucose is commonly used as a substrate because most soil microorganisms can readily utilize it as a carbon source. The substrate-induced respiration (SIR) method was modified and adapted to measure fungal, bacterial and total microbial contributions to glucose-induced respiration and the potentially active microbial biomass on decaying plant residues of different composition. Decomposing residues from natural and agricultural ecosystems were chopped and sieved to include the >1 mm fraction for routine SIR analyses on a continuous flow-through respiration system. Substrate induced respiration is a main factor for the assessment of the soil microbial activity. This technique is already used widely in soil microbial studies. Different factors such as the source of carbon, temperature and incubation may play a significant role in the amount of SIR. Therefore, optimizing the test conditions is one of the important criteria for SIR determination. For this purpose, statistical methods such as central composite design (CCD) and response surface method can be used as a useful tool for determining optimal conditions. This study was carried out to model and compare the effect of carbon source (glucose), temperature and incubation time on the SIR of forest and agricultural soils.
Materials and Methods: In this research, 40 experiments were conducted for two soil types including agricultural soil (with relatively low organic matter content) and forest soil (with relatively high organic matter content). Soil samples were collected from the topsoil (0-20 cm) layer. In the laboratory, all visible roots were removed and the soil samples were divided into two parts. One part was kept in plastic bottles at 4°C for SIR analysis. And the rest was air dried in the shade at laboratory temperature for chemical and physical analysis. Electrical conductivity (EC) and pH were determined in saturated soil extract and organic carbon persent (%OC) was determined by di-chromate oxidation. Soil texture was determined using a Bouyoucos hydrometer in a soil suspension. Response surface methodology based on the central composite design was applied in modeling procedure. Different ranges of the independent variables including glucose (0.5-10 mg g-1), incubation time (1-10 hr), and temperature (15-30˚C) were used in central composite design experiments. Totally, 40 experiments based on the coded values of the independent variables were conducted for two soils.
Results and Discussion: Experimental results indicated that the SIR in forest soil is two times greater than the agricultural soil, which may be related to the higher organic matter content and more microbial activity in this soil. Results also revealed the efficiency of the central composite design in predicting the SIR of forest (R2= 0.823) and agricultural (R2=0.919) soils. Among the three independent variables, the linear effect of temperature on the SIR were significant for both soils. However, the substrate (glucose) content has more significant effect in forest soil in comparison with agricultural soil which may be associated with the higher decomposable organic matter content of the forest soil. Glucose enhancement didn’t have significant effect on SIR alteration rate which can be attributed to low organic matter content in agricultural soil. Totally, with increasing time and temperature, the amount of SIR was significantly increased, however with increasing glucose, SIR amount was not significantly increased especially in the agricultural soil. In the forest soil, the process of SIR changes is clearly distinct in response to independent variables compared to agricultural soil. Maximum levels of the SIR in forest soil is clearly associated to the highest time and glucose levels. This indicates that increasing glucose and sufficient time in the forest soil, which contains high amounts of digestible organic matter, can stimulate microorganisms to decompose more organic matter and it outcome is increasing SIR.
Conclusion: This study indicated the high efficiency of response surface methodology in SIR modeling for both forest and agricultural soils. However, the quantitative amounts of SIR were very different in two soils. The amounts of SIR in the forest soil were almost twice relative to agricultural soil. In the forest soil, the amounts of glucose and temperature were as the main variables in increasing SIR, while the temperature and time variables were more determinant in agricultural soil on it.
H. Arfania; Abbas Samadi; F. Asadzadeh; E. Sepehr
Abstract
Introduction: Phosphorus (P) is an essential nutrient for all life forms. In aquatic environments, P is a double-edged sword. In some areas, habitat biodiversity is strongly limited by low P bioavailability, while in others, P inputs in excess of plant needs have led to pollution of water bodies and ...
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Introduction: Phosphorus (P) is an essential nutrient for all life forms. In aquatic environments, P is a double-edged sword. In some areas, habitat biodiversity is strongly limited by low P bioavailability, while in others, P inputs in excess of plant needs have led to pollution of water bodies and eutrophication. There is little information available on P status in river sediments by single chemical extraction and its correlation with algae growth in Iran. This study was performed to select proper single chemical extraction methods by algal bioassay. The quantity of P estimated by different extractions methods depends on sediment characteristics such as calcium carbonate, pH, clay and organic matter contents. Therefore, this study was conducted in western rivers of the Lake Urmia to get an insight into P status in sediments by using single chemical and biological P assay.
Materials and Methods: The lakeUrmia basin has the second largest water resources in Iran with Mediterranean climate. Italso has the largest hypersaline lake in the world. There is a significant phytoplankton growth and also some dense algal blooms occurring during years with low salinity in wetlands and lagoons. Thirty four river sediment samples from seven main rivers of the Lake Urmia basin were collected from depth of 0-10 cm to evaluate algae (SenedesmusObliquus) P bioavalability by single chemical extraction. Selection of extractantis based on different mechanism of extraction. Cluster analysis was conducted on 17 sediment samples selected for algal bioassay.Pearson simple correlation and multivariate analysis were also performed.
Results and Discussion:Average total P concentrations of the sediments were343-654, 456 mg kg-1. Sodium bicarbonate 0.5 Mextractable P (Olsen-P) varied from 0.48 to 8.42 mg kg-1. Sediments from upper reach had considerably higher total and bioavailable P concentration in comparison with lower reach sediment. The low reach sediments of two rivers had higher Olsen extractable P than the threshold value of 20 mg kg-1indicating possible release which poses a threat to aquatic environment.Upper reach sediments had higher restoration potential, but algal bloom was observed in low reach part of rivers, particularly Simineh and Mahabad Chai. Land use changes, discharge of sewage from rural and urban section, industrial activity and cycling of river borne P are the main reasons for algal bloom in wetlands and lagoons around the lake.Principal component analysis (PCA) performed on the data identified three PC which explained 83.3% of total variation and silt and sand had higher loading values. Active calcium carbonate equivalent (ACCE) was negatively correlated with sand in the first PC. Different extractions were positively correlated with each other. The Mehlich III and Olsen-P extraction methods were significantly correlated and the predicted values were same. The average rank order of P extraction by singleextractantswas Cowell >Mehlich III >NaOH 0.1 M > Olsen > Morgan > AB-DTPA > Bray II.Extractants had different long-term and short-term potential to extract algal available P. The Cowell extractable P concentrations of sediments varied from 1.44 to 88.0 mg kg-1.This extractant was correlated significantly with algal growth and selected as the best P single extraction method among allextractants. The high correlation between 0.1 M NaOH and algae growth indicates the sensitivity of P bioavailability to redox conditions in river system. Algae (SenedesmusObliquus) was able to use P from different sediment components because its growth was correlated with Cowell, Mehlich III, NaOH 0.1M, Olsen and Morgan.
Conclusion: Legacy P (sediment P) evaluation by chemical extractants gives new insight into P bioavailability in river sediments of the Urmia Lake. The results of this work showed that Cowell extractant could be used to estimate algal available P in studied river sediments. Similarity between Olsen-P and Mehlich-P in estimating bioavailable P suggests that Mehlich III-P can be substituted for Olsen-P in studied sediments.For sustainable P management, monitoring P status by single chemical extraction methods is necessary. Phosphorous fertilizer application around the Lake Urmia basin lands should be conducted based onthe P soil test to avoid any aquatic pollution. Care must be taken in lower reach river sediments because of fragile ecosystems such as wetlands and lagoons. Further investigations are also needed to evaluate legacy P bioavailability by temporal and spatial variability.
F. Afrasiabi; H. Khodaverdiloo; F. Asadzadeh
Abstract
Introduction Particle size distribution (PSD) is one of the most fundamental features of soil physics that is widely used as the most common input for predicting several key soil attributes. The mathematically representing the PSD provides several benefits to soil mechanics, physics, and hydrodynamics ...
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Introduction Particle size distribution (PSD) is one of the most fundamental features of soil physics that is widely used as the most common input for predicting several key soil attributes. The mathematically representing the PSD provides several benefits to soil mechanics, physics, and hydrodynamics as well as helps to convert PSD data of various particle size classification systems to the desired one. Consequently, the correct and consistent descriptions of soil PSD by using mathematical functions is necessary.
The PSD models have often been evaluated in terms of their general performance for predicting the entire PSD curve. However, a given model may be feasible and globally perform well to generate the whole PSD curve but locally may fail to predict some specific points on the curve. To our knowledge, as well as, PSD models have not been widely tested for salt-affected soils with different levels of salinity/sodicity.
The aim of this study was to determine the error structure of several more accurate PSD models in selected soil samples with different levels of salinity and sodicity.
Materials and Methods Twenty four locations neighboring the western edge of threatened hypersaline Lake Urmia were sampled in this study. The locations were selected considering the available soil maps so that soils with wide range of salinity/sodicity were sampled. Selected physical and chemical properties of the soil samples were determined by standard methods. The performance of six PSD models including Modified Logistic Growth (MLG), Fredlund type models with three (Fred-4p) and four (Fred-3p) parameters, Anderson (AD), ONL, and Weibull (Wei), which have been reported as the most accurate PSD models by previous studies, was evaluated by using different efficiency criteria that offer various performances depending on the range of particle sizes.
An iterative nonlinear optimization procedure was used to fit the observed cumulative PSD data of the soils to the PSD models. Since every statistical criterion evaluates a part and some (and not all) aspects of the correspondence between measured and predicted values, we suggest that an effective assessment of model performance should include a suitable combination of criteria. Furthermore, dependence of the models performance to the range of soil particle sizes was examined.
Results and Discussion The soils differed widely in their EC (range = 85dS/m and CV = 159%), ESP (range = 67 % and CV = 71 %), and PSD (CV of clay and silt particles, 48 and 55 %, respectively). Soil textural class of the soils was differed widely from sandy loam to clay. All the soils were calcareous and alkaline.
The results showed that according to the efficiency criteria, including R2 (coefficient of determination), RMSE (Root Mean Square Error) and Er (Relative Error), all of the models have high efficiency, so that, the lowest average value of R2 in models was 0.992 and the maximum value of RMSE and Er was 0.028 and 0.045, respectively.
Prediction error of the models was dependent on the diameter for which we predict the cumulative fraction and decreases with increasing of the soil particles diameter. The performance of the models showed a significant quadratic polynomial relationship with sand content of the samples, so that, the studied models had the lowest performance in soils containing 30 to 45 percent sand.
The point-to-point error structure of model represents a decrease in systematic error in estimating coarse soil particles, while the models over-estimated the relative frequency of the fine soil particles. In addition, the values of relative error were also lower for coarse particles of soil, so that, the Wei model (for example) had the lowest Er value for 100 to 500 μm diameter soil particles. The relatively high correlations between parameters of Fred-3p, MLG and ONL models show insights to reduce the number of their parameters.
Furthermore, parameters a and c of MLG model, parameters μ and α of ONL model and parameter α and m of Fred-3p model had a statistically significant correlations. The relatively high correlations between parameters of the PSD models show insights to reduce the number of their parameters which increases their applicability.
Conclusion The studied models generally performed well to predict the whole PSD curve, but their performance was particle size dependent. This implies that, one should consider the range of sizes of soil particles for which the model are used. A model might be accurate enough for predicting some ranges of particle diameter or the whole PSD, but not for particular range of particle sizes. Using such models might lead to large errors in predicting the specific PSD range of interest.
F. Asadzadeh; manoochehr gorji; A. Vaezi; S. Mirzaee
Abstract
Introduction: Field plots are widely used in studies related to the measurements of soil loss and modeling of erosion processes. Research efforts are needed to investigate factors affecting the data quality of plots. Spatial scale or size of plots is one of these factors which directly affects measuring ...
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Introduction: Field plots are widely used in studies related to the measurements of soil loss and modeling of erosion processes. Research efforts are needed to investigate factors affecting the data quality of plots. Spatial scale or size of plots is one of these factors which directly affects measuring runoff and soil loss by means of field plots. The effect of plot size on measured runoff or soil loss from natural plots is known as plot scale effect. On the other hand, variability of runoff and sediment yield from replicated filed plots is a main source of uncertainty in measurement of erosion from plots which should be considered in plot data interpretation processes. Therefore, there is a demand for knowledge of soil erosion processes occurring in plots of different sizes and of factors that determine natural variability, as a basis for obtaining soil loss data of good quality. This study was carried out to investigate the combined effects of these two factors by measurement of runoff and soil loss from replicated plots with different sizes.
Materials and Methods: In order to evaluate the variability of runoff and soil loss data seven plots, differing in width and length, were constructed in a uniform slope of 9% at three replicates at Koohin Research Station in Qazvin province. The plots were ploughed up to down slope in September 2011. Each plot was isolated using soil beds with a height of 30 cm, to direct generated surface runoff to the lower part of the plots. Runoff collecting systems composed of gutters, pipes and tankswere installed at the end of each plot. During the two-year study period of 2011-2012, plots were maintained in bare conditions and runoff and soil loss were measured for each single event. Precipitation amounts and characteristics were directly measured by an automatic recording tipping-bucket rain gauge located about 200 m from the experimental plots. The entire runoff volume including eroded sediment was measured on storm basis using the collection tanks. The collected runoff from each plot was then mixed thoroughly and a sample was taken for determining sediment concentration by weight. The per-storm soil loss was then obtained.
Results and Discussion: A wide range of rainfall characteristics were observed during the study period.The results indicated that the maximum amount of coefficients of variation (CVs) for runoff and soil loss from replicated plots were 60 and 80 percent, respectively, which were considerably higher than the variability of soil characteristics from these plots. CV of runoff and soil loss data among the replicates decreased as a power function of mean runoff (R2= 0.661, P