Shohreh Moradpour; MOjgan Entezari; Shamsollah Ayoubi; Salman Naimi
Abstract
Impacts of land use and geomorphology on some heavy metal concentrations in a part of Zayandehroud dam watershed IntroductionWith the rapid development of industry and urbanization, soil pollution with heavy metals as a result ecosystem destruction has attracted global attention. Pollutants are considered ...
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Impacts of land use and geomorphology on some heavy metal concentrations in a part of Zayandehroud dam watershed IntroductionWith the rapid development of industry and urbanization, soil pollution with heavy metals as a result ecosystem destruction has attracted global attention. Pollutants are considered environmental threats and among pollutants, heavy metals are known for their non-degradability and physiological effects on living organisms even at low concentrations. The close correlation of magnetic properties and heavy metals shows that magnetic measurement is an efficient and cheap tool to detect heavy metal contamination in soils affected by heavy industries and traffic pollution. Magnetic minerals in soil may be inherited from parent rocks (lithogenic origin), pedogenesis (pedogenic origin), or may result from human activities (secondary ferromagnetic materials). The concentration of metals can be influenced by geomorphology and various soil properties such as organic carbon, electrical conductivity. Land use directly or indirectly affects the geochemical behaviors of heavy metals through regulating soil properties. The main objectives of this study were to investigate the effect of land use change on magnetic receptivity and the concentration of some heavy metals including zinc, copper, iron, nickel, chromium, cobalt and manganese in the 20 cm soil surface layers, and to explore spatial distribution of magnetic receptivity and heavy metals under different types of land use and geomorphological units in the studied area.Materials and MethodsThe present research was conducted in Isfahan province in the center of Iran with an area of 227 Km2. This area has an average temperature of 9.8 oC and an average annual rainfall of 324 mm and an altitude of 2380 meters a.s.l. Based on Kopen's classification, the climate was classified as semi-arid with cold winters. Geologically, it belongs to the Sanandaj-Sirjan zone, the dominant rocks of the area include limestone, shale limestone, slate and Quaternary sediments. The most important land uses in the region included pasture, rainfed and irrigated agriculture, and in terms of geomorphology, the region comprised river plains and pediments. Soil sampling was done by stratified random method. A total of 100 samples were collected from the surface layer (0-20 cm depth) in the summer of 2021. Magnetic susceptibility was measured at high and low frequencies using Bartington MS2 dual frequency sensor. The concentration of heavy metals including iron, zinc, manganese, nickel, copper, chromium and cobalt were measured by atomic absorption spectroscopy. pH, organic carbon, calcium carbonate, electrical conductivity were measured in all samples. Spearman's correlation coefficient was used to check the correlation between different parameters. Analysis of variance was applied to evaluate the effects of geomorphology and land use on heavy metals and magnetic susceptibility. Spatial analysis was performed for heavy metals and magnetic susceptibility, and the maps were prepared in ArcGIS v.10.7 software.Results and DiscussionThe results showed that there was a negative and significant correlation between calcium carbonate, heavy metals and geomorphology. There was no significant correlation between organic carbon and heavy metals in land uses. But there was a negative correlation in the river plains and alluvium. There is a significant negative correlation between electrical conductivity, copper, manganese, and nickel. In the use of agricultural lands and river plains, there is a positive correlation between low-frequency magnetic susceptibility and high-frequency magnetic receptivity with electrical conductivity. Also, pH showed a significant negative correlation with magnetic susceptibility in pasture land and had no relationship in other land uses. There is a positive correlation between calcium carbonate and frequency-dependent magnetic susceptibility in agricultural land use and river plains. There is a significant positive correlation between heavy metals and magnetic susceptibility in pediments and some land uses, especially in rainfed lands. The results of analysis of variance showed significant difference (p*<0.05) in land use regarding heavy metal concentrations. In this analysis, there was a significant difference between cobalt, nickel and manganese elements according to land use, and the magnetic susceptibility among the studied geomorphic surfaces. According to the results of the test, there was a significant difference for heavy metals in various geomorphic surfaces. The content of iron, chromium, cobalt, nickel and manganese in river plains and pediment had significant differences with hills.ConclusionThe present study was conducted with the aim of clarifying the effect of land use and geomorphology on magnetic susceptibility and concentration of heavy metals in a part of the Zayandeh River watershed in Isfahan province. The average of nickel and manganese in the soils of the study area is higher than the normal range, due to parent materials effects and agricultural activities (plowing and irrigation) accelerate the soil formation processes and increase the amount of these elements in the soil. The highest concentration of cobalt, iron, zinc, copper, nickel and chromium elements were observed in dryland farming. In addition, investigating the spatial distribution of magnetic receptivity values and heavy metals in different places are significantly different. Higher values of magnetic susceptibility were seen in the center of the studied area. Spatial distribution of heavy metals iron and chromium are concentrated in the center of the region and other metals are concentrated in the west and northwest. Probably, parent materials such as shale, dolomite, limestone and sandstone and weathering and release of elements in the soil increase the concentration of these elements in the region.Keywords: Geomorphology, land use, LSD test, kriging
Kamran Azizi; Shamsollah Ayoubi; Kamal Nabiollahi
Abstract
Introduction: The parent material and geology have a significant contribution to heavy metal contents and magnetic susceptibility in soils. Magnetic susceptibility is known as the extent of a material’s impact on the magnetic field, which depends on the concentration and the type of magnetic minerals ...
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Introduction: The parent material and geology have a significant contribution to heavy metal contents and magnetic susceptibility in soils. Magnetic susceptibility is known as the extent of a material’s impact on the magnetic field, which depends on the concentration and the type of magnetic minerals in the soil. Magnetic susceptibility measurement is fast, easy, economically convenient, and non-destructive. Mass magnetic susceptibility and frequency-dependent magnetic susceptibility are among the conventionally used parameters. The type of land use is among the main factors influencing magnetic susceptibility distribution in soil. Besides, soil magnetic susceptibility is affected by slope position and different soil properties such as soil organic matter and carbonates. A variety of relationships have been observed between heavy metal concentrations and magnetic susceptibility across various types of soil, parent material and climatic regimes. Generally, magnetic properties have positively correlated with the type and concentration of magnetic minerals as well as the particle size of soil fractions. Besides, magnetic susceptibility has a weak negative correlation with diamagnetic components such as quartz, gypsum, calcite, and organic materials. The main objectives of this study were to i) determine the relationships between magnetic susceptibility (MS) and concentration of some heavy metals such as copper, iron, zinc, and manganese in the surface soils and ii) explore the impacts of land use and geomorphological units on the variability of heavy metals and MS in a semi-arid region in the west of Iran.Materials and Methods: The present study was conducted in Kurdistan province located in western Iran, the area is about 110,000 ha and mean altitude of 2277m above sea level. The area has an average annual temperature of 10.20 °C and an average annual rainfall of 369.8 mm, which dominantly occurs in spring and winter. Besides, the area has soil moisture and temperature regimes of Xeric and Mesic, respectively. The lithological setting of the studied area includes reddish of sandy marls and marl sandstone, river deposits, alluvium-cultivated land, and granite. The studied area was divided into different parts in terms of geomorphology and land use. Soil sampling was done using the stratified random sampling approach. A total of 347 samples were collected from the surface layers (0-30 cm depth) of the studied area. Magnetic susceptibility at both high and low frequencies was measured using a Bartington MS2 dual-frequency sensor. The amounts of all the selected heavy metals including iron, zinc, manganese, copper, and nickel were measured using atomic absorption spectrophotometer. Soil particle sizes, acidity, SOC, CCE and electrical conductivity were measured in all soil samples. The concentration factor and Tomlinson’s Pollution Load Index were calculated. The Spearman correlation coefficient was used to examine the correlation between different parameters. The analysis of variance was used to evaluate the effects of geomorphology and land use on heavy metals and magnetic susceptibility. Spatial analysis was done conducted for some variables (Fe, Mn, Zn, Ni, Cu, and χlf) and the map of variables were created in ArcGIS v.13 software.Results and Discussion: The results showed that the positive significant correlations were observed between heavy metals and silt content and negative significant correlations were observed between heavy metals and sand content. Fine soil fraction compared with coarse fraction has the higher specific surfaces and more susceptible to attract heavy metals. Moreover, positive and significant correlations were obtained between the SOC and heavy metals across various land use types and geomorphic units. Organic matters have a high cation exchange capacity, therefore they adsorb heavy metals and hold them on their surfaces. pH and CCE showed negative and significant correlations with heavy metals and magnetic susceptibility. The positive correlation between heavy metals and magnetic susceptibility in agricultural land, piedmont, and river plains units observed. Also, PLI and CF have a positive correlation with magnetic susceptibility. The t-test showed that a significant difference between agricultural lands and non-agricultural land types and ANOVA results in various geomorphic units of the study area indicated that the magnetic susceptibility between piedmont and mountainous areas were significantly different. Conclusion: Results indicated a significant correlation between magnetic susceptibility and heavy metals. Besides, the magnetic properties of the soil are influenced by its physical and chemical properties that have large impacts on reducing or improving its magnetic field. Significant differences were observed between agricultural lands and non-agricultural lands as well as mountainous and piedmont areas that had different parent materials. These results indicate the great impact of parent materials constituting the soil on the absence or presence of diamagnetism in the region.
Micromorphology and Clay mineralogy
Masoumeh Pourmasoumi Parashkouh; Farhad Khormali; Shams Ollah Ayoubi; Farshad Kiani; Martin Kehl; Eva Lehndorff
Abstract
Introduction The loess-paleosol sequences in Northern Iran are important archives that represent several cycles of Quaternary climate change and can be used to complete the information gap on loess between Europe and central Asia. Last interglacial soils derived from loess in northern Iran is represented ...
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Introduction The loess-paleosol sequences in Northern Iran are important archives that represent several cycles of Quaternary climate change and can be used to complete the information gap on loess between Europe and central Asia. Last interglacial soils derived from loess in northern Iran is represented by strongly developed Bt horizons of forest soils. In Golestan and Mazandaran area, soils under the forest are mainly classified as Alfisols or Luvisols. Interestingly, E horizons are generally not found in these soils. In the Caspian Lowlands, a pronounced precipitation gradient is reflected in mean annual precipitation rates decreasing from about 1850 mm at Bandar Anzali in the west to about 435 mm at Gonbad- e Kavoos in the east. The results of the loess climosequence in Northern Iran showed that with increasing precipitation, soil pH and calcium carbonate contents decrease, whereas soil organic carbon, clay content, and cation exchange capacity increase. For years, many efforts to quantify the soil properties led to the provision of indices of soil development. Among these indices are forms and ratios of iron, morphological, and micromorphological indices. Many studies have been carried out on the loess-paleosol sequences and modern loess soils in Northern Iran with focus on micromorphology, mineralogy, and dating but more investigation is needed with an emphasis on the forest soils with well-pronounced clay illuviation as a proxy for paleo-moisture. For this purpose, we used micromorphology and soil color indices to report the effects of precipitation gradient on the variability in the formation of soils under forest vegetation. Materials and Methods The study area is located at the northern slopes of Alborz Mountain Ranges, covered with Caspian or Hyrcanian deciduous forests. Field sampling started in summer 2015. More than ten soil pedons with loess parent material were investigated based on former studies. Finally, six representative modern pedons were selected and dug in an east-west direction on loess deposits. The climate data shows that precipitation varies from 500 mm in Qapan (Pedon 1) to up to 800 mm in Neka. Physiochemical properties of soils were studied using standard methods. Thin section prepared for soil micromorphological studies were studied and interpreted based on Bullock et al. and Stoops guideline using a polarizing microscope. The micromorphological index of soil development (MISECA), suggested by Khormali et al (2003), was calculated. Also, color indices were calculated based on Hurst (1977), Torrent (1983), and Alexander (1985) by using the Munsell color chart. In all color indices, Munsell color hue converts to a single number. Results and Discussion The results showed that the downward decalcification and the subsequent clay illuviation were the main criteria influencing the assessment of soil development in this study. So, all of the soils host argillic and calcic horizons and are classified as Alfisols and Mollisols. Micromorphological studies confirmed the morphology studies in the field and the results of physico-chemical analyses. MISECA index showed pedological changes in different pedons in the studied areas. A significant positive relationship between climate gradient (increasing rainfall) and MISECA index was found. The area and thickness of clay coatings show an increasing trend with rainfall. Occurrence and preservation of clay coatings are more pronounced in more humid regions with illite and vermiculite as the dominant clay minerals. These minerals reduce the shrink/swell potential and increase the number of clay coatings present. In Argillic horizons of all pedons, except Toshan, dominant b-fabric is speckled due to carbonate leaching, while in Toshan, it is striated b-fabric. In calcite horizon, b-fabric is crystallitic. The correlation of various forms of iron with three color indices of Hurst, Torrent, and Alexander showed that Torrent and Alexander indices were better than the other one for the study area. Moreover, there was a good correlation between MISECA and Torrent color index. Conclusion The results showed that the soil evolution in the studied areas is strongly influenced by soil formation factors, especially in a climate which shows a change in the micromorphological characteristics of soils. With increasing the rainfall from the east to the west in this gradient, the amount and thickness of clay coating, as well as secondary calcium carbonate accumulation, change significantly. In addition, the micromorphological and color indices of soil evolution can be used as two indicators for assessing the effects of rainfall gradient on soil formation in northern Iran. On the other hand, knowledge of the development of modern loess-derived soils could help to better understand the paleoenvironment.
Soil Genesis and Classification
Farideh Abbaszadeh Afshar
Abstract
Introduction Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. DSM is the computer-assisted production of digital maps of soil ...
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Introduction Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. DSM is the computer-assisted production of digital maps of soil type and soil properties. It typically implies use of mathematical and statistical models that combine information from soil observations with information contained in correlated variables and remote sensing images. Machine learning is a general term for a broad set of models used to discover patterns in data and to make predictions. Although machine learning is most often applied to large databases, it is an attractive tool for learning about and making spatial predictions of soil classes because knowledge about relationships between soil classes and environmental covariates is often poorly understood. Our objective was to compare multiple machine learning models (multinomial regression logistic, boosted regression trees and decision tree) for predicting soil great groups at Bam distinct in Kerman province. Materials and Methods The study area, Bam district was located between 58°4΄17˝ to 58°28΄8˝ E longitudes and 28°52΄51˝ to 29°9΄29˝ N latitudes (Fig. 1), at Kerman province, (Southeastern Iran). The area is surrounded by mountains (dominantly limestone and volcanic) from northwest toward southeast with major landforms included young alluvial fans and pediment, clay flat and hills. The mean annual precipitation, temperature and potential evapotranspiration are respectively 64 mm, 23.8◦C and 3000 mm with Aridic and Hyper thermic soil moisture and temperate regimes Stratified sampling scheme were defined in 100000 hectares, and 126 soil profiles were excavated and described by Key of soil taxonomy. Our objective was to perform and compare multiple machine learning models for predicting soil taxonomic classes (great group level). The models were used in this study including, multinomial logistic regression (MLR), boosted regression trees (BRT) and decision tree (DT). We used 80/20 training/testing split (80% of the pedon observations were used for model training and 20% for model testing). Kappa index (KI), overall accuracy (OC), Brier scores (BS), User accuracy (UA) and producer accuracy (PA) were used to compare model accuracy. Results and Discussion The profile description revealed the presence of two soil orders: Entisols and Aridisols that, subdivided in six suborders and eight great groups: Haplosalids, Haplocambids, Haplocalcids, Haplogypsids, Calcigypsids, Calciargids, Petrocalcids and Torriorthents. This testifies to the wide pedodiversity of the study area, considering that is characterized by the presence of eight soils great groups. Results showed that the geomorphology map contributed importantly to the prediction accuracy. This can be explained by the fact that the geomorphological surfaces have formed recently, or during a geological period with soil formation under conditions close to those of current processes in the arid regions. Terrain attributes and finally remote sensing indices after geomorphic surface were imported as predictors in the prediction. The best prediction result was obtained when characteristics derived from terrain, remote sensing and geomorphological processes were used together and when differentiation of geomorphological processes and overall heterogeneity identification and stratification of the study area was made. In areas where the distribution of predictors was more homogenous, the models can better understand and connect predictors and response. The spatial distribution of soils in the study area followed the distribution pattern of most geomorphological and terrain attributes. The results of model comparing indicated that decision tree was consistently the most accurate. The results of prediction accuracy of soil groups showed that the highest accuracy related Haplosalids, Calcigypsids and Petrocalcids soil great groups. The lowest of predictive quality was observed for Haplocalcids in three approaches. As a reliable and flexible approach, decision tree could be used successfully to prepare continuous digital soil maps. Conclusion The application of decision trees for prediction of soil types could be a promising alternative. In digital soil mapping, the best prediction result was obtained when parameters derived from terrain, remote sensing and geomorphological processes were used together and when differentiation of geomorphological processes and overall heterogeneity identification and stratification of the study area was made. In areas where the distribution of predictors was more homogenous, the models can better understand and connect predictors and response. Altogether, an extended digital terrain analysis approach and clear description of geomorphological, geological and pedological processes could be a promising key technology in future soil mapping.
Soil Physics, Erosion and Conservation
Hamid Kelishadi; Mohammad Reza Mosaddeghi; Shamsollah Ayoubi; Hossein Asadi
Abstract
Introduction Soil erosion is one of the major obstacles to sustainable development. A large part of Iran has an arid and semi-arid climate, without vegetation with suitable density or even completely without vegetation. Therefore, many parts of the country face high erosion and soil losses. Previous ...
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Introduction Soil erosion is one of the major obstacles to sustainable development. A large part of Iran has an arid and semi-arid climate, without vegetation with suitable density or even completely without vegetation. Therefore, many parts of the country face high erosion and soil losses. Previous studies showed an increased trend of soil erosion in Iran. Because in situ measurement of soil erosion at the farm or watershed scale is expensive and time-consuming, estimation of soil erosion from easy and ready parameters can be useful. It is well-known that aggregate stability can affect soil erosion. There are many methods developed to measure soil aggregate stability, but there is no specific method that can be used for a wide range of soil types under different land uses. This study was done to compare different methods of aggregate stability determination (i.e., splash rate measurement, shear strength measured with fall-cone penetrometer and wet sieving). Materials and Methods Twenty-eight soil samples with different textures, equivalent calcium carbonate, and organic matter were collected from surface soil layers in Isfahan and Chaharmahal-va-Bakhtiari provinces. Particles size distribution of studied the soil was measured. Very coarse sand (VCS), coarse sand (CS), medium sand (MS), fine sand (FS) and very fine sand (VFS) were measured according to ASTM sieves. Also, four components of silt (0.035-0.05, 0.02-0.035, 0.01-0.02 and 0.002-0.01 mm) were measured according to Stock's law by the pipette method. Geometric mean diameter and geometric standard deviation of particles were calculated by Shirazi and Boeresma (1984) relations. Soil splash rate (S) was measured with rainfall simulator, near-saturated soil shear strength (τ) was determined using the fall-cone penetrometer, and mean weight diameter (MWD) and geometric mean diameter (GMD) of soil aggregates were measured by the wet sieving. Results and Discussion The results of this study showed that the sand, silt and clay contents were, respectively, in the ranges of 1.5-51%, 34-73% and 11-35% in the studied soils. Most of the sand particles belonged to the FS and VFS (0.05-0.25 mm) fractions and most of the silt fraction was in the very fine silt (0.002-0.01 mm) fraction. The range of organic matter was 0.08 to 8.8% and calcium carbonate equivalent varied in the range between 10% and 63%. Generally, soil aggregate stability was low and splash erosion was high in the studied soils. The results showed that S showed significant correlations with sand, silt, and geometric mean diameter and geometric standard deviation calculated using all particle fractions, VCS, CS, MS, FS, fine silt and very fine silt. Soil shear strength (τ) had significant correlations with silt, very fine silt, geometric mean diameter and geometric standard deviation. The GMD and MWD had significant correlations with soil organic carbon. The results showed that S had significant and negative correlations with τ and GMD, and there were significant and positive correlations between τ with GMD and MWD. The S was mainly dependent on particle size distribution, while GMD and MWD mainly depended on soil organic carbon. However, both particle size distribution and soil organic carbon would affect τ. This finding might be justified by differences between mechanisms which are responsible for particles detachment. The energies induced by raindrop impact and slaking are the main forces and mechanisms responsible for detachment of particles in splash erosion and wet sieving tests, respectively while the cohesive forces between particles mainly govern soil strength in the fall-cone penetrometer test. The studied soils were clustered based on intrinsic soil properties (i.e., texture, CaCO3 and organic carbon) by using K-means method in MATLAB software, in order to evaluate the capability of different methods in different soil groups. The least significant difference (LSD) test was used in a completely randomized design for mean’ comparisons between the clusters. The mean comparison results showed that the three methods similarly predicted the variation of aggregate stability in different soil clusters. The results of clustering showed that the soil cluster with high organic matter, silt and clay contents and low sand content was more stable than other clusters. Conclusion Three methods similarly predicted the variation of aggregate stability in different soil groups; therefore, the methods might be used alternatively for aggregate stability determination. Fall-cone penetrometer can be introduced as an in situ method for evaluation of aggregate stability against splash erosion.
M. Karami; S. Ayoubi; H. Khademi
