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Soil petroleum hydrocarbon contamination in the wetlands could cause ecological risk, especially through leakage into water reservoirs. So, the detection of the spatial variability of total petroleum hydrocarbons (TPH) in these soils is very crucial. The variability of TPH and its associations with magnetic susceptibility (χ

A b s t r a c t. The potential use of optimized support vector machines with simulated annealing algorithm in developing prediction functions for estimating soil aggregate stability and soil shear strength was evaluated. The predictive capabilities of support vector machines in comparison with traditional regression prediction functions were also studied. In results, the support vector machines achieved greater accuracy in predicting both soil shear strength and soil aggregate stability properties comparing to traditional multiple-linear regression. The coefficient of correlation (R) between the measured and predicted soil shear strength values using the support vector machine model was 0.98 while it was 0.52 using the multiple-linear regression model. Furthermore, a lower mean square error value of 0.06 obtained using the support vector machine model in prediction of soil shear strength as compared to the multiple-linear regression model. The ERROR% value for soil aggregate stability prediction using the multiple-linear regression model was 14.59% while a lower ERROR% value of 4.29% was observed for the support vector machine model. The mean square error values for soil aggregate stability prediction using the multiplelinear regression and support vector machine models were 0.001 and 0.012, respectively. It appears that utilization of optimized support vector machine approach with simulated annealing algorithm in developing soil property prediction functions could be a suitable alternative to commonly used regression methods. K e y w o r d s: soft computing, support vector machines, simulated annealing algorithm, soil shear strength, aggregate stability

The present study was conducted to evaluate the effectiveness of combining proximal, and remote sensing with environmental variables for predicting USDA (United States Department of Agriculture) so...

Near-saturated hydraulic properties are the key parameters for water transport models in the unsaturated zone and essential for management practices. This study was conducted to compare efficacy of multiple linear regression (MLR) and hybrid method of genetic algorithm with artificial neural network (GA-ANN) for prediction of near-saturated soil hydraulic properties in Moghan plain, north-western Iran. The results of MLR analysis indicated that this method had low potential to predict near-saturated soil hydraulic properties in the study area, which only could explain 14–38% of variability in the studied properties. Otherwise, GA-ANN was much higher powerful that could explain about 35–80% of total variability in the mentioned properties in the study area. The results of sensitivity analysis suggest that soil particle size distribution, organic matter, electrical conductivity and relative bulk density were the most crucial with variety of priorities for explaining variability of the near-saturated soil hydraulic properties in the study area in the semiarid region. In overall, it was concluded that application of intelligent system using the easily available soil properties as predictors could provide reliable estimates of near-saturated soil hydraulic properties at the filed scale.

In the context of the continued increased global uptake of fingerprinting procedures to explore fluvial sediment sources, far less attention has been paid to dust source tracing and especially using different particle size fractions and low-cost tracers such as colour and magnetic susceptibility. The objective of this study, therefore, was to apportion local dust storm source contributions for the < 63-μm and 63–125-μm fractions of dust samples in a case study in central Iran. Colour and magnetic susceptibility properties were measured on 62 source samples and six dust storm samples. Statistical methods were used to select four different composite fingerprints for discriminating the dust sediment sources. These statistical approaches comprised (1) the Kruskal–Wallis H test (KW-H), (2) a combination of KW-H and discriminant function analysis (DFA), (3) a combination of KW-H and principal components and classification analysis (PCCA), and (4) a combination of KW-H and a general classification and regression tree model (GCRTM). Local dust source contributions were ascribed using a Bayesian un-mixing model using the final composite fingerprints. For both the < 63- and 63–125-μm fractions, the different composite signatures consistently suggested that alluvial fan material was the dominant source of the dust samples. The root mean square differences between the apportionment results using the different fingerprints ranged from 0.5 to 1.6% for the < 63-μm fraction and from 1.8 to 5.8% for the 63–125-μm fraction. The Wald-Wolfowitz runs test was used to compare the posterior distributions of the predicted source proportions created using the alternative final composite fingerprints and the results indicated that most of the pairwise comparisons were significantly different (p ≤ 0.05). For the < 63-μm fraction, the RMSE and MAE estimates of divergence between the modelled and known virtual source mixtures using the different final composite signatures ranged between 1.5 and 23.4% (with a corresponding mean value of 9.4%). The equivalent estimates for the 63–125-μm fraction were 1.2–20.1% (8.3%). The findings clearly demonstrate that colour and magnetic susceptibility tracers offer low-cost options for apportioning dust sources.

The wheat production variability is not well-understood in hilly region, especially in loess-derived soils of Golestan province in Iran with a sub-humid climate. Topography can greatly influence the production of agricultural crops by affecting soil quality. A study area located in Golestan province was selected in order to assess the spatial variability of wheat production and to develop regression models between the crop, soil properties, and topography attributes. The samples of wheat and soil were randomly taken from 100 points at different hillslope positions (i.e., shoulder, back-, foot-, and toe-slope). The soil physicochemical analysis and the measurement of wheat yield components were conducted. The digital elevation model (DEM; 10 m resolution) was used, and the topographic attributes (i.e., elevation, slope, wetness index, stream power index, curvature, erosivity factor, and watershed specific area) were calculated. The results showed that the greatest total yield and the highest grain yield were estimated to be 14.53 and 4.41 ton ha−1, respectively, in areas with a slope of less than 10%, which were significantly higher than those in the steep areas (slope classes of 10–30% and > 30%). The highest and the lowest total yields, with average values of 15.82 and 5.68 ton ha−1, were observed in the toeslope and shoulder slope positions, respectively. The greatest grain yields were obtained from the foot- and toeslope positions with the average values of 4.61 and 4.66 ton ha−1, respectively. The topographic curvature and wetness index had a significant correlation with the yield of wheat. According to the regression equations, topographic indexes can well justify the spatial variability of wheat yield, indicating the importance of these factors by influencing the distribution of moisture during the process of wheat production in the study region. The enhancements of wheat yield components in the lower slope positions could be attributed to an increase in soil depth and plant available water as well as to the accumulation of further soil organic matter and nutrient elements, including nitrogen and potassium, in such positions as a result of soil redistribution. Moreover, the results illustrated that by using easy accessable, cheap, and none destructive data (DEM derivatives and soil properties); it is possible to predict the production yield of wheat with a reliable estimation. We concluded that for better farming management and productivity in hilly regions, topographic attributes should be considered for plantation. Therefore, this study introduces the most suitable slope positions and topographic attributes for crop production with the least soil degradation. Shoulder and backslope positions are the most unsuitable slopes possibly better for orchards while toeslopes and footslopes could be used for intensive crop production.

The simultaneous effect of the environmental factors as determinants of soil invertebrate community structures is little understood. This study was carried out at two depths (0–10, 10–20 cm) in the forest ecosystem located in Golestan province, north of Iran. The abundance, diversity, and richness of twelve taxonomic groups of soil invertebrates were modeled using three groups of environmental attributes consisted of topographic attributes and vegetation indices, soil properties and tree diversity indices as predictors. Genetic algorithm was applied to identify and select the effective environmental attributes; the non-linear machine learning, random forest (RF), was employed to predict abundance, diversity, and richness of invertebrates and to determine the most important variables controlling the horizontal and vertical distribution of invertebrates in the forest ecosystem. Results showed that RF was an accurate model for predicting abundance (RMSE = 0.15), diversity (RMSE = 0.18) and richness (overall accuracy = 0.51, kappa = 0.34) of soil invertebrates at the first depth. Similarly, at the second depth, RF showed a good prediction for abundance (RMSE = 0.20), diversity (RMSE = 0.18) and richness (overall accuracy = 0.23, kappa = 0.12) of soil invertebrates. Horizontal distribution of invertebrates' abundance was affected by soil microbial respiration (Resp), land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Shannon index of trees and soil moisture, while vertical distribution of invertebrates' abundance was more related to soil organic carbon (SOC) and soil moisture. Variable importance analysis confirmed that horizontal and vertical types of distribution of Shannon index of soil invertebrates were affected by the same factors, including land temperature, NDVI, soil respiration and moisture. Also, soil moisture, soil respiration, NDVI and richness of trees had significant effects on horizontal distribution of soil invertebrates' richness, but silt, Normalized Ratio Vegetation Index (NRVI), soil respiration and moisture had positive effects on vertical distribution of richness. In conclusion, that digital soil mapping (DSM) model could be apparently considered as a powerful and economical method in biodiversity modeling. Other supplementary data such as invertebrates feeding web are also suggested as the input dataset for further investigations.

The present study was conducted to explore the relationships between magnetic measures and some heavy metals in some igneous rocks in north-western Iran. For this purpose, four major rocks including ultrabasic, basalt, granite and andesite were selected and totally 60 samples were collected. The collected samples were analyzed for magnetic measurements ( $$ \chi_{\text{lf}} $$ , $$ \chi_{\text{hf}} $$ , $$ \chi_{\text{fd}} $$ ) and heavy metals concentration (Fe, Cr, Cu, Zn, Co, Mn and Ni) by atomic absorption spectroscopy. Some samples were analyzed by XRD for iron mineral characterization. The results indicated that the highest and lowest $$ \chi_{\text{lf}} $$ and all measured heavy metals were found in ultrabasic (as a basic rock) and in granite (as an acidic rock), respectively. X-ray analysis confirmed the higher presence of magnetite/maghemite in basic rocks. Positive and significant correlations were found between magnetic susceptibility at low frequency and all heavy metals’ concentration except for Ni. Overall, magnetic susceptibility serves as a preliminary assessment of rock samples, providing rapid, non-destructive, economical and easy information about heavy metal concentration in igneous rocks in the study area.

In order to investigate the development of forest soils formed on loess, six representative modern soil pedons were selected along a precipitation gradient extending from eastern Golestan (mean annual precipitation, MAP = 500 mm) to eastern Mazandaran Provinces (MAP = 800 mm). Physiochemical, micromorphological and magnetic properties, as well as clay mineralogy of soils were studied using standard methods. Soils are mainly classified as Alfisols and Mollisols. Downward decalcification and the subsequent clay illuviation were the main criteria of soil development in all study areas. Pedogenic magnetic susceptibility of pedons studied varied systematically across the precipitation gradient in Northern Iran, increasing from 14.66 × 10−8 m3 kg−1 at the eastern part to 83.75 × 10−8 m3 kg−1 at the western margin of this transect. The frequencydependent magnetic susceptibility showed an increasing trend with rainfall as well. The micromorphological study of soils indicated that there is a positive relationship between climate gradient (increasing rainfall) and the micromorphological index of soil development (MISECA). The area and thickness of clay coatings showed an increasing trend with rainfall. Grain size analysis indicates that pedogenic processes are responsible for changing original grain size distribution of loess in our soils. The correlation achieved among modern soil properties and precipitation could be applied to the buried paleosols in the whole study area to refer degree of paleosol development and to reconstruct the paleoclimate.

We examine the potential of magnetic susceptibility measurements to discriminate different soil drainage classes in the Gandoman region, central Iran. Four soil drainage classes, comprising poorly drained (PD), somewhat poorly drained (SPD), moderately well drained (MWD) and well drained (WD), were identified, and a total number of 48 soil profiles were excavated and studied. The soil samples were collected from all studied profiles from the genetic horizons individually. Magnetic susceptibility was measured at both low (0.46 kHz) and high (4.6 kHz) frequencies. The crystallized and amorphous iron forms were also measured using citrate-bicarbonate-dithionite solution and oxalate-ammonium extracts, respectively. The highest magnetic susceptibility was observed in WD soils, whereas the lowest susceptibility was observed in PD soils. The results of the predictor models developed by discriminate analysis showed that the use of magnetic susceptibility and iron forms could correctly predict about 90.9, 78.6, 85.7 and 88.9% of all profiles in WD, MWD, SPD and PD classes, respectively. Overall, the results indicate that magnetic susceptibility could be applied as a marker for the discrimination of drainage classes in the study area. Magnetic susceptibility is thus a quickly accessible and low-cost indicator for soil drainage classes for landownerships and subsequent analyses.

The objective of this study was to characterize the morphological and magnetic properties of Sari loess-paleosol section in northern Iran for paleopedologic and paleoenvironmental interpretation. The section consisted of a modern soil (MS) and three paleosols (PS1, PS2, PS3) separated by loess layers (LS1, LS2 and LS3). Based on particle size distribution, clay mineralogy, carbonates distribution and size of secondary carbonates, pedogenic development of the soils was in order of PS3>PS2>PS1=MS. Presence of redoximorphic features in PS3 was attributed to alternate stagnic saturation due to local water or high precipitation. Dominance of smectite and vermiculite as well as large carbonated dolls in PS3 indicated suitable environment and sufficient time for pedogenic development. Magnetic properties (χlf and χfd%) were distinctly higher in MS, PS1 and PS2 when compared to loess layers. The Lowest magnetic properties values were observed in PS3 which can be the result of ferrimagnetic minerals destruction under hydromorphic conditions. The highest Fed content occurred in PS3, however, low χlf/Fed ratio indicated that majority of the iron minerals in PS3 are not magnetic. In conclusion, the particle size distribution, clay mineralogy and carbonates features were indicative of pedogenesis intensity, whereas, magnetic properties were useful to characterize the pedogenic environment.

Viscosity, density, pour point, flash point, and oxidation stability are some quality properties of engine oil. The entrance of any contamination into the engine oil can change its properties. In the present study the effect of calcite and silica sand dust particles on the quality of engine oil, namely, Sepahan Oil Production API, SC-CC, SAE 40, was investigated. The presence, amount, and particle size of silica and calcite in the engine oil were determined by X-ray diffraction (XRD), inductively coupled plasma spectroscopy (ICP), and energy dispersive X-ray spectroscopy (EDX), respectively. The concentrations of the silica and calcite particles (0, 1, 2, and 3 g/L) were used according to the dust conditions in the east of Isfahan, Iran. The quality of the oil was measured after 20 hours of the engine operation. The results showed viscosity, density, and the pour point of the engine oil were significantly (P

Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model (DEM) and the Landsat Enhanced Thematic Mapper (ETM), respectively. These factors were contrasted for 334 soil samples (depth of 0–30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon (SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions.

The characterization of magnetic minerals and the relationship of these minerals to the magnetic susceptibility of soils that have developed on various parent materials can provide valuable information to various disciplines, such as soil evolution and environmental science. The aim of the study reported here was to investigate variations in the magnetic susceptibility (χ) of soils in western Iran due to differences in lithology and to examine the relationship of χ to ferrimagnetic minerals. Eighty samples were collected from eight parent materials taken from both intact rocks and associated soils. The soil parent materials included a range of igneous and sedimentary rocks, such as ultrabasic rocks (Eocene), basalt (Eocene), andesite (Eocene), limestone (Permian), shale (Cretaceous), marl (Cretaceous), and the Qom formation (partially consolidated fine evaporative materials, early Miocene). The 80 samples were analyzed for χ using a dual-frequency magnetic sensor and for mineralogy using X-ray diffraction (XRD). The highest χ values were found in the ultrabasic rocks and associated soils, while the lowest χ values were observed in the limestone rocks and associated soils. The pedogenic processes significantly enhanced the χ values of soils developed on the sedimentary rocks due to the formation of ferrimagnetic minerals. In contrast, χ values decreased as a result of pedogenic processes in soils developed on igneous rocks due to the dilution effects of diamagnetic materials, such as halite, calcite, phyllosilicates, and organic matter. The significant positive correlation between the XRD peak intensity of the maghemite/magnetite particles and χ values confirmed that χ values in soils are largely controlled by the distribution and content of ferrimagnetic minerals. These results show that χ measurements can be used to quantify low concentrations of ferrimagnetic minerals in the soils of semiarid regions.

Increasing demand for food production, global change, and growing population are the enormous challenges in recent decades. Accurate soil maps and adequate models are indispensable tools to assist managers, scientists, and decision-makers in addressing these challenges. Legacy soil polygon maps at national and regional scales are available widely, but lack detail, and therefore effective methods such as digital soil mapping (DSM) are needed to disaggregate these maps. The objective of this study was to disaggregate a legacy 1:1,000,000 soil map by three methods of disaggregation: a supervised classification method (DSMART algorithm) and two unsupervised classification methods including fuzzy c-means (FCM) and k-means (KM) clustering in Borujen region, Chaharmahal-Va-Bakhtiari Province, Central Iran for both great group and subgroup Taxonomic levels. Although field validation indicated that the accuracy of the disaggregated soil maps was lower than that of the conventional soil map at both levels of Soil Taxonomy, disaggregated approaches produced more detailed soil maps when compared with the first, second, and third most probable soil classes of the conventional soil map. The higher overall accuracy of the conventional soil map was due to soil association units which consist of more than one soil taxonomic class. FCM and DSMART methods produced more accurate and detailed disaggregated soil maps than KM clustering algorithm at the great group and subgroup levels, respectively. We concluded that the decision on what method to use depends on the map, the level of available information (map detail), available expert knowledge, and the availability of the soil unit composition percentages in the soil map legend.

Knowledge about distribution of soil properties over the landscape is required for a variety of land management applications and resources, modeling, and monitoring practices. The main aim of this research was to conduct a spatially prediction of the top soil properties such as soil organic carbon (SOC), calcium carbonate equivalent (CCE), and clay content using digital soil mapping (DSM) approaches in Borujen region, Chaharmahal-Va-Bakhtiari province, central Iran. To achieve this goal, a total of 334 soil samples were collected from 0 to 30 cm depth. Three non-linear models including Cubist (Cu), Random Forest (RF), Regression Tree (RT) and a Multiple Linear Regression (MLR) were used to link environmental covariates and the studied soil properties. The environmental covariates were obtained from a digital elevation model (DEM) and satellite imagery (Landsat Enhanced Thematic Mapper; ETM). The model was calibrated and validated by the 10-fold cross-validation approach. Root mean square error (RMSE) and coefficient of determination (R2) were used to determine the performance of the models, and relative RMSE (RMSE%) was used to define prediction accuracy. According to the RMSE and R2, Cu and RF resulted in the most accurate predictions for CCE (R2 = 0.30 and RMSE = 9.52) and clay contents (R2 = 0.15 and RMSE = 7.86), respectively, while both of RF and Cu models showed the highest performance to predict SOC content (R2 = 0.55). Results showed that remote sensing covariates (Ratio Vegetation Index and band 4) were the most important variables to explain the variability of SOC and CCE content, but only topographic attributes were responsible for clay content variation. According to RMSE% results, it could be concluded that the best model is not necessarily able to make the most accurate estimation. This study recommended that more observations and denser sampling should be carried out in the entire study area. Alternatively, stratified sampling by elevation in homogeneous sub-areas was recommended. The stratified sampling probably will increase the performance of models.

In this study, pedotransfer functions were developed using magnetic measures and soil properties to evaluate the concentrations of some heavy metals (Cr, Co, Fe, Cu, Ni, Mn, and Zn) in soils developed on some igneous rocks (gabbro, diorite-gabbro, gabbro-diorite, monzodiorite, spilite basalt, granite, and porphyritic-granite) in Kurdistan province, western Iran. A total number of 105 samples from both rocks and the associated surface soils were taken; magnetic susceptibility at two frequencies, concentrations of selected metals, and physcio-chemical properties of soils were then measured. The highest concentrations of metals were obtained for Co, Ni, Fe, Mn, and Cu in soils developed on gabbro and for Zn and Cr in soils developed on porphyritic-granite and spilite basalt, respectively. Magnetic measurements in soils and associated rocks could explain 78, 74, 77, 72, 75, 68, and 69% of the total variability of Zn, Cu, Ni, Fe, Mn, Co, and Cr concentrations in the studied soils. Inclusion of soil physcio-chemical properties did not significantly improve the prediction. Therefore, it seems that magnetic measurements, as fast, non-destructive, and cost-effective tools, could solely be used to successfully predict heavy metals in natural ecosystems, especially in reconnaissance scale studies.

This research was conducted to evaluate the utilization of magnetic susceptibility measurements in the assessment of metal concentrations in soils developed on a range of parent materials in northwestern Iran. Eighty surface soil samples were collected from eight parent rocks including ultrabasic rocks, basalt, andesite, granite, marl, limestone, Qom formation, and shale. The collected samples were assessed to determine magnetic susceptibility at low frequency (χlf) and concentrations of some metals comprising chromium (Cr), iron (Fe), copper (Cu), nickel (Ni), zinc (Zn), cobalt (Co), and manganese (Mn). The results showed that the highest levels of metals and χlf were observed in basic and ultrabasic soils. Strong positive correlations (P 0.01) detected between χlf and Fe (0.87), Mn (0.78), Zn (0.74), Ni (0.90), Co (0.78), and Cr (0.90) in all samples indicated a potential for using magnetic susceptibility in semi-quantitative estimation of metal concentrations in soils of natural ecosystems. Multiple linear regression between metal contents and χ

Due to the heavy costs of wind erosion control measures, the correct selection of technical methods is indispensable for a sustainable land management in arid and semiarid regions. Thus, th...

Temperature is a key factor that can affect soil properties and processes. Surface tension-viscous flow (STVF) theory is widely used to explain the temperature effects on soil hydraulic properties. We hypothesized that one of the reasons for deviation of measured soil water retention data from the STVF theory predictions is due to the temperature effects on soil structural stability (i.e., near-saturated pore size distribution [PSD]). Therefore, the objective of this study was to evaluate the effect of temperature on soil structural stability as characterized using the high energy moisture characteristic (HEMC) method in a wide range of arid and semi-arid soils of Iran. Aggregates of 28 soil samples were fast-wetted to mimic the natural conditions of soils during rainfall and irrigation. Four ambient temperatures (5, 10, 15 and 30 °C) were applied using an incubator during the wetting of aggregates, and after that HEMCs in the matric suction (h) range of 2 to 50 hPa were measured. Both van Genuchten (VG) and modified van Genuchten (MVG) models were fitted to the HEMC data and structural stability indices were calculated. The results generally showed that ambient temperature significantly altered soil PSD and structural stability, and as temperature increased: (i) an expansion of entrapped air initiated by the formation of microbubbles, was enhanced, (ii) water content at near full saturation h = 2 hPa (θ2hPa) decreased, and at h = 50 hPa (θ50hPa) it increased, leading to a reduction in the volume of drainable pores [VDP], and thus (iii) the soil structural index [SI] and slope at the inflection point of HEMC [Si] decreased, and (iv) amount of macropores (h, 2–12 hPa) was markedly decreased, however, micropores (h, 12–50 hPa) were not notably affected or were increased at high temperature due to intensive shifting of macropores into micropores. These findings imply that soil structure was damaged due to loosening effects of high temperatures on inter-particles bonds and increased repulsive forces between clay particles. Cluster analysis showed that the effect of ambient temperature on the structural stability was greatest in carbonate-rich soils with low clay and organic carbon (OC) contents and was least in the soils rich in OC. The results of this study could be important for projecting the effect of global warming and climate change on soil structure and erosion.

Post-fire erosion is a main concern to society because it has inflicted serious damages in managed ecosystems. In this study, the impacts of fire and time after fire events on soil erosion (as predicted using 137Cs technique) and on some soil chemical and physical properties were investigated in steep rangelands of western Iran. Three sites in rangelands with similar slope gradients and parent materials were selected, and within each site, the burnt (1, 5, and 10 years after the fire events) and the unburnt treatments were studied and soil samples were collected from five depths (0–2.5, 2.5–5, 5–10, 10–20, and 20–40 cm) with three replicates. The results indicated that soil organic matter (SOM), total nitrogen (TN), available phosphorus (Pava), available potassium (Kava), electrical conductivity (EC), and bulk density (BD) were significantly different between burnt and unburnt treatments for two times (1 and 5 years) after fire events. No significant difference by Duncan’s test was obtained for these properties between the 10 and 5 years after fire events. In addition, clay and sand contents and magnetic measures (χlf, χhf) were significantly different between burnt and unburnt treatments for all the three times after fire. The results of soil erosion by the 137Cs technique showed that profile distribution model (PDM) estimated the mean soil erosion rate of 38.9 and 23.02 Mg ha−1 year−1 in the three studied years in the burnt and the unburnt rangelands, respectively. Fire events increased soil erosion rate and altered soil physical, chemical, and magnetic properties in the studied steep rangelands. Overall, the results confirmed that the 137Cs technique could be used as a rapid and efficient model to determine soil degradation in the rangelands. The fire diminished soil organic matter and, subsequently, reduced aggregate stability, and increased soil erosion and degradation in the burnt rangelands. Hence, understanding historical contexts of fire occurrences is paramount to increase our capacity for ecological transformations and management in the face of the critical situation.

There was an error in the original publication [1] in ‘4.1. The Web Service Advantages and Limitations’, on page 18, in a sentence regarding Soil-Spec4GG. The sentence was incorrectly typed/inserted. The construction of the paragraph positioned the Soil-Spec in the wrong place, and gave a misinterpretation. The authors strongly state that Soil-Spec4GG is a confidently reliable project. Our idea in the texts was to emphasize the importance of this project, but our mistyping created the opposite. We humbly apologize and ratify that it was not an intentional error. We hope that this effort maintains the respect of the scientific community. A correction has been made to 4.1. The Web Service Advantages and Limitations. Replaced “…other ongoing global spectral community efforts (e.g., Soil-Spec4GG) are more vertical with researchers subsuming people’s spectral data without a data sharing policy that fully acknowledges and credits the user’s labor and costs of field data collection”. with “Other global spectral communities are also making similar efforts as our work which will increase the spectroscopy efforts (e.g., Soil-Spec4GG). On the other side, there are vertical groups with researchers subsuming people’s spectral data without a data sharing policy that fully acknowledges and credits the user’s labor and costs of field data collection”. Supplementary Materials, Table S1, indicated in the Materials and Methods. Consider the following footnotes to this material: The open access databases mentioned: Lucas and ICRAF are available in http://esdac.jrc.ec.europa.eu/content/lucas-2009-topsoil-data (accessed on 21 February 2022) and https://doi.org/10.34725/DVN/MFHA9C (accessed on 21 February 2022), respectively. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original publication has also been updated.

This study was executed to explore soil redistribution and soil quality changes induced by land degradation and then rehabilitation by orchard plantation in different slope positions in a semi-arid region in central Iran. A total of 72 surface soil samples (0–30 cm) were collected from three land uses (natural rangelands, dryland farming, and apple orchards) in four slope positions (shoulder, backslope, footslope, and toeslope). The soil physicochemical properties and magnetic parameters were measured, and soil redistribution was determined in the selected soil samples using the 137Cs technique. The results showed that rangeland degradation and, subsequently, rainfed cultivation, led to a significant decline in the soil quality indicators, such as soil organic matter (SOM), total nitrogen (TN), available potassium (Kava), and available phosphorous (Pava), thus incurring further soil loss, as determined by the 137Cs technique. Conversely, the conversion and rehabilitation of drylands to apple orchards cultivated on the contour terraces improved soil quality significantly and decreased soil loss (p < 0.05) and soil quality grade (p < 0.01). Additionally, the findings indicated that slope positions relative to land use change had a reasonable impact on the variability of soil properties and soil loss and deposition. The results of 137Cs analysis showed that the drylands had the highest soil loss (185.3 t ha−1 yr−1) and maximum sedimentation (182. 5 t ha−1 yr−1) in the shoulder and footslope positions, respectively. The random forest model applied between 137Cs inventory and soil properties indicated that calcium carbonate equivalent (CCE), TN, Pava, Kava, and bulk density (ρb) could explain 75% of the total variability in 137Cs inventory with high R2 (0.94) and low RMSE (111.29). Magnetic measurements have shown great potential as a cost-effective and fast method for assessing soil redistribution in hilly regions, as confirmed by the findings of the 137Cs analysis, which agreed well with the magnetic susceptibility at low frequency (χlf). Overall, the results confirmed that restoring abandoned dryland by orchard cultivation may improve soil quality and diminish soil loss in the semi-arid region of Iran. However, further research is required to assess other aspects of the ecosystem affected by this restoration.

In the digital soil mapping (DSM) framework, machine learning models quantify the relationship between soil observations and environmental covariates. Generally, the most commonly used covariates (MCC; e.g., topographic attributes and single-time remote sensing data, and legacy maps) were employed in DSM studies. Additionally, remote sensing time-series (RST) data can provide useful information for soil mapping. Therefore, the main aims of the study are to compare the MCC, the monthly Sentinel-2 time-series of vegetation indices dataset, and the combination of datasets (MCC + RST) for soil organic carbon (SOC) prediction in an arid agroecosystem in Iran. We used different machine learning algorithms, including random forest (RF), Cubist, support vector machine (SVM), and partial least square regression (PLSR). A total of 237 soil samples at 0–20 cm depths were collected. The 5-fold cross-validation technique was used to evaluate the modeling performance, and 50 bootstrap models were applied to quantify the prediction uncertainty. The results showed that the Cubist model performed the best with the MCC dataset (R2 = 0.35, RMSE = 0.26%) and the combined dataset of MCC and RST (R2 = 0.33, RMSE = 0.27%), while the RF model showed better results for the RST dataset (R2 = 0.10, RMSE = 0.31%). Soil properties could explain the SOC variation in MCC and combined datasets (66.35% and 50.82%, respectively), while NDVI was the most controlling factor in the RST (50.22%). Accordingly, results showed that time-series vegetation indices did not have enough potential to increase SOC prediction accuracy. However, the combination of MCC and RST datasets produced SOC spatial maps with lower uncertainty. Therefore, future studies are required to explicitly explain the efficiency of time-series remotely-sensed data and their interrelationship with environmental covariates to predict SOC in arid regions with low SOC content.

Clear-cutting of Oak forests and intensive cultivation on the hilly regions of the Zagros Mountains has led to plausible threats on the natural ecosystem and catastrophic floods in western Iran. This study was conducted to explore the effects of clear-cutting of natural forests in different slope positions on some soil chemical and physical properties as well as quantification of soil redistribution using magnetic susceptibility. Two adjacent sites, including natural forest and cultivated lands, were selected and a total of six transects at three soil depths (0–10, 10–20, and 20–30 cm) and a total of seventy-two soil samples were examined. Soil properties such as particle size distributions, bulk density (ρb), calcium carbonate equivalent (CCE), soil organic matter (SOM), and magnetic susceptibility at low and high frequencies were measured. Results indicated that clear-cutting and cultivation for 50 years significantly (p < 0.05) increased ρb and CCE and reduced SOM and TN. Moreover, magnetic susceptibility at four slope positions was significantly (p < 0.05) lower compared with the natural forest. Using a simple proportional model to estimate the soil loss or gain and comparing the mean χlf in any given point confirmed a high rate of soil loss in cultivated sites. In the forest soils, low soil loss in shoulder position and high rate of deposition in footslope and backslope positions were obtained. In general, it is revealed that magnetic measures could provide valuable estimates of mid-term soil erosion and sedimentation in the hilly region following land use changes.

The soil carbon pool which is the sum of soil organic carbon (SOC) and soil inorganic carbon (SIC) is the second largest active store of carbon after the oceans and it is an important component of the global carbon cycle. Hence, accurate estimation of SOC and SIC as important carbon reservoirs in terrestrial ecosystems using fast, inexpensive and non-destructive methods is crucial for planning different climate change policies. The aim of the current research was to examine the effectiveness of Vis-NIR (visible and near-infrared spectroscopy: 350–2500 nm) and MIR (mid-infrared spectroscopy: 4000–400 cm−1) to characterize and estimate soil organic matter (SOM) and carbonates as main components of soil carbon stocks in Juneqan, Charmahal va Bakhtiari, Iran. To do so, a total of 548 soil samples from this area were collected (October 2015) and analyzed in laboratory (August 2017). In order to develop models capable of predicting SOM and carbonates content, seven spectral preprocessing methods comprising Absorbance (Abs), De-trending (Det), Continuum removal (CR), Savitzky-Golay derivatives (SGD), standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and Normalization by range (NBR) were conducted along with five multivariate methods including Random Forest (RF), Partial Least-Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Gaussian Process Regression (GPR). The content of carbonates caused spectral reflectance intensity to augment on several ranges of spectrum and strong absorption feature at 2338 nm in the Vis-NIR and 714, 850, 870, 1796, 2150 and 2510 cm−1 in the MIR spectra range. SOM absorbed energy in several ranges, but also showed specific peaks in MIR. Both facts are associated with the structure of carbonates and SOM and its interaction with energy. The best combination of preprocessing and calibration models for carbonates quantification in Vis-NIR spectra was Det/PLSR (R2= 0.74, RPD= 2.19, RMSE= 6.45). For SOM, it was Det/PLSR (R2= 0.82, RPD= 2.41, RMSE= 0.75). The Det/RF (R2= 0.87, RPD= 2.44, RMSE= 0.66) for the quantification of SOM and MSC/RF (R2= 0.84, RPD= 2.84, RMSE= 5.50) for carbonates in MIR spectra range showed the greatest results. The stronger occurrence of spectral bands in MIR as well as the specificity of the absorption features indicated that this range produced better predictions. The obtained results highlighted the significant role of soil spectroscopy technique in predicting SOC and soil carbonates as key components of soil carbon stocks in the study area. Therefore, this technique can be used as a more cost-effective, time saving and nondestructive alternative to traditional methods of soil analysis.

The objective of the present study was to evaluate the efficiency of soil magnetic parameters for assessment of soil drainage classes in Juneqan district, Charmahal and Bakhtiari province, western Iran. Four soil drainage classes including well drained (WD), moderately well drained (MWD), intermittent poor drained (IPD) and poorly drainage (PD) were selected. A total of 89 soil pedons were described and soil samples were collected within the moisture control section. Magnetic susceptibility (MS) at high (χhf) and low (χlf) frequencies and frequency-dependent MS (χfd) were evaluated in the laboratory. Poorly crystalline iron (Feo) and pedogenic iron (Fed) values of all soil samples were also measured. The results revealed that among the four drainage classes, PD class showed the lowest χlf and χhf values, greatest of Feo and Feo/Fed, lowest contents of Fed, as well as the highest average increase of χlf on heating (at 500 °C). However, all mentioned features showed an inverse trend in the WD class as compared to PD. The results of discriminant analysis demonstrated that magnetic measures could prosperously discriminate between the selected drainage classes in this study area (average accuracy = 83.1%). Therefore, it can be concluded that MS technique could be used as a powerful, nondestructive and fast technique for separation of soil drainage classes in the present case.

Land-use type under different topographic conditions and human activities affects soil development. We investigated the effects of land-use, topography and human activity on soil classification changes in the Toshan watershed in northern Iran. Seven representative pedons derived from loess parent materials were studied on different land-uses and topographic positions. The studied pedons in forest (FO) on backslopes and footslope were classified as Calcic Haploxeralfs and Typic Haploxeralfs, respectively. The soils in abandoned lands (AB) and orchards (OR), where formerly under natural forests, located on the shoulder and backslopes positions were classified as Calcic Haploxeralfs and Vertic Haploxeralfs, respectively. Well-developed argillic horizons as indicators for higher degrees of soil evolution were observed in more-stable areas under the natural forest or less disturbed areas. Clay lessivage through these soil profiles have led to formation of Typic or Calcic Haploxeralfs, while under croplands (CP) were classified as Typic Calcixerepts. Conversion of sloping deforested areas to CP along with inappropriate management have accelerated soil erosion, resulting in unstable conditions in which decalcification and formation of developed soils cannot occur. Paddy cultivation in flat areas has caused to reduced conditions and formation of Typic Haplaquepts. Because of unfavorable conditions for chemical weathering (e.g. lower water retention compared to more-stable areas) no vermiculite was detected in the CP. The results showed that evolution and classification of the studied soils were strongly affected by land-use type, topography and management.

Soil information is essential for sustainable management of ecosystems. In many parts of Iran, soil information is either not available or difficult to obtain. Therefore, when no detailed map or soil information is accessible in a region of interest, one way to obtain information is to extrapolate information from other parts having soil information. This study was conducted to determine whether - machine-learning extrapolation method extracted from the reference region, i.e. Zarand can estimate soil classes in the interest region, i.e. Bam and reduce the costs of soil mapping. To identify similarities between reference and interest regions, homology of soil forming factors was determined using Gower's similarity index. Then, the multinomial logistic regression was extracted from the reference region and applied into the interest region to estimate soil classes. Moreover, soil classes were predicted in the interest region using direct soil observations; finally, the accuracy of the soil maps obtained from both methods was assessed. Based on Gower index, the study regions, namely Bam and Zarand, were to some extent similar in term of soil forming factors. The results showed that although the soil map derived from the extrapolation process indicated appropriate spatial coverage of soil classes in the interest area, the resultant predictive map of calibrated-in process was slightly more accurate, i.e. higher κ, lower Brier scores. Acceptable levels of predictive accuracy (60%) were achieved using extrapolated model while costs simultaneously significantly lowered. This study put forward the view that the extrapolation method was quite useful in predicting soil classes within areas where soil mapping by calibrated-in method might be too costly or time consuming or where soil observations may not be sufficient. Nevertheless, this research encouraged us to use extrapolated method to fill the gaps in the present soil map of Iran and to apply it as the base map to increase and improve the efficiency of digital soil mapping.

Knowledge about the spatial variability of soil aggregate stability indices, soil organic carbon (SOC) in various aggregate sizes, and aggregation across the landscape is crucial for sustainable land use planning and management practices. Direct traditional measurements for the target variables, as mentioned above, are time-consuming and expensive. Thus, this study attempts to spatially predict the soil aggregate stability indices, including mean weight diameter-MWD, geometric mean diameter-GMD, water-stable aggregates-WSA, and SOC in various aggregate fractions using digital soil mapping and machine learning models using the environmental covariates as the time and cost-effective approaches. Thus, a total of 100 soil surface samples (0–10 cm depth) were collected from the natural forest, tea plantation, and paddy rice field land uses, and soil aggregate stability indices were determined following laboratory analyses. The machine learning models, including random forest (RF), k-nearest neighbors (kNN), support vector machine (SVM), artificial neural network (ANN), and the ensemble of four single models, were trained using the repeated 10-fold cross-validation method. The models were evaluated by the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and normalized RMSE (nRMSE). The modeling results demonstrated that the RF model outperformed for MWD (R2 = 0.74, nRMSE = 24.28), GMD (R2 = 0.75, nRMSE = 12.72), and WSA (R2 = 0.58, nRMSE = 10.40), while kNN and SVM models resulted in the best prediction of SOC in (meso and micro-aggregates (RMSE = 1.03 and 0.88)) and macroaggregates (RMSE = 1.49), respectively. However, the ensemble model increased the prediction accuracies for all soil targets (RI ≥ 15.78%). Moreover, the variable importance analysis showed that soil properties such as soil organic matter (SOM) and remote sense-data mainly explained the variation of soil aggregate stability indices and SOC in various aggregate fractions. Overall, the results revealed that the machine learning-based models could accurately predict the soil aggregate stability and associated SOC, and the produced maps can be a baseline map for land use planning and decision making.

We explore changes in magnetic susceptibility (χlf), and various forms of Fe and ferrimagnetic minerals after incubation by crude oil in four different textural soils. Four soil classes: clay, sandy, silt loam and sandy loam, were collected in central Iran and incubated with 0, 2.5, 5.0, 7.5 and 10% w/w of crude oil at 80% moisture of field capacity for 1, 3 and 6 months. After each period of incubation, various forms of Fe using extraction techniques, χlf and total petroleum hydrocarbon (TPH) were measured. In addition selected samples from the four soil types were analyzed by XRD for characterization of ferrimagnetic minerals. The results showed that all forms of iron and magnetic susceptibility increased significantly with increasing concentration of crude oil and incubation time. The highest dithionite extractable iron (Fed), χlf, as well as the ratio of Fed to oxalate extractable iron (Feo) were obtained for 10% w/w crude oil after 6 months incubation due to formation of ferrimagnetic minerals. The increase in the concertation of ferrimagnetic minerals (magnetite/maghemite) was verified by XRD. Significant positive correlations were obtained among χlf, Fed and TPH after 6 months of incubation. The results from this study confirm that magnetic methods can be used as a quick and cost-effective means for evaluation of TPH in polluted soils during the remediation, as compared to the chemical analyses.

There is an urgent need for reliable and cost-effective sediment source tracing techniques for apportioning aeolian sediment (sand dune) sources for guiding the selection of best management practices for wind erosion control. Accordingly, the main aim of this study was to quantify the contributions of aeolian sources to sand dune target sediment samples collected in a case study in central Iran using a source fingerprinting procedure based on low-cost fingerprints comprising colour and magnetic tracers. Colour (RGB), magnetic susceptibility (χlf and χhf) and 13 colour and magnetic indices were measured on 54 aeolian sediment source samples and ten aeolian target sediment samples. Three different composite fingerprints for discriminating and apportioning the aeolian sediment sources were selected based on a combination of statistical tests comprising the Kruskal–Wallis H test (KW-H), discriminant function analysis (DFA), principal component & classification analysis (PCCA), and a general classification & regression tree (GC&RT) model. The Modified MixSIR Bayesian un-mixing model was used to apportion aeolian source contributions using the final composite fingerprints. The composite signatures all suggested that the salt flat plain was the dominant (average 63%, and standard deviation, SD, 5.9%) source of the aeolian target sediment samples, whilst agricultural land was second (average 63%, SD 5.6%,) most important. The root mean square difference between the apportionment results based on the three composite fingerprints ranged from 0.2% to 8.3%. Pairwise comparisons of the posterior distributions for the predicted source proportions generated using the three composite signatures showed that eight of 12 pairwise comparisons were not significantly different. Virtual mixture accuracy tests of the fingerprinting models using the three composite signatures suggested errors ranging between 2.2%−20.6% (with a mean of 9.9%), 1.4%−17.0% (mean value 8.3%), and 0.03%−1.0% (mean value 0.8%). The results support the use of low-cost colour and magnetic tracers by investigations into aeolian sediment provenance.

This study reports the seasonal and spatial variations of atmospheric dust deposition rates (DDR) and dust-borne heavy metals concentrations in the city of Isfahan and its surrounding areas in central Iran. Dust samples were collected from 67 different sites on a monthly basis from June 2012 to May 2013 and topsoil samples were taken only once from the same sites. Fall and winter seasons exhibited the lowest DDR due to the higher precipitation while the highest rate was observed in the summer season. The northern and central parts of the desert land in the study area recorded the highest annual DDR with a mean value of 61.24 ton km−2 year−1. Seasonal distribution of dust-borne heavy metals concentrations showed that almost all the elements followed the trend winter > fall ≥ spring > summer. Spatial distributions of dust-borne Cd, Cu, Ni, Pb, and Zn almost followed the same pattern with highest concentrations in the western stretches of the study area and in the city of Isfahan. The highest concentration of Hg and As were observed in the urban and desert rural areas. Cr recorded its highest concentration in the urban area while dust-borne Co exhibited a fairly uniform distribution over the whole study area. Results of crustal enrichment factor (EFc) analysis showed that anthropogenic sources contribute a substantial amount of all studied elements in dust particles rather than crustal origin. Fossil fuel, vehicle traffic, and industrial activities seem to be the most important anthropogenic factors responsible for dust elemental pollution in the study area.

The efficiency of different digital and conventional soil mapping approaches to produce categorical maps of soil types is determined by cost, sample size, accuracy and the selected taxonomic level. The efficiency of digital and conventional soil mapping approaches was examined in the semi-arid region of Borujen, central Iran. This research aimed to (i) compare two digital soil mapping approaches including Multinomial logistic regression and random forest, with the conventional soil mapping approach at four soil taxonomic levels (order, suborder, great group and subgroup levels), (ii) validate the predicted soil maps by the same validation data set to determine the best method for producing the soil maps, and (iii) select the best soil taxonomic level by different approaches at three sample sizes (100, 80, and 60 point observations), in two scenarios with and without a geomorphology map as a spatial covariate. In most predicted maps, using both digital soil mapping approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map, although differences between the scenarios with and without the geomorphology map were not significant. Employing the geomorphology map increased map purity and the Kappa index, and led to a decrease in the ‘noisiness’ of soil maps. Multinomial logistic regression had better performance at higher taxonomic levels (order and suborder levels); however, random forest showed better performance at lower taxonomic levels (great group and subgroup levels). Multinomial logistic regression was less sensitive than random forest to a decrease in the number of training observations. The conventional soil mapping method produced a map with larger minimum polygon size because of traditional cartographic criteria used to make the geological map 1:100,000 (on which the conventional soil mapping map was largely based). Likewise, conventional soil mapping map had also a larger average polygon size that resulted in a lower level of detail. Multinomial logistic regression at the order level (map purity of 0.80), random forest at the suborder (map purity of 0.72) and great group level (map purity of 0.60), and conventional soil mapping at the subgroup level (map purity of 0.48) produced the most accurate maps in the study area. The multinomial logistic regression method was identified as the most effective approach based on a combined index of map purity, map information content, and map production cost. The combined index also showed that smaller sample size led to a preference for the order level, while a larger sample size led to a preference for the great group level.

The effects of parent rock types on soil evolution in arid areas were studied in a sequence of soils, derived from different lithologies in the Kopet Dagh basin (Northeastern Iran) using micromorphology, clay mineralogy, magnetic susceptibility and physico-chemical properties. The selected parent rocks and associated soils were shale (Haplocalcids), claystone (Haplotorrerts), gypsiferous marl (Haplogypsids), limestones (Haplocalcids and Torriorthents), siltstone and sandstones (Torriorthents). The results showed that the properties and development of soils were mainly affected by grain size and mineralogy of parent materials. Soil magnetic susceptibility (χlf) variations were attributed to the types of parent material and pedogenic processes. Redistribution of calcite and gypsum in soil profiles and natural and pedogenic formation of ferrimagnetic minerals were responsible for χlf variations. The soils clay mineral origins were found to be mainly of inheritance from parent materials. Smectite was the dominant clay mineral of the most soils. Based on the micromorphological index of soil evolution (MISECA), the soils studied were categorized into weakly developed Orthents, weakly to moderately developed Aridisols (Gypsids and Calcids) and moderately developed soils including Calcids and Torrerts. The degree of microstructure development, alteration of weatherable minerals and calcitic features were the most important criteria influencing assessment of soil development degree by MISECA index. The vertic features were only observed in soils of claystone in which there were considerable amounts of clay and smectite. High amounts of gypsum and low smectite content were mainly responsible for the lack of vertic behavior in other fine grained soils derived from shale and marl.

Magnetic susceptibility is a fast, inexpensive and reliable technique for estimating and monitoring the anthropogenic contamination of soil with heavy metals. However, it is essential to determine the factors affecting magnetic susceptibility before applying this technique to environmental studies. The objectives of this study were to investigate i) the effect of parent materials and land use on the magnetic susceptibility and concentrations of Fe, Ni, Pb and Zn, and ii) capability of magnetic susceptibility as an indicator of anthropogenic heavy metals contamination of soil in Mashhad plain, northeastern Iran. One hundred seventy-eight composite surface soil samples (0–10 cm) were taken. The aqua-regia extractable concentrations of Fe, Ni, Zn and Pb were determined by atomic absorption spectroscopy. Magnetic susceptibility at low and high frequency (χ lf and χ hf ) were measured and frequency dependent susceptibility (χ fd ) was calculated. The average concentrations of Fe, Ni, Pb and Zn were 22,812, 61.4, 74.1 and 31.6 mg kg − 1 , respectively. The highest contents of Pb (69.1 mg kg − 1 ) and Zn (149 mg kg − 1 ) were observed in urban area. The highest concentration of Ni was 41,538 mg kg − 1 observed in the soils developed from ultramafic rocks. Magnetic susceptibility varied from 20.3 on marly sediments to 311.8 × 10 − 8 m 3 kg − 1 on ultramafic rocks. A positive strong correlation (P value r = 0.88) was obtained between Ni and χ lf . There were no significant relationships between Zn and Pb with χ lf, therefore it seems that magnetic susceptibility has not been affected significantly by anthropogenic activities which enhanced Pb and Zn concentrations in urban soils. The results indicated that magnetic susceptibility was mainly controlled by Ni containing minerals with lithogenic origin. Therefore, in the soils studied, magnetic susceptibility could not be employed as indicator of anthropogenic contamination of soil with heavy metals.

137 Cs technique has been widely used for the evaluation rates and patterns of soil erosion and deposition. This technique requires an accurate estimate of the values of 137 Cs inventory at the reference site. This study was conducted to evaluate the variability of the inventory of 137 Cs regarding to the sampling program including sample size, distance and sampling method at a reference site located in vicinity of Fereydan district in Isfahan province, west-central Iran. Two 3 × 8 grids were established comprising large grid (35 m length and 8 m width), and small grid (24 m length and 6 m width). At each grid intersection two soil samples were collected from 0 to 15 cm and 15–30 cm depths, totally 96 soil samples from 48 sampling points. Coefficients of variation for 137 Cs inventory in the soil samples was relatively low (CV = 15%), and the sampling distance and methods used did not significantly affect the 137 Cs inventories across the studied reference site. To obtain a satisfactory estimate of the mean 137 Cs activity in the reference sites, particularly those located in the semiarid regions, it is recommended to collect at least four samples along in a grid pattern 3 m apart.

Soil organic carbon (SOC) storage is a basic measure used to study soil productivity, hydrology and the balance among greenhouse gases. Variation of SOC is controlled by environmental factors such as land use and topography. Toshan watershed located in northern Iran was selected to study the effects of different land uses i.e. forest (FO), cropland (CP), orchard (OR) and abandoned land (AB) on different slope gradients and aspects on SOC both in surface (0–30 cm) and subsurface (30–100 cm) layers. A total of 364 soil samples plus 1638 undisturbed ones were collected from two soil layers in 182 sampling sites. Results showed that the surface 30 cm soil layer was solely responsible for 54.8% of SOC density. On average, FO with 22.84 kg m− 2 had the highest SOC density in 0–100 cm layer. Deforestation and agricultural activities have resulted in a significant 48.2% decrease of SOC density in 0–30 cm soil layer. North facing slope (N) aspect and also flat area of all land uses had the higher SOC density compared to east (E) and west facing slope (W) aspects in this subhumid region in 0–100 cm layer. Generally, in the upper 100 cm soil layer of deforested lands, gentle and moderate slopes had higher SOC density than steeper slopes. There was a positive significant correlation between SOC density and clay content. The largest amount of SOC storage was observed in the surface 30 cm layer accounting for 74,907.94 Mg (54.4% of total SOC storage), indicating the important key role of topsoil in conserving SOC. FO with one-third proportion of the total area stored the largest amount of SOC (39,325.55 Mg; 52.5%) in surface layer. In conclusion, protection of forest lands is higher important to increase SOC storage. Agricultural activities on steep E and W aspects in deforested lands must be reduced or prohibited. The subsoil has almost the same contribution to SOC storage and therefore should be carefully considered for management measures. Generally, interaction between environmental factors on storing SOC and also rate of carbon loss to the atmosphere was significant.