Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China.

Soil salinization and sodification, the primary causes of land degradation and desertification in arid and semi-arid regions, demand effective monitoring for sustainable land management. This study explores the utility of partial least square (PLS) latent variables (LVs) derived from visible and near-infrared (Vis-NIR) spectroscopy, combined with remote sensing (RS) and auxiliary variables, to predict electrical conductivity (EC) and sodium absorption ratio (SAR) in northern Xinjiang, China. Using 90 soil samples from the Karamay district, machine learning models (Random Forest, Support Vector Regression, Cubist) were tested in four scenarios. Modeling results showed that RS and Land use alone were unreliable predictors, but the addition of topographic attributes significantly improved the prediction accuracy for both EC and SAR. The incorporation of PLS LVs derived from Vis-NIR spectroscopy led to the highest performance by the Random Forest model for EC (CCC = 0.83, R2 = 0.80, nRMSE = 0.48, RPD = 2.12) and SAR (CCC = 0.78, R2 = 0.74, nRMSE = 0.58, RPD = 2.25). The variable importance analysis identified PLS LVs, certain topographic attributes (e.g., valley depth, elevation, channel network base level, diffuse insolation), and specific RS data (i.e., polarization index of VV + VH) as the most influential predictors in the study area. This study affirms the efficiency of Vis-NIR data for digital soil mapping, offering a cost-effective solution. In conclusion, the integration of proximal soil sensing techniques and highly relevant topographic attributes with the RF model has the potential to yield a reliable spatial model for mapping soil EC and SAR. This integrated approach allows for the delineation of hazardous zones, which in turn enables the consideration of best management practices and contributes to the reduction of the risk of degradation in salt-affected and sodicity-affected soils.

Pine needles as bioindicator and biomagnetic indicator of selected metals in the street dust, a case study from southeastern Iran.

Among the different approaches currently being used to evaluate the contamination level of street dust, the magnetic susceptibility of dust and urban tree leaves has received little attention. The key objectives of this study were: (i) to investigate the feasibility of using pine needles as a bioindicator and biomagnetic indicator for estimating the concentration of selected metals in street dust, and (ii) to predict the contamination level of street dust by selected metals using magnetic susceptibility. Street dust and pine tree needle samples were taken from 60 locations in three adjacent cities in Kerman province (Kerman, Rafsanjan, and Sirjan), southeastern Iran. The total concentrations of selected metals, including Cu, Zn, Fe, Mn, Ni, and Pb, and the magnetic susceptibility (χlf and χhf) values of both pine tree needles and street dust samples were determined. Among the three cities studied, samples from Kerman showed the highest magnetic susceptibility and metal concentration values. This could be attributed to the larger size and much higher population density of this city, with more industrial activities and urban traffic than the other two cities investigated. The results also showed that the concentrations of metals in pine needles were strongly correlated (p < 0.01) with those in street dust. The magnetic susceptibility of pine needles and the concentrations of Fe, Pb, Zn, Cu, Ni, and Mn in street dust showed a statistically significant correlation (p < 0.01). A strong and statistically significant correlation (p < 0.01) was also found between magnetic susceptibility and the concentration of metals in pine needles. In conclusion, strong relationships between magnetic properties and metal concentrations of pine needles with those of street dust samples seem to make pine needles a good bioindicator and biomagnetic estimator of the contamination level of metals in street dust.

Digital exploration of selected heavy metals using Random Forest and a set of environmental covariates at the watershed scale.

The current study was established for predicting some selected heavy metals (HMs) including Zn, Mn, Fe, Co, Cr, Ni, and Cu, by applying random forest (RF) and a set of environmental covariates at watershed scale. The objectives were to find out the most effective combination of variables and controlling factors on the variability of HMs in a semiarid watershed in central Iran. One hundred locations were selected in the given watershed in the hypercube manner and soil samples from a surface 0-20 cm depth and concentration of HMs and some soil properties were measured in the laboratory. Three scenarios of input variables were defined for HMs prediction. The results revealed that the first scenario (remote sensing + topographic attributes) explained about 27-34% of the variability in HMs. Inclusion of a thematic map to the scenario I, improved the prediction accuracy for all HMs. Scenario III (remote sensing data+ topographic attributes + soil properties) was the most efficient scenario for prediction of HMs with R2 values ranging from 0.32 for Cu to 0.42 for Fe. Similarly, the lowest nRMSE was found for all HMs in scenario III, ranging from 0.271 for Fe to 0.351 for Cu. Among the soil properties, clay content and magnetic susceptibility were the most important variables, and also some remote sensing data (Carbonate index, Soil adjusted vegetation index, Band2, and Band7) and topographic attributes (mainly control soil redistribution along the landscape) were the most efficient variables for estimating HMs. We concluded that the RF model with a combination of remote sensing data, topographic attributes, and assisting of thematic maps such as land use in the studied watershed could reliably predict HMs content.

Magnetic susceptibility as a proxy for detection of total petroleum hydrocarbons in contaminated wetlands.

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 (χlf) in contaminated soils around the Shadegan pond in southern Iran was investigated. TPH varied from 2.1 to 18.1% (w/w), by the variation of χlf from 14.08 to 713.93 × 10-8 m3 kg-1. The highest variability (coefficient of variation, CV = 107.12%) was obtained for χlf indicating significant impacts of magnetic minerals induced by crude oil contamination. High positive correlations were detected among TPH, χlf, and different forms of iron (Fed: extracted by CBD, Feo: extracted by oxalate, and Fet: total iron). The results of mineralogy by powdery XRD and scanning electron microscopy (SEM), also revealed the formation of ferrimagnetic minerals (magnetite, maghemite) during the biodegradation of petroleum hydrocarbons. The stepwise multiple regression analysis showed that χlf and Fed made a great contribution and could explain about 74% of TPH variability in the studied sites. For the extension of this cost-effective and rapid technique, further work is needed to assay saturation isothermal remnant magnetization and isothermal remanet magnetization in contaminated sites.

Comparison of Different Machine Learning Methods for Predicting Cation Exchange Capacity Using Environmental and Remote Sensing Data.

This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomorphology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Accordingly, the collection of ninety-seven soil samples was performed from the surface layer (0-20 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the laboratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R2 = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R2 = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index-TRI) and remotely sensed data (ferric oxides, normalized difference moisture index-NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area.

Storm dust source fingerprinting for different particle size fractions using colour and magnetic susceptibility and a Bayesian un-mixing model.

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.

Use of magnetic susceptibility to assess metals concentration in soils developed on a range of parent materials.

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 χlf showed that Ni, Zn, Mn, and Co could explain 77% of the total variance in χlf in the study area. K-means cluster analysis categorized the studied soils into three groups based on metals and χlf variability. Clustering of soils based on their parent rocks and use of further magnetic measures, i.e., saturation isothermal remanent magnetization (SIRM), isothermal remanent magnetization (IRM100mT) and natural remanent magnetization (NRM) are expected to improve the accuracy of metal concentration predictions in natural soils of the study area.

Particle Size Distribution of Heavy Metals and Magnetic Susceptibility in an Industrial Site.

This study was conducted to explore the relationships between magnetic susceptibility and some soil heavy metals concentrations in various particle sizes in an industrial site, central Iran. Soils were partitioned into five fractions (< 28, 28-75, 75-150, 150-300, and 300-2000 µm). Heavy metals concentrations including Zn, Pb, Fe, Cu, Ni and Mn and magnetic susceptibility were determined in bulk soil samples and all fractions in 60 soil samples collected from the depth of 0-5 cm. The studied heavy metals except for Pb and Fe displayed a substantial enrichment in the < 28 µm. These two elements seemed to be independent of the selected size fractions. Magnetic minerals are specially linked with medium size fractions including 28-75, 75-150 and 150-300 µm. The highest correlations were found for < 28 µm and heavy metals followed by 150-300 µm fraction which are susceptible to wind erosion risk in an arid environment.

Erodibility of calcareous soils as influenced by land use and intrinsic soil properties in a semiarid region of central Iran.

The most important properties affecting the soil loss and runoff were investigated, and the effects of land use on the soil properties, together with the erodibility indices in a semiarid zone, central Iran, were evaluated. The locations of 100 positions were acquired by cLHS and 0-5-cm surface soil layer samples were used for laboratory analyses from the Borujen Region, Chaharmahal-Va-Bakhtiari Province, central Iran. To measure in situ runoff and soil erodibility of three different land uses comprising dryland, irrigated farming, and rangeland, a portable rainfall simulator was used. The results showed that the high variations (coefficient of variation, CV) were obtained for electrical conductivity (EC), mean weight diameter (MWD), soil organic carbon (SOC), and soil erodibility indices including runoff volume, soil loss, and sediment concentration (CV ~ 43.6-77.4%). Soil erodibility indices showed positive and significant correlations with bulk density and negative correlations with SOC, MWD, clay content, and soil shear strength in the area under investigation. The values of runoff in the dryland, irrigated farming, and rangeland were found 1.5, 28.9, and 58.7 cm3; soil loss in the dryland, irrigated farming, and rangeland were observed 0.25, 2.96, and 76.8 g; and the amount of sediment concentration in the dryland, irrigated farming, and rangeland were found 0.01, 0.11, and 0.15 g cm-3. It is suggested that further investigations should be carried out on soil erodibility and the potential of sediment yield in various land uses with varying topography and soil properties in semiarid regions of Iran facing the high risk of soil loss.

Variability of 137Cs inventory at a reference site in west-central Iran.

137Cs 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 137Cs inventory at the reference site. This study was conducted to evaluate the variability of the inventory of 137Cs 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 137Cs inventory in the soil samples was relatively low (CV = 15%), and the sampling distance and methods used did not significantly affect the 137Cs inventories across the studied reference site. To obtain a satisfactory estimate of the mean 137Cs 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.

Relationships of 137Cs inventory with magnetic measures of calcareous soils of hilly region in Iran.

Erosion is a natural process, but it has been dramatically increased by human activities; and this adversely influences soil productivity and environmental quality. For quantification of soil erosion, several techniques including the use of Cs-137 have been employed. This study was conducted to explore the relationships of Cs-137 inventory with magnetic properties in calcareous soils in western Iran. Ten transects were selected in the hilly region in Chelgerd district of Iran. Soil samples from 0 to 30 and 30-50 cm depths were collected from fifty points to determine Cs-137 inventory, magnetic measures and selected physico-chemical properties (in total there were 100 soil samples). The results showed that simple mass balance model (SMBM) estimated a gross erosion rate of 29.6 t ha(-1) yr(-1) and a net soil deposition of 21.8 t ha(-1) yr(-1); hence, a net soil loss of 9.6 t ha(-1) yr(-1) and a sediment delivery ratio of 31.4%. Simple linear regression and non-linear regression analysis showed that mass magnetic susceptibility (χ(lf)) explained only 33.64% and 45% of variability in Cs-137 in the transects studied. The results of multiple linear regression analysis of (137)Cs with magnetic parameters and physico-chemical properties indicated that extractable potassium and χ(lf) explained approximately 61% of the total variability in (137)Cs in the area studied. Overall, the results suggest that further research is needed for the use of magnetic characteristics as an alternative technique in place Cs-137 methodology for calcareous soils.

Soil redistribution rate and its relationship with soil organic carbon and total nitrogen using 137Cs technique in a cultivated complex hillslope in western Iran.

The spatial pattern of soil redistribution rate was investigated using cesium-137 ((137)Cs) within a cultivated complex hillslope in western Iran. The relationship between soil redistribution rate and soil organic carbon and total nitrogen pattern were studied using co-regionalization analysis. Ninety-one soil cores were sampled for (137)Cs, total nitrogen, and soil organic carbon measurements. The simplified mass balance model estimated a gross erosion rate of 29.8 t ha(-1) yr(-1) and a net soil deposition rate of 21.8 t ha(-1) yr(-1); hence, a net soil loss rate of 8 t ha(-1) yr(-1). This magnitude of soil erosion rate is higher than the acceptable rate in semiarid regions. Co-regionalization analysis and co-dispersive coefficients among the selected variables showed that only a small fraction of the variability in total nitrogen and soil organic carbon could be explained by soil redistribution and that the remaining might be the result of different management practices by local farmers.