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.

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.

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.

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 (χ) in contaminated soils around the Shadegan pond in southern Iran was investigated.

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.

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.

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).

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.

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.

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

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 Cs inventory at the reference site. This study was conducted to evaluate the variability of the inventory of 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.

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.

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.