Predicting the spatio-temporal distribution of absorbable heavy metals in soil is needed to identify the potential contaminant sources and develop appropriate management plans to control these hazardous pollutants. Therefore, our aim was to develop a model to predict soil adsorbable heavy metals in arid regions of Iran from 1986 to 2016. Soil adsorbable heavy metals were measured in 201 samples from locations selected using the Latin hypercube sampling method in 2016. A random forest (RF) model was used to determine the relationship between a suite of geospatial predictors derived from remote sensing and digital elevation model data with georeferenced measurements of soil absorbable heavy metals. The trained RF model from 2016 was used to reconstruct the spatial distribution of soil absorbable heavy metals at three historical timesteps (1986, 1999, and 2010). Results indicated that the RF model was effective at predicting the distribution of heavy metals with coefficients of determination of 0.53, 0.59, 0.41, 0.45, and 0.60 for Fe, Mn, Ni, Pb, and Zn, respectively. The predicted maps showed high spatio-temporal variability; for example, there were substantial increases in Pb (the 1.5–2 mg/kg⁻¹ class) where its distribution increased by ~25% from 1988 to 2016—similar trends were observed for the other heavy metals. This study provides insights into the spatio-temporal trends and the potential causes of soil heavy metal contamination to facilitate appropriate planning and management strategies to prevent, control, and reduce the impact of heavy metal contamination in soils.

Soil organic carbon (SOC) is an essential property of soil, and understanding its spatial patterns is critical to understanding vegetation management, soil degradation, and environmental issues. This study applies a framework using remote sensing data and digital soil mapping techniques to examine the spatiotemporal dynamics of SOC for the Yazd-Ardakan Plain, Iran, from 1986 to 2016. Here, a conditioned Latin hypercube sampling method was used to select 201 sampling sites. A set of 37 environmental predictors were obtained from Landsat imagery taken in 1986, 1999, 2010 and 2016. Here, SOC was modeled for 2016 using the Random Forest (RF), support vector regression (SVR), and artificial neural networks (ANN) machine-learners by correlating environmental predictors with soil data. The results showed that RF yielded the highest accuracy (R² = 0.53), compared to the other two learners. By performing a variable importance analysis of the RF model, normalized difference vegetation index, modified vegetation index, and ground-adjusted vegetation index were determined to be the most important environmental predictors. By applying the model calibrated from 2016 data to 1986, 1999 and 2010, the results showed a substantial decrease in SOC; these decreases in SOC were mainly attributed to land use changes and agricultural activities.

The present study was conducted for the spatial distribution and concentration evaluation of heavy metals, including Cu, Cd, Mn, Fe, Zn, and Pb, within 102 soil samples collected from Kushk Mine in Bafgh, Iran. This work employed hierarchical clustering analysis (HCA), principal component analysis (PCA), and spatial distribution patterns, to perform element distribution evaluation within the area. The distributions of heavy metals in the entire area were exhibited in the form of maps. The average concentrations of Cd, Cu, Mn, Fe, Zn, and Pb were found to be 0.39, 0.26, 5.3, 4.1, 51.9, and 40.9 mg.kg⁻¹, respectively. Based on the PCA and HCA findings, the heavy metals were divided into two groups. The first group included Pb, Cd, Zn, and Cu. In the first group, altered threshold‐surpassing anthropogenic and lithogenic pollution was found to be the main factor accounting for Pb and Zn. The second group involved Fe and Mn, which could be impacted by either anthropogenic and lithogenic factors. Furthermore, the geo‐statistical results demonstrated higher contents of the heavy metals in the south of the mine and in the vicinity of the mine tailings. It may be concluded from the results that the heavy metal contents of the area are impacted by anthropogenic and lithogenic factors.

Seaweed has developed as a cure with the capability to spare the biological system, because of its eco-friendliness, reasonableness, accessibility, and active ion expulsion rate. This work summarizes the result of saltiness on the organ of plants, and this biomass is revered because the driving redress material among the accessible materials too grandstand the bio activity part of seaweed extricate disengage and its adequacy as a valuable, on morphological and physiological characteristics of peppermint underneath completely different irrigation water salinity was conducted a factorial experiment supported a totally irregular style with four replications within the Yazd University greenhouse in 2020. Experimental factors included salinity stress at four levels of (0, 2, 4 and 8 dS/m) and, seaweed powder application at four levels of (0, 1, 2 and 4 ppt ). Morphological traits or growth parameters and physiological characteristics (relative water content, photosynthetic pigments, total soluble carbohydrate, proline amino acid) assessed. our results demonstrated that seaweed led to changes in peppermint growth and physiology which improved the plant salt stress response. Examination of interaction effects showed was a significant effect on total chlorophyll, chlorophyll a, chlorophyll b, carotenoids (Photosynthetic pigment) and, total soluble carbohydrate at the level of 1% (p≤0.01), and in examining morphological traits results demonstrated that fresh and dry weight of leaves, aerial components, total fresh and dry biomass and harvest index were significant at the level of 1% (p≤0.01). The use of seaweed at 4 ppt under stress-free conditions resulted in the highest weight of leaves, aerial, and total biomass. The highest relative water content was related to the without salinity stress (65.8%). The highest proline content was obtained at 4 and 8 dS/m salinity levels, which showed a rise of 37.5% compared to the management treatment. In general, the results showed that the employment of 2 and 4 ppt seaweed at high levels of salinity (4 and 8 dS/m) reduced the adverse effects of salinity stress by improved physiological traits, together with chemical change pigments and amino acid content. This analysis grandstands the bio activity part of seaweed growth biomass and its adequacy as a helpful, cheap, and reasonable.