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.

Soilless systems have become increasingly popular as effective solutions for regions with infertile soil, low water availability, limited space, and environmental pollution. There is limited information on the role of soilless culture in the production of medicinal plants. While some research has examined growth rates and yields, there is not enough data on how these systems affect nutrient uptake, physiological properties, and water use efficiency (WUE) in medicinal plants. This research investigated soilless systems as alternative techniques for cultivating watercress (Nasturtium officinale). The study was conducted using a completely randomized design with five replications and assessed the impact of different cultivation systems (hydroponic, aquaponic, and soil) on the growth of watercress. The results showed that cultivation systems had significant effects on morphological, physiological and nutrient content of watercress (P < 0.01). When grown using hydroponics and aquaponics, watercress exhibited a 58.2 and 54.3 % increase in height, a 104.7 and 59.2 % increase in root length, a 20.1 and 72.9 % increase in leaves, a 44.3 and 11.4 % increase in lateral branches, a 58.5 and 35.3 % increase in leaf area, and a 46.8 and 81 % increase in yield, respectively, than the soil-based system. The soil-based system promoted higher levels of chlorophyll a and b, while the soilless systems exhibited higher amounts of carotenoids, protein, proline, and relative water content (P < 0.01). The aquaponics demonstrated the highest N, P, Mg, S, and Na, while the soil system displayed the highest Ca, Fe, and Zn concentrations. The higher amount Fe and Zn in soil system can be attributed to soil organic matter, which plays a role in chelating micronutrients and enhancing their accessibility for plant absorption. Different cultivation systems significantly affected the daily water usage and WUE. Daily water decreased by 39 and 34.4 % in the hydroponic and aquaponic, respectively than soil-based system. WUE in the hydroponic and aquaponic was 2.45 and 2.78 higher than in the soil. Overall, soilless systems resulted in faster plant growth and higher yields. This efficiency can lead to reduced inputs and less environmental impact than traditional farming. Further investigation is needed to assess the economic feasibility of growing medicinal plants using soilless methods.

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.