Evaluation of the geochemical background of soil in a hyper-arid zone using a multivariate statistical methodology: The case of the city of Antofagasta in the Atacama Desert.

This study investigates the geochemical background and factors influencing the variability of 19 environmentally relevant elements in the soils of Antofagasta, Chile, a region known for its extensive mining activities. Employing robust multivariate statistical techniques on a dataset of 94 soil samples, we identified four main factors explaining 70% of the total variance in elemental concentrations. These four factors reflect the influence of Jurassic volcanic rocks, intrusive rocks, marine sediments, and mafic to intermediate intrusive rocks. Cluster analysis revealed three distinct geochemical populations, each reflecting a unique combination of natural and anthropogenic influences. We established background concentrations for each element within these clusters using robust statistical methods. Geostatistical analysis, employing inverse distance weighted interpolation, produced factor distribution maps that, when integrated with geological data, provided insights into lithological and anthropogenic influences on soil geochemistry. Our findings highlight the complex interplay between natural geological processes, the region's unique arid climate, and anthropogenic activities in shaping the geochemical landscape of Antofagasta. This study contributes to the understanding of geochemical backgrounds in mining-intensive, arid regions and provides a methodological framework applicable to similar environments worldwide.

The scientific landscape of phytoremediation of tailings: a bibliometric and scientometric analysis.

This article seeks to evaluate the scientific landscape of the phytoremediation of mine tailings through a series of bibliometric and scientometric techniques. Phytoremediation has emerged as a sustainable approach to remediate metal-contaminated mine waste areas. A scientometric analysis of 913 publications indexed in Web of Science from 1999 to 2023 was conducted using CiteSpace. The results reveal an expanding, interdisciplinary field with environmental sciences as the core category. Keyword analysis of 561 nodes and 2,825 links shows a focus on plant-metal interactions, microbial partnerships, bioavailability, and field validation. Co-citation analysis of 1,032 nodes and 2,944 links identifies seminal works on native species, plant-microbe interactions, and amendments. Temporal mapping of 15 co-citation clusters indicates a progression from early risk assessments and native plant inquiries to integrated biological systems, economic feasibility, and sustainability considerations. Recent trends emphasize multidimensional factors influencing adoption, such as plant-soil-microbe interactions, organic amendments, and field-scale performance evaluation. The findings demonstrate an intensifying translation of phytoremediation from scientific novelty to engineering practice. This quantitative and qualitative analysis of research trends aids in understanding the development of phytoremediation for mine tailings. The results provide valuable insights for researchers and practitioners in this evolving field.

An extension of the characteristic curve model of plant species behavior in heavy metal soils.

This article proposes a mathematical model to characterize phytoremediation processes in soils contaminated with heavy metals. In particular, the proposed model constructs characteristic curves for the concentrations of several metals (As, Cd, Cu, Fe, Pb, Sb, and Zn) in soils and plants based on the experimental data retrieved from several bibliographical sources comprising 305 vegetal species. The proposed model is an extension of previous models of characteristic curves in phytoremediation processes developed by Lam et al. for root measurements using the bioconcentration factor. However, the proposed model extends this approach to consider roots, as well as aerial parts and shoots of the plant, while at the same time providing a less complex mathematical formula compared to the original. The final model shows an adjusted R2 of 0.712, and all its parameters are considered statistically significant. The model may be used to assess samples from a given plant species to identify its potential as an accumulator in the context of soil phytoremediation processes. Furthermore, a simplified version of the model was constructed using an approximation to provide an easy-to-compute alternative that is valid for concentrations below 37,000 mg/kg. This simplified model shows results similar to the original model for concentrations below this threshold and it uses an adjusted factor defined as [Formula: see text] that must be compared with a threshold depending on the metal, type of measurement, and target (e.g., accumulator or hyperaccumulator). The full model construction shows that 90 out of the 305 species assessed have a potential behavior as accumulators and 10 of them as hyperaccumulators. Finally, out of the 1405 experimental measurements, 1177 were shown to be accumulators or hyperaccumulators. In particular, 85% of the results coincide with the reported values, thus validating the proposed model.

Characteristic curve modeling of plant species behavior in soils with heavy metals.

Many vegetal species can accumulate great amounts of metallic elements in their tissues. For this reason, they are called metal hyperaccumulators. An indicator of great interest in environmental sciences is the bioconcentration factor because it is recognized for establishing the potential accumulation of chemicals in organisms. Particularly in soil phytoremediation processes, it measures the capacity of a certain plant to capture metals, in terms of soil concentration. According to their behavior, four types of plants can be distinguished regarding soil concentration increase: indicator, excluder, accumulator, and hyperaccumulator. This study proposes a new model to categorize plants according to their behavior related to soil concentration increase, using several characteristic curves obtained from 1288 experimental measurements collected from different bibliographic sources. The metals analyzed were Cu, Fe, Pb, and Zn. The proposed model is obtained through linear regression and nonlinear transformations to model the expected behavior of plants in high concentration conditions. In particular, the basic equation of the model has three key components to represent the expected concentration in the plant root given the final soil concentration level, the type of species, and specific metal: a linear factor that determines the growth for low concentration values, an exponential factor that determines its decrease for high concentration values, and a logarithmic factor that limits the maximum value that can be reached in practice and influences the decay for high concentration values. After fitting the experimental data using linear regression, the proposed model has a 0.084 R2 determination coefficient and all of its parameters are considered significant. Furthermore, it shows that 60 of the 257 species assessed behave as accumulators and 10 of them as hyperaccumulators. The main contribution of this model is its ability to handle soils with high concentrations, where it would be hard for plants to achieve concentrations similar to or higher than the substrate containing them. Thus, the conventional criterion of the bioconcentration factor would incorrectly categorize a plant as an excluder. In contrast, this new model allows assessing plant effectiveness in a phytoremediation process of highly concentrated affected sites, such as mine tailings.