Salt-affected soils in the Lopez de Bertodano Formation, polar semi-desert of Seymour Island, Antarctica: topographic or lithological controls?

Salinization is one of the main pedogenetic processes occurring on the semiarid zone of the Weddel sea, like in Seymour Island, where salt-affected soils are widespread. This study aimed to investigate whether topography controls the distribution of salt-affected soils on Seymour Island, particularly with reference to the Lopez de Bertodano Formation. Chemical and physical results from 45 soil profiles were used, where morphometric variables were extracted.

Ground temperature trend and active layer dynamics in the Fildes Peninsula, King George Island - Marine Antarctica.

Ground temperature's sensitivity to climate change has garnered attention. This study aimed to monitor and analyze temporal trends and estimate Active Layer Thickness from a monitoring point at Fildes Peninsula, King George Island, in Antarctica. Quality control and consistency analysis were performed on the data.

Resistance to adverse conditions and characterization of Cladosporium species from marine and terrestrial Antarctic samples.

Microbial adaptations to extreme environments can lead to biotechnological applications. This study aimed to evaluate the resistance of Antarctic Cladosporium to adverse conditions (temperature, salinity, UV radiation, and nutrients) and refine their taxonomy. Sequencing and phylogenetic analysis using ITS-act markers resulted in a more accurate taxonomic identification, revealing the presence of five different species, belonging to the complexes C.

Potential of plant species adapted to semi-arid conditions for phytoremediation of contaminated soils.

Physical and chemical remediation techniques used in contaminated areas are expensive and damaging to the soil structure. Biological alternatives, such as phytoremediation, are economical and applicable to large areas. The main limitation of phytoremediation is identifying plants that are both capable of stabilizing and/or absorbing metals from soil and adapted to edaphoclimatic conditions of the contaminated areas.

Predictive modeling of contents of potentially toxic elements using morphometric data, proximal sensing, and chemical and physical properties of soils under mining influence.

Several anthropic activities, especially mining, have contributed to the exacerbation of contents of potentially toxic elements in soils around the world. Mines can release a large amount of direct sources of contaminants into the environment, and even after the mines are no longer being exploited, the environmental liabilities generated may continue to provide contamination risks. Potentially toxic elements (PTEs), when present in the environment, can enter the food chain, promoting serious risks to human health and the ecosystem.

Soil predictors are crucial for modelling vegetation distribution and its responses to climate change.

Bioclimatic envelope models have been extensively used to predict the vegetation dynamics in response to climate changes. However, they are prone to the uncertainties arising from General Circulation Models (GCMs), classification algorithms and predictors, with low-resolution results and little detail at the regional level. Novel research has focused on the improvement of these models through a combination of climate and soil predictors to enhance ecological consistency.

Effect of environmental covariable selection in the hydrological modeling using machine learning models to predict daily streamflow.

There are different methods for predicting streamflow, and, recently machine learning has been widely used for this purpose. This technique uses a wide set of covariables in the prediction process that must undergo a selection to increase the precision and stability of the models. Thus, this work aimed to analyze the effect of covariable selection with Recursive Feature Elimination (RFE) and Forward Feature Selection (FFS) in the performance of machine learning models to predict daily streamflow.

Machine learning models for streamflow regionalization in a tropical watershed.

This study aims to assess different machine learning approaches for streamflow regionalization in a tropical watershed, analyzing their advantages and limitations, and to point the benefits of using them for water resources management. The algorithms applied were: Random Forest, Earth and linear model. The response variables were the three types of minimum streamflow (Q, Q and Q), besides the long-term average streamflow (Q).