Soil erosion after forest fires: evaluation of mitigation measures applied to drainage channels in the northwest of Portugal

The development and implementation of measures which promote the reduction of the impacts of forest fires on soils is imperative and should be part of any strategy for forest and soil preservation and recovery, especially considering the actual scenario of continuous growth in the number of fires and burnt area. Consequently, with the dendrocaustologic reality that has characterized the Portuguese mainland in recent decades, a research project promoted by the Center for the Study of Geography and Spatial Planning (CEGOT) was implemented with the objective of applying several erosion mitigation measures in a burned area of the Peneda-Geres National Park in NW Portugal. This paper therefore seeks to present the measures applied in the study area within the project Soil Protec, relating to triggered channel processes and the results of preliminary observations concerning the evaluation of the effectiveness of erosion mitigation measures implemented, as well as their cost/benefit ratio.

Soil attributes dynamics evaluation after prescribed burning practice in northwestern Portugal forest

The Portuguese northern forests are often and severely affected by wildfires during the summer season. These occurrences affect significant and rudely all ecosystems, namely soil, fauna and flora. Preventive actions such as prescribed burnings and clear-cut logging are frequently used and have showed a significant reduction of the natural wildfires occurrences. In Portugal, and due to some technical and operational conditions, prescribed burnings in forests are the most common preventive action used to reduce the existing fuel hazard. The overall impacts of this preventive action on Portuguese ecosystems are complex and not fully understood. This work reports to the study of a prescribed burning impact in soil chemical properties, namely pH, humidity and organic matter, by monitoring the soil self-recovery capacity. The experiments were carried out in soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, Portugal, who was able to maintain itself intact from prescribed burnings from four years. The composed soil samples were collected from five plots at three different layers (0-3cm, 3-6cm and 6-18cm) 1 day before prescribed fire and after the prescribed fire. The results have shown that the dynamic equilibrium in soil was affected significantly.

Determination of total petroleum hydrocarbons in soil from different locations using infrared spectrophotometry and gas chromatography

Total petroleum hydrocarbons (TPH) are important environmental contaminants which are toxic to human and environmental receptors. Several analytical methods have been used to quantify TPH levels in contaminated soils, specifically through infrared spectrometry (IR) and gas chromatography (GC). Despite being two of the most used techniques, some issues remain that have been inadequately studied: a) applicability of both techniques to soils contaminated with two distinct types of fuel (petrol and diesel), b) influence of the soil natural organic matter content on the results achieved by various analytical methods, and c) evaluation of the performance of both techniques in analyses of soils with different levels of contamination (presumably non-contaminated and potentially contaminated). The main objectives of this work were to answer these questions and to provide more complete information about the potentials and limitations of GC and IR techniques. The results led us to the following conclusions: a) IR analysis of soils contaminated with petrol is not suitable due to volatilisation losses, b) there is a significant influence of organic matter in IR analysis, and c) both techniques demonstrated the capacity to accurately quantify TPH in soils, irrespective of their contamination levels.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.