Impact of prescribed fire on the chemical properties of soil

Prescribed fire is a common forest management tool used in Portugal to reduce the fuel load availability and minimize the occurrence of wildfires. In addition, the use of this technique also causes an impact to ecosystems. In this presentation we propose to illustrate some results of our project in two forest sites, both located in Northwest Portugal, where the effect of prescribed fire on soil properties were recorded during a period of 6 months. Changes in soil moisture, organic matter, soil pH and iron, were examined by Principal Component Analysis multivariate statistics technique in order to determine impact of prescribed fire on these soil properties in these two different types of soils and determine the period of time that these forest soils need to recover to their pre-fire conditions, if they can indeed recover. Although the time allocated to this study does not allow for a widespread conclusion, the data analysis clearly indicates that the pH values are positively correlated with iron values at both sites. In addition, geomorphologic differences between both sampling sites, Gramelas and Anjos, are relevant as the soils’ properties considered have shown different performances in time. The use of prescribed fire produced a lower impact in soils originated from more amended bedrock and therefore with a ticker humus covering (Gramelas) than in more rocky soils with less litter covering (Anjos) after six months after the prescribed fire occurrence.

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.

Predictivity Strength of the Spatial Variability of Phenanthrene Sorption Across Two Sandy Loam Fields

Sorption is commonly agreed to be the major process underlying the transport and fate of polycyclic aromatic hydrocarbons (PAHs) in soils. However, there is still a scarcity of studies focusing on spatial variability at the field scale in particular. In order to investigate the variation in the field of phenanthrene sorption, bulk topsoil samples were taken in a 15 × 15-m grid from the plough layer in two sandy loam fields with different texture and organic carbon (OC) contents (140 samples in total). Batch experiments were performed using the adsorption method. Values for the partition coefficient K d (L kg−1) and the organic carbon partition coefficient K OC (L kg−1) agreed with the most frequently used models for PAH partitioning, as OC revealed a higher affinity for sorption. More complex models using different OC compartments, such as non-complexed organic carbon (NCOC) and complexed organic carbon (COC) separately, performed better than single K OC models, particularly for a subset including samples with Dexter n < 10 and OC <0.04 kg kg−1. The selected threshold revealed that K OC-based models proved to be applicable for more organic fields, while two-component models proved to be more accurate for the prediction of K d and retardation factor (R) for less organic soils. Moreover, OC did not fully reflect the changes in phenanthrene retardation in the field with lower OC content (Faardrup). Bulk density and available water content influenced the phenanthrene transport mechanism phenomenon.

Metodologias no Controlo de Compactação em Aterros

O presente trabalho pretende abordar aspectos relacionados com o controlo de compactação em aterros, com base na avaliação de parâmetros “in situ”, tais como: pesos volúmicos, teores em água, graus de compactação e módulos de deformabilidade. Recorrendo aos métodos correntes no controlo de compactação, como o ensaio de carga em placa, gamadensímetro, garrafa e ao ensaio de deflectómetro de impacto portátil. Visa-se comparar os resultados obtidos, nas diferentes condições, de modo a possibilitar alcançar correlações entre os ensaios, bem como determinar aqueles que apresentam maior grau de confiança técnico e vantagens operacionais e económicas. De modo a garantir os índices de qualidade da obra é necessário fazer cumprir os critérios exigidos pelo caderno de encargos, nomeadamente nos parâmetros de avaliação do controlo de compactação, para que estas satisfaçam o seu estado funcional e estrutural. Neste tipo de obras os cadernos de encargos de referência em Portugal são os das Estradas de Portigal (EP) e da Brisa, Auto-estradas de Portugal (Brisa), os quais se baseiam em recomendações de classificações de solos. No contexto experimental, foram efectuados dois estudos com condições e materiais diferentes, em obras pertencentes à empresa Mota-Engil Engenharia e Construções, S.A. Nas campanhas de ensaios foram realizados ensaios “in situ” na obra da Subconcessão do Douro Interior – Lote 6 – IC5 – Troço Murça/ Nó de Pombal nas camadas de sub-base e base num agregado de granulometria extensa, e na obra de modernização do troço ferroviário Bombel e Vidigal a Évora, em solos.