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Alvaro Veiga

Publications and source records attributed to Alvaro Veiga.

2 recordsLinked to original sources
PubMed2006

Validation of Ucides cordatus as a bioindicator of oil contamination and bioavailability in mangroves by evaluating sediment and crab PAH records.

This study is aimed at verifying the relevance of Ucides cordatus as a bioindicator of oil contamination and PAH bioavailability in mangrove sediments. For this, crabs and sediment cores were sampled from five mangroves, including an area suspected of contamination derived from an MF380 oil spillage, and analyzed for the 16 PAH in the USEPA priority list as well as for the five series of alkylated homologues. Concentrations in sediments varied from 35 microg kg-1 in the lower core layer of the control area to 33,000 microg kg-1 in the upper layer of the most contaminated area. Total PAH contents in crabs varied from 206 to 62,000 microg kg-1 and were closely correlated to that in sediments. In general, individual PAH profiles in both matrices were in good agreement. Phenanthrenes, however, were more predominant in crabs making up to 30-46% of the Total PAH. Accumulation factors found in the range of 0.7 to 35 were highly variable even after normalizing concentrations for organic carbon and lipid content. Survival in highly contaminated environment and reliable record of environmental contamination in the tissue provide evidence that U. cordatus is an excellent bioindicator for oil in mangroves.

Animals↗
PubMed2005

A flexible coefficient smooth transition time series model.

In this paper, we consider a flexible smooth transition autoregressive (STAR) model with multiple regimes and multiple transition variables. This formulation can be interpreted as a time varying linear model where the coefficients are the outputs of a single hidden layer feedforward neural network. This proposal has the major advantage of nesting several nonlinear models, such as, the self-exciting threshold autoregressive (SETAR), the autoregressive neural network (AR-NN), and the logistic STAR models. Furthermore, if the neural network is interpreted as a nonparametric universal approximation to any Borel measurable function, our formulation is directly comparable to the functional coefficient autoregressive (FAR) and the single-index coefficient regression models. A model building procedure is developed based on statistical inference arguments. A Monte Carlo experiment showed that the procedure works in small samples, and its performance improves, as it should, in medium size samples. Several real examples are also addressed.

Algorithms↗