Exploring lava‐flow hazards at Pico Island,Azores Archipelago (Portugal)

Pico, the youngest island of the Azores Archipelago (Portugal), is characterized by a central volcano and a 30‐km‐long fissure zone. Its eruption rate is the highest of the Azores islands, with more than 35 eruptions in the last 2000 years. Here, we estimate the lava‐flow hazard for Pico Island by combining the vent opening probability derived from the spatial distribution of eruptive fissures, the classes of expected eruptions inferred from the physical and chemical characteristics of historical eruptions, and the lava‐flow paths simulated by the MAGFLOW model. The most likely area to host new eruptions is along a WNW–ESE trend centred on the central volcano, with the highest hazard affecting the two main residential zones of Lajes do Pico and Madalena. Our analysis is the first attempt to assess the lava‐flow hazard for Pico Island, and may have important implications for decision‐making in territorial management and future land‐use planning.     

Spatial vent opening probability map of Etna volcano (Sicily, Italy)

We produce a spatial probability map of vent opening (susceptibility map) at Etna, using a statistical analysis of structural features of flank eruptions of the last 2?ky. We exploit a detailed knowledge of the volcano structures, including the modalities of shallow magma transfer deriving from dike and dike-fed fissure eruptions analysis on historical eruptions. Assuming the location of future vents will have the same causal factors as the past eruptions, we converted the geological and structural data in distinct and weighted probability density functions, which were included in a non-homogeneous Poisson process to obtain the susceptibility map. The highest probability of new eruptive vents opening falls within a N-S aligned area passing through the Summit Craters down to about 2,000?m?a.s.l. on the southern flank. Other zones of high probability follow the North-East, East-North-East, West, and South Rifts, the latter reaching low altitudes (～400?m). Less susceptible areas are found around the faults cutting the upper portions of Etna, including the western portion of the Pernicana fault and the northern extent of the Ragalna fault. This structural-based susceptibility map is a crucial step in forecasting lava flow hazards at Etna, providing a support tool for decision makers.     

Isolation and characterization of two crude oil-degrading yeast strains, Yarrowia lipolytica PG-20 and PG-32, from the Persian Gulf

Among six crude oil-degrading yeasts that were isolated from an oil-polluted area in the Persian Gulf, two yeast strains showed high degradation activity of aliphatic hydrocarbons. From an analysis of 18S rRNA sequences and biochemical characteristics, these strains were identified as Yarrowia lipolytica strains PG-20 and PG-32. Gas Chromatography (GC) analysis of the crude oil remaining in the culture medium after 1 week at 30°C showed that the strains PG-20 and PG-32 degraded 68% and 58% of crude oil, respectively. The optimal growth condition and biodegradation of hydrocarbons was in ONR medium with an acidic pH (pH5). These two strains may degrade aliphatic hydrocarbons more efficiently than aromatic hydrocarbons, although strain PG-20 had better degradation than strain PG-32. The two Y. lipolytica strains reduce surface tension when cultured on hydrocarbon substrates (1% v/v). These strains showed a cell surface hydrophobicity higher than 70%. These results suggested that Y. lipolytica strains PG-20 and PG-32 have high crude oil degrading activity due to their high emulsifying activity and cell hydrophobicity. In conclusion, these yeast strains can be useful for the bioremediation process in the Persian Gulf and decreasing oil pollution in this marine ecosystem.     

Modeling Ocean Currents Through Complex Random Fields Indexed in Time

Surface ocean currents are often of interest in environmental monitoring. These vectorial data can be reasonably treated as a finite realization of a complex-valued random field, where the decomposition in modulus (current speed) and direction (current direction) of the current field is natural. Moreover, when observations are also available for different time points (other than at several locations), it is useful to evaluate the evolution of their complex correlation over time (rather than in space) and the corresponding modeling which is required for estimation purposes. This paper illustrates a first approach where the temporal profile of surface ocean currents is considered. After introducing the fundamental aspects of the complex formalism of a random field indexed in time, a new class of models suitable for including the temporal component is proposed and applied to describe the time-varying complex covariance function of current data. The analysis concerns ocean current observations, taken hourly on 30 April 2016 through high frequency radar systems at some stations located in the Northeastern Caribbean Sea. The selected complex covariance model indexed in time is used for estimation purposes and its reliability is confirmed by a numerical analysis.

     

Testing the type of non-separability and some classes of space-time covariance function models

In statistical space-time modeling, the use of non-separable covariance functions is often more realistic than separable models. In the literature, various tests for separability may justify this choice. However, in case of rejection of the separability hypothesis, none of these tests include testing for the type of non-separability of space-time covariance functions. This is an important and further significant step for choosing a class of models. In this paper a method for testing positive and negative non-separability is given; moreover, an approach for testing some well known classes of space-time covariance function models has been proposed. The performance of the tests has been shown using real and simulated data.     

Isolation and characterization of crude-oil-degrading bacteria from the Persian Gulf and the Caspian Sea

Twenty-five crude-oil-degrading bacteria were isolated from oil-contaminated sites in the Persian Gulf and the Caspian Sea. Based on a high growth rate on crude oil and on hydrocarbon degradation ability, 11 strains were selected from the 25 isolated strains for further study. Determination of the nucleotide sequence of the 16S rRNA gene showed that these isolated strains belonged to genera Acinetobacter, Pseudomonas, Gordonia, Rhodococcus, Cobetia, Halomonas, Alcanivorax, Marinobacter and Microbacterium. Among the 11 isolates, strains BS (Acinetobacter calcoaceticus, 98%) and PG-12 (Alcanivorax dieselolei, 98%) were the most effective in degrading crude oil. Rate of crude-oil degradation of 82% (isolate BS) and 71% (isolate PG-12) were observed after 1 week of cultivation in mineral medium. These strains had high emulsification activity and biosurfactant production. GC-MS analysis showed that A. dieselolei PG-12 can degrade different alkanes in crude oil. Screening of the distribution of the alkane hydroxylase gene in 25 isolates in relation to the source of isolation indicated that the group (II) alkane hydroxylase is prevalent in the Caspian Sea, but in the Persian Gulf, the frequency of the group (III) alkane hydroxylase gene is greater than that of the group (II) alkane hydroxylase gene.