Lava flow susceptibility map of mt etna based on numerical simulations

We constructed maps of probability of lava inundation using computer simulations considering the past eruptive behaviour of the Mt. Etna volcano and data deriving from monitoring networks. The basic a priori assumption is that new volcanoes will not form far from existing ones and that such a distribution can be performed using a Cauchy kernel. Geophysical data are useful to update or fine tune the initial Cauchy kernel to better reflect the distribution of future volcanism. In order to obtain a final susceptibility map, a statistical analysis permits a classification of Etna’s flank eruptions into twelve types. The simulation method consists of creating a probability surface of the location of future eruption vents and segmenting the region according to the most likely historical eruption on which to base the simulation. The paths of lava flows were calculated using the MAGFLOW Cellular Automata (CA) model, allowing us to simulate the discharge rate dependent spread of lava as a function of time

Assessment and modeling of lava flow hazard on Mt. Etna volcano

A methodology for constructing a probability map of lava inundation by considering the past eruptive behavior of the Mt. Etna volcano is described. The basic a priori assumption is that new vents will not form far from existing ones and that such a distribution can be performed using a Gaussian kernel. The methodology follows several steps: computation of a susceptibility map that provides the spatial probability of vent opening; evaluation of the temporal probability for the occurrence of the hazard during the considered time interval; characterization of the expected eruptions; numerical simulations of lava flow paths and elaboration of the hazard map. The application of MAGFLOW code, a physical-mathematical model, for simulating the lava flow paths represents the central part of this methodology for the hazard assessment at Mt. Etna. The simulation approach, to assess lava flow hazard, provides a more robust and locally accurate analysis than a simple probabilistic approach and accounts for the influence of the actual topography on the path of future lava flows

Modelling lava flows by Cellular Nonlinear Networks (CNN): preliminary results

International audienceThe forecasting of lava flow paths is a complex problem in which temperature, rheology and flux-rate all vary with space and time. The problem is more difficult to solve when lava runs down a real topography, considering that the relations between characteristic parameters of flow are typically nonlinear. An alternative approach to this problem that does not use standard differential equation methods is Cellular Nonlinear Networks (CNNs). The CNN paradigm is a natural and flexible framework for describing locally interconnected, simple, dynamic systems that have a lattice-like structure. They consist of arrays of essentially simple, nonlinearly coupled dynamic circuits containing linear and non-linear elements able to process large amounts of information in real time. Two different approaches have been implemented in simulating some lava flows. Firstly, a typical technique of the CNNs to analyze spatio-temporal phenomena (as Autowaves) in 2-D and in 3-D has been utilized. Secondly, the CNNs have been used as solvers of partial differential equations of the Navier-Stokes treatment of Newtonian flow

Assessment and modeling of lava flow hazard on Etna volcano

A methodology for constructing a probability map of lava inundation by considering the past eruptive behavior of the Mt Etna volcano is described. The basic a priori assumption is that new vents will not form far from existing ones and that such a distribution can be performed using a Gaussian kernel. The methodology is based on several steps: computation of susceptibility map that provides the spatial probability of vent opening; evaluation of the temporal probability for the occurrence of the hazard during the considered time interval; characterization of the expected eruptions; numerical simulations of lava flow paths, and elaboration of the hazard map. The application of MAGFLOW code, a physical-mathematical model, for simulating the lava flow paths represents the central part of this methodology for the hazard assessment at Etna. The simulation approach, to assess lava flow hazard, provides a more robust and locally accurate analysis than a simple probabilistic approach and accounts for the influence of the actual topography on the path of future lava flows

The TRPM4 channel inhibitor 9-phenanthrol

The phenanthrene-derivative 9-phenanthrol is a recently identified inhibitor of the transient receptor potential melastatin (TRPM) 4 channel, a Ca2+-activated non-selective cation channel whose mechanism of action remains to be determined. Subsequent studies performed on other ion channels confirm the specificity of the drug for TRPM4. In addition, 9-phenanthrol modulates a variety of physiological processes through TRPM4 current inhibition and thus exerts beneficial effects in several pathological conditions. 9-Phenanthrol modulates smooth muscle contraction in bladder and cerebral arteries, affects spontaneous activity in neurons and in the heart, and reduces lipopolysaccharide-induced cell death. Among promising potential applications, 9-phenanthrol exerts cardioprotective effects against ischaemia-reperfusion injuries and reduces ischaemic stroke injuries. In addition to reviewing the biophysical effects of 9-phenanthrol, here we present information about its appropriate use in physiological studies and possible clinical applications

Simulations of the 2004 lava flow at etna volcano by the magflow cellular automata model

Lava flows represent a challenge for physically based modeling, since the mechanical properties of lava change over time. This change is ruled by a temperature field, which needs to be modeled. MAGFLOW Cellular Automata (CA) model was developed for physically based simulations of lava flows in near real-time. We introduced an algorithm based on the Monte Carlo approach to solve the anisotropic problem. As transition rule of CA, a steady state solution of Navier-Stokes equations was adopted in the case of isothermal laminar pressure-driven Bingham fluid. For the cooling mechanism, we consider the radiative heat loss only from the surface of the flow, and the change of the temperature due to mixture of lavas between cells with different temperatures. The model was applied to reproduce a real lava flow occurred during the 2004-2005 Etna eruption. The simulations were computed using three different empirical relationships between viscosity and temperature

Dendritic Calcium Activity Precedes Inspiratory Bursts in preBotzinger Complex Neurons

Medullary interneurons of the preBotzinger complex assemble excitatory networks that produce inspiratory-related neural rhythms, but the importance of somatodendritic conductances in rhythm generation is still incompletely understood. Synaptic input may cause Ca(2+) accumulation postsynaptically to evoke a Ca(2+)-activated inward current that contributes to inspiratory burst generation. We measured Ca(2+) transients by two-photon imaging dendrites while recording neuronal somata electrophysiologically. Dendritic Ca(2+) accumulation frequently precedes inspiratory bursts, particularly at recording sites 50-300 mu m distal from the soma. Preinspiratory Ca(2+) transients occur in hotspots, not ubiquitously, in dendrites. Ca(2+) activity propagates orthodromically toward the soma (and antidromically to more distal regions of the dendrite) at rapid rates (300-700 mu m/s). These high propagation rates suggest that dendritic Ca(2+) activates an inward current to electrotonically depolarize the soma, rather than propagate as a regenerative Ca(2+) wave. These data provide new evidence that respiratory rhythmogenesis may depend on dendritic burst-generating conductances activated in the context of network activity

Simulations of the 2004 lava flow at Etna volcano by the magflow cellular automata model

Lava flows represent a challenge for physically based modeling, since the mechanical properties of lava change over time. This change is ruled by a temperature field, which needs to be modeled. MAGFLOW Cellular Automata (CA) model was developed for physically based simulations of lava flows in near real-time. We introduced an algorithm based on the Monte Carlo approach to solve the anisotropic problem. As transition rule of CA, a steady state solution of Navier-Stokes equations was adopted in the case of isothermal laminar pressure-driven Bingham fluid. For the cooling mechanism, we consider the radiative heat loss only from the surface of the flow, and the change of the temperature due to mixture of lavas between cells with different temperatures. The model was applied to reproduce a real lava flow occurred during the 2004-2005 Etna eruption. The simulations were computed using three different empirical relationships between viscosity and temperature

QVAST: a new Quantum GIS plugin for estimating volcanic susceptibility

One of the most important tasks of modern volcanology is the construction of hazard maps simulating different eruptive scenarios that can be used in risk-based decision making in land-use planning and emergency management. The first step in the quantitative assessment of volcanic hazards is the development of susceptibility maps (i.e., the spatial probability of a future vent opening given the past eruptive activity of a volcano). This challenging issue is generally tackled using probabilistic methods that use the calculation of a kernel function at each data location to estimate probability density functions (PDFs). The smoothness and the modeling ability of the kernel function are controlled by the smoothing parameter, also known as the bandwidth. Here we present a new tool, QVAST, part of the open-source geographic information system Quantum GIS, which is designed to create user-friendly quantitative assessments of volcanic susceptibility. QVAST allows the selection of an appropriate method for evaluating the bandwidth for the kernel function on the basis of the input parameters and the shapefile geometry, and can also evaluate the PDF with the Gaussian kernel. When different input data sets are available for the area, the total susceptibility map is obtained by assigning different weights to each of the PDFs, which are then combined via a weighted summation and modeled in a non-homogeneous Poisson process. The potential of QVAST, developed in a free and user-friendly environment, is here shown through its application in the volcanic fields of Lanzarote (Canary Islands) and La Garrotxa (NE Spain)

The HOTSAT volcano monitoring system based on combined use of SEVIRI and MODIS multispectral data

Spaceborne remote sensing of high-temperature volcanic features offers an excellent opportunity to monitor the onset and development of new eruptive activity. To provide a basis for real-time response during eruptive events, we designed and developed the volcano monitoring system that we call HOTSAT. This multiplatform system can elaborate both Moderate Resolution Imaging Spectroradiometer (MODIS) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, and it is here applied to the monitoring of the Etna volcano. The main advantage of this approach is that the different features of both of these sensors can be used. It can be refreshed every 15 min due to the high frequency of the SEVIRI acquisition, and it can detect smaller and/or less intense thermal anomalies through the MODIS data. The system consists of data preprocessing, detection of volcano hotspots, and radiative power estimation. To locate thermal anomalies, a new contextual algorithm is introduced that takes advantage of both the spectral and spatial comparison methods. The derivation of the radiative power is carried out at all ‘hot’ pixels using the middle infrared radiance technique. The whole processing chain was tested during the 2008 Etna eruption. The results show the robustness of the system after it detected the lava fountain that occurred on May 10 through the SEVIRI data, and the very beginning of the eruption on May 13 through the MODIS data analysis