Search Results (5)

Satellite microwave (MW) and millimetre-wave (MMW) passive sensors can be used to detect volcanic clouds because of their sensitivity to larger volcanic particles (i.e., size bigger than 20 µm). In this work, we combine the MW-MMW observations with thermal-infrared (TIR) radiometric data from the Low Earth Orbit (LEO) spectroradiometer to have a complete characterisation of volcanic plumes. We describe new physical-statistical methods, which combine machine learning techniques, aimed at detecting and retrieving volcanic clouds of two highly explosive eruptions: the 2014 Kelud and 2015 Calbuco test cases. For the detection procedure, we compare the well-known split-window methods with a machine learning algorithm named random forest (RF). Our work highlights how the machine learning method is suitable to detect volcanic clouds using different spectral signatures without fixing a threshold. Moreover, the RF model allows images to be automatically processed with promising results (90% of the area correctly identified). For the retrieval procedure of the mass of volcanic particles, we consider two methods, one based on the maximum likelihood estimation (MLE) and one using the neural network (NN) architecture. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands. Summing the MW-MMW and TIR estimates, we obtain the following masses: 1.11 ± 0.40 × 10¹¹ kg (MLE method) and 1.32 ± 0.47 × 10¹¹ kg (NN method) for Kelud; 4.48 ± 1.61 × 10¹⁰ kg (MLE method) and 4.32 ± 1.56 × 10¹⁰ kg (NN method) for Calbuco. This work shows how machine learning techniques can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near-source volcanic clouds. Full article

Lactate is a metabolite produced during anaerobic glycolysis for ATP resynthesis, which accumulates during hypoxia and muscle contraction. Tobacco smoking significantly increases blood lactate. Here we conducted a counter-balanced crossover study to examine whether this effect is associated with inhaling nicotine or burned carbon particles. Fifteen male smokers (aged 23 to 26 years) were randomized into 3 inhalation conditions: tobacco smoking, nicotine vaping, and nicotine-free vaping, conducted two days apart. An electronic thermal evaporator (e-cigarette) was used for vaping. We have observed an increased blood lactate (+62%, main effect: p < 0.01) and a decreased blood glucose (−12%, main effect: p < 0.05) during thermal air inhalations regardless of the content delivered. Exercise-induced lactate accumulation and shuttle run performance were similar for the 3 inhalation conditions. Tobacco smoking slightly increased the resting heart rate above the two vaping conditions (p < 0.05), implicating the role of burned carbon particles on sympathetic stimulation, independent of nicotine and thermal air. The exercise response in the heart rate was similar for the 3 conditions. The results of the study suggest that acute hypoxia was induced by breathing thermal air. This may explain the reciprocal increases in lactate and decreases in glucose. The impaired lung function in oxygen delivery of tobacco smoking is unrelated to nicotine. Full article

This paper quantifies the tropical stratospheric aerosol content as impacted by volcanic events over the 2013–2019 period. We use global model simulations by the Whole Atmosphere Community Climate Model (WACCM) which is part of the Community Earth System Model version 1.0 (CESM1). WACCM is associated with the Community Aerosol and Radiation Model for Atmospheres (CARMA) sectional aerosol microphysics model which includes full sulphur chemical and microphysical cycles with no a priori assumption on particle size. Five main volcanic events (Kelud, Calbuco, Ambae, Raikoke and Ulawun) have been reported and are shown to have significantly influenced the stratospheric aerosol layer in the tropics, either through direct injection in this region or through transport from extra-tropical latitudes. Space-borne data as well as ground-based lidar and balloon-borne in situ observations are used to evaluate the model calculations in terms of aerosol content, vertical distribution, optical and microphysical properties, transport and residence time of the various volcanic plumes. Overall, zonal mean model results reproduce the occurrence and vertical extents of the plumes derived from satellite observations but shows some discrepancies for absolute values of extinction and of stratospheric aerosol optical depth (SAOD). Features of meridional transport of the plumes emitted from extra-tropical latitudes are captured by the model but simulated absolute values of SAOD differ from 6 to 200% among the various eruptions. Simulations tend to agree well with observed in situ vertical profiles for the Kelud and Calbuco plumes but this is likely to depend on the period for which comparison is done. Some explanations for the model–measurement discrepancies are discussed such as the inaccurate knowledge of the injection parameters and the presence of ash not accounted in the simulations. Full article

The largest explosive volcanic eruptions produce umbrella clouds that drive ash radially outward, enlarging the area that impacts aviation and ground-based communities. Models must consider the effects of umbrella spreading when forecasting hazards from these eruptions. In this paper we test a version of the advection–dispersion model Ash3d that considers umbrella spreading by comparing its simulations with observations from three well-documented umbrella-forming eruptions: (1) the 15 June 1991 eruption of Pinatubo (Philippines); (2) the 13 February 2014 eruption of Kelud (Indonesia); and (3) phase 2 of the 22–23 April 2015 eruption of Calbuco (Chile). In volume, these eruptions ranged from several cubic kilometers dense-rock equivalent (DRE) for Pinatubo to about one tenth for Calbuco. In mass eruption rate (MER), they ranged from 10⁸–10⁹ kg s⁻¹ at Pinatubo to 9–16 × 10⁶ kg s⁻¹ at Calbuco. For each case we ran simulations that considered umbrella growth and ones that did not. All umbrella-cloud simulations produced a cloud whose area was within ~25% of the observed cloud by the end of the eruption. By the eruption end, the simulated areas of the Pinatubo, Kelud, and Calbuco clouds were 851, 53.2, and 100 × 10³ km² respectively. These areas were 2.2, 2.2, and 1.5 times the areas calculated in simulations that ignored umbrella growth. For Pinatubo and Kelud, the umbrella simulations provided better agreement with the observed cloud area than the non-umbrella simulations. Each of these simulations extended 24 h from the eruption start. After the eruption ended, the difference in cloud area (umbrella minus non-umbrella) at Pinatubo persisted for many hours; at Kelud it diminished and became negative after 14 h and at Calbuco it became negative after ~23 h. The negative differences were inferred to result from the fact that non-umbrella simulations distributed ash over a wider vertical extent in the plume, and that wind shear spread the cloud out in multiple directions. Thus, for some smaller eruptions, wind shear can produce a larger cloud than might be produced by umbrella spreading alone. Full article

Large explosive eruptions can result in the formation of an umbrella cloud which rapidly expands, spreading ash out radially from the volcano. The lateral spread by the intrusive gravity current dominates the transport of the ash cloud. Hence, to accurately forecast the transport of ash from large eruptions, lateral spread of umbrella clouds needs to be represented within volcanic ash transport and dispersion models. Here, we describe an umbrella cloud parameterisation which has been implemented into an operational Lagrangian model and consider how it may be used during an eruption when information concerning the eruption is limited and model runtime is key. We examine different relations for the volume flow rate into the umbrella, and the rate of spreading within the cloud. The scheme is validated against historic eruptions of differing scales (Pinatubo 1991, Kelud 2014, Calbuco 2015 and Eyjafjallajökull 2010) by comparing model predictions with satellite observations. Reasonable predictions of umbrella cloud spread are achieved using an estimated volume flow rate from the empirical equation by Bursik et al. and the observed eruption height. We show how model predictions can be refined during an ongoing eruption as further information and observations become available. Full article