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Field-portable Open Path Fourier Transform Infrared (OP-FTIR) spectrometers can be used to remotely measure the composition of volcanic plumes using absorption spectroscopy, providing invaluable data on total gas emissions. Quantifying the temporal evolution of gas compositions during an eruption helps develop models of volcanic processes and aids in eruption forecasting. Absorption measurements require a viewing geometry which aligns infrared source, plume, and instrument, which can be challenging. Here, we present a fast retrieval algorithm to estimate quantities of gas, ash and sulphate aerosols from thermal emission OP-FTIR measurements, and the results from two pilot campaigns on Stromboli volcano in Italy in 2019 and 2021. We validate the method by comparing time series of SO2 slant column densities retrieved using our method with those obtained from a conventional UV spectrometer, demonstrating that the two methods generally agree to within a factor of 2. The algorithm correctly identifies ash-rich plumes and gas bursts associated with explosions and quantifies the mass column densities and particle sizes of ash and sulphate aerosols (SA) in the plume. We compare the ash sizes retrieved using our method with the particle size distribution (PSD) of an ash sample collected during the period of measurements in 2019 by flying a Remotely Piloted Aircraft System into the path of a drifting ash plume and find that both modes of the bimodal PSD (a fine fraction with diameter around 5–10 μm and a coarse fraction around 65 μm) are identified within our datasets at different times. We measure a decrease in the retrieved ash particle size with distance downwind, consistent with settling of larger particles, which we also observed visually. We measure a decrease in the SO2/SA ratio as the plume travels downwind, coupled with an increase in measured SA particle size (range 2–6 μm), suggesting rapid hygroscopic particle growth and/or SO2 oxidation. We propose that infrared emission spectroscopy can be used to examine physical and chemical changes during plume transport and opens the possibility of remote night-time monitoring of volcanic plume emissions. These ground-based analyses may also aid the refinement of satellite-based aerosol retrievals.

Volcano-seismic event classification represents a fundamental component of volcanic monitoring. Recent advances in techniques for the automatic classification of volcano-seismic events using supervised deep learning models achieve high accuracy. However, these deep learning models require a large, labelled training dataset to successfully train a generalisable model. We develop an approach to volcano-seismic event classification making use of active learning, where a machine learning model actively selects the training data which it learns from. We apply a diversity-based active learning approach, which works by selecting new training points which are most dissimilar from points already in the model according to a distance-based calculation applied to the model features. We combine the active learning with an existing volcano-seismic event classifier and apply the model to data from two volcanoes: Nevado del Ruiz, Colombia and Llaima, Chile. We find that models with data selected using an active learning approach achieve better testing accuracy and AUC (Area Under the Receiver Operating Characteristic Curve) than models with data selected using random sampling. Additionally, active learning decreases the labelling burden for the Nevado del Ruiz dataset but offers no increase in performance for the Llaima dataset. To explain these results, we visualise the features from the two datasets and suggest that active learning can reduce the quantity of labelled data required for less separable data, such as the Nevado del Ruiz dataset. This study represents the first evaluation of an active learning approach in volcano-seismology.

Among the hazards posed by volcanoes are the emissions of gases and particles that can affect air quality and damage agriculture and infrastructure. A recent intense episode of volcanic degassing associated with severe impacts on air quality accompanied the 2018 lower East Rift Zone (LERZ) eruption of Kīlauea volcano, Hawai'i. This resulted in a major increase in gas emission rates with respect to usual emission values for this volcano, along with a shift in the source of the dominant plume to a populated area on the lower flank of the volcano. This led to reduced air quality in downwind communities. We analyse open-access data from the permanent air quality monitoring networks operated by the Hawai'i Department of Health (HDOH) and National Park Service (NPS), and report on measurements of atmospheric sulfur dioxide (SO2) between 2007 and 2018 and PM2.5 (aerosol particulate matter with diameter

Transient micronutrient enrichment of the surface ocean can enhance phytoplankton growth rates and alter microbial community structure with an ensuing spectrum of biogeochemical feedbacks. Strong phytoplankton responses to micronutrients supplied by volcanic ash have been reported recently. Here we: (i) synthesize findings from these recent studies; (ii) report the results of a new remote sensing study of ash fertilization; and (iii) calculate theoretical bounds of ash-fertilized carbon export. Our synthesis highlights that phytoplankton responses to ash do not always simply mimic that of iron amendment; the exact mechanisms for this are likely biogeochemically important but are not yet well understood. Inherent optical properties of ash-loaded seawater suggest rhyolitic ash biases routine satellite chlorophyll-a estimation upwards by more than an order of magnitude for waters with 0.5 mg chlorophyll-a m-3. For this reason post-ash-deposition chlorophyll-a changes in oligotrophic waters detected via standard Case 1 (open ocean) algorithms should be interpreted with caution. Remote sensing analysis of historic events with a bias less than a factor of 2 provided limited stand-alone evidence for ash-fertilization. Confounding factors were poor coverage, incoherent ash dispersal, and ambiguity ascribing biomass changes to ash supply over other potential drivers. Using current estimates of iron release and carbon export efficiencies, uncertainty bounds of ash-fertilized carbon export for 3 events are presented. Patagonian iron supply to the Southern Ocean from volcanic eruptions is less than that of windblown dust on thousand year timescales but can dominate supply at shorter timescales. Reducing uncertainties in remote sensing of phytoplankton response and nutrient release from ash are avenues for enabling assessment of the oceanic response to large-scale transient nutrient enrichment.