Voronoi-based analysis of clustering dynamics in experimental volcanic ash clouds

TL;DR

This study uses Voronoi analysis to understand how volcanic ash particles cluster in the air, which affects how they spread and settle, improving predictions of ash dispersal.

Contribution

The paper introduces a novel experimental framework using Voronoi tessellation to quantify clustering dynamics in volcanic ash clouds.

Findings

Particle-driven convection intensifies with decreasing particle size.

Clustering affects settling, allowing smaller particles to settle faster than larger ones.

Clustering enhances particle interactions and likely promotes aggregation.

Abstract

Explosive volcanic eruptions inject large amounts of ash into the atmosphere, where it disperses regionally and globally, posing risks to aviation, infrastructure, and public health. Accurate ash dispersal forecasting is crucial for hazard mitigation, yet current models primarily rely on eruption source parameters, such as particle size distribution, while largely neglecting evolving atmospheric ash distributions. Turbulence-driven particle interactions generate dense clusters that travel faster than isolated particles, shortening the residence time of fine ash and potentially boosting collision and aggregation rates. These processes remain poorly constrained. Here, we present an experimental framework to quantify clustering in controlled ash columns over particle volume fractions φ = 10–5–10–2. Using Laacher See ash (1000–63 µm), we vary particle size distributions and release rates, acquire high-speed laser-illuminated videos for particle tracking, and apply Voronoi tessellation to quantify preferential concentration. We find that particle-driven convection intensifies with decreasing size, while varying φ modulates clustering across all sizes < 500 µm. Clustering produces strongly inhomogeneous distributions within the column, enhances particle–particle interactions, and likely promotes aggregation. It also affects settling, as smaller particles within clusters can settle faster than larger, unclustered ones, thus challenging traditional assumptions that link particle size to settling velocity. Incorporating these dynamics into dispersal models, and accounting for their signatures in remote-sensing retrievals, should improve forecast accuracy and refine our understanding of volcanic ash transport and deposition.

The online version contains supplementary material available at 10.1007/s00445-025-01933-x.

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