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

Capponi, A., Cimarelli C., Mininni P. (2025)

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 distribu
tions 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 set
tling 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.