English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Mt. Baekdu’s eruption precursors are continuously observed and have become a global social issue. Volcanic activities in neighboring Japan are also active. There are no direct risks of proximity-related disasters in South Korea from the volcanic eruptions at Japan or Mt. Baekdu; however, severe impacts are expected from the spread of volcanic ash. Numerical analysis models are generally used to predict and analyze the diffusion of volcanic ash, and each numerical analysis model has its own limitations caused by the computational algorithm it employs. In this study, we analyzed the PUFF–UAF model, an ash dispersion model based on the Lagrangian approach, and observed that the number of particles used in tracking substantially affected the results. Even with the presence of millions of particles, the concentration of ash predicted by the PUFF–UAF model does not accurately represent the dispersion. To overcome this deficit and utilize the computational efficiency of the Lagrangian model, we developed a PUFF–Gaussian model to consider the dispersive nature of ash by applying the Gaussian dispersion theory to the results of the PUFF–UAF model. The results of the proposed method were compared with the field measurements from actual volcanic eruptions, and the comparison showed that the proposed method can produce reasonably accurate predictions for ash dispersion.

Development of PUFF–Gaussian dispersion model for the prediction of atmospheric distribution of particle concentration