Refinement and Quantitative Evaluation of a Monte Carlo Model for Wind-Driven PM Emissions from Industrial Granular Materials

A physical–mathematical model was previously developed to estimate dust emissions from granular surfaces exposed to wind erosion. The model is based on the main physical mechanisms governing wind-driven dust emissions, whereby the release of fine particles is controlled by saltation and the associated sandblasting process. A probabilistic Monte Carlo approach is used to simulate saltator impacts on the erodible surface and estimate Particulate Matter (PM) emissions from the mass of elementary particles released during each collision. While the original study provided only a qualitative assessment, the present work introduces computational refinements and presents the first quantitative evaluation of the model in terms of both numerical performance and the physical consistency of the predicted PM emission behaviour. The algorithm was modified by introducing a fixed number of simulated impacts, thereby reducing computational cost. The revised model was applied to lead and zinc sulphide concentrates from an industrial plant in Sardinia (Italy) to evaluate the effects of the proposed model improvements. The assessment focused on (i) the sensitivity of the simulated emissions to the number of simulated impacts and (ii) the ability of the revised model to reproduce the PM emission magnitude, sandblasting efficiency, and their dependence on wind friction velocity. The results show that reducing the number of simulated impacts from 10,000 to 500 resulted in a median relative difference of 3.35% in the simulated PM emissions compared with the highest-sampling configuration investigated, while reducing the computational time by approximately 95%. Moreover, the model reproduces emission magnitudes and key emission parameters generally consistent with those reported in the literature for materials with similar physical properties. Overall, the revised model provides an efficient, physically based tool for estimating PM emissions from industrial granular materials.