Abstract
Fugitive dust has increasingly been an area of agency attention. The mining area specifically is highly prone to fugitive dust generation due to the large number of dust generating activities, such as haul truck roads, tailings basins, stockpiles, and mine pits. Because most of the mining operations are subject to the natural elements, addressing fugitive dust generation from weather events can be difficult. Most of the methods for addressing fugitive dust are reactionary, such as water trucks and applying dust suppressants once dust is visible. These measures are good and appropriate, however, for weather events that our out of the facility’s control they can sometimes be too late. In this talk, I will present two methods for developing site-specific fugitive dust prediction models based on dynamical and statistical relationships with local weather data. I will walk through a typical physical dust generation mechanism and also discuss different statistical methods used to develop site-specific prediction models. Additionally, I will discuss how these models are being used in alert tool systems to provide effective and proactive dust suppressant measures.