Dr. Sukumar Bandopadhyay
The generation and entrainment of dust is inherent to many of the unit and auxiliary operations of mining. In surface mining, in general, most of these operations are carried out in open atmosphere and the resulting dust is generally released to the atmosphere. Dusts generated from these open sources are, by definition, fugitive dusts and the sources, fugitive sources. Many communities and countries have set environmental standards for ambient airborne dust concentrations produced by mining operations; these are enforced by their respective regulatory agencies (e.g. MSHA and EPA in the United States). Even where there are no standards, or comparatively less stringent regulations, there is increasing recognition of the health, safety, environmental and economic benefits yielded by a well-designed particulate control program.
Fugitive dusts can be a source of threats to the health and safety of miners and communities. The particular threat is dependent on the type of dust. Coal, silica and metallic dusts have been associated with serious health consequences. When mineral operations take place close to populated communities, effects on the general public, livestock, and vegetation are major issues. Fugitive dusts also have the potential to affect production and productivity in a number of ways: they can cause poor visibility for operators; they affect the moving parts of machinery, leading to failures, downtime and repairs; dusts can settle on various surfaces and structures, creating both waste and a need for regular clean-up. Finally, they can be a public relations nightmare, a major cause for bad publicity, damaging the image of mining companies in the local and national media.
An additional concern to the mining industry is the potential for fugitive dust to contain metals. Along with total suspended solids, metals are also regulated and must be assessed as a component of a site’s fugitive emissions. In addition, many mine sites in arctic regions often undergo strong atmospheric inversions during the winters, which are characterized by extremely cold temperatures and little wind, conditions that tend to intensify the effects of fugitive emissions. These inversions are common during Arctic winters due to a lack of insolation and atmospheric conditions. In general, fine dust seems to be generated more often in the winter due to freeze-dry conditions, from mid-October to May.
As opposed to dust sources in other industrial activities, dust sources in mining are several, spatially distributed, generally non-point and present several challenges to successful dust control. The amount and the characteristics of the dusts generated and dispersed are affected by a number of natural and cultural factors such as the topography, meteorological conditions, dust sources and the amount of dust produced determined by such factors as material mined, mining method, and methods used to load and haul the mined materials, as well as the control measures in place. While haul roads account for the bulk of fugitive dust (over 80%), all operations such as top soil removal, drilling, blasting, loading and dumping generate and entrain dust. Particle sizes ranges from under 10 microns to over 100 microns. Though programs for dust control incorporate well-known methods of prevention, suppression, reduction and isolation strategies, often in combination, it has been difficult to fully control the generation and entrainment of dust.
The entrained dust is dispersed by small-scale airflows and transported to farther distances by larger scale airflows. This dispersion and transport by wind in an open-pit environment is a complex process, making the open-pit airborne dust assessment and control very challenging. Developing more effective methods for dust control requires the application of computer-oriented mathematical simulation models of pollutant dispersion.
While governmental agencies in the U.S. and elsewhere are in the forefront of atmospheric dispersion modeling, much of their work is focused on large-scale regional or global issues affecting public policy and safety. For example, The U.S. EPA has developed and approved models such as AERMOD, CALPUFF and ISC3 for atmospheric dispersion studies and these have been applied extensively in many industrial settings in both United States and abroad. Reed (2005) has provided an excellent summary of significant dust dispersion models for mining operations, noting the importance of variability of airflow velocities and directions in surface mining and the difficulty in interpreting model results. The performance of these models when applied to surface mining sources has been questionable, often over-predicting concentrations (Cole and Zapert, 1995; Long, 2011).
Advances in mathematical and computer fields in recent years have enabled better modeling of the dust dispersion phenomenon around mines using computational fluid dynamics (CFD). However, there is a need for a good understanding of the physical, mathematical and computational aspects and limitations. The importance of the location of the emission sources, the direction and strength of the prevailing wind, and the pit configuration were identified as major factors by Lowndes et al. (2008) in their CFD model study of dust dispersion in a limestone quarry. Silvester et al. (2009) developed and applied a computational fluid dynamics (CFD) model to study the dispersion and deposition of fugitive mineral dust particles generated during rock blasting operations. While this study concluded that depending on the location of the bench blast within the quarry and the direction of the wind, a mass fraction of between 0.3 and 0.6 of the emitted mineral particles was retained within the quarry, the stability of the prevailing atmospheric conditions would also be an influencing factor on deposition. Flores et al. (2013, 2014) applied a CFD modeling approach to study dust dispersion in the Chuquicamata copper open pit. Matejicek et al. (2008) discussed the need to manage a wide range of spatio-temporal data and numerical simulations to study the impact of dust transport on communities; their case study used spatial data from 3D laser scanners, GPS measurements, meteorological observations, and results from numerical models to perform a risk assessment of dust deposition in areas of interest.
This brief literature review underscores the need for more comprehensive data collection and analytical procedures to address the surface mine dust dispersion problem. Indeed, Reed explicitly notes in his specific study of mining dispersion models that dust dispersion modeling of mining facilities needs to be advanced (2005). This proposal outlines a new approach combining several advances in computer modeling, data collection and analytical procedures to design dust control and dispersion plans for open pit mines.