This report presents the significant dust dispersion models that are pertinent to the mining industry in an effort to highlight past and current dust modeling exercises.

The Arctic region contains vast mineral resources and mining of these resources is a major activity in several countries, including the United States. With the advancement of open-pit mining technology, the depth to which minerals can be profitably mined has increased, resulting in deeper pits than ever before. This increase in depth has several inherent challenges for mining operations. The ventilation of an open-pit mine is mostly dependent on natural airflow patterns. The dispersion behavior of the pollutants generated in a mine is also dependent on the atmospheric conditions. The control of fugitive dust in high-latitude open-pit mines is challenging due to unique atmospheric phenomena resulting in complicated flow regimes as well as atmospheric inversion due to the lack of adequate insolation during prolonged winter seasons. The development of a computational fluid dynamics (CFD) model of an open-pit mine is challenging due to the presence of several sharp and irregular features at the pit surface. A good quality mesh of the model domain is a prerequisite for convergence in solution. Besides good quality meshing, choices of various simulation setup parameters have significant impact in convergence or divergence of the simulation. Appropriate choices of simulation type, boundary and initial conditions, time stepping and various convergence criteria are important for realistic simulation of a model domain. Environmental conditions in the mine vary from season to season; hence, fugitive dust dispersion simulations using a commercial CFD software are conducted for various seasonal conditions along with several cloud conditions. Clear sky and cloudy sky conditions result in different radiative and turbulent energy fluxes. In each scenario, fugitive dust particles varying in size (PM0.1 to PM10) and concentrations are generated at various locations of the selected mine. The simulation results predict a speedy removal of fugitive dust in summer. However, during winter, the presence of an inversion layer in the open-pit results in extensive retention of fugitive dust. For removal of the atmospheric inversion during winter, it is observed that the presence of cloud cover and convective wind are the most important factors.

Mining operations generates substantial quantities of airborne respirable dust, which leads to the development of lung disease in mine workers. Coal worker's pneumoconiosis and silicosis are lung diseases that have adversely impacted the health of thousands of mine workers. The increasing trend of opencast mining leads to release of huge amount of dust. These air borne dust particles, generally below 100 micron in size, are environmentally nuisance and cause health hazards as an ill effect of mining activities. Opencast extraction activities like drilling, blasting, material handling and transport are a potential source of air pollution. Therefore, a detailed study on emission sources and quantification of pollutant concentration by means of dispersion modeling is required to access the environmental impact of a opencast mine. On the basis of the predicted increments to air pollutant concentrations, an effective mitigation and environmental plan can be devised for sensitive areas. In the present study, Air quality modeling has been attempted using AERMOD. Line source & Volume source modeling has been carried out for haul road and open pit respectively. From the modeling exercise, dust concentrations at certain receptor locations have been predicted and it was found that the resultant SPM level at these locations will remain within the NAAQS norms.

Wind erosion, transport and deposition of particulate matter can have significant impacts on the environment. It is observed that about 40% of the global land area and 30% of the earth's population lives in semiarid environments which are especially susceptible to wind erosion and airborne transport of contaminants. With the increased desertification caused by land use changes, anthropogenic activities and projected climate change impacts windblown dust will likely become more significant. An important anthropogenic source of windblown dust in this region is associated with mining operations including tailings impoundments. Tailings are especially susceptible to erosion due to their fine grain composition, lack of vegetative coverage and high height compared to the surrounding topography. This study is focused on emissions, dispersion and deposition of windblown dust from the Iron King mine tailings in Dewey-Humboldt, Arizona, a Superfund site. The tailings impoundment is heavily contaminated with lead and arsenic and is located directly adjacent to the town of Dewey-Humboldt. The study includes in situ field measurements, computational fluid dynamic modeling and the development of a windblown dust deposition forecasting model that predicts deposition patterns of dust originating from the tailings impoundment. Two instrumented eddy flux towers were setup on the tailings impoundment to monitor the aeolian and meteorological conditions. The in situ observations were used in conjunction with a computational fluid dynamic (CFD) model to simulate the transport of windblown dust from the mine tailings to the surrounding region. The CFD model simulations include gaseous plume dispersion to simulate the transport of the fine aerosols, while individual particle transport was used to track the trajectories of larger particles and to monitor their deposition locations. The CFD simulations were used to estimate deposition of tailings dust and identify topographic mechanisms that influence deposition. Simulation results indicated that particles preferentially deposit in regions of topographic upslope. In addition, turbulent wind fields enhanced deposition in the wake region downwind of the tailings. This study also describes a deposition forecasting model (DFM) that can be used to forecast the transport and deposition of windblown dust originating from a mine tailings impoundment. The DFM uses in situ observations from the tailings and theoretical simulations of aerosol transport to parameterize the model. The model was verified through the use of inverted-disc deposition samplers. The deposition forecasting model was initialized using data from an operational Weather Research and Forecasting (WRF) model and the forecast deposition patterns were compared to the inverted-disc samples through gravimetric, chemical composition and lead isotopic analysis. The DFM was verified over several month-long observing periods by comparing transects of arsenic and lead tracers measured by the samplers to the DFM PM2-- forecast. Results from the sampling periods indicated that the DFM was able to accurately capture the regional deposition patterns of the tailings dust up to 1 km. Lead isotopes were used for source apportionment and showed spatial patterns consistent with the DFM and the observed weather conditions. By providing reasonably accurate estimates of contaminant deposition rates, the DFM can improve the assessment of human health impacts caused by windblown dust from the Iron King tailings impoundment.