Modeling the emission, transport and deposition of contaminated dust from a mine tailing site.

Mining operations are potential sources of airborne particulate metal and metalloid contaminants through both direct smelter emissions and wind erosion of mine tailings. The warmer, drier conditions predicted for the Southwestern US by climate models may make contaminated atmospheric dust and aerosols increasingly important, due to potential deleterious effects on human health and ecology. Dust emissions and dispersion of contaminants from the Iron King Mine tailings in Dewey-Humboldt, Arizona, a Superfund site, are currently being investigated through in situ field measurements and computational fluid dynamics modeling. These tailings are significantly contaminated with lead and arsenic with an average soil concentration of 1616 and 1420 ppm, respectively. Similar levels of these contaminants have also been measured in soil samples taken from the area surrounding the mine tailings. Using a computational fluid dynamics model, we have been able to model dust transport from the mine tailings to the surrounding region. The model includes a distributed Eulerian model to simulate fine aerosol transport and a Lagrangian approach to model fate and transport of larger particles. In order to improve the accuracy of the dust transport simulations both regional topographical features and local weather patterns have been incorporated into the model simulations.

Understanding oil and gas pneumatic controllers in the Denver-Julesburg basin using optical gas imaging.

In the spring of 2018, a 10-day field study was conducted in Colorado's Denver-Julesburg oil and natural gas production basin to improve information on well pad pneumatic controller (PC) populations and identify PCs with potential maintenance issues (MIs) causing excess emissions through a novel optical gas imaging (OGI) survey approach. A total of 500 natural gas-emitting PCs servicing 102 wells (4.9 PCs/well) were surveyed at 31 facilities operated by seven different companies. The PCs were characterized by their designed operational function and applications, with 83% of the PC population identified as intermittent PCs (IPCs). An OGI inspection protocol was used to investigate emissions on 447 working PCs from this set. OGI detected continuous emissions from 11.3% of observed IPCs and these were classified as experiencing some level of MIs. OGI imaging modes were observed to have a significant effect on emission detectability with high sensitivity mode detection rates being approximately 2 times higher compared to auto mode. Fourteen snapshot emission measurements (not including actuations) were conducted on IPCs in this category using a high-volume sampling device with augmented quality assurance procedures with observed emissions rates ranging from 0.1 up to 31.3 scf/hr (mean = 2.8 scf/hr). For PCs with continuous depressurization type (CPC), 36.8% had continuous emissions observed by OGI. Four supporting emission measurements were conducted on CPCs with one unit exceeding the low bleed regulatory emission threshold with an emission rate of 9.9 scf/hr (mean = 4.2 scf/hr). Additional information was collected on PC actuation events, as observed with OGI, which showed a strong correlation between observed actuation events and facility production compared to observed continuous emissions caused by MIs which did not correlate with facility production.Implications: A novel survey approach of pneumatic controllers at oil and natural gas production facilities in the Denver-Julesburg basin, using optical gas imaging and supporting emission measurements, was demonstrated as an effective method to identify controllers with potential maintenance issues causing excess emissions. The results of the pneumatic controller and optical gas imaging surveys improved information on pneumatic controller populations within the basin and also demonstrated the significant effect optical gas imaging modes have on emission detections.

Assessment of Uinta Basin Oil and Natural Gas Well Pad Pneumatic Controller Emissions.

In the fall of 2016, a field study was conducted in the Uinta Basin Utah to improve information on oil and natural gas well pad pneumatic controllers (PCs). A total of 80 PC systems at five oil sites (supporting six wells) and three gas sites (supporting 12 wells) were surveyed, and emissions data were produced using a combination of measurements and engineering emission estimates. Ninety-six percent of the PCs surveyed were low actuation frequency intermittent vent type. The overall whole gas emission rate for the study was estimated at 0.36 scf/h with the majority of emissions occurring from three continuous vent PCs (1.0 scf/h average) and eleven (14%) malfunctioning intermittent vent PC systems (1.6 scf/h average). Oil sites employed, on average 10.3 PC systems per well compared to 1.5 for gas sites. Oil and gas sites had group average PC emission rates of 0.28 scf/h and 0.67 scf/h, respectively, with this difference due in part to site selection procedures. The PC system types encountered, the engineering emissions estimate approach, and comparisons to measurements are described. Survey methods included identification of malfunctioning PC systems and emission measurements with augmented high volume sampling and installed mass flow meters, each providing a somewhat different picture of emissions that are elucidated through example cases.

Windblown Dust Deposition Forecasting and Spread of Contamination around Mine Tailings.

Wind erosion, transport and deposition of windblown dust from anthropogenic sources, such as mine tailings impoundments, can have significant effects on the surrounding environment. The lack of vegetation and the vertical protrusion of the mine tailings above the neighboring terrain make the tailings susceptible to wind erosion. Modeling the erosion, transport and deposition of particulate matter from mine tailings is a challenge for many reasons, including heterogeneity of the soil surface, vegetative canopy coverage, dynamic meteorological conditions and topographic influences. In this work, a previously developed Deposition Forecasting Model (DFM) that is specifically designed to model the transport of particulate matter from mine tailings impoundments is verified using dust collection and topsoil measurements. The DFM is initialized using data from an operational Weather Research and Forecasting (WRF) model. The forecast deposition patterns are compared to dust collected by inverted-disc samplers and determined through gravimetric, chemical composition and lead isotopic analysis. The DFM is capable of predicting dust deposition patterns from the tailings impoundment to the surrounding area. The methodology and approach employed in this work can be generalized to other contaminated sites from which dust transport to the local environment can be assessed as a potential route for human exposure.

Simulation of windblown dust transport from a mine tailings impoundment using a computational fluid dynamics model.

Mining operations are potential sources of airborne particulate metal and metalloid contaminants through both direct smelter emissions and wind erosion of mine tailings. The warmer, drier conditions predicted for the Southwestern US by climate models may make contaminated atmospheric dust and aerosols increasingly important, due to potential deleterious effects on human health and ecology. Dust emissions and dispersion of dust and aerosol from the Iron King Mine tailings in Dewey-Humboldt, Arizona, a Superfund site, are currently being investigated through in situ field measurements and computational fluid dynamics modeling. These tailings are heavily contaminated with lead and arsenic. Using a computational fluid dynamics model, we model dust transport from the mine tailings to the surrounding region. The model includes gaseous plume dispersion to simulate the transport of the fine aerosols, while individual particle transport is used to track the trajectories of larger particles and to monitor their deposition locations. In order to improve the accuracy of the dust transport simulations, both regional topographical features and local weather patterns have been incorporated into the model simulations. Results show that local topography and wind velocity profiles are the major factors that control deposition.