Presented here is a new dust modeling framework that uses a backward-Lagrangian particle dispersion model coupled with a dust emission model, both driven by meteorological data from the Weather Research and Forecasting (WRF) Model. This new modeling framework was tested for the spring of 2010 at multiple sites across northern Utah. Initial model results for March–April 2010 showed that the model was able to replicate the 27–28 April 2010 dust event; however, it was unable to reproduce a significant wind-blown dust event on 30 March 2010. During this event, the model significantly underestimated PM2.5 concentrations (4.7 vs 38.7 μg m−3) along the Wasatch Front. The backward-Lagrangian approach presented here allowed for the easy identification of dust source regions with misrepresented land cover and soil types, which required an update to WRF. In addition, changes were also applied to the dust emission model to better account for dust emitted from dry lake basins. These updates significantly improved dust model simulations, with the modeled PM2.5 comparing much more favorably to observations (average of 30.3 μg m−3). In addition, these updates also improved the timing of the frontal passage within WRF. The dust model was also applied in a forecasting setting, with the model able to replicate the magnitude of a large dust event, albeit with a 2-h lag. These results suggest that the dust modeling framework presented here has potential to replicate past dust events, identify source regions of dust, and be used for short-term forecasting applications.

Projecting future-year emission inventories in the oil and gas sector is complicated by the fact that there is a life cycle to the amount of production from individual wells and thus from well fields in aggregate. Here we present a method to account for that fact in support of regulatory policy development. This approach also has application to air quality modeling inventories by adding a second tier of refinement to the projection methodology. Currently, modeling studies account for the future decrease in emissions due to new regulations based on the year those regulations are scheduled to take effect. The addition of a year-by-year accounting of production decline provides a more accurate picture of emissions from older, uncontrolled sources. This proof of concept approach is focused solely on oil production; however, it could be used for the activity and components of natural gas production to compile a complete inventory for a given area.