Simulation of Diurnal Green House Gas (GHG) Emission Flux from a Complex Mining Facility Using WRF

Green House Gas (GHG) emission is a global climate challenge and the mining sector is a large source of GHG emission. Unlike stack emissions, area fugitive emissions from mines complicate GHG flux estimation due to surface variation in land use, topography, and GHG mixing ratios distributed unevenly. In addition, meteorological effects at meso and micro scales confound dispersion and GHG flux emission estimation. In this study we have used the Weather Research and Forecasting (WRF) 4.0 model to simulate the GHG passive tracer dispersion and flux in a complex heterogeneous mining facility in Northern Alberta, Canada. The model was initialized using the Global Data Assimilation System (GDAS) dataset with a spatial resolution of 0.25° and a temporal resolution of 6 hours. Five nested domains with horizontal grid resolutions of 10000 m in the outermost domain (5) and 124 m in the innermost domain (1) were used. The model contained 90 vertical levels. We used Mellor-Yamada-Janjic (MYJ) (1994) Planetary Boundary Layer (PBL) scheme. To construct the most recent topography of the mining facility, in domains 1, 2, and 3, the standard Global 30 Arc-Second (GTOPO 30s) dataset in WRF was used, which provides a horizontal resolution of 900 m. For domains 4 and 5, the Shuttle Radar Topography Mission (SRTM) 1s dataset with a horizontal resolution of 30 m was used. For domain 5, the background resolution (SRTM 1s) was overwritten with a Light Detection And Ranging (LiDAR) dataset with a horizontal resolution down to 1 m. In addition to specifying land use classifications of Moderate Resolution Imaging Spectroradiometer (MODIS) 30s by Friedl et al. 2010, we have enabled the Subin et al. 2012 and Gu et al. 2015 lake model in WRF to properly simulate the thermodynamics and aerodynamics of water bodies at the site by specifying the pond and lakes at the facility as water bodies. GHG transport was simulated using passive tracer dispersion in WRF. Simulations were conducted for the period of May 18-28, 2018, coinciding with a rigorous field observation campaign to provide boundary and initial conditions for the model. Meteorological fields and particularly surface level GHG mixing ratios were measured in various locations. In order to find the variation in GHG flux under different diurnal times, we conducted the simulations for blocks of four hour intervals, where surface level GHG mixing ratios were inserted as constants in the model, while meteorological fields were updated according to the GDAS dataset. Results show that in the night-time and early-morning time intervals, when the Atmospheric Boundary Layer (ABL) is thermally stable, GHG emission flux is reduced, whereas in the midday time interval, and at highly convective condition, GHG emission flux is enhanced. This study quantifies diurnal variation of GHG flux from area fugitive emission sources at a mining facility. The methodology can be applied to other situations where large scale land surface modification requires meso and micro scale Numerical Weather Prediction (NWP) in dispersion modelling of atmospheric pollutants. Such detailed quantification is required for more accurate estimations of GHG flux and therefore potential air quality and climate impacts.