A chemical transport model study of plume rise and particle size distribution for the Athabasca oil sands

Abstract

Abstract.We evaluate four high-resolution model simulations of pollutant emissions, chemical transformation and downwind transport for the Athabasca oil sands using the Global Environmental Multiscale -Modelling Air-quality and Chemistry (GEM-MACH) model using surface monitoring network and aircraft observations of multiple pollutants, for simulations spanning a 15 time period corresponding to an aircraft measurement campaign in the region in summer 2013. We have focussed here on the impact of different representations of the model's aerosol size distribution and plume-rise parameterization on model results. The use of a more finely resolved representation of the aerosol size distribution was found to have a significant impact on model performance, reducing the magnitude of the original surface PM 2.5 negative biases by 32%. 20We compared model predictions of SO 2 , NO 2 , and speciated particulate matter concentrations from simulations employing the commonly-used Briggs (1984) plume-rise algorithms to redistribute emissions from large stacks with stack plume observations. As in our companion paper (Gordon et al., 2018), we found these algorithms resulted in under-predictions of plume rise, with 39 to 60% of predicted plume heights falling below half of the observed plume heights. However, we found here that a layered buoyancy approach for stable to neutral atmospheres, coupled with the assumption of free rise in 25 convectively unstable atmospheres, resulted in much better model performance, both for atmospheric constituent concentrations and the predicted height of the plumes. Persistent issues with over-fumigation of plumes in the model were linked to positive biases in the predicted temperatures between the surface and 1km elevation. These in turn may lead to overestimates of near-surface diffusivity, resulting in excessive fumigation.Atmos. Chem. Phys. Discuss., https://doi

A Comparison of Plume Rise Algorithms to Stack Plume Measurements in the Athabasca Oil Sands

“…For model resolutions of greater than 2 km (c.f. Akingunola et al, 2017), the plumes will all have reached their equilibrium height within one grid-square distance of emission. Had the measurements been too close to the stacks, some overestimation may have occurred due to the measured plumes not reaching their maximum height.…”

“…While the above formulae are used in GEM-MACH and other models, we also examine a layer-165 based approach suggested by Briggs, described below, and the companion paper (Akingunola et al (2017) examines the impact of this approach within the GEM-MACH model itself.…”

The use of hierarchical clustering for the design of optimized monitoring networks

“…GEM-MACH simulations have been previously evaluated for both NO 2 and SO 2 concentrations against monitoring network data and satellite observations and cross-compared to the output of other air quality models in Im et al (2015), Wang et al (2015), Makar et al (2015a, b), and Moran et al (2016). Further evaluation of GEM-MACH on the high-resolution domain used here can be found in and Akingunola et al (2018).…”

“…These are compared to observations in order to evaluate the model's performance (for traditional evaluations using the model output used herein, cf. Makar et al, 2018;Akingunola et al, 2018;Stroud et al, 2018). We introduce here for the first time the concept of the use of these time series of air quality model output, combined with hierarchical clustering analysis, as a surrogate for station data, for the purposes of monitoring network analysis and design.…”

“…4a) already show many clusters composed of one or few stations, with the model showing slightly more clusters than the observations (21 clusters versus 25, respectively). As noted earlier, SO 2 in this region is emitted mainly by point sources, and the use of annual emissions data with an assumed temporal allocation, along with the additional inherent difficulties in accurately predicting plume rise (Akingunola et al, 2018), makes the reproduction of the time record of SO 2 by the model a challenge. Inaccuracies in both the emissions and the model meteorology may contribute to these differences.…”