Articles | Volume 18, issue 12
Atmos. Chem. Phys., 18, 8667–8688, 2018
https://doi.org/10.5194/acp-18-8667-2018

Special issue: Atmospheric emissions from oil sands development and their...

Atmos. Chem. Phys., 18, 8667–8688, 2018
https://doi.org/10.5194/acp-18-8667-2018

Research article 20 Jun 2018

Research article | 20 Jun 2018

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

Ayodeji Akingunola et al.

Data sets

Monitoring air quality in Alberta oil sands ECCC https://www.canada.ca/en/environment-climate-change/services/oil-sands-monitoring/monitoring-air-quality-alberta-oil-sands.html

Environment and Climate Change Canada and Alberta Environment and Parks, Executive Summary JOSM https://www.canada.ca/en/environment-climate-change/services/science-technology/publications/joint-oil-sands-monitoring-emissions-report.html

Environment and Climate Change Canada, AEMERA, and Alberta Environment and Parks JOSM http://aep.alberta.ca/air/reports-data/documents/JOSM-EmissionsInventoryReport-Jun2016.pdf

Environment and Climate Change Canada and Alberta Environment and Parks JOSM http://ec.gc.ca/data_donnees/SSB-OSM_Air/Air/Emissions_inventory_files/

Historical monitoring data WBEA http://www.wbea.org/network-and-data/historical-monitoring-data

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Short summary
We examine the manner in which air-quality models simulate lofting of buoyant plumes of emissions from stacks (plume rise) and the impact of the level of detail in algorithms simulating particles' variation in size (particle size distribution). The most commonly used plume rise algorithm underestimates the height of plumes compared to observations, while a revised algorithm has much better performance. A 12-bin size distribution reduced the forecast 2-bin size distribution bias error by 32 %.
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