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

Akingunola, Ayodeji; Makar, Paul A.; Zhang, Junhua; Darlington, Andrea; Li, Shao-Meng; Gordon, Mark; Moran, Michael D.; Zheng, Qiong

doi:https://doi.org/10.5194/acp-18-8667-2018

Articles | Volume 18, issue 12

https://doi.org/10.5194/acp-18-8667-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Atmospheric emissions from oil sands development and their...

https://doi.org/10.5194/acp-18-8667-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 12

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, Paul A. Makar, Junhua Zhang, Andrea Darlington, Shao-Meng Li, Mark Gordon, Michael D. Moran, and Qiong Zheng

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

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 %.