Articles | Volume 18, issue 18
https://doi.org/10.5194/acp-18-13531-2018
https://doi.org/10.5194/acp-18-13531-2018
Research article
 | 
25 Sep 2018
Research article |  | 25 Sep 2018

Improving air quality model predictions of organic species using measurement-derived organic gaseous and particle emissions in a petrochemical-dominated region

Craig A. Stroud, Paul A. Makar, Junhua Zhang, Michael D. Moran, Ayodeji Akingunola, Shao-Meng Li, Amy Leithead, Katherine Hayden, and May Siu

Viewed

Total article views: 2,576 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,682 834 60 2,576 306 54 50
  • HTML: 1,682
  • PDF: 834
  • XML: 60
  • Total: 2,576
  • Supplement: 306
  • BibTeX: 54
  • EndNote: 50
Views and downloads (calculated since 04 Apr 2018)
Cumulative views and downloads (calculated since 04 Apr 2018)

Viewed (geographical distribution)

Total article views: 2,576 (including HTML, PDF, and XML) Thereof 2,607 with geography defined and -31 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 16 Jul 2024
Download
Short summary
It is shown that using measurement-derived volatile organic compound (VOC) and organic aerosol (OA) emissions in the GEM-MACH air quality model provides better overall predictions compared to using bottom-up emission inventories. This work was done to better constrain the fugitive organic emissions from the Athabasca oil sands region, which are a challenge to estimate with bottom-up emission approaches. We use observations from the 2013 Joint Oil Sands Monitoring study.
Altmetrics
Final-revised paper
Preprint