Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.509 IF 5.509
  • IF 5-year value: 5.689 IF 5-year 5.689
  • CiteScore value: 5.44 CiteScore 5.44
  • SNIP value: 1.519 SNIP 1.519
  • SJR value: 3.032 SJR 3.032
  • IPP value: 5.37 IPP 5.37
  • h5-index value: 86 h5-index 86
  • Scimago H index value: 161 Scimago H index 161
Discussion papers | Copyright
https://doi.org/10.5194/acp-2018-93
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Apr 2018

Research article | 04 Apr 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Air Quality Predictions using Measurement-Derived Organic Gaseous and Particle Emissions in a Petrochemical-Dominated Region

Craig A. Stroud1, Paul A. Makar1, Junhua Zhang1, Michael D. Moran1, Ayodeji Akingunola1, Shao-Meng Li1, Amy Leithead1, Katherine Hayden1, and May Siu2 Craig A. Stroud et al.
  • 1Air Quality Research Division, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, Ontario, M3H 5T4, Canada
  • 2Air Quality Research Division, Environment and Climate Change Canada, 335 River Road, Ottawa, Ontario, K1V 1C7, Canada

Abstract. This study assesses the impact of revised volatile organic compound (VOC) and organic aerosol (OA) emissions estimates in the GEM-MACH (Global Environmental Multiscale‒Modelling Air Quality and CHemistry) chemical transport model, driven with two different emissions input datasets, using observations from the 2013 Joint Oil Sands Monitoring (JOSM) intensive field study. The first emissions dataset (base-case run) makes use of regulatory reported VOC and particulate matter emissions data for the large oil sands mining facilities in northeastern Alberta, Canada, while the second emissions dataset (sensitivity run) uses emissions estimates based on box-flight aircraft observations around specific facilities (Li et al., 2017, Zhang et al., 2017) and a mass-balance analysis (Gordon et al., 2015) to derive total facility emission rates. The preparation of model-ready emissions files for the base-case and sensitivity run is described in an accompanying paper by Zhang et al. (2017).

The large increases in VOC and OA emissions in the revised emissions data set for four large oil sands mining facilities were found to improve the modeled VOC and OA concentration maxima in plumes from these facilities, as shown with the 99th percentile statistic and illustrated by case studies. The results show that the VOC emission speciation profile from each oil sand facility is unique and different from standard petrochemical-refinery emission speciation profiles used for other regions in North America. A feedback between larger long-chain alkane emissions and higher secondary organic aerosol (SOA) concentrations was found to be significant for some facilities and improved OA predictions for those plumes. The use of the revised emissions data resulted in a large improvement of the model OA bias; however, the decrease in OA correlation coefficient suggests the need for further improvements to model organic aerosol emissions and formation processes. Including intermediate volatile organic compound (IVOC) emissions as precursors to SOA and spatially allocating more PM1 POA emissions (primary organic aerosol of 1.0μm or less in diameter) to mine-face locations are both recommended to improve OA bias and correlation further. A systematic bias in the background OA was also predicted on most flights, likely due to under-predictions in biogenic SOA formation. Overall, the weight of evidence suggests that the new aircraft-observation-derived organic emissions help to constrain better the fugitive organic emissions, which are a challenge to estimate in the creation of bottom up emission inventories. This work shows that the use of facility-specific emissions, based on direct observations, rather than generic emission factors and speciation profiles can result in improvements to model predictions of VOCs and OA. Emissions estimation techniques, such as those used to construct the inventories in our study, may therefore have beneficial impacts when applied to other regions with large sources of VOCs and OA.

Download & links
Craig A. Stroud et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Co-Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Craig A. Stroud et al.
Craig A. Stroud et al.
Viewed
Total article views: 254 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
193 50 11 254 20 6 5
  • HTML: 193
  • PDF: 50
  • XML: 11
  • Total: 254
  • Supplement: 20
  • BibTeX: 6
  • EndNote: 5
Views and downloads (calculated since 04 Apr 2018)
Cumulative views and downloads (calculated since 04 Apr 2018)
Viewed (geographical distribution)
Total article views: 254 (including HTML, PDF, and XML) Thereof 252 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited
Saved
No saved metrics found.
Discussed
No discussed metrics found.
Latest update: 17 Jul 2018
Publications Copernicus
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 generic petrochemical emission factors and speciation profiles. 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 Joint Oil Sands Monitoring Study.
It is shown that using measurement-derived volatile organic compound (VOC) and organic aerosol...
Citation
Share