Articles | Volume 18, issue 18
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

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 (CTM) on air quality model predictions of organic species for the Athabasca oil sands (OS) region in Northern Alberta, Canada. The first emissions data set that was evaluated (base-case run) makes use of regulatory-reported VOC and particulate matter emissions data for the large oil sands mining facilities. The second emissions data set (sensitivity run) uses total facility emissions and speciation profiles derived from box-flight aircraft observations around specific facilities. Large increases in some VOC and OA emissions in the revised-emissions data set for four large oil sands mining facilities and decreases for others were found to improve the modeled VOC and OA concentration maxima in facility plumes, 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 significant increase in the correlation coefficient is reported for the long-chain alkane predictions against observations when using the revised emissions based on aircraft observations. For some facilities, larger long-chain alkane emissions resulted in higher secondary organic aerosol (SOA) production, which improved OA predictions in those plumes. Overall, the use of the revised-emissions data resulted in an improvement of the model mean OA bias; however, a decrease in the OA correlation coefficient and a remaining negative bias suggests the need for further improvements to model OA emissions and formation processes. The weight of evidence suggests that the top-down emission estimation technique helps to better constrain the fugitive organic emissions in the oil sands region, which are a challenge to estimate given the size and complexity of the oil sands operations and the number of steps in the process chain from bitumen extraction to refined oil product. This work shows that the top-down emissions estimation technique may help to constrain bottom-up emission inventories in other industrial regions of the world with large sources of VOCs and OA.

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.
Final-revised paper