Articles | Volume 18, issue 18
Atmos. Chem. Phys., 18, 13531–13545, 2018
https://doi.org/10.5194/acp-18-13531-2018

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

Atmos. Chem. Phys., 18, 13531–13545, 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 et al.

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Cited articles

Barsanti, K. C., Carlton, A. G., and Chung, S. H.: Analyzing experimental data and model parameters: implications for predictions of SOA using chemical transport models, Atmos. Chem. Phys., 13, 12073–12088, https://doi.org/10.5194/acp-13-12073-2013, 2013.
Cappa, C. D. and Jimenez, J. L.: Quantitative estimates of the volatility of ambient organic aerosol, Atmos. Chem. Phys., 10, 5409–5424, https://doi.org/10.5194/acp-10-5409-2010, 2010.
Cappa, C. D. and Wilson, K. R.: Evolution of organic aerosol mass spectra upon heating: implications for OA phase and partitioning behavior, Atmos. Chem. Phys., 11, 1895–1911, https://doi.org/10.5194/acp-11-1895-2011, 2011.
Chai, T., Kim, H.-C., Lee, P., Tong, D., Pan, L., Tang, Y., Huang, J., McQueen, J., Tsidulko, M., and Stajner, I.: Evaluation of the United States National Air Quality Forecast Capability experimental real-time predictions in 2010 using Air Quality System ozone and NO2 measurements, Geosci. Model Dev., 6, 1831–1850, https://doi.org/10.5194/gmd-6-1831-2013, 2013.
Cho, S., McEachern, P., Morris, R., Shah, T., Johnson, J., and Nopmongcol, U.: Emission sources sensitivity study for ground-level ozone and PM2.5 due to oil sands development using air quality modeling system: Part I – model evaluation for current year base case simulation, Atmos. Environ., 55, 533–541, 2012.
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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.
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