A New Plume Rise Algorithm – Incorporating the Thermodynamic Effects of Water for Plume Rise Prediction in Air Quality Models

Plume rise is commonly parameterized based on ambient atmospheric conditions and emission source metrics (e.g. stack effluent temperature and exit momentum), with empirical formulae (e.g., Briggs, 1984) employed in large-scale air-quality models (e.g. Environment and Climate Change Canada’s GEM-MACH model). Past evaluations against observed plume heights emitted from industrial sources (e.g., Canadian Oil Sands) have attributed the discrepancies between observed and predicted plume heights to various causes, such as spatial variability of meteorological fields between observation and stack locations and/or inaccuracies in model meteorological predictions. It has been shown that stack-location-specific meteorology and layered (vertical) calculation of plume buoyancy can improve predicted plume heights (Akingunola et al. 2018).  However, more recent observations have shown that predicted plume heights remain biased low relative to aircraft observations of well-characterized SO₂ plumes, particularly under colder winter conditions, and demonstrate the need for further improvements to plume rise predictions. 
We introduce a new algorithm for plume rise calculation, which incorporates thermodynamic effects of the emitted water vapour from industrial stack combustion sources on the resulting calculation of plume height. The high temperature effluent from these stacks usually contain significant amounts of combustion-generated water. As the plume rises and cools, this water vapour condenses, increasing plume temperature and buoyancy through the release of latent heat, which can result in additional plume rise. We have developed a revised plume rise algorithm for implementation within the regional models, through combining the Briggs’ empirical parameterization with concepts of cloud parcel thermodynamic effects for the release or uptake of latent heat associated with the phase change of water. Our results show significant improvement in model plume rise prediction, through evaluation against SO₂ plumes observed during a 2018 aircraft campaign over the Canadian Oil Sands. We also discuss results from long-term (15-month duration) model simulations with the new versus the original algorithm, along with evaluations against aircraft-based and surface monitoring network observed concentrations. The potential impact of the condensed in-plume liquid water on aqueous phase chemistry will also be discussed. This work is the first plume rise algorithm to incorporate the effects of latent heat release of both combustion-emitted and in-plume ambient-entrained water, for implementation in air quality models. 

References

• Akingunola, A., Makar, P. A., Zhang, J., Darlington, A., Li, S.-M., Gordon, M., Moran, M. D., and Zheng, Q.: A chemical transport model study of plume-rise and particle size distribution for the Athabasca oil sands, Atmos. Chem. Phys., 18, 8667–8688, https://doi.org/10.5194/acp-18-8667-2018, 2018.
• Briggs, G. A.: Plume rise and buoyancy effects, atmospheric sciences and power production, in: DOE/TIC-27601 (DE84005177), edited by: Randerson, D., TN, Technical Information Center, US Dept. of Energy, Oak Ridge, USA, 327–366, 1984.