Atmospheric Chemistry and Physics (Aug 2021)

Vehicle-induced turbulence and atmospheric pollution

  • P. A. Makar,
  • C. Stroud,
  • A. Akingunola,
  • J. Zhang,
  • S. Ren,
  • P. Cheung,
  • Q. Zheng

DOI
https://doi.org/10.5194/acp-21-12291-2021
Journal volume & issue
Vol. 21
pp. 12291 – 12316

Abstract

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Theoretical models of the Earth's atmosphere adhere to an underlying concept of flow driven by radiative transfer and the nature of the surface over which the flow is taking place: heat from the sun and/or anthropogenic sources are the sole sources of energy driving atmospheric constituent transport. However, another source of energy is prevalent in the human environment at the very local scale – the transfer of kinetic energy from moving vehicles to the atmosphere. We show that this source of energy, due to being co-located with combustion emissions, can influence their vertical distribution to the extent of having a significant influence on lower-troposphere pollutant concentrations throughout North America. The effect of vehicle-induced turbulence on freshly emitted chemicals remains notable even when taking into account more complex urban radiative transfer-driven turbulence theories at high resolution. We have designed a parameterization to account for the at-source vertical transport of freshly emitted pollutants from mobile emissions resulting from vehicle-induced turbulence, in analogy to sub-grid-scale parameterizations for plume rise emissions from large stacks. This parameterization allows vehicle-induced turbulence to be represented at the scales inherent in 3D chemical transport models, allowing this process to be represented over larger regions than is currently feasible with large eddy simulation models. Including this sub-grid-scale parameterization for the vertical transport of emitted pollutants due to vehicle-induced turbulence in a 3D chemical transport model of the atmosphere reduces pre-existing North American nitrogen dioxide biases by a factor of 8 and improves most model performance scores for nitrogen dioxide, particulate matter, and ozone (for example, reductions in root mean square errors of 20 %, 9 %, and 0.5 %, respectively).