Vehicle-induced turbulence and atmospheric pollution
-
Published:2021-08-17
Issue:16
Volume:21
Page:12291-12316
-
ISSN:1680-7324
-
Container-title:Atmospheric Chemistry and Physics
-
language:en
-
Short-container-title:Atmos. Chem. Phys.
Author:
Makar Paul A.,Stroud Craig,Akingunola Ayodeji,Zhang Junhua,Ren Shuzhan,Cheung Philip,Zheng Qiong
Abstract
Abstract. 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).
Publisher
Copernicus GmbH
Subject
Atmospheric Science
Reference60 articles.
1. Abelsohn, A. and Steib, D. M.: Health effects of outdoor air pollution: approach to counseling patients using the Air Quality Health Index, Can. Fam. Physician, 57, 881–887, 2011. 2. Adelman, Z., Baek, B. H., Brandmeyer, J., Seppanen, C., Naess, B., and Yang, D.: Spatial Surrogate Development for 2014 Emissions Modeling Platforms, 2017 International Emissions Inventory Conference, 14–18 August, Baltimore, MD, USA, available at: https://www.epa.gov/sites/production/files/2017-11/documents/surrogate_developement.pdf (last access: 21 July 2021), 2017. 3. 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. 4. Bethke, K.-H., Baumgartner, S., Gabele, M., Hounam, D., Kemptner, E., Klement, E., Krieger, G., and Erxleben, R.: Air- and spaceborne monitoring of road traffic using SAR moving target indication – Project TRAMRAD, ISPRS J. Photogramm., 61, 243–259, 2006. 5. Bou-Zeid, E., Meneveau, C., and Parlange, M. B.: Large-eddy simulation of neutral atmospheric boundary layer flow over heterogeneous surfaces: Blending height and effective surface roughness, Water Resour. Res., 40, W02505, https://doi.org/10.1029/2003WR002475, 2004.
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|