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The treatment of uncertainties in reactive pollution dispersion models at urban scales

Lookup NU author(s): Dr Paul Goodman

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

The ability to predict NO2 concentrations ([NO­2]) within urban street networks is important for the evaluation of strategies to reduce exposure to NO2. However, models aiming to make such predictions involve the coupling of several complex processes: traffic emissions under different levels of congestion; dispersion via turbulent mixing; chemical processes of relevance at the street scale. Parameterisations of these processes are challenging to quantify with precision. Predictions are therefore subject to uncertainties and these should be taken into account when models are used in decision making. This paper presents an analysis of mean [NO­2] predictions from such a complex modelling system applied to a street canyon within the city of York, UK including the treatment of model uncertainties and their causes. The model system consists of a micro-scale traffic simulation and emissions model, a Reynolds Averaged turbulent flow model coupled to a reactive Lagrangian particle dispersion model. In particular the analysis focuses on the sensitivity of predicted in-street increments of [NO­2] at different locations in the street to uncertainties in the model inputs. These include physical characteristics such as background wind direction, temperature and background ozone concentrations; traffic parameters such as overall demand and primary NO2 fraction in the exhaust; as well as model parameterisations such as roughness lengths, turbulent time- and length-scales and chemical reaction rate coefficients. Predicted [NO­2] is shown to be relatively robust with respect to model parameterisations, although there are significant sensitivities to the activation energy for the reaction NO+O3 as well as the canyon wall roughness length. Under off-peak traffic conditions, demand is the key traffic parameter. Under peak conditions where the network saturates, road-side [NO­2] is relatively insensitive to changes in demand and more sensitive to the primary NO2 fraction. The most important physical parameter was found to be the background wind direction. The study highlights the key parameters required for reliable [NO­2] estimations suggesting that accurate reference measurements for wind direction should be a critical part of air quality assessments for in-street locations. It also highlights the importance of street scale chemical processes in forming road-side [NO­2], particularly for regions of high NOx emissions.


Publication metadata

Author(s): Tomlin AS, Ziehn T, Goodman P, Tate JE, Dixon NS

Publication type: Article

Publication status: Published

Journal: Faraday Discussions

Year: 2016

Volume: 189

Pages: 567-587

Print publication date: 14/07/2016

Online publication date: 30/11/2015

Acceptance date: 27/11/2015

Date deposited: 11/08/2016

ISSN (print): 1359-6640

ISSN (electronic): 1364-5498

Publisher: The Royal Society of Chemistry

URL: http://dx.doi.org/10.1039/C5FD00159E

DOI: 10.1039/C5FD00159E


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