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Analysis of air quality within a street canyon using statistical and dispersion modelling techniques

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JournalAtmospheric Environment
DatePublished - Dec 2007
Issue number39
Number of pages11
Pages (from-to)9195-9205
Original languageEnglish


The dispersion model, ADMS-Urban, alongside the statistical modelling technique, generalized additive modelling, have been used to predict hourly NO x and nitrogen dioxide (NO 2) concentrations at a busy street canyon location and the results compared with measurements. Generalized additive models (GAMs) were constructed for NO 2 and NO x concentrations using input data required to run ADMS-Urban. Bivariate polar plots have been produced from the wind flow (speed and direction) and pollution data (measured and predicted concentrations) to provide further information regarding the complex wind-pollutant interactions in an urban street canyon. The predictions made with the GAMs show excellent agreement with measured concentrations at this location, reproducing both the magnitude of NO x and NO 2 concentrations and also the wind speed-wind direction dependence of pollutant sources within the canyon. However, the predictions made with ADMS-Urban under-estimated the measured NO x by 11% and NO 2 by 21% and there are clear differences in the bivariate polar plots. Several sensitivity tests were carried out with ADMS-Urban in an attempt to produce predictions in closer agreement to those measured at Gillygate. Increasing the primary NO 2 fraction in ADMS-Urban (from 10% to 20%) had a considerable effect on the predictions made with this model, increasing NO 2 predictions by ∼20%. However, the bivariate plots still showed major differences to those of the measurements. This work illustrates that generalized additive modelling is a useful tool for investigating complex wind-pollutant interactions within a street canyon. © 2007 Elsevier Ltd. All rights reserved.

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Cited By :24

Export Date: 19 July 2016

    Research areas

  • ADMS-Urban, Generalized additive models, NO, Urban air quality

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