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Prediction of ultrafine particle number concentrations in urban environments by means of Gaussian process regression based on measurements of oxides of nitrogen

Reggente, M, Peters, J, Theunis, J, Van Poppel, M, Rademaker, M, Kumar, P and De Baets, B (2014) Prediction of ultrafine particle number concentrations in urban environments by means of Gaussian process regression based on measurements of oxides of nitrogen Environmental Modelling & Software, 61. pp. 135-150.

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Abstract

Abstract Gaussian process regression is used to predict ultrafine particle (UFP) number concentrations. We infer their number concentrations based on the concentrations of NO, NO2, CO and O3 at half hour and 5 min resolution. Because UFP number concentrations follow from a dynamic process, we have used a non-stationary kernel based on the addition of a linear and a rational quadratic kernel. Simultaneous measurements of UFP and gaseous pollutants were carried out during one month at three sampling locations situated within a 1 km2 area in a Belgian city, Antwerp. The method proposed provides accurate predictions when using NO and NO2 as covariates and less accurate predictions when using CO and O3. We have also evaluated the models for different training periods and we have found that a training period of at least seven days is suitable to let the models learn the UFP number concentration dynamics in different typologies of traffic.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Civil and Environmental Engineering
Authors :
AuthorsEmailORCID
Reggente, MUNSPECIFIEDUNSPECIFIED
Peters, JUNSPECIFIEDUNSPECIFIED
Theunis, JUNSPECIFIEDUNSPECIFIED
Van Poppel, MUNSPECIFIEDUNSPECIFIED
Rademaker, MUNSPECIFIEDUNSPECIFIED
Kumar, PUNSPECIFIEDUNSPECIFIED
De Baets, BUNSPECIFIEDUNSPECIFIED
Date : November 2014
Identification Number : 10.1016/j.envsoft.2014.07.012
Uncontrolled Keywords : Ultrafine particles, Number distributions, Street canyon, Traffic emissions, Gaussian process regression, Urban air pollution
Related URLs :
Additional Information : NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version will be published in Environmental Modelling & Software, 61, November 2014, DOI 10.1016/j.envsoft.2014.07.012.
Depositing User : Symplectic Elements
Date Deposited : 09 Sep 2014 09:58
Last Modified : 13 Sep 2014 01:33
URI: http://epubs.surrey.ac.uk/id/eprint/805904

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