Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 61, Issue -, Pages 135-150Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2014.07.012
Keywords
Ultrafine particles; Number distributions; Street canyon; Traffic emissions; Gaussian process regression; Urban air pollution
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Funding
- IWT-Vlaanderen (Flemish Agency for Innovation by Science and Technology)
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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 O-3 at half hour and 5 min resolution. Because UFP number concentrations follow from a dynamic process, we have used a nonstationary 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 km(2) 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 O-3. 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. (C) 2014 Elsevier Ltd. All rights reserved.
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