A comparison of strategies for estimation of ultrafine particle number concentrations in urban air pollution monitoring networks. (April 2015)

• Record Type: Journal Article
• Authors: Reggente, Matteo; Peters, Jan; Theunis, Jan; Van Poppel, Martine; Rademaker, Michael; De Baets, Bernard; Kumar, Prashant
• Is Part Of: Environmental pollution. Volume 199(2015)
• Page Start: 209
• Page End: 218
• Publication Date: 2015-04
• Subjects: Ultrafine particles estimation -- Urban air pollution -- Pollution monitoring network -- Gaussian process regression -- Statistical modelling; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2015.01.034
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: We propose three estimation strategies (local, remote and mixed) for ultrafine particles (UFP) at three sites in an urban air pollution monitoring network. Estimates are obtained through Gaussian process regression based on concentrations of gaseous pollutants (NOx, O3, CO) and UFP. As local strategy, we use local measurements of gaseous pollutants (local covariates) to estimate UFP at the same site. As remote strategy, we use measurements of gaseous pollutants and UFP from two independent sites (remote covariates) to estimate UFP at a third site. As mixed strategy, we use local and remote covariates to estimate UFP. The results suggest: UFP can be estimated with good accuracy based on NOx measurements at the same location; it is possible to estimate UFP at one location based on measurements of NOx or UFP at two remote locations; the addition of remote UFP to local NOx, O3 or CO measurements improves models' performance. Highlights: UFP number concentrations are estimated using Gaussian process regression. The independent variables include local and/or remote gaseous measurements. Three modelling strategies (local, remote and mixed) used for UFP estimations. NOx was the most important independent variable. The best models explained >90% of the variance. Abstract : UFP can be estimated with good accuracy at one location based on NOx measurements at the same location and based on measurements of NOx or UFP at two remote locations.