A polynomial approximation of the traffic contributions for kriging-based interpolation of urban air quality model. (July 2018)

• Record Type: Journal Article
• Authors: Beauchamp, Maxime; Malherbe, Laure; de Fouquet, Chantal; Létinois, Laurent; Tognet, Frédéric
• Is Part Of: Environmental modelling & software. Volume 105(2018)
• Page Start: 132
• Page End: 152
• Publication Date: 2018-07
• Subjects: Geostatistics -- Kriging -- Air pollution -- Urban scale -- Series expansion drift; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.03.033
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The European directives for ambient air quality require to assess areas where air pollutant concentrations exceed a regulatory threshold. As the spatial distribution of the pollutant is not exactly known, deterministic atmospheric dispersion models are commonly used to supplement the observational network. To reduce the computational time, the simulations are made on irregular grids, especially in urban areas where the grid is refined close to the roads. An interpolation method is then necessary to map the dispersion model at any location. We propose a new geostatistical approach based on kriging with external drift to distinguish the information along and across the roads. An exponential function is introduced to describe the decrease of the concentrations across the roads. Its series expansion is used to build a set of polynomial auxiliary predictors with unknown coefficients. This framework leads to a drift that is more generic and flexible in the kriging system. Highlights: Kriging is used to combine an atmospheric dispersion model and NOx emissions inventory to interpolate the model outputs. An external drift framework is involved to distinguish information along and across the roads in the estimation. The expansion series of the exponential is used to build a set of polynomial drifts with unknown coefficients. This kriging-based estimator is compared to classic interpolation techniques used in NO2 air quality simulation data.