Re-framing the Gaussian dispersion model as a nonlinear regression scheme for retrospective air quality assessment at a high spatial and temporal resolution. (March 2020)

• Record Type: Journal Article
• Authors: Chen, Shimon; Yuval, ; Broday, David M.
• Is Part Of: Environmental modelling & software. Volume 125(2020)
• Publication Date: 2020-03
• Subjects: Air quality modelling -- Data assimilation -- Exposure assessment -- Gaussian dispersion -- Nitrogen oxides -- Regression models; 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.2019.104620
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Regression models (e.g. Land-Use Regression) are currently the most popular way to estimate retrospective exposures to air pollution. However, these models lack important features of atmospheric dispersion. We developed a new non-linear air quality regression model which is based on the physical grounds of the well-established and commonly applied Gaussian dispersion model. This was achieved through parametrization of the basic Gaussian model (including its standard deviations) and optimizing the parameters to provide a least-squares fit with ambient measurements at each individual time-point. The new model (GaussODM) outperformed both a simpler regression model and a benchmark interpolation model in predicting spatial ambient nitrogen oxides (NOx ) concentrations. The GaussODM enables a deeper understanding of the relationship between air pollution and adverse health effects. This is partly because it is better adapted at incorporating meteorological data and the effects of elevated emissions compared with previously available air pollution regression models. Highlights: The Gaussian dispersion model was reformulated as a non-linear regression model. The model incorporates high spatial density traffic volume input and shows its impact. The new model is highly adapted at manifesting the effects of elevated emissions. Our results agree with physical understandings of atmospheric dispersion processes. Exposure estimates were responsive to varying emissions and dispersion conditions.