Estimating the spatiotemporal variation of NO2 concentration using an adaptive neuro-fuzzy inference system. (February 2018)

• Record Type: Journal Article
• Authors: Yeganeh, Bijan; Hewson, Michael G.; Clifford, Samuel; Tavassoli, Ahmad; Knibbs, Luke D.; Morawska, Lidia
• Is Part Of: Environmental modelling & software. Volume 100(2018)
• Page Start: 222
• Page End: 235
• Publication Date: 2018-02
• Subjects: NO2 -- Satellite data -- ANFIS -- Spatiotemporal -- Transport model -- Australia; 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.2017.11.031
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Statistical modelling has been successfully used to estimate the variations of NO2 concentration, but employing new modelling techniques can make these estimations far more accurate. To do so, for the first time in application to spatiotemporal air pollution modelling, we employed a soft computing algorithm called adaptive neuro-fuzzy inference system (ANFIS) to estimate the NO2 variations. Comprehensive data sets were investigated to determine the most effective predictors for the modelling process, including land use, meteorological, satellite, and traffic variables. We have demonstrated that using selected satellite, traffic, meteorological, and land use predictors in modelling increased the R 2 by 21%, and decreased the root mean square error (RMSE) by 47% compared with the model only trained by land use and meteorological predictors. The ANFIS model found to have better performance and higher accuracy than the multiple regression model. Our best model, captures 91% of the spatiotemporal variability of monthly mean NO2 concentrations at 1 km spatial resolution (RMSE 1.49 ppb) in a selected area of Australia. Highlights: We used ANFIS to develop a satellite-based model for estimating NO2 concentration. We used comprehensive satellite-based, traffic, meteorological and land use data. We used traffic congestion data to provide a better measure of traffic dynamics. We evaluated the effects of satellite and traffic data on modelling performance.