Long-term forecasting of nitrogen dioxide ambient levels in metropolitan areas using the discrete-time Markov model. (September 2018)

• Record Type: Journal Article
• Authors: Nebenzal, Asaf; Fishbain, Barak
• Is Part Of: Environmental modelling & software. Volume 107(2018)
• Page Start: 175
• Page End: 185
• Publication Date: 2018-09
• Subjects: Air pollution modeling -- Discrete-time Markov model -- Long-term forecasting -- Modeling -- Risk assessment -- Nitrogen dioxide (NO2); 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.2018.06.001
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Air pollution management and control are key factors in maintaining sustainable societies. Air quality forecasting may significantly advance these tasks. While short-term forecasting, a few days into the future, is a well-established research domain, there is no method for long-term forecasting (e.g., the pollution level distribution in the upcoming months or years). This paper introduces and defines long-term air pollution forecasting, where long-term refers to estimating pollution levels in the next few months or years. A Discrete-Time-Markov-based model for forecasting ambient nitrogen oxides patterns is presented. The model accurately forecasts overall pollution level distributions, and the expectancy for tomorrow's pollution level given today's level, based on longitudinal historical data. It thus characterizes the temporal behavior of pollution. The model was applied to five distinctive regions in Israel and Australia and was compared against several forecasting methods and was shown to provide better results with a relatively lower total error rate. Graphical abstract: Image Highlights: Introducing and Defining Long-Term Air Pollution Forecasting. Devising Discrete Time Markov Model for long-term air pollution forecasting. Predicting long-term (annual) pollution levels. Predicting daily pollution level gradients.