Authors: T.S. Lira; M.A.S. Barrozo; A.J. Assis
Addresses: Federal University of Espirito Santo, Department of Engineering and Computation, Sao Mateus, ES, Brazil. ' School of Chemical Engineering Federal University of Uberlândia, Uberlandia, MG, Brazil. ' School of Chemical Engineering, Federal University of Uberlandia, Av. Joao Naves de Avila, 2121, 38408-100, Uberlandia, MG, Brazil
Abstract: Particulate air pollution causes a wide range of effects on human health, including disorders of the respiratory and cardiovascular systems, asthma and can cause mortality. Hence, the development of an efficient air quality forecasting and early warning system is an obvious and imperative need. The objective of this work was to investigate this forecasting possibility using linear models (such as ARX, ARMAX, output-error and Box-Jenkins), and Neural Networks (NNs). The input data for the models were meteorological variables and the 24-h average PM10 concentration of the present day, while the output was the 24-h average PM10 concentration predicted for a 3-day horizon. The results revealed that all the models yield fairly good estimates, but the Box-Jenkins model showed the best fit and predictability.
Keywords: air quality forecasting; linear modelling; neural networks; particulate matter; public health; air pollution; Brazil; early warning systems.
World Review of Science, Technology and Sustainable Development, 2011 Vol.8 No.2/3/4, pp.135 - 147
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 13 Dec 2011 *