Atmospheric particulate matter (PM) is one of the pollutants that may have a signiﬁcant impact on human health. Data collected over 7 years from the air quality monitoring station at the LD-III steelworks, belonging to the Arcelor-Mittal Steel Company, located in the metropolitan area of Aviles (Principality of Asturias, Northern Spain), is analyzed using four different mathematical models: vector autoregressive moving-average, autoregressive integrated moving-average (ARIMA), multilayer perceptron neural networks and support vector machines with regression. Measured monthly, the average concentration of pollutants (SO , NO and NO ) and PM (particles with a diameter less than 10 lm) is used as 2 2 10 input to forecast the monthly average concentration of PM from one to 7 months ahead. Simulations showed that the ARIMA model performs better than the other models when forecasting 1 month ahead, while in the forecast from one to 9 months ahead the best performance is given by the support vector regression. Keywords Support vector regression (SVR) Multilayer perceptron (MLP) Vector autoregressive moving-average (VARMA) Autoregressive integrated moving-average (ARIMA) Monthly PM concentration Pollution episode 1 Introduction construction of the factory of ENSIDESA, a large steel mill currently part of Arcelor Mittal
Stochastic Environmental Research and Risk Assessment – Springer Journals
Published: Jun 1, 2018
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