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Estimation of PM10 concentration from air quality data in the vicinity of a major steelworks site in the metropolitan area of Avilés (Northern Spain) using machine learning techniques

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Abstract

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 Avilés (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 (SO2, NO and NO2) and PM10 (particles with a diameter less than  10 μm) is used as input to forecast the monthly average concentration of PM10 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.

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Acknowledgements

The authors wish to thank the General Directorate of Prevention and Environmental Control (from Ministry of Infrastructure, Spatial Planning and Environment of the Principality of Asturias) for providing the experimental dataset used in this research. Additionally, we would like to thank Anthony Ashworth for his revision of English grammar and spelling of the manuscript.

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Correspondence to P. J. García Nieto.

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García Nieto, P.J., Sánchez Lasheras, F., García-Gonzalo, E. et al. Estimation of PM10 concentration from air quality data in the vicinity of a major steelworks site in the metropolitan area of Avilés (Northern Spain) using machine learning techniques. Stoch Environ Res Risk Assess 32, 3287–3298 (2018). https://doi.org/10.1007/s00477-018-1565-6

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