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

Estimation of PM10 concentration from air quality data in the vicinity of a major steelworks site... 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Stochastic Environmental Research and Risk Assessment Springer Journals

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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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Environment; Math. Appl. in Environmental Science; Earth Sciences, general; Probability Theory and Stochastic Processes; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Computational Intelligence; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution
ISSN
1436-3240
eISSN
1436-3259
D.O.I.
10.1007/s00477-018-1565-6
Publisher site
See Article on Publisher Site

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 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

Journal

Stochastic Environmental Research and Risk AssessmentSpringer Journals

Published: Jun 1, 2018

References

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