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Virtual Sensor to Estimate Air Pollution Heavy Metals Using Bioindicators

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17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (SOCO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 531))

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Abstract

The main objective of this work is to demonstrate that a set of bioindicators linked to the lichen Lobaria Pulmonaria and the bryophyte called Leucodon Sciuroides are adequate predictors of air pollution heavy metals (HM). A study case was performed in Oran, a port and coastal city in northwestern Algeria, located on the coast of the Mediterranean Sea. Each of the HM has been modelled using a machine learning procedure and in the experiments, the artificial neural networks (ANN) produces always better and more accurate results than multiple linear regression (MLR). Furthermore, good obtained results (R correlation coefficient greater than 0.9) demonstrate the main hypotheses and could be used as a virtual sensor.

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Acknowledgements

This work is part of the research project RTI2018-098160-B-I00 supported by ‘MICINN’ Programa Estatal de I+D+i Orientada a ‘Los Retos de la Sociedad’.

Author information

Authors and Affiliations

  1. Higher Technical Engineering School, University of Cádiz, Avd. Ramón Puyol s/n, 11202, Algeciras (Cádiz), Spain

    María Inmaculada Rodríguez-García, Arantxa M. Ortega León, Javier González-Enrique & Ignacio J. Turias

  2. Département le Vivant et L’Environnement, Université des Sciences et de la Technologie Mohamed Boudiaf, El M’Naouar, Algérie

    Nawel Kouadria

Authors
  1. María Inmaculada Rodríguez-García
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  2. Nawel Kouadria
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  3. Arantxa M. Ortega León
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  4. Javier González-Enrique
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  5. Ignacio J. Turias
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Corresponding author

Correspondence to María Inmaculada Rodríguez-García .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. University of León, León, Spain

    Hilde Pérez García

  3. Mechanical Engineering Department, University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martinez-de-Pison

  4. Inteligencia Artificial, University of Oviedo, A Coruña, La Coruña, Spain

    José Ramón Villar Flecha

  5. Data Science and Big Data Lab, Pablo de Olavide University, Sevilla, Spain

    Alicia Troncoso Lora

  6. University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  7. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  8. School of engineering, Pablo Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  9. DIGIP, University of Bergamo, Dalmine, Bergamo, Italy

    Giuseppe Psaila

  10. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  11. Department of Computing Science, University of Salamanca, Salamanca, Spain

    Emilio S. Corchado Rodriguez

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Rodríguez-García, M.I., Kouadria, N., León, A.M.O., González-Enrique, J., Turias, I.J. (2023). Virtual Sensor to Estimate Air Pollution Heavy Metals Using Bioindicators. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_20

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