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Machine Learning Models to Predict Critical Episodes of Environmental Pollution for PM2.5 and PM10 in Talca, Chile

Author

Listed:
  • Gonzálo Carreño

    (Faculty of Engineering Sciences, Universidad Católica del Maule, Talca 3480112, Chile)

  • Xaviera A. López-Cortés

    (Faculty of Engineering Sciences, Universidad Católica del Maule, Talca 3480112, Chile
    Department of Computer Science and Industries, Universidad Católica del Maule, Talca 3480112, Chile)

  • Carolina Marchant

    (Faculty of Basic Sciences, Universidad Católica del Maule, Talca 3480112, Chile
    ANID-Millennium Science Initiative Program-Millennium Nucleus Center for the Discovery of Structures in Complex Data, Santiago 7820244, Chile)

Abstract

One of the main environmental problems that affects people’s health and quality of life is air pollution by particulate matter. Chile has nine of the ten most polluted cities in South America according to a report presented in 2019 by Greenpeace and AirVisual that measured the air quality index based on the levels of fine particles. Most Chilean cities are highly contaminated by particulate matter, especially during the months of April to August (the critical episode management period). The objective of this study is to predict particulate matter levels based on meteorological and climatic features, such as temperature, wind speed, wind direction, precipitation and relative air humidity in Talca, Chile, during the critical episode management periods between 2014 and 2018. Predictive models based on machine learning techniques were used, considering training datasets with meteorological and climatic data, and particulate matter levels from the three air quality monitoring stations in Talca, Chile. We carried out the training of 24 models to predict particulate matter levels considering the 24-h average and average between 05:00 to 11:00 p.m. For the model testing, data from the year 2018 during the critical episode management period were used. The obtained results indicate that our models are able to effectively predict levels of particulate matter, enabling correct management of critical episodes, especially for alert, pre-emergency and emergency conditions. We used the cross-platform and open-source programming language Python for the development and implementation of the proposed models and R-project for some visualizations.

Suggested Citation

  • Gonzálo Carreño & Xaviera A. López-Cortés & Carolina Marchant, 2022. "Machine Learning Models to Predict Critical Episodes of Environmental Pollution for PM2.5 and PM10 in Talca, Chile," Mathematics, MDPI, vol. 10(3), pages 1-17, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:3:p:373-:d:734077
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    References listed on IDEAS

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    1. Carolina Marchant & Víctor Leiva & George Christakos & M. Fernanda Cavieres, 2019. "Monitoring urban environmental pollution by bivariate control charts: New methodology and case study in Santiago, Chile," Environmetrics, John Wiley & Sons, Ltd., vol. 30(5), August.
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    Cited by:

    1. Nicole Jeldes & Germán Ibacache-Pulgar & Carolina Marchant & Javier Linkolk López-Gonzales, 2022. "Modeling Air Pollution Using Partially Varying Coefficient Models with Heavy Tails," Mathematics, MDPI, vol. 10(19), pages 1-24, October.

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