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Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels

Author

Listed:
  • Sandra Ceballos-Santos

    (Department of Chemical and Biomolecular Engineering, University of Cantabria, 39005 Santander, Spain)

  • Jaime González-Pardo

    (Department of Chemical and Biomolecular Engineering, University of Cantabria, 39005 Santander, Spain)

  • David C. Carslaw

    (Wolfson Atmospheric Chemistry Laboratories, University of York, York YO10 5DD, UK
    Ricardo Energy & Environment, Didcot OX11 0QR, UK)

  • Ana Santurtún

    (Unit of Legal Medicine, Department of Physiology and Pharmacology, University of Cantabria, 39011 Santander, Spain)

  • Miguel Santibáñez

    (Global Health Research Group, Department of Nursing, University of Cantabria, 39008 Santander, Spain
    Research Nursing Group, IDIVAL, Calle Cardenal Herrera Oria s/n, 39011 Santander, Spain)

  • Ignacio Fernández-Olmo

    (Department of Chemical and Biomolecular Engineering, University of Cantabria, 39005 Santander, Spain)

Abstract

The global COVID-19 pandemic that began in late December 2019 led to unprecedented lockdowns worldwide, providing a unique opportunity to investigate in detail the impacts of restricted anthropogenic emissions on air quality. A wide range of strategies and approaches exist to achieve this. In this paper, we use the “deweather” R package, based on Boosted Regression Tree (BRT) models, first to remove the influences of meteorology and emission trend patterns from NO, NO 2 , PM 10 and O 3 data series, and then to calculate the relative changes in air pollutant levels in 2020 with respect to the previous seven years (2013–2019). Data from a northern Spanish region, Cantabria, with all types of monitoring stations (traffic, urban background, industrial and rural) were used, dividing the calendar year into eight periods according to the intensity of government restrictions. The results showed mean reductions in the lockdown period above −50% for NO x , around −10% for PM 10 and below −5% for O 3 . Small differences were found between the relative changes obtained from normalised data with respect to those from observations. These results highlight the importance of developing an integrated policy to reduce anthropogenic emissions and the need to move towards sustainable mobility to ensure safer air quality levels, as pre-existing concentrations in some cases exceed the safe threshold.

Suggested Citation

  • Sandra Ceballos-Santos & Jaime González-Pardo & David C. Carslaw & Ana Santurtún & Miguel Santibáñez & Ignacio Fernández-Olmo, 2021. "Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels," IJERPH, MDPI, vol. 18(24), pages 1-18, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:24:p:13347-:d:705686
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    Cited by:

    1. Reza Salehi & Santhana Krishnan & Mohd Nasrullah & Sumate Chaiprapat, 2023. "Using Machine Learning to Predict the Performance of a Cross-Flow Ultrafiltration Membrane in Xylose Reductase Separation," Sustainability, MDPI, vol. 15(5), pages 1-27, February.
    2. Mario Lovrić & Mario Antunović & Iva Šunić & Matej Vuković & Simonas Kecorius & Mark Kröll & Ivan Bešlić & Ranka Godec & Gordana Pehnec & Bernhard C. Geiger & Stuart K. Grange & Iva Šimić, 2022. "Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia," IJERPH, MDPI, vol. 19(11), pages 1-16, June.

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