A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK

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• arXiv

Abstract

In March 2020 the United Kingdom (UK) entered a nationwide lockdown period due to the Covid-19 pandemic. As a result, levels of nitrogen dioxide (NO2) in the atmosphere dropped. In this work, we use 550,134 NO2 data points from 237 stations in the UK to build a spatiotemporal Gaussian process capable of predicting NO2 levels across the entire UK. We integrate several covariate datasets to enhance the model’s ability to capture the complex spatiotemporal dynamics of NO2. Our numerical analyses show that, within two weeks of a UK lockdown being imposed, UK NO2 levels dropped 36.8%. Further, we show that as a direct result of lockdown NO2 levels were 29-38% lower than what they would have been had no lockdown occurred. In accompaniment to these numerical results, we provide a software framework that allows practitioners to easily and efficiently fit similar models.

Citation

Pinder, T., Hollaway, M., Nemeth, C., Young, P.J. and Leslie, D., 2021. A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK. arXiv preprint arXiv:2104.10979.

@article{pinder2021probabilistic,
  title={A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK},
  author={Pinder, Thomas and Hollaway, Michael and Nemeth, Christopher and Young, Paul J and Leslie, David},
  journal={arXiv preprint arXiv:2104.10979},
  year={2021}
}