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

Research output: Contribution to Journal/Magazine › Journal article

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A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK. / Pinder, Thomas; Hollaway, Michael; Nemeth, Christopher et al.
In: arxiv.org, 22.04.2021.

Research output: Contribution to Journal/Magazine › Journal article

Harvard

Pinder, T, Hollaway, M, Nemeth, C, Young, PJ & Leslie, D 2021, 'A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK', arxiv.org.

APA

Pinder, T., Hollaway, M., Nemeth, C., Young, P. J., & Leslie, D. (2021). A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK. arxiv.org.

Vancouver

Pinder T, Hollaway M, Nemeth C, Young PJ, Leslie D. A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK. arxiv.org. 2021 Apr 22.

Author

Pinder, Thomas ; Hollaway, Michael ; Nemeth, Christopher et al. / A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK. In: arxiv.org. 2021.

Bibtex

@article{ec14d3899c4645cf852b878ab9a08922,
title = "A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK",
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. ",
keywords = "stat.AP",
author = "Thomas Pinder and Michael Hollaway and Christopher Nemeth and Young, {Paul J.} and David Leslie",
note = "14 pages, 4 figures",
year = "2021",
month = apr,
day = "22",
language = "English",
journal = "arxiv.org",

}

RIS

TY - JOUR

T1 - A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK

AU - Pinder, Thomas

AU - Hollaway, Michael

AU - Nemeth, Christopher

AU - Young, Paul J.

AU - Leslie, David

N1 - 14 pages, 4 figures

PY - 2021/4/22

Y1 - 2021/4/22

N2 - 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.

AB - 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.

KW - stat.AP

M3 - Journal article

JO - arxiv.org

JF - arxiv.org

ER -