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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-270
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-270
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model evaluation paper 02 Sep 2020

Submitted as: model evaluation paper | 02 Sep 2020

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This preprint is currently under review for the journal GMD.

Evaluating the use of Facebook's Prophet model v0.6 in forecasting concentrations of NO2 at single sites across the UK and in response to the COVID-19 lockdown in Manchester, England

David Topping1, David Watts2, Hugh Coe1, James Evans3, Thomas J. Bannan1, Douglas Lowe4, Caroline Jay5, and Jonathan W. Taylor1 David Topping et al.
  • 1Department of Earth and Environmental Science, The University of Manchester, Oxford Road, Manchester, M13 9PL
  • 2Transport for Greater Manchester, 2 Piccadilly Place, Manchester, M1 3BG
  • 3Department of Geography, The University of Manchester, Oxford Road, Manchester, M13 9PL
  • 4ResearchIT, The University of Manchester, Oxford Road, Manchester, M13 9PL
  • 5Department of Computer Science, The University of Manchester, Oxford Road, Manchester, M13 9PL

Abstract. Time-series forecasting methods have often been used to mitigate some of the challenges associated with deploying chemical transport models at high resolution for use at local scales. In this study we deploy and evaluate Facebook’s Prophet model v0.6 in predicting hourly concentrations of Nitrogen Dioxide [NO2] over a 2 year period [2018–2019] across the UK’s Automatic Urban and Rural Network (AURN). Results indicate promising performance when comparing absolute values, diurnal trends and seasonality, with discrepancies increasing when the site is classified as having a larger contribution from regional sources and non-local sources. Using mobility and traffic volume data in the model fitting process allowed us to evaluate the ability of the model to forecast levels at two sites in Manchester where there were significant reductions in traffic levels during the COVID-19 lock-down, defined as a national state of restricted access. Prior to lock-down, comparison between hourly concentrations from the Prophet forecast and observations are significantly better compared with predictions from the EMEP regional model. Despite the simplified approach of fitting to derived NO2-per-traffic volume over a 5 year period, trends in absolute NO2 reductions and diurnal profiles were captured well at Manchester Piccadilly. However at a second site, in Sharston, we found that reliance on historical NO2-per-traffic volume resulted in errors in the prediction as the nature of local traffic changed under the COVID-19 lock-down; correlating with an increase in the Heavy Goods Vehicle fleet [HGV] relative to other forms of traffic. Ancillary meteorological information and predictions from the EMEP model enabled identification of significant contributions from regional sources during the lock-down period. These periods coincide with noticeable differences between measured and forecast values from Prophet. Overall the Prophet model offers a relatively effective and simple way to make predictions about NO2 at local levels. The source code to reproduce and expand on the work presented in this paper is made openly available.

David Topping et al.

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Latest update: 28 Sep 2020
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Short summary
Time-series forecasting methods have often been used to mitigate some of the challenges associated with deploying chemical transport models. In this study we deploy and evaluate Facebook’s Prophetmodel v0.6 in predicting hourly concentrations of Nitrogen Dioxide [NO2]. et. Overall we find the Prophet model offers a relatively effective and simple way to make predictions about NO2 at local levels.
Time-series forecasting methods have often been used to mitigate some of the challenges...
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