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Abstract: This repository contains all the data used for the article "Estimating changes in air pollutant levels due to COVID-19 lockdown measures based on a business-as-usual prediction scenario using data mining models: A case-study for urban traffic sites in Spain", submitted to Environmental Software & Modelling by J. González-Pardo et al. (2022) published in Science of the Total Environment (STOTEN). This research was developed in the framework of the project "Contaminación atmosférica y COVID-19: ¿Qué podemos aprender de esta pandemia?", selected in the Extraordinary BBVA Foundation grant call for SARS-CoV-2 and COVID-19 research proposals, within the area of ecology and veterinary science.

For the sake of reproducibility, it includes Jupyter notebooks with worked examples which allow to reproduce the results shown in that paper. During the course of this research the pyaemet python library has been developed in order to download daily meteorological observations from the Spanish Met Service (AEMET) via its OpenData API REST and it is needed to perform the data curation process.

Repository: Zenodo

 Year of publication: 2021

 DOI: 10.5281/zenodo.5642868

 Full citation: González-Pardo, J., Manzanas, R. & Ceballos-Santos, S. (2021). AirQualityCOVID-dataset. [Dataset]. (Version 1) Zenodo.




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