Preprints
https://doi.org/10.5194/essd-2020-380
https://doi.org/10.5194/essd-2020-380

  14 Jan 2021

14 Jan 2021

Review status: a revised version of this preprint is currently under review for the journal ESSD.

AQ-Bench: A Benchmark Dataset for Machine Learning on Global Air Quality Metrics

Clara Betancourt1, Timo Stomberg1, Scarlet Stadtler1, Ribana Roscher2, and Martin G. Schultz1 Clara Betancourt et al.
  • 1Jülich Supercomputing Centre, Jülich Research Centre, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
  • 2Institute of Geodesy and Geoinformation, University of Bonn, Nußallee 17, 53115 Bonn, Germany

Abstract. With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air quality based on this metadata. AQ-Bench contains a unique collection of aggregated air quality data from the years 2010–2014 and metadata at more than 5500 air quality monitoring stations all over the world, provided by the first Tropospheric Ozone Assessment Report (TOAR). It focuses in particular on metrics of tropospheric ozone, which has a detrimental effect on climate, human morbidity and mortality, as well as crop yields. We validate these data as a machine learning benchmark by providing a well-defined task together with a suitable evaluation metric. Baseline scores obtained from a linear regression method, a fully connected neural network and random forest are provided for reference. AQ-Bench offers a low-threshold entrance for all machine learners with an interest in environmental science and for atmospheric scientists who are interested in applying machine learning techniques. It enables them to start with a real-world problem relevant to humans and nature. The dataset and introductory machine learning code are available at https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f (Betancourt et al., 2020) and https://gitlab.version.fz-juelich.de/toar/ozone-mapping . AQ-Bench thus provides a blueprint for environmental benchmark datasets as well as an example for data re-use according to the FAIR principles.

Clara Betancourt et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2020-380', Anonymous Referee #1, 04 Feb 2021
  • RC2: 'Comment on essd-2020-380', Anonymous Referee #2, 09 Mar 2021
  • AC1: 'Reply to RC1', Clara Betancourt, 09 Apr 2021
  • AC2: 'Reply to RC2', Clara Betancourt, 09 Apr 2021

Clara Betancourt et al.

Data sets

AQ-Bench Clara Betancourt, Timo Stomberg, Scarlet Stadtler, Ribana Roscher, and Martin G. Schultz https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f

Clara Betancourt et al.

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Short summary
With the AQ-Bench dataset, we contribute to shared data usage and machine learning methods in the field of environmental science. The AQ-Bench dataset contains air quality data and metadata at more than 5500 air quality observation stations all over the world. The dataset offers a low-threshold entrance to machine learning on a real world environmental dataset. AQ-Bench thus provides a blueprint for environmental benchmark datasets.