Abstract
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. The purpose of this dataset is to produce estimates of various long-term ozone metrics based on time-independent local site conditions. We combine this task 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 and validation. 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/esde/machine-learning/aq-bench (Betancourt et al., 2021). AQ-Bench thus provides a blueprint for environmental benchmark datasets as well as an example for data re-use according to the FAIR principles.
Highlights
In recent years, machine learning has achieved remarkable success in areas such as pattern, image and speech recognition by usage of increasing computing power, innovative algorithms and high data availability (Krizhevsky et al, 2012; Amodei et al, 2016; Silver et al, 2016)
With the AQ-Bench dataset, we aim to fill this gap and provide a dataset of global long-term air quality metrics and metadata compiled from the TOAR database (Tropospheric Ozone Assessment Report; Schultz et al, 2017)
To make these data usable for machine learning developments, this paper describes the specific task of mapping between the metadata and the air quality metrics
Summary
Machine learning has achieved remarkable success in areas such as pattern, image and speech recognition by usage of increasing computing power, innovative algorithms and high data availability (Krizhevsky et al, 2012; Amodei et al, 2016; Silver et al, 2016). This has aroused the interest of environmental scientists in exploring the application of machine learning and data-driven methods in their fields. Ozone is a scientifically interesting candidate for machine learning applications: it is influenced by many inter-
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