Articles | Volume 13, issue 6
https://doi.org/10.5194/essd-13-3013-2021
https://doi.org/10.5194/essd-13-3013-2021
Data description paper
 | 
24 Jun 2021
Data description paper |  | 24 Jun 2021

AQ-Bench: a benchmark dataset for machine learning on global air quality metrics

Clara Betancourt, Timo Stomberg, Ribana Roscher, Martin G. Schultz, and Scarlet Stadtler

Related authors

Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties
Clara Betancourt, Timo T. Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Martin G. Schultz, Ribana Roscher, Julia Kowalski, and Scarlet Stadtler
Geosci. Model Dev., 15, 4331–4354, https://doi.org/10.5194/gmd-15-4331-2022,https://doi.org/10.5194/gmd-15-4331-2022, 2022
Short summary
Firewood residential heating – local versus remote influence on the aerosol burden
Clara Betancourt, Christoph Küppers, Tammarat Piansawan, Uta Sager, Andrea B. Hoyer, Heinz Kaminski, Gerhard Rapp, Astrid C. John, Miriam Küpper, Ulrich Quass, Thomas Kuhlbusch, Jochen Rudolph, Astrid Kiendler-Scharr, and Iulia Gensch
Atmos. Chem. Phys., 21, 5953–5964, https://doi.org/10.5194/acp-21-5953-2021,https://doi.org/10.5194/acp-21-5953-2021, 2021
Short summary

Related subject area

Data, Algorithms, and Models
Improved maps of surface water bodies, large dams, reservoirs, and lakes in China
Xinxin Wang, Xiangming Xiao, Yuanwei Qin, Jinwei Dong, Jihua Wu, and Bo Li
Earth Syst. Sci. Data, 14, 3757–3771, https://doi.org/10.5194/essd-14-3757-2022,https://doi.org/10.5194/essd-14-3757-2022, 2022
Short summary
The Fengyun-3D (FY-3D) global active fire product: principle, methodology and validation
Jie Chen, Qi Yao, Ziyue Chen, Manchun Li, Zhaozhan Hao, Cheng Liu, Wei Zheng, Miaoqing Xu, Xiao Chen, Jing Yang, Qiancheng Lv, and Bingbo Gao
Earth Syst. Sci. Data, 14, 3489–3508, https://doi.org/10.5194/essd-14-3489-2022,https://doi.org/10.5194/essd-14-3489-2022, 2022
Short summary
A high-resolution inland surface water body dataset for the tundra and boreal forests of North America
Yijie Sui, Min Feng, Chunling Wang, and Xin Li
Earth Syst. Sci. Data, 14, 3349–3363, https://doi.org/10.5194/essd-14-3349-2022,https://doi.org/10.5194/essd-14-3349-2022, 2022
Short summary
A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan
Amy McNally, Jossy Jacob, Kristi Arsenault, Kimberly Slinski, Daniel P. Sarmiento, Andrew Hoell, Shahriar Pervez, James Rowland, Mike Budde, Sujay Kumar, Christa Peters-Lidard, and James P. Verdin
Earth Syst. Sci. Data, 14, 3115–3135, https://doi.org/10.5194/essd-14-3115-2022,https://doi.org/10.5194/essd-14-3115-2022, 2022
Short summary
HOTRUNZ: an open-access 1 km resolution monthly 1910–2019 time series of interpolated temperature and rainfall grids with associated uncertainty for New Zealand
Thomas R. Etherington, George L. W. Perry, and Janet M. Wilmshurst
Earth Syst. Sci. Data, 14, 2817–2832, https://doi.org/10.5194/essd-14-2817-2022,https://doi.org/10.5194/essd-14-2817-2022, 2022
Short summary

Cited articles

Amante, C. and Eakins, B. W.: ETOPO1 arc-minute global relief model: procedures, data sources and analysis, Tech. rep., NOAA National Geophysical Data Center, available at: https://repository.library.noaa.gov/view/noaa/1163/noaa_1163_DS1.pdf (last access: 21 June 2021), 2009. a
Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., Chen, G., Chen, J., Chen, J., Chen, Z., Chrzanowski, M., Coates, A., Diamos, G., Ding, K., Du, N., Elsen, E., Engel, J., Fang, W., Fan, L., Fougner, C., Gao, L., Gong, C., Hannun, A., Han, T., Johannes, L., Jiang, B., Ju, C., Jun, B., LeGresley, P., Lin, L., Liu, J., Liu, Y., Li, W., Li, X., Ma, D., Narang, S., Ng, A., Ozair, S., Peng, Y., Prenger, R., Qian, S., Quan, Z., Raiman, J., Rao, V., Satheesh, S., Seetapun, D., Sengupta, S., Srinet, K., Sriram, A., Tang, H., Tang, L., Wang, C., Wang, J., Wang, K., Wang, Y., Wang, Z., Wang, Z., Wu, S., Wei, L., Xiao, B., Xie, W., Xie, Y., Yogatama, D., Yuan, B., Zhan, J., and Zhu, Z.: Deep Speech 2: End-to-End Speech Recognition in English and Mandarin, arXiv [preprint], arXiv:1512.02595, pp. 173–182, 8 December 2015. a
Benkovitz, C. M., Scholtz, M. T., Pacyna, J., Tarrasón, L., Dignon, J., Voldner, E. C., Spiro, P. A., Logan, J. A., and Graedel, T.: Global gridded inventories of anthropogenic emissions of sulfur and nitrogen, J. Geophys. Res.-Atmos., 101, 29239–29253, https://doi.org/10.1029/96JD00126, 1996. a
Betancourt, C., Stomberg, T., Stadtler, S., Roscher, R., and Schultz, M. G.: AQ-Bench, B2SHARE [data set], http://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f, 2020. a, b, c
Betancourt, C., Stadtler, S., and Stomberg, T.: AQ-Bench Git repository, GitLab – JSC [data set], available at: https://gitlab.version.fz-juelich.de/esde/machine-learning/aq-bench, last access: 21 June 2021. a, b, c
Download
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 from 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.
Altmetrics
Final-revised paper
Preprint