Articles | Volume 13, issue 6
https://doi.org/10.5194/essd-13-3013-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-3013-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
24 Jun 2021
Data description paper |
| 24 Jun 2021
| 24 Jun 2021
AQ-Bench: a benchmark dataset for machine learning on global air quality metrics
Clara Betancourt
Jülich Supercomputing Centre, Jülich Research Centre, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Timo Stomberg
Jülich Supercomputing Centre, Jülich Research Centre, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Institute of Geodesy and Geoinformation, University of Bonn, Nußallee 17, 53115 Bonn, Germany
Ribana Roscher
Institute of Geodesy and Geoinformation, University of Bonn, Nußallee 17, 53115 Bonn, Germany
Martin G. Schultz
CORRESPONDING AUTHOR
Jülich Supercomputing Centre, Jülich Research Centre, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
Scarlet Stadtler
Jülich Supercomputing Centre, Jülich Research Centre, Wilhelm-Johnen-Straße, 52425 Jülich, Germany
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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
Brasseur, G., Orlando, J. J., and Tyndall, G. S. (Eds.): Atmospheric chemistry
and global change, 3 edn., Oxford University Press, Oxford, UK, 1999. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
Caselli, M., Trizio, L., de Gennaro, G., and Ielpo, P.: A Simple
Feedforward Neural Network for the PM10 Forecasting: Comparison
with a Radial Basis Function Network and a Multivariate Linear
Regression Model, Water Air Soil Poll., 201, 365–377,
https://doi.org/10.1007/s11270-008-9950-2, 2009. a
Chang, K.-L., Petropavlovskikh, I., Copper, O. R., Schultz, M. G., and Wang,
T.: Regional trend analysis of surface ozone observations from monitoring
networks in eastern North America, Europe and East Asia, Elem. Sci. Anth., 5, 50, https://doi.org/10.1525/elementa.243, 2017. a, b
Chollet, F. et al.: Keras, available at: https://keras.io (last access: 21 June 2021), 2015. a
CIESIN: Gridded Population of the World, Version 3 (GPWv3): Population Count
Grid, Center for International Earth Science Information Network – CIESIN –
Columbia University, United Nations Food and Agriculture Programme – FAO, and
Centro Internacional de Agricultura Tropical – CIAT, Palisades, NY: NASA
Socioeconomic Data and Applications Center (SEDAC),
https://doi.org/10.7927/H4639MPP, 2005. a
Comrie, A. C.: Comparing Neural Networks and Regression Models for
Ozone Forecasting, J. Air Waste Manage.,
47, 653–663, https://doi.org/10.1080/10473289.1997.10463925, 1997. a
Cooper, O. R., Parrish, D. D., Ziemke, J., Balashov, N. V., Cupeiro, M.,
Galbally, I. E., Gilge, S., Horowitz, L., Jensen, N. R., Lamarque, J.-F.,
Naik, V., Oltmans, S. J., Schwab, J., Shindell, D. T., Thompson, A. M.,
Thouret, V., Wang, Y., and Zbinden, R. M.: Global distribution and trends of
tropospheric ozone: An observation-based review, Elementa: Science of the
Anthropocene, 2, 29, https://doi.org/10.12952/journal.elementa.000029, 2014. a
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L.: ImageNet:
A Large-Scale Hierarchical Image Database, in: 2009 IEEE Conference on
Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June 2009, pp. 248–255,
https://doi.org/10.1109/CVPR.2009.5206848, 2009. a
Duboue, P.: The Art of Feature Engineering: Essentials for Machine Learning, 1 edn.,
Cambridge University Press, Cambridge, UK, https://doi.org/10.1017/9781108671682, 2020. a
Ebert-Uphoff, I., Thompson, D. R., Demir, I., Gel, Y. R., Karpatne, A.,
Guereque, M., Kumar, V., Cabral-Cano, E., and Smyth, P.: A vision for the
development of benchmarks to bridge geoscience and data science, in:
Proceedings of the 7th International Workshop on Climate Informatics,
Boulder, CL, USA, 20–22 September 2017, 2017. a, b
Elkamel, A., Abdul-Wahab, S., Bouhamra, W., and Alper, E.: Measurement and
prediction of ozone levels around a heavily industrialized area: a neural
network approach, Adv. Environ. Res., 5, 47–59,
https://doi.org/10.1016/S1093-0191(00)00042-3, 2001. a
Emberson, L., Ashmore, M., Cambridge, H., Simpson, D., and Tuovinen, J.-P.:
Modelling stomatal ozone flux across Europe, Environ. Pollut., 109,
403–413, https://doi.org/10.1016/S0269-7491(00)00043-9, 2000. a
Field, R., Goldstone, M., Lester, J., and Perry, R.: The sources and behaviour
of tropospheric anthropogenic volatile hydrocarbons, Atmos. Environ. A-Gen., 26, 2983–2996, https://doi.org/10.1016/0960-1686(92)90290-2,
1992. a
Fleming, Z. L., Doherty, R. M., Von Schneidemesser, E., Malley, C. S., Cooper,
O. R., Pinto, J. P., Colette, A., Xu, X., Simpson, D., Schultz, M. G.,
Lefohn, A. S., Hamad, S., Moolla, R., Solberg, S., and Feng, Z.: Tropospheric
Ozone Assessment Report: Present-day ozone distribution and trends relevant
to human health, Elem. Sci. Anth., 6, 12, https://doi.org/10.1525/elementa.273, 2018. a
Gaudel, A., Cooper, O. R., Ancellet, G., Barret, B., Boynard, A., Burrows,
J. P., Clerbaux, C., Coheur, P. F., Cuesta, J., Cuevas, E., Doniki, S.,
Dufour, G., Ebojie, F., Foret, G., Garcia, O., Granados Muños, M. J.,
Hannigan, J. W., Hase, F., Huang, G., Hassler, B., Hurtmans, D., Jaffe, D.,
Jones, N., Kalabokas, P., Kerridge, B., Kulawik, S. S., Latter, B., Leblanc,
T., Le Flochmoën, E., Lin, W., Liu, J., Liu, X., Mahieu, E.,
McClure-Begley, A., Neu, J. L., Osman, M., Palm, M., Petetin, H.,
Petropavlovskikh, I., Querel, R., Rahpoe, N., Rozanov, A., Schultz, M. G.,
Schwab, J., Siddans, R., Smale, D., Steinbacher, M., Tanimoto, H., Tarasick,
D. W., Thouret, V., Thompson, A. M., Trickl, T., Weatherhead, E., Wespes, C.,
Worden, H. M., Vigouroux, C., Xu, X., Zeng, G., and Ziemke, J.: Tropospheric
Ozone Assessment Report: Present-day distribution and trends of tropospheric
ozone relevant to climate and global atmospheric chemistry model evaluation,
Elem. Sci. Anth., 6, 39, https://doi.org/10.1525/elementa.291, 2018. a
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y.: Deep learning, 1 edn., MIT press Cambridge, Cambridge, UK, 2016. a
He, H. and Garcia, E. A.: Learning from Imbalanced Data, IEEE Transactions
on Knowledge and Data Engineering, 21, 1263–1284,
https://doi.org/10.1109/TKDE.2008.239, 2009. a
Jacob, D. J.: Heterogeneous chemistry and tropospheric ozone, Atmos.
Environ., 34, 2131–2159,
https://doi.org/10.1016/S1352-2310(99)00462-8, 2000. a
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi, B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution, Atmos. Chem. Phys., 15, 11411–11432, https://doi.org/10.5194/acp-15-11411-2015, 2015. a
Kaiser, A., Scheifinger, H., Spangl, W., Weiss, A., Gilge, S., Fricke, W.,
Ries, L., Cemas, D., and Jesenovec, B.: Transport of nitrogen oxides, carbon
monoxide and ozone to the alpine global atmosphere watch stations
Jungfraujoch (Switzerland), Zugspitze and Hohenpeißenberg (Germany),
Sonnblick (Austria) and Mt. Krvavec (Slovenia), Atmos. Environ., 41,
9273–9287, https://doi.org/10.1016/j.atmosenv.2007.09.027, 2007. a
Kelp, M. M., Jacob, D. J., Kutz, J. N., Marshall, J. D., and Tessum, C. W.:
Toward Stable, General Machine-Learned Models of the Atmospheric Chemical
System, J. Geophys. Res.-Atmos., 125, e2020JD032759,
https://doi.org/10.1029/2020JD032759, 2020. a
Kierdorf, J., Garcke, J., Behley, J., Cheeseman, T., and Roscher, R.: What
Identifies a Whale by its Fluke? on the Benefit of Interpretable Machine
Learning for Whale Identification, ISPRS Annals of the Photogrammetry, Remote
Sensing and Spatial Information Sciences, 2, 1005–1012, 2020. a
Kleinert, F., Leufen, L. H., and Schultz, M. G.: IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany, Geosci. Model Dev., 14, 1–25, https://doi.org/10.5194/gmd-14-1-2021, 2021. a
Koffi, B., Dentener, F., Janssens-Maenhout, G., Guizzardi, D., Crippa, M.,
Diehl, T., Galmarini, S., and Solazzo, E.: Hemispheric Transport Air
Pollution (HTAP): Specification of the HTAP2 experiments – Ensuring harmonized
modelling, Tech. rep., EUR 28255 EN, Luxembourg: Publications Office of the
European Union, 2016. a
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: ImageNet Classification with
Deep Convolutional Neural Networks, in: Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3–6, 2012, Lake Tahoe, Nevada, United States,
https://doi.org/10.1145/3065386, pp. 1097–1105, 2012. a
Krotkov, N. A., McLinden, C. A., Li, C., Lamsal, L. N., Celarier, E. A., Marchenko, S. V., Swartz, W. H., Bucsela, E. J., Joiner, J., Duncan, B. N., Boersma, K. F., Veefkind, J. P., Levelt, P. F., Fioletov, V. E., Dickerson, R. R., He, H., Lu, Z., and Streets, D. G.: Aura OMI observations of regional SO2 and NO2 pollution changes from 2005 to 2015, Atmos. Chem. Phys., 16, 4605–4629, https://doi.org/10.5194/acp-16-4605-2016, 2016. a
LeCun, Y., Cortes, C., and Burges, C. J.: MNIST handwritten digit database,
available at: http://yann.lecun.com/exdb/mnist/ (last access: 21 June 2021), 2010. a
Lefohn, A. S., Malley, C. S., Smith, L., Wells, B., Hazucha, M., Simon, H.,
Naik, V., Mills, G., Schultz, M. G., Paoletti, E., De Marco, A., Xu, X.,
Zhang, L., Wang, T., Neufeld, H. S., Musselman, R. C., Tarasick, D., Brauer,
M., Feng, Z., Tang, H., Kobayashi, K., Sicard, P., Solberg, S., and Gerosa,
G.: Tropospheric ozone assessment report: Global ozone metrics for climate
change, human health, and crop/ecosystem research, Elem. Sci. Anth., 6, 27,
https://doi.org/10.1525/elementa.279, 2018. a, b
Luhar, A. K., Woodhouse, M. T., and Galbally, I. E.: A revised global ozone dry deposition estimate based on a new two-layer parameterisation for air–sea exchange and the multi-year MACC composition reanalysis, Atmos. Chem. Phys., 18, 4329–4348, https://doi.org/10.5194/acp-18-4329-2018, 2018. a
Mills, G., Hayes, F., Simpson, D., Emberson, L., Norris, D., Harmens, H., and
Büker, P.: Evidence of widespread effects of ozone on crops and (semi-)
natural vegetation in Europe (1990–2006) in relation to AOT40-and flux-based
risk maps, Glob. Change Biol., 17, 592–613, 2011. a
Mills, G., Pleijel, H., Malley, C. S., Sinha, B., Cooper, O. R., Schultz,
M. G., Neufeld, H. S., Simpson, D., Sharps, K., Feng, Z., Gerosa, G.,
Harmens, H., Kobayashi, K., Saxena, P., Paoletti, E., Sinha, V., and Xu, X.:
Tropospheric Ozone Assessment Report: Present-day tropospheric ozone
distribution and trends relevant to vegetation, Elem. Sci. Anth., 6, 47,
https://doi.org/10.1525/elementa.302, 2018. a
Monks, P. S., Archibald, A. T., Colette, A., Cooper, O., Coyle, M., Derwent, R., Fowler, D., Granier, C., Law, K. S., Mills, G. E., Stevenson, D. S., Tarasova, O., Thouret, V., von Schneidemesser, E., Sommariva, R., Wild, O., and Williams, M. L.: Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer, Atmos. Chem. Phys., 15, 8889–8973, https://doi.org/10.5194/acp-15-8889-2015, 2015. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn.
Res., 12, 2825–2830,
available at: https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf (last access: 21 June 2021),
2011. a
Porter, W. C., Heald, C. L., Cooley, D., and Russell, B.: Investigating the observed sensitivities of air-quality extremes to meteorological drivers via quantile regression, Atmos. Chem. Phys., 15, 10349–10366, https://doi.org/10.5194/acp-15-10349-2015, 2015. a
Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., and Thuerey,
N.: WeatherBench: A benchmark dataset for data-driven weather forecasting,
J. Adv. Model. Earth Sy., 12, e2020MS002203,
https://doi.org/10.1029/2020MS002203, 2020. a
Roscher, R., Bohn, B., Duarte, M. F., and Garcke, J.: Explainable machine
learning for scientific insights and discoveries, IEEE Access, 8,
42200–42216, https://doi.org/10.1109/ACCESS.2020.2976199, 2020. a
Sayeed, A., Choi, Y., Eslami, E., Lops, Y., Roy, A., and Jung, J.: Using a deep
convolutional neural network to predict 2017 ozone concentrations, 24 hours
in advance, Neural Networks, 121, 396–408,
https://doi.org/10.1016/j.neunet.2019.09.033, 2020. a
Schmitz, S., Towers, S., Villena, G., Caseiro, A., Wegener, R., Klemp, D., Langer, I., Meier, F., and von Schneidemesser, E.: Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-489, in review, 2021. a
Schraudner, M., Langebartels, C., and Sandermann, H.: Changes in the
biochemical status of plant cells induced by the environmental pollutant
ozone, Physiol. Plantarum, 100, 274–280,
https://doi.org/10.1111/j.1399-3054.1997.tb04783.x, 1997. a
Schultz, M. G., Jacob, D. J., Wang, Y., Logan, J. A., Atlas, E. L., Blake,
D. R., Blake, N. J., Bradshaw, J. D., Browell, E. V., Fenn, M. A., Flocke F., Gregory, G. L., Heikes, B. G., Sachse, G. W., Sandholm, S. T., Shetter, R. E., Singh, H. B., and Talbot, R. W.: On
the origin of tropospheric ozone and NOx over the tropical South Pacific,
J. Geophys. Res.-Atmos., 104, 5829–5843,
https://doi.org/10.1029/98JD02309, 1999. a
Schultz, M. G., Schröder, S., Lyapina, O., Cooper, O., Galbally, I.,
Petropavlovskikh, I., Von Schneidemesser, E., Tanimoto, H., Elshorbany, Y.,
Naja, M., Seguel, R., Dauert, U., Eckhardt, P., Feigenspahn, S., Fiebig, M.,
Hjellbrekke, A.-G., Hong, Y.-D., Christian Kjeld, P., Koide, H., Lear, G.,
Tarasick, D., Ueno, M., Wallasch, M., Baumgardner, D., Chuang, M.-T.,
Gillett, R., Lee, M., Molloy, S., Moolla, R., Wang, T., Sharps, K., Adame,
J. A., Ancellet, G., Apadula, F., Artaxo, P., Barlasina, M., Bogucka, M.,
Bonasoni, P., Chang, L., Colomb, A., Cuevas, E., Cupeiro, M., Degorska, A.,
Ding, A., Fröhlich, M., Frolova, M., Gadhavi, H., Gheusi, F., Gilge, S.,
Gonzalez, M. Y., Gros, V., Hamad, S. H., Helmig, D., Henriques, D.,
Hermansen, O., Holla, R., Huber, J., Im, U., Jaffe, D. A., Komala, N.,
Kubistin, D., Lam, K.-S., Laurila, T., Lee, H., Levy, I., Mazzoleni, C.,
Mazzoleni, L., McClure-Begley, A., Mohamad, M., Murovic, M., Navarro-Comas,
M., Nicodim, F., Parrish, D., Read, K. A., Reid, N., Ries, L., Saxena, P.,
Schwab, J. J., Scorgie, Y., Senik, I., Simmonds, P., Sinha, V., Skorokhod,
A., Spain, G., Spangl, W., Spoor, R., Springston, S. R., Steer, K.,
Steinbacher, M., Suharguniyawan, E., Torre, P., Trickl, T., Weili, L.,
Weller, R., Xu, X., Xue, L., and Zhiqiang, M.: Tropospheric Ozone Assessment
Report: Database and Metrics Data of Global Surface Ozone Observations, Elem.
Sci. Anth., 5, 58, https://doi.org/10.1525/elementa.244, 2017. a, b, c, d, e, f, g, h, i
Schultz M. G., Betancourt C., Gong B., Kleinert F., Langguth M., Leufen L. H., Mozaffari A., and Stadtler S.: Can deep learning beat numerical weather prediction?, Philos. T. R. Soc. A., 379, 20200097, https://doi.org/10.1098/rsta.2020.0097, 2021. a, b
Sillman, S.: The relation between ozone, NOx and hydrocarbons in urban and
polluted rural environments, Atmos. Environ., 33, 1821–1845, 1999. a
Silva, S. J., Heald, C. L., Ravela, S., Mammarella, I., and Munger, J. W.: A
Deep Learning Parameterization for Ozone Dry Deposition
Velocities, Geophys. Res. Lett., 46, 983–989,
https://doi.org/10.1029/2018GL081049, 2019. a
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche,
G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M.,
Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., and Hassabis, D.: Mastering the game of Go with deep neural networks and tree search,
Nature, 529, 484–489, https://doi.org/10.1038/nature16961, 2016. a
Simpson, D., Winiwarter, W., Börjesson, G., Cinderby, S., Ferreiro, A.,
Guenther, A., Hewitt, C. N., Janson, R., Khalil, M. A. K., Owen, S., Pierce, T. E., Puxbaum, H., Shearer, M., Skiba, U., Steinbrecher, R., Tarrasón, L., and Öquist, M. G.:
Inventorying emissions from nature in Europe, J. Geophys.
Res.-Atmos., 104, 8113–8152, https://doi.org/10.1029/98JD02747, 1999. a
Tarasick, D., Galbally, I. E., Cooper, O. R., Schultz, M. G., Ancellet, G.,
Leblanc, T., Wallington, T. J., Ziemke, J., Liu, X., Steinbacher, M.,
Staehelin, J., Vigouroux, C., Hannigan, J. W., García, O., Foret, G., Zanis,
P., Weatherhead, E., Petropavlovskikh, I., Worden, H., Osman, M., Liu, J.,
Chang, K.-L., Gaudel, A., Lin, M., Granados-Muñoz, M., Thompson, A. M.,
Oltmans, S. J., Cuesta, J., Dufour, G., Thouret, V., Hassler, B., Trickl, T.,
and Neu, J. L.: Tropospheric Ozone Assessment Report: Tropospheric ozone from
1877 to 2016, observed levels, trends and uncertainties, Elem. Sci. Anth., 7,
39, https://doi.org/10.1525/elementa.376, 2019. a
Veldkamp, E. and Keller, M.: Fertilizer-induced nitric oxide emissions from
agricultural soils, Nutr. Cycl. Agroecosys., 48, 69–77,
https://doi.org/10.1023/A:1009725319290, 1997. a
Wagstaff, K.: Machine learning that matters, arXiv [preprint],
arXiv:1206.4656, 18 June 2012. a
Wang, S., Ma, Y., Wang, Z., Wang, L., Chi, X., Ding, A., Yao, M., Li, Y., Li, Q., Wu, M., Zhang, L., Xiao, Y., and Zhang, Y.: Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: impacts of COVID-19 pandemic lockdown, Atmos. Chem. Phys., 21, 7199–7215, https://doi.org/10.5194/acp-21-7199-2021, 2021. a
Wang, Y., Choi, Y., Zeng, T., Davis, D., Buhr, M., Huey, L. G., and Neff, W.:
Assessing the photochemical impact of snow NOx emissions over Antarctica
during ANTCI 2003, Atmos. Environ., 41, 3944–3958,
https://doi.org/10.1016/j.atmosenv.2007.01.056, 2007. a
Wessel, P., Luis, J. F., Uieda, L., Scharroo, R., Wobbe, F., Smith, W. H. F.,
and Tian, D.: The Generic Mapping Tools Version 6, Geochem. Geophy.
Geosy., 20, 5556–5564, https://doi.org/10.1029/2019GC008515, 2019. a
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M.,
Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E.,
Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O.,
Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J.,
Groth, P., Goble, C., Grethe, J. S., Heringa, J., ’t Hoen, P. A. C., Hooft,
R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A.,
Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R.,
Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz,
M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J.,
Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.:
The FAIR Guiding Principles for scientific data management and stewardship,
Scientific Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18, 2016.
a
Wise, E. K. and Comrie, A. C.: Extending the Kolmogorov–Zurbenko filter:
application to ozone, particulate matter, and meteorological trends, J. Air Waste Manage., 55, 1208–1216,
https://doi.org/10.1080/10473289.2005.10464718, 2005. a
Xu, J., Ma, J. Z., Zhang, X. L., Xu, X. B., Xu, X. F., Lin, W. L., Wang, Y., Meng, W., and Ma, Z. Q.: Measurements of ozone and its precursors in Beijing during summertime: impact of urban plumes on ozone pollution in downwind rural areas, Atmos. Chem. Phys., 11, 12241–12252, https://doi.org/10.5194/acp-11-12241-2011, 2011. a
Xu, X., Lin, W., Xu, W., Jin, J., Wang, Y., Zhang, G., Zhang, X., Ma, Z., Dong,
Y., Ma, Q., Yu, D., Li, Z., Wang, D., and Zhao, H.: Tropospheric Ozone
Assessment Report: Long-term changes of regional ozone in China: implications
for human health and ecosystem impacts, Elem. Sci. Anth., 8, 13,
https://doi.org/10.1525/elementa.409, 2020. a
Yi, J. and Prybutok, V. R.: A neural network model forecasting for prediction
of daily maximum ozone concentration in an industrialized urban area,
Environ. Pollut., 92, 349–357, 1996. a
Young, P. J., Naik, V., Fiore, A. M., Gaudel, A., Guo, J., Lin, M. Y., Neu,
J. L., Parrish, D. D., Rieder, H. E., Schnell, J. L., Tilmes, S., Wild, O.,
Zhang, L., Ziemke, J. R., Brandt, J., Delcloo, A., Doherty, R. M., Geels, C.,
Hegglin, M. I., Hu, L., Im, U., Kumar, R., Luhar, A., Murray, L., Plummer,
D., Rodriguez, J., Saiz-Lopez, A., Schultz, M. G., Woodhouse, M. T., and
Zeng, G.: Tropospheric Ozone Assessment Report: Assessment of global-scale
model performance for global and regional ozone distributions, variability,
and trends, Elem. Sci. Anth., 6, 10, https://doi.org/10.1525/elementa.265, 2018. a
Zhang, Y. and Yang, Q.: A survey on multi-task learning, arXiv [preprint],
arXiv:1707.08114, 25 July 2017. a
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.
With the AQ-Bench dataset, we contribute to shared data usage and machine learning methods in...
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