Correction to: A hybrid CNN-Transformer model for ozone concentration prediction

Air Quality, Atmosphere & Health (2022) 15:1695–1697 https://doi.org/10.1007/s11869-022-01224-w CORRECTION Correction to: A hybrid CNN‑Transformer model for ozone concentration prediction Yibin Chen1 · Xiaomin Chen1 · Ailan Xu2 · Qiang Sun1 · Xiaoyan Peng3 Published online: 9 July 2022 © Springer Nature B.V. 2022 Correction to: Air Quality, Atmosphere & Health https://doi.org/10.1007/s11869-022-01197-w The original version of this article unfortunately contained mistakes due to exchanged of figures 1 and 2. The corrected figures are shown below: The original article has been corrected. The original article can be found online at https://doi.org/10.1007/ s11869-022-01197-w. * Qiang Sun Yibin Chen Xiaomin Chen Ailan Xu Xiaoyan Peng 1 School of Information Science and Technology, Nantong University, Nantong 226019, China 2 Jiangsu Province Nantong Environmental Monitoring Centre, Nantong 226019, China 3 Nantong Meteorological Bureau, Nantong 226019, China 13 Vol.:(0123456789) 1696 Fig. 1  Distribution of monitoring stations with the elevation of the region 13 Air Quality, Atmosphere & Health (2022) 15:1695–1697 Air Quality, Atmosphere & Health (2022) 15:1695–1697 1697 Fig. 2  System architecture of the CNN-Transformer model Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 13  (...truncated)