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<p>In recent years, the frequent occurrence of high-concentration ozone pollution events has attracted widespread attention from all sectors of society. Using high spatial resolution data to accurately calculate near-surface ozone concentrations is crucial to solving ozone pollution and health assessment problems. This study used a downscaling model based on mutual information (MI) entropy to obtain a near-surface ozone dataset for China with a resolution of 1&#xa0;km from 2021 to 2022. The main findings include: (1) The downscaling model based on MI entropy has a good downscaling effect. (2) The distribution of near-surface ozone across China is generally high in the east and low in the west. Higher concentrations of ozone are concentrated in industrialized areas, the Qinghai-Tibet Plateau, and coastal areas. (3) During an ozone pollution process in Guangdong Province between 7 and 12 December 2021, the near-surface ozone concentration first expanded and then contracted over time, and high concentrations of ozone were concentrated in Jiangmen, Foshan and Guangzhou. This high ozone event is mainly driven by meteorological conditions conducive to photochemical reactions, specifically sunny days with long sunshine hours and high temperatures. Additionally, the unique geographical conditions of Guangdong Province lead to significant impacts from local circulation, which causes the accumulation and exacerbation of ozone pollution. The high spatial resolution dataset improves the spatiotemporal coverage of ozone data, in order to provide scientific and effective data and theoretical support for ozone pollution control.</p> Graphical abstract <p></p>

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China Surface Ozone Remote Sensing Dataset in 2021–2022 at 1 km Spatial Resolution

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

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  • Shan Zhang,
  • Yong Xue,
  • Xiaolu Ling,
  • Fujia Zhang,
  • Liying Han,
  • Zhenglong Li,
  • Ziqi Liu

摘要

In recent years, the frequent occurrence of high-concentration ozone pollution events has attracted widespread attention from all sectors of society. Using high spatial resolution data to accurately calculate near-surface ozone concentrations is crucial to solving ozone pollution and health assessment problems. This study used a downscaling model based on mutual information (MI) entropy to obtain a near-surface ozone dataset for China with a resolution of 1 km from 2021 to 2022. The main findings include: (1) The downscaling model based on MI entropy has a good downscaling effect. (2) The distribution of near-surface ozone across China is generally high in the east and low in the west. Higher concentrations of ozone are concentrated in industrialized areas, the Qinghai-Tibet Plateau, and coastal areas. (3) During an ozone pollution process in Guangdong Province between 7 and 12 December 2021, the near-surface ozone concentration first expanded and then contracted over time, and high concentrations of ozone were concentrated in Jiangmen, Foshan and Guangzhou. This high ozone event is mainly driven by meteorological conditions conducive to photochemical reactions, specifically sunny days with long sunshine hours and high temperatures. Additionally, the unique geographical conditions of Guangdong Province lead to significant impacts from local circulation, which causes the accumulation and exacerbation of ozone pollution. The high spatial resolution dataset improves the spatiotemporal coverage of ozone data, in order to provide scientific and effective data and theoretical support for ozone pollution control.

Graphical abstract