Analysis of Vertical Distribution Changes and Influencing Factors of Tropospheric Ozone in China from 2005 to 2020 Based on Multi-Source Data

Int J Environ Res Public Health. 2022 Oct 3;19(19):12653. doi: 10.3390/ijerph191912653.

Abstract

The vertical distribution of the tropospheric ozone column concentration (OCC) in China from 2005 to 2020 was analysed based on the ozone profile product of the ozone monitoring instrument (OMI). The annual average OCC in the lower troposphere (OCCLT) showed an increasing trend, with an average annual increase of 0.143 DU. The OCC in the middle troposphere showed a downward trend, with an average annual decrease of 0.091 DU. There was a significant negative correlation between the ozone changes in the two layers. The monthly average results show that the peak values of OCCLT occur in May or June, the middle troposphere is significantly influenced by topographic conditions, and the upper troposphere is mainly affected by latitude. Analysis based on multi-source data shows that the reduction in nitrogen oxides (NOx) and the increase in volatile organic compounds (VOCs) weakened the titration of ozone generation, resulting in the increase in OCCLT. The increase in vegetation is closely related to the increase in OCCLT, with a correlation coefficient of up to 0.875. The near-surface temperature increased significantly, which strengthened the photochemical reaction of ozone. In addition, the increase in boundary layer height also plays a positive role in the increase in OCCLT.

Keywords: OMI; cause analysis; remote sensing vertical monitoring; spatiotemporal change; tropospheric ozone.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollutants* / analysis
  • Atmosphere / analysis
  • China
  • Environmental Monitoring
  • Nitrogen Oxides / analysis
  • Ozone* / analysis
  • Temperature
  • Volatile Organic Compounds* / analysis

Substances

  • Air Pollutants
  • Nitrogen Oxides
  • Volatile Organic Compounds
  • Ozone

Grants and funding

This research was funded by the “National Natural Science Foundation of China, grant number 41901294, 41771535”, the “Technology Department of Sichuan Province Foundation, grant number 21YYJC3604”, “the Key Research and Development Projects of Sichuan Science and Technology, grant number 2022YFS0482”, the “Sichuan Science and Technology Program, grant number 2020YFG0144”, and the “FengYun Application Pioneering Project, grant number FY-APP-2021.0208”.