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High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning

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Global carbon monoxide (CO) pollution poses health risks, especially in China and India. Wildfires significantly increase CO levels, impacting air quality and mortality, necessitating continuous monitoring.

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Public Health

Background:

  • Ambient carbon monoxide (CO) is a major air pollutant with significant health implications.
  • CO contributes to the formation of secondary pollutants like ozone (O3).
  • Natural events, such as wildfires, can drastically increase CO concentrations.

Purpose of the Study:

  • To elucidate global CO pollution patterns and associated health risks.
  • To assess the influence of natural events, specifically wildfires, on CO levels.
  • To develop a high-resolution model for estimating daily global CO concentrations.

Main Methods:

  • Utilized artificial intelligence (AI) big data techniques, specifically a Convolutional Neural Network (CNN)-based Residual Network (ResNet) model.
  • Integrated the TROPOMI Total Column of atmospheric CO (TCCO) product with reanalysis datasets.
  • Achieved high spatial resolution (0.07°) estimations for daily global CO concentrations from June 2018 to May 2021.

Main Results:

  • High CO concentrations were observed in northern and central China and northern India, particularly during winter.
  • Wildfires caused significant CO increases: 60.0% in Indochina, 28.7% in the Amazon, and 40.8% in Central Africa.
  • Estimated 23,400 short-term mortality cases related to CO exposure in China in 2019.

Conclusions:

  • Ongoing monitoring of CO levels and their health impacts is crucial.
  • The developed AI model provides accurate, high-resolution global CO estimations.
  • Publicly available daily surface CO concentration data supports future sustainable environmental studies.