Atmospheric Carbon Monoxide Measurements and Modeling

Atmospheric carbon monoxide (CO) is a short-lived trace gas arising from incomplete combustion of fossil fuels, biomass burning and natural processes such as oxidation of volatile organic compounds. It plays a key role in atmospheric chemistry by modulating the concentration of the hydroxyl radical (OH), which in turn regulates the lifetimes of methane and other pollutants. Measurement approaches include ground-based in situ stations, mobile laboratories, Fourier-transform infrared spectroscopy and aircraft campaigns. These datasets are complemented by global satellite observations from both polar-orbiting and geostationary platforms, exploiting thermal infrared and shortwave infrared bands to retrieve total columns or boundary-layer concentrations. Retrieval algorithms rely on radiative transfer models, averaging-kernel techniques and increasingly on interpretable machine-learning frameworks to resolve spatiotemporal variability under diverse cloud and surface conditions. Modeling efforts employ chemical transport models and Earth system models, often coupled with inversion methods to infer emissions and sinks by reconciling observations with prior inventories. Advances in high-resolution data assimilation, multi-species Bayesian inversion and hybrid physical–statistical approaches have improved our understanding of regional sources, transport pathways and the long-term CO budget. The integration of state-of-the-art measurements and models underpins efforts to monitor air quality, assess combustion efficiency and guide mitigation strategies on global to urban scales.

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Atmospheric Carbon Monoxide Measurements and Modeling publication trend

The graph below shows the total number of articles in atmospheric carbon monoxide measurements and modeling across all publications each year (not limited to Nature Index journals).

Technical terms

Hydroxyl radical (OH): The primary atmospheric oxidant responsible for removing CO and other trace gases.

Radiative transfer model: A computational representation of the passage and absorption of radiation through the atmosphere used in remote-sensing retrievals.

Bayesian inversion: A statistical technique that combines observational data with prior information to infer spatial and temporal emission estimates.

SHapley Additive Explanations (SHAP): A method for interpreting machine-learning predictions by quantifying the contribution of each input feature.

Geostationary satellite: A satellite in a fixed orbital position relative to the Earth, providing continuous regional coverage for monitoring atmospheric trace gases.

References

1. Synergistic observation of FY-4A&4B to estimate CO concentration in China: combining interpretable machine learning to reveal the influencing mechanisms of CO variations. npj Climate and Atmospheric Science (2024).
2. Diurnal Carbon Monoxide Retrieval from FY-4B/GIIRS Using a Novel Machine Learning Method. Journal of Remote Sensing (2024).
3. Global atmospheric carbon monoxide budget 2000–2017 inferred from multi-species atmospheric inversions. Earth System Science Data (2019).