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Air pollution modulates trends and variability of the global methane budget

Nature volume 642, pages 369–375 (2025)Cite this article

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Abstract

Air pollution affects climate through various complex interactions1. It perturbs the Earth’s radiative energy balance and alters the atmospheric oxidation capacity, which determines the lifetimes of short-lived climate forcers, such as methane1. A key mechanism in this dynamic is the impact of air pollutants on the hydroxyl radical (OH), the most important oxidant in the troposphere, which accounts for approximately 90% of the methane chemical sink2. However, a comprehensive quantification of the interactions between air pollutants, OH and methane over decadal timescales remains incomplete2. Here we develop an integrated observation-driven and model-driven approach to quantify how variations in key air pollutants influence the methane chemical sink and alter the methane budget. Our results indicate that, from 2005 to 2021, enhanced tropospheric ozone, increased water vapour and decreased carbon monoxide levels collectively contributed to a 1.3–2.0 Tg year−1 increase per year in the global methane sink, thereby buffering atmospheric methane growth rates. This increase in the methane sink was primarily concentrated in tropical regions and exhibited a north–south asymmetry. Periods of high methane growth were typically linked to abrupt OH level declines driven by fluctuations in air pollutants, especially during extreme events such as mega wildfires and the COVID-19 pandemic. Our study suggests a trade-off between O3 pollution control and methane removal mediated by OH and highlights the risk of increasing carbon monoxide emissions from widespread wildfires.

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Fig. 1: Year-to-year variations in tropospheric OH concentrations and CH4 growth rates.
Fig. 2: Global tropospheric [OH] and CH4 sink changes driven by individual OH precursors.
Fig. 3: Spatial distribution of decadal changes in tropospheric [OH] and CH4 sink.
Fig. 4: Contributions of individual OH precursors to global CH4 sink changes during unusual years.

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Data availability

The atmospheric composition concentrations simulated by GEOSCCM and CESM1 CAM4-chem were downloaded from https://data.ceda.ac.uk/badc. The atmospheric methane growth rate from the NOAA/GML observational network was obtained from https://gml.noaa.gov/ccgg/trends_ch4/. The CAMS EAC4 chemical reanalysis and EGG4 were downloaded from https://www.ecmwf.int/en/research/climate-reanalysis/cams-reanalysis. The TCR-2 chemical reanalysis data were downloaded from https://tes.jpl.nasa.gov/tes/chemical-reanalysis/. The MERRA-2 reanalysis data were downloaded from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2. The ERA5 reanalysis data were downloaded from https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. The AURA OMI/MLS tropospheric O3 column was downloaded from https://acd-ext.gsfc.nasa.gov/Data_services/cloud_slice/new_data.html. The total O3 column of SBUV Merged Ozone Data Set was downloaded from https://acd-ext.gsfc.nasa.gov/Data_services/merged/instruments.html. The QA4ECV tropospheric NO2 product was downloaded from https://www.temis.nl/airpollution/no2.php. The NASA tropospheric NO2 products were downloaded from https://disc.gsfc.nasa.gov/datasets/OMNO2d_003/summary. The CEDS emission inventory was downloaded from https://zenodo.org/records/4741285. The EDGAR emission inventory was downloaded from https://edgar.jrc.ec.europa.eu/. The global gridded distribution of the OH changes relative to 2005 owing to individual OH precursors and the source data are available at Figshare (https://doi.org/10.6084/m9.figshare.27850596). Source data are provided with this paper.

Code availability

Code and documentation for the chemical box model DSMACC are available at https://github.com/barronh/DSMACC.

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Acknowledgements

The study was supported by the National Natural Science Foundation of China (grant numbers 42305101, 42375096 and 22188102). B.Z. acknowledges support from the Shenzhen Science and Technology Program (grant number ZDSYS20220606100806014). Y.Z. acknowledges support from the Shandong Provincial Natural Science Foundation (grant number 2022HWYQ-066). P.C. acknowledges support from the European Space Agency Climate Space RECCAP2-CS project (ESA ESRIN/4000144908) and the CALIPSO project funded by the generosity of Schmidt Science.

Author information

Authors and Affiliations

  1. State Key Laboratory of Physical Oceanography, Ocean University of China, Qingdao, China

    Yuanhong Zhao & Shengmin Lu

  2. Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Bo Zheng & Yifan Li

  3. State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China

    Bo Zheng

  4. Laboratoire des Sciences du Climat et de l’Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, Gif-sur-Yvette, France

    Marielle Saunois, Philippe Ciais & Philippe Bousquet

  5. Institute of Climate and Energy Systems, Stratosphere (ICE-4), Forschungszentrum Jülich, Jülich, Germany

    Michaela I. Hegglin

  6. Department of Meteorology, University of Reading, Reading, UK

    Michaela I. Hegglin

  7. Institute of Atmospheric and Environmental Research, University of Wuppertal, Wuppertal, Germany

    Michaela I. Hegglin

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Contributions

B.Z. and Y.Z. designed and conceptualized the study. Y.Z. performed DSMACC simulations and created the figures. B.Z. and Y. Z. wrote the original draft. M.S., P.C., M.I.H., S.L., Y.L. and P.B. reviewed and commented on the manuscript.

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Correspondence to Bo Zheng.

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Extended data figures and tables

Extended Data Fig. 1 Comparison of the global tropospheric mean [OH] with MCF-based and HFC-based inversions.

Tropospheric [OH] variations calculated using MOZART (pink) and GEOS-Chem (purple) chemical mechanisms (n = 144) are compared with MCF-based inversions from the 3D model as described by Patra et al.29 and by Naus et al.28, as well as with HFC-based inversions from the box model as described by Thompson et al.30.

Source Data

Extended Data Fig. 2 Contributions of NOx from the free troposphere to interannual variation of global [OH]trop-M and decadal changes in the chemical loss of CH4.

a, Interannual variations of [OH]trop-M. b, Decadal changes in the chemical loss of CH4 (n = 3). Box plots and error bars indicate the means and ranges of individual calculations, each represented by grey circles.

Source Data

Extended Data Fig. 3 Contributions of individual OH precursors to decadal changes in tropospheric [OH] in South Asia.

a–c, Spatial patterns of decadal changes in mean tropospheric OH concentrations ([OH]trop-M) driven by tropospheric O3, H2O(g) and boundary-layer NOx. d, Regional mean [OH]trop-M changes over South Asia. The n values are the same as in Fig. 2. Box plots and error bars indicate the means and ranges of individual calculations, each represented by grey circles.

Source Data

Extended Data Fig. 4 Contributions of boundary-layer NOx to anomalies in [OH]trop-M during the COVID-19 lockdown in 2020.

The lockdown period is March 2020 to May 2020 for the Eastern United States and Western Europe and February 2020 to March 2020 for Eastern China. Box plots and error bars indicate the means and ranges of individual calculations, each represented by grey circles (n = 3).

Source Data

Extended Data Fig. 5 Regional contributions of individual OH precursors to global CH4 sink changes during unusual years.

a, The rapid increase in the global CH4 sink following 2007 (mean values of 2008–2009 minus 2006–2007). b, Anomaly in the global CH4 sink during the El Niño event starting in 2015 (mean values of May 2015 to April 2016 minus May 2014 to April 2015). c,d, Anomalies in the global CH4 sink during the COVID-19 lockdown in 2020 (2020 minus 2019) and 2021 (2021 minus 2019). The n values are the same as in Fig. 2. Box plots and error bars indicate the means and ranges of individual calculations, each represented by grey circles.

Source Data

Extended Data Fig. 6 Contributions of NOx from the boundary layer, free troposphere and whole troposphere to global [OH]trop-M and CH4 sink changes during unusual years.

The unusual years include the El Niño event starting in 2015 (mean values of May 2015 to April 2016 minus May 2014 to April 2015) and the COVID-19 lockdown in 2020 (2020 minus 2019). a, [OH]trop-M. b, Chemical loss of CH4. Box plots and error bars indicate the means and ranges of individual calculations, each represented by grey circles (n = 3).

Source Data

Extended Data Fig. 7 Conceptual diagram illustrating how air pollution modulates global OH radicals and the CH4 chemical sink.

Red arrows indicate positive contributions and blue arrows show negative contributions. Grey arrows represent the interactions between climate and air quality measures, global warming and air pollution. The image elements in this figure (for example, plane, cattle, buildings and trees) are derived from https://pixabay.com/.

Extended Data Table 1 Satellite retrieval products used in this work
Full size table
Extended Data Table 2 Reanalysis datasets used in this work
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1–5 and Supplementary Table 1.

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Zhao, Y., Zheng, B., Saunois, M. et al. Air pollution modulates trends and variability of the global methane budget. Nature 642, 369–375 (2025). https://doi.org/10.1038/s41586-025-09004-z

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