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Recovery of ecosystem productivity in China due to the Clean Air Action plan

Nature Geoscience volume 17, pages 1233–1239 (2024)Cite this article

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Abstract

Severe air pollution reduces ecosystem carbon assimilation through the vegetation damaging effects of ozone and by altering the climate through aerosol effects, exacerbating global warming. In response, China implemented the Clean Air Action plan in 2013 to reduce anthropogenic emissions. Here we assess the impact of air pollution reductions due to the Clean Air Action plan on net primary productivity (NPP) in China during the period 2014–2020 using multiple measurements, process-based models and machine learning algorithms. The Clean Air Action plan led to a national NPP increase of 26.3 ± 27.9 TgC yr−1, of which 20.1 ± 10.9 TgC yr−1 is attributed to aerosol reductions, driven by both the enhanced light availability as a result of decreased black carbon concentrations and the increased precipitation caused by weakened aerosol climatic effects. The impact of ozone amelioration became more important over time, surpassing the effects of aerosol reduction by 2020, and is expected to drive future NPP recovery. Two machine learning models simulated similar NPP recoveries of 42.8 ± 26.8 TgC yr−1 and 43.4 ± 30.1 TgC yr−1. Our study highlights substantial carbon gains from controlling aerosols and surface ozone, underscoring the co-benefits of regulating air pollution for public health and carbon neutrality in China.

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Fig. 1: Evaluation of the AOD and O3 concentrations in China for May–September.
Fig. 2: NPP recovery following the changes in aerosols and O3 due to the CAA plan.
Fig. 3: The year-to-year NPP recovery attributable to CAA-induced changes in aerosols and surface O3 in China.
Fig. 4: NPP changes and trends in China due to the CAA plan and LCC.
Fig. 5: NPP responses to aerosol and O3 changes in different CAA phases.

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Data availability

All simulations that support the finding of this study and source data underlying Figs. 1–5 are publicly available via Figshare at https://doi.org/10.6084/m9.figshare.27186126.v1 (ref. 62). The data used in this study are available publicly from the following databases. GLASS 8-day 500 m NPP and leaf area index data were obtained from the University of Maryland at http://www.glass.umd.edu/. The land-cover data in China are available via the National Ecosystem Research Network Data Center at https://doi.org/10.12199/nesdc.ecodb.rs.2023.015 (ref. 63). Gridded PM2.5 and MDA8 O3 data were downloaded from Tsinghua University at http://tapdata.org.cn. The meteorological datasets are from the ERA-5 reanalysis (https://www.ecmwf.int/) and monthly spatial CO2 data are available in ref. 44.

Code availability

The codes for the processed-based and data-driven models are publicly available from the following sources. The codes for iMAPLE are available via Figshare at https://doi.org/10.6084/m9.figshare.23593578.v1 (ref. 64) and CRM is openly available at https://www.ess.uci.edu/~zender/. GEOS-Chem is developed and updated by the Atmospheric Chemistry Modeling Group at Harvard University and is available at https://acmg.seas.harvard.edu/geos/. The Python codes for XGB and RF used in the data-driven models are available at https://xgboost.readthedocs.io/en/latest/python/ and https://scikit-learn.org/stable/modules/ensemble.html, respectively.

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Acknowledgements

This work was jointly supported by the National Natural Science Foundation of China (grant numbers 42293323 and 42275128 to X.Y.), the Natural Science Foundation of Jiangsu Province (grant number BK20220031 to X.Y.) and the National University of Defense Technology Independent Research Project (grant number ZK24-52 to H.Z.).

Author information

Author notes

  1. These authors contributed equally: Hao Zhou, Xu Yue.

Authors and Affiliations

  1. College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China

    Hao Zhou

  2. Key Laboratory of High Impact Weather (special), China Meteorological Administration, Changsha, China

    Hao Zhou

  3. Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing, China

    Xu Yue, Huibin Dai, Jun Zhu & Hong Liao

  4. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, China

    Guannan Geng

  5. Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Wenping Yuan

  6. Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, USA

    Jiquan Chen

  7. Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, China

    Guofeng Shen

  8. State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Tianyi Zhang

  9. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, NUIST, Nanjing, China

    Tianyi Zhang

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Contributions

H.Z., X.Y. and H.L. conceived this project. X.Y. led the research and was responsible for communicating with all authors about the data, methods and results. H.Z. collected observational data and performed simulations with input from H.D., G.G. and W.Y. H.Z. completed data analyses and the first draft of the paper. X.Y. and H.L. reviewed and edited the paper. J.C., G.S., T.Z. and J.Z. helped to revise the paper.

Corresponding author

Correspondence to Xu Yue.

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Nature Geoscience thanks Xitian Cai, William Collins and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Spatial pattern of NPP changes due to baseline air pollutants.

Results shown are the changes of NPP (g C m−2 day−1) during May-September caused by (a) aerosols, (b) ozone, and (c) their combined effects during 2014-2020 from simulations with and without air pollutants. The aerosol impacts are further separated into (d) radiative and (e) climatic effects on NPP. The national total changes in NPP (Tg C yr−1) are shown on each panel. Basemaps (including inset maps) of China are adopted from the Resource and Environment Science and Data Center, ref. 38.

Extended Data Fig. 2 Responses of NPP to the radiative effects of baseline aerosol species.

Results shown are the impacts of five aerosol species including (a) black carbon, (b) organic carbon, (c) sulfate and nitrate, (d) sea salt and (e) dust on May-September NPP in China during 2014-2020 through perturbations in diffuse and direct radiation. Please notice the differences in color scales. The units are g C m−2 day−1. Basemaps (including inset maps) of China are adopted from the Resource and Environment Science and Data Center, ref. 38.

Extended Data Fig. 3 Responses of NPP to baseline aerosol climatic effects.

Results shown are the impacts of aerosol-induced changes in (a) temperature and (b) precipitation on May-September NPP (g C m−2 day−1) in China during 2014-2020. Basemaps (including inset maps) of China are adopted from the Resource and Environment Science and Data Center, ref. 38.

Extended Data Fig. 4 NPP recovery due to changes in aerosol radiative and climatic effects following CAA plans in China.

Results shown are the changes in May-September NPP (g C m−2 day−1) due to the CAA-induced changes in aerosol (a) radiative and (b) climatic effects during 2014-2020. Basemaps (including inset maps) of China are adopted from the Resource and Environment Science and Data Center, ref. 38.

Extended Data Fig. 5 NPP responses to changes in aerosol radiative and climatic effects following CAA plans in China.

Results shown are the changes in May-September NPP (g C m−2 day−1) due to the CAA-induced changes in aerosol radiative effects from (a) BC, (b) OC, and (c) sulfate and nitrate, as well as those of aerosol climatic effects from perturbations in (d) temperature and (e) precipitation. Basemaps (including inset maps) of China are adopted from the Resource and Environment Science and Data Center, ref. 38.

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Supplementary Sections 1 and 2, Tables 1–4 and Figs. 1–16.

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Zhou, H., Yue, X., Dai, H. et al. Recovery of ecosystem productivity in China due to the Clean Air Action plan. Nat. Geosci. 17, 1233–1239 (2024). https://doi.org/10.1038/s41561-024-01586-z

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