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Sustained emission reductions have restrained the ozone pollution over China

Nature Geoscience volume 16, pages 967–974 (2023)Cite this article

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Abstract

Near-surface ozone pollution, associated with complex responses to changing precursor emissions and meteorological conditions, has become one of the biggest challenges in China’s air quality management. Here, we present the spatiotemporal evolution of ozone concentrations from 2010 to 2021 using measurements of the national air quality monitoring network. We evaluate the effectiveness of the national air pollution control programme, including Phase 1 (2013–2017) and Phase 2 (2018–2021), in reducing the ozone level over China, using an optimized machine learning approach, high-resolution emission estimates and an improved air quality model. We find that while emission changes in Phase 1 increased the ozone level over the five highly developed regions, further reductions of nitrogen oxide emissions in Phase 2 have generally constrained the ozone pollution. The changing effect of emission controls on ozone pollution is due to the shift in the prevailing regime for ozone formation and the weakened effects of aerosol declines, as emission reductions continue. We further find that current emission controls have been more effective in rural locales in four of the five regions, and more effective in summer than winter. Therefore, further control of ozone pollution should consider these regional and seasonal variations to identify the most important precursors for the pollution.

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Fig. 1: Spatial distribution of average warm-season (April–September) MDA8 ozone concentrations during 2010–2021.
Fig. 2: The interannual variations of MDA8DEMET in urban and rural areas by region for 2013–2021.
Fig. 3: The interannual changes of MDA8DEMET by region.
Fig. 4: The shifts of ozone formation regime by season and region between 2013 and 2021.
Fig. 5: The weakened effect of aerosol reduction on ozone enhancement.

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Data availability

The source data for figures (including the gridded data of MDA8 ozone concentration) are available at the figshare repository https://doi.org/10.6084/m9.figshare.23790609. Source data are provided with this paper.

Code availability

The code of the CMAQ model is available at https://github.com/USEPA/CMAQ/tree/5.2. The machine learning code used for predicting the ozone concentration is available at the figshare repository https://doi.org/10.6084/m9.figshare.23790609.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (41922052 to Y.Z. and 42177080 to Y.Z. and Y.W.) and the Sze Family Charitable Foundation to C.P.N.

Author information

Authors and Affiliations

  1. State Key Laboratory of Pollution Control and Resource Reuse and School of Environment, Nanjing University, Nanjing, China

    Yutong Wang & Yu Zhao

  2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China

    Yu Zhao

  3. School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai, China

    Yiming Liu

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

    Yueqi Jiang & Jia Xing

  5. Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

    Bo Zheng

  6. Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA

    Yang Liu

  7. China National Environmental Monitoring Centre, Beijing, China

    Shuai Wang

  8. Harvard-China Project on Energy, Economy and Environment, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Chris P. Nielsen

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Contributions

Y.Z. and Y.W. designed the research. Y.W. performed the research. Yiming Liu, Yang Liu, Y.J., J.X. and C.P.N. interpreted the data. B.Z. provided emission inventory. S.W. provided observational ozone data. Y.W., Y.Z. and C.P.N. wrote the paper with input from all co-authors.

Corresponding author

Correspondence to Yu Zhao.

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Nature Geoscience thanks the anonymous reviewers 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 Time series and interannual trends of MDA8 ozone concentrations for East China during 2010–2012, 2013–2017 and 2018–2021 for urban (a) and rural areas (b).

The smooth grey lines and dotted purple lines represent time series of predicted and observed MDA8 ozone averaged for each month, respectively. The black, blue and red lines represent the linear trends of MDA8 ozone for 2010–2012, 2013–2017 and 2018–2021, respectively. The annual variation rates (μg/m3/yr) during different periods, with significance levels (*p < 0.05) are presented (The numbers in the parentheses indicate the relative annual variation rate (%/yr)). Summer indicates June-August and winter indicates December-February.

Source data

Extended Data Fig. 2 The difference in CMAQ-simulated MDA8 ozone for East China due to the anthropogenic emission change from 2013 to 2017 (a) and 2018–2021 (b).

The meteorological field is fixed at 2017 level in the simulation.

Source data

Extended Data Fig. 3 The relationships between MDA8 ozone, satellite-based NO2 and HCHO TVCD in the five key regions for selected years during 2013–2021.

MDA8 ozone are predicted with machine learning model in this work, and NO2 and HCHO TVCD are obtained from OMI products (see Methods of the article). The solid, dashed and dotted lines indicate the HCHO/NO2 ratio at 1, 2 and 4.

Source data

Extended Data Fig. 4 The growth of MDA8 ozone-PM2.5 partial correlation coefficients (PCORs) and the decline in PM2.5 concentrations for the five key regions during 2013–2021.

The solid blue lines and solid green lines indicate the time series of MDA8 ozone-PM2.5 PCORs and PM2.5 concentrations, respectively. The dashed blue lines indicate the linear trends of MDA8 ozone-PM2.5 PCORs. The annual growth rates of PCORs with significance levels (*p < 0.05) are presented.

Source data

Extended Data Fig. 5 Spatial distribution of the simulated MDA8 ozone (μg/m3) responding to the changes in the effects of aerosol in summer from 2013 to 2021.

The aerosol affects ozone via altering photolysis rates (a) and all heterogeneous reactions (b).

Source data

Extended Data Fig. 6 The differences in winter (December) MDA8 ozone simulated with and without the aerosol effect in CMAQ are presented for 2013 (a), 2017 (b), and 2021 (c).

The national average ‘efficiency’ of aerosol suppression on MDA8 ozone (that is, ΔCO3/CPM2.5) is given in the bottom left corner of each panel.

Source data

Extended Data Fig. 7 Expanded ozone pollution seasons in China.

The evolution of monthly average MDA8 ozone concentrations (a), and monthly distributions of satellite-derived TVCD of HCHO (b) and NO2 (c) during 2010–2021 are shown for East China. The data source of TVCD is described in the Methods of the article.

Source data

Supplementary information

Supplementary Information

Supplementary Text Section, Figs. 1–18 and Tables 1–9.

Source data

Source Data Fig. 1

Gridded data of average warm-season MDA8 ozone concentration during 2010–2021.

Source Data Fig. 2

Interannual changes of MDA8DEMET in urban and rural areas for different regions for 2013–2021.

Source Data Fig. 3

Annual variation rates of MDA8DEMET for Phase 1 and Phase 2 at monthly scale over different regions.

Source Data Fig. 4

Gridded data of shifts in ozone formation regime between 2013 and 2021 for different regions and seasons; the fractions of total area undergoing different types of regime shift by season for the five key regions.

Source Data Fig. 5

Gridded data of the effect of aerosol reduction on ozone enhancement.

Source Data Extended Data Fig. 1

Interannual changes of MDA8 ozone concentrations for East China during different periods for urban and rural areas.

Source Data Extended Data Fig. 2

Gridded data of the difference in CMAQ-simulated MDA8 ozone for East China due to the anthropogenic emission change from different periods.

Source Data Extended Data Fig. 3

MDA8 ozone, satellite-based NO2 and HCHO TVCD in different regions and years.

Source Data Extended Data Fig. 4

Partial correlation coefficients of MDA8 ozone-PM2.5 and the PM2.5 concentrations for the five key regions during 2013–2021.

Source Data Extended Data Fig. 5

Gridded data of simulated MDA8 ozone concentrations responding to the changes in aerosol effects in summer from 2013 to 2021.

Source Data Extended Data Fig. 6

Gridded data of the differences in winter MDA8 ozone simulated with and without the aerosol effect in CMAQ model.

Source Data Extended Data Fig. 7

Monthly average MDA8 ozone concentrations and satellite-derived TVCD of HCHO and NO2 during 2010–2021 for East China.

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Wang, Y., Zhao, Y., Liu, Y. et al. Sustained emission reductions have restrained the ozone pollution over China. Nat. Geosci. 16, 967–974 (2023). https://doi.org/10.1038/s41561-023-01284-2

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