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A two-pollutant strategy for improving ozone and particulate air quality in China

Nature Geoscience volume 12, pages 906–910 (2019)Cite this article

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Abstract

Fine particulate matter (PM2.5) decreased by 30–40% across China during 2013–2017 in response to the governmental Clean Air Action. However, surface ozone pollution worsened over the same period. Model simulations have suggested that the increase in ozone could be driven by the decrease in PM2.5, because PM2.5 scavenges hydroperoxy (HO2) and NOx radicals that would otherwise produce ozone. Here we show observational evidence for this effect with 2013–2018 summer data of hourly ozone and PM2.5 concentrations from 106 sites in the North China Plain. The observations show suppression of ozone pollution at high PM2.5 concentrations, consistent with a model simulation in which PM2.5 scavenging of HO2 and NOx depresses ozone concentrations by 25 ppb relative to PM2.5-free conditions. PM2.5 chemistry makes ozone pollution less sensitive to NOx emission controls, emphasizing the need for controlling emissions of volatile organic compounds (VOCs), which so far have not decreased in China. The new 2018–2020 Clean Air Action plan calls for a 10% decrease in VOC emissions that should begin to reverse the long-term ozone increase even as PM2.5 continues to decrease. Aggressive reduction of NOx and aromatic VOC emissions should be particularly effective for decreasing both PM2.5 and ozone.

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Fig. 1: Ozone–PM2.5 relationship in China in summer.
Fig. 2: HOx radical sinks in the North China Plain.
Fig. 3: Response of mean summer MDA8 surface ozone concentrations in the North China Plain to reductions in NOx and VOC emissions relative to 2017 levels.
Fig. 4: Expected ozone concentration change in the North China Plain under the 2018–2020 Clean Air Action plan.

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Data availability

The surface measurements for PM2.5 and ozone from China’s Ministry of Ecology and Environment can be downloaded from beijingair.sinaapp.com. The anthropogenic emission inventory is available from www.meicmodel.org. The MERRA-2 reanalysis data are from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2. The GEOS-Chem simulation results are available from the corresponding authors on request

Code availability

The GEOS-Chem model code is open source (https://doi.org/10.5281/zenodo.2658178). Code for calculation and data processing is available from the corresponding authors on request.

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Acknowledgements

This work is a contribution from the Harvard–NUIST Joint Laboratory for Air Quality and Climate. H.L. is supported by National Natural Science Foundation of China Grant 91744311. We thank China’s Ministry of Ecology and Environment for running the nationwide observation network and publishing hourly concentrations of air pollutants.

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Authors and Affiliations

  1. Harvard-NUIST Joint Laboratory for Air Quality and Climate, Nanjing University of Information Science and Technology, Nanjing, China

    Ke Li

  2. Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA

    Ke Li, Daniel J. Jacob, Viral Shah, Lu Shen, Kelvin H. Bates & Shixian Zhai

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

    Hong Liao & Jia Zhu

  4. Department of Earth System Science, Tsinghua University, Beijing, China

    Qiang Zhang

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Contributions

K.L. and D.J.J. conceived the study. K.L. performed the analysis. H.L., J.Z., V.S., L.S. and S.Z. contributed to interpreting the data. V.S. and K.H.B. helped with model simulations. Q.Z. provided emission data. K.L. wrote the draft of the paper with D.J.J. and H.L. All authors contributed to discussing and improving the paper.

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Correspondence to Daniel J. Jacob or Hong Liao.

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Li, K., Jacob, D.J., Liao, H. et al. A two-pollutant strategy for improving ozone and particulate air quality in China. Nat. Geosci. 12, 906–910 (2019). https://doi.org/10.1038/s41561-019-0464-x

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