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Control of particulate nitrate air pollution in China

Nature Geoscience volume 14, pages 389–395 (2021)Cite this article

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Abstract

The concentration of fine particulate matter (PM2.5) across China has decreased by 30–50% over the period 2013–2018 due to stringent emission controls. However, the nitrate component of PM2.5 has not responded effectively to decreasing emissions of nitrogen oxides and has actually increased during winter haze pollution events in the North China Plain. Here, we show that the GEOS-Chem atmospheric chemistry model successfully simulates the nitrate concentrations and trends. We find that winter mean nitrate would have increased over 2013–2018 were it not for favourable meteorology. The principal cause of this nitrate increase is weaker deposition. The fraction of total inorganic nitrate as particulate nitrate instead of gaseous nitric acid over the North China Plain in winter increased from 90% in 2013 to 98% in 2017, as emissions of nitrogen oxides and sulfur dioxide decreased while ammonia emissions remained high. This small increase in the particulate fraction greatly slows down deposition of total inorganic nitrate and hence drives the particulate nitrate increase. Our results suggest that decreasing ammonia emissions would decrease particulate nitrate by driving faster deposition of total inorganic nitrate. Decreasing nitrogen oxide emissions is less effective because it drives faster oxidation of nitrogen oxides and slower deposition of total inorganic nitrate.

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Fig. 1: PM2.5 nitrate concentrations in China and comparisons between observations and GEOS-Chem model results.
Fig. 2: PM2.5 and nitrate trends in Beijing.
Fig. 3: 2013–2017 trends of PM2.5 nitrate concentrations in the North China Plain relative to 2013 values.
Fig. 4: Factors contributing to the 2013–2017 trends of PM2.5 nitrate over the North China Plain.
Fig. 5: Percent changes of wintertime PM2.5 nitrate in response to emission reductions in the North China Plain relative to 2017.

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Data availability

Surface PM2.5 observations across China from the China Ministry of Ecology and Environment (MEE) national network can be downloaded from quotsoft.net/air. The anthropogenic emission inventory is from www.meicmodel.org. MERRA-2 reanalysis data are from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/. Information about the observed PM2.5 species concentrations used in this work are summarized in the Supplementary Table. PM2.5 species observation data are deposited at https://doi.org/10.7910/DVN/VHFTLQ. The National Nitrogen Deposition Monitoring Network (NNDMN) version 1.0 database is from ref. 35. Source data are provided with this paper.

Code availability

The GEOS-Chem model code version 12.3.1 is open source (https://doi.org/10.5281/zenodo.2633278). Code for calculations and data processing is available from the corresponding author upon request.

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Acknowledgements

This work was funded by the Harvard–NUIST Joint Laboratory for Air Quality and Climate, the Samsung PM2.5 Strategic Research Program and Samsung Advanced Institute of Technology. H.L. is supported by the National Key Research and Development Program of China (grant no. 2019YFA0606804). G.L. and F.Y. acknowledge funding support from NASA under grant no. NNX17AG35G. Y.S. acknowledges support from the Beijing Municipal Natural Science Foundation (8202049).

Author information

Authors and Affiliations

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

    Shixian Zhai, Daniel J. Jacob, Viral Shah, Ke Li, Jonathan M. Moch, Kelvin H. Bates, Shaojie Song & Lu Shen

  2. School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China

    Xuan Wang

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

    Zirui Liu, Tianxue Wen, Yele Sun & Yuesi Wang

  4. Key Laboratory of Coastal Environment and Resources of Zhejiang Province (KLaCER), School of Engineering, Westlake University, Hangzhou, Zhejiang, China

    Yuzhong Zhang

  5. Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China

    Yuzhong Zhang

  6. Atmospheric Sciences Research Center, University at Albany, Albany, NY, USA

    Gan Luo & Fangqun Yu

  7. College of Energy and Environmental Engineering, Hebei University of Engineering, Handan, China

    Litao Wang & Mengyao Qi

  8. Institute for Environmental and Climate Research, Jinan University, Guangzhou, China

    Jun Tao

  9. Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, Beijing, China

    Ke Gui

  10. Zhejiang Institute of Meteorological Sciences, Hangzhou, China

    Honghui Xu

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

    Qiang Zhang

  12. Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China

    Tianliang Zhao

  13. Samsung Advanced Institute of Technology, Suwon-si, Gyeonggi-do, Republic of Korea

    Hyun Chul Lee & Hyoungwoo Choi

  14. 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

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  1. Shixian Zhai
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  2. Daniel J. Jacob
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Contributions

S.Z., D.J.J. and H.L. designed research. S.Z. performed research. X.W., V.S., J.M.M., K.H.B., L.S., G.L. and F.Y. helped with model simulations. Z.L., T.W., Y.S., L.W., M.Q., J.T., K.G., H.X., T.Z. and Y.W. helped with data collection. X.W., V.S., K.L., S.S., Y.Z., H.C.L. and H.C. helped with results interpretation. Q.Z. provided the MEIC emission inventory. S.Z. and D.J.J. wrote the paper with input from all other authors.

Corresponding author

Correspondence to Daniel J. Jacob.

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The authors declare no competing interests.

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Peer review information Nature Geoscience thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Rebecca Neely.

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Extended data

Extended Data Fig. 1 Spatial distribution of measured (filled circles) and modeled (gridded background) 3-year (2013-2015) averaged summer mean and winter mean nitrate wet deposition fluxes.

Measurements are from the National Nitrogen Deposition Monitoring Network (NNDMN) version 1.0 database35. Comprehensive global evaluation of the updated wet scavenging scheme can be found in refs. 34,69.

Source data

Extended Data Fig. 2 Spatial and seasonal patterns of the mass concentrations of PM2.5 and its major components (OA, BC, sulfate, nitrate, and ammonium) over China in 2013.

a-f, Spatial distributions of observed annual mean concentrations (circles) are compared to the GEOS-Chem model (background). g-i, Scatter plots of observed and modeled monthly mean sulfate, nitrate, and ammonium concentrations for winter (December-January-February; filled circles) and summer (June-July-August; open circles). Also shown in panels g-i are the 1:1 lines, the correlation coefficients (r) between model and observations, and the corresponding reduced-major-axis regressions and slopes. PM2.5 observations are from the China Ministry of Ecology and the Environment (MEE) national air quality monitoring network. OA and BC observations in Beijing, Handan, and Shanghai are from refs. 70,71,72. Sulfate, nitrate, and ammonium observations are from the Campaign on Atmospheric Aerosol Research network of China (CARE-China)36,48.

Source data

Extended Data Fig. 3 Same as Extended Data Fig. 1 but for the year 2015 including January, February, July, and December.

Observations are from ref. 37. Here we only show sites that have both winter and summer observations, and summer observations for these sites are mostly for July.

Source data

Extended Data Fig. 4 Time series of monthly mean PM2.5 nitrate at Nanjing from 2013 to 2017.

GEOS-Chem results (blue dotted lines) are compared to observations (black solid lines). Observations are from the Station for Observing Regional Processes of the Earth System (SORPES; 118.97° E, 32.1° N) in Nanjing, and are detected by the Monitor for AeRosols and GAses in Ambient air (MARGA; Metrohm, Switzerland)3,73. The abnormally low nitrate in summer 2013 is mainly due to meteorological influence (Supplementary Fig. 3).

Source data

Extended Data Fig. 5 Linear regression trends of temperature and RH from 2013 to 2017 for annual mean, summer, and winter conditions.

Temperature and RH are from the MERRA-2 reanalysis data from the NASA Goddard Earth Sciences (GES) Data and Information Services Center (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/). The dashed rectangles define the North China Plain region (113.75°-118.75° E, 35°-41° N).

Source data

Extended Data Fig. 6 Thermodynamic regime for ammonium nitrate particulate formation in the North China Plain in winter.

The figure shows the molar ratio R = [NH3T] /(2 × [SO42-] + [NO3T]) as a function of sulfate-nitrate-ammonium (SNA) PM2.5 concentrations in daily mean GEOS-Chem results for the North China Plain in winters 2013-2017. Formation of nitrate PM2.5 is nitrate-limited if R > 1 (ammonia in excess) and ammonia-limited if R < 1 (nitrate in excess). The black dashed line indicates R = 1. This figure can be compared to Fig. 4a from ref. 44 which showed the same plot for observations in Beijing in December 2015 and December 2016. Bisulfate (HSO4-) in acid particles would modify the acid-base balance but we find from ISORROPIA II calculations that it accounts for less than 5% of total sulfate in the model, consistent with wintertime Beijing observations44.

Source data

Extended Data Fig. 7 2013-2017 trends of PM2.5 nitrate, the particulate fraction of total nitrate ([NO3-]/[NO3T] molar ratio), and NO3T lifetime against deposition simulated by GEOS-Chem without implementation of the new wet deposition scheme in ref. 34.

Results are from GEOS-Chem driven by 2013 and 2017 MEIC emissions with 2017 meteorology applied to the two years.

Source data

Extended Data Fig. 8

Similar to Fig. 5 in the main text but for percent changes of mean total PM2.5 in response to emission reductions averaged over the North China Plain relative to 2017.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–6 and references.

Supplementary Table

Summary information for observation data.

Source data

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Zhai, S., Jacob, D.J., Wang, X. et al. Control of particulate nitrate air pollution in China. Nat. Geosci. 14, 389–395 (2021). https://doi.org/10.1038/s41561-021-00726-z

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