Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Control of particulate nitrate air pollution in China

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

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.

References

  1. 1.

    Action Plan on Prevention and Control of Air Pollution (in Chinese) (Chinese State Council, 2013); http://www.gov.cn/zwgk/2013-09/12/content_2486773.htm

  2. 2.

    Zhai, S. et al. Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology. Atmos. Chem. Phys. 19, 11031–11041 (2019).

    Google Scholar 

  3. 3.

    Zhang, Q. et al. Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc. Natl Acad. Sci. USA 116, 24463–24469 (2019).

    Google Scholar 

  4. 4.

    Liu, M. et al. Rapid SO2 emission reductions significantly increase tropospheric ammonia concentrations over the North China Plain. Atmos. Chem. Phys. 18, 17933–17943 (2018).

    Google Scholar 

  5. 5.

    Zhou, W. et al. Response of aerosol chemistry to clean air action in Beijing, China: insights from two-year ACSM measurements and model simulations. Environ. Pollut. 255, 113345 (2019).

    Google Scholar 

  6. 6.

    Zheng, B. et al. Trends in China’s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. 18, 14095–14111 (2018).

    Google Scholar 

  7. 7.

    Gao, M. et al. China’s emission control strategies have suppressed unfavorable influences of climate on wintertime PM2.5 concentrations in Beijing since 2002. Atmos. Chem. Phys. 20, 1497–1505 (2020).

    Google Scholar 

  8. 8.

    Regional Air Quality Has Improved Significantly, but Prevention and Control of Air Pollution Still Has a Long Way to Go (in Chinese) (National Center for Atmospheric Pollution Control, 2020); https://mp.weixin.qq.com/s/5KoDFQrqtmJ4OXiL3LWlug

  9. 9.

    Fu, X. et al. Persistent heavy winter nitrate pollution driven by increased photochemical oxidants in northern China. Environ. Sci. Technol. 54, 3881–3889 (2020).

    Google Scholar 

  10. 10.

    Cheng, J. et al. Dominant role of emission reduction in PM2.5 air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis. Atmos. Chem. Phys. 19, 6125–6146 (2019).

    Google Scholar 

  11. 11.

    Shao, P. et al. Characterizing remarkable changes of severe haze events and chemical compositions in multi-size airborne particles (PM1, PM2.5 and PM10) from January 2013 to 2016–2017 winter in Beijing, China. Atmos. Environ. 189, 133–144 (2018).

    Google Scholar 

  12. 12.

    Xu, W. et al. Changes in aerosol chemistry from 2014 to 2016 in winter in Beijing: insights from high-resolution aerosol mass spectrometry. J. Geophys. Res. Atmos. 124, 1132–1147 (2019).

    Google Scholar 

  13. 13.

    Song, S. et al. Thermodynamic modeling suggests declines in water uptake and acidity of inorganic aerosols in Beijing winter haze events during 2014/2015–2018/2019. Environ. Sci. Technol. Lett. 6, 3881–3889 (2019).

    Google Scholar 

  14. 14.

    Li, H. et al. Rapid transition in winter aerosol composition in Beijing from 2014 to 2017: response to clean air actions. Atmos. Chem. Phys. 19, 11485–11499 (2019).

    Google Scholar 

  15. 15.

    Lu, K. et al. Fast photochemistry in wintertime haze: consequences for pollution mitigation strategies. Environ. Sci. Technol. 53, 10676–10684 (2019).

    Google Scholar 

  16. 16.

    Xu, Q. et al. Nitrate dominates the chemical composition of PM2.5 during haze event in Beijing, China. Sci. Total Environ. 689, 1293–1303 (2019).

    Google Scholar 

  17. 17.

    Pan, Y. et al. Fossil fuel combustion-related emissions dominate atmospheric ammonia sources during severe haze episodes: evidence from 15N-stable isotope in size-resolved aerosol ammonium. Environ. Sci. Technol. 50, 8049–8056 (2016).

    Google Scholar 

  18. 18.

    Sun, K. et al. Vehicle emissions as an important urban ammonia source in the United States and China. Environ. Sci. Technol. 51, 2472–2481 (2017).

    Google Scholar 

  19. 19.

    Chang, Y. et al. The importance of vehicle emissions as a source of atmospheric ammonia in the megacity of Shanghai. Atmos. Chem. Phys. 16, 3577–3594 (2016).

    Google Scholar 

  20. 20.

    Bhattarai, N. et al. Sources of gaseous NH3 in urban Beijing from parallel sampling of NH3 and NH4+, their nitrogen isotope measurement and modeling. Sci. Total Environ. 747, 141361 (2020).

    Google Scholar 

  21. 21.

    Guo, H. et al. Effectiveness of ammonia reduction on control of fine particle nitrate. Atmos. Chem. Phys. 18, 12241–12256 (2018).

    Google Scholar 

  22. 22.

    Wang, G. et al. Persistent sulfate formation from London fog to Chinese haze. Proc. Natl Acad. Sci. USA 113, 13630–13635 (2016).

    Google Scholar 

  23. 23.

    Lachatre, M. et al. The unintended consequence of SO2 and NO2 regulations over China: increase of ammonia levels and impact on PM2.5 concentrations. Atmos. Chem. Phys. 19, 6701–6716 (2019).

    Google Scholar 

  24. 24.

    Fu, X. et al. Increasing ammonia concentrations reduce the effectiveness of particle pollution control achieved via SO2 and NOx emissions reduction in East China. Environ. Sci. Technol. Lett. 4, 221–227 (2017).

    Google Scholar 

  25. 25.

    Shah, V. et al. Effect of changing NOx lifetime on the seasonality and long-term trends of satellite-observed tropospheric NO2 columns over China. Atmos. Chem. Phys. 20, 1483–1495 (2020).

    Google Scholar 

  26. 26.

    Jaeglé, L. et al. Nitrogen oxides emissions, chemistry, deposition and export over the Northeast United States during the WINTER Aircraft Campaign. J. Geophys. Res. Atmos. 123, 12368–12393 (2018).

    Google Scholar 

  27. 27.

    Wang, H. et al. Fast particulate nitrate formation via N2O5 uptake aloft in winter in Beijing. Atmos. Chem. Phys. 18, 10483–10495 (2018).

    Google Scholar 

  28. 28.

    Wang, H. et al. High N2O5 concentrations observed in urban Beijing: implications of a large nitrate formation pathway. Environ. Sci. Technol. Lett. 4, 416–420 (2017).

    Google Scholar 

  29. 29.

    Leung, D. M. et al. Wintertime particulate matter decrease buffered by unfavorable chemical processes despite emissions reductions in China. Geophys. Res. Lett. 47, e2020GL087721 (2020).

    Google Scholar 

  30. 30.

    Womack, C. C. et al. An odd oxygen framework for wintertime ammonium nitrate aerosol pollution in urban areas: NOx and VOC control as mitigation strategies. Geophys. Res. Lett. 46, 4971–4979 (2019).

    Google Scholar 

  31. 31.

    Geng, G. et al. Chemical composition of ambient PM2.5 over China and relationship to precursor emissions during 2005–2012. Atmos. Chem. Phys. 17, 1–25 (2017).

    Google Scholar 

  32. 32.

    Li, K. et al. A two-pollutant strategy for improving ozone and particulate air quality in China. Nat. Geosci. 12, 906–910 (2019).

    Google Scholar 

  33. 33.

    Lu, X. et al. Exploring 2016–2017 surface ozone pollution over China: source contributions and meteorological influences. Atmos. Chem. Phys. 19, 8339–8361 (2019).

    Google Scholar 

  34. 34.

    Luo, G., Yu, F. & Schwab, J. Revised treatment of wet scavenging processes dramatically improves GEOS-Chem 12.0.0 simulations of nitric acid, nitrate and ammonium over the United States. Geosci. Model Dev. 12, 3439–3447 (2019).

    Google Scholar 

  35. 35.

    Xu, W., Zhang, L. & Liu, X. A database of atmospheric nitrogen concentration and deposition from the nationwide monitoring network in China. Sci. Data 6, 51 (2019).

    Google Scholar 

  36. 36.

    Liu, Z. et al. Characteristics of PM2.5 mass concentrations and chemical species in urban and background areas of China: emerging results from the CARE-China network. Atmos. Chem. Phys. 18, 8849–8871 (2018).

    Google Scholar 

  37. 37.

    Liu, M. et al. Ammonia emission control in China would mitigate haze pollution and nitrogen deposition, but worsen acid rain. Proc. Natl Acad. Sci. USA 116, 7760–7765 (2019).

    Google Scholar 

  38. 38.

    Xie, Y. et al. Characteristics of chemical composition and seasonal variations of PM2.5 in Shijiazhuang, China: impact of primary emissions and secondary formation. Sci. Total Environ. 677, 215–229 (2019).

    Google Scholar 

  39. 39.

    Duan, J. et al. Summertime and wintertime atmospheric processes of secondary aerosol in Beijing. Atmos. Chem. Phys. 20, 3793–3807 (2020).

    Google Scholar 

  40. 40.

    Li, H. et al. Nitrate-driven urban haze pollution during summertime over the North China Plain. Atmos. Chem. Phys. 18, 5293–5306 (2018).

    Google Scholar 

  41. 41.

    Seinfeld, J. H & Pandis, S. N. Atmospheric Chemistry and Physics (Wiley, 2016).

  42. 42.

    Nenes, A. et al. Aerosol acidity and liquid water content regulate the dry deposition of inorganic reactive nitrogen. Atmos. Chem. Phys. Discuss. 2020, 1–25 (2020).

    Google Scholar 

  43. 43.

    Zhang, L. et al. Nitrogen deposition to the United States: distribution, sources and processes. Atmos. Chem. Phys. 12, 4539–4554 (2012).

    Google Scholar 

  44. 44.

    Xu, Z. et al. High efficiency of livestock ammonia emission controls in alleviating particulate nitrate during a severe winter haze episode in northern China. Atmos. Chem. Phys. 19, 5605–5613 (2019).

    Google Scholar 

  45. 45.

    Tan, Z. et al. Wintertime photochemistry in Beijing: observations of ROx radical concentrations in the North China Plain during the BEST-ONE campaign. Atmos. Chem. Phys. 18, 12391–12411 (2018).

    Google Scholar 

  46. 46.

    Geng, G. et al. Impact of China’s air pollution prevention and control action plan on PM2.5 chemical composition over eastern China. Sci. China Earth Sci. 62, 1872–1884 (2019).

    Google Scholar 

  47. 47.

    Wexler, A. S. & Seinfeld, J. H. Analysis of aerosol ammonium nitrate: departures from equilibrium during SCAQS. Atmos. Environ. A 26, 579–591 (1992).

    Google Scholar 

  48. 48.

    Xin, J. et al. The campaign on atmospheric aerosol research network of china: CARE-China. Bull. Am. Meteorol. 96, 1137–1155 (2014).

    Google Scholar 

  49. 49.

    Kim, P. S. et al. Sources, seasonality, and trends of southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem chemical transport model. Atmos. Chem. Phys. 15, 10411–10433 (2015).

    Google Scholar 

  50. 50.

    Pye, H. O. T. et al. Effect of changes in climate and emissions on future sulfate–nitrate–ammonium aerosol levels in the United States. J. Geophys. Res. Atmos. 114, D01205 (2009).

    Google Scholar 

  51. 51.

    Mao, J. et al. Ozone and organic nitrates over the eastern United States: sensitivity to isoprene chemistry. J. Geophys. Res. Atmos. 118, 11256–11268 (2013).

    Google Scholar 

  52. 52.

    Sherwen, T. et al. Global impacts of tropospheric halogens (Cl, Br, I) on oxidants and composition in GEOS-Chem. Atmos. Chem. Phys. 16, 12239–12271 (2016).

    Google Scholar 

  53. 53.

    Dang, R. & Liao, H. Severe winter haze days in the Beijing–Tianjin–Hebei region from 1985 to 2017 and the roles of anthropogenic emissions and meteorology. Atmos. Chem. Phys. 19, 10801–10816 (2019).

    Google Scholar 

  54. 54.

    Li, M. et al. MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP. Atmos. Chem. Phys. 17, 34813–34869 (2017).

    Google Scholar 

  55. 55.

    Philip, S. et al. Spatially and seasonally resolved estimate of the ratio of organic mass to organic carbon. Atmos. Environ. 87, 34–40 (2014).

    Google Scholar 

  56. 56.

    Philip, S. et al. Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models. Environ. Res. Lett. 12, 044018 (2017).

    Google Scholar 

  57. 57.

    Murray, L. T., Jacob, D. J., Logan, J. A., Hudman, R. C. & Koshak, W. J. Optimized regional and interannual variability of lightning in a global chemical transport model constrained by LIS/OTD satellite data. J. Geophys. Res. Atmos. 117, D20307 (2012).

    Google Scholar 

  58. 58.

    Hudman, R. C. et al. Steps towards a mechanistic model of global soil nitric oxide emissions: implementation and space based-constraints. Atmos. Chem. Phys. 12, 7779–7795 (2012).

    Google Scholar 

  59. 59.

    van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).

    Google Scholar 

  60. 60.

    Shao, J. et al. Heterogeneous sulfate aerosol formation mechanisms during wintertime Chinese haze events: air quality model assessment using observations of sulfate oxygen isotopes in Beijing. Atmos. Chem. Phys. 19, 6107–6123 (2019).

    Google Scholar 

  61. 61.

    Fountoukis, C. & Nenes, A. ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+–Ca2+–Mg2+–NH4+–Na+–SO42−–NO3–Cl–H2O aerosols. Atmos. Chem. Phys. 7, 4639–4659 (2007).

    Google Scholar 

  62. 62.

    Fairlie, T. D., Jacob, D. J. & Park, R. J. The impact of transpacific transport of mineral dust in the United States. Atmos. Environ. 41, 1251–1266 (2007).

    Google Scholar 

  63. 63.

    Jaegle, L., Quinn, P. K., Bates, T. S., Alexander, B. & Lin, J. T. Global distribution of sea salt aerosols: new constraints from in situ and remote sensing observations. Atmos. Chem. Phys. 11, 3137–3157 (2011).

    Google Scholar 

  64. 64.

    Wesely, M. L. Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models. Atmos. Environ. 23, 1293–1304 (1989).

    Google Scholar 

  65. 65.

    Liu, H., Jacob, D. J., Bey, I. & Yantosca, R. M. Constraints from 210Pb and 7Be on wet deposition and transport in a global three-dimensional chemical tracer model driven by assimilated meteorological fields. J. Geophys. Res. Atmos. 106, 12109–12128 (2001).

    Google Scholar 

  66. 66.

    Amos, H. M. et al. Gas-particle partitioning of atmospheric Hg(ii) and its effect on global mercury deposition. Atmos. Chem. Phys. 12, 591–603 (2012).

    Google Scholar 

  67. 67.

    Wang, Q. et al. Sources of carbonaceous aerosols and deposited black carbon in the Arctic in winter–spring: implications for radiative forcing. Atmos. Chem. Phys. 11, 12453–12473 (2011).

    Google Scholar 

  68. 68.

    Wang, Q. et al. Global budget and radiative forcing of black carbon aerosol: constraints from pole-to-pole (HIPPO) observations across the Pacific. J. Geophys. Res. Atmos. 119, 195–206 (2014).

    Google Scholar 

  69. 69.

    Luo, G., Yu, F. & Moch, J. M. Further improvement of wet process treatments in GEOS-Chem v12.6.0: impact on global distributions of aerosols and aerosol precursors. Geosci. Model Dev. 13, 2879–2903 (2020).

    Google Scholar 

  70. 70.

    Ji, D. et al. Impact of air pollution control measures and regional transport on carbonaceous aerosols in fine particulate matter in urban Beijing, China: insights gained from long-term measurement. Atmos. Chem. Phys. 19, 8569–8590 (2019).

    Google Scholar 

  71. 71.

    Zhao, L. et al. Changes of chemical composition and source apportionment of PM2.5 during 2013–2017 in urban Handan, China. Atmos. Environ. 206, 119–131 (2019).

    Google Scholar 

  72. 72.

    Chang, Y. et al. Assessment of carbonaceous aerosols in Shanghai, China—Part 1: long-term evolution, seasonal variations and meteorological effects. Atmos. Chem. Phys. 17, 9945–9964 (2017).

    Google Scholar 

  73. 73.

    Ding, A. et al. Significant reduction of PM2.5 in eastern China due to regional-scale emission control: evidence from SORPES in 2011–2018. Atmos. Chem. Phys. 19, 11791–11801 (2019).

    Google Scholar 

Download references

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

Affiliations

Authors

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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing