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Increasing contribution of nighttime nitrogen chemistry to wintertime haze formation in Beijing observed during COVID-19 lockdowns

Nature Geoscience volume 16, pages 975–981 (2023)Cite this article

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Abstract

Nitrate comprises the largest fraction of fine particulate matter in China during severe haze. Consequently, strict control of nitrogen oxides (NOx) emissions has been regarded as an effective measure to combat air pollution. However, this notion is challenged by the persistent severe haze pollution observed during the COVID-19 lockdown when NOx levels substantially declined. Here we present direct field evidence that diminished nitrogen monoxide (NO) during the lockdown activated nocturnal nitrogen chemistry, driving severe haze formation. First, dinitrogen pentoxide (N2O5) heterogeneous reactions dominate particulate nitrate (pNO3−) formation during severe pollution, explaining the higher-than-normal pNO3− fraction in fine particulate matter despite the substantial NOx reduction. Second, N2O5 heterogeneous reactions provide a large source of chlorine radicals on the following day, contributing drastically to the oxidation of volatile organic compounds, and thus the formation of oxygenated organic molecules and secondary organic aerosol. Our findings highlight the increasing importance of such nocturnal nitrogen chemistry in haze formation caused by NOx reduction, motivating refinements to future air pollution control strategies.

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Fig. 1: Relative contributions of PM2.5 components and concentrations of main nitrous species in different PM2.5 levels in the pre-lockdown and lockdown periods.
Fig. 2: Comparison between the contributions of the HNO3 pathway and N2O5 pathway to pNO3− formation.
Fig. 3: Relative contribution of VOC oxidation by different atmospheric oxidants, including Cl radicals, OH radicals, NO3 radicals and O3.
Fig. 4: The amplified nocturnal nitrogen chemistry and impacts on air quality.

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Data availability

The observation data that support the main findings of this study are available at Zenodo (https://doi.org/10.5281/zenodo.8195559).

Data on concentrations of air pollutants for Madrid, Helsinki, Los Angeles and San Francisco can be found at https://datos.madrid.es/portal/site/egob, https://smear.avaa.csc.fi/, https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing and https://www.arb.ca.gov/aqmis2/aqdselect.php?tab=specialrpt, respectively. Source data are provided with this paper.

Code availability

Data processing techniques are available on request from Chao Yan (chaoyan@nju.edu.cn).

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) project (92044301, 42220104006, 42075101 and 41975154), the Academy of Finland (1251427, 1139656, 296628, 306853, 316114 and 311932), the Finnish Centre of Excellence (1141135 and 307331), the European Union’s Horizon 2020 programme (ERC, project no.742206 ‘ATM-GTP’, no. 850614 ‘CHAPAs’ and no. 895875 ‘NPF-PANDA’), the trans-national ERA-PLANET project SMURBS (project no. 689443) under the EU Horizon 2020 Framework Programme, the European Regional Development Fund, the Urban Innovative Actions initiative (HOPE; Healthy Outdoor Premises for Everyone, project no. UIA03− 240), MegaSense by Business Finland (grant no. 7517/31/2018) and Academy of Finland Flagship funding (grant no. 337549). The Beijing University of Chemical Technology team is supported by the National Natural Science Foundation of China (42275117) and the Beijing Natural Science Foundation (8232041). Y.J.T. acknowledges the funding support from the National Natural Science Foundation of China (42175118) and the Guangdong Basic and Applied Basic Research Foundation (2022A1515010852). The CSIC team acknowledges the funding support from the European Research Council Executive Agency under the European Union’s Horizon 2020 Research and Innovation Programme (project ERC‐2016‐COG, project no. 726349 CLIMAHAL to A.S.-L.). The Tsinghua University team acknowledges the National Natural Science Foundation of China (22188102) and Samsung PM2.5 SRP. N.M.D. acknowledges the US National Science Foundation grant AGS2132089. H.W. acknowledges the funding support from the National Natural Science Foundation of China (42175111). The Indian Institute of Tropical Meteorology is funded by the Ministry of Earth Sciences, Government of India. We acknowledge the German federal environmental agency for kindly providing us with the O3, NOx and PM2.5 and PM10 data in Frankfurt and Berlin. The data at the Frankfurt sites were measured by Hessisches Landesamt für Naturschutz, Umwelt und Geologie and the data at the Berlin sites were measured by Senatsverwaltung für Umwelt, Mobilität, Verbraucher- und Klimaschutz. We thank them for their great effort. We acknowledge the Madrid Air Quality Monitoring Network, Smart SMEAR Network, India Central Pollution Control Board and California Air Resources Board for the NOx, O3, CO and PM2.5 open data sources. We thank X. Huang and the modelling group at Nanjing University for their useful discussion regarding the vertical distribution of air pollution.

Author information

Author notes

  1. These authors contributed equally: Chao Yan, Yee Jun Tham.

Authors and Affiliations

  1. Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, China

    Chao Yan, Yishuo Guo, Wei Ma, Junlei Zhan, Chenjie Hua, Feixue Zheng, Xin Chen, Yongchun Liu & Markku Kulmala

  2. Joint International Research Laboratory of Atmospheric and Earth System Science, School of Atmospheric Sciences, Nanjing University, Nanjing, China

    Chao Yan, Wei Nie, Yuanyuan Li, Aijun Ding & Markku Kulmala

  3. Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland

    Chao Yan, Men Xia, Federico Bianchi, Veli-Matti Kerminen & Markku Kulmala

  4. School of Marine Sciences, Sun Yat-sen University, Zhuhai, China

    Yee Jun Tham

  5. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China

    Men Xia

  6. School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China

    Haichao Wang

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

    Chenjuan Deng, Yiran Li & Jingkun Jiang

  8. Department of Atmospheric Chemistry and Climate, Institute of Physical Chemistry Blas Cabrera, CSIC, Madrid, Spain

    Qinyi Li, Carlos A. Cuevas & Alfonso Saiz-Lopez

  9. State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of China Meteorological Administration (CMA), Chinese Academy of Meteorological Sciences, Beijing, China

    Gen Zhang

  10. Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

    Anoop S. Mahajan

  11. State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, China

    Dan Dan Huang

  12. Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China

    Zhe Wang

  13. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Yele Sun

  14. Aerodyne Research Inc., Billerica, MA, USA

    Douglas R. Worsnop

  15. Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, PA, USA

    Neil M. Donahue

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  1. Chao Yan
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  2. Yee Jun Tham
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Contributions

C.Y., Y.J.T., A.D. and M.K. designed the study. C.Y., Y.J.T, W.N, H.W., Y.G., W.M., J.Z., C.H., Yu.L., C.D., Yi.L., F.Z., X.C., G.Z., D.D.H., Z.W., Y.S., F.B., J.J., D.R.W. and Y. Liu collected and analysed the data. M.X, Q.L., A.S.M., C.A.C. and A.S.-L. conducted the model development and simulations. C.Y., Y.J.T., V.-M.K., N.M.D. and M.K. wrote the manuscript. All authors participated in relevant scientific discussions and commented on the manuscript.

Corresponding authors

Correspondence to Yee Jun Tham, Aijun Ding or Markku Kulmala.

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The authors declare no competing interests.

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Peer review information

Nature Geoscience thanks Matthias Beekmann 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 Time series of reactive nitrogen species, including NO, NO2, N2O5, ClNO2, and PM2.5 chemical composition before and after lockdown.

The yellow-shaded areas denote daytime. As explained in the text, the dashed vertical line represents the separation of the pre-lockdown and lockdown periods.

Extended Data Fig. 2 Diurnal variations of NO (a) and O3 (b) concentrations in the pre-lockdown and lockdown periods.

Data of clean conditions (PM2.5 < 50 μg m−3) and during polluted conditions (PM2.5 > 150 μg m−3) on the left and right panels, respectively. The solid lines denote the median values, shaded areas represent the range of 25 %–75 % variations, and empty squares indicate the mean values.

Extended Data Fig. 3 Derivation of N2O5 uptake coefficients (γ) using the linear-regression method (see Methods) (a) and determination of ClNO2 yield (b).

In Panel (a), the (τNO3)−1 is the inverse of NO3 lifetime, c denotes the molecular speed of N2O5 that is 240 m s−1, S denotes the concentration of particle surface area, and Keq denotes the equilibrium rate constant between NO3 and N2O5. In this plot, the slope and intersect of the linear regression indicate uptake coefficient and kNO3, respectively. Data are color-coded by RH, which has a major influence on the slope. In Panel (b), the mass concentration of nitrate is converted to a mixing ratio. The slope that equals ф / (2- ф) is fitted as 0.29, and hence ф is calculated as 0.45. Since nitrate formation through N2O5 heterogeneous reaction is very limited in the pre-lockdown period, we only use data in the lockdown period to determine the uptake coefficient of N2O5 and the yield of ClNO2.

Extended Data Fig. 4 Diurnal patterns of \(p{{\rm{NO}}}_{3}^{-}\) in different pollution scenarios.

Clean and polluted conditions are defined as PM2.5 < 50 μg m−3 and PM2.5 > 150 μg m−3, respectively. The concentration of \(p{{\rm{NO}}}_{3}^{-}\) is normalized to CO mixing ratio to eliminate the possible influence of air mass transport and fresh emission.

Extended Data Fig. 5 Comparisons of NO, NO2, O3 concentrations, pNO3-/PM2.5, and pNO3-/NO2 between the pre-lockdown and lockdown periods from different cities in China.

Panels a, b, and c show the results from Shanghai, Nanjing, Shijiazhuang, respectively. The centre, bounds of box and whiskers represent the median, 25 (75) percentiles, and 5 (95) percentiles of the data, respectively. Note that only data from polluted nights were used for this analysis. As the averaged PM2.5 level differs in NCP and YRD, the polluted condition is defined as PM2.5 > 150 μg m-3 for Shijiazhuang and PM2.5 > 75 μg m-3 for Nanjing and Shanghai.

Extended Data Fig. 6 The strong correlation (R = 0.99) between ClNO2 and Cl2.

Red and blue empty circles denote data collected during the pre-lockdown and lockdown periods, respectively.

Extended Data Fig. 7 Time series of estimated Cl and OH concentrations.

Panels a and b show the time series of simulated Cl radical and OH radical, respectively. The details of the estimation are provided in Methods. The yellow-shaded area represents the daytime.

Extended Data Fig. 8 The increase of aromatic-OOM concentration (Δ[aromatic-OOM]) as a dependent of reacted monocyclic aromatics weighted by OOM yield.

(a), and the correlation coefficient between Δ[aromatic-OOM] and the yield-weighted reacted aromatics. In panel (a), Δ[aromatic-OOM]i is calculated as the difference between the daily maximum and minimum aromatic-OOM concentration, as illustrated in Supplementary Fig. 1 in Supporting Information. The calculation of yield-weighted reacted aromatics is demonstrated in detail in Methods. In panel (b), the two dashed lines denote the range of relative OOM yield (YCl:YOH) obtained for alpha-pinene oxidation in previous studies.

Extended Data Fig. 9 Observations of nighttime NOx (NO and NO2) and O3 levels in major cities around the globe.

The world map illustrates the global NOx emission in 2020. The corresponding location of the city is shown by an alphabet (a–m) on the map. The reported NO (cyan bar), NO2 (blue bar), and O3 (red bar) data are the average values between 10 p.m. and 4 a.m. (local time) during the winter. Relatively polluted (denoted by ‘P’) and clean (denoted by ‘C’) conditions are separated based on PM2.5 levels, or CO concentrations if PM2.5 concentration is not available. In cities with high pollution levels (that is, cities in China and India), ‘P’ and ‘C’ conditions are separated based on PM2.5 < 50 µg m−3 and PM2.5 > 150 µg m−3, respectively. In European and US cities, where PM2.5 barely exceeded 150 µg m−3, 25 and 75 quantiles of PM2.5 (or CO, when the PM2.5 is not available) are used to define the relatively polluted and clean conditions. Details of the measurement sites, durations, and data sources are provided in Table S2 in the Supplementary Information.

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Supplementary Discussion, Supplementary Figs. 1–3, and Supplementary Tables 1 and 2.

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Yan, C., Tham, Y.J., Nie, W. et al. Increasing contribution of nighttime nitrogen chemistry to wintertime haze formation in Beijing observed during COVID-19 lockdowns. Nat. Geosci. 16, 975–981 (2023). https://doi.org/10.1038/s41561-023-01285-1

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