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Secondary organic aerosol formed by condensing anthropogenic vapours over China’s megacities

Nature Geoscience volume 15, pages 255–261 (2022)Cite this article

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Abstract

Secondary organic aerosol contributes a significant fraction to aerosol mass and toxicity. Low-volatility organic vapours are critical intermediates connecting the oxidation of volatile organic compounds to secondary organic aerosol formation. However, the direct measurement of intermediate vapours poses a great challenge. Here we present coordinated measurements of oxygenated organic molecules in the three most urbanized regions of China and determine their likely precursors, enabling us to connect secondary organic aerosol formation to various volatile organic compounds. We show that the oxidation of anthropogenic volatile organic compounds dominates oxygenated organic molecule formation, with an approximately 40% contribution from aromatics and a 40% contribution from aliphatic hydrocarbons (predominantly alkanes), a previously under-accounted class of volatile organic compounds. The irreversible condensation of these anthropogenic oxygenated organic molecules increases significantly in highly polluted conditions, accounting for a major fraction of the production of secondary organic aerosol. We find that the distribution of oxygenated organic molecules and their formation pathways are largely the same across the urbanized regions. This suggests that uniform mitigation strategies could be effective in solving air pollution issues across these highly populated city clusters.

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Fig. 1: Distribution of OOMs in Beijing, Nanjing, Shanghai and Hong Kong.
Fig. 2: Characteristics of aliphatic-OOMs (left) and aromatic-OOMs (right) in Nanjing.
Fig. 3: Factors influencing on OOM formation and the subsequent impact on PM2.5 pollution.
Fig. 4: Contribution of OOMs to SOA.

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Data availability

The observation data that support the main findings of this study are available at figshare (https://doi.org/10.6084/m9.figshare.14526801.v1). The aerosol optical depth data used in this work are archived at https://atmosphere-imager.gsfc.nasa.gov/products/monthly. Source data are provided with this paper.

Code availability

Data processing techniques are available on request from the corresponding author.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) project (92044301, 41875175, 42075101, 21806108, 91744204 and 22188102), the Jiangsu Provincial Collaborative Innovation Center of Climate Change, Samsung PM2.5 SRP, the Research Grants Council of Hong Kong Special Administrative Region (grants nos. T24/504/17-N and 15265516), the Shanghai Rising-Star Program (19QB1402900) and the US National Science Foundation (AGS1801897). K.R.D. acknowledges support by the Swiss National Science Foundation mobility grant P2EZP2_181599. We thank Y. Liu for processing aerosol optical depth data. The Hong Kong team would like to acknowledge the HKPolyU University Research Facility in Chemical and Environmental Analysis (UCEA) for equipment support, and the Hong Kong Environmental Protection Department for providing access to the team to conduct measurements at Cape D’Aguilar Supersite AQMS and for sharing the trace gas, PM2.5 and VOCs data at the Supersite.

Author information

Author notes

  1. These authors contributed equally: Wei Nie, Chao Yan, Dan Dan Huang, Zhe Wang.

Authors and Affiliations

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

    Wei Nie, Chao Yan, Yuliang Liu, Zhengning Xu, Yuanyuan Li, Zheng Xu, Ximeng Qi, Peng Sun, Jiaping Wang, Tuukka Petäjä, Xuguang Chi, Veli-Matti Kerminen, Markku Kulmala & Aijun Ding

  2. National Observation and Research Station for Atmospheric Processes and Environmental Change in Yangtze River Delta, Nanjing, Jiangsu Province, China

    Wei Nie, Yuliang Liu, Zhengning Xu, Yuanyuan Li, Zheng Xu, Ximeng Qi, Peng Sun, Jiaping Wang & Aijun Ding

  3. 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, Feixue Zheng, Yongchun Liu & Markku Kulmala

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

    Chao Yan, Kaspar R. Dallenbach, Federico Bianchi, Tuukka Petäjä, Yanjun Zhang, Veli-Matti Kerminen, Mikael Ehn, Markku Kulmala & Douglas Worsnop

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

    Dan Dan Huang, Liping Qiao, Qian Wang, Min Zhou, Hongli Wang & Cheng Huang

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

    Zhe Wang & Penggang Zheng

  7. State Key Joint Laboratory of Environment Simulation and Pollution Control, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, School of Environment, Tsinghua University, Beijing, China

    Xiaohui Qiao, Xiaoxiao Li, Rujing Yin & Jingkun Jiang

  8. Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau, China

    Linhui Tian & Yong Jie Li

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

    Penggang Zheng, Chuan Yu, Dawen Yao, Hai Guo, Shuncheng Lee & Tao Wang

  10. Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, Villigen, Switzerland

    Kaspar R. Dallenbach

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

    Mingyi Wang, Meredith Schervish & Neil M. Donahue

  12. State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China

    Sainan Wang

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

    Penglin Ye & Douglas Worsnop

  14. Frontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing, China

    Aijun Ding

Authors
  1. Wei Nie
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  2. Chao Yan
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Contributions

W.N., C. Yan, D.D.H., Z.W., A.D., J.J. and M.K. designed the study. W.N., C. Yan, D.D.H., Z.W., Yuliang Liu, X. Qiao, Y.G., L.T. and P.Z. analysed the data. W.N., C. Yan, D.D.H., Z.W., A.D., J.J., M.E. and N.M.D. wrote the manuscript. Yuliang Liu, Zhengning Xu, Y. Li, X. Qiao, Y.G. and P.Z. collected other research materials. All authors participated in relevant scientific discussion and commented on the manuscript.

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Correspondence to Jingkun Jiang or Aijun Ding.

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Nature Geoscience thanks Jacqueline Hamilton, Ru-Jin Huang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Xujia Jiang and Tom Richardson, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Time series of observed OOMs and related parameters in (a) Beijing, (b) Nanjing, (c) Shanghai, and (d) Hong Kong.

Variables include UVB (JO1D in Shanghai), Temperature, RH, NOx, O3, primary organic aerosol (POA), secondary organic aerosol (SOA), total OOMs and nitrophenols.

Extended Data Fig. 2 Correlation of OOMs measured by nitrate- and iodide- CIMS (A-F), C5-10 CHO OOMs with different number of oxygen (G) C5-10 CHO OOMs and (H) C5-10 CHON OOMs in Beijing.

Time period of data is from 1st to 28th January 2020. The numbers in the brackets are the corresponding number of common species that can be detected by both instruments. R denotes the person correlation coefficient.

Extended Data Fig. 3 Overview of the PMF results.

(a) comparison of PMF results from the four different cities; (b) an example of one of the common factors: profile and diurnal variation of common factor 6 (night-time monoterpene factor). The dark yellow dots in the diurnal plot represent the mean value; the line and the shaded area represent 50th percentile and 25th−75th percentile, respectively. (c) mass defect plot of common factor 6 in Nanjing.

Extended Data Fig. 4 A decision-tree based workflow.

A decision-tree based workflow to identify the precursors of detected OOM.

Extended Data Fig. 5 The accuracy of workflow tested by known OOM peak lists of (a) Nitrate-CIMS and (b) I-CIMS from laboratory experiments.

Superscripts 1, 2, 3 in panel (a) refer to (1) Molteni et al., 2018, (2) Garmash et al., 2020, and (3) Wang et al., 2021. There are 4 different experiments for the oxidation of benzene: (*) Experiment in a flow reactor, (**) Experiment with low OH/VOC in the JPAC chamber, (***) Experiment with high OH/VOC in the JPAC chamber, and (****) Experiment affected by NOx in the JPAC chamber. Peak lists used in panel (b) are from Mehra et al., 2020.

Extended Data Fig. 6 The distributions of the total observed OOMs (upper panel), aliphatic-OOMs (middle panel) and aromatic-OOMs (lower panel) grouped by (a) the numbers of carbon (nC), (b) effective oxygen (nO-eff), (c) nitrogen (nN) and (d) double bond equivalent (DBE) in four different cities.

Columns in blue, green, orange and purple represent Beijing, Nanjing, Shanghai and Hong Kong, respectively.

Extended Data Fig. 7 Volatility distribution of observed OOMs in 4 megacities at (a) observed actual temperature, and (b) 300 K.

Columns in blue, green, orange and purple represent Beijing, Nanjing, Shanghai and Hong Kong, respectively.

Extended Data Fig. 8 OOMs condensation flux as a dependent of PM2.5 in (a) Beijing, (b) Nanjing, (c) Shanghai, and (d) Hong Kong.

Aliphatic-OOMs in red, aromatic-OOMs in blue, isoprene-OOMs in bright yellow, monoterpene-OOMs in dark yellow and undistinguished OOMs in grey.

Extended Data Fig. 9 Correlation between detected OOMs from different precursor classes and PM2.5 in Beijing, Nanjing, Shanghai and Hong Kong.

Aliphatic-OOMs in red, aromatic-OOMs in blue, isoprene-OOMs in bright yellow, monoterpene-OOMs in dark yellow and undistinguished OOMs in grey.

Extended Data Fig. 10 Case showing the selection of SOA formation episode.

A stable weather condition is critical for selecting SOA formation episodes to exclude the influence of air mass changes. Four parameters, including wind speed, wind direction, boundary layer height and water concentrations, are used for the judgement. For one sampling period, when these four parameters kept stable, it can be identified as a SOA episode. These selected SOA episodes mostly concentrated around noontime or night-time time when the boundary layer was stable. In total, 16 episodes in Beijing, 19 episodes in Nanjing, 8 episodes in Shanghai, and 18 episodes in Hong Kong were selected for further analysis. We also test the potential uncertainties from case selection by varying the start- and end-point of the case. Cases with the uncertainties of d(SOA)/dt higher than 30% are excluded.

Supplementary information

Supplementary Information

Supplementary information of sampling sites and measurements, Figs. 1–3 and Tables 1–3.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Molecular composition data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

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Nie, W., Yan, C., Huang, D.D. et al. Secondary organic aerosol formed by condensing anthropogenic vapours over China’s megacities. Nat. Geosci. 15, 255–261 (2022). https://doi.org/10.1038/s41561-022-00922-5

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