The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China
Open accessAbstract
To control the spread of the 2019 novel coronavirus (COVID-19), China imposed nationwide restrictions on the movement of its population (lockdown) after the Chinese New Year of 2020, leading to large reductions in economic activities and associated emissions. Despite such large decreases in primary pollution, there were nonetheless several periods of heavy haze pollution in eastern China, raising questions about the well-established relationship between human activities and air quality. Here, using comprehensive measurements and modeling, we show that the haze during the COVID lockdown was driven by enhancements of secondary pollution. In particular, large decreases in NOx emissions from transportation increased ozone and nighttime NO3 radical formation, and these increases in atmospheric oxidizing capacity in turn facilitated the formation of secondary particulate matter. Our results, afforded by the tragic natural experiment of the COVID-19 pandemic, indicate that haze mitigation depends upon a coordinated and balanced strategy for controlling multiple pollutants.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The contribution of outdoor air pollution sources to premature mortality on a global scale
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., & Pozzer, A. (2015) · Nature · doi:10.1038/nature15371 · Cited by 5,977
Tian, H., Liu, Y., Li, Y., Wu, C.-H., Chen, B., Kraemer, M. U. G., Li, B., Cai, J., Xu, B., Yang, Q., et al. (2020) · Science · doi:10.1126/science.abb6105 · Cited by 2,010
Self-citationAnthropogenic drivers of 2013–2017 trends in summer surface ozone in China
Li, K., Jacob, D. J., Liao, H., Shen, L., Zhang, Q., & Bates, K. H. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1812168116 · Cited by 1,804
Self-citation
Cited by
Scattering and absorbing aerosols in the climate system
Li, J., Carlson, B. E., Yung, Y. L., Lv, D., Hansen, J., Penner, J. E., Liao, H., Ramaswamy, V., Kahn, R. A., Zhang, P., et al. (2022) · Nature Reviews Earth & Environment · doi:10.1038/s43017-022-00296-7 · Cited by 561
Self-citationImpact of city lockdown on the air quality of COVID-19-hit of Wuhan city
Lian, X., Huang, J., Huang, R., Liu, C., Wang, L., & Zhang, T. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.140556 · Cited by 319
Opportunistic experiments to constrain aerosol effective radiative forcing
Christensen, M. W., Gettelman, A., Cermak, J., Dagan, G., Diamond, M., Douglas, A., Feingold, G., Glassmeier, F., Goren, T., Grosvenor, D. P., et al. (2022) · Atmospheric Chemistry and Physics · doi:10.5194/acp-22-641-2022 · Cited by 138
Ming, Y., Loeb, N. G., Lin, P., Shen, Z., Naik, V., Singer, C. E., Ward, R. X., Paulot, F., Zhang, Z., Bellouin, N., et al. (2021) · Geophysical Research Letters · doi:10.1029/2020gl091699 · Cited by 39
Bera, B., Bhattacharjee, S., Shit, P. K., Sengupta, N., & Saha, S. (2022) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-021-02033-w · Cited by 10
Metadata
- Authors
- Huang, X., Ding, A., Gao, J., Zheng, B., Zhou, D., Qi, X., Tang, R., Wang, J., Ren, C., Nie, W., Chi, X., Xu, Z., Chen, L., Li, Y., Che, F., Pang, N., Wang, H., Tong, D., Qin, W., Cheng, W., Liu, W., Fu, Q., Liu, B., Chai, F., Davis, S. J., Zhang, Q., & He, K.
- Venue
- National Science Review
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-06-18
- Volume
- 8
- Issue
- 2
- Pages
- nwaa137-nwaa137
- ISSN
- 2053-714X (ISSN-L)
- 2095-5138
- OpenAlex
- W3036686135
- Cited by
- 960
- References
- 3
- FWCI
- 67.37
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. https://app.cochat.ai/papers/10.1093/nsr/nwaa137
- The paper
- Huang, X., Ding, A., Gao, J., Zheng, B., Zhou, D., Qi, X., Tang, R., Wang, J., Ren, C., Nie, W., Chi, X., Xu, Z., Chen, L., Li, Y., Che, F., Pang, N., Wang, H., Tong, D., Qin, W., . . . He, K. (2021). Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China. National Science Review. https://doi.org/10.1093/nsr/nwaa137
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
