The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Understanding meteorological influences on PM 2.5 concentrations across China: a temporal and spatial perspective
Open accessAbstract
Abstract. With frequent air pollution episodes in China, growing research emphasis has been put on quantifying meteorological influences on PM2.5 concentrations. However, these studies mainly focus on isolated cities, whilst meteorological influences on PM2.5 concentrations at the national scale have not yet been examined comprehensively. This research employs the CCM (convergent cross-mapping) method to understand the influence of individual meteorological factors on local PM2.5 concentrations in 188 monitoring cities across China. Results indicate that meteorological influences on PM2.5 concentrations have notable seasonal and regional variations. For the heavily polluted North China region, when PM2.5 concentrations are high, meteorological influences on PM2.5 concentrations are strong. The dominant meteorological influence for PM2.5 concentrations varies across locations and demonstrates regional similarities. For the most polluted winter, the dominant meteorological driver for local PM2.5 concentrations is mainly the wind within the North China region, whilst precipitation is the dominant meteorological influence for most coastal regions. At the national scale, the influence of temperature, humidity and wind on PM2.5 concentrations is much larger than that of other meteorological factors. Amongst eight factors, temperature exerts the strongest and most stable influence on national PM2.5 concentrations in all seasons. Due to notable temporal and spatial differences in meteorological influences on local PM2.5 concentrations, this research suggests pertinent environmental projects for air quality improvement should be designed accordingly for specific regions.
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
Detecting Causality in Complex Ecosystems
Sugihara, G., May, R., Ye, H., Hsieh, C.-H., Deyle, E., Fogarty, M., & Munch, S. (2012) · Science · doi:10.1126/science.1227079 · Cited by 2,539
Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series
Sugihara, G., & May, R. M. (1990) · Nature · doi:10.1038/344734a0 · Cited by 1,932
Granger, C. W. J. (1980) · Journal of Economic Dynamics and Control · doi:10.1016/0165-1889(80)90069-x · Cited by 1,746
Chemical characterization and source apportionment of PM 2.5 in Beijing: seasonal perspective
Zhang, R., Jing, J., Tao, J., Hsu, S.-C., Wang, G., Cao, J., Lee, C. S. L., Zhu, L., Chen, Z., Zhao, Y., et al. (2013) · Atmospheric Chemistry and Physics · doi:10.5194/acp-13-7053-2013 · Cited by 1,206
Self-citationTai, A. P. K., Mickley, L. J., & Jacob, D. J. (2010) · Atmospheric Environment · doi:10.1016/j.atmosenv.2010.06.060 · Cited by 1,048
He, J., Gong, S., Yu, Y., Yu, L., Wu, L., Mao, H., Song, C., Zhao, S., Liu, H., Li, X., et al. (2017) · Environmental Pollution · doi:10.1016/j.envpol.2017.01.050 · Cited by 750
Testing for Causality: A Personal Viewpoint
Granger, C. W. J. (2010) · Essays in Econometrics · doi:10.1017/cbo9780511753978.003 · Cited by 735
Inhalable Microorganisms in Beijing’s PM2.5 and PM10 Pollutants during a Severe Smog Event
Cao, C., Jiang, W., Wang, B., Fang, J., Lang, J., Tian, G., Jiang, J., & Zhu, T. F. (2014) · Environmental Science & Technology · doi:10.1021/es4048472 · Cited by 669
Zhang, H., Wang, Y., Hu, J., Ying, Q., & Hu, X.-M. (2015) · Environmental Research · doi:10.1016/j.envres.2015.04.004 · Cited by 583
Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing
Ma, Z., Hu, X., Huang, L., Bi, J., & Liu, Y. (2014) · Environmental Science & Technology · doi:10.1021/es5009399 · Cited by 568
Cited by
Zhai, S., Jacob, D. J., Wang, X., Shen, L., Li, K., Zhang, Y., Gui, K., Zhao, T., & Liao, H. (2019) · Atmospheric Chemistry and Physics · doi:10.5194/acp-19-11031-2019 · Cited by 785
Cheng, J., Su, J., Cui, T., Li, X., Dong, X., Sun, F., Yang, Y., Tong, D., Zheng, Y., Li, Y., et al. (2019) · Atmospheric Chemistry and Physics · doi:10.5194/acp-19-6125-2019 · Cited by 437
Li, X., Zhou, L.-X., Yang, L.-L., Huang, X.-L., Wang, N., Hu, Y.-G., Tang, E.-J., Xiao, H., Zhou, Y.-M., Li, Y.-F., et al. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-20236-8 · Cited by 10
Metadata
- Authors
- Chen, Z., Xie, X., Cai, J., Chen, D., Gao, B., He, B., Cheng, N., & Xu, B.
- Venue
- Atmospheric Chemistry and Physics
- Publisher
- Copernicus GmbH
- Published
- 2018-04-19
- Volume
- 18
- Issue
- 8
- Pages
- 5343-5358
- ISSN
- 1680-7316 (ISSN-L)
- 1680-7324
- OpenAlex
- W2739452932
- Cited by
- 256
- References
- 39
- FWCI
- 9.44
- 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: Understanding meteorological influences on PM 2.5 concentrations across China: a temporal and spatial perspective. https://app.cochat.ai/papers/10.5194/acp-18-5343-2018
- The paper
- Chen, Z., Xie, X., Cai, J., Chen, D., Gao, B., He, B., Cheng, N., & Xu, B. (2018). Understanding meteorological influences on PM 2.5 concentrations across China: a temporal and spatial perspective. Atmospheric Chemistry and Physics. https://doi.org/10.5194/acp-18-5343-2018
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.