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Journal of Meteorological Research

, Volume 33, Issue 4, pp 765–776 | Cite as

Dominant Synoptic Patterns and Their Relationships with PM2.5 Pollution in Winter over the Beijing-Tianjin-Hebei and Yangtze River Delta Regions in China

  • Yuzhi LiuEmail author
  • Bing Wang
  • Qingzhe Zhu
  • Run Luo
  • Chuqiao Wu
  • Rui Jia
Regular Atricle
  • 7 Downloads

Abstract

This paper concerns about the episodes of PM25 pollution that frequently occur in China in winter months. The severity of PM2.5 pollution is strongly dependent on the synoptic-scale atmospheric conditions. We combined PM2.5 concentration data and meteorological data with the Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT4) to investigate the dominant synoptic patterns and their relationships with PM2.5 pollution over the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions in the winters of 2014–17. The transport of PM2.5 from the BTH to YRD regions was examined by using cluster analysis and HYSPLIT4. It is found that the level of PM2.5 pollution over the BTH region was higher than that over the YRD region. The concentration of PM2.5 in the atmosphere was more closely related to meteorological factors over the BTH region. The episodes of PM2.5 pollution over the BTH region in winter were related to weather patterns such as the rear of a high-pressure system approaching the sea, a high-pressure field, a saddle pressure field, and the leading edge of a cold front. By contrast, PM2.5 pollution episodes in the YRD region in winter were mainly associated with the external transport of cold air, a high-pressure field, and a uniform pressure field. Cluster analysis shows that the trajectories of PM2.5 were significantly different under different weather patterns. PM2.5 would be transported from the BTH to the YRD within 48 h when the PM2.5 pollution episodes were associated with three different kinds of weather patterns: the rear of a high-pressure system approaching the sea, the high-pressure field, and the leading edge of a cold front over the BTH region. This suggests a possible method to predict PM2.5 pollution episodes based on synoptic-scale patterns.

Key words

PM2.5 pollution episodes synoptic patterns Beijing-Tianjin-Hebei (BTH) Yangtze River Delta (YRD) 

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Notes

Acknowledgments

The PM2.5 monitoring data were obtained from the China National Environmental Monitoring Center of the Chinese Ministry of Environmental Protection. The meteorological data were from the China Meteorological Administration. The authors gratefully acknowledge the efforts of these institutions in making these data available online.

References

  1. Batterman, S., L. Z. Xu, F. Chen, et al., 2016: Characteristics of PM2.5 concentrations across Beijing during 2013–2015. Atmos. Environ., 145, 104–114, doi:  https://doi.org/10.1016/j.atmosenv.2016.08.060.CrossRefGoogle Scholar
  2. Cao, J. J., H. M. Xu, Q. Xu, et al., 2012: Fine particulate matter constituents and cardiopulmonary mortality in a heavily polluted Chinese city. Environ. Health Persp., 120, 373–378, doi:  https://doi.org/10.1289/ehp.1103671.CrossRefGoogle Scholar
  3. Chen, X., X. L. Wang, Y. Y. Yue, et al., 2018: Study on weather classification of PM2.5 heavy pollution in Hubei Province. Environ. Sci. Technol., 41, 54–64, doi:  https://doi.org/10.19672/j.cnki.1003-6504.2018.11.009. (in Chinese)Google Scholar
  4. Chen, Y. R. X., and Y. L. Luo, 2018: Analysis of paths and sources of moisture for the South China rainfall during the presummer rainy season of 1979–2014. J. Meteor. Res., 32, 744–757, doi:  https://doi.org/10.1007/s13351-018-8069-7.CrossRefGoogle Scholar
  5. Gao, S., R. Tian, B. Guo, et al., 2018: Characteristics of PM2.5 concentration and its relations with meteorological factors in typical cities of the Yangtze River Delta. Sci. Technol. Eng., 18, 142–155, doi:  https://doi.org/10.3969/j.issn.1671-1815.2018.09.021. (in Chinese)Google Scholar
  6. Guo, X. B., and H. Y. Wei, 2013: Progress on the health effects of ambient PM2.5 pollution. Chinese Sci. Bull., 58, 1171–1177, doi:  https://doi.org/10.1360/972013-147. (in Chinese)CrossRefGoogle Scholar
  7. Han, L. J., W. Q. Zhou, W. F. Li, et al., 2014: Impact of urbanization level on urban air quality: A case of fine particles (PM2.5) in Chinese cities. Environ. Pollut., 194, 163–170, doi:  https://doi.org/10.1016/j.envpol.2014.07.022. (in Chinese)CrossRefGoogle Scholar
  8. Hu, S. L., and H. N. Liu, 2017: Effects of PM2.5 on the urban radiation and air temperature in Hefei. J. Meteor. Sci., 37, 78–85, doi:  https://doi.org/10.3969/2015jms.0077. (in Chinese)Google Scholar
  9. Huang, J. P., Q. Fu, J. Su, et al., 2009: Taklimakan dust aerosol radiative heating derived from CALIPSO observations using the Fu-Liou radiation model with CERES constraints. Atmos. Chem. Phys., 9, 4011–4021, doi:  https://doi.org/10.5194/acp-9-4011-2009.CrossRefGoogle Scholar
  10. Jia, R., Y. Z. Liu, S. Hua, et al., 2018: Estimation of the aerosol radiative effect over the Tibetan Plateau based on the latest CALIPSO product. J. Meteor. Res., 32, 707–722, doi:  https://doi.org/10.1007/s13351-018-8060-3.CrossRefGoogle Scholar
  11. Jin, Y., D. W. Song, and X. P. Wu, 2015: Meteorological conditions and weather situation of PM2.5 air pollution in Chang-zhou during 2012–2013. J. Environ. Sci. Manag., 40, 46–50, doi:  https://doi.org/10.3969/j.issn.1673-1212.2015.01.012. (in Chinese)Google Scholar
  12. Kan, H. D., R. J. Chen, and S. L. Tong, 2012: Ambient air pollution, climate change, and population health in China. Environ. Int., 42, 10–19, doi:  https://doi.org/10.1016/j.envint.2011.03.003.CrossRefGoogle Scholar
  13. Leung, D. M., A. P. K. Tai, L. J. Mickley, et al., 2018: Synoptic meteorological modes of variability for fine particulate matter (PM2.5) air quality in major metropolitan regions of China. Atmos. Chem. Phys., 18, 6733–6748, doi:  https://doi.org/10.5194/acp-18-6733-2018.CrossRefGoogle Scholar
  14. Leung, Y. K., M. C. Wu, and K. K. Yeung, 2009: A study on the relationship among visibility, atmospheric suspended particulate concentration, and meteorological conditions in Hong Kong. Acta Meteor. Sinica, 23, 250–260.Google Scholar
  15. Li, S. X., B. Zou, X. Q. Liu, et al., 2017: Pollution status and spatial-temporal variations of PM2.5 in China during 2013–2015. Res. Environ. Sci., 30, 678–687, doi:  https://doi.org/10.13198/j.issn.1001-6929.2017.01.93. (in Chinese)Google Scholar
  16. Liu, Y. L., Q. M. Sun, M. Y. Zhong, et al., 2016: Temporal and spatial distribution characteristics of PM2.5 in Chongqing urban areas. Environ. Sci., 37, 1219–1229, doi:  https://doi.org/10.13227/j.hjkx.2016.04.005. (in Chinese)Google Scholar
  17. Liu, Y. Z., R. Jia, T. Dai, et al., 2014: A review of aerosol optical properties and radiative effects. J. Meteor. Res., 28, 1003–1028, doi:  https://doi.org/10.1007/s13351-014-4045-z.CrossRefGoogle Scholar
  18. Lu, F., D. Q. Xu, Y. B. Cheng, et al., 2015: Systematic review and meta-analysis of the adverse health effects of ambient PM2.5 and PM10 pollution in the Chinese population. Environ. Res., 136, 196–204, doi:  https://doi.org/10.1016/j.envres.2014.06.029.CrossRefGoogle Scholar
  19. Ma, X. H., X. N. Liao, Y. X. Tang, et al., 2017: Weather pattern and case analysis of air heavy pollution days in Beijing. J. Meteor. Environ., 33, 53–60, doi:  https://doi.org/10.3969/j.issn.1673-503X.2017.05.007. (in Chinese)Google Scholar
  20. Ma, Y. X., Y. X. Zhao, S. X. Yang, et al., 2017: Short-term effects of ambient air pollution on emergency room admissions due to cardiovascular causes in Beijing, China. Environ. Pollut., 230, 974–980, doi:  https://doi.org/10.1016/j.envpol.2017.06.104.CrossRefGoogle Scholar
  21. Ma, Y. X., S. X. Yang, J. D. Zhou, et al., 2018: Effect of ambient air pollution on emergency room admissions for respiratory diseases in Beijing, China. Atmos. Environ., 191, 320–327, doi:  https://doi.org/10.1016/j.atmosenv.2018.08.027.CrossRefGoogle Scholar
  22. Ma, Z. W., X. F. Hu, A. M. Sayer, et al., 2016: Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013. Environ. Health Perspect., 124, 184–192, doi:  https://doi.org/10.1289/ehp.1409481.CrossRefGoogle Scholar
  23. Madaniyazi, L., T. Nagashima, Y. M. Guo, et al., 2015: Projecting fine particulate matter-related mortality in East China. Environ. Sci. Technol., 49, 11141–11150, doi:  https://doi.org/10.1021/acs.est.5b01478.CrossRefGoogle Scholar
  24. Mao, W. L., J. H. Xu, D. B. Lu, et al., 2017: An analysis of the spatial-temporal pattern and influencing fctors of PM2.5 in the Yangtze River Delta in 2015. Resour. Environ. Yangtze Basin, 26, 264–272, doi:  https://doi.org/10.11870/cjlyzyyhj201702012.Google Scholar
  25. Meng, C. C., L. T. Wang, F. F. Zhang, et al., 2016: Characteristics of concentrations and water-soluble inorganic ions in PM2.5 in Handan City, Hebei Province, China. Atmos. Res., 171, 133–146, doi:  https://doi.org/10.1016/j.atmosres.2015.12.013.CrossRefGoogle Scholar
  26. Meng, M., and Q. S. Zhou, 2014: Progress in PM2.5-caused tumor neovascularization and Metastasis. Sci. Technol. Rev., 32, 52–57, doi:  https://doi.org/10.3981/j.issn.l000-7857.2014.26.007. (in Chinese)Google Scholar
  27. Miao, X. Y., H. L. Zhan, K. Zhao, et al., 2018: Terahertz-dependent PM2.5 monitoring and grading in the atmosphere. Sci. China Phys. Mech. Astron., 61, 104211, doi:  https://doi.org/10.1007/s11433-018-9237-1.CrossRefGoogle Scholar
  28. Nowak, D. J., S. Hirabayashi, A. Bodine, et al., 2013: Modeled PM2.5 removal by trees in ten U.S. cities and associated health effects. Environ. Pollut., 178, 395–402, doi:  https://doi.org/10.1016/j.envpol.2013.03.050.CrossRefGoogle Scholar
  29. Pan, B. F., Y. L. Zhao, J. J. Li, et al., 2012: Analysis of the scavenging efficiency on PM2.5 concentration of some kinds of meteorological factors. Environ. Sci. Technol., 25, 41–44, doi:  https://doi.org/10.3969/j.issn.1674-4829.2012.06.012. (in Chinese)Google Scholar
  30. Sun, J. H., H. J. Wang, J. Wei, et al., 2016: The sources and transportation of water vapor in persistent heavy rainfall events in the Yangtze-Huaihe River Valley. Acta Meteor. Sinica, 74, 542–555, doi:  https://doi.org/10.11676/qxxb2016.047. (in Chinese)Google Scholar
  31. Tao, J., T. T. Cheng, R. J. Zhang, et al., 2013: Chemical composition of PM2.5 at an urban site of Chengdu in southwestern China. Adv. Atmos. Sci., 30, 1070–1084, doi:  https://doi.org/10.1007/s00376-012-2168-7.CrossRefGoogle Scholar
  32. Wang, A. Y., Y. Pan, and Y. B. Tong, 2015: Research of temporal and spatial distribution of air pollution in the major cities of the Yangtze River Delta. Environ. Prot. Sci., 41, 131–136, doi:  https://doi.org/10.3969/j.issn.1004-6216.2015.05.025. (in Chinese)Google Scholar
  33. Wang, G. L., J. J. Xue, and J. Z. Zhang, 2016: Analysis of spatial-temporal distribution characteristics and main cause of air pollution in Beijing-Tianjin-Hebei Region in 2014. Meteor. Environ. Sci., 39, 34–42, doi:  https://doi.org/10.16765/j.cnki.1673-7148.2016.01.005. (in Chinese)Google Scholar
  34. Wang, H. C., Z. B. Wu, J. B. Zhou, et al., 2015: Relationship between PM2.5 concentration and meteorological elements at Shangdianzi station of Beijing. J. Meteor. Environ., 31, 99–104, doi: https://doi.org/10.3969/j.issn.1673-503X.2015.05.014. (in Chinese)Google Scholar
  35. Wang, Q., M. Liu, Y. P. Yu, et al., 2016: Characterization and source apportionment of PM2.5-bound polycyclic aromatic hydrocarbons from Shanghai city, China. Environ. Pollut., 218, 118–128, doi:  https://doi.org/10.1016/j.envpol.2016.08.037.CrossRefGoogle Scholar
  36. Wang, X. Y., Y. P. Jiang, H. N. Liu, et al., 2016: Analysis of the characteristics of different weather condition impact on air quality in Hangzhou by case studies. Environ. Monit. For., 8, 1–8. (in Chinese)Google Scholar
  37. Wang, Y. S., L. Yao, L. L. Wang, et al., 2014: Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Sci. China Earth Sci., 57, 14–25, doi:  https://doi.org/10.1007/s11430-013-4773-4.CrossRefGoogle Scholar
  38. Wang, Z. B., C. L. Fang, G. Xu, et al., 2015: Spatial-temporal characteristics of the PM2.5 in China in 2014. Acta Geogr. Sinica, 70, 1720–1734, doi:  https://doi.org/10.11821/dlxb201511003. (in Chinese)Google Scholar
  39. Xu, J. M., L. Y. Chang, J. H. Ma, et al., 2016: Objective synoptic weather classification on PM2.5 pollution during autumn and winter seasons in Shanghai. Acta Sci. Circum., 36, 4303–4314, doi:  https://doi.org/10.13671/j.hjkxxb.2016.0224. (in Chinese)Google Scholar
  40. Xue, T., Y. X. Zheng, G. N. Geng, et al., 2017: Fusing observational, satellite remote sensing and air quality model simulated data to estimate spatiotemporal variations of PM2.5 exposure in China. Remote Sens., 9, 221, doi:  https://doi.org/10.3390/rs9030221.CrossRefGoogle Scholar
  41. Yan, X., W. Z. Shi, W. J. Zhao, et al., 2014: Impact of aerosols and atmospheric particles on plant leaf proteins. Atmos. Environ., 88, 115–122, doi:  https://doi.org/10.1016/j.atmosenv.2014.01.044.CrossRefGoogle Scholar
  42. Yang, X. C., W. J. Zhao, Q. L. Xiong, et al., 2017: Spatial-temporal distribution of PM2.5 in Beijing-Tianjin-Hebei (BTH) area in 2016 and its relationship with meteorological factors. Ecol. Environ. Sci., 26, 1747–1754, doi:  https://doi.org/10.16258/j.cnki.1674-5906.2017.10.014. (in Chinese)Google Scholar
  43. Yang, X., X. L. Zhang, Y. Z. Kang, et al., 2017: Circulation weather type classification for air pollution over the Beijing-Tianjin-Hebei region during winter. China Environ. Sci., 37, 3201–3209, doi:  https://doi.org/10.3969/j.issn.1000-6923.2017.09.001. (in Chinese)Google Scholar
  44. Zhang, J. P., T. Zhu, Q. H. Zhang, et al., 2012: The impact of circulation patterns on regional transport pathways and air quality over Beijing and its surroundings. Atmos. Chem. Phys., 12, 5031–5053, doi:  https://doi.org/10.5194/acp-12-5031-2012.CrossRefGoogle Scholar
  45. Zhang, Y. J., X. Chen, G. D. Xie, et al., 2015: Pollution status and spatial distribution of PM2.5 in China. Resour. Sci., 37, 1339–1346. (in Chinese)Google Scholar
  46. Zhang, Y. L., and F. Cao, 2015: Fine particulate matter (PM2.5) in China at a city level. Sci. Rep., 5, 14884, doi:  https://doi.org/10.1038/srep14884.CrossRefGoogle Scholar
  47. Zhang, Y. Y., J. L. Lang, S. Y. Cheng, et al., 2018: Chemical composition and sources of PM1 and PM2.5 in Beijing in autumn. Sci. Total Environ., 630, 72–82, doi:  https://doi.org/10.1016/j.scitotenv.2018.02.151.CrossRefGoogle Scholar
  48. Zhao, S. P., Y. Yu, D. H. Qin, et al., 2019: Analyses of regional pollution and transportation of PM2.5 and ozone in the city clusters of Sichuan Basin, China. Atmos. Pollut. Res., 10, 374–385, doi:  https://doi.org/10.1016/j.apr.2018.08.014.CrossRefGoogle Scholar
  49. Zhou, Y. M., and X. Y. Zhao, 2017: Correlation analysis between PM2.5 concentration and meteorological factors in Beijing area. Acta Sci. Nat. Univ. Pekinens., 53, 111–124, doi:  https://doi.org/10.13209/j.0479-8023.2017.002. (in Chinese)Google Scholar
  50. Zhu, Q. Z., Y. Z. Liu, R. Jia, et al., 2018: A numerical simulation study on the impact of smoke aerosols from Russian forest fires on the air pollution over Asia. Atmos. Environ., 182, 263–274, doi:  https://doi.org/10.1016/j.atmosenv.2018.03.052.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  • Yuzhi Liu
    • 1
    Email author
  • Bing Wang
    • 1
    • 2
  • Qingzhe Zhu
    • 1
  • Run Luo
    • 1
  • Chuqiao Wu
    • 1
  • Rui Jia
    • 1
  1. 1.Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric SciencesLanzhou UniversityLanzhouChina
  2. 2.Meteorological Observatory Unit 95021YichangChina

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