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The macroscopic mechanisms and associated atmospheric precursor environmental capacities that lead to secondary fine particle pollution

  • Dahai XuEmail author
  • Junming Chen
Research Paper
  • 4 Downloads

Abstract

This paper establishes the kinetic equations in atmospheric chemistry that describe the macroscopic mechanisms of secondary fine particle pollution generated by precursors during atmospheric self-purification. The dynamic and static solutions of these equations can be applied to calculate quantitative relationships between the concentration ratio of precursors and secondary fine particles as well as the physical clearance power of the atmosphere, chemical reaction rate, and the scale of a contaminated area. The dynamic solution presented here therefore corresponds with a theoretical formula for calculating the overall rate constant for the oxidation reaction of reducing pollutants in the actual atmosphere based on their local concentrations and meteorological monitoring data. In addition, the static solution presented in this paper reveals the functional relationship between the concentration of secondary fine particles and precursor emission rate as well as atmospheric self-purification capacity. This result can be applied to determine the atmospheric environmental capacity of a precursor. Hourly records collected over the last 40 years from 378 weather stations in mainland China as well as the spatiotemporal distribution sequence of overall oxidation reaction rates from precursors show that when the reference concentration limit of secondary fine particles is 100 μmol m-3, the atmospheric environmental capacity of total precursors can be calculated as 24890×1010 mol yr-1. Thus, when the annual average concentration limit of given fine particles is 35 μg m-3 and the ratio of sulfate and nitrate to 30% and 20% of the total amount of fine particles, the capacities of SO2, NOx and NH3 are 1255, 1344, and 832 (1010g yr-1), respectively. The clearance density of precursors for different return periods across mainland China under above conditions are also provided in this study.

Keywords

Atmospheric chemical kinetic equations Precursor of secondary fine particle Overall oxidation reaction rate Atmospheric self-purification power Atmospheric environmental capacity Clearance density Return period 

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Notes

Acknowledgements

Thanks to the reviewers for their valuable constructive comments during the review. This study was supported by S & T Development Program (Grant No. CAMS 2018KJ026).

References

  1. Alkezweeny A J, Powell D C. 1977. Estimation of transformation rate of SO2 to SO4 from atmospheric concentration data. Atmos Environ, 11: 179–182CrossRefGoogle Scholar
  2. Byun D, Schere K L. 2006. Review of the governing equations, computational algorithms, and other components of the Models-3 community multiscale air quality (CMAQ) modeling system. Appl Mech Rev, 59: 51–77CrossRefGoogle Scholar
  3. Cao L X, Geng H, Yao C T, Zhao L, Duan P L, Xuan Y Y, Li H. 2014. Characteristics of chemical constituents of fine particulate matter during haze episode in Taiyuan City (in Chinese). China Environ Sci, 34: 837–843Google Scholar
  4. Cheng Y L, Wang X S, Liu Z R, Bai Y H, Li J L. 2008. A new method for quantitatively characterizing atmospheric oxidation capacity. Sci China Ser B-Chem, 51: 1102–1109CrossRefGoogle Scholar
  5. Cooper J A, Watson Jr J G. 1980. Receptor oriented methods of air particulate source apportionment. J Air Pollut Control Assoc, 30: 1116–1125CrossRefGoogle Scholar
  6. Deng L Q, Li H, Chai F H, Lun X X, Chen Y Z, Wang F W, Ni R X. 2011. The pollution characteristics of the atmospheric fine particles and related gaseous pollutants in the northeastern urban area of Beijing (in Chinese). China Environ Sci, 31: 1064–1070Google Scholar
  7. Grell G A, Peckham S E, Schmitz R, McKeen S A, Frost G, Skamarock W C, Eder B. 2005. Fully coupled “online” chemistry within the WRF model. Atmos Environ, 39: 6957–6975CrossRefGoogle Scholar
  8. Jiang L, Zhu B, Wang H L, Sha D D, Shi S S. 2017. Characteristics of water-soluble ions in the haze and mist days in winter in Yangtze River Delta (in Chinese). China Environ Sci, 37: 3601–3610Google Scholar
  9. Li Y, Tang W, Ding F, He Y J, Zhu X Y, Meng F. 2017. Study on a method for calculating conversion coefficient of secondary sulfate and nitrate in atmosphere (in Chinese). Environ Pollut Control, 39: 1348–1352Google Scholar
  10. Lin Y, Ye Z X, Yang H J, Zhang J, Zhu Y M. 2017. Pollution level and source apportionment of atmospheric particles PM1 in downtown area of Chengdu (in Chinese). China Environ Sci, 37: 3220–3226Google Scholar
  11. Ma J Z, Wang W, Chen Y, Liu H J, Yan P, Ding G A, Wang M L, Sun J, Lelieveld J. 2012. The IPAC-NC field campaign: A pollution and oxidization pool in the lower atmosphere over Huabei, China. Atmos Chem Phys, 12: 3883–3908CrossRefGoogle Scholar
  12. Mei Y, Zhang W T, Yang Y, Zhao Y, Li L L. 2018. Uncertainty assessment of PM2.5 probability mapping by using spatio-temporal indicator kriging (in Chinese). China Environ Sci, 38: 35–43Google Scholar
  13. Paatero P, Tapper U. 1993. Analysis of different modes of factor analysis as least squares fit problems. Chemo Intell Lab Syst, 18: 183–194CrossRefGoogle Scholar
  14. Pasquill F, Smith F B. 1983. Atmosperic Diffusion. 3rd ed. New York: Ellis Horwood Limited. 383Google Scholar
  15. Gu F T, Hu M, Wang W, Li M R, Guo Q F, Wu Z J. 2016. Characteristics of PM2.5 pollution winter and spring of Beijing during 2009–2010 (in Chinese). China Environ Sci, 36: 2578–2584Google Scholar
  16. State Environmental Protection Administration. 1991. Handbook of Total Methods for Controlling Urban Air Pollution (in Chinese). Beijing: China Science Press. 164–170Google Scholar
  17. Seinfeld J H, Pandis S N. 2016. Atmospheric Chemistry and Physics: From Air Pollution to Climate Chang. New Jersey: John Wiley & SonsGoogle Scholar
  18. Tang X Y, Zhang Y H, Shao M. 2006. Atmospheric Environmental Chemistry (in Chinese). 2nd ed. Beijing: Higher Education Press. 125–210Google Scholar
  19. Wei F F, Liu W, Lu X B, Wang Q G, Ge Y, Hao J. 2017. Temporal and spatial characteristics of PM2.5 in Nanjing (in Chinese). China Environ Sci, 37: 2866–2876Google Scholar
  20. Xing J P, Shao L Y, Li H, Guo W, Wang W H, Zuo X C. 2016. ATR-FTIR characteristics of organic functional groups and inorganic ions of the haze PM2.5 in Beijing (in Chinese). China Environ Sci, 36: 1654–1659Google Scholar
  21. Xu D H, Zhu R. 2000. Atmospheric advective and dispersion nonstatic boxmodel for prediction of the potential index of airborne pollutant. Quart J Appl Meteorol, 11: 1–12Google Scholar
  22. Xu D H, Wang Y. 2013. Plume footprints analysis for determining the bearing capacity of atmospheric environment (in Chinese). Acta Scientiae Circumstantiae, 33: 1734–1740Google Scholar
  23. Xu D H, Wang Y, Zhu R. 2016. The atmospheric environmental capacity coefficient cumulative frequency curve fitting and its application (in Chinese). China Environ Sci, 36: 2913–2922Google Scholar
  24. Xu D H, Wang Y, Zhu R. 2018. Atmospheric environmental capacity and urban atmospheric load in mainland China. Sci China Earth Sci, 61: 33–46CrossRefGoogle Scholar
  25. Xu H, Xiao Z M, Kong J, Yuan J, Li P, Guan Y C, Deng X W, Zhang Y F, Han S Q. 2017. Characteristic of atmospheric heavy pollution episodes in Winter of Tianjin (in Chinese). China Environ Sci, 37: 1239–1246Google Scholar
  26. Xue W B, Fu F, Wang J N, He K B, Lei Y, Yang J T, Wang S X, Han B P. 2014. Modeling study on atmospheric environment capacity of major pollutants constrained by PM2.5 compliance of Chinese cities (in Chinese). China Environ Sci, 34: 2490–2496Google Scholar
  27. Yang G X, He Q Q, Lu H C. 1991. Meso-Meteorology (in Chinese). Beijing: China Meteorological Press. 2–4Google Scholar
  28. Zhu R, Zhang C J, Mei M. 2018. Climate characteristics and application of atmospheric self-purification capacity index (in Chinese). China Environ Sci, 38: 3601–3610Google Scholar
  29. Zhang Y H, Duan Y S, Gao S, Wei H P, Sha F, Cai Y, Shen L P. 2011. Characteristics of fine particulate matter during a typical air pollution episode in Shanghai urban area (in Chinese). China Environ Sci, 31: 1115–1121Google Scholar
  30. China National Standard GB/T34299-2017. 2018. Grades of Atmospheric Self-Purification Capability (in Chinese). Beijing: Standard PressGoogle Scholar
  31. Zhou T, Yan C Q, Li X W, Cai J, Guo X S, Wang R, Wu Y S, Zeng L M, Zhu W, Zhang Y H, Zheng M. 2017. Chemical characteristics and sources of PM2.5 in urban and rural sites in the North China Plain during summer (in Chinese). China Environ Sci, 37: 3227–3236Google Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Severe Weather and Key Laboratory of Atmospheric Chemistry of China Meteorological AdministrationChinese Academy of Meteorological SciencesBeijingChina

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