Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2018, Vol. 29 ›› Issue (10): 151 doi: 10.1007/s41365-018-0481-2

• NUCLEAR CHEMISTRY,RADIOCHEMISTRY,RADIOPHARMACEUTICALS AND NUCLEAR MEDICINE • Previous Articles    

Evaluation of correlation between PM2.5 and radon-progeny equilibrium factor in radon chamber

Li-Fan Li, Rui Chen, Shu-Min Zhou, Bin Tang   

  1. Engineering Research Center of Nuclear Technology Application, Ministry of Education, East China University of Technology, Nanchang 330013, China
  • Contact: Shu-Min Zhou E-mail:smzhou@ecit.cn
  • Supported by:

    The work was supported by the National Natural Science Foundation of China (No. 11565002), the Jiang Xi Young Science Foundation Project (No. GJJ150558), and Open Funds of Engineering Research Center of Nuclear Technology Application of Ministry of Education (No. HJSJYB2017-2).

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Li-Fan Li, Rui Chen, Shu-Min Zhou, Bin Tang. Evaluation of correlation between PM2.5 and radon-progeny equilibrium factor in radon chamber.Nuclear Science and Techniques, 2018, 29(10): 151     doi: 10.1007/s41365-018-0481-2
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Abstract:

The dosage of environmental radon progeny is typically estimated according to the environmental radon exposure and the recommended radon-progeny equilibrium factor, F. To investigate the relationship between PM2.5 and the radon-progeny equilibrium factor, cigarettes are used to simulate the haze–fog in a multi-functional radon chamber to achieve a stable radon concentration environment. A radon detector and a portable laser aerosol spectrometer are used to obtain the values for Cmean PM2.5, CRn, and Cp. The results show that the mean values of F conform with the typical value recommended by the United Nations Scientific Committee on the Effects of Atomic Radiation and are within the reasonable range of 0.1–0.9. In this study, a positive correlation is observed between the F values and PM2.5 concentrations.

Key words: PM2.5, Radon chamber, Equilibrium factor, Linear regression