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Inversion of PM2.5 atmospheric refractivity profile based on AlexNet model from the perspective of electromagnetic wave propagation

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Abstract

Human civilization has reached an unprecedented height, but the industrialization of economic development also brings global warming, ozone depletion, acid rain, fresh water resources crisis, energy shortage, and environmental problems. In autumn and winter, haze becomes the usual state in the modern society, and PM2.5 has been becoming an important form of air pollution. The research found that PM2.5 brings great influence to the human body or daily life. To some extent, the PM2.5 also affects the propagation of electromagnetic waves near the ground, reducing the transmission performance of electromagnetic wave. Based on Mie scattering theory, this paper qualitatively analyzed the scattering effects of PM2.5 particles on every frequency band of electromagnetic wave in daily use. Then the paper takes the satellite navigation signals as a research example, selecting university of Wyoming Davis stations in Antarctica sounding data by measuring the tropospheric atmospheric meteorological parameters (including the atmosphere pressure, geopotential height of different layers, dew point temperature, relative humidity and specific humidity, wind direction, wind speed, and temperature). The paper inversed the refractive index distribution of the troposphere based on AlexNet model and described the error quantitatively. The simulation results show that the estimated error is less than 5.1455%, proving the high accuracy of the AlexNet model. To test the influence of PM2.5, the paper takes Jiuquan, a city with serious pollution, as an example. Comparison between the inversion results and IGS products shows that high concentration of PM2.5 pollution has little influence on the inversion of refractive index profile.

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References

  1. Feng M, Chen D, Wang X (2018) Classification of fabric patterns image based on improved log-AlexNet. 2018 11th CISP-BMEI, 1–6

  2. He K, Yang F, Ma Y et al (2001) The characteristics of PM2.5 in Beijing, China[J]. Atmos Environ 35(29):4959–4970

  3. Li JM, Shu TT, Lin LK et al (2013) An improved linear regression algorithm for inverting atmospheric refractive index by microwave radiometer [J]. JoRS 28(2):316–320

  4. Li ZF, Yang HL, Wang YL et al (2015) A new method for modeling atmospheric refractive index profiles [J]. HPLAPB 27(10):103255 (1) -103255 (6)

  5. Lin LK (2011) Study on inversion of ground-based atmospheric refractive index profiles using GNSS signals [D]. Nanjing University of Posts and Telecommunications, Nanjing

  6. Lin LK, Zhao ZW, Zhang YR et al (2008) Tropospheric refractivity profiling based on single ground-based GPS[C]. Proceedings of ICMMT, Nanjing, pp 788–791

  7. Lowry AR, Rocken C, Sokolovskiy SV et al (2002) Vertical profiling of atmospheric refractivity from ground-based GPS[J]. Radio Sci 37(3):1041–1059

  8. Mishchenko ML, Travis LD, Lacis AA (2002) Book review: scattering, absorption, and emission of light by small particles / Cambridge university press,2002[J]. Space Sci Rev 101:442

  9. Qiao WB, Tian WC, Tian Y, Yang Q, Wang YN, Zhang JZ (2019) The forecasting of PM2.5 using a hybrid model based on wavelet transform and an improved deep learning algorithm. IEEE Access, Volume 7:142814–142825

  10. Sze V, Chen YH, Yang TJ, Emer JS (2017) Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE 105(12):2295–2329

  11. Wang DQ (2015) Study on the scattering characteristics of major pollutants in high concentration aerosols such as haze [D]. Xi'an electronic and science University5

  12. Wang, Z, Long Z (2018) Pm2.5 Prediction Based on Neural Network. 2018 11th ICICTA, 44–47

  13. Wang XM, Zheng YT, Lei T (2019) PM2.5 Concentration Prediction Model Based on Chaotic Genetic Neural Network. CCDC, 1759–1764. https://doi.org/10.1109/CCDC.2019.8832724

  14. Xiao L, Yan Q, Deng S (2017) Scene classification with improved AlexNet model. In: 2017 12th international conference on ISKE, pp 1–6

  15. Xie J (2017) Deep neural network for PM2.5 pollution forecasting based on manifold learning. In: 2017 international conference on SDPC, pp 236–240. https://doi.org/10.1109/SDPC.2017.52

  16. Yang B, Chen Q (2017) PM2.5 concentration estimation based on image quality assessment. 2017 4th IAPR ACPR, 676–681. https://doi.org/10.1109/ACPR.2017.42

  17. Zhang HM, Sun XX (2013) Study on prediction of atmospheric PM2.5 based on RBF neural network. 2013 FICoDMA, 1287–1289

  18. Zhao ZW, Wu ZS (2000) Millimeter-wave attenuation due to fog and clouds[C]. International Conference on Infrared and Millimeter Waves Conference Digest IEEE 2002:1607–1615

  19. Zhao J, Dou J, Ge K (2019) Application of LDHA-BP in prediction of atmospheric PM2.5 concentration. 2019 IEEE 3rd information technology, (ITNEC), 2239–2245. https://doi.org/10.1109/ITNEC.2019.8729040

  20. Zhu H, Lu X (2016) The prediction of PM2.5 value based on ARMA and improved BP neural network model. In: 2016 international conference on INCoS, pp 515–517. https://doi.org/10.1109/INCoS.2016.81

  21. Zhu QL, Wu ZS, Zhao ZW, et al (2010) Real time atmosphere sensing from singular ground-based GPS Station[C]. The 27th PIERS, Xi'an

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Correspondence to ChengJun Guo.

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Guo, C., Xu, Y. & Tian, Z. Inversion of PM2.5 atmospheric refractivity profile based on AlexNet model from the perspective of electromagnetic wave propagation. Environ Sci Pollut Res (2020). https://doi.org/10.1007/s11356-020-07703-w

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Keywords

  • Environmental pollution
  • PM2.5
  • Scattering theory
  • Refractive index
  • AlexNet
  • Inversion