Advertisement

Granger-Causality Mining in Atmospheric Visibility Based on Deep Learning

  • Bo Liu
  • Xi HeEmail author
  • Jianqiang Li
  • Guangzhi Qu
  • Jianlei Lang
  • Rentao Gu
Conference paper
  • 8 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 551)

Abstract

Causal relationship mining of multi-dimensional meteorological time series data can reveal the potential connection between visibility and other influencing factors, and it is meaningful in terms of environmental management, air pollution chasing, and haze control. However, because causality analysis based on statistical methods or traditional machine learning techniques cannot represent the complex relationship between visibility and its influencing factors, as an extension of traditional Granger-causality analysis, we propose a causality mining method based on the seq2seq-LSTM deep learning model. The method can profoundly reveal the hidden relationship between different features and visibility and the regular pattern of bad weather, which can provide theoretical support for air pollution control.

Keywords

Granger-causality Deep learning Multidimensional time series 

Notes

Acknowledgment

This work is supported by National Natural Science Foundation of China (61702021), Beijing Natural Science Foundation (4174082), and General Program of Science and Technology Plans of Beijing Education Committee (SQKM201710005021).

References

  1. 1.
    Ma, Z., Li, H., Yu, P.: The impact of fog on Heyuan Expressway traffic. Guangdong Meteorol. 4, 61–62 (2006)Google Scholar
  2. 2.
    Holzworth, G.E., Maga, J.A.: A method for analyzing the trend in visibility. J. Air Pollut. Control Ass. 10(6), 430–435 (1960)CrossRefGoogle Scholar
  3. 3.
    Craig, C.D., Faulkenberrv, G.D.: The application of ridit analysis to detect trends in visibility. Atmos. Environ. 13, 617–1622 (1979)CrossRefGoogle Scholar
  4. 4.
    Lee, D.O.: Regional variations in long-term visibility trends in the UK 1962–1990. Geography 79, 108–121 (1994)Google Scholar
  5. 5.
    Doyle, M., Dorling, S.: Visibility trends in the UK 1950–1997. Atmos. Environ. 36, 3161–3172 (2002)CrossRefGoogle Scholar
  6. 6.
    Sloane, C.S.: Visibility trends-I. Methods of analysis. Atmos. Environ. 16(1), 41–51 (1982)Google Scholar
  7. 7.
    Charlson, R.J.: Atmospheric visibility related to aerosol mass concentration: a review. Environ. Sci. Technol. 3(10), 913–918 (1969)CrossRefGoogle Scholar
  8. 8.
    Wen, C.C., Yeh, H.H.: Comparative influences of airborne pollutants and meteorological parameters on atmospheric visibility and turbidity. Atmos. Res. 96, 496–509 (2010)CrossRefGoogle Scholar
  9. 9.
    Leaderer, B.P., Holford, T.R., Stowijk, J.A.J.: Relationship between sulfate aerosol and visibility. J. Air Pollut. Control Assoc. 29, 154 (1979)CrossRefGoogle Scholar
  10. 10.
    Trijonis, J.: Visibility in California. J. Air Pollut. Control Assoc. 32(2), 165–169 (1982)CrossRefGoogle Scholar
  11. 11.
    Yuan, C.S., Lee, C.G., Liu, S.H., et al.: Correlation of atmospheric visibility with chemical composition of Kaohsiung aerosols. Atmos. Res. 82, 663–679 (2006)CrossRefGoogle Scholar
  12. 12.
    Malm, W.C., Sisler, J.F., Huffman, D., et al.: Spatial and seasonal trends in particle concentration and optical extinction in the United States. J. Geophys. Res. 99(D1), 1347–1370 (1994)CrossRefGoogle Scholar
  13. 13.
    Chen, J., Zhao, C.S., Ma, N., et al.: A parameterization of low visibilities for hazy days in the North China Plain. At mos. Chem. Phys. 12, 4935–4950 (2012)CrossRefGoogle Scholar
  14. 14.
    Cheng, Y.F., Eichler, H., Wiedensohler, A., et al.: Mixing state of elemental carbon and non-light-absorbing aerosol components derived from in situ particle optical properties at Xinken in Pearl River Delta of China. J. Geophys. Res. 111, D20204 (2006)CrossRefGoogle Scholar
  15. 15.
    Fuller, K.A., Malm, W.C., Kreidenweis, S.M.: Effects of mixing on extinction by carbonaceous particles. J. Geophys. Res. 14(D13), 15941–15954 (1999)CrossRefGoogle Scholar
  16. 16.
    Ma, N., Zhao, C.S., Müller, T., et al.: A new method to determine the mixing state of light absorbing carbonaceous using the measured aerosol optical properties and number size distributions. Atmos. Chem. Phys. 12, 2381–2397 (2012).  https://doi.org/10.5194/acp-12-2381-2012CrossRefGoogle Scholar
  17. 17.
    Yu, H., Wu, C., Wu, D., et al.: Size distributions of elemental carbon and its contribution to light extinction in urban and rural locations in the Pearl River Delta Region, China. Atmos. Chem. Phys. 10, 5107–5119 (2010)CrossRefGoogle Scholar
  18. 18.
    Zhang, L., Zhang, C., Wang, B., et al.: Evolution characteristics and physical analysis ofatmospheric visibility in Beijing expressway. Chin. J. Atmos. Sci. 32(6), 1229–1240 (2008)Google Scholar
  19. 19.
    Wang, S., Zhang, X., Xu, X.: Statistical analysis of the change law of atmospheric visibility and its influencing factors in Beijing. Meteorol. Sci. Technol. 31(2), 109–114 (2003)Google Scholar
  20. 20.
    Ling, H., Junlin, A., Bin, Z.: Analysis of the change law and influence factors of atmospheric visibility in Nanjing. J. Atmos. Sci. 01(37), 91–98 (2014)Google Scholar
  21. 21.
    Fu, G., Zhang, Y., Zhang, Q., et al.: Analysis of characteristics of low visibility events in Hebei Province. Meteorology 39(08), 1042–1049 (2018)Google Scholar
  22. 22.
    Zhang, L., Zhang, C., Wang, B., et al.: Diagnosis and physical analysis of visibility and atmospheric dynamics and thermal factors of Beijing Airport Expressway. Clim. Environ. Res. 05(13), 260–272 (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Bo Liu
    • 1
  • Xi He
    • 1
    Email author
  • Jianqiang Li
    • 1
  • Guangzhi Qu
    • 2
  • Jianlei Lang
    • 3
  • Rentao Gu
    • 4
  1. 1.Faculty of Information Technology, School of Software EngineeringBeijing University of TechnologyBeijingChina
  2. 2.Computer Science and Engineering DepartmentOakland UniversityRochesterUSA
  3. 3.Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental and Energy EngineeringBeijing University of TechnologyBeijingChina
  4. 4.Beijing Laboratory of Advanced Information Networks, School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations