research-article

A Method for Mining Granger Causality Relationship on Atmospheric Visibility

Authors:

Rentao GuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 5

Article No.: 92, Pages 1 - 16

https://doi.org/10.1145/3447681

Published: 29 May 2021 Publication History

Abstract

Atmospheric visibility is an indicator of atmospheric transparency and its range directly reflects the quality of the atmospheric environment. With the acceleration of industrialization and urbanization, the natural environment has suffered some damages. In recent decades, the level of atmospheric visibility shows an overall downward trend. A decrease in atmospheric visibility will lead to a higher frequency of haze, which will seriously affect people's normal life, and also have a significant negative economic impact. The causal relationship mining of atmospheric visibility can reveal the potential relation between visibility and other influencing factors, which is very important in environmental management, air pollution control and haze control. However, causality mining based on statistical methods and traditional machine learning techniques usually achieve qualitative results that are hard to measure the degree of causality accurately. This article proposed the seq2seq-LSTM Granger causality analysis method for mining the causality relationship between atmospheric visibility and its influencing factors. In the experimental part, by comparing with methods such as linear regression, random forest, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting, it turns out that the visibility prediction accuracy based on the seq2seq-LSTM model is about 10% higher than traditional machine learning methods. Therefore, the causal relationship mining based on this method can deeply reveal the implicit relationship between them and provide theoretical support for air pollution control.

References

[1]

Ma Zusheng, Li Hanbin, and Yu Ping. 2006. The impact of fog on Heyuan Expressway traffic. Guangdong Meteorol 4, 16 (2006), 61–62.

[2]

G. E. Holzworth and J. A. Maga. 1960. A method for analyzing the trend in visibility. J Air Pollut Control Ass 10, 6 (1960), 430–435.

[3]

C. D. Craig and G. D. Faulkenberrv. 1979. The application of ridit analysis to detect trends in visibility. Atmos Environ 13, 12 (1979), 617–1622.

[4]

D. O. Lee. 1994. Regional variations in long-term visibility trends in the UK. 1962-1990. Geography 79, 2 (1994), 108–121.

[5]

M. Doyle and S Dorling. 2002. Visibility trends in the UK 1950-1997. Atmos Environ 36, 19 (2002), 3161–3172.

[6]

C. S. Sloane. 1982. Visibility trends—I. Methods of analysis. Atmos Environ 16, 1 (1982), 41–51.

[7]

R. J. Charlson. 1969. Atmospheric visibility related to aerosol mass concentration: A review. Environ Sci Technol 3, 10 (1969), 913–918.

[8]

C. C. Wen and H. H. Yeh. 2010. Comparative influences of airborne pollutants and meteorological parameters on atmospheric visibility and turbidity. Atmos Res 96, 4 (2010), 496–509.

[9]

B. P. Leaderer, T. R. Holford, and J. A. J. Stowijk. 1982. Relationship between sulfate aerosol and visibility. J Air Pollut Control Assoc 29, 2 (1979), 154.

[10]

J. Trijonis. 1982. Visibility in California. J Air Pollut Control Assoc 32, 2 (1982), 165–169.

[11]

C. S. Yuan, C. G. Lee, S. H. Liu, J. C. Chang, C. Yuang, and H. Y. Yang. 2006. Correlation of atmospheric visibility with chemical composition of Kaohsiung aerosols. Atmos Res 82, 3–4 (2006), 663–679.

[12]

W. C. Malm, J. F. Sisler, D. Huffman et al. 1994. Spatial and seasonal trends in particle concentration and optical extinction in the United States. J Geophys Res 99, D1 (1994), 1347–1370.

[13]

J. Chen, C. S. Zhao, N. Ma, et al. 2012. A parameterization of low visibilities for hazy days in the North China Plain. Atmos Chem Phys 12, 11 (2012), 4935–4950.

[14]

Y. F. Cheng, H. Eichler, A. Wiedensohler, et al. 2006. 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, D20 (2006), D20204.

[15]

K. A. Fuller, W. C. Malm, and S. M. Kreidenweis. 1999. Effects of mixing on extinction by carbonaceous particles. J Geophys Res 14, D13 (1999), 15941–15954.

[16]

N. Ma, C. S. Zhao, T. Müller, et al. 2012. 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, 5 (2012), 2381–2397.

[17]

H. Yu, C. Wu, D. Wu, et al. 2010. 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, 11 (2010), 5107–5119.

[18]

Zhang Lina, Zhang Chaolin, Wang Bizheng, et al. 2008. Evolution characteristics and physical analysis of atmospheric visibility in Beijing expressway 32, 6 (2008), 1229—1240.

[19]

Fan Yinqi and Li Chunqiang. 2008. Study on the change trend of atmospheric visibility in Beijing, Tianjin and Hebei provinces from 1980 to 2003. Plateau Meteorol 12, 6 (2008), 1393–1400.

[20]

Wang Shuying, Zhang Xiaoling, and Xu Xiaofeng. 2003. Statistical analysis of the change law of atmospheric visibility and its influencing factors in beijing. Meteorol Sci Technol 31, 2 (2003), 109–114.

[21]

Ma Yanjun, Zuo Hongchao, Zhang Yunhai, et al. 2005. Analysis of atmospheric visibility and influence factors of urban agglomerations in central Liaoning. Plateau Meteorol 24, 4 (2005), 623–628.

[22]

Wang Shouqiang and Huo Guoan. 2013. Analysis of the change trend of atmospheric visibility and its influencing factors. Modern Agric Sci Technol 4, 151 (2013), 235–236.

[23]

Ma Zhiqiang, Zhao Xiujuan, Meng Wei, et al. 2012. Comparative analysis of the influence of fog and helium on atmospheric visibility in Beijing. Environ Sci Res 11, 25 (2012), 1208–1214.

[24]

Hou Ling, An Junlin, and Zhu Bin. 2014. Analysis of the change law and influence factors of atmospheric visibility in Nanjing. J Atmos Sci 01, 37 (2014), 91–98.

[25]

Fu Guiqin, Zhang Yingxin, and Zhang Qinghong. 2018. Analysis of characteristics of low visibility events in Hebei province. Meteorology 39, 08 (2018), 1042–1049.

[26]

Zhang Lina, Zhang Chaolin, Wang Bizheng, et al. 2008. Diagnosis and physical analysis of visibility and atmospheric dynamics and thermal factors of Beijing Airport Expressway. Clim Environ Res 05, 13 (2008), 260–272.

[27]

Li Xingmin, Dong Zipeng, Chen Wei, et al. 2014. Study on the influence of Guanzhong aerosol on atmospheric visibility in Shaanxi. Plateau Meteorol 33, 5 (2014), 1289–1296.

[28]

Doyen Sahoo, Steven C. H. Hoi, and Bin Li. 2019. Large scale online multiple kernel regression with application to time-series prediction. ACM Trans Knowl Discov Data 13, 1 (2019), 33 pages.

Digital Library

[29]

C. Papagiannopoulou, D. G. Miralles, S. Decubber, M. Demuzere, N. E. C. Verhoest, W. A. Dorigo, and W. Waegeman. 2017. A non-linear Granger-causality framework to investigate climate–vegetation dynamics. Geosci Model Dev 10, 5 (2017), 1945–1960.

[30]

Jin Junli, Yan Peng, and Ma Zhiqiang, et al. 2014. Characteristics of PM2.5 change in Beijing and surrounding areas from January to March 2013. J Appl Meteorol Sci 25, 6 (2014), 690–700.

[31]

T. Sebastian, V. Jeyaseelan, and L. Jeyaseelan, et al. 2019. Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models. Stat Methods Med Res 28, 5 (2019), 1552–1563.

[32]

Wu H., Cai Y. Wu Y., et al. 2017. Time series analysis of weekly influenza-like illness rate using a one-year period of factors in random forest regression. Biosci Trends 11, 3 (2017), 292–296.

[33]

S. Liu, Y. Cui, Y. Ma, et al. 2018. Short-term load forecasting based on GBDT combinatorial optimization. In Proceedings of the 2018 2nd IEEE Conference on Energy Internet and Energy System Integration. IEEE, 1–5.

[34]

G. Ke, Q. Meng, T. Finley, et al. 2017. Lightgbm: A highly efficient gradient boosting decision tree. In Proceedings of the Advances in Neural Information Processing Systems. 3146–3154.

[35]

T. Chen and C. Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785–794.

[36]

J. Ma, J. C. P. Cheng, Z. Xu, et al. 2020. Identification of the most influential areas for air pollution control using XGBoost and grid importance rank. Journal of Cleaner Production 274, 122835 (2020).

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A Method for Mining Granger Causality Relationship on Atmospheric Visibility

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 5

October 2021

508 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3461317

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

Copyright © 2021 ACM.

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Association for Computing Machinery

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Publication History

Published: 29 May 2021

Accepted: 01 January 2021

Revised: 01 December 2020

Received: 01 July 2019

Published in TKDD Volume 15, Issue 5

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Natural Science Foundation of China
Grant of China Scholarship Council
Beijing Natural Science Foundation

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https://doi.org/10.3390/s24020348
Qu YFang YJi SYuan CWu HZhu SQin HQue F(2024)Deep Learning-Based Atmospheric Visibility DetectionAtmosphere10.3390/atmos1511139415:11(1394)Online publication date: 19-Nov-2024
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