Skip to main content
SpringerLink
Log in

A Multi-scale Periodic Study of PM2.5 Concentration in the Yangtze River Delta of China Based on Empirical Mode Decomposition-Wavelet Analysis

  • Chapter
  • First Online:
Economic Impacts and Emergency Management of Disasters in China

  • 633 Accesses

Abstract

With the acceleration of industrialization and urbanization, the problem of air pollution in China has become increasingly serious. Particulate matter (PM) is a representative indicator of pollutants, and it is of great significance to carry out targeted treatment by studying its periodicity of concentration. In this paper, as a data mining information technology, the Empirical Mode Decomposition-Wavelet Analysis (EMD-WA) model is used to conduct a multi-scale periodic study of the PM2.5 concentration time sequence in the Yangtze River Delta region in China and it is found that: (1) through the decomposition and reconstruction of the EMD-WA model, the period characteristics of four scales from short to long can be obtained, which are seasonal, short, medium and long period terms respectively; (2) the PM2.5 concentration in the Yangtze River Delta region shows obvious multi-scale periodicity for the four scales, which includes a seasonal cycle of 46 days (about 1.5 months), a short cycle of 101 days (about 3.5 months), a medium cycle of 294 days (about 10 months), and a long cycle of 671 days (about 22.5 months), respectively. (3) The results are consistent in terms of season, short and middle cycle scales, in north (Jiangsu), east (Shanghai), south (Zhejiang) and west (Anhui) of the Yangtze River Delta region, but there are significant differences in the terms of long cycle scales. (4) The PM2.5 concentration still shows obvious periodicity within 240 h during severe haze in the Yangtze River Delta region. This paper provides a framework for the government to make policies on energy conservation, emission reduction and air pollution control, and also provides a strong basis for haze prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    These conclusions are basically consistent with the other study. For example, Wang et al. (2020) found hourly concentrations of PM2.5 from 2015 to 2018 in the cities of the Yangtze River Delta have two dominant periods: an annual cycle on the time scale of 250–480 days and a semi-annual cycle on the time scale of 130–220 days.

References

  • Addo, P. M., Billio, M., & Guégan, G. (2014). Nonlinear dynamics and wavelets for business cycle analysis. Berlin: Springer International Publishing.

    Book  Google Scholar 

  • Bao, C., & Fang, C. L. (2007). Water resources constraint force on urbanization in water deficient regions: A case study of the Hexi Corridor, arid area of NW China. Ecological Economics,62(3–4), 508–517.

    Article  Google Scholar 

  • Baubeau, P., & Cazelles, B. (2009). French economic cycles: A wavelet analysis of French retrospective GNP series. Cliometrica,3(3), 275–300.

    Article  Google Scholar 

  • Berdiev, A. N., & Chang, C. P. (2015). Business cycle synchronization in Asia-Pacific: New evidence from wavelet analysis. Journal of Asian Economics,37, 20–33.

    Article  Google Scholar 

  • Carvalho, M. D., Rodrigues, P. C., & Rua, A. (2012). Tracking the us business cycle with a singular spectrum analysis. Economics Letters,114, 32–35.

    Article  Google Scholar 

  • Cendejas, J. L., Muñoz, F. F., & Fernández-de-Pinedo, N. (2017). A contribution to the analysis of historical economic fluctuations (1870–2010): Filtering, spurious cycles, and unobserved component modeling. Cliometrica,11(1), 93–125.

    Article  Google Scholar 

  • Cekim, H. O. (2020). Forecasting PM10 concentrations using time series models: A case of the most polluted cities in Turkey. Environmental Science and Pollution Research,27, 25612–25624.

    Article  Google Scholar 

  • Chen, X. B., Yin, L. R., Fan, Y. L., Song, L. H., Ji, T. T., Liu, Y., Tian, J. W., & Zheng, W. F. (2020). Temporal evolution characteristics of PM2. 5 concentration based on continuous wavelet transform. Science of the Total Environment, 699, 134244.

    Google Scholar 

  • Cui, J. T. (1995). Wavelet Analysis. Xian: Xi’an Jiaotong University Press.

    Google Scholar 

  • Fiffer, M., Kang, Choong-Min., Requia, M. J., & Koutrakis, P. (2020). Long-term impact of PM2.5 mass and sulfur reductions on ultrafine particle trends in Boston, MA from 1999 to 2018. Journal of the Air & Waste Management Association,70(7):700–707.

    Google Scholar 

  • Flandrin, P., Rilling, G., & Gonçalves, P. (2004). Empirical mode decomposition as a filter bank. IEEE Signal Processing Letters,11(2), 112–114.

    Article  Google Scholar 

  • Gu, K. H., Shi, H. X., Zhang, S., Fan, S. X., Xu, J. M., & Tan, J. G. (2015). Variation characteristics of PM2.5 levels and the influence of meteorological conditions on Chongming Island in Shanghai. Resources and Environment in the Yangtze Basin,24(12), 2108–2116.

    Google Scholar 

  • Hansen, J., Sato, M. K. I., Ruedy, R., Nazarenko, L., Lacis, A., Schmidt, G. A., & Bell, N. (2005). Efficacy of climate forcings. Journal of Geophysical Research: Atmospheres,110(D18).

    Google Scholar 

  • He, J. L., & Song, W. L. (2013). Measurement and analysis of ocean economic cyclical changes based on filtering method. Marine Science Bulletin,32(1), 1–7.

    Google Scholar 

  • Huang, N., Shen, Z., Long, S. R., & Wu, M. C. (1971). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A-Mathematical Physical & Engineering Sciences,1998(454), 903–995.

    Google Scholar 

  • Huang, N. E., Shen, Z., & Long, S. R. (1999). A new view of nonlinear water waves: The Hilbert spectrum. Annual Review of Fluid Mechanics,31(1), 417–457.

    Article  Google Scholar 

  • Kuo, S. M., & Lee, B. H. (1988). Fast Fourier transform and its applications.

    Google Scholar 

  • Li, C. G., Tian, Y. X., & He, J. R. (2012). Prediction model of AC algorithm based on EMD decomposition combined with GMDH and its application. Journal of Systems & Management,21(1), 105–110.

    Google Scholar 

  • Li, X. F., Chu, J. H., Yu, L. D., Zhu, G. H., & Wang, G. F. (2011). Variational characteristics of PM2.5 concentration in a monitoring site in Beijing urban area. Journal of Beijing Normal University (Natural Science),47(3), 258–261.

    Google Scholar 

  • Li, X. Y., Jin, M. J., Chen, K., Xiang, H. Q., & Liu, Q. M. (2007). The case-crossover studies of air particulate matter pollution and cardiovascular disease death. China Environmental Science,27(5), 657–660.

    Article  Google Scholar 

  • Li, Z. M., Sun, Z. B., S. X., Liao, X. N., Zhang, X. L., Xiong, Y. J., & Ma, X. H. (2017). Using Morlet wavelet analysis to analyze multiple time scale periodically in PM2.5 in Beijing. China Environmental Science,37(2), 407–415.

    Google Scholar 

  • Liang, M. Y., Dong, L., & Tao, J. (2007). Pollution Level of the Airborne Particulate Matter (PM2.5) During the Haze Period in Winter in Guangzhou. Environmental Monitoring in China, (5), 52–54 + 70.

    Google Scholar 

  • Mao, W. L., Xu, J. H., Lu, D. B., Yang, D. Y., & Zhao, J. N. (2017). An analysis of the spatial-temporal pattern and influencing factors of PM2.5 in the Yangtze River Delta in 2015. Resources and Environment in the Yangtze Basin,26 (2), 264–272.

    Google Scholar 

  • Pedregal, D. J. (2003). Filter-Design and Model-Based Analysis of Economic Cycles. Documento de trabajo, 13.

    Google Scholar 

  • Shen, L., Cheng, S., Gunson, A. J., & Wan, H. (2005). Urbanization, sustainability and the utilization of energy and mineral resources in China. Cities,22(4), 287–302.

    Article  Google Scholar 

  • Sella, L., Vivaldo, G., Ghil, M., & Groth, A. (2010). Economic cycles and their synchronization: Spectral analysis of macroeconomic series from Italy, The Netherlands, and the UK. EGU General Assembly Conference Abstracts,12, 11847.

    Google Scholar 

  • Song, M., & Wang, S. (2018). Market competition, green technology progress and comparative advantages in China. Management Decision,56(1), 188–203.

    Article  Google Scholar 

  • Malm, W. C., Sisler, J. F., Huffman, D., Eldred, R. A., & Cahill, T. A. (1994). Spatial and seasonal trends in particle concentration and optical extinction in the United States. Journal of Geophysical Research: Atmospheres,99(D1), 1347–1370.

    Article  Google Scholar 

  • Marczak, M., & Gomez, V. (2015). Cyclicality of real wages in the USA and Germany: New insights from wavelet analysis. Economic Modelling,47, 40–52.

    Article  Google Scholar 

  • Mohr, M. F. (2006). The missing cycle in the HP filter and the measurement of cyclically-adjusted budget balances. SSRN Electronic Journal, 73–111.

    Google Scholar 

  • Wang, F., Han, Y. L., & Zhao, Y. (2017). Spatial-temporal variations of PM10 and PM2.5 on different time-scales in Taiyuan. Ecology and Environmental Sciences,26(9), 1521–1528.

    Google Scholar 

  • Wang, G. C., & Wang, P. C. (2014). PM2.5 Pollution in China and Its Harmfulness to Human Health. Science & Technology Review,32(26), 72–78.

    Google Scholar 

  • Wang, J. J., Lu, X. M., Yan, Y. T., Zhou, L. G., & Ma, W. C. (2020). Spatiotemporal characteristics of PM 2.5 concentration in the Yangtze River Delta urban agglomeration, China on the application of big data and wavelet analysis. Science of The Total Environment, 724.

    Google Scholar 

  • Wang, W. B., Fei, P. S., & Yi, X. M. (2010). Prediction of China stock market based on EMD and neural network. Systems Engineering Theory & Practice,30(6), 1027–1033.

    Google Scholar 

  • Wu, H. H., Kuang, H. B., Meng, B., & Feng, W. W. (2018). Study on the periodic characteristics of BDI index based on EMD-WA model. Systems Engineering C Characteristics,38(06), 1586–1598.

    Google Scholar 

  • Wu, X. H., Cao, Y. L., Xiao, Y., & Guo, J. (2018b). Finding of urban rainstorm and waterlogging disasters based on microblogging data and the location-routing problem model of urban emergency logistics. Annals of Operations Research, 1–32.

    Google Scholar 

  • Wu, X. H., Chen, Y. F., Zhao, P., Guo, J., & Ma, Z. X. (2019). Study of haze emission efficiency based on new co-opetition data envelopment analysis. Expert Systems,3, 1–12.

    Google Scholar 

  • Wu, X. H., Wang, Z. J., Gao, G., Guo, J., & Xue, P. P. (2020). Disaster probability, optimal government expenditure for disaster prevention and mitigation, and expected economic growth. Science of the Total Environment,709, 135888.

    Article  Google Scholar 

  • Wu, X. H., Xu, Z., Liu, H., Guo, J., & Zhou, L. (2019b). What are the impacts of tropical cyclones on employment? An analysis based on meta-regression. Weather, Climate, and Society,11(April), 259–275. https://doi.org/10.1175/WCAS-D-18-0052.1.

  • Yang, X. X., Feng, L. H., & Wei, P. (2012). Air particulate matter PM2.5 in Beijing and its harm. Frontier Science,6(1), 22–31.

    Google Scholar 

  • Yogo, M. (2008). Measuring business cycles: A wavelet analysis of economic time series. Economics Letters,100, 208–212.

    Article  Google Scholar 

  • Yu, L., Wang, S., & Lai, K. K. (2008). Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics,30(5), 2623–2635.

    Article  Google Scholar 

  • Zhang, W. A. (2014). Study on the dynamic and mechanism of regional economical disparity in western china—Multi-scale analysis based on EMD Method. Journal of Applied Statistics and Management,33(6), 951–964.

    Google Scholar 

  • Zhang, Z. S., Tao, J., Xie, S. D., Zhou, L. D., Song, D. L., Zhang, P., Cao, J. J., & Luo, L. (2013). Seasonal variations and source apportionment of PM2.5 at urban area of Chengdu. Acta Scientiae Circumstantiate,33(11), 2947–2952.

    Google Scholar 

  • Zhao, X. J. (2008). Seasonal and daily variation characteristics of PM2.5 concentration in urban and suburban areas of Beijing. China Meteorological Society: China Meteorological Society, 11.

    Google Scholar 

  • Zhang, X., Lai, K. K., & Wang, S. Y. (2008). A new approach for crude oil price analysis based on empirical mode decomposition. Energy Economics,30(3), 905–918.

    Article  Google Scholar 

  • Zheng, Z. G. (2010). Empirical modal analysis and wavelet analysis (pp. 1–2). Beijing: Meteorological publishing house.

    Google Scholar 

  • Zhou, J., Zhang Y. J., Xiang, D., & Han, Z. Y. (2018). The periodicity and cause analysis of PM2.5 in Taiyuan. Ecology and Environmental Sciences,27(3), 527–532.

    Google Scholar 

Download references

Acknowledgements

Shaoli He, Weihang Sun also made great contributions to this manuscript. We express our heartfelt thanks to them. This research was supported by: National Social and Scientific Fund Program of China (18ZDA052; 17BGL142; 16ZDA047); The Natural Science Foundation of China (91546117, 71373131).

Author information

Authors and Affiliations

  1. School of Economics and Management, Shanghai Maritime University, Shanghai, China

    Prof. Xianhua Wu & Prof. Ji Guo

  2. Collaborative Innovation Center on Climate and Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

    Prof. Xianhua Wu & Prof. Ji Guo

Authors
  1. Prof. Xianhua Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prof. Ji Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xianhua Wu .

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wu, X., Guo, J. (2021). A Multi-scale Periodic Study of PM2.5 Concentration in the Yangtze River Delta of China Based on Empirical Mode Decomposition-Wavelet Analysis. In: Economic Impacts and Emergency Management of Disasters in China. Springer, Singapore. https://doi.org/10.1007/978-981-16-1319-7_2

Download citation

Publish with us

Policies and ethics

Search

Navigation