Using the seasonal FGM(1,1) model to predict the air quality indicators in Xingtai and Handan

  • LF WuEmail author
  • Nu Li
  • Ting Zhao
Research Article


The air pollution problem in Xingtai and Handan is the focus of public attention. The seasonal gray model with fractional order accumulation is proposed to predict the quarterly concentrations of PM2.5, PM10, NO2, and CO in Xingtai and Handan. The new model has higher forecasting performance and can describe the characteristics of seasonal fluctuation very well. The forecasting results indicated that except for the PM10 in Xingtai that will increase slowly, the other indicators in the two places will decrease. The changes of the air quality indicator concentration in different quarters are obvious, and in the same quarter tend to be stable. Except for CO and NO2 in some seasons, other indicators are in the state of exceeding the standard. The effect of air pollution control is not good. The governance needs to be further strengthened.


Xingtai Handan Air indicators Gray model Seasonal factors 


Funding information

The relevant researches in this paper are supported by the National Natural Science Foundation of China (71871084, 71401051) and the project of high-level talent in Hebei province.


  1. Cheng L, Kunquan L (2017) Application of fuzzy comprehensive assessment grounded on double weighing factors in assessment of Nanjing air quality. Chin J Environ Eng 11(12):6386–6392Google Scholar
  2. Gao X, Wu L (2019) Using fractional order weakening buffer operator to forecast the main indices of online shopping in China. Grey Systems Theory and Application 9(1):128–140CrossRefGoogle Scholar
  3. GB 3095-2012 Ambient air quality standards. Ministry of Environmental Protection. 2012-02-29Google Scholar
  4. Heidarinejada Z, Kavosib A, Mousapour H et al (2018) Data on evaluation of AQI for different season in Kerman, Iran, 2015. Data Brief 20:1917–1923CrossRefGoogle Scholar
  5. Jie L (2017) Analysis and simulation of spatial and temporal characteristics of air quality in Wuhan based on LUR model. Central China Normal UniversityGoogle Scholar
  6. Jie X, Yusen D,Yanmin H et al (2011) The Season Changing of Air Quality in Shanghai and the Analysis of High Pollution Cases. Environ Sci Technol 34(S1):116–118+173Google Scholar
  7. Jun H, Yanqun Z (2009) Amending GM(1,1) model by season exponent and its application in the air quality forecast. Henan Science 27(07):779–782Google Scholar
  8. Kim MJ, Rao BS, Kang OY et al (2012) Monitoring and prediction of indoor air quality (IAQ) in subway or metro systems using season dependent models. Energ Buildings 46:48–55CrossRefGoogle Scholar
  9. Lei W, Qingyu G, Feifei W et al (2018) Association between heating seasons and criteria air pollutants in three provincial capitals in northern China: spatiotemporal variation and sources contribution. Build Environ 132:233–244CrossRefGoogle Scholar
  10. Lifeng W, Sifeng L, Ligen Y et al (2013) Grey system model with the fractional order accumulation. Commun Nonlinear Sci Numer Simulat 18(7):1775–1785CrossRefGoogle Scholar
  11. Qiao X, Ying Q, Li X, Zhang H, Hu J, Tang Y, Chen X (2018) Source apportionment of PM2.5 for 25 Chinese provincial capitals and municipalities using a source-oriented community multiscale air quality model. Sci Total Environ 612:462–471CrossRefGoogle Scholar
  12. Robichaud A, Ménard R (2014) Multi-year objective analyses of warm season ground-level ozone and PM2.5 over North America using real-time observations and Canadian operational air quality models. Atmos Chem Phys 14(4):1769–1800CrossRefGoogle Scholar
  13. Wang ZX, Li Q, Pei L-L (2018) A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors. Energy 154:522–534CrossRefGoogle Scholar
  14. Weixue L, Huifang X (2017) Application of seasonal adjustment model in the study of air pollution index in Anhui province. Modern Business Trade Industry (33):176–177Google Scholar
  15. Zhijuan S, Jun B, Zongwei M et al (2017) Seasonal trends of indoor fine particulate matter and its determinants in urban residences in Nanjing, China. Build Environ 125:319–325CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Economics and ManagementHebei University of EngineeringHandanChina

Personalised recommendations