Pollutant Profile Estimation Using Unscented Kalman Filter

  • S. MetiaEmail author
  • S. D. Oduro
  • A. P. SinhaEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 591)


In this paper, we develop an estimation model for carbon monoxide (CO) air pollution concentrations. CO is an important pollutant which is used to calculate an air quality index (AQI). AQI becomes less reliable as the proportion of data missing due to equipment failure and periods of calibration increases. This paper presents the Unscented Kalman filter (UKF) to predict missing data of atmospheric carbon monoxide concentrations using the time series data of monitoring stations.


Carbon monoxide (CO) Unscented Kalman filter (UKF) Air quality index (AQI) 


  1. 1.
    Zhou Y, Mao H, Demerjian K, Hogrefe C, Liu J (2017) Regional and hemispheric influences on temporal variability in baseline carbon monoxide and ozone over the Northeast US. Atmos Environ 164:309–324CrossRefGoogle Scholar
  2. 2.
    Gaeggelera K, Prevota ASH, Dommena J, Legreid G, Reimann S, Baltensperger U (2008) Residential wood burning in an Alpine valley as a source for oxygenated volatile organic compounds, hydrocarbons and organic acids. Atmos Environ 42(35):8278–8287CrossRefGoogle Scholar
  3. 3.
    Spinazze A, Cattaneo A, Garramone G, Cavallo DM (2013) Temporal variation of size-fractionated particulate matter and carbon monoxide in selected microenvironments of the milan urban area. J Occup Environ Hyg 10(11):652–662CrossRefGoogle Scholar
  4. 4.
    Dahms TE, Younis LT, Wiens RD, Zarnegar S, Byers SL, Chaitman BR (1993) Effects of carbon monoxide exposure in patients with documented cardiac arrhythmias. J Am Coll Cardiol 21(2):442–450CrossRefGoogle Scholar
  5. 5.
    Mohajer NA, Zuidema C, Sousan S, Hallett L, Tatum M, Rule AM, Thomas G, Peters TM, Koehle K (2018) Evaluation of low-cost electro-chemical sensors for environmental monitoring of ozone, nitrogen dioxide, and carbon monoxide. J Occup Environ Hyg 15(2):87–98CrossRefGoogle Scholar
  6. 6.
    Omidvarborna H, Baawain M, Al-Mamun A (2018) Ambient air quality and exposure assessment study of the Gulf Cooperation Council countries: a critical review. Sci Total Environ 636:437–448CrossRefGoogle Scholar
  7. 7.
    Zolghadri A, Cazaurang F (2006) Adaptive nonlinear state-space modelling for the prediction of daily mean PM10 concentrations. Environ Model Softw 21(6):885–894CrossRefGoogle Scholar
  8. 8.
    Hartikainen J, Särkkä S (2010) Kalman filtering and smoothing solutions to temporal Gaussian process regression models. In: 2010 IEEE international workshop on machine learning for signal processing, August 2010, pp. 379–384Google Scholar
  9. 9.
    Arasaratnam I, Haykin S (2009) Cubature Kalman filters. IEEE Trans Autom Control 54(6):1254–1269MathSciNetCrossRefGoogle Scholar
  10. 10.
    Julier S, Uhlmann J, Durrant-Whyte HF (2000) A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans Autom Control 45(3):477–482MathSciNetCrossRefGoogle Scholar
  11. 11.
    Reddy JBV, Dash PK, Samantaray R, Moharana AK (2009) Fast tracking of power quality disturbance signals using an optimized unscented filter. IEEE Trans Instrum Meas 58(12):3943–3952CrossRefGoogle Scholar
  12. 12.
    Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear estimation. Proc IEEE 92(3):401–422CrossRefGoogle Scholar
  13. 13.
    Metia S, Oduro SD, Duc HN, Ha Q (2016) Inverse air-pollutant emission and prediction using extended fractional Kalman filtering. IEEE J Sel Top Appl Earth Obs Remote Sens 9(5):2051–2063CrossRefGoogle Scholar
  14. 14.
    Metia S, Ha QP, Duc HN, Azzi M, Estimation of power plant emissions with unscented Kalman filter. IEEE J Sel Top Appl Earth Obs Remote Sens 11(8):2763–2772CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Faculty of Engineering and ITUniversity of Technology SydneySydneyAustralia
  2. 2.Department of Mechanical and Automotive Technology Education College of Technology Education KumasiUniversity of Education WinnebaKumasiGhana
  3. 3.Department of Electronics and Communication EngineeringBIT SindriDhanbadIndia

Personalised recommendations