Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Multivariate statistical monitoring of subway indoor air quality using dynamic concurrent partial least squares

  • 67 Accesses


To maintain the health level of indoor air quality (IAQ) in subway stations, the data-driven multivariate statistical method concurrent partial least squares (CPLS) has been successfully applied for output-relevant and input-relevant sensor faults detection. To cope with the dynamic problem of IAQ data, the augmented matrices are applied to CPLS (DCPLS) to achieve the better performance. DCPLS method simultaneously decomposes the input and output data spaces into five subspaces for comprehensive monitoring: a joint input-output subspace, an output principal subspace, an output-residual subspace, an input-principal subspace, and an input-residual subspace. Results of using the underground IAQ data in a subway station demonstrate that the monitoring capability of DCPLS is superior than those of PLS and CPLS. More specifically, the fault detection rates of the bias of PM10 and PM2.5 using DCPLS can be improved by approximately 13% and 15%, respectively, in comparison with those of CPLS.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12


  1. Bachoual R, Boczkowski J, Goven D, Amara N, Tabet L, On D, Leçon-Malas V, Aubier M, Lanone S (2007) Biological effects of particles from the Paris subway system. Chem Res Toxicol 20(10):1426–1433

  2. Bräuner EV, Frederiksen M, Kolarik B, Gunnarsen L (2014) Typical benign indoor aerosol concentrations in public spaces and designing biosensors for pathogen detection: a review. Build Environ 82:190–202

  3. Ge Z, Song Z, Gao F (2013) Review of recent research on data-based process monitoring. Ind Eng Chem Res 52(10):3543–3562

  4. Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17

  5. Jun BH (2011) Fault detection using dynamic time warping (DTW) algorithm and discriminant analysis for swine wastewater treatment. J Hazard Mater 185(1):262–268

  6. Karlsson HL, Nilsson L, Möller L (2005) Subway particles are more genotoxic than street particles and induce oxidative stress in cultured human lung cells. Chem Res Toxicol 18(1):19–23

  7. Kim Y-S, Kim JT, Kim I-W, Kim J-C, Yoo C (2010a) Multivariate monitoring and local interpretation of indoor air quality in Seoul's metro system. Environ Eng Sci 27(9):721–731

  8. Kim Y, Kim M, Lim J, Kim JT, Yoo C (2010b) Predictive monitoring and diagnosis of periodic air pollution in a subway station. J Hazard Mater 183(1–3):448–459

  9. Kim M, Liu H, Kim JT, Yoo C (2013) Sensor fault identification and reconstruction of indoor air quality (IAQ) data using a multivariate non-Gaussian model in underground building space. Energy Build 66:384–394

  10. Kim M, Liu H, Kim JT, Yoo C (2014) Evaluation of passenger health risk assessment of sustainable indoor air quality monitoring in metro systems based on a non-Gaussian dynamic sensor validation method. J Hazard Mater 278:124–133

  11. Kim M, Braatz RD, Kim JT, Yoo C (2015) Indoor air quality control for improving passenger health in subway platforms using an outdoor air quality dependent ventilation system. Build Environ 92:407–417

  12. Ku W, Storer RH, Georgakis C (1995) Disturbance detection and isolation by dynamic principal component analysis. Chemom Intell Lab Syst 30(1):179–196

  13. Kwon S-B, Jeong W, Park D, Kim K-T, Cho KH (2015) A multivariate study for characterizing particulate matter (PM10, PM2.5, and PM1) in Seoul metropolitan subway stations. Korea J Hazard Mater 297:295–303

  14. Lee J-M, Yoo C, Lee I-B (2004) Statistical monitoring of dynamic processes based on dynamic independent component analysis. Chem Eng Sci 59(14):2995–3006

  15. Lee S, Liu H, Kim M, Kim JT, Yoo C (2014) Online monitoring and interpretation of periodic diurnal and seasonal variations of indoor air pollutants in a subway station using parallel factor analysis (PARAFAC). Energy Build 68:87–98

  16. Lindgren F, Geladi P, Wold S (1993) The kernel algorithm for PLS. J Chemom 7(1):45–59

  17. Liu H, Yoo C (2016) A robust localized soft sensor for particulate matter modeling in Seoul metro systems. J Hazard Mater 305:209–218

  18. Liu Y, Huang D, Li Y (2012a) Development of interval soft sensors using enhanced just-in-time learning and inductive confidence predictor. Ind Eng Chem Res 51(8):3356–3367

  19. Liu H, Kim M, Kang O, Sankararao B, Kim J, Kim J-C, Yoo CK (2012b) Sensor validation for monitoring indoor air quality in a subway station. Indoor Built Environ 21(1):205–221

  20. Liu H, Huang M, Kim J, Yoo C (2013) Adaptive neuro-fuzzy inference system based faulty sensor monitoring of indoor air quality in a subway station. Korean J Chem Eng 30(3):528–539

  21. Liu Y, Liu B, Zhao X, Xie M (2018a) A mixture of variational canonical correlation analysis for nonlinear and quality-relevant process monitoring. IEEE Trans Ind Electron 65(8):6478–6486

  22. Liu H, Yang C, Huang M, Wang D, Yoo C (2018b) Modeling of subway indoor air quality using Gaussian process regression. J Hazard Mater 359:266–273

  23. Macgregor JF, Jaeckle C, Kiparissides C, Koutoudi M (1994) Process monitoring and diagnosis by multiblock PLS methods. AICHE J 40(5):826–838

  24. Ni W, Tan SK, Ng WJ, Brown SD (2012) Localized, adaptive recursive partial least squares regression for dynamic system modeling. Ind Eng Chem Res 51(23):8025–8039

  25. Passalía C, Alfano OM, Brandi RJ (2012) A methodology for modeling photocatalytic reactors for indoor pollution control using previously estimated kinetic parameters. J Hazard Mater 211-212:357–365

  26. Qin SJ (2003) Statistical process monitoring: basics and beyond. J Chemom 17(8–9):480–502

  27. Qin SJ (2012) Survey on data-driven industrial process monitoring and diagnosis. Annu Rev Control 36(2):220–234

  28. Qin SJ, Zheng Y (2013) Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures. AICHE J 59(2):496–504

  29. Seaton A, Cherrie J, Dennekamp M, Donaldson K, Hurley JF, Tran CL (2005) The London underground: dust and hazards to health. Occup Environ Med 62(6):355–362

  30. Shi H, Kim MJ, Liu H, Yoo CK (2016) Process modeling based on nonlinear PLS models using a prior knowledge-driven time difference method. J Taiwan Inst Chem E:6993–6105

  31. Wise BM, Gallagher NB (1996) The process chemometrics approach to process monitoring and fault detection. J Process Control 6(6):329–348

  32. Zhou D, Li G, Qin SJ (2010) Total projection to latent structures for process monitoring. AICHE J 56(1):168–178

Download references


This study was supported by the Foundation of Nanjing Forestry University (No. GXL029), a grant from the Subway Fine Dust Reduction Technology Development Project of the Ministry of Land Infrastructure and Transport (19QPPW-B152306-01) and the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (No. NRF-2019H1D3A1A02071051).

Author information

Correspondence to Hongbin Liu or ChangKyoo Yoo.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Responsible Editor: Philippe Garrigues

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Liu, H., Yang, C., Huang, M. et al. Multivariate statistical monitoring of subway indoor air quality using dynamic concurrent partial least squares. Environ Sci Pollut Res 27, 4159–4169 (2020).

Download citation


  • Concurrent partial least squares
  • Dynamic process monitoring
  • Fault detection
  • Indoor air quality
  • Subway systems