Skip to main content
SpringerLink
Log in

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

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

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 via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • 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

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

    Article  CAS  Google Scholar 

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

    CAS  Google Scholar 

Download references

Funding

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

Author notes

  1. Hongbin Liu and Chong Yang contributed equally to this work.

Authors and Affiliations

  1. Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, 210037, China

    Hongbin Liu & Chong Yang

  2. Department of Environmental Science and Engineering, College of Engineering, Kyung Hee University, Yongin, 446701, South Korea

    Hongbin Liu & ChangKyoo Yoo

  3. Environmental Research Institute, Key Laboratory of Theoretical Chemistry of Environment Ministry of Education, South China Normal University, Guangzhou, 510631, China

    Mingzhi Huang

Authors
  1. Hongbin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingzhi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. ChangKyoo Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Hongbin Liu or ChangKyoo Yoo.

Additional information

Responsible Editor: Philippe Garrigues

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. 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). https://doi.org/10.1007/s11356-019-06935-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-019-06935-9

Keywords

Search

Navigation