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

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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.

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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).

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Correspondence to Hongbin Liu or ChangKyoo Yoo.

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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

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Keywords

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