Van Reeuwijk Lab
Published February 26, 2024 | Version v1
Computational notebook Open

RPCA methods for pattern extraction, hotspot identification and signal correction on NO2 time-series from the London Air Quality Network database

Authors/Creators

  • 1. ROR icon Imperial College London
  • 2. ROR icon University of Copenhagen

Description

This code presents the main commands used to compute the results on NO2 data from a dense network of low-cost sensors in Camden, London. It presents the use of robust principal component analysis (RPCA) to perform pattern extraction, pollution peaks identification, and signal correction.

Bogaert, M., Mouritzen, C., Johnson, M. S. and van Reeuwijk, M. (2024) RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London. Science of The Total Environment. Volume 925, 171522. ISSN 0048-9697. DOI: 10.1016/j.scitotenv.2024.171522.

Please cite this paper if you use the code.

Files

RPCA-Code-Bogaert.zip

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Additional details

Software

Programming language
Python

References

  • Brunton, S. L. & Kutz, J. N. (2022) Data-driven science and engineering: machine learning, dynamical systems, and control. Cambridge University Press, Cambridge, United Kingdom, New York, NY
  • London Air Quality Network (2023) Data Downloads. URL: https: //www.londonair.org.uk/london/asp/datadownload.asp