RPCA methods for pattern extraction, hotspot identification and signal correction on NO2 time-series from the London Air Quality Network database
Bogaert, Martin; Mouritzen, Christian; Johnson, Matthew Stanley; van Reeuwijk, Maarten;
RPCA methods for pattern extraction, hotspot identification and signal correction on NO2 time-series from the London Air Quality Network database
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.
- Imperial College London United Kingdom
- University of Copenhagen Denmark
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These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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