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Particulate air pollution in the Copenhagen metro part 2: Low-cost sensors and micro-environment classification

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  • Hugo S. Russell
  • Niklas Kappelt, AirLabs, University of Copenhagen
  • ,
  • Dafni Fessa
  • ,
  • Louise B. Frederickson
  • Evangelos Bagkis, Aristotle University of Thessaloniki
  • ,
  • Pantelis Apostolidis, Aristotle University of Thessaloniki
  • ,
  • Kostas Karatzas, Aristotle University of Thessaloniki
  • ,
  • Johan A. Schmidt, Devlabs
  • ,
  • Ole Hertel
  • Matthew S. Johnson, AirLabs, University of Copenhagen

In this study fine particulate matter (PM2.5) levels throughout the Copenhagen metro system are measured for the first time and found to be ∼10 times the roadside levels in Copenhagen. In this Part 2 article, low-cost sensor (LCS) nodes designed for personal-exposure monitoring are tested against a conventional mid-range device (TSI DustTrak), and gravimetric methods. The nodes were found to be effective for personal exposure measurements inside the metro system, with R2 values of > 0.8 at 1-min and > 0.9 at 5-min time-resolution, with an average slope of 1.01 in both cases, in comparison to the reference, which is impressive for this dynamic environment. Micro-environment (ME) classification techniques are also developed and tested, involving the use of auxiliary sensors, measuring light, carbon dioxide, humidity, temperature and motion. The output from these sensors is used to distinguish between specific MEs, namely, being aboard trains travelling above- or under- ground, with 83 % accuracy, and determining whether sensors were aboard a train or stationary at a platform with 92 % accuracy. This information was used to show a 143 % increase in mean PM2.5 concentration for underground sections relative to overground, and 22 % increase for train vs. platform measurements. The ME classification method can also be used to improve calibration models, assist in accurate exposure assessment based on detailed time-activity patterns, and facilitate field studies that do not require personnel to record time-activity diaries.

Original languageEnglish
Article number107645
JournalEnvironment International
Publication statusPublished - Dec 2022

    Research areas

  • Low-cost sensors, Machine learning, Metro, Micro-environment, Particulate matter, Personal exposure monitoring

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