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

Hugo S Russell; Niklas Kappelt; Dafni Fessa; Louise B Frederickson; Evangelos Bagkis; Pantelis Apostolidis; Kostas Karatzas; Johan A Schmidt; Ole Hertel; Matthew S Johnson

doi:10.1016/j.envint.2022.107645

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

Environ Int. 2022 Dec:170:107645. doi: 10.1016/j.envint.2022.107645. Epub 2022 Nov 17.

Authors

Affiliations

¹ Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark; AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark.
² AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Department of Chemistry, Copenhagen University, DK-2100 Copenhagen, Denmark.
³ Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark.
⁴ Environmental Informatics Research Group, School of Mechanical Engineering, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece.
⁵ Devlabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark.
⁶ Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark; Department of Ecoscience, Aarhus University, DK-4000 Roskilde, Denmark.
⁷ AirLabs, Nannasgade 28, DK-2200 Copenhagen N, Denmark; Department of Chemistry, Copenhagen University, DK-2100 Copenhagen, Denmark. Electronic address: msj@chem.ku.dk.

PMID: 36434885
DOI: 10.1016/j.envint.2022.107645

Abstract

In this study fine particulate matter (PM_2.5) 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 PM_2.5 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.

Keywords: Low-cost sensors; Machine learning; Metro; Micro-environment; Particulate matter; Personal exposure monitoring.

MeSH terms

Air Pollution*