23 Jun 2022
23 Jun 2022

Are dense networks of low-cost nodes better at monitoring air pollution? A case study in Staffordshire

Louise Bøge Frederickson1,2,3, Ruta Sidaraviciute3, Johan Albrecht Schmidt3, Ole Hertel5, and Matthew Stanley Johnson3,4 Louise Bøge Frederickson et al.
  • 1Department of Environmental Science, Aarhus University, Frederiksborgvej 399, DK-4000 Roskilde, Denmark
  • 2Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, Frederiksborgvej 399, DK-4000 Roskilde, Denmark
  • 3AirLabs Denmark, Nannasgade 28, DK-2200 Copenhagen N, Denmark
  • 4Department of Chemistry, University of Copenhagen, Universitetsparken 5, DK-2100 Copenhagen Ø, Denmark
  • 5Department of Ecoscience, Aarhus University, Frederiksborgvej 399, DK-4000 Roskilde, Denmark

Abstract. Air pollution exhibits hyper-local variation, especially near emissions sources. In addition to people's time-activity patterns, this variation is the most critical element determining exposure. Pollution exposure is time-activity and path-dependent with specific behaviors such as mode of commuting and time spent near a roadway or in a park playing a decisive role. Compared to conventional air pollution monitoring stations, nodes containing low-cost air pollution sensors can be deployed with very high density. Monitoring stations are often tasked with characterizing regional air pollution and are therefore placed away from local sources, leaving the additional burden of local emissions such as traffic uncharacterized. In this study, a network of 18 nodes using low-cost air pollution sensors was deployed in Newcastle-under-Lyme, Staffordshire, UK, in June 2020. Each node measured a range of species including nitrogen dioxide (NO2), ozone (O3) and particulate matter (PM2.5 and PM10); this study focuses on NO2 and PM2.5 over a one year period from August 1, 2020 to October 1, 2021. A simple and effective temperature, scale and offset correction was able to overcome data quality issues associated with temperature bias in the NO2 readings. In its recent update, the World Health Organization dramatically reduced annual exposure limit values from 40 to 10 μg m-3 for NO2 and from 10 to 5 μg m-3 for PM2.5. We found the average annual mean NO2 concentration for the network was 17.5 μg m-3, and 8.1 μg m-3 for PM2.5. While in exceedance of the WHO guideline levels, these average concentrations do not exceed legally binding UK/EU standards. The network average NO2 concentration was 12.5 μg m-3 higher than values reported by a nearby regional air quality monitoring station, showing the critical importance of monitoring close to sources before pollution is diluted. We demonstrate how data from a low-cost air pollution sensor network can reveal insights into patterns of air pollution and help determine whether sources are local or non-local. With spectral analysis, we investigate the variation of the pollution levels and identify typical periodicities. Both NO2 and PM2.5 have contributions from high-frequency sources, however, the low-frequency sources are significantly different. Using spectral analysis, we determine that at least 54.3 ± 4.3 % of NO2 is from local sources, whereas in contrast, only 37.9 ± 3.5 % of PM2.5 is local.

Louise Bøge Frederickson et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-407', Anonymous Referee #1, 19 Jul 2022
  • RC2: 'Comment on egusphere-2022-407', Anonymous Referee #2, 26 Jul 2022

Louise Bøge Frederickson et al.

Louise Bøge Frederickson et al.


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Short summary
Low cost sensors see additional pollution that is not seen with traditional regional air quality monitoring stations. This additional local pollution is sufficient to cause exceedance of the World Health Organization exposure thresholds. Analysis shows that a significant amount of the NO2 pollution we observe is local, mainly due to road traffic. This article demonstrates how networks of nodes containing low cost pollution sensors can powerfully extend existing monitoring programs.