TY - JOUR

T1 - Are dense networks of low-cost nodes really useful for monitoring air pollution? A case study in Staffordshire

AU - Frederickson, Louise Boge

AU - Sidaraviciute, Ruta

AU - Schmidt, Johan Albrecht

AU - Hertel, Ole

AU - Johnson, Matthew Stanley

PY - 2022

Y1 - 2022

N2 - 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 behaviours 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. 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 (O-3), and particulate matter (PM2.5 and PM10); this study focuses on NO2 and PM2.5 over a 1-year period from 1 August 2020 to 1 October 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 (WHO) dramatically reduced annual exposure limit values from 40 to 10 mu g m(-3) for NO2 and from 10 to 5 mu g m(-3) for PM2.5. We found that the average annual mean NO2 concentration for the network was 17.5 mu g m(-3) and 8.1 mu g 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 mu g 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.

AB - 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 behaviours 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. 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 (O-3), and particulate matter (PM2.5 and PM10); this study focuses on NO2 and PM2.5 over a 1-year period from 1 August 2020 to 1 October 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 (WHO) dramatically reduced annual exposure limit values from 40 to 10 mu g m(-3) for NO2 and from 10 to 5 mu g m(-3) for PM2.5. We found that the average annual mean NO2 concentration for the network was 17.5 mu g m(-3) and 8.1 mu g 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 mu g 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.

KW - SPECTRAL-ANALYSIS

KW - ELECTROCHEMICAL SENSORS

KW - FIELD CALIBRATION

KW - AVAILABLE SENSORS

KW - POLAR PLOTS

KW - OUTDOOR AIR

KW - TIME-SERIES

KW - QUALITY

KW - POLLUTANTS

KW - PART

U2 - 10.5194/acp-22-13949-2022

DO - 10.5194/acp-22-13949-2022

M3 - Journal article

VL - 22

SP - 13949

EP - 13965

JO - Atmospheric Chemistry and Physics

JF - Atmospheric Chemistry and Physics

SN - 1680-7316

IS - 21

ER -