资源环境科技发展态势分析平台

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 (PM_2.5 and PM₁₀); this study focuses on NO₂ and PM_2.5 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 PM_2.5. We found the average annual mean NO₂ concentration for the network was 17.5 μg m^-3, and 8.1 μg m^-3 for PM_2.5. 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 PM_2.5 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 PM_2.5 is local.