This paper by our talented team in Copenhagen was published in the prestigious international peer-reviewed journal Atmospheric Chemistry and Physics. They used data from the Staffordshire network to answer the question, ‘Are dense networks of low-cost nodes better at monitoring air pollution?’
They found that the dense network sees a lot of pollution that is not seen by the regional monitoring stations, and that additional local pollution is what pushes concentrations over the WHO exposure threshold. The local component of pollution is large and often invisible to the regional stations.
‘We determine that at least 54.3 ± 4.3 % of NO 2 is from local sources, whereas in contrast, only 37.9 ± 3.5 % of PM 2.5 is local.’
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.
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 AirNodes using low-cost air pollution sensors was deployed in Newcastle-under-Lyme, Staffordshire, UK, in June 2020.
‘The network average NO2 concentration was 12.5 µg m−3 higher than values reported by a nearby regional air quality monitoring station. This demonstrates the critical importance of monitoring close to sources before pollution is diluted.’
We found that data from our low-cost air pollution sensor network revealed insights into patterns of air pollution, and helped determine whether sources were local or non-local.
Read the full paper here