A study on the performance of low-cost sensors for source apportionment at an urban background site

Abstract

Abstract. Knowledge of air pollution sources is important in policymaking and air pollution mitigation. Until recently, source apportion analyses were limited and only possible with the use of expensive regulatory-grade instruments. In the present study we applied a two-step positive matrix factorisation (PMF) receptor analysis at a background site in Birmingham, UK using data acquired by low-cost sensors (LCSs). The application of PMF allowed for the identification of the sources that affect the local air quality, clearly separating different sources of particulate matter (PM) pollution. Furthermore, the method allowed for the contribution of different air pollution sources to the overall air quality at the site to be estimated, thereby providing pollution source apportionment. The use of data from regulatory-grade (RG) instruments further confirmed the reliability of the results, as well as further clarifying the particulate matter composition and origin. Compared with the results from a previous analysis, in which a k-means clustering algorithm was used, a good consistency between the k means and PMF results was found in pinpointing and separating the sources of pollution that affect the site. The potential and limitations of each method when used with low-cost sensor data are highlighted. The analysis presented in this study paves the way for more extensive use of LCSs for atmospheric applications, receptor modelling and source apportionment. Here, we present the infrastructure for understanding the factors that affect air quality at a significantly lower cost than previously possible. This should provide new opportunities for regulatory and indicative monitoring for both scientific and industrial applications.

The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK

“…This sensor was chosen for a variety of reasons, firstly, the unit cost of the OPC-N3 is relatively low (∼£250) when compared to reference grade OPC instruments. Secondly, it is currently the low-cost OPC sensor with the most independent testing and validation in the scientific literature [ 31 , 45 ]. Thirdly, access to raw sensor information (voltages) available remotely enables high-fidelity, independent data analysis and data quality assurance processes to be undertaken.…”

Low-Cost Investigation into Sources of PM2.5 in Kinshasa, Democratic Republic of the Congo

“…We follow the methodology of Hagan et al (2019) and Yang et al (2022), 21,22 where further details can be found. The method is similar to Bousiotis et al (2022) 44 and Bousiotis et al (2023) 45 who use positive matrix factorization (PMF) to infer aerosol sources. Our time series input matrix includes the entire campaign at 5 min resolution of CO, O 3 , NO, and NO 2 signals, expressed in change in voltage between the working and auxiliary electrodes (ΔmV), and the particle counts per volume (cm −3 ) in 6 size bins from the Alphasense OPC-N3.…”

Indoor air quality monitoring and source apportionment using low-cost sensors

“…Bousiotis et al (2021) used receptor-based k-means clustering with concurrent measurements from regulatory-grade instruments and LCS to identify sources of pollution at a site in Birmingham, UK. Bousiotis et al (2022) then went on to apply PMF receptor-based analysis to apportion sources using data collected from LCS. This study also used data from reference-grade instruments and aerosol chemical speciation monitors to verify their analysis.…”