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

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.