17 Mar 2022
17 Mar 2022
Status: a revised version of this preprint is currently under review for the journal AMT.

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

Dimitrios Bousiotis1, David Beddows1, Ajit Singh1, Molly Haugen2, Sebastián Diez3, Pete Edwards3, Adam Boies2, Roy Harrison1, and Francis Pope1 Dimitrios Bousiotis et al.
  • 1Division of Environmental Health and Risk Management, School of Geography, Earth and Environmental Sciences University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
  • 2Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom
  • 3Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, Heslington, York YO10 5DD, United Kingdom

Abstract. While the measurements of atmospheric pollutants are useful in understanding the level of the air quality at a given area, receptor models are equally important in assessing the sources of these pollutants and the extent of their effect, helping in policy making to deal with air pollution problems. Such analyses were limited and were attempted until recently only 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 using data acquired by low-cost sensors (LCS). Using PMF, the identification of the sources that affect the air quality at the background site in Birmingham provided results that were consistent with a previous study at the site, even though in different measuring periods, but also clearly separated the anticipated sources of particulate matter (PM) and pollution. Additionally, the method supplied a metric for the contribution of different sources to the overall air quality at the site, thus 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. Comparing the results from a previous analysis, in which a k-means clustering algorithm was used, a good consistency between the results was found, and 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 LCS for atmospheric applications and receptor modelling. Here, we present the infrastructure for understanding the factors that affect the air quality at a significantly lower cost that previously possible, thus opening up multiple new opportunities for regulatory and indicative monitoring for both scientific and industrial applications.

Dimitrios Bousiotis et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-84', Anonymous Referee #1, 28 Apr 2022
  • RC2: 'Comment on amt-2022-84', Anonymous Referee #2, 30 Apr 2022

Dimitrios Bousiotis et al.

Dimitrios Bousiotis et al.


Total article views: 419 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
306 99 14 419 17 4 4
  • HTML: 306
  • PDF: 99
  • XML: 14
  • Total: 419
  • Supplement: 17
  • BibTeX: 4
  • EndNote: 4
Views and downloads (calculated since 17 Mar 2022)
Cumulative views and downloads (calculated since 17 Mar 2022)

Viewed (geographical distribution)

Total article views: 399 (including HTML, PDF, and XML) Thereof 399 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 24 May 2022
Short summary
In the last decade, low cost sensors have revolutionised the field of air quality monitoring. This paper extends the ability of low cost sensors to not only measure air pollution but to understand where the pollution comes from. This “source apportionment” is a critical step in air quality management to allow for the mitigation of air pollution. The techniques developed in this paper has the potential for great impact in both research and industrial applications.