Articles | Volume 14, issue 8
Research article
18 Aug 2021
Research article |  | 18 Aug 2021

Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability

Peer Nowack, Lev Konstantinovskiy, Hannah Gardiner, and John Cant

Related authors

Sensitivities of cloud radiative effects to large-scale meteorology and aerosols from global observations
Hendrik Andersen, Jan Cermak, Alyson Douglas, Timothy A. Myers, Peer Nowack, Philip Stier, Casey J. Wall, and Sarah Wilson Kemsley
EGUsphere,,, 2023
Short summary
A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019
Xiang Weng, Grant L. Forster, and Peer Nowack
Atmos. Chem. Phys., 22, 8385–8402,,, 2022
Short summary
An unsupervised learning approach to identifying blocking events: the case of European summer
Carl Thomas, Apostolos Voulgarakis, Gerald Lim, Joanna Haigh, and Peer Nowack
Weather Clim. Dynam., 2, 581–608,,, 2021
Short summary
The importance of antecedent vegetation and drought conditions as global drivers of burnt area
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879,,, 2021
Short summary
Evaluating stratospheric ozone and water vapour changes in CMIP6 models from 1850 to 2100
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061,,, 2021
Short summary

Related subject area

Subject: Gases | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise
Blake Actkinson and Robert J. Griffin
Atmos. Meas. Tech., 16, 3547–3559,,, 2023
Short summary
Characterising the methane gas and environmental response of the Figaro Taguchi Gas Sensor (TGS) 2611-E00
Adil Shah, Olivier Laurent, Luc Lienhardt, Grégoire Broquet, Rodrigo Rivera Martinez, Elisa Allegrini, and Philippe Ciais
Atmos. Meas. Tech., 16, 3391–3419,,, 2023
Short summary
Reducing errors on estimates of the carbon uptake period based on time series of atmospheric CO2
Theertha Kariyathan, Ana Bastos, Julia Marshall, Wouter Peters, Pieter Tans, and Markus Reichstein
Atmos. Meas. Tech., 16, 3299–3312,,, 2023
Short summary
Generalized Kendrick analysis for improved visualization of atmospheric mass spectral data
Mitchell W. Alton, Harald J. Stark, Manjula R. Canagaratna, and Eleanor C. Browne
Atmos. Meas. Tech., 16, 3273–3282,,, 2023
Short summary
Determination of NOx emission rates of inland ships from onshore measurements
Kai Krause, Folkard Wittrock, Andreas Richter, Dieter Busch, Anton Bergen, John P. Burrows, Steffen Freitag, and Olesia Halbherr
Atmos. Meas. Tech., 16, 1767–1787,,, 2023
Short summary

Cited articles

Bishop, C. M.: Pattern recognition and machine learning, Springer Science+Business Media, Singapore, 2006. a, b
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,, 2001. a, b
Breiman, L. and Friedman, J. H.: Predicting multivariate responses in multiple linear regression, J. Roy. Stat. Soc.-B, 59, 3–54,, 1997. a
Casey, J. G. and Hannigan, M. P.: Testing the performance of field calibration techniques for low-cost gas sensors in new deployment locations: across a county line and across Colorado, Atmos. Meas. Tech., 11, 6351–6378,, 2018. a, b
Casey, J. G., Collier-Oxandale, A., and Hannigan, M.: Performance of artificial neural networks and linear models to quantify 4 trace gas species in an oil and gas production region with low-cost sensors, Sensor. Actuat. B-Chem., 283, 504–514,, 2019. a
Short summary
Machine learning (ML) calibration techniques could be an effective way to improve the performance of low-cost air pollution sensors. Here we provide novel insights from case studies within the urban area of London, UK, where we compared the performance of three ML techniques to calibrate low-cost measurements of NO2 and PM10. In particular, we highlight the key issue of the method-dependent robustness in maintaining calibration skill after transferring sensors to different measurement sites.