Articles | Volume 12, issue 2
https://doi.org/10.5194/amt-12-1325-2019
https://doi.org/10.5194/amt-12-1325-2019
Research article
 | 
28 Feb 2019
Research article |  | 28 Feb 2019

An improved low-power measurement of ambient NO2 and O3 combining electrochemical sensor clusters and machine learning

Kate R. Smith, Peter M. Edwards, Peter D. Ivatt, James D. Lee, Freya Squires, Chengliang Dai, Richard E. Peltier, Mat J. Evans, Yele Sun, and Alastair C. Lewis

Related authors

Long-term evaluation of commercial air quality sensors: an overview from the QUANT (Quantification of Utility of Atmospheric Network Technologies) study
Sebastian Diez, Stuart Lacy, Hugh Coe, Josefina Urquiza, Max Priestman, Michael Flynn, Nicholas Marsden, Nicholas A. Martin, Stefan Gillott, Thomas Bannan, and Pete M. Edwards
Atmos. Meas. Tech., 17, 3809–3827, https://doi.org/10.5194/amt-17-3809-2024,https://doi.org/10.5194/amt-17-3809-2024, 2024
Short summary
Investigating the contribution of grown new particles to cloud condensation nuclei with largely varying preexisting particles – Part 2: Modeling chemical drivers and 3-D new particle formation occurrence
Ming Chu, Xing Wei, Shangfei Hai, Yang Gao, Huiwang Gao, Yujiao Zhu, Biwu Chu, Nan Ma, Juan Hong, Yele Sun, and Xiaohong Yao
Atmos. Chem. Phys., 24, 6769–6786, https://doi.org/10.5194/acp-24-6769-2024,https://doi.org/10.5194/acp-24-6769-2024, 2024
Short summary
Non-sea-salt aerosols that contain trace bromine and iodine are widespread in the remote troposphere
Gregory P. Schill, Karl D. Froyd, Daniel M. Murphy, Christina J. Williamson, Charles Brock, Tomás Sherwen, Mat J. Evans, Eric A. Ray, Eric C. Apel, Rebecca S. Hornbrook, Alan J. Hills, Jeff Peischl, Tomas B. Ryerson, Chelsea R. Thompson, Ilann Bourgeois, Donald R. Blake, Joshua P. DiGangi, and Glenn S. Diskin
EGUsphere, https://doi.org/10.5194/egusphere-2024-1399,https://doi.org/10.5194/egusphere-2024-1399, 2024
Short summary
The impact of multi-decadal changes in VOC speciation on urban ozone chemistry: a case study in Birmingham, United Kingdom
Jianghao Li, Alastair C. Lewis, Jim R. Hopkins, Stephen J. Andrews, Tim Murrells, Neil Passant, Ben Richmond, Siqi Hou, William J. Bloss, Roy M. Harrison, and Zongbo Shi
Atmos. Chem. Phys., 24, 6219–6231, https://doi.org/10.5194/acp-24-6219-2024,https://doi.org/10.5194/acp-24-6219-2024, 2024
Short summary
NAQPMS-PDAF v2.0: A Novel Hybrid Nonlinear Data Assimilation System for Improved Simulation of PM2.5 Chemical Components
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-78,https://doi.org/10.5194/gmd-2024-78, 2024
Preprint under review for GMD
Short summary

Related subject area

Subject: Gases | Technique: In Situ Measurement | Topic: Validation and Intercomparisons
Preparation of low-concentration H2 test gas mixtures in ambient air for calibration of H2 sensors
Niklas Karbach, Lisa Höhler, Peter Hoor, Heiko Bozem, Nicole Bobrowski, and Thorsten Hoffmann
Atmos. Meas. Tech., 17, 4081–4086, https://doi.org/10.5194/amt-17-4081-2024,https://doi.org/10.5194/amt-17-4081-2024, 2024
Short summary
Pico-Light H2O: intercomparison of in situ water vapour measurements during the AsA 2022 campaign
Mélanie Ghysels, Georges Durry, Nadir Amarouche, Dale Hurst, Emrys Hall, Kensy Xiong, Jean-Charles Dupont, Jean-Christophe Samake, Fabien Frérot, Raghed Bejjani, and Emmanuel D. Riviere
Atmos. Meas. Tech., 17, 3495–3513, https://doi.org/10.5194/amt-17-3495-2024,https://doi.org/10.5194/amt-17-3495-2024, 2024
Short summary
Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment
Andrew R. Whitehill, Melissa Lunden, Brian LaFranchi, Surender Kaushik, and Paul A. Solomon
Atmos. Meas. Tech., 17, 2991–3009, https://doi.org/10.5194/amt-17-2991-2024,https://doi.org/10.5194/amt-17-2991-2024, 2024
Short summary
Intercomparison of eddy-covariance software for urban tall-tower sites
Changxing Lan, Matthias Mauder, Stavros Stagakis, Benjamin Loubet, Claudio D'Onofrio, Stefan Metzger, David Durden, and Pedro-Henrique Herig-Coimbra
Atmos. Meas. Tech., 17, 2649–2669, https://doi.org/10.5194/amt-17-2649-2024,https://doi.org/10.5194/amt-17-2649-2024, 2024
Short summary
Assessment of current methane emission quantification techniques for natural gas midstream applications
Yunsong Liu, Jean-Daniel Paris, Gregoire Broquet, Violeta Bescós Roy, Tania Meixus Fernandez, Rasmus Andersen, Andrés Russu Berlanga, Emil Christensen, Yann Courtois, Sebastian Dominok, Corentin Dussenne, Travis Eckert, Andrew Finlayson, Aurora Fernández de la Fuente, Catlin Gunn, Ram Hashmonay, Juliano Grigoleto Hayashi, Jonathan Helmore, Soeren Honsel, Fabrizio Innocenti, Matti Irjala, Torgrim Log, Cristina Lopez, Francisco Cortés Martínez, Jonathan Martinez, Adrien Massardier, Helle Gottschalk Nygaard, Paula Agregan Reboredo, Elodie Rousset, Axel Scherello, Matthias Ulbricht, Damien Weidmann, Oliver Williams, Nigel Yarrow, Murès Zarea, Robert Ziegler, Jean Sciare, Mihalis Vrekoussis, and Philippe Bousquet
Atmos. Meas. Tech., 17, 1633–1649, https://doi.org/10.5194/amt-17-1633-2024,https://doi.org/10.5194/amt-17-1633-2024, 2024
Short summary

Cited articles

Broday, D. M., Arpaci, A., Bartonova, A., Castell-Balaguer, N., Cole-Hunter, T., Dauge, F. R., Fishbain, B., Jones, R. L., Galea, K., Jovasevic-Stojanovic, M., Kocman, D., Martinez-Iñiguez, T., Nieuwenhuijsen, M., Robinson, J., Svecova, V., and Thai, P.: Wireless distributed environmental sensor networks for air pollution measurement-the promise and the current reality, Sensors, 17, 2263, https://doi.org/10.3390/s17102263, 2017. 
Caron, A., Redon, N., Hanoune, B., and Coddeville, P.: Performances and limitations of electronic gas sensors to investigate an indoor air quality event, Build. Environ., 107, 19–28, https://doi.org/10.1016/j.buildenv.2016.07.006, 2016. 
Chan, C. K. and Yao, X.: Air pollution in mega cities in China, Atmos. Environ., 42, 1–42, https://doi.org/10.1016/j.atmosenv.2007.09.003, 2008. 
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, 13–17 August 2016 San Francisco, CA, USA, https://doi.org/10.1145/2939672.2939785, 2016. 
Edwards, P., Smith, K., Lewis, A., and Ivatt, P.: Low cost sensor in field calibrations (training and test data) – Beijing 2017, https://doi.org/10.15124/1a0c64b0-433b-4eec-b5c7-64d3de0a0351, 2017. 
Download
Short summary
Clusters of low-cost, low-power atmospheric gas sensors were built into a sensor instrument to monitor NO2 and O3 in Beijing, alongside reference instruments, aiming to improve the reliability of sensor measurements. Clustering identical sensors and using the median sensor signal was used to minimize drift over short and medium timescales. Three different machine learning techniques were used for all the sensor data in an attempt to correct for cross-interferences, which worked to some degree.