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

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., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024,https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Theoretical Framework for Measuring Cloud Effective Supersaturation Fluctuations with an Advanced Optical System
Ye Kuang, Jiangchuan Tao, Hanbin Xu, Li Liu, Pengfei Liu, Wanyun Xu, Weiqi Xu, Yele Sun, and Chunsheng Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2698,https://doi.org/10.5194/egusphere-2024-2698, 2024
Short summary
An improved estimate of inorganic iodine emissions from the ocean using a coupled surface microlayer box model
Ryan J. Pound, Lucy V. Brown, Mat J. Evans, and Lucy J. Carpenter
Atmos. Chem. Phys., 24, 9899–9921, https://doi.org/10.5194/acp-24-9899-2024,https://doi.org/10.5194/acp-24-9899-2024, 2024
Short summary
Hygroscopic growth and activation changed submicron aerosol composition and properties in the North China Plain
Weiqi Xu, Ye Kuang, Wanyun Xu, Zhiqiang Zhang, Biao Luo, Xiaoyi Zhang, Jiangchuang Tao, Hongqin Qiao, Li Liu, and Yele Sun
Atmos. Chem. Phys., 24, 9387–9399, https://doi.org/10.5194/acp-24-9387-2024,https://doi.org/10.5194/acp-24-9387-2024, 2024
Short summary
Markedly different impacts of primary emissions and secondary aerosol formation on aerosol mixing states revealed by simultaneous measurements of CCNC, H(/V)TDMA, and SP2
Jiangchuan Tao, Biao Luo, Weiqi Xu, Gang Zhao, Hanbin Xu, Biao Xue, Miaomiao Zhai, Wanyun Xu, Huarong Zhao, Sanxue Ren, Guangsheng Zhou, Li Liu, Ye Kuang, and Yele Sun
Atmos. Chem. Phys., 24, 9131–9154, https://doi.org/10.5194/acp-24-9131-2024,https://doi.org/10.5194/acp-24-9131-2024, 2024
Short summary

Related subject area

Subject: Gases | Technique: In Situ Measurement | Topic: Validation and Intercomparisons
Alternate materials for the capture and quantification of gaseous oxidized mercury in the atmosphere
Livia Lown, Sarrah M. Dunham-Cheatham, Seth N. Lyman, and Mae S. Gustin
Atmos. Meas. Tech., 17, 6397–6413, https://doi.org/10.5194/amt-17-6397-2024,https://doi.org/10.5194/amt-17-6397-2024, 2024
Short summary
Lower-cost eddy covariance for CO2 and H2O fluxes over grassland and agroforestry
Justus G. V. van Ramshorst, Alexander Knohl, José Ángel Callejas-Rodelas, Robert Clement, Timothy C. Hill, Lukas Siebicke, and Christian Markwitz
Atmos. Meas. Tech., 17, 6047–6071, https://doi.org/10.5194/amt-17-6047-2024,https://doi.org/10.5194/amt-17-6047-2024, 2024
Short summary
Towards a high quality in-situ observation network for oxygenated volatile organic compounds (OVOCs) in Europe: transferring traceability to the International System of Units (SI) to the field
Maitane Iturrate-Garcia, Thérèse Salameh, Paul Schlauri, Annarita Baldan, Martin K. Vollmer, Evdokia Stratigou, Sebastian Dusanter, Jianrong Li, Stefan Persijn, Anja Claude, Rupert Holzinger, Christophe Sutour, Tatiana Macé, Yasin Elshorbany, Andreas Ackermann, Céline Pascale, and Stefan Reimann
EGUsphere, https://doi.org/10.5194/egusphere-2024-2236,https://doi.org/10.5194/egusphere-2024-2236, 2024
Short summary
Evaluation of optimized flux chamber design for measurement of ammonia emission after field application of slurry with full-scale farm machinery
Johanna Pedersen, Sasha D. Hafner, Andreas Pacholski, Valthor I. Karlsson, Li Rong, Rodrigo Labouriau, and Jesper N. Kamp
Atmos. Meas. Tech., 17, 4493–4505, https://doi.org/10.5194/amt-17-4493-2024,https://doi.org/10.5194/amt-17-4493-2024, 2024
Short summary
Methodology and uncertainty estimation for measurements of methane leakage in a manufactured house
Anna Karion, Michael F. Link, Rileigh Robertson, Tyler Boyle, and Dustin Poppendieck
EGUsphere, https://doi.org/10.5194/egusphere-2024-2129,https://doi.org/10.5194/egusphere-2024-2129, 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.