Articles | Volume 15, issue 2
Atmos. Meas. Tech., 15, 321–334, 2022
https://doi.org/10.5194/amt-15-321-2022
Atmos. Meas. Tech., 15, 321–334, 2022
https://doi.org/10.5194/amt-15-321-2022
Research article
21 Jan 2022
Research article | 21 Jan 2022

Evaluating uncertainty in sensor networks for urban air pollution insights

Daniel R. Peters et al.

Related authors

Improving NOx emission estimates in Beijing using network observations and a perturbed emissions ensemble
Le Yuan, Olalekan A. M. Popoola, Christina Hood, David Carruthers, Roderic L. Jones, Haitong Zhe Sun, Huan Liu, Qiang Zhang, and Alexander T. Archibald
Atmos. Chem. Phys., 22, 8617–8637, https://doi.org/10.5194/acp-22-8617-2022,https://doi.org/10.5194/acp-22-8617-2022, 2022
Short summary
In-situ measurements of NH3: instrument performance and applicability
Marsailidh M. Twigg, Augustinus J. C. Berkhout, Nicholas Cowan, Sabine Crunaire, Enrico Dammers, Volker Ebert, Vincent Gaudion, Marty Haaima, Christoph Häni, Lewis John, Matthew R. Jones, Bjorn Kamps, John Kentisbeer, Thomas Kupper, Sarah R. Leeson, Daiana Leuenberger, Nils O. B. Lüttschwager, Ulla Makkonen, Nicholas A. Martin, David Missler, Duncan Mounsor, Albrecht Neftel, Chad Nelson, Eiko Nemitz, Rutger Oudwater, Celine Pascale, Jean-Eudes Petit, Andrea Pogany, Nathalie Redon, Jörg Sintermann, Amy Stephens, Mark A. Sutton, Yuk S. Tang, Rens Zijlmans, Christine F. Braban, and Bernhard Niederhauser
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-107,https://doi.org/10.5194/amt-2022-107, 2022
Preprint under review for AMT
Short summary
Air pollution measurement errors: Is your data fit for purpose?
Sebastian Diez, Stuart Lacy, Thomas Bannan, Michael Flynn, Tom Gardiner, David Harrison, Nicholas Mardsen, Nick Martin, Katie Read, and Pete M. Edwards
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-58,https://doi.org/10.5194/amt-2022-58, 2022
Revised manuscript accepted for AMT
Short summary
Modelling spatiotemporal variations of the canopy layer urban heat island in Beijing at the neighbourhood scale
Michael Biggart, Jenny Stocker, Ruth M. Doherty, Oliver Wild, David Carruthers, Sue Grimmond, Yiqun Han, Pingqing Fu, and Simone Kotthaus
Atmos. Chem. Phys., 21, 13687–13711, https://doi.org/10.5194/acp-21-13687-2021,https://doi.org/10.5194/acp-21-13687-2021, 2021
Short summary
Observations of speciated isoprene nitrates in Beijing: implications for isoprene chemistry
Claire E. Reeves, Graham P. Mills, Lisa K. Whalley, W. Joe F. Acton, William J. Bloss, Leigh R. Crilley, Sue Grimmond, Dwayne E. Heard, C. Nicholas Hewitt, James R. Hopkins, Simone Kotthaus, Louisa J. Kramer, Roderic L. Jones, James D. Lee, Yanhui Liu, Bin Ouyang, Eloise Slater, Freya Squires, Xinming Wang, Robert Woodward-Massey, and Chunxiang Ye
Atmos. Chem. Phys., 21, 6315–6330, https://doi.org/10.5194/acp-21-6315-2021,https://doi.org/10.5194/acp-21-6315-2021, 2021
Short summary

Related subject area

Subject: Gases | Technique: In Situ Measurement | Topic: Validation and Intercomparisons
Performance characterization of low-cost air quality sensors for off-grid deployment in rural Malawi
Ashley S. Bittner, Eben S. Cross, David H. Hagan, Carl Malings, Eric Lipsky, and Andrew P. Grieshop
Atmos. Meas. Tech., 15, 3353–3376, https://doi.org/10.5194/amt-15-3353-2022,https://doi.org/10.5194/amt-15-3353-2022, 2022
Short summary
Comment on “Comparison of ozone measurement methods in biomass burning smoke: an evaluation under field and laboratory conditions” by Long et al. (2021)
Noah Bernays, Daniel A. Jaffe, Irina Petropavlovskikh, and Peter Effertz
Atmos. Meas. Tech., 15, 3189–3192, https://doi.org/10.5194/amt-15-3189-2022,https://doi.org/10.5194/amt-15-3189-2022, 2022
Short summary
Homogenization of the Observatoire de Haute Provence electrochemical concentration cell (ECC) ozonesonde data record: comparison with lidar and satellite observations
Gérard Ancellet, Sophie Godin-Beekmann, Herman G. J. Smit, Ryan M. Stauffer, Roeland Van Malderen, Renaud Bodichon, and Andrea Pazmiño
Atmos. Meas. Tech., 15, 3105–3120, https://doi.org/10.5194/amt-15-3105-2022,https://doi.org/10.5194/amt-15-3105-2022, 2022
Short summary
Long-term behavior and stability of calibration models for NO and NO2 low-cost sensors
Horim Kim, Michael Müller, Stephan Henne, and Christoph Hüglin
Atmos. Meas. Tech., 15, 2979–2992, https://doi.org/10.5194/amt-15-2979-2022,https://doi.org/10.5194/amt-15-2979-2022, 2022
Short summary
Controlled-release experiment to investigate uncertainties in UAV-based emission quantification for methane point sources
Randulph Morales, Jonas Ravelid, Katarina Vinkovic, Piotr Korbeń, Béla Tuzson, Lukas Emmenegger, Huilin Chen, Martina Schmidt, Sebastian Humbel, and Dominik Brunner
Atmos. Meas. Tech., 15, 2177–2198, https://doi.org/10.5194/amt-15-2177-2022,https://doi.org/10.5194/amt-15-2177-2022, 2022
Short summary

Cited articles

Apte, J. S., Messier, K. P., Gani, S., Brauer, M., Kirchstetter, T. W., Lunden, M. M., Marshall, J. D., Portier, C. J., Vermeulen, R. C. H., and Hamburg, S. P.: High-resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data, Environ. Sci. Technol., 51, 6999–7008, https://doi.org/10.1021/acs.est.7b00891, 2017. 
AQMesh: https://www.aqmesh.com/products/aqmesh/, last access: 15 June 2021. 
AQ-SPEC: AQMesh (v.4.0) – field evaluation, South Coast AQMD, available at: http://www.aqmd.gov/aq-spec/sensordetail/aqmesh-(v.4.0) (last access: 7 January 2022), Diamond Bar, CA, 2015. 
Bi, J., Stowell, J., Seto, E. Y. W., English, P. B., Al-Hamdan, M. Z., Kinney, P. L., Freedman, F. R., and Liu, Y.: Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA, Environ. Res., 180, 108810, https://doi.org/10.1016/j.envres.2019.108810, 2020. 
Breathe London: AQMesh fixed sensor network data quality assurance and control procedures, available at: https://www.globalcleanair.org/files/2021/01/Breathe-London-Fixed-Sensor-Network-QAQC-Procedures.pdf (last access: 7 January 2022), 2020. 
Download
Short summary
We present more than 2 years of NO2 pollution measurements from a sensor network in Greater London and compare results to an extensive network of expensive reference-grade monitors. We show the ability of our lower-cost network to generate robust insights about local air pollution. We also show how irregularities in sensor performance lead to some uncertainty in results and demonstrate ways that future users can characterize and mitigate uncertainties to get the most value from sensor data.