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.

Data sets

Breathe London Stationary Breathe London https://openaq.org/#/project/28967

Data Downloads LondonAir https://www.londonair.org.uk/london/asp/datadownload.asp

worldmet: Import Surface Meteorological Data from NOAA Integrated Surface Database (ISD) D. Carslaw https://CRAN.R-project.org/package=worldmet

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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.