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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
12 Mar 2018
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Measurement Techniques (AMT) and is expected to appear here in due course.
Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application
Alessandro Bigi1, Michael Mueller2, Stuart K. Grange3, Grazia Ghermandi1, and Christoph Hueglin2 1‘Enzo Ferrari’ Department of Engineering, University of Modena and Reggio Emilia, Modena, Italy
2Empa, Swiss Federal Institute for Materials Science and Technology, Duebendorf, Switzerland
3Wolfson Atmospheric Chemistry Laboratory, University of York, York, United Kingdom
Abstract. Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices is being delivered to the public notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using 3 different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4–month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, precision and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE < 5 ppb, R2 between 0.74–0.95 and MAE between 2–4 ppb. The combined use of both NO and NO2 sensor output in the estimate of each pollutant showed some contribution by NO sensor to NO2 estimate and vice-versa. All algorithms exhibited a drift ranging between 5–10 ppb for Random Forest and 15 ppb for Multivariate Linear regression at the end of the deployment. The lowest concentration correctly estimated, with a 25 % relative expanded uncertainty, resulted in ca. 15–20 ppb and it was provided by the non-linear algorithms. As an assessment for the suitability of the tested sensors for a targeted application, the probability of resolving hourly concentration difference in cities was investigated. It was found that NO concentration differences of 5–10 ppb (8–10 for NO2) can reliably be detected (90 % confidence), depending on the air pollution level. The findings of this study, although derived from a specific sensor type and sensor model, base on a flexible methodology and have a large potential to explore the performance of other low cost sensors, different in target pollutant and sensing technology.
Citation: Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech. Discuss.,, in review, 2018.
Alessandro Bigi et al.
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
RC1: 'Bigi et al.', Anonymous Referee #1, 27 Mar 2018 Printer-friendly Version 
AC1: 'Reply to comments by Anonymous Referee #1', Alessandro Bigi, 29 May 2018 Printer-friendly Version Supplement 
RC2: 'Interactive general comments', Anonymous Referee #2, 31 Mar 2018 Printer-friendly Version 
AC2: 'Reply to comments by Anonymous Referee #2', Alessandro Bigi, 29 May 2018 Printer-friendly Version Supplement 
Alessandro Bigi et al.
Alessandro Bigi et al.


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Short summary
Low cost sensors for monitoring atmospheric pollution are growing in popularity worldwide, nonetheless the expectations from these devices were seldom met, due to several problems, therefore urging for more research. In this study we focussed on sensor performance within the realistic framework of an initial calibration next to a reference instrument and the subsequent distant deployment. We assessed the uncertainty of these sensors and their suitability to map intra-urban gradients of NO/NO2.
Low cost sensors for monitoring atmospheric pollution are growing in popularity worldwide,...