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Volume 11, issue 6 | Copyright
Atmos. Meas. Tech., 11, 3717-3735, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Jun 2018

Research article | 26 Jun 2018

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 Alessandro Bigi et al.
  • 1“Enzo Ferrari” Department of Engineering, University of Modena and Reggio Emilia, Modena, Italy
  • 2Empa, Swiss Federal Laboratories for Materials Science and Technology, Duebendorf, Switzerland
  • 3Wolfson Atmospheric Chemistry Laboratory, University of York, York, UK

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 are 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 three 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, noise 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  < 5ppb, R2 between 0.74 and 0.95 and MAE between 2 and 4ppb. 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 and 10ppb for Random Forest and 15ppb for Multivariate Linear Regression at the end of the deployment. The lowest concentration correctly estimated, with a 25% relative expanded uncertainty, resulted in ca. 15–20ppb and 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 5–10ppb (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, are based on a flexible methodology and have extensive potential for exploring the performance of other low cost sensors, that are different in their target pollutant and sensing technology.

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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, thus urging for more research. This study focuses on sensor performance within the realistic framework of an initial calibration next to a reference instrument and the subsequent distant deployment. Within this framework, 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....