Design of an ozone and nitrogen dioxide sensor unit and its long-term operation within a sensor network in the city of Zurich

Abstract

Abstract. This study focuses on the investigation and quantification of low-cost sensor performance in application fields such as the extension of traditional air quality monitoring networks or the replacement of diffusion tubes. For this, sensor units consisting of two boxes featuring NO2 and O3 low-cost sensors and wireless data transfer were engineered. The sensor units were initially operated at air quality monitoring sites for three months for performance analysis and initial calibration. Afterwards, they were relocated and operated within a sensor network consisting of six locations for more than one year. Our analyses show that the employed O3 and NO2 sensors can be accurate to 2–5 and 5–7 ppb, respectively, during the first three months of operation. This accuracy, however, could not be maintained during their operation within the sensor network related to changes in sensor behaviour. Hence, the low-cost sensors in our configuration do not reach the accuracy level of NO2 diffusion tubes. Tests in the laboratory revealed that changes in relative humidity can impact the signal of the employed NO2 sensors similarly as changes in ambient NO2 concentration. All the employed low-cost sensors need to be individually calibrated. Best performance of NO2 sensors is achieved when the calibration models include also time dependent parameters accounting for changes in sensor response over time. Accordingly, an effective procedure for continuous data control and correction is essential for obtaining meaningful data. It is demonstrated that linking the measurements from low-cost sensors to the high quality measurements from routine air quality monitoring stations is an effective procedure for both tasks provided that time periods can be identified when pollutant concentrations can be accurately predicted at sensor locations.

Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements

“…The details of the HDMR algorithms used here are discussed in detail elsewhere (Li and Rabitz, 2010;Sipilä et al, 2010;. One of the key underlying tenets of the HDMR framework is that many input-output relationships for complex physical systems can be captured adequately by low-order combinations of input variables, even in systems with high dimensionality in input variables.…”

Effect of Relative Humidity and Air Temperature on the Results Obtained from Low-Cost Gas Sensors for Ambient Air Quality Measurements

“…Some researchers used the second technique that consisted on the deployment of the low-cost sensors near reference monitoring stations or professional measuring instruments. These colocation measurements helped to compare and calibrate the low-cost sensors according to the data obtained from reference instruments [ 25 , 26 , 27 , 28 , 29 ]. In this case, the advantage is that the low-cost sensors were exposed directly to the desired environment in which they are to be deployed.…”

Citizen sensing for environmental information

“…The accuracy of the measurements can be improved by including temperature and humidity measurements from other low-cost sensors in a multilinear regression approach. It is worth noting that more advanced calibration algorithms such as by Cross et al (2017) and Mueller et al (2017) could give better results, but this is not the focus of this paper. It is hard to quantify an optimal length of a calibration period without having a proper understanding of the sensor degradation rate beforehand.…”

“…When a sensor device is switched on for service, the electrochemical cell must be stabilized by the potentiostatic circuit which can take a few hours due to the high capacitance of the working electrode (AAN 105-03, 2009). Furthermore, when the sensor is transported to another environment the sudden change in RH causes an equilibrium distortion with a relaxation time of about 2h (Mueller et al, 2017). The startup-effect is translated by the calibration model as a strong positive NO2 peak, which should be filtered out.…”

“…For instance, Cross et al (2017) applied a high-dimensional model representation method for calibration which significantly improved the sensor performance in their experiment. As indicated by Mueller et al (2017) who compared different models for ozone and nitrogen dioxide sensor calibration, more advanced models should be studied to improve the sensor performance in different environmental conditions. The calibration of air quality sensors still has open scientific questions and is not fully studied yet .…”