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 3 months for performance analysis and initial calibration. Afterwards, they were relocated and operated within a sensor network consisting of six locations for more than 1 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 3 months of operation. This accuracy, however, could not be maintained during their operation within the sensor network related to changes in sensor behaviour. For most of the O3 sensors a decrease in sensitivity was encountered over time, clearly impacting the data quality. The NO2 low-cost sensors in our configuration exhibited better performance but did not reach the accuracy level of NO2 diffusion tubes (∼ 2 ppb for uncorrected 14-day average concentrations). Tests in the laboratory revealed that changes in relative humidity can impact the signal of the employed NO2 sensors similarly to 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 also include 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.

Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring

“…As a result, we applied κ-Köhler theory (Petters and Kreidenweis, 2007), which describes the relationship between particle hygroscopicity and volume by a sin- Table 2. Slopes of measured PM mass concentrations of the reference instruments (TEOM and GRIMM) against the OPC-N2.…”

“…However, the question remains as to whether the un-L. R. Crilley et al: Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) certain quality of data from these low-cost sensors can be of value when attempting to determine pollutant concentrations at high spatial resolution (Kumar et al, 2015). Sensors for both gases and particles can suffer from drift and a number of interference artefacts such as relative humidity (RH), temperature and other gas-phase species (Lewis et al, 2016;Mueller et al, 2017;Popoola et al, 2016). Despite these challenges, recent work has shown that low-cost gas sensors can be deployed in large-scale networks provided appropriate corrections for known artefacts are applied (Borrego et al, 2016;Mead et al, 2013;Mueller et al, 2017), with clustering of multiple gas sensors into one unit shown to be an effective methodology (Lewis et al, 2016;Mueller et al, 2017;Smith et al, 2017).…”

“…Sensors for both gases and particles can suffer from drift and a number of interference artefacts such as relative humidity (RH), temperature and other gas-phase species (Lewis et al, 2016;Mueller et al, 2017;Popoola et al, 2016). Despite these challenges, recent work has shown that low-cost gas sensors can be deployed in large-scale networks provided appropriate corrections for known artefacts are applied (Borrego et al, 2016;Mead et al, 2013;Mueller et al, 2017), with clustering of multiple gas sensors into one unit shown to be an effective methodology (Lewis et al, 2016;Mueller et al, 2017;Smith et al, 2017). For low-cost particle sensors, their reported performance across the literature is somewhat mixed (Borrego et al, 2016;Castellini et al, 2014;Sousan et al, 2016;Viana et al, 2015) and can depend on the type of particle sensor employed.…”

Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments

“…There are multiple advantages to this approach: the reference instruments are regularly calibrated, the reference measurement data are generally made publicly available (e.g., EPA AirNow, US EPA, 2017;OpenAQ, Hasenkopf, 2017), and the calibrations are carried out under ambient conditions that are (at least partially) representative of the sensor measurements to be made. Indeed, the effectiveness of co-location has been demonstrated in several recent studies, with sensor outputs (voltages) and other environmental parameters (e.g., temperature) related to the true concentration values (from the reference instruments) via some form of regression from either parametric models (Jiao et al, 2016;Lewis et al, 2015;Masson et al, 2015;Mueller et al, 2017;Popoola et al, 2016;Sadighi et al, 2017;Smith et al, 2017) or machine-learning/nonparametric methods (Cross et al, 2017;Spinelle et al, 2015;Zimmerman et al, 2017).…”

“…However, accurate calibration of such sensors poses a major challenge. Even setting aside the logistical difficulties associated with calibrating a large number of sensors distributed throughout a network, there are specific technical challenges that can limit the accuracy of any calibration; these include the sensitivity of sensors to environmental conditions (temperature and relative humidity, RH) (Cross et al, 2017;Masson et al, 2015;Mead et al, 2013;Popoola et al, 2016), cross sensitivities to other (sometimes unknown or unmeasured) atmospheric species (Lewis et al, 2015;Mueller et al, 2017;Spinelle et al, 2015;Zimmerman et al, 2017), and long-term sensitivity decay (drift) associated with the evaporation of electrolyte solution (Mead et al, 2013;Smith et al, 2017).…”