Performance of NO, NO<sub>2</sub> low cost sensors and three calibration approaches within a real world application

Abstract

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 < 5 ppb, R2 between 0.74 and 0.95 and MAE between 2 and 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 and 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 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, 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.

Review of the Performance of Low-Cost Sensors for Air Quality Monitoring

“…Fortunately, the majority of these studies also report the slope and intercept of the regression line between LCS data and reference measurements that describe the possible bias of LCS data. A few studies also report the RMSE [10,20,22,36,[41][42][43]51,52,58,60,62,63,85] which clearly indicates that the magnitude of the error in LCS data is also sensitive to extreme values and outliers. Only a few studies report the measurement uncertainty [10,22,25,30,48,52,59,61].…”

“…The majority of the reviewed works reported R 2 value as a main metric when comparing LCS with reference measurements. Table 2 clearly shows that only a few records were found for the measurements reporting mean absolute error (MAE), bias, and RMSE [42,49,[52][53][54]56]. However, we would like to stress that other statistical parameters, such as the mean normalized bias (MNB), mean normalized error (MNE), and the root mean square error (RMSE), are also very important in evaluating the relationship between LCS and reference instruments.…”

“…In addition, 19 projects evaluating OEMs, SSys, or both, and reporting quantitative comparisons of LCS data and reference measurements were identified. They include the Air Quality Egg, Air Quality Station, AirCasting [19,[35][36][37], Carnegie Mellon [36,38], CitiSense [30], Cairsense [39], Developer Kit [19], HKEPD/14-02771 [40], making-sense.eu [41], communitysensing.org [32], MacPoll.eu [20], OpenSense II [42,43], Proof of Concept AirSensEUR [22], and SNAQ Heathrow [44,45]. Out of the 1423 records collected from literature, we identified 1188 records (197 OEMs and 991 SSys) from 89 alive LCS (24 OEMs and 65 SSys) and 235 records (123 OEMs and 112 SSys) from 23 "non active" (or discontinued) LCS (7 OEMs and 16 SSys).…”

Hierarchical network design for nitrogen dioxide measurement in urban environments

“…Common calibration procedures involve factory calibration, where the sensors are calibrated in the laboratory under controlled conditions, and field calibration, where low-cost sensors are colocated with regulatory monitoring instruments. The former approach is often not appropriate due to interactions with other gases as well as the effects of different meteorological conditions (temperature, humidity, wind speed) that are not accounted for (Bigi et al, 2018;Cross et al, 2017;Lewis et al, 2016;Spinelle et al, 2017). The latter approach must be repeated periodically to ensure data reliability, which is not only resource and time consuming, but can also lead to data gaps, unknown errors associated with long-term drift and issues related to sensor handling and transport during relocation (Bigi et al, 2018).…”

A Comparative Study of Calibration Methods for Low-Cost Ozone Sensors in IoT Platforms

“…For this reason, much research has focused on the interaction of environmental conditions such as temperature and relative humidity [3], [4], [7], [8], [9] or on the interactions of other pollutants [10], [11] with respect to one pollutant sensor. In addition, there is recently a greater interest in comparing and studying [11], [12], [13], [14], [15] how signal processing techniques behave for calibrating different air pollution lowcost sensors in IoT platforms. Many of these investigations focus on comparing what is the error obtained using several linear and non-linear machine learning algorithms.…”