Seasonal Influence on the Performance of Low-Cost NO2 Sensor Calibrations

Abstract

Low-cost sensor technology has been available for several years and has the potential to complement official monitoring networks. The current generation of nitrogen dioxide (NO2) sensors suffers from various technical problems. This study explores the added value of calibration models based on (multiple) linear regression including cross terms on the performance of an electrochemical NO2 sensor, the B43F manufactured by Alphasense. Sensor data were collected in duplicate at four reference sites in the Netherlands over a period of one year. It is shown that a calibration, using O3 and temperature in addition to a reference NO2 measurement, improves the prediction in terms of R2 from less than 0.5 to 0.69–0.84. The uncertainty of the calibrated sensors meets the Data Quality Objective for indicative methods specified by the EU directive in some cases and it was verified that the sensor signal itself remains an important predictor in the multilinear regressions. In practice, these sensors are likely to be calibrated over a period (much) shorter than one year. This study shows the dependence of the quality of the calibrated signal on the choice of these short (monthly) calibration and validation periods. This information will be valuable for determining short-period calibration strategies.

In situ drift correction for a low-cost NO2 sensor network

“…LCS calibration often involves calibration models that are trained and tested using collocated reference data. These calibration models can include linear regression, 9 multivariate linear regression, 16 support vector regression, 17 generalized additive models, 18 random forest models, 10 machine learning algorithms, 19 or hybrid models. 14 For electrochemical-based NO 2 LCSs, sensor drift, seasonal bias, ozone cross-interference, and sensor degradation are all issues that should be accounted for in the calibration.…”

“…Sun et al 20 attached an additional NO 2 removal system to their sensors in order to perform periodic auto-zeroing of their LCSs to correct for drift. Ratingen et al 16 showed that calibrations developed in winter months experience poor performance in summer months with different environmental conditions. Hossain et al 21 have demonstrated the importance of a chemical filter upstream of the electrochemical cell in limiting ozone cross-sensitivity, while Li et al 22 have shown that even with a chemical filter present this scrubber will degrade over time leading to sensor failure.…”

“…In addition to an ozone scrubber, correcting for ozone interference using site-specific ozone measurements in post-processing calibration has also been performed in several studies. 9,11,13,15,16,23…”

Calibration of SO2 and NO2 Electrochemical Sensors via a Training and Testing Method in an Industrial Coastal Environment

“…Despite these advances in sensor calibration, statistical approaches can be sensitive to seasonal effects. For example, an algorithm based on data obtained in summer may not fit well for data obtained in another season [ 19 ]. Therefore, repeat calibrations during different seasons may be necessary for sensors used for a long time throughout the year.…”

“…Moreover, although studies show that sensors from the same manufacturer often perform similarly, this cannot be guaranteed in all cases. Evidence indicates that a calibration model based on training data collected from an individual sensor may not work well when applied to another sensor, even a sensor of the same model and brand [ 19 , 20 , 21 ].…”