Seasonal Influence on the Performance of Low-Cost NO2 Sensor Calibrations.
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.
['Ratingen|Sjoerd van|SV|', 'Vonk|Jan|J|', 'Blokhuis|Christa|C|', 'Wesseling|Joost|J|', 'Tielemans|Erik|E|', 'Weijers|Ernie|E|']
seasonal influence NO2 sensor calibration validation measurement uncertainty multivariate linear regression ozone