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.

Highlights

  • Within the framework of the European Air Quality Directive [1], it is possible to use supplementary techniques for indicative measurements

  • The behavior of individual sensors is discussed by designing severallinear regression models. These were constructed for the Alphasense NO2-B43F sensor [17] to predict continuous reference data and are based on the predictor variables: NO2 measured by the sensor, temperature, relative humidity and ozone in ambient air

  • We chose the Alphasense NO2-B43F, a popular, low-cost electrochemical sensor for measuring ambient NO2. This sensor is part of the platform developed by the Joint Research Centre (JRC) of the European Commission: AirSensEUR

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Summary

Introduction

Within the framework of the European Air Quality Directive [1], it is possible to use supplementary techniques for indicative measurements. The behavior of individual sensors is discussed by designing several (multi-)linear regression models These were constructed for the Alphasense NO2-B43F sensor [17] to predict continuous reference data and are based on the predictor variables: NO2 measured by the sensor, temperature, relative humidity and ozone (either by reference instrumentation or by a sensor) in ambient air. Such terms describe the effect that the relationship between a given (independent) predictor and the outcome may depend on other predictor variables The performance of such models is expressed here in several validation metrics including the expanded relative measurement uncertainty which, following [18,19], was compared with the Data Quality Objectives (DQO) as defined in the Air Quality Directive. The raw measurement data provided by the sensors are stored in a central database on a 1 min base

Field Deployment
Validation
Presentation of the Dataset
Full Text
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