Metrology for sensor networks: metrological traceability and measurement uncertainties for air quality monitoring

Abstract

In situ calibration of sensors delivering SI traceable measurement results still provides an open question to the design and operation of sensor networks. Particularly when addressing low-cost sensors, currently, the use of sensor networks for air quality monitoring is limited by the low or unknown accuracy of measurements that they can achieve, while the data quality of individual sensor networks is mainly derived by algorithms. Standardization bodies like DIN and CEN therefore announced the need for investigations of validation methods on gas phase species and particulate matter on the one hand side, and for the development of fully digitized quality assurance/quality control and calibration techniques for sensor networks on the other (CEN/CENELEC, Opportunity for Standardisation to Contribute to the European Partnership on Metrology EPM under Horizon Europe). This contribution concentrates on the metrological traceability of sensor networks for air quality monitoring to the international system of units (SI) based on FAIRified intra-network communications (M. Wilkinson, et al., “The FAIR guiding principles for scientific data management and stewardship,” Sci. Data, vol. 3, 2016, Art. no. 160018) and including delocalized Optical Gas Standards operated according to the digital TILSAM method (O. Werhahn, et al., The TILSAM Method Adapted into Optical Gas Standards – Complementing Gaseous Reference Materials, PTB Open Access Repository, 2021). Informed by related activities in EURAMET (Partnership project FunSNM, EMNs COO & POLMO, TC-IM 1551) (European Metrology Network Climate and Ocean Observation (COO), European Metrology Network Pollution Monitoring (POLMO), EURAMET Project TC-IM 1551, Project Database) this contribution discusses the importance of measurement uncertainties in the context of sensor networks, comprising different sensor principles and promoting an efficient uptake of state-of-the-art methods. We discuss how the sensor network case can be addressed with sensors individually using the GUM principles (Joint Committee for Guides in Metrology, Guide to the Expression of Uncertainty in Measurement (GUM), JCGM 100: 2008 (E)). For sensor network measurements becoming metrologically traceable to the SI, documented and unbroken chains of calibrations need to be implemented each contributing to the measurement uncertainty. This applies to each individual sensor of the network including the potential gold standard among them, but also to the network’s output viewed as a single entity. The contribution provides first approaches to be tested and validated that are underpinned by fundamental design strategies for sensor networks. It follows on practical applications in real world scenarios aside from model uncertainties discussed in artificial intelligence prospects.