Metrology for sensor networks: metrological traceability and measurement uncertainties for air quality monitoring
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Sascha Eichstädt
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Olav Werhahn
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.
Zusammenfassung
Die In-situ-Kalibrierung von Sensoren, die SI-rückführbare Messergebnisse liefern, ist nach wie vor eine offene Frage für den Entwurf und den Betrieb von Sensornetzen. Insbesondere bei kostengünstigen Sensoren ist der Einsatz von Sensornetzwerken zur Überwachung der Luftqualität derzeit durch die geringe oder unbekannte Genauigkeit der Messungen, die sie erreichen können, begrenzt, während die Datenqualität einzelner Sensornetzwerke hauptsächlich durch Algorithmen bestimmt wird. Normungsgremien wie DIN und CEN haben daher einerseits Untersuchungen von Validierungsmethoden für Gasphasenspezies und Feinstaub und andererseits die Entwicklung von volldigitalisierten Qualitätssicherungs-/Qualitätskontroll- und Kalibrierverfahren für Sensornetzwerke angekündigt (CEN/CENELEC, Opportunity for Standardisation to Contribute to the European Partnership on Metrology EPM under Horizon Europe). Dieser Beitrag konzentriert sich auf die messtechnische Rückführung von Sensornetzwerken zur Überwachung der Luftqualität auf das internationale Einheitensystem (SI) auf der Grundlage einer FAIRifizierten netzwerkinternen Kommunikation (M. Wilkinson, et al., “The FAIR guiding principles for scientific data management and stewardship,” Sci. Data, vol. 3, 2016, Art. no. 160018) und unter Einbeziehung delokalisierter optischer Gasnormale, die nach der digitalen TILSAM-Methode betrieben werden (O. Werhahn, et al., The TILSAM Method Adapted into Optical Gas Standards – Complementing Gaseous Reference Materials, PTB Open Access Repository, 2021). Auf der Grundlage verwandter Aktivitäten im Rahmen von EURAMET (Partnerschaftsprojekt 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) wird in diesem Beitrag die Bedeutung von Messunsicherheiten im Zusammenhang mit Sensornetzwerken erörtert, die verschiedene Sensorprinzipien umfassen und eine effiziente Übernahme von Methoden nach dem Stand der Technik fördern. Wir erörtern, wie der Fall der Sensornetzwerke mit einzelnen Sensoren unter Verwendung der GUM-Prinzipien (Joint Committee for Guides in Metrology, Guide to the Expression of Uncertainty in Measurement (GUM), JCGM 100: 2008 (E)) angegangen werden kann. Damit Sensornetzwerkmessungen metrologisch auf das SI rückführbar werden, müssen dokumentierte und ununterbrochene Kalibrierungsketten implementiert werden, die jeweils zur Messunsicherheit beitragen. Dies gilt für jeden einzelnen Sensor des Netzwerks, einschließlich des potenziellen Goldstandards unter ihnen, aber auch für den Ausgang des Netzwerks als eine Einheit betrachtet. Der Beitrag liefert erste zu testende und zu validierende Ansätze, die durch grundlegende Entwurfsstrategien für Sensornetzwerke untermauert werden. Er geht auf praktische Anwendungen in realen Szenarien ein, abgesehen von Modellunsicherheiten, die in Perspektiven der künstlichen Intelligenz diskutiert werden.
About the authors
Sascha Eichstädt is the leader of the department “Metrology for digital transformation” at the Physikalisch-Technische Bundesanstalt (PTB). He received his Diploma in Mathematics in 2008 at the Humboldt University Berlin, and his PhD in Theoretical Physics in 2012 at the Technical University Berlin. In 2008, he joined PTB. He chaired the EURAMET working group “Metrology for digital transformation” from 2020-2022. Sascha Eichstädt is chairing the OIML Digitalisation Task Group since 2022 and the IMEKO Technical Committee on Digitalisation since 2021.
Olav Werhahn holds the position as Executive Secretary of the Innovation Cluster Environment & Climate at PTB since 8/2023. Before, Dr. Werhahn served two years as Executive Secretary of the JCRB at the BIPM Headquarters in Sèvres (near Paris), France from 2021 to 2023. From 2015 to 2021 he worked as Head of Working Group “Spectrometric Gas Analysis” at PTB. He started at PTB in 2000 as PostDoc in various divisions and contributed his expertise to laser spectroscopic metrology aspects.
Acknowledgments
The authors acknowledge continued communication with experts from gas metrology and digital transformation. Hereon, OW expresses his gratitude particularly to J. Nwaboh and Z. Qu for discussing optical gas standards and to J. C. Petersen from the Danish Fundamental Metrology (DFM) for his enthusiasm collaborating on the TILSAM method.
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Research ethics: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no competing interests.
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Research funding: None declared.
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Data availability: The raw data can be obtained on request from the corresponding author.
References
[1] Joint Committee for Guides in Metrology, International Vocabulary of Metrology, vol. 200, JCGM, 2012. Available at: https://www.bipm.org/documents/20126/2071204/JCGM_200_2012.pdf/.Suche in Google Scholar
[2] Joint Committee for Guides in Metrology, Guide to the Expression of Uncertainty in Measurement (GUM), JCGM 100: 2008 (E), 2008. Available at: https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf.Suche in Google Scholar
[3] CEN/CENELEC, Opportunity for Standardisation to Contribute to the European Partnership on Metrology EPM under Horizon Europe, 2023. Available at: https://metpart.eu/component/edocman/call-2023-cen-002/download.html?Itemid=0.Suche in Google Scholar
[4] B. Seeger and T. Bruns, “Primary calibration of mechanical sensors with digital output for dynamic applications,” Acta IMEKO, vol. 10, no. 3, pp. 177–184, 2021. https://doi.org/10.21014/acta_imeko.v10i3.1075.Suche in Google Scholar
[5] D. Smorgon and V. Fernicola, “Assuring measurement traceability to ATE systems for MEMS temperature sensors testing and calibration,” in Intervento presentato al convegno METROLOGY FOR INDUSTRY 4.0 & IoT, Turin, Italy, 2020.Suche in Google Scholar
[6] G. Tancev and F. G. Toro, “Stochastic online calibration of low-cost gas sensor networks with mobile references,” IEEE Access, vol. 10, pp. 13901–13910, 2022. https://doi.org/10.1109/access.2022.3145945.Suche in Google Scholar
[7] M. Gruber, “Consensus-based online co-calibration for networks of homogeneous sensors in IIoT environments under consideration of semantic knowledge,”, Ph.D. thesis, FU Berlin, Berlin, 2023, submitted.Suche in Google Scholar
[8] A. Vedurmudi, K. Janzen, M. Nagler, and S. Eichstädt, “Uncertainty-aware temperature interpolation for measurement rooms using ordinary Kriging,” Meas. Sci. Technol., vol. 34, no. 6, 2023, Art. no. 064007. https://doi.org/10.1088/1361-6501/acc2d8.Suche in Google Scholar
[9] V. A. Vasiliev and P. S. Chernov, “Smart sensors, sensor networks, and digital interfaces general problems of metrology and measurement technique,” Meas. Tech., vol. 55, pp. 1115–1119, 2013. https://doi.org/10.1007/s11018-012-0094-9.Suche in Google Scholar
[10] O. Werhahn, et al.., The TILSAM Method Adapted into Optical Gas Standards – Complementing Gaseous Reference Materials, PTB Open Access Repository, 2021. Available at: https://oar.ptb.de/files/download/EMPIR.16ENG05.CA.20211013.pdf.Suche in Google Scholar
[11] EURAMET, Comparison of 100 µmol/mol HCl in N2, TC-MC Project no. 1498, 2023. Available at: https://www.euramet.org/technical-committees/tc-projects/details/project/comparison-of-100-umolmol-hcl-in-n2.Suche in Google Scholar
[12] B. Buchholz, B. Kühnreich, H. G. J. Smit, and V. Ebert, “Validation of an extractive, airborne, compact TDL spectrometer for atmospheric humidity sensing by blind intercomparison,” Appl. Phys. B, vol. 110, no. 2, pp. 249–262, 2013. https://doi.org/10.1007/s00340-012-5143-1.Suche in Google Scholar
[13] A. Afchine, et al.., “Ice particle sampling from aircraft- influence of the probing position on the ice water content,” Atmos. Meas. Tech., vol. 11, no. 7, pp. 4015–4031, 2018. https://doi.org/10.5194/amt-11-4015-2018.10.5194/amt-11-4015-2018Suche in Google Scholar
[14] M. Grosso, et al.., Digital TILSAM Systems – Providing FAIR Data and SI Traceability to Smart Sensor Networks for Air Quality Monitoring, Conference Contribution Congress International de Metrologie (CIM), März, 2023. Session S7.Suche in Google Scholar
[15] International des Poids et Mesures, Transforming the International System of Units for a Digital World, CIPM/2023-I-B4h.1, 2023. Available at: https://www.bipm.org/documents/20126/17806960/CIPM+Vision+2023+-+SI+for+a+Digital+World.pdf/85183b2b-791b-02cc-bed5-7660b36eb0b5.Suche in Google Scholar
[16] European Metrology Network Climate and Ocean Observation (COO), European Metrology Network Pollution Monitoring (POLMO), 2024. Available at: https://www.euramet.org/european-metrology-networks.Suche in Google Scholar
[17] J. A. Nwaboh, Z. Qu, O. Werhahn, and V. Ebert, “Towards an optical gas standard for traceable calibration-free and direct NO2 concentration measurements,” Appl. Sci., vol. 11, no. 12, p. 5361, 2021. https://doi.org/10.3390/app11125361.Suche in Google Scholar
[18] M. Wilkinson, et al.., “The FAIR guiding principles for scientific data management and stewardship,” Sci. Data, vol. 3, 2016, Art. no. 160018. https://doi.org/10.1038/sdata.2016.18.Suche in Google Scholar PubMed PubMed Central
[19] A. J. Lewis, A. Yacoot, M. J. T. Milton, and A. J. Lancaster, “A digital framework for realising the SI—a proposal for the metre,” Metrologia, vol. 59, no. 4, 2022, Art. no. 044004.10.1088/1681-7575/ac7fceSuche in Google Scholar
[20] S. Eichstädt, “Metrology for the digital age, DeGruyter series of measurement,” in Metrological Infrastructure, 2023. Chapter 6.10.1515/9783110715835-006Suche in Google Scholar
[21] DIN, DKE, Szenarien zur Digitalisierung der Normung und Normen Whitepaper der Initiative Digitale Standards (IDiS), Berlin, Deutsches Institut für Normung, 2021.Suche in Google Scholar
[22] S. Eichstädt, M. Gruber, and A. Vedurmudi, “Integrating metrological principles into the Internet of Things: A digital maturity model for sensor network metrology,” tm – Tech. Mess., vol. 91, no. 1, pp. 17–31, 2024. https://doi.org/10.1515/teme-2023-0103.Suche in Google Scholar
[23] S. Hackel, et al.., “The fundamental architecture of the DCC, Measurement,” Sensors, vol. 18, 2021, Art. no. 100354. https://doi.org/10.1016/j.measen.2021.100354.Suche in Google Scholar
[24] H. Koch, “Improving DCC-results by post-processing,” in Proceedings of the 2nd International DCC Conference, 2022.Suche in Google Scholar
[25] Bureau International des Poids et Mesures, Permanent Link for a Given CMC, 2023. Available at: https://www.bipm.org/documents/20126/43909385/QUICK_START_CMC-identifiers.pdf/.Suche in Google Scholar
[26] EURAMET Project 1551, EURAMET Project Database, 2024. Available at: https://www.euramet.org/technical-committees/tc-projects.Suche in Google Scholar
[27] DIN, Allgemeine Anforderungen an die Kompetenz von Prüf- und Kalibrierlaboratorien (ISO/IEC 17025:2017); deutsche und englische Fassung EN ISO/IEC 17025:2017, Berlin, Deutsches Institut für Normung, 2017.Suche in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Editorial
- Measurement uncertainty, data quality and data-driven modelling
- Research Articles
- Metrology for sensor networks: metrological traceability and measurement uncertainties for air quality monitoring
- Proof of concept and new developments on a Kibble extension
- Leveraging measurement data quality by adoption of the FAIR guiding principles
- Combination of generic novelty detection and supervised classification pipelines for industrial condition monitoring
- Test of conformance or non-conformance with geometrical specifications
- Data-driven modeling in metrology – A short introduction, current developments and future perspectives
Artikel in diesem Heft
- Frontmatter
- Editorial
- Measurement uncertainty, data quality and data-driven modelling
- Research Articles
- Metrology for sensor networks: metrological traceability and measurement uncertainties for air quality monitoring
- Proof of concept and new developments on a Kibble extension
- Leveraging measurement data quality by adoption of the FAIR guiding principles
- Combination of generic novelty detection and supervised classification pipelines for industrial condition monitoring
- Test of conformance or non-conformance with geometrical specifications
- Data-driven modeling in metrology – A short introduction, current developments and future perspectives
