Data quality control considerations in multivariate environmental monitoring: experience of the French coastal network SOMLIT

Elsa Breton, Nicolas Savoye, Peggy Rimmelin-Maury, Benoit Sautour, Eric Goberville, Arnaud Lheureux, Thierry Cariou, Sophie Ferreira, Hélène Agogué, Samir Alliouane, Fabien Aubert, Sébastien Aubin, Eric Berthebaud, Hadrien Blayac, Lucie Blondel, Cédric Boulart, Yann Bozec, Sarah Bureau, Arnaud Caillo, Arnaud CauvinJean-Baptiste Cazes, Léo Chasselin, Pascal Claquin, Pascal Conan, Marie-Ange Cordier, Laurence Costes, Romain Crec’hriou, Olivier Crispi, Muriel Crouvoisier, Valérie David, Yolanda Del Amo, Hortense De Lary, Gaspard Delebecq, Jeremy Devesa, Aurélien Domeau, Maria Durozier, Claire Emery, Eric Feunteun, Juliette Fauchot, Valérie Gentilhomme, Sandrine Geslin, Mélanie Giraud, Karine Grangeré, Gerald Grégori, Emilie Grossteffan, Aurore Gueux, Julien Guillaudeau, Gael Guillou, Manon Harrewyn, Orianne Jolly, Florence Jude-Lemeilleur, Ian Salter, Laurent Seront

Research output: Contribution to journalArticlepeer-review

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Abstract

Introduction: While crucial to ensuring the production of accurate and high-quality data—and to avoid erroneous conclusions—data quality control (QC) in environmental monitoring datasets is still poorly documented.
Methods: With a focus on annual inter-laboratory comparison (ILC) exercises performed in the context of the French coastal monitoring SOMLIT network, we share here a pragmatic approach to QC, which allows the calculation of systematic and random errors, measurement uncertainty, and individual performance. After an overview of the different QC actions applied to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises, and statistical methodology specially adapted to small environmental networks (<20 laboratories) and multivariate datasets. Finally, the expanded uncertainty of measurement for 20 environmental variables routinely measured by SOMLIT from discrete sampling—including Essential Ocean Variables—is provided.
Results, Discussion, Conclusion: The examination of the temporal variations (2001–2021) in the repeatability, reproducibility, and trueness of the SOMLIT network over time confirms the essential role of ILC exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets.
Original languageEnglish
Number of pages12
JournalFrontiers in Marine Science
Volume10
DOIs
Publication statusPublished - 26 Apr 2023

Keywords

  • SOMLIT
  • Chlorophyll
  • Time-series
  • Environmental monitoring network
  • Data quality control
  • Inter-laboratory comparison exercises
  • Measurement uncertainty
  • Analyst performance
  • Multivariate dataset

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