Data quality control considerations in multivariate environmental monitoring: experience of the French coastal network SOMLIT - Breton, Elsa; Savoye, Nicolas; Rimmelin-Maury, Peggy; Sautour, Benoit; Goberville, Eric; Lheureux, Arnaud; Cariou, Thierry; Ferreira, Sophie; Agogué, Hélène; Alliouane, Samir; Aubert, Fabien; Aubin, Sébastien; et al. (Frontiers in Marine Science, 2023)
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. 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 dataset. Finally, the expanded uncertainty of measurement for twenty environmental variables routinely measured by SOMLIT from discrete sampling - including Essential Ocean Variables - is provided. 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 inter-laboratory exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets.