Heavy Metals in the Adriatic-Ionian Seas: A Case Study to Illustrate the Challenges in Data Management When Dealing With Regional Datasets - Molina Jack, Maria Eugenia; Bakiu, Rigers; Castelli, Ana; Čermelj, Branko; Fafanđel, Maja; Georgopoulou, Christina; Giorgi, Giordano; Iona, Athanasia; Ivankovic, Damir; Kralj, Martina; Partescano, Elena; Rotini, Alice; et al. (Frontiers in Marine Science, 2020)
Harmonization of monitoring protocols and analytical methods is a crucial issue for transnational marine environmental status assessment, yet not the only one. Coherent data management and quality control become very relevant when environmental status is assessed at regional or subregional scale (e.g. for the Mediterranean or the Adriatic Sea), thus requiring data from different sources. Heavy metals are among the main targets of monitoring activities. Significant efforts have been dedicated to share best practices for monitoring and assessment of ecosystem status and to strengthen the network of national, regional and European large data infrastructures in order to facilitate the access to data among countries. Data comparability and interoperability depend not only on sampling and analytical protocols but also on how data and metadata are managed, quality controlled and made accessible. Interoperability is guaranteed by using common metadata and data formats, and standard vocabularies to assure homogeneous syntax and semantics. Data management of contaminants is complex and challenging due to the high number of information required on sampling and analytical procedures, high heterogeneity in matrix characteristics, but also to the large and increasing number of pollutants. Procedures for quality control on heterogeneous datasets provided by multiple sources are not yet uniform and consolidated. Additional knowledge and reliable long time-series of data are needed to evaluate typical ranges of contaminant concentration. The analysis of a coherent and harmonized regional dataset can provide the basis for a multi-step quality control procedure, which can be further improved as knowledge increases during data validation process.