2026-09-09T17:44:55Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/112512026-01-22T01:05:40Zcom_10259_4249com_10259_5086com_10259_2604col_10259_4250

Russo, Raffaele Emanuele Fattobene, Martina Zamponi, Silvia Conti, Paolo Herrero Gutiérrez, Ana Berrettoni, Mario 2026-01-21T07:44:48Z 2026-01-21T07:44:48Z 2026-01 https://hdl.handle.net/10259/11251 10.3390/molecules31020232 1420-3049 Environmental element monitoring is essential for assessing environmental quality, identifying pollution sources, evaluating ecological risks, and understanding long-term contamination trends. Modern monitoring campaigns routinely generate large volumes of complex data that require advanced analytical strategies. This study applied chemometric techniques to analyze elements and BVOCs (biogenic volatile organic compounds) measured from Posidonia oceanica and related environmental matrices (seawater, sediment, and rhizomes) during three sampling campaigns in the Tremiti Islands (Italy). Twenty-two trace elements were quantified, and BVOC profiles were obtained from the leaf samples. The dataset was analyzed using a combination of univariate visualizations, unsupervised and supervised multivariate techniques, and multi-way methods. PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares-Discriminant Analysis) revealed distinct spatial (leaf section) and temporal (sampling period) trends, supported by consistent elemental markers. A low-level data fusion approach integrating BVOC and element data improved group discrimination and interpretability. PARAFAC (PARAllel FACtor analysis) applied to a three-way array successfully separated background trends from meaningful compositional changes, uncovering latent structures across chemical, spatial, and temporal dimensions. This work illustrates the usefulness of chemometrics in environmental monitoring and the effectiveness of combining multivariate tools and data fusion to improve the interpretability of complex environmental datasets. The methodology used in this study is fully generalizable and applicable to other environmental multi-way datasets. he PhD fellowship of M.F. was co-funded by PNRR resources from the European Union (Innovative PhD programs addressing the innovation needs of companies, CUP J11J22001830006) and by Ecotechsystems srl, Ancona (Marche, Italy). application/pdf eng MDPI Molecules. 2026, V. 31, n. 2, p. 232-252 https://doi.org/10.3390/molecules31020232 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Environmental monitoring Elements BVOCs Posidonia oceanica Sediments Chemometrics N-way techniques PARAFAC Vigilancia ambiental Environmental monitoring Aguas marinas-Contaminación Marine pollution Exploring Environmental Element Monitoring Data Using Chemometric Techniques: A Practical Case Study from the Tremiti Islands (Italy) info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Molecules 31 2 232 252