Advanced multivariate techniques for the classification and pollution of marine sediments due to aquaculture

Eleni G Farmaki; Nikolaos S Thomaidis; Ioannis N Pasias; Nikolaos I Rousis; Cecile Baulard; Leonidas Papaharisis; Constantinos E Efstathiou

doi:10.1016/j.scitotenv.2020.144617

Advanced multivariate techniques for the classification and pollution of marine sediments due to aquaculture

Sci Total Environ. 2021 Apr 1:763:144617. doi: 10.1016/j.scitotenv.2020.144617. Epub 2020 Dec 24.

Authors

Eleni G Farmaki¹, Nikolaos S Thomaidis², Ioannis N Pasias¹, Nikolaos I Rousis¹, Cecile Baulard³, Leonidas Papaharisis³, Constantinos E Efstathiou¹

Affiliations

¹ Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771 Athens, Greece.
² Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15771 Athens, Greece. Electronic address: ntho@chem.uoa.gr.
³ Nireus Aquaculture S.A., 1st klm. Koropiou-Varis Avenue, 19400 Koropi, Greece.

PMID: 33385839
DOI: 10.1016/j.scitotenv.2020.144617

Abstract

Aquaculture production has globally increased and its environmental impact is not well understood and assessed yet. Therefore, in this work nine metals and metalloids (Cu, Cd, Pb, Hg, Ni, Fe, Mn, Zn and As) and three nutrients (P, N and C) that seem to accumulate in marine sediments, were determined under the fish cages (zero distance) and about 50 and 100 m away from them, in three aquacultures in Greece. The analysis of these data is crucial due to the negative impact of the intensive aquaculture activities on fish population, human health and marine environment. This study investigated the environmental impact associated with aquaculture cages on marine sediments, using Supervised Artificial Neural Networks (ANNs) in parallel with Classification Trees (CTs). Optimised models were constructed in order to detect the significance of each variable, predict the origin of the sediment samples and successfully visualise their results. Three popular ANN architectures, as multi-layer perceptrons (MLPs), radial basis function (RBF) and counter propagation artificial neural networks (CP-ANNs) were used to assess the impact of the intensive aquaculture activities on marine sediments. In addition, more traditional multivariate chemometric techniques like CTs were applied to the same data set for comparison purposes. The modelling study showed that P, N, Cu, Cd were the most critical (and polluting) factors of those metals studied. Moreover, single-element models achieved elevated predictive percentages. The results were justified due to the usual practices used for fish feeding or cages maintenance.

Keywords: Classification trees; Counter propagation artificial neural networks; Environmental pollution; Metals and metalloids.