Predictive Innovative Methods for Aquatic Heavy Metals Pollution Based on Bioindicators in Support of Blue Economy in the Danube River Basin

Ira-Adeline Simionov, Dragoș Sebastian Cristea, Ștefan-Mihai Petrea, Alina Mogodan, Roxana Jijie, Elena Ciornea, Mircea Nicoară, Maria Magdalena Turek Rahoveanu and Victor Cristea
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Ira-Adeline Simionov: Multidisciplinary Research Platform (ReForm)-MoRAS Research Center, “Dunărea de Jos” University of Galaţi, 800008 Galați, Romania
Dragoș Sebastian Cristea: Faculty of Economics and Business Administration, “Dunărea de Jos” University of Galaţi, 800008 Galaţi, Romania
Ștefan-Mihai Petrea: Department of Food Science, Food Engineering, Biotechnology and Aquaculture, Faculty of Food Science and Engineering, “Dunărea de Jos” University of Galaţi, 800008 Galați, Romania
Alina Mogodan: Department of Food Science, Food Engineering, Biotechnology and Aquaculture, Faculty of Food Science and Engineering, “Dunărea de Jos” University of Galaţi, 800008 Galați, Romania
Roxana Jijie: Department of Exact and Natural Sciences, Institute for Interdisciplinary Research, “Alexandru Ioan Cuza” University of Iasi, 700107 Iasi, Romania
Elena Ciornea: Department of Biology, Faculty of Biology, “Alexandru Ioan Cuza” University of Iasi, 700505 Iasi, Romania
Mircea Nicoară: Department of Biology, Faculty of Biology, “Alexandru Ioan Cuza” University of Iasi, 700505 Iasi, Romania
Maria Magdalena Turek Rahoveanu: Faculty of Engineering and Agronomy in Braila, “Dunărea de Jos” University of Galaţi, 810017 Braila, Romania
Victor Cristea: Multidisciplinary Research Platform (ReForm)-MoRAS Research Center, “Dunărea de Jos” University of Galaţi, 800008 Galați, Romania

Sustainability, 2021, vol. 13, issue 16, 1-41

Abstract: Heavy metal pollution is still present in the Danube River basin, due to intensive naval and agricultural activities conducted in the area. Therefore, continuous monitoring of this pivotal aquatic macro-system is necessary, through the development and optimization of monitoring methodologies. The main objective of the present study was to develop a prediction model for heavy metals accumulation in biological tissues, based on field gathered data which uses bioindicators (fish) and oxidative stress (OS) biomarkers. Samples of water and fish were collected from the lower sector of Danube River (DR), Danube Delta (DD) and Black Sea (BS). The following indicators were analyzed in samples: cadmium (Cd), lead (Pb), iron (Fe), zinc (Zn), copper (Cu) (in water and fish tissues), respectively, catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx), malondialdehyde (MDA) (in fish tissues). The pollution index (PI) was calculated to identify the most polluted studied ecosystem, which revealed that Danube River is seriously affected by the presence of Fe (IP = 4887) and strongly affected by the presence of Zn (IP = 4.49). The concentration of Cd in fish muscle tissue was above the maximum permitted level (0.05 µg/g) by the EU regulation. From all analyzed OS biomarkers, MDA registered the highest median values in fish muscle (145.7 nmol/mg protein in DR, 201.03 nmol/mg protein in DD, 148.58 nmol/mg protein in BS) and fish liver (200.28 nmol/mg protein in DR, 163.67 nmol/mg protein, 158.51 nmol/mg protein), compared to CAT, SOD and GPx. The prediction of Cd, Pb, Zn, Fe and Cu in fish hepatic and muscle tissue was determined based on CAT, SOD, GPx and MDA, by using non-linear tree-based RF prediction models. The analysis emphasizes that MDA in hepatic tissue is the most important independent variable for predicting heavy metals in fish muscle and tissues at BS coast, followed by GPx in both hepatic and muscle tissues. The RF analytical framework revealed that CAT in muscle tissue, respectively, MDA and GPx in hepatic tissues are most common predictors for determining the heavy metals concentration in both muscle and hepatic tissues in DD area. For DR, the MDA in muscle, followed by MDA in hepatic tissue are the main predictors in RF analysis.

Keywords: oxidative stress; heavy metals; Danube River; Black Sea; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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