Abstract
The aim of this work was to statistically evaluate the presence of various contaminants in fish in the copper mining-impacted Morphou Bay. Multivariate data techniques were used to analyse the contamination in seasonally encountered fish. Principal component analysis was used to classify the contaminants with respect to factor loadings. Red mullet, a local deep feeder, was found to be affected at 66% of factor loadings in variance, with a linear correlation between arsenic (As) and copper (Cu). Agglomerative hierarchical clustering linked three similar clusters: (As, Cu, Se, Zn), (Cd, Cr, Hg), and (Ni, Pb) and we merged them in a dendrogram. The self-organising maps were developed by combining the artificial neural network analysis classified data with the sub-patterns, while considering the results of principal component and clustering analysis. The resultant maps effectively distinguished the correlations between the contaminants in the sampled fish. Based on the results, we suggest that human consumption of fish from the polluted area should only take place with caution and that the study be repeated using a more elaborate sampling strategy.
Zusammenfassung
Das Ziel dieser Arbeit war es, das Vorhandensein verschiedener Schad-stoffe in Fischen in der vom Kupferabbau beeinflussten Morphou Bucht statistisch zu untersuchen. Multivariate Datenverfahren wurden verwendet, um die Belastung in saisonal angetroffenen Fischen zu analysieren. Die Hauptkomponentenanalyse wurde genutzt, um die Schadstoffe in Bezug auf die Faktorladungen zu klassifizieren. Mit einer Faktorladung, die 66 % der Varianz erklärt, zeigte sich für Rotbarben eine lineare Korrelation zwischen Arsen (As) und Kupfer (Cu). Mit der agglo-merativen hierarchischen Clusteranalyse wurden drei Cluster: (As, Cu, Se, Zn), (Cd, Cr, Hg) und (Ni, Pb) differenziert und in einem Dendrogramm dargestellt. Durch Kombination der klassifizierten Daten aus der künstlichen neuronalen Netzwerkanalyse mit den Untergruppen wurden Kohonennetze erstellt. Dabei wurden die Ergebnisse der Hauptkomponenten- und Clusteranalyse berücksichtigt. Die so erzeugten Karten zeigten eine deutliche Korrelation zwischen den Schadstoffen in den beprobten Fischen. Basierend auf den Ergebnissen, empfehlen wir den Verzehr von Fischen aus belasteten Gebieten nur unter Vorsicht. Außerdem sollte die Studie mit einer verbesserten Probenahme¬strategie wiederholt werden.
Resumen
El objetivo de este trabajo fue evaluar estadísticamente la presencia de diversos contaminantes en peces en la bahía de Morphou, afectada por la extracción de cobre. Se usaron técnicas de datos multivariantes para analizar la contaminación en peces de temporada. El análisis del componente principal se utilizó para clasificar los contaminantes con respecto a los factores de carga. El salmonete, un comedero profundo local, se encontró afectado al 66% de las cargas de factores en varianza, con una correlación lineal entre el arsénico (As) y el cobre (Cu). La clusterización jerárquica aglomerativa vincula tres clusters similares: (As, Cu, Se, Zn), (Cd, Cr, Hg) y (Ni, Pb) y fueron fusionados en un dendrograma. Los mapas autoorganizados se desarrollaron combinando los datos clasificados del análisis de red neuronal artificial con los subpatrones, mientras se consideraban los resultados del análisis del componente principal y del análisis por clusters. Los mapas resultantes distinguieron efectivamente las correlaciones entre los contaminantes en los peces muestreados. En base a los resultados, sugerimos que el consumo humano de pescado del área contaminada sólo debe tener lugar con precaución y que el estudio debe repetirse utilizando una estrategia de muestreo más elaborada.
抽象
文章旨在统计评价受铜矿开采影响的Morphou海湾鱼体内各类污染物。多变量分析方法用以分析季节性鱼体内污染。采用主成分分析进行污染物因子载荷分类。红鲻鱼是一种地方性深水鱼,公共因子方差的因子载荷贡献率达66%,砷(As)与铜(Cu)呈线性关系。层次聚类分析将污染物为三个集群(As, Cu, Se, Zn)、(Cd, Cr, Hg)和 (Ni, Pb),并绘制于一张树形图。考虑主成分分析和聚类分析结果,通过人工神经网络建立了自组织图。自组织图能够有效地区分样本鱼体内污染物。基于研究结果,建议人类小心食用污染区鱼类,建议采用更精细取样方案进行重复试验。
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Acknowledgements
The data was collected by one of us (A.K.) within the scope of a Near East University Research Project (YDU/2009-08). We also acknowledge support from the Turkish Technological and Research Council (TUBITAK-YDABAG) for the various analyses (Dr. Deniz Sarica, chemist). We also thank Dr. S. Hutchinson Blue for linguistic corrections.
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Karafistan, A., Gemikonakli, E. Contaminant Evaluation in Fish from the Mining-Impacted Morphou Bay, Cyprus, Using Statistical and Artificial Neural Network Analysis. Mine Water Environ 38, 178–186 (2019). https://doi.org/10.1007/s10230-018-0559-4
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DOI: https://doi.org/10.1007/s10230-018-0559-4