![]() Other studies developed prediction models of heavy metals in the nematode ( Caenorhabditis elegans), in the water, and sediment matrix. For instance, mathematical models (One Compartment Approach) were developed in order to predict internal concentrations of organic chemicals in fish. Several studies have generated prediction models. Įcotoxicological models can be developed for selected fish species, which have the capacity to accumulate pollutants within their tissues. The Regional Organization for the Protection of the Marine Environment recommends using collected fish as an environmental monitoring tool in order to minimize the costs related to field sampling studies. Metal analysis is the most direct procedure for the quantification of these elements in the environment however, this approach involves high financial costs. Moreover, the turbot does not undertake long and transboundary migrations therefore it is an important instrument in assessing water pollution. In general, toxic metals accumulate more in benthic fish species when compared to pelagic ones. The feeding ecology of the turbot is benthivorous, which is, it feeds on organic detritus and small preys inhabiting the sediment superficial layers. įlatfish, such as the turbot ( Psetta maxima maeotica, Linnaeus, 1758), are accepted by the scientific communities as good bioindicators of heavy metal pollution in the aquatic environment, due to their association to bottom sediments. It is important that the chosen bioindicator fish species is not migratory and, thus, can accurately indicate pollution levels in a certain study area, have a superior trophic level (carnivorous or piscivorous) and a specific diet. The response of fish to environmental pollution is species specific. ![]() Furthermore, alkali metals, such as sodium (Na) or potassium (K), and alkaline earth metals, such as calcium (Ca) or magnesium (Mg), influence the bioaccumulation capacity of trace metals in fish. Both essential and non-essential metals may become toxic above a specific threshold. Other trace metals, such as lead (Pb) and cadmium (Cd) are non-essential elements and do not have any biological role in the fish’s organism. Trace metals, such as iron (Fe), zinc (Zn), copper (Cu), cobalt (Co), manganese (Mn), nickel (Ni), chromium (Cr), or selenium (S), are essential elements that are involved in the normal metabolism of fish. By using fish as bioindicators, direct data on the bioavailable fraction of heavy metals can be obtained. įish are suitable bioindicators of metal pollution due to their capacity to accumulate higher metal concentrations in their tissues, as compared to the concentrations present in the surrounding water. In the aquatic environment, fish have been widely utilized as environmental bioindicators of microplastic pollution metal pollution, sewage sludge pollution, suspended solid pollution, polychlorinated biphenyl (PCBs) pollution, and polycyclic aromatic hydrocarbon (PAH) pollution. The models can be used for improving the knowledge and economic efficiency of linked heavy metals food safety and environment pollution studies.Īmong all of the pollutants, metals are the most hazardous substances due to their potential for accumulation, magnification, persistence and wide distribution within the water table, sediments, and aquatic organisms. Both machine learning MLR and non-linear tree-based RF prediction models were identified to be suitable for predicting the heavy metal concentration from both turbot muscle and liver tissues. The non-linear tree-based RF prediction models (over 70% prediction accuracy) were identified for As, Cd, Cu, K, Mg, and Zn in muscle tissue and As, Ca, Cd, Mg, and Fe in turbot liver tissue. Significant MLR models were recorded for Ca, Fe, Mg, and Na in muscle tissue and K, Cu, Zn, and Na in turbot liver tissue. The models were based on data that were provided from scientific literature, attributed to 11 heavy metals (As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Zn) from both muscle and liver tissues of turbot exemplars. For multiple linear regression (MLR) models, the stepwise method was used, while non-linear models were developed by applying random forest (RF) algorithm. The present study uses a machine learning approach, which is based on multiple linear and non-linear models, in order to effectively estimate the concentrations of heavy metals in both turbot muscle and liver tissues. ![]() ![]() ![]() Demersal fish species, such as turbot ( Psetta maxima maeotica), are accepted by the scientific communities as suitable bioindicators of heavy metal pollution in the aquatic environment. Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. ![]()
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