Patterns of PCB-138 Bioaccumulation in Small Pelagic Fish from the Eastern Mediterranean Sea Using Explainable Machine Learning Prediction

Stojić, Andreja; Mustać, Bosiljka; Jovanović, Gordana; Stojanovic, Jasna; Perišić, Mirjana; Stanišić, Svetlana; Romanić, Snježana Herceg

doi:10.1007/978-3-030-72711-6_10

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…The value of this parameter is dynamically reduced over the iterations, giving an additional focus on a stronger FA search in the latter rounds. Initially, sm is set to 0.8, being reduced over time according to Equation (9).…”

Section: Proposed Modified Sine Cosine Algorithmmentioning

confidence: 99%

“…In this study, based on our previous research [8][9][10][11][12][13][14][15], we have applied a novel approach based on the XGBoost model to identify the factors which are mostly associated with the observed B[a]P concentrations and the environmental conditions which support and facilitate B[a]P level dynamics and its interactions with other polluting species. The XGBoost itself is an efficient model; nevertheless, its hyperparameters require tuning for each particular prediction task in order to achieve good performance on the observed dataset.…”

Section: Introductionmentioning

confidence: 99%

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Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate

Jovanović

Perišić

Bačanin

et al. 2023

Toxics

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Polycyclic aromatic hydrocarbons (PAHs) refer to a group of several hundred compounds, among which 16 are identified as priority pollutants, due to their adverse health effects, frequency of occurrence, and potential for human exposure. This study is focused on benzo(a)pyrene, being considered an indicator of exposure to a PAH carcinogenic mixture. For this purpose, we have applied the XGBoost model to a two-year database of pollutant concentrations and meteorological parameters, with the aim to identify the factors which were mostly associated with the observed benzo(a)pyrene concentrations and to describe types of environments that supported the interactions between benzo(a)pyrene and other polluting species. The pollutant data were collected at the energy industry center in Serbia, in the vicinity of coal mining areas and power stations, where the observed benzo(a)pyrene maximum concentration for a study period reached 43.7 ngm−3. The metaheuristics algorithm has been used to optimize the XGBoost hyperparameters, and the results have been compared to the results of XGBoost models tuned by eight other cutting-edge metaheuristics algorithms. The best-produced model was later on interpreted by applying Shapley Additive exPlanations (SHAP). As indicated by mean absolute SHAP values, the temperature at the surface, arsenic, PM10, and total nitrogen oxide (NOx) concentrations appear to be the major factors affecting benzo(a)pyrene concentrations and its environmental fate.

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Section: Proposed Modified Sine Cosine Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate

Jovanović

Perišić

Bačanin

et al. 2023

Toxics

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Efficiency of clustering methods and self-organizing maps of Adriatic sardines and anchovies regarding organochlorine and fatty acid burden

Dučić,

Herceg Romanić,

Mustać

et al. 2024

Environ Sci Pollut Res

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Polychlorinated biphenyls in mussels, small pelagic fish, tuna, turtles, and dolphins from the Croatian Adriatic Sea waters: an overview of the last two decades of monitoring

Herceg Romanić,

Mendaš,

Fingler

et al. 2024

Archives of Industrial Hygiene and Toxicology

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This review summarises our two decades of polychlorinated biphenyl (PCB) monitoring in different marine organisms along the eastern Adriatic Sea. The aim was to gain an insight into the trends of PCB distribution in order to evaluate the effectiveness of past and current legislation and suggest further action. Here we mainly focus on PCB levels in wild and farmed Mediterranean mussels, wild and farmed bluefin tuna, loggerhead sea turtles, common bottlenose dolphins, and small pelagic fish. The use of artificial intelligence and advanced statistics enabled an insight into the influence of various variables on the uptake of PCBs in the investigated organisms as well as into their mutual dependence. Our findings suggest that PCBs in small pelagic fish and mussels reflect global pollution and that high levels in dolphins and wild tuna tissues raise particular concern, as they confirm their biomagnification up the food chain. Therefore, the ongoing PCB monitoring should focus on predatory species in particular to help us better understand PCB contamination in marine ecosystems in our efforts to protect the environment and human health.

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Patterns of PCB-138 Bioaccumulation in Small Pelagic Fish from the Eastern Mediterranean Sea Using Explainable Machine Learning Prediction

Cited by 4 publications

References 16 publications

Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate

Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate

Efficiency of clustering methods and self-organizing maps of Adriatic sardines and anchovies regarding organochlorine and fatty acid burden

Polychlorinated biphenyls in mussels, small pelagic fish, tuna, turtles, and dolphins from the Croatian Adriatic Sea waters: an overview of the last two decades of monitoring

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