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Volume 11
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10.3390/toxics11040394
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Article

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

by
Gordana Jovanovic
1,2,*,
Mirjana Perisic
1,2,
Nebojsa Bacanin
2,
Miodrag Zivkovic
2,
Svetlana Stanisic
2,
Ivana Strumberger
2,
Filip Alimpic
1 and
Andreja Stojic
1,2
1
Institute of Physics Belgrade, National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
2
Faculty of Informatics and Computing, Singidunum University, 11000 Belgrade, Serbia
*
Author to whom correspondence should be addressed.
Toxics 2023, 11(4), 394; https://doi.org/10.3390/toxics11040394
Submission received: 25 February 2023 / Revised: 17 April 2023 / Accepted: 19 April 2023 / Published: 21 April 2023
(This article belongs to the Special Issue Environmental Monitoring and Analysis of Persistent Organic Pollutants)
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Abstract

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.
Keywords: machine learning; extreme gradient boosting; metaheuristics optimization; swarm intelligence; explainable artificial intelligence; sine cosine algorithm; benzo(a)pyrene machine learning; extreme gradient boosting; metaheuristics optimization; swarm intelligence; explainable artificial intelligence; sine cosine algorithm; benzo(a)pyrene

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MDPI and ACS Style

Jovanovic, G.; Perisic, M.; Bacanin, N.; Zivkovic, M.; Stanisic, S.; Strumberger, I.; Alimpic, F.; Stojic, A. Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate. Toxics 2023, 11, 394. https://doi.org/10.3390/toxics11040394

AMA Style

Jovanovic G, Perisic M, Bacanin N, Zivkovic M, Stanisic S, Strumberger I, Alimpic F, Stojic A. Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate. Toxics. 2023; 11(4):394. https://doi.org/10.3390/toxics11040394

Chicago/Turabian Style

Jovanovic, Gordana, Mirjana Perisic, Nebojsa Bacanin, Miodrag Zivkovic, Svetlana Stanisic, Ivana Strumberger, Filip Alimpic, and Andreja Stojic. 2023. "Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate" Toxics 11, no. 4: 394. https://doi.org/10.3390/toxics11040394

APA Style

Jovanovic, G., Perisic, M., Bacanin, N., Zivkovic, M., Stanisic, S., Strumberger, I., Alimpic, F., & Stojic, A. (2023). Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate. Toxics, 11(4), 394. https://doi.org/10.3390/toxics11040394

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