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

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.

Computer-Vision Unmanned Aerial Vehicle Detection System Using YOLOv8 Architectures

“…Moreover, hybrid methods between machine/deep learning and metaheuristics excel in other application domains as well, as evidenced by numerous successful recent applications including medicine [22,13,8,27,32,6,24], agriculture [25], environmental monitoring [5,20], economy [13,41,38] and power grids [29,14,3,39,45]. Other notable applications include weather forecasting [21], cloud computing [7,33,4,9], wireless sensor networks [46,11,44] and intrusion detection [35,36,23,15].…”

Environmental Monitoring and Analysis of Persistent Organic Pollutants

“…The article “Potential of Coupling Metaheuristics-Optimized-XGBoost and SHAP in Revealing PAHs Environmental Fate” by a team of Serbian researchers [ 3 ] describes the use of an optimized XGBoost model and the Shapley Additive exPlanations (artificial intelligence) method to analyse a set of two years’ worth of data on air pollutant measurements (PM 10 atmospheric particles, particle-bound benzo(a)pyrene, Pb, As, Cd, Ni, gaseous pollutants NO, NO 2 , NOx, and SO 2 ) and meteorological parameters. The authors distinguish types of environments characterized by specific interactions between benzo(a)pyrene, other polluting species, and meteorological conditions and explain how advanced artificial intelligence-based modelling can contribute to a better understanding of complex factors that determine the fate of air polluting species in the environment.…”