Explainable extreme gradient boosting tree-based prediction of toluene, ethylbenzene and xylene wet deposition

Časopis: Science of The Total Environment

Volume 653

ISSN: 0048-9697

DOI: 10.1016/j.scitotenv.2018.10.368

Stranice: 140-147

Link: https://doi.org/10.1016/j.scitotenv.2018.10.368

Current research suggests that, apart from photochemical reactions, toluene, ethylbenzene and xylene (TEX) removal from ambient air might be affected by atmospheric precipitation, depending on the concentrations and water solubility of the compounds, Henry's law, physico-chemical properties of the water, as well as the frequency and intensity of precipitation events. Nevertheless, existing knowledge of the role that wet deposition plays in biogeochemical cycles of volatile species remains insufficient, and this topic requires more scientific effort to be explored and understood. In this study, we employed the eXtreme Gradient Boosting tree ensemble for revealing TEX transfer from ambient air to rainwater, and applied a novel SHapley Additive exPlanations feature attribution framework to examine the relevance of the monitored parameters and identify key factors that govern wet deposition of TEX. According to the results, main impacts, including ambient air TEX concentrations, and rainwater and air temperatures, and occasional, but less important impacts, including wind speed, air pressure, turbidity, and total organic carbon, NO3-, Cl- and K+ rainwater concentration, shaped TEX partition between gaseous and aqueous phases during rain events.
Ključne reči: BTEX, Machine learning, Multiphase system, SHAP, Wet deposition, XGBoost