Literature DB >> 30408662

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

Andreja Stojić1, Nenad Stanić2, Gordana Vuković3, Svetlana Stanišić2, Mirjana Perišić3, Andrej Šoštarić4, Lazar Lazić5.   

Abstract

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.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  BTEX; Machine learning; Multiphase system; SHAP; Wet deposition; XGBoost

Year:  2018        PMID: 30408662     DOI: 10.1016/j.scitotenv.2018.10.368

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  5 in total

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Journal:  Front Med (Lausanne)       Date:  2022-03-08

4.  Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP.

Authors:  Yan Shi; Ying Zou; Jihua Liu; Yuanyuan Wang; Yingbin Chen; Fang Sun; Zhi Yang; Guanghe Cui; Xijun Zhu; Xu Cui; Feifei Liu
Journal:  Front Oncol       Date:  2022-08-26       Impact factor: 5.738

5.  Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach.

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  5 in total

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