Document Type
Article
Publication Date
5-5-2025
Abstract
In recent decades, the environmental detection of various organic compounds (OCs) has highlighted the limitations of conventional soil-water sorption models, which simplify complex experimental conditions and often overlook OCs with polyfunctional and ionizable structures. To address these shortcomings, we compiled a comprehensive soil-water sorption dataset encompassing 20,945 data points for 419 OCs with various functional groups and 1037 different soils. Meta-analysis of the dataset revealed the trends of soil sorption associated with OC substructures, soil properties, and solution conditions. Machine learning models employing the XGBoost algorithm, in conjunction with MACCS fingerprints and experimental conditions, were developed to cover the entire spectrum of speciation for cationic, neutral, and anionic species. Among these, the individual models tailored to each speciation achieved an overall root-mean-square-error value of 0.32 for log Kd. Model interpretation revealed that the models correctly understood the contributions of various substructures, such as multiple aromatic rings and nitrogen or oxygen atoms, to sorption. The models were also found to accurately capture isotherm nonlinearity and the pH effect on the sorption of ionizable OCs. Finally, utilizing soil properties from the Harmonized World Soil Database, the models predicted the sorption of diverse OCs based on global soil properties under simulated environmental scenarios.
Keywords
chemical speciation, model interpretation, organic contaminants, soil sorption, soil-water environments
Language
English
Publication Title
Journal of Hazardous Materials
Grant
2020-67019-31019
Rights
© 2025 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/), which permits non-commercial copying and redistribution of the material in any medium or format, provided the original work is not changed in any way and is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Sun, Jiachun; Zhang, Kai; and Zhang, Huichun, "Predicting Sorption of Diverse Organic Compounds in Soil-Water Systems: Meta-Analysis, Machine Learning Modeling, and Global Soil Mapping" (2025). Faculty Scholarship. 1360.
https://commons.case.edu/facultyworks/1360
Manuscript Version
Final Publisher Version