Deciphering heavy metal adsorption capacity of soil based on its physicochemical properties and adsorption reaction time using machine learning
Authors:
- Jianle WangJWJianle Wang
- Xueming LiuXLXueming Liu
- Weijie LiWLWeijie Li
- Yuliang TuYTYuliang Tu
- Hong DengHDHong Deng
- Jianwei DuJDJianwei Du
- 2 May 2025
- journal article
- Published by Elsevier in Journal of Environmental Chemical Engineering
- Vol. 13 (3)
Abstract
No abstract availableFunding Information
- Natural Science Foundation of Guangdong Province (2024A1515011395)
- National Natural Science Foundation of China (22476055)
This publication has 55 references indexed in Scilit:
- Effects of the increases in soil pH and pH buffering capacity induced by crop residue biochars on available Cd contents in acidic paddy soilsChemosphere, 2022
- Differential transformation mechanisms of exotic Cr(VI) in agricultural soils with contrasting physio-chemical and biological propertiesChemosphere, 2021
- New insights into hydrogen uptake on porous carbon materials via explainable machine learningCarbon, 2021
- Soil heavy metal pollution and food safety in China: Effects, sources and removing technologyChemosphere, 2021
- How Machine Learning Will Transform BiomedicineCell, 2020
- Ecological risk assessment of heavy metals in sediments and water from the coastal areas of the Bohai Sea and the Yellow SeaEnvironment International, 2020
- Comparison of common spatial interpolation methods for analyzing pollutant spatial distributions at contaminated sitesEnvironmental Geochemistry and Health, 2019
- New insights on Cr(VI) retention by ferrihydrite in the presence of Fe(II)Chemosphere, 2019
- The effects of soil properties and temperature on the adsorption isotherms of lead on some temperate and semiarid surface soils of IranEnvironmental Chemistry Letters, 2009
- Adsorption of arsenate on soils. Part 2: Modeling the relationship between adsorption capacity and soil physiochemical properties using 16 Chinese soilsEnvironmental Pollution, 2005