期刊
SCIENCE OF THE TOTAL ENVIRONMENT
卷 665, 期 -, 页码 1168-1181出版社
ELSEVIER
DOI: 10.1016/j.scitotenv.2019.01.345
关键词
Engineered nanoparticles; Mobility; Porous media; Predictive models
资金
- Ferdowsi University of Mashhad
- Ministry of Science, Research and Technology, Islamic Republic of Iran
- University of South Australia
- University of South Australia within the Environmental Contaminants Group
- Australian government under the Australian Research Council (ARC) [FT120100799]
- Australian Research Council [FT120100799] Funding Source: Australian Research Council
Understanding the transport behaviour of new and emerging materials such as engineered nanoparticles (ENPs) is vital for the accurate assessment of their functionality and fate in environmental systems. Predicting ENP mobility in soil systems based on common attributes of either soil or ENPs is of significant interest as an alternative to conducting laborious and time consuming column experiments. Thus this study investigates the importance of different soil properties and experimental conditions on titanium dioxide nanoparticles (nTiO(2)) mobility in real soil media and also evaluates four different modelling approaches including Multiple Linear Regression (MLR), Classification and Regression Tree (CART), Random Forest (RF) and Artificial Neural Network (ANN) for predicting nTiO2 mobility in soil media. The performance of both ANN and RF models were good for predicting nTiO2 transport in soil media, with ANN predictions being slightly superior to RF with less generalization errors. However, RF had the advantage of requiring less input predictors. In comparison the MLR model exhibited poor performance in both calibration and validation datasets, and while the validity of CART was almost acceptable in the calibration dataset, its efficiency was poor for the validation dataset. In addition to soil solution chemistry and hydraulic properties, other important factors having a major contribution to nTiO(2) transport through soil included soil fracture associated properties and the existence of preferential flows. (c) 2019 Published by Elsevier B.V.
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