A Deeper Look at Plant Uptake of Environmental Contaminants using Intelligent Approaches


Uptake of contaminants from the groundwater is one pathway of interest, and efforts have been made to relate root exposure to transloation throughout the plant, termed the transpiration stream concentration factor (TSCF). This work utilized machine learning techniques and statistcal analysis to improve the understanding of plant uptake and translocation of emerging contaminants. Neural network (NN) was used to develop a reliable model for predicting TSCF using physicochemical properties of compounds. Fuzzy logic was as a technique to examine the simultaneous impact of properties on TSCF, and interactions between compound properties. The significant and effective compound properties were determined using stepwise and forward regression as two widely used statiscal techniques. Clustering was used for detecting the hidden structures in the plant uptake data set. The NN predicted the TSCF with improved accuracy compared to mechanistic models. We also delivered new insight to compound properteis and their importance in transmembrane migration. The sensitivity analysis indicated that log Kow, molecular weight, hydrogen bond donor, and rotatable bonds are the most important properties. The results of fuzzy logic demonstrated that the relationship between molecular weight and log Kow with TSCF are both bell-shape and sigmoidal. The employed clustering algorithms all discovered two major distinct clusters in the data set.


Electrical and Computer Engineering

Second Department

Civil, Architectural and Environmental Engineering

Research Center/Lab(s)

Center for High Performance Computing Research


This work was supported by National Science Foundation under Award Number 1606036, the Mary K. Finley Endowment, and the Missouri S&T Intelligent Systems Center.

Keywords and Phrases

Clustering algorithms; Computer circuits; Contamination; Fuzzy logic; Groundwater; Groundwater pollution; Hydrogen bonds; Learning systems; Molecular weight; Neural networks; Sensitivity analysis; Emerging contaminants; Environmental contaminant; Hydrogen bond donors; Machine learning techniques; Physicochemical property; Physio-chemical properties; Plant uptake; Transpiration stream concentration factor; River pollution; Artificial neural network; Biological uptake; Concentration (composition); Fuzzy mathematics; Physicochemical properties; Pollutant; Regression analysis; Adult; Article; Fuzzy logic; Human; Hydrogen bond; Machine learning; Molecular weight; Physical chemistry; Sensitivity analysis; Sweating

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Article - Journal

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© 2019 Elsevier, All rights reserved.

Publication Date

01 Feb 2019