Title

Using Artificial Neural Network to Investigate Physiological Changes and Cerium Oxide Nanoparticles and Cadmium Uptake by Brassica Napus Plants

Abstract

Heavy metals and emerging engineered nanoparticles (ENPs) are two current environmental concerns that have attracted considerable attention. Cerium oxide nanoparticles (CeO2NPs) are now used in a plethora of industrial products, while cadmium (Cd) is a great environmental concern because of its toxicity to animals and humans. Up to now, the interactions between heavy metals, nanoparticles and plants have not been extensively studied. The main objectives of this study were (i) to determine the synergistic effects of Cd and CeO2NPs on the physiological parameters of Brassica and their accumulation in plant tissues and (ii) to explore the underlying physiological/phenotypical effects that drive these specific changes in plant accumulation using Artificial Neural Network (ANN) as an alternative methodology to modeling and simulating plant uptake of Ce and Cd. The combinations of three cadmium levels (0 [control] and 0.25 and 1 mg/kg of dry soil) and two CeO2NPs concentrations (0 [control] and 500 mg/kg of dry soil) were investigated. The results showed high interactions of co-existing CeO2NPs and Cd on plant uptake of these metal elements and their interactive effects on plant physiology. ANN also identified key physiological factors affecting plant uptake of co-occurring Cd and CeO2NPs. Specifically, the results showed that root fresh weight and the net photosynthesis rate are parameters governing Ce uptake in plant leaves and roots while root fresh weight and Fv/Fm ratio are parameters affecting Cd uptake in leaves and roots. Overall, ANN is a capable approach to model plant uptake of co-occurring CeO2NPs and Cd.

Department(s)

Civil, Architectural and Environmental Engineering

Comments

This work was supported by National Science Foundation under Award CBET #1606036.

Keywords and Phrases

Artificial neural network; Brassica napus; Cadmium; Canola; Cerium oxide nanoparticles

International Standard Serial Number (ISSN)

0269-7491; 1873-6424

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Elsevier Ltd, All rights reserved.

PubMed ID

30577006

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