AIMC Topic: Soil Pollutants
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Effects of corn straw on dissipation of polycyclic aromatic hydrocarbons and potential application of backpropagation artificial neural network prediction model for PAHs bioremediation.
Ecotoxicology and environmental safety
Oct 10, 2019
In order to provide a viable option for remediation of PAHs-contaminated soils, a greenhouse experiment was conducted to assess the effect of corn straw amendment (1%, 2%, 4% or 6%, w/w) on dissipation of aged polycyclic aromatic hydrocarbons (PAHs) ...
Artificial neural network model to predict transport parameters of reactive solutes from basic soil properties.
Environmental pollution (Barking, Essex : 1987)
Oct 10, 2019
Measurement of solute-transport parameters through soils for a wide range of solute- and soil-types is time-consuming, laborious, expensive and practically impossible. So, indirect methods for estimating the transport parameters by pedo-transfer func...
Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security.
The Science of the total environment
Aug 20, 2019
When water and solutes enter the plant root through the epidermis, organic contaminants in solution either cross the root membranes and transport through the vascular pathways to the aerial tissues or accumulate in the plant roots. The accumulation o...
Modelling and Optimizing Pyrene Removal from the Soil by Phytoremediation using Response Surface Methodology, Artificial Neural Networks, and Genetic Algorithm.
Chemosphere
Jul 30, 2019
This study aimed to model and optimize pyrene removal from the soil contaminated by sorghum bicolor plant using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) with Genetic Algorithm (GA) approach. Here, the effects of indole a...
Artificial Neural Networks (ANNs) and Response Surface Methodology (RSM) Approach for Modelling the Optimization of Chromium (VI) Reduction by Newly Isolated Strain NS-MIE from Agricultural Soil.
BioMed research international
May 26, 2019
Numerous technologies and approaches have been used in the past few decades to remove hexavalent chromium (Cr[VI]) in wastewater and the environment. However, these conventional technologies are not economical and efficient in removing Cr(VI) at a ve...
Using artificial neural network to investigate physiological changes and cerium oxide nanoparticles and cadmium uptake by Brassica napus plants.
Environmental pollution (Barking, Essex : 1987)
Dec 12, 2018
Heavy metals and emerging engineered nanoparticles (ENPs) are two current environmental concerns that have attracted considerable attention. Cerium oxide nanoparticles (CeONPs) are now used in a plethora of industrial products, while cadmium (Cd) is ...
A coupled multivariate statistics, geostatistical and machine-learning approach to address soil pollution in a prototypical Hg-mining site in a natural reserve.
Chemosphere
Nov 26, 2018
The impact of mining activities on the environment is vast. In this regard, many mines were operating well before the introduction of environmental law. This is particularly true of cinnabar mines, whose activity has declined for decades due to growi...
A novel method for predicting cadmium concentration in rice grain using genetic algorithm and back-propagation neural network based on soil properties.
Environmental science and pollution research international
Oct 24, 2018
Heavy metal pollution is a global ecological safety issue, especially in crops, where it directly threatens regional ecological security and human health. In this study, the back-propagation (BP) neural network optimized by the genetic algorithm (GA)...
Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models.
Chemosphere
Oct 11, 2018
Empirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of co...
Analysis of the adsorption and retention models for Cd, Cr, Cu, Ni, Pb, and Zn through neural networks: selection of variables and competitive model.
Environmental science and pollution research international
Jun 29, 2018
In this study, the neural networks are used to predict and explain the behavior of different edaphological variables in the adsorption and retention of heavy metals, both isolated and competing. A comparison with the results obtained using multiple r...
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