AIMC Topic: Environmental Restoration and Remediation

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Effective allocation of resources in water pollution treatment alternatives: a multi-stage gray group decision-making method based on hesitant fuzzy linguistic term sets.

Environmental science and pollution research international
With the significant economic shift, water pollution treatment has gradually become a key problem which needs to be deeply investigated for the sustainable development of China. In the face of specific water pollution incidents, multiple alternatives...

Multifunctional and biodegradable self-propelled protein motors.

Nature communications
A diversity of self-propelled chemical motors, based on Marangoni propulsive forces, has been developed in recent years. However, most motors are non-functional due to poor performance, a lack of control, and the use of toxic materials. To overcome t...

Priorization of River Restoration by Coupling Soil and Water Assessment Tool (SWAT) and Support Vector Machine (SVM) Models in the Taizi River Basin, Northern China.

International journal of environmental research and public health
Identifying priority zones for river restoration is important for biodiversity conservation and catchment management. However, limited data due to the difficulty of field collection has led to research to better understand the ecological status withi...

An Information System for Brownfield Regeneration: providing customised information according to stakeholders' characteristics and needs.

Journal of environmental management
In the EU brownfield presence is still considered a widespread problem. Even though, in the last decades, many research projects and initiatives developed a wealth of methods, guidelines, tools and technologies aimed at supporting brownfield regenera...

Compound washing remediation and response surface analysis of lead-contaminated soil in mining area by fermentation broth and saponin.

Environmental science and pollution research international
The development of eluent is the key to soil washing remediation, and a compound eluent was constructed using the prepared citric acid fermentation broth and saponin in this study. It displayed a good washing performance for Pb, Cu, Cr, and Cd in red...

Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of "hot" particles.

The Science of the total environment
The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can pre...

Machine learning-driven optimization of arsenic phytoextraction using amendments.

Ecotoxicology and environmental safety
Exogenous amendments are crucial for enhancing the remediation efficiency of arsenic-contaminated soils by Pteris vittata. However, their effectiveness is unstable due to various factors, and neglecting their economic costs hinder broader application...

Application of machine learning in microwave remediation of total petroleum hydrocarbon contaminated soil: Prediction and key factor identification.

Journal of environmental management
Microwave thermal remediation (TPH) is a promising remediation method for petroleum hydrocarbon contaminated soils due to its high energy efficiency and rapid heating capacity. However, the complexity of influencing factors and their nonlinear intera...

Integrating machine learning and reliability analysis: A novel approach to predicting heavy metal removal efficiency using biochar.

Ecotoxicology and environmental safety
Soil contamination with heavy metals (HMs) presents critical environmental and public health risks due to their long-term persistence and tendency to bioaccumulate. Biochar has gained recognition as an effective amendment for HM immobilization, owing...

Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks.

International journal of phytoremediation
Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied...