AIMC Topic: Environmental Restoration and Remediation

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Evaluating Ecosystem Services of Ecological Restoration in Mining Areas: A Modified Choice Experiment Approach.

Environmental management
Unsustainable mining practices have led to severe ecosystem degradation in mining areas. Ecological restoration plays a crucial role in reinstating the ecosystem service functions of these areas. A monetary evaluation of the ecosystem service value g...

A novel integrated framework for long-term assessment of ecosystem service degradation and restoration prioritization in a semi-arid rift valley landscape.

Environmental monitoring and assessment
Wetland ecosystems in Africa's semi-arid rift valleys are crucial for supporting biodiversity, regulating water systems, and sustaining livelihoods; however, they are rapidly deteriorating due to agricultural expansion and urbanization. Previous asse...

Depth-dependent heterogeneity in topsoil stockpiles influences plant-microbe interactions and revegetation success in arid mine reclamation.

The Science of the total environment
Covering mine tailings with uncontaminated soil is a common strategy to mitigate environmental impacts and promote ecosystem recovery during post-mining land reclamation. Topsoil is often stockpiled for future use as a capping layer, but prolonged st...

Integrating artificial intelligence with microbial biotechnology for sustainable environmental remediation.

Environmental monitoring and assessment
This narrative review examines the significant advances of artificial intelligence (AI) in enhancing the identification and microbial degradation of environmentally persistent compounds, addressing major issues in pollution monitoring and management....

Hydrogeochemical and machine learning evidences for release and attenuation mechanisms of chromium contamination in a partially PRB remediation of shallow groundwater.

Environmental pollution (Barking, Essex : 1987)
This study aimed to investigate the release and attenuation mechanisms of Cr(VI) in a shallow aquifer system and evaluate the remediation performance of a permeable reactive barrier (PRB) in central China. A hydrogeochemical and machine learning fram...

Predicting arsenic bioaccessibility: A global data-driven machine learning approach and its implication for reducing carbon emissions.

Journal of hazardous materials
Site-specific arsenic (As) bioaccessibility data can improve the accuracy of health risk assessments, but direct measurements are costly and time-consuming. Even when available, measured values such as the mean still yield remediation targets below n...

Emerging technologies for assessing the occurrence, fate, effects, and remediation of plastics in the environment.

Environmental monitoring and assessment
Plastic pollution and contamination originates from raw material handling, polymerization, compounding, and fabrication, contributing to environmental accumulation. Advanced analytical techniques such as Fourier transform infrared, Raman spectroscopy...

Addressing data handling shortcomings in machine learning studies on biochar for heavy metal remediation.

Journal of hazardous materials
Recent advancements in machine learning (ML) technologies have significantly enhanced their applications in environmental sciences, particularly in the domains of soil and water remediation. This paper reviews recent studies that employ ML to optimiz...

Assessment of wetland ecological restoration effect based on fuzzy analytic hierarchy process: a case study of Tianjin Qilihai Wetland.

Environmental monitoring and assessment
Scientific evaluation of the effectiveness of ecological restoration could provide support for sustainable management and protection of wetlands. However, due to the multiple and difficult to quantify factors affecting wetlands, commonly used spatiot...

Innovations in plastic remediation: Catalytic degradation and machine learning for sustainable solutions.

Journal of contaminant hydrology
Plastic pollution is an extreme environmental threat, necessitating novel restoration solutions. The present investigation investigates the integration of machine learning (ML) techniques with catalytic degradation processes to improve plastic waste ...