Groundwater vulnerability is the term used to define how easy or how hard it is for a contaminant released at the land surface to reach groundwater. In other words, it defines a “degree of insulation” of an aquifer from pollution. Soil and groundwater vulnerability are important issues all over the world due to the increasing level of contamination by heavy metals, radionuclides, agricultural contaminants, and other pollutants. Shallow aquifers are usually directly at risk due to a sudden pollution of the land surface, but deep aquifers are generally sheltered from serious detrimental risk unless threatened by faulty drilling or injection of pollutants, etc. Since the 1960s, the concept of groundwater vulnerability has gradually evolved from a mere assessment of hydrogeological factors to a numerical assessment of the contamination transport and risk using modern Geographical Information Systems (GIS) and machine learning (ML) approaches.
This Research Topic calls for research papers aimed at:
• Experimental and numerical deterministic and statistical methods, including the application of GIS and ML techniques.
• Results of field and modelling investigations conducted for mapping of groundwater vulnerability at different scales.
• Geochemical processes, including sorption-desorption processes.
• Effects of climate changes, including land surface and groundwater flooding.
• Assessment of the attenuation ability of pollutants within the unsaturated (vadose) and saturated (aquifer) zones.
• Other research topics related to the assessment of soil and groundwater vulnerability and protectability.
Keywords:
Groundwater Vulnerability Assessment, Risk Assessment and Decision Making, Remediation and Stewardship, Prediction and Uncertainty Evaluation, Field Observations, Numerical Modelling, Groundwater and Soil Pollution, Machine Learning, GIS and Mapping, Artificial Intelligence (AI) and Machine Learning Models
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Groundwater vulnerability is the term used to define how easy or how hard it is for a contaminant released at the land surface to reach groundwater. In other words, it defines a “degree of insulation” of an aquifer from pollution. Soil and groundwater vulnerability are important issues all over the world due to the increasing level of contamination by heavy metals, radionuclides, agricultural contaminants, and other pollutants. Shallow aquifers are usually directly at risk due to a sudden pollution of the land surface, but deep aquifers are generally sheltered from serious detrimental risk unless threatened by faulty drilling or injection of pollutants, etc. Since the 1960s, the concept of groundwater vulnerability has gradually evolved from a mere assessment of hydrogeological factors to a numerical assessment of the contamination transport and risk using modern Geographical Information Systems (GIS) and machine learning (ML) approaches.
This Research Topic calls for research papers aimed at:
• Experimental and numerical deterministic and statistical methods, including the application of GIS and ML techniques.
• Results of field and modelling investigations conducted for mapping of groundwater vulnerability at different scales.
• Geochemical processes, including sorption-desorption processes.
• Effects of climate changes, including land surface and groundwater flooding.
• Assessment of the attenuation ability of pollutants within the unsaturated (vadose) and saturated (aquifer) zones.
• Other research topics related to the assessment of soil and groundwater vulnerability and protectability.
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Keywords:
Groundwater Vulnerability Assessment, Risk Assessment and Decision Making, Remediation and Stewardship, Prediction and Uncertainty Evaluation, Field Observations, Numerical Modelling, Groundwater and Soil Pollution, Machine Learning, GIS and Mapping, Artificial Intelligence (AI) and Machine Learning Models
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
