An Approach for Selecting a Model for the Assessment of Potentially Contaminated Sites

Area of Science:

• Environmental Science
• Geospatial Analysis
• Risk Assessment

Background:

• Assessing potentially contaminated sites (PCS) is often costly and complex.
• There is a need for efficient and reliable methods to select appropriate site assessment models.
• Existing models vary in their predictive accuracy for contaminant plume behavior.

Purpose of the Study:

• To develop and validate an approach for selecting the most suitable model for potentially contaminated sites (PCS).
• To rank analytical/empirical models based on their ability to predict maximum plume length (Lmax).
• To provide a framework for informed model selection based on user-defined criteria.

Main Methods:

• Utilized over 100 field site datasets to evaluate four analytical/empirical models.
• Analyzed field plume length (Lf) data, finding it follows a log-normal distribution.
• Developed a modified Akaike Information Criterion (AICmod) and employed the Analytical Hierarchy Process (AHP) for model ranking.

Main Results:

• The study established thresholds for underestimating, overestimating, and overly-overestimating plume lengths.
• Both AICmod and AHP provided distinct rankings for the evaluated models.
• Field plume length (Lf) data exhibited a log-normal distribution, a key finding for model validation.

Conclusions:

• The developed approach effectively ranks models for potentially contaminated sites (PCS) assessment.
• AICmod and AHP offer robust methods for model selection, considering predictive accuracy and complexity.
• This framework supports informed decision-making in environmental site assessment, optimizing resource allocation.