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

Abstract

Assessment of potentially contaminated sites (PCS) can be expensive; hence, simple and less demanding methods and models are required. This work attempts to provide an approach that can aid in selecting the most appropriate model for the PCS. The developed method uses over 100 field site data to evaluate four test models (analytical/empirical) that provide the maximum plume length (L_max ), which is used as a principal model ranking quantity in this work. Analysis of site data shows that field plume length (L_f ) follows a log-normal distribution. Subsequently, L_max is delineated with respect to L_f using a threshold probability as underestimating, overestimating, and overly-overestimating. Akaike information criterion (AIC) and analytical hierarchy process (AHP) are considered to support the threshold approach results. The classical AIC is modified (to AIC_mod ) to fit the term represented by the difference between L_f and L_max . Additionally, the threshold factors as a product of subjective weights are added to the AIC_mod . Using L_f and L_max , the AIC_mod provides a distinct ranking of the test models. For the AHP approach, the goodness of fit, underestimation, overly overestimation, and model complexity are the four chosen criteria. Similar to AIC_mod , the AHP approach provides a distinct ranking of the test models. The final decision on the best fitting model can be made on user criteria following the scheme developed in this work.