Sources of Uncertainty in the Application of the Equilibrium Partitioning Approach to Sediment Quality Assessment

Abstract

One widely discussed method to assess biological impact of contaminated sediments is through the application of the equilibrium partitioning model. The partitioning model can be used to develop normalized chemical concentrations that can be correlated to biological effects resulting from sediment contamination. The application of the equilibrium partitioning model requires the measurement or estimation of several model variables. However, the combined uncertainties in the determination of these variables may limit the usefulness of this approach. Uncertainty in estimated pore water effects concentrations and analytical variability of three chemical measurements was investigated using Monte Carlo simulation. Results of Monte Carlo simulations indicated that the levels of analytical uncertainties in the study were acceptable. However, the results also revealed that analytical variability can significantly impair interpretation of the data and should be considered in the choice among different analytical methods. Investigation of duplicate field samples collected in the study indicated an additional source of uncertainty from the occurrence of spatially heterogeneous contaminant distributions.

Environmental quality assessment on a river system polluted by mining activities

“…In this study, other chemical forms of heavy metals, except SEM, were referred to as inactive species and excluded in the derivation of K p,i and SQVs. Moreover, abuse or misuse of EqP-based SQVs, in which the quality values are often mechanically regarded as the clean-up regulations or goals ( Coates and Delfino, 1993;Landrum, 1995), should be avoided as well.…”

Assessing and managing contaminated sediments: Part I, developing an effective investigation and risk evaluation strategy

“…Uncertainty, and how it is handled, can be communicated in a number of quantitative and qualitative ways, including various statistical treatments and the use of models such as Monte Carlo simulations or weighted decision criteria analysis (Coates and Delfino 1993; Johnston et al 2002). Sampling design and data interpretation should be done with care.…”