Evaluation of Numerical Sediment Quality Targets for the St. Louis River Area of Concern

Abstract

Numerical sediment quality targets (SQTs) for the protection of sediment-dwelling organisms have been established for the St. Louis River Area of Concern (AOC), 1 of 42 current AOCs in the Great Lakes basin. The two types of SQTs were established primarily from consensus-based sediment quality guidelines. Level I SQTs are intended to identify contaminant concentrations below which harmful effects on sediment-dwelling organisms are unlikely to be observed. Level II SQTs are intended to identify contaminant concentrations above which harmful effects on sediment-dwelling organisms are likely to be observed. The predictive ability of the numerical SQTs was evaluated using the matching sediment chemistry and toxicity data set for the St. Louis River AOC. This evaluation involved determination of the incidence of toxicity to amphipods ( Hyalella azteca) and midges (Chironomus tentans) within five ranges of Level II SQT quotients (i.e., mean probable effect concentration quotients [PEC-Qs]). The incidence of toxicity was determined based on the results of 10-day toxicity tests with amphipods (endpoints: survival and growth) and 10-day toxicity tests with midges (endpoints: survival and growth). For both toxicity tests, the incidence of toxicity increased as the mean PEC-Q ranges increased. The incidence of toxicity observed in these tests was also compared to that for other geographic areas in the Great Lakes region and in North America for 10- to 14-day amphipod (H. azteca) and 10- to 14-day midge (C. tentans or C. riparius) toxicity tests. In general, the predictive ability of the mean PEC-Qs was similar across geographic areas. The results of these predictive ability evaluations indicate that collectively the mean PEC-Qs provide a reliable basis for classifying sediments as toxic or not toxic in the St. Louis River AOC, in the larger geographic areas of the Great Lakes, and elsewhere in North America.

Evaluation of sediment quality guidelines derived using the screening-level concentration approach for application at uranium operations in Saskatchewan, Canada

“…Sediment quality guidelines have been used in a variety of ways. They are used in the management of dredged materials (Casado-Martinez et al 2006) and as sediment quality remediation targets (Crane et al 2002;Crane and MacDonald 2003). They are solely used or integrated as a line of evidence into environmental risk assessments to evaluate the potential for adverse effects on aquatic life (Chang et al 2004;Long et al 2006;Thompson et al 2005;Apitz et al 2007).…”

Comparative sediment quality guideline performance for predicting sediment toxicity in Southern California, USA

“…Similar results were reported in a study conducted in the St. Louis River, USA. Crane et al [13] found that their data set contained few highly contaminated samples, which resulted in low toxicity efficiency and sensitivity.…”

“…Chemical mixtures present in each sediment sample were addressed in three of the empirical approaches by normalizing each chemical to its respective SQG value and then summarizing the values as the mean quotient (e.g., mean ERMq, mean SQGQ1q, mean consensus MECq). The mean quotient values for empirical guidelines have been widely used by others [10,[13][14][15][16][17]21,22]. The data were normalized for the EqP for organics approach by calculating the toxic units represented by each chemical and then summarized by calculating the sum of the toxic units.…”

“…Previous studies have shown that the performance (predictive ability) of particular SQGs is variable when applied to large‐scale data sets [9–12]. Variations in predictive ability among SQG approaches have also been observed when regional data sets have been analyzed [13]. Regional differences in sediment characteristics (e.g., grain size or total organic carbon) or in the concentrations or contaminants types may influence the predictive ability of SQGs [12].…”

Integrated Risk Assessments for the Management of Contaminated Sediments in Estuaries and Coastal Systems

“…These have been developed in an attempt to account for the potential effects of multiple contaminants (assuming simple additivity), as well as the magnitude of exceedance above relevant SQGs. Various SQGQ methods have been developed and tested for their ability to accurately predict toxicity (Burmaster et al, 1991;Hyland et al, 1999;Thompson et al, 1999;Bombardier and Blaise, 2000;MacDonald et al, 2000b;Fairey et al, 2001;Ingersoll et al, 2001Ingersoll et al, , 2002Linkov et al, 2001;Long et al, 2002;Birch and Taylor, 2002;Crane et al, 2002;Grapentine et al, 2002b;Hyland et al, 2003;Tannenbaum et al, 2003;Riba et al, 2003). Because the mSQGQs generate a single number for each sam ple or site, which integrates information on all contaminants considered, it is, like the index-based values described below, a valuable communication graphical toolsites can be ranked or maps can be contoured to illustrate relative chemical screening risk (see Figure 3).…”