An evaluation of methods for calculating mean sediment quality guideline quotients as indicators of contamination and acute toxicity to amphipods by chemical mixtures

Abstract

Mean sediment quality guideline quotients (mean SQGQs) were developed to represent the presence of chemical mixtures in sediments and are derived by normalizing a suite of chemicals to their respective numerical sediment quality guidelines (SQGs). Mean SQGQs incorporate the number of SQGs exceeded and the degree to which they are exceeded and are used for comparison with observed biological effects in the laboratory or field. The current research makes it clear, however, that the number and type of SQGs used in the derivation of these mean quotients can influence the ability of mean SQGQ values to correctly predict acute toxicity to marine amphipods in laboratory toxicity tests. To determine the optimal predictive ability of mean SQGQs, a total of 18 different chemical combinations were developed and compared. The ability of each set of mean SQGQs to correctly predict the presence and absence of acute toxicity to amphipods was determined using three independent databases (n = 605, 2753, 226). Calculated mean SQGQ values for all chemical combinations ranged from 0.002 to 100. The mean SQGQ that was most predictive of acute toxicity to amphipods is calculated as SQGQ1 = ((sigma ([cadmium]/4.21 )([copper]/270)([lead]/ 12.18)([silver]/1.77)([zinc]/ 410)([total chlordane]/6)([dieldrin]/8)([total PAHoc]/1,800)([total PCB]/400))/9). Both the incidence and magnitude of acute toxicity to amphipods increased with increasing SQGQI values. To provide better comparability between regions and national surveys, SQGQ1 is recommended to serve as the standard method for combination of chemicals and respective SQGs when calculating mean SQGQs.

Use of national-scale data to examine human-mediated additions of heavy metals to wetland soils of the US

“…Given that there are 12 elements included in the HMI, the range of possible scores is 0 to 12. We used an equal weighting of 1 assigned to each element because potential effects of human-mediated inputs of heavy metals to wetland soils are often additive (Swartz et al 1988; Fairey et al 2001; Chu and Chow 2002; Norwood et al 2003). To be clear, these thresholds were designed to be assessment tools and do not indicate human health responses, biological responses, mobilization of heavy metals, or ecological condition—these are research questions that are outside the scope of this dataset and this study.…”

“…To be clear, these thresholds were designed to be assessment tools and do not indicate human health responses, biological responses, mobilization of heavy metals, or ecological condition—these are research questions that are outside the scope of this dataset and this study. However, the additive approach we use for the HMI is similar to that used to develop sediment quality guidelines (SQGs), which are used in monitoring and assessment to predict when chemical concentrations are likely to be associated with a measurable biological response (e.g., Long et al 1998; Fairey et al 2001). …”

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).…”

“…In this method, the SQG sum is divided by the number of contaminants measured. One of the most successful is the SQGQ1 approach of Fairey et al (2001), which examines the mSQGQ concentration of nine parameters: Cd, Cu, Pb, Ag, Zn, total chlordane, dieldrin, total polycyclic aro matic hydrocarbon (PAH), and total polychlorinated biphenyl (PCB), using SQGs selected from different sources for different contaminants. However, while normalization goes some way toward making sediments more comparable, this method can also cause artifacts.…”

Use of Receiver Operating Characteristic Curves to Evaluate Sediment Quality Guidelines for Metals

“…To date, most evaluations of the effectiveness of the various SQGs have focused on measures of predictive ability only [16][17][18][19][20][21][22]. For example, Long et al [16] criticized the SEM and AVS approaches because although they provide adequate negative predictive power, they provide low positive predictive power when compared to empirically derived guidelines.…”