Comparison of national and regional sediment quality guidelines for classifying sediment toxicity in California

Abstract

A number of sediment quality guidelines (SQGs) have been developed for relating chemical concentrations in sediment to their potential for effects on benthic macroinvertebrates, but there have been few studies evaluating the relative effectiveness of different SQG approaches. Here we apply 6 empirical SQG approaches to assess how well they predict toxicity in California sediments. Four of the SQG approaches were nationally derived indices that were established in previous studies: effects range median (ERM), logistic regression model (LRM), sediment quality guideline quotient 1 (SQGQ1), and Consensus. Two approaches were variations of nationally derived approaches that were recalibrated to California-specific data (CA LRM and CA ERM). Each SQG approach was applied to a standardized set of matched chemistry and toxicity data for California and an index of the aggregate magnitude of contamination (e.g., mean SQG quotient or maximum probability of toxicity) was calculated. A set of 3 thresholds for classification of the results into 4 categories of predicted toxicity was established for each SQG approach using a statistical optimization procedure. The performance of each SQG approach was evaluated in terms of correlation and categorical classification accuracy. Each SQG index had a significant, but low, correlation with toxicity and was able to correctly classify the level of toxicity for up to 40% of samples. The CA LRM had the best overall performance, but the magnitude of differences in classification accuracy among the SQG approaches was relatively small. Recalibration of the indices using California data improved performance of the LRM, but not the ERM. The LRM approach is more amenable to revision than other national SQGs, which is a desirable attribute for use in programs where the ability to incorporate new information or chemicals of concern is important. The use of a consistent threshold development approach appeared to be a more important factor than type of SQG approach in determining SQG performance. The relatively small change in classification accuracy obtained with regional calibration of these SQG approaches suggests that further calibration and normalization efforts are likely to have limited success in improving classification accuracy associated with biological effects. Fundamental changes to both SQG components and conceptual approach are needed to obtain substantial improvements in performance. These changes include updating the guideline values to include current use pesticides, as well as developing improved approaches that account for changes in contaminant bioavailability.

Framework for interpreting sediment quality triad data

“…A combination of 2 sediment chemistry indices is used to determine the magnitude of chemical exposure at each site: the logistic regression models calibrated to California data (CA LRM) and the chemical score index (CSI). The CA LRM uses a set of logistic regression models to predict the probability of sediment toxicity based on the concentrations of 12 sediment contaminants (Bay et al 2008; Field et al 2002; USEPA 2005). To determine the probability of toxicity for the target constituents, the concentration data for each are entered in the following logistic regression equation: where p is the probability of observing a toxic effect; B0 is the intercept parameter (contaminant‐specific); B1 is the slope parameter (contaminant‐specific); and x is the log of the concentration of the contaminant of interest.…”

Hydrocarbon Pollution and Potential Ecological Risk of Heavy Metals in the Sediments of the Oturuba Creek, Niger Delta, Nigeria

“…River bottom or deposited channel materials represent the closest approximation of sediment provenance, movement and deposition, however directly linking sediment chemistry data to observed adverse biological effects on organisms is problematic (USEPA, 2005), hence, a few screening guidelines, indices or benchmarks (below which toxic effects are not expected to occur and above which toxic effects are usually expected) relate chemical concentrations in sediments to their "potential for biological effects" (Bay et al, 2012). Ezekwe and Utong (2017) Buchman (2008) presented sediment screening levels based on Threshold Effects Level (TEL), Effects Range-Low (ERL), and Probable Effects Level (PEL) for evaluating sediment quality.…”

The Sequential Probability Ratio Test: An efficient alternative to exact binomial testing for Clean Water Act 303(d) evaluation

“…4 For listing evaluations based on sediment quality in bays and estuaries, California has adopted a sediment quality objective based on a “multiple lines of evidence” approach that considers contaminant levels, sediment toxicity and sediment macrofaunal community condition. 5–7 These multiple lines of evidence are integrated and assessed to determine whether the sediment quality objective has been attained at a given station 8 which reduces the multiple possible considerations for sediment quality into a binary decision variable suitable for evaluating exceedance frequency and responding to the 303(d) listing and delisting requirements of the CWA.…”