L. Jay Field

A Preliminary Evaluation of Sediment Quality Assessment Values for Freshwater Ecosystems

Abstract Sediment quality assessment values were developed using a weight of evidence approach in which matching biological and chemical data from numerous modelling, laboratory, and field studies performed on freshwater sediments were compiled and analyzed. Two assessment values (a threshold effect level (TEL) and a probable effect level(PEL)) were derived for 23 substances, including eight trace metals, six individual polycyclic aromatic hydrocarbons (PAHs), total polychlorinated biphenyls (PCBs), and eight pesticides. The two values defined three ranges of chemical concentrations; those that were (1) rarely, (2) occasionally, and (3) frequently associated with adverse biological effects. An evaluation of the percent incidence of adverse biological effects within the three concentration ranges indicated that the reliability of the TELs (i.e., the degree to which the TELs represent concentrations within the data set below which adverse effects rarely occur) was consistently good. However, this preliminary evaluation indicated that most of the PELs were less reliable (i.e., they did not adequately represent concentrations within the data set above which adverse effects frequently occur). Nonetheless, these values were often comparable to other biological effects-based assessment values (which were themselves reliable), which increased the level of confidence that could be placed in our values. This method is being used as a basis for developing national sediment quality guidelines for freshwater systems in Canada and sediment effect concentrations as part of the Assessment and Remediation of Contaminated Sediments (ARCS) program in the Great Lakes.

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

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.

Development and evaluation of sediment quality guidelines based on benthic macrofauna responses.

Toxicity-based sediment quality guidelines (SQGs) are often used to assess the potential of sediment contamination to adversely affect benthic macrofauna, yet the correspondence of these guidelines to benthic community condition is poorly documented. This study compares the performance of 5 toxicity-based SQG approaches to a new benthos-based SQG approach relative to changes in benthic community condition. Four of the toxicity-based SQG approaches--effects range median, logistic regression modeling (LRM), sediment quality guideline quotient 1 (SQGQ1), and consensus--were derived in previous national studies in the United States, and one was developed as a regional variation of LRM calibrated to California data. The new benthos-based SQG approach, chemical score index, was derived from Southern California benthic community data. The chemical-specific guidelines for each approach were applied to matched chemical concentration, amphipod mortality, and benthic macrofauna abundance data for Southern California. Respective results for each SQG approach were then combined into a summary metric describing the overall contamination magnitude (e.g., mean quotient) and assessed in accordance with a set of thresholds in order to classify stations into 4 categories of expected biological effect. Results for each SQG approach were significantly correlated with changes in sediment toxicity and benthic community condition. Cumulative frequency plots and effect category thresholds for toxicity and benthic community condition were similar, indicating that both types of effect measures had similar sensitivity and specificity of response to contamination level. In terms of discriminating among multiple levels of benthic community condition, the toxicity-based SQG indices illustrated moderate capabilities, similar to those for multiple levels of toxicity. The National LRM, California LRM, and the chemical score index had the highest overall agreement with benthic categories. However, only the benthos-based chemical score index was consistently among the highest performing SQG indices for all measures of association (correlation, percent agreement, and weighted kappa) for both toxicity and benthos.

Baseline Ecological Risk Assessment of the Calcasieu Estuary, Louisiana: Part 3. An Evaluation of the Risks to Benthic Invertebrates Associated With Exposure to Contaminated Sediments

The sediments in the Calcasieu Estuary are contaminated with a wide variety of chemicals of potential concern (COPCs), including heavy metals, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, phthalates, chlorinated benzenes, and polychlorinated dibenzo-p-dioxins and dibenzofurans. The sources of these COPCs include both point and non–point source discharges. As part of a baseline ecological risk assessment, the risks to benthic invertebrates posed by exposure to sediment-associated COPCs were assessed using five lines of evidence, including whole-sediment chemistry, pore-water chemistry, whole-sediment toxicity, pore-water toxicity, and benthic invertebrate community structure. The results of this assessment indicated that exposure to whole sediments and/or pore water from the Calcasieu Estuary generally posed low risks to benthic invertebrate communities (i.e., risks were classified as low for 68% of the sampling locations investigated). However, incremental risks to benthic invertebrates (i.e., compared with those associated with exposure to conditions in reference areas) were indicated for 32% of the sampling locations within the estuary. Of the three areas of concern (AOCs) investigated, the risks to benthic invertebrates were highest in the Bayou d’Inde AOC; risks were generally lower in the Upper Calcasieu River AOC and Middle Calcasieu River AOC. The areas showing the highest risks to sediment-dwelling organisms were generally located in the vicinity of point source discharges of COPCs. These results provided risk managers with the information required to make decisions regarding the need for remedial actions at the site.

Re-visiting projections of PCBs in Lower Hudson River fish using model emulation.

Remedial decision making at large contaminated sediment sites with bioaccumulative contaminants often relies on complex mechanistic models to forecast future concentrations and compare remedial alternatives. Remedial decision-making for the Hudson River PCBs Superfund site involved predictions of future levels of PCBs in Upper Hudson River (UHR) and Lower Hudson River (LHR) fish. This study applied model emulation to evaluate the impact of updated sediment concentrations on the original mechanistic model projections of time to reach risk-based target thresholds in fish in the LHR under Monitored Natural Attenuation (MNA) and the selected dredging remedy. The model emulation approach used a combination of nonlinear and linear regression models to estimate UHR water PCBs as a function of UHR sediment PCBs and to estimate fish concentrations in the LHR as a function of UHR water PCBs, respectively. Model emulation captured temporal changes in sediment, water, and fish PCBs predicted by the mechanistic model over the emulation period. The emulated model, using updated sediment concentrations and a revised estimate of recovery rate, matched the trend in annual monitoring data for white perch and largemouth bass in the LHR between 1997 and 2014. Our best predictions based on the emulated model indicate that the projected time to reach fish tissue risk-based thresholds in the LHR will take decades longer than the original mechanistic model projections.

Regional models for sediment toxicity assessment

The present study describes approaches to improve the performance of empirical models developed from a large nationwide data set to predict sediment toxicity from chemistry for regional applications. The authors developed 4 multiple chemical (PMax ) models selected from individual chemical models developed using 1) a previously published approach applied to the nationwide data set; 2) a broader array of response and explanatory variables (e.g., different normalization approaches and toxicity classifications) applied to the nationwide data set; 3) a data set from the New York/New Jersey, USA, region; and 4) both nationwide and regional data sets. The models were calibrated using the regional data set. Performance was tested using an independent data set from the same region. The performance of the final PMax model developed using the calibration process substantially improved over that of the uncalibrated PMax model developed using the nationwide data set. The improvements were achieved by selecting the best performing individual chemical models and eliminating those that performed poorly when applied together. Although the best performing PMax model included both nationwide and region-specific models, the performance of the PMax model derived using only nationwide models was nearly as good. These results suggest that calibrating nationwide models to a regional data set may be both a more efficient and effective approach for improving model performance than developing region-specific models. This article is a US Government work and is in the public domain in the USA.