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.

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.

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.

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.

Baseline ecological risk assessment of the Calcasieu Estuary, Louisiana: Part 2. An evaluation of the predictive ability of effects-based sediment-quality guidelines.

Three sets of effects-based sediment-quality guidelines (SQGs) were evaluated to support the selection of sediment-quality benchmarks for assessing risks to benthic invertebrates in the Calcasieu Estuary, Louisiana. These SQGs included probable effect concentrations (PECs), effects range median values (ERMs), and logistic regression model (LRMs)-based T₅₀ values. The results of this investigation indicate that all three sets of SQGs tend to underestimate sediment toxicity in the Calcasieu Estuary (i.

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.

Predicting sediment toxicity using logistic regression: a concentration-addition approach.

The question posed in this article is how useful the chemical concentration measurements for predicting the outcome of sediment toxicity tests are. Using matched data on sediment toxicity and sediment chemical concentrations from a number of studies, we investigated several approaches for predicting toxicity based on multiple logistic regression with concentration-addition models. Three models were found to meet criteria for acceptability.

Predicting amphipod toxicity from sediment chemistry using logistic regression models.

Individual chemical logistic regression models were developed for 37 chemicals of potential concern in contaminated sediments to predict the probability of toxicity, based on the standard 10-d survival test for the marine amphipods Ampelisca abdita and Rhepoxynius abronius. These models were derived from a large database of matching sediment chemistry and toxicity data, which includes contaminant gradients from a variety of habitats in coastal North America. Chemical concentrations corresponding to a 20, 50, and 80% probability of observing sediment toxicity (T20, T50, and T80 values) were calculated to illustrate the potential for deriving application-specific sediment effect concentrations and to provide probability ranges for evaluating the reliability of the models.