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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. Environ Toxicol Chem 2014;33:708–717. © 2013 SETAC. This article is a US Government work and is in the public domain in the USA.

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. Integr Environ Assess Manag 2012; 8: 597–609. © 2012 SETAC

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. Integr Environ Assess Manag 2012; 8: 610–624. © 2011 SETAC

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.

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.

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. The first model uses individual chemicals selected using stepwise selection. The second uses derived variables to reflect combined metal contamination, polycyclic aromatic hydrocarbon (PAH) contamination, and the interaction between metals and PAHs. The third and final model is a separate species model with derived variables. Overall, these models suggest that toxicity may be correctly predicted approximately 77% of the time, although prediction is better for samples identified as nontoxic than for those known to be toxic.

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. The individual chemical regression models were combined into a single model, using either the maximum (P(Max) model) or average (P(Avg) model) probability predicted from the chemicals analyzed in a sample, to estimate the probability of toxicity for a sample. The average predicted probability of toxicity (from the P(Max) model) within probability quartiles closely matched the incidence of toxicity within the same ranges, demonstrating the overall reliability of the P(Max) model for the database that was used to derive the model. The magnitude of the toxic effect (decreased survival) in the amphipod test increased as the predicted probability of toxicity increased. Users have a number of options for applying the logistic models, including estimating the probability of observing acute toxicity to estuarine and marine amphipods in 10-d toxicity tests at any given chemical concentration or estimating the chemical concentrations that correspond to specific probabilities of observing sediment toxicity.

This paper describes the use of logistic-regression modeling for evaluating matching sediment chemistry and toxicity data. Contaminant-specific logistic models were used to estimate the percentage of samples expected to be toxic at a given concentration. These models enable users to select the probability of effects of concern corresponding to their specific assessment or management objective or to estimate the probability of observing specific biological effects at any contaminant concentration. The models were developed using a large database (n = 2,524) of matching saltwater sediment chemistry and toxicity data for field-collected samples compiled from a number of different sources and geographic areas. The models for seven chemicals selected as examples showed a wide range in goodness of fit, reflecting high variability in toxicity at low concentrations and limited data on toxicity at higher concentrations for some chemicals. The models for individual test endpoints (e.g., amphipod mortality) provided a better fit to the data than the models based on all endpoints combined. A comparison of the relative sensitivity of two amphipod species to specific contaminants illustrated an important application of the logistic model approach.

Abstract-Mat~hing'syno~tically collected chemical and laboratory bioassay data (n = 1.068) were compiled from analyses of surficial sediment samples collecred during 1990 to 1993 to evaluate the predictive ability of sediment quality guidelines (SQGs). specifically, effects range-low (ERL). effects range-median (ERM). threshold effects level (TELL and probable eifects level (PEL) values. Dam were acquired from surveys of sediment quality periomed in estuaries along the Atlantic. Pacific. and Gulf of hlexico coasts. Samples were classified as either nontoxic (p > 0.05 re!ative to controls). marginally toxic @ < 0.05 only). or highly toxic @ < 0.05 and response greater than minimum significant difference :elalive to controls). This analysis indic-red that. when nor exceeded. [he ERLs and TELs were highly predictive of nontoxicity. The percenrages of samples that were highly toxic generally increased with increasing numbers of guidelines (panicularly the EX.\.ls and PELS) that were exceeded. Also. the incidence of toxicity increased with increases in concentntions of mixtures of chemicals normalized to (divided by) the SQGs. The ERhls and PELs indicated high predictive ability in samples in which many substances exceeded these concentrations. Suggestions are provided on the uses of these estimates of the predictive ability oi sediment ~uidelines.

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.

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.

As a result of the Exxon Valdez oil spill, many residents of villages in Prince William Sound, Lower Cook Inlet, the Alaska Peninsula, and Kodiak Island were alarmed by the prospect that their subsistence food supply might have been contaminated by oil and thus present an increased health risk. Although major oil spills have occurred in the past, no database has been developed on human health effects resulting from the consumption of oil-contaminated food. This information gap and the time lag of several months required to develop relevant data were the principal impediments to rapidly addressing the villagers’ concerns. The National Oceanic and Atmospheric Administration (NOAA) was asked by the federal on-scene coordinator and the Alaska Oil Spill Health Task Force to take an active role in addressing the subsistence food safety concerns of native villagers. Specific issues of concern to villagers included the following: Were subsistence foods contaminated as a result of the spill? Could subsistence foods be screened or tested by villagers to detect contamination? Did consumption of subsistence foods pose a health risk, either immediate (acute) or long-term (chronic)? What species in what areas were safe to eat? Answering these questions involved researching the screening and testing equipment and protocols in existence, developing and implementing a sampling program, selecting techniques and a laboratory to conduct the analyses, and identifying experts to evaluate the analytical data as they might relate to the human health risk. This paper describes the activities and findings of the NOAA subsistence program; it does not address occupational health issues associated with the oil spill.

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. The individual chemical regression models were combined into a single model, using either the maximum (P(Max) model) or average (P(Avg) model) probability predicted from the chemicals analyzed in a sample, to estimate the probability of toxicity for a sample. The average predicted probability of toxicity (from the P(Max) model) within probability quartiles closely matched the incidence of toxicity within the same ranges, demonstrating the overall reliability of the P(Max) model for the database that was used to derive the model. The magnitude of the toxic effect (decreased survival) in the amphipod test increased as the predicted probability of toxicity increased. Users have a number of options for applying the logistic models, including estimating the probability of observing acute toxicity to estuarine and marine amphipods in 10-d toxicity tests at any given chemical concentration or estimating the chemical concentrations that correspond to specific probabilities of observing sediment toxicity.

Five species of rockfish (Sebastes spp.) were examined off southeast Alaska for Sarcotaces arcticus (Copepoda). Only Sebastes ciliatus was infected. Sarcotaces were compared to those found in 7 species of Sebastes collected off California and those from Molva dypterygia collected off Norway. As a result of these comparisons it is suggested that Sarcotaces komaii be synonymized with S. arcticus. It is possible that S. arcticus is synonymous with Sarcotaces verrucosus. Studies on aspects of the host-parasite relationship indicated that younger fish have a higher prevalence of infection and more multiple infections. There is no host-parasite size relationship. Seventy-five percent of the female parasites were associated with males, 85% had eggs and 10% nauplius larvae. Females ranged in size from 20 to 50 mm. Smaller specimens were represented by degenerated cysts. There was no indication of parasite-induced host mortality. The absence of infection in some species may be more a result of the hosts' natural history and geographic distribution than parasite specificity. In the course of a study on nearshore demersal fishes off southeast Alaska, numerous individuals from 5 species of rockfish (Sebastes) were examined for Sarcotaces (Copepoda: Phylichthyidae). Only Sebastes ciliatus was found to harbor Sarcotaces. This copepod, Sarcotaces arcticus Collett, 1874, was compared to specimens recovered from incidental collections of Sebastes spp. off southern and northern California. This report discusses some aspects of the host-parasite relationship between S. arcticus and S. ciliatus and suggests changes in the taxonomic status of Sarcotaces komaii Shiino, 1953. MATERIALS AND METHODS Five Sebastes spp. were collected off southeast Alaska from June to August 1981. Specimens were collected at 0 to 30 m by spear using SCUBA and hook-andline and at 80 to 120 m by longline and a jigging machine. Sebastes spp. (number hosts necropsied): S. ciliatus (411); Sebastes brevispinis (90); Sebastes ruberrimus (141); Sebastes entomelas (125); and Sebastes melanops (797). Hosts were aged by otolith ring count. The text will refer to "small" and "large" populations of S. ciliatus. The "small" populations are fish between 140 to 340 mm (3 to 12 yr old) and the "large" between 350 to 500 mm (12 to 27+ yr old). Although there is some overlap, the "large" populations were usually found in deeper water than the "small." Comparisons between samples were standardized for host size, sex, Received 5 September 1984; revised 6 November 1985; accepted 10 December 1984. * School of Fisheries and Sciences, University of Alaska, Juneau, Alaska 99801. t School of Fisheries, University of Washington, Seattle, Washington 98195. seasons and depth of collection. Hence, not all comparisons have the same sample sizes. Infected Sebastes spp. off California included (host, number infected, collection, location, date): Sebastes auriculatus, 7, Tiburon, Sept. 1980; S. entomelas, 2, Moss Landing, Aug. 1982 and, 1, Point Pinos, Oct. 1982; Sebastes flavidus, 4, Moss Landing, June 1983; Sebastes melanops, 2, Monterey Bay, June 1982; S. rubrivinctus, 1, Bodega Bay, May 1981; Sebastes semicinctus, 1, Los Angeles, June 1982; and Sebastes serranoides, 2, Farallon Island, June 1981. The Molva dypterygia was collected by Dr. Bjmrn Berland, Zoological Laboratory, Bergen, Norway in September 1970. The tissues containing the Sarcotaces specimens were rehydrated in glycerine in March 1984. The northeastern Pacific Sarcotaces were compared to these specimens because the type specimen of S. arcticus was described from M. dypterygia (as Molva abyssorum) off Norway. Linear regression was used to assess the host-parasite size relationship and comparison between percentages to test prevalence of infections (Bailey, 1974). Parasites were formalin fixed at necropsy. Specimens to be examined by scanning electron microscopy were prepared by routine methods and examined on a NANOLAB 7 (SEM). All measurements were taken from formalin fixed specimens. The taxonomy of the Sebastes spp. is from Eschmeyer et al. (1983).