Refining our understanding of metal bioavailability in sediments using information from porewater: Application of a multimetal biotic ligand model as an extension of the equilibrium partitioning sediment benchmarks

The equilibrium partitioning sediment benchmarks (ESBs) derived by the US Environmental Protection Agency (USEPA) in 2005 provide a mechanistic framework for understanding metal bioavailability in sediments by considering equilibrium partitioning (EqP) theory, which predicts that metal bioavailability in sediments is determined largely by partitioning to sediment particles. Factors that favor the partitioning of metals to sediment particles, such as the presence of acid volatile sulfide (AVS) and sediment organic matter, reduce metal bioavailability to benthic organisms. Because ESBs link metal bioavailability to partitioning to particles, they also predict that measuring metals in porewater can lead to a more accurate assessment of bioavailability and toxicity to benthic organisms. At the time of their development, sediment ESBs based on the analysis of porewater metal concentrations were limited to comparison with hardness-dependent metals criteria for the calculation of interstitial water benchmark units (IWBUs). However, the multimetal biotic ligand model (mBLM) provides a more comprehensive assessment of porewater metal concentrations, because it considers factors in addition to hardness, such as pH and dissolved organic carbon, and allows for interactions between metals. To evaluate the utility of the various sediment and porewater ESBs, four Hyalella azteca bioassay studies were identified that included sediment and porewater measurements of metals and porewater bioavailability parameters. Evaluations of excess simultaneously extracted metals, IWBUs, and mBLM toxic units (TUs) were compared among the bioassay studies. For porewater, IWBUs and mBLM TUs were calculated using porewater metal concentrations from samples collected using centrifugation and peepers. The percentage of correct predictions of toxicity was calculated for each benchmark comparison. The mBLM-based assessment using peeper data provided the most accurate predictions for the greatest number of samples among the evaluation methods considered. This evaluation demonstrates the value of porewater-based evaluations in conjunction with sediment chemistry in understanding toxicity observed in bioassay studies. Integr Environ Assess Manag 2022;18:1335-1347. © 2021 SETAC.

Collection and use of porewater data from sediment bioassay studies for understanding exposure to bioavailable metals

The US Environmental Protection Agency Procedures for the Derivation of Equilibrium Partitioning Sediment Benchmarks (ESBs) for the Protection of Benthic Organisms: Metal Mixtures (Cadmium, Copper, Lead, Nickel, Silver and Zinc) equilibrium partitioning approach causally link metal concentrations and toxicological effects; they apply to sediment and porewater (i.e., interstitial water). The evaluation of bioavailable metal concentrations in porewater, using tools such as the biotic ligand model, provides an advancement that complements sediment-based evaluations. However, porewater characterization is less commonly performed in sediment bioassays than sediment chemistry characterization due to the difficulty and expense of porewater collection as well as concerns about interpretation of porewater data. This study discusses the advantages and disadvantages of different porewater extraction methods for analysis of metals and bioavailability parameters during laboratory sediment bioassays, with a focus on peepers and centrifugation. The purpose is to provide recommendations to generate bioassay porewater data of sufficient quality for use in risk-based decision-making, such as for regulated cleanup actions. Comparisons of paired data from previous bioassay studies indicate that metal porewater concentrations collected via centrifugation tend to be higher than those collected via peepers. However, centrifugation disrupts the redox status of the sediment; also, metal concentrations can vary markedly based on centrifugation conditions. Data to compare the concentrations of peeper- and centrifugation-collected bioavailability parameters (e.g., major ions, pH) are much more limited, but indicate smaller differences than those observed for metal concentrations. While peepers can be sampled without altering the redox status of the porewater, the small volume of porewater peepers collected is enough for metal concentration analysis, but insufficient for analysis of all metal bioavailability parameters. Given the benefits of metal collection via peepers, it is optimal to use centrifugation and peepers in tandem for bioassay porewater collection to improve bioavailability predictions. Environ Assess Manag 2022;18:1321-1334. © 2021 SETAC.

Sediment toxicity data and excess simultaneously extracted metals from field-collected samples: Comparison to United States Environmental Protection Agency benchmarks

US Environmental Protection Agency (USEPA) Procedures for the Derivation of Equilibrium Partitioning Sediment Benchmarks (ESBs) for the Protection of Benthic Organisms: Metal Mixtures are based on the principle that metals toxicity to benthic organisms is determined by bioavailable metals concentrations in porewater. One ESB is based on the difference between simultaneously extracted metal (SEM) and acid volatile sulfide (AVS) concentrations in sediment (excess SEM). The excess SEM ESBs include a lower uncertainty bound, below which most samples (95%) are expected to be "nontoxic" (defined as a bioassay mortality rate ≤24%), and an upper uncertainty bound, above which most samples (95%) are expected to be "toxic" (defined as a mortality rate >24%). Samples that fall between the upper and lower bounds are classified as "uncertain." Excess SEM ESBs can, in principle, be improved by normalizing for organic carbon (OC). OC is a binding phase that reduces metals bioavailability. OC normalization should improve the accuracy of bioavailable metal concentration estimates, thus tightening uncertainty bounds. We evaluated field-collected sediments from 13 studies with excess SEM, OC, and bioassay data (n = 740). Use of the OC-normalized excess SEM benchmarks did not improve prediction accuracy. The ESB model predicts OC-normalized excess SEM exceeding the upper benchmark even when toxicity is not observed, because error in the OC normalization model increases at low OC concentrations. To minimize the likelihood of incorrectly identifying nontoxic samples as toxic, we recommend that OC normalization of excess SEM should not be considered for sediments with an OC concentration <1% and is questionable for sediments with an OC concentration of 1%-4%. Additional focused studies are needed to confirm or refine the minimum sediment OC concentrations that are applicable for reducing uncertainty in toxicity predictions due to excess SEM. Integr Environ Assess Manag 2022;18:174-186. © 2021 SETAC.

Setting site-specific water-quality standards by using tissue residue thresholds and bioaccumulation data. Part 2. Calculating site-specific selenium water-quality standards for protecting fish and birds

In a companion paper, a method for deriving tissue residue-based site-specific water-quality standards (SSWQSs) was described. In this paper, the methodology is applied to selenium (Se) as an example. Models were developed to describe Se bioaccumulation in aquatic-dependent bird eggs and whole fish. A simple log-linear model best described Se accumulation in bird eggs (r2 = 0.50). For fish, separate hockey stick regressions were developed for lentic (r2 = 0.65) and lotic environments (r2 = 0.37). The low r2 value for the lotic fish model precludes its reliable use at this time. Corresponding tissue residue criteria (i.e., tissue thresholds) for bird eggs and whole fish also were identified and example model predictions were made. The models were able to predict SSWQSs over a wide range of water-tissue combinations that might be encountered in the environment. The models also were shown to be sensitive to variability in measured tissue residues with relatively small changes in variability (as characterized by the standard error) resulting in relatively large differences in SSWQSs.

Setting site-specific water-quality standards by using tissue residue criteria and bioaccumulation data. Part 1. Methodology

We have developed a method for determining site-specific water-quality standards (SSWQSs) for substances regulated based on tissue residues. The method uses a multisite regression model to solve for the conditional prior probability density function (PDF) on water concentration, given that tissue concentration equals a tissue residue threshold. The method then uses site-specific water and tissue concentration data to update the probabilities on a Monte Carlo sample of the prior PDF by using Bayesian Monte Carlo analysis. The resultant posterior PDF identifies the water concentration that, if met at the site, would provide a desired level of confidence of meeting the tissue residue threshold contingent on model assumptions. This allows for derivation of a SSWQS. The method is fully reproducible, statistically rigorous, and easily implemented. A useful property of the method is that the model is sensitive to the amount of site-specific data available, that is, a more conservative or protective number (water concentration) is derived when the data set is small or the variance is large. Likewise, as the site water concentration increases above the water-quality standard, more site-specific information is needed to demonstrate a safe concentration at the site. A companion paper demonstrates the method by using selenium as an example.

Risk-based environmental remediation: Bayesian Monte Carlo analysis and the expected value of sample information

A methodology that simulates outcomes from future data collection programs, utilizes Bayesian Monte Carlo analysis to predict the resulting reduction in uncertainty in an environmental fate-and-transport model, and estimates the expected value of this reduction in uncertainty to a risk-based environmental remediation decision is illustrated considering polychlorinated biphenyl (PCB) sediment contamination and uptake by winter flounder in New Bedford Harbor, MA. The expected value of sample information (EVSI), the difference between the expected loss of the optimal decision based on the prior uncertainty analysis and the expected loss of the optimal decision from an updated information state, is calculated for several sampling plan. For the illustrative application we have posed, the EVSI for a sampling plan of two data points is $9.4 million, for five data points is $10.4 million, and for ten data points is $11.5 million. The EVSI for sampling plans involving larger numbers of data points is bounded by the expected value of perfect information, $15.6 million. A sensitivity analysis is conducted to examine the effect of selected model structure and parametric assumptions on the optimal decision and the EVSI. The optimal decision (total area to be dredged) is sensitive to the assumption of linearity between PCB sediment concentration and flounder PCB body burden and to the assumed relationship between area dredged and the harbor-wide average sediment PCB concentration; these assumptions also have a moderate impact on the computed EVSI. The EVSI is most sensitive to the unit cost of remediation and rather insensitive to the penalty cost associated with under-remediation.