John Toll

Site-specific approach for setting water quality criteria for selenium : differences between lotic and lentic systems

Results of an in-depth review of the literature indicates there are significant differences in the bioaccumulation of selenium by fishes and invertebrates from lotic (flowing) and lentic (standing) water bodies and that selenate is much less bioaccumulative than selenite. Bioaccumulation in fish is a factor of 10 or more higher in lentic systems as compared to lotic systems. These differences are a function of selenium’s site-specific biogeochemical cycling. Further, we observed considerable variation in bird accumulation of selenium from site to site. To account for differences in bioaccumulation potential of selenium we developed a residue-based Bayesian Monte Carlo model to derive site-specific selenium water quality criteria protective of fish and sensitive avian species. The approach uses data from a given site of interest to calibrate a model based on data from several other similar sites. When evaluating a specific site, the range of water and tissue concentrations is typically limited. This makes it difficult to use site-specific data to identify a water concentration sufficiently low that tissue concentrations do not exceed the tissue-effect threshold. Data from several similar sites provide a broader range of water and tissue residue concentrations that allow for an appropriate statistical extrapolation of the data to the site of interest. The Bayesian Monte Carlo model accounts for the significant site-to-site variability that exists in the relationship between water selenium and the mean tissue residue. In practice, data from similar sites are pooled to define a set of possible water and mean tissue residue relationships. This set of possible relationships is then used with data from the site of interest to determine which relationships, from the set of possibilities, best fit the specific site. Once we have determined which set of possible relationships fit the specific site, we extrapolate from the observed water concentration to a water concentration that results in a tissue residue concentration less than or equal to a chronic effect threshold. This value becomes the chronic water quality criterion. Adams, W.J., J.E. Toll, K.V. Brix, L.M. Tear and D.K. DeForest. 2000. Site-specific approach for setting water quality criteria for selenium: differences between lotic and lentic systems. Proceedings Mine Reclamation Symposium: Selenium Session; Sponsored by Ministry of Energy and Mines, Williams Lake, British Columbia, Canada, June 21-22, 2000.

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.

Assessing population‐level effects of zinc exposure to brown trout (Salmo trutta) in the Arkansas River at Leadville, Colorado

We assessed population-level risk to upper Arkansas River brown trout (Salmo trutta L.) due to juvenile exposure to Zn. During spring, individuals in the sensitive young-of-the-year life stage are exposed to elevated Zn concentrations from acid mine drainage. We built and used a simple life-history population model for the risk assessment, with survival and fecundity parameter values drawn from published data on brown trout populations located in the United States and Europe. From experimental data, we derived a toxicity model to predict mortality in brown trout fry after chronic exposure to Zn. We tested sensitivity of risk estimates to uncertainties in the life-history parameters. We reached 5 conclusions. First, population projections are highly uncertain. A wide range of estimates for brown trout population growth is consistent with the scientific literature. The low end of this range corresponds to an unsustainable population, a physically unrealistic condition due to combining minimum parameter values from several studies. The upper end of the range corresponds to an annual population growth rate of 281%. Second, excess mortality from Zn exposure is relatively more predictable. Using our exposure-response model for excess mortality to brown trout fry due to Zn exposure in the upper Arkansas River at the mouth of California Gulch in the years 2000 to 2005, we derived a mean estimate of 6.1% excess mortality (90% confidence interval = 1.6%-14.1%). Third, population projections are sensitive to all the parameters that contribute to the onset of reproduction. The weight of evidence suggests that young-of-the-year survival is most important; it is inconclusive about the ranking of other parameters. Fourth, population-level risk from Zn exposure is sensitive to young-of-the-year survival. If young-of-the-year survival exceeds 20% to 25%, then the marginal effect of excess juvenile mortality on population growth is low. The potential effect increases if young-of-the-year survival is less than 20%. Fifth, the effect of Zn on population growth is predictable despite high uncertainty in population projections. The estimate was insensitive to model uncertainties. This work could be useful to ecological risk assessors and managers interested in using population-level endpoints in other risk assessments.

From sediment to tissue and tissue to sediment: an evaluation of statistical bioaccumulation models.

Biota-sediment accumulation factors (BSAFs) and biota-sediment accumulation regressions (BSARs) are statistical models that may be used to estimate tissue chemical concentrations from sediment chemical concentrations or vice versa. Biota-sediment accumulation factors and BSARs are used to fill tissue concentration data gaps, set sediment preliminary remediation goals (PRGs), and make projections about the effectiveness of potential sediment cleanup projects in reducing tissue chemical concentrations. We explored field-based, benthic invertebrate biota-sediment chemical concentration relationships using data from the US Environmental Protection Agency (USEPA) Mid-Continent Ecology Division (MED) BSAF database. Approximately two thirds of the 262 relationships investigated were very poor (r(2)  < 0.3 or p-value ≥ 0.05); for some of the biota-sediment relationships that did have a significant nonzero slope (p-value < 0.05), lipid-normalized tissue concentrations tended to decrease as the colocated organic carbon (OC)-normalized sediment concentration increased. Biota-sediment relationships were further evaluated for 3 of the 262 datasets. Biota-sediment accumulation factors, linear regressions, model II regressions, illustrative sediment PRGs, and confidence intervals (CIs) were calculated for each of the three examples. These examples illustrate some basic but important statistical practices that should be followed before selecting a BSAR or BSAF or relying on these simple models of biota-sediment relationships to support consequential management decisions. These practices include the following: one should not assume that the relationship between chemical concentrations in tissue and sediment is necessarily linear, one should not assume the model intercept to be zero, and one should not place too much stock on models that are heavily influenced by one or a few high chemical concentration data points. People will continue to use statistical models of field-based biota-sediment chemical concentration relationships to support sediment investigations and remedial action decisions. However, it should not be assumed that the models will be reliable. In developing and applying BSAFs and BSARs, it is essential that best practices are followed and model limitations and uncertainties are understood, acknowledged, and quantified as much as possible.

Recommendations for the Derivation and Use of Biota-Sediment Bioaccumulation Models for Carcinogenic PAHs†

Carcinogenic polycyclic aromatic hydrocarbons (cPAHs) are important sediment contaminants that can pose health risks to people who eat shellfish from contaminated sites. Biota-sediment accumulation factors (BSAFs) are quotients of colocated lipid-normalized tissue concentrations and organic carbon (OC)-normalized sediment concentrations, whereas biota-sediment accumulation regressions (BSARs) are models describing the relationships between these tissue and sediment concentrations. BSAR/Fs (BSARs and/or BSAFs) are commonly used to back-calculate sediment preliminary remediation goals (PRGs) from target tissue concentrations; the PRGs are then used to set target action levels (i.e., sediment concentrations above which remedial actions will be prescribed). The cPAH BSAR/Fs reported across sites and species are highly variable due to both site- and species-specific differences and inconsistent BSAR/F calculation methods and assumptions. We reviewed past studies, identified best practices for developing BSAR/Fs, and compiled publicly available colocated tissue and sediment data for 7 cPAHs from 13 sites across the United States. Of the 249 unique cPAH data sets compiled for various species, only 17 yielded acceptable BSAR/Fs, 16 of which were for clams. The influence of BSAR/Fs on sediment remedial action decisions and costs can be disproportionate to the quality of the statistical models from which they are derived. Therefore, it is important to establish and follow best practices for deriving BSAR/Fs and for deciding whether and how BSAR/Fs should be used. Based on our review and analysis, we highlight the advantages of relying on BSARs and propose a consistent method for deriving and judging the reliability of these relationships. We also offer guidance for evaluating the ramifications of BSAR uncertainty on remedial decision making at contaminated sediment sites, and we discuss alternative ways to make risk management decisions in the absence of a reliable site-specific BSAR. Integr Environ Assess Manag 2017;13:1060-1071.

Recommendations for the derivation and use of biota–sediment bioaccumulation models for carcinogenic polycyclic aromatic hydrocarbons

Carcinogenic polycyclic aromatic hydrocarbons (cPAHs) are important sediment contaminants that can pose health risks to people who eat shellfish from contaminated sites. Biota-sediment accumulation factors (BSAFs) are quotients of colocated lipid-normalized tissue concentrations and organic carbon (OC)-normalized sediment concentrations, whereas biota-sediment accumulation regressions (BSARs) are models describing the relationships between these tissue and sediment concentrations. BSAR/Fs (BSARs and/or BSAFs) are commonly used to back-calculate sediment preliminary remediation goals (PRGs) from target tissue concentrations; the PRGs are then used to set target action levels (i.e., sediment concentrations above which remedial actions will be prescribed). The cPAH BSAR/Fs reported across sites and species are highly variable due to both site- and species-specific differences and inconsistent BSAR/F calculation methods and assumptions. We reviewed past studies, identified best practices for developing BSAR/Fs, and compiled publicly available colocated tissue and sediment data for 7 cPAHs from 13 sites across the United States. Of the 249 unique cPAH data sets compiled for various species, only 17 yielded acceptable BSAR/Fs, 16 of which were for clams. The influence of BSAR/Fs on sediment remedial action decisions and costs can be disproportionate to the quality of the statistical models from which they are derived. Therefore, it is important to establish and follow best practices for deriving BSAR/Fs and for deciding whether and how BSAR/Fs should be used. Based on our review and analysis, we highlight the advantages of relying on BSARs and propose a consistent method for deriving and judging the reliability of these relationships. We also offer guidance for evaluating the ramifications of BSAR uncertainty on remedial decision making at contaminated sediment sites, and we discuss alternative ways to make risk management decisions in the absence of a reliable site-specific BSAR. Integr Environ Assess Manag 2017;13:1060-1071.