Kevin J. Farley

A Surface Precipitation Model for the Sorption of Cations on Metal Oxides

Abstract A new model for the sorption of cations on metal oxides is formulated which allows for a continuum between surface reactions and precipitation. The model extends the surface complexation approach by considering precipitation on the solid to be described by the formation of a solid solution whose composition varies continuously between that of the original solid and a pure precipitate of the sorbing cation. The ability of the surface precipitation model to describe the equilibrium sorption of metal cations on amorphous iron hydroxide is demonstrated. The model can also be extended to describe cation competition and anion sorption.

Role of Coagulation in the Kinetics of Sedimentation

The combined results of analytical, numerical, and laboratory studies are used in examining the kinetic be- havior of sedimentation in well-mixed systems. The rate of mass removal of solids, dC/dt, as a function of mass concentration C, can be described as the sum of three power laws, dC/dt = -BdsC2.3 - B,hC1.g - BbC1.3, each term of which corresponds to a particular coagulation mecha- nism: differential settling, shear, and Brownian motion. Empirical relationships for the coefficients Bds, Bsh, and Bb as a function of system parameters are provided. Introduction Understanding sedimentation kinetics is important for determining the performance of treatment processes and assessing the impact of waste disposal in natural waters. Sedimentation rates are now typically described by a fixed distribution of settling velocities, corresponding to the discrete settling of individual particles. Experimental results (1-7) however have shown the importance of par- ticle-particle interactions, most notably coagulation, re- sulting in the aggregation of smaller particles into larger ones. According to Stokes law, large particles settle much faster than smaller ones, and overall sedimentation rates can thus be dramatically affected by coagulation. Basic coagulation theory, which is largely attributable to the work of Smoluchowski (8,9), is presented elsewhere (5-7,lO-12). Combining coagulation theory with Stokes law for gravity settling yields the dynamic equation of sedimentation kinetics. No general analytical solution exists for this equation. Investigators have thus relied on laboratory observations (3-6, 13), simplified analytical solutions (5, 6,14), and numerical simulations (12,15) to determine sedimentation behavior. A number of problems are associated with these individual approaches. First, extrapolating experimental settling rates to field Eonditions is difficult since dispersion is not represented in laboratory studies and the appropriate scaling of the laboratory results is unknown. Second, simplified analytical solutions, namely those developed by Hunt (5, 6) and Morel and Schiff (14), describe the mass removal of solids by a sec- ond-order dependency on mass concentration. Although this second-order rate law appears to be an adequate de- scription of laboratory data, it has only been tested over a limited range, encompassing less than an order of mag- nitude change in suspended mass. Further testing of this rate law is clearly needed. Finally, numerical simulations based on the dynamic equation for sedimentation have so far been performed for case-specific applications-providing little insight into the general behavior of sedimentation. Since no systematic comparison of numerical results with laboratory or field data has been performed, the applica- bility of the numerical simulations remains questionable. The goal of this study is to combine results of laboratory, analytical, and numerical studies for predicting the rate of solids removal from water. Simplified descriptions of sedimentation behavior previously hypothesized by Hunt (5,6), Morel and Schiff (14), and Farley (16) are used as a starting point and tested in a series of numerical simu-

Comparing laboratory and field measured bioaccumulation endpoints

An approach for comparing laboratory and field measures of bioaccumulation is presented to facilitate the interpretation of different sources of bioaccumulation data. Differences in numerical scales and units are eliminated by converting the data to dimensionless fugacity (or concentration-normalized) ratios. The approach expresses bioaccumulation metrics in terms of the equilibrium status of the chemical, with respect to a reference phase. When the fugacity ratios of the bioaccumulation metrics are plotted, the degree of variability within and across metrics is easily visualized for a given chemical because their numerical scales are the same for all endpoints. Fugacity ratios greater than 1 indicate an increase in chemical thermodynamic activity in organisms with respect to a reference phase (e.g., biomagnification). Fugacity ratios less than 1 indicate a decrease in chemical thermodynamic activity in organisms with respect to a reference phase (e.g., biodilution). This method provides a holistic, weight-of-evidence approach for assessing the biomagnification potential of individual chemicals because bioconcentration factors, bioaccumulation factors, biota-sediment accumulation factors, biomagnification factors, biota-suspended solids accumulation factors, and trophic magnification factors can be included in the evaluation. The approach is illustrated using a total 2393 measured data points from 171 reports, for 15 nonionic organic chemicals that were selected based on data availability, a range of physicochemical partitioning properties, and biotransformation rates. Laboratory and field fugacity ratios derived from the various bioaccumulation metrics were generally consistent in categorizing substances with respect to either an increased or decreased thermodynamic status in biota, i.e., biomagnification or biodilution, respectively. The proposed comparative bioaccumulation endpoint assessment method could therefore be considered for decision making in a chemicals management context.

Metal Mixtures Modeling Evaluation project: 1. Background

Despite more than 5 decades of aquatic toxicity tests conducted with metal mixtures, there is still a need to understand how metals interact in mixtures and to predict their toxicity more accurately than what is currently done. The present study provides a background for understanding the terminology, regulatory framework, qualitative and quantitative concepts, experimental approaches, and visualization and data-analysis methods for chemical mixtures, with an emphasis on bioavailability and metal-metal interactions in mixtures of waterborne metals. In addition, a Monte Carlo-type randomization statistical approach to test for nonadditive toxicity is presented, and an example with a binary-metal toxicity data set demonstrates the challenge involved in inferring statistically significant nonadditive toxicity. This background sets the stage for the toxicity results, data analyses, and bioavailability models related to metal mixtures that are described in the remaining articles in this special section from the Metal Mixture Modeling Evaluation project and workshop. It is concluded that although qualitative terminology such as additive and nonadditive toxicity can be useful to convey general concepts, failure to expand beyond that limited perspective could impede progress in understanding and predicting metal mixture toxicity. Instead of focusing on whether a given metal mixture causes additive or nonadditive toxicity, effort should be directed to develop models that can accurately predict the toxicity of metal mixtures.

Metal Mixture Modeling Evaluation project: 2. Comparison of four modeling approaches

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the US Geological Survey (USA), HDR|HydroQual (USA), and the Centre for Ecology and Hydrology (United Kingdom) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME workshop in Brussels, Belgium (May 2012), is provided in the present study. Overall, the models were found to be similar in structure (free ion activities computed by the Windermere humic aqueous model [WHAM]; specific or nonspecific binding of metals/cations in or on the organism; specification of metal potency factors or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single vs multiple types of binding sites on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong interrelationships among the model parameters (binding constants, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed.

Comparing laboratory‐ and field‐measured biota–sediment accumulation factors

Standardized laboratory protocols for measuring the accumulation of chemicals from sediments are used in assessing new and existing chemicals, evaluating navigational dredging materials, and establishing site-specific biota-sediment accumulation factors (BSAFs) for contaminated sediment sites. The BSAFs resulting from the testing protocols provide insight into the behavior and risks associated with individual chemicals. In addition to laboratory measurement, BSAFs can also be calculated from field data, including samples from studies using in situ exposure chambers and caging studies. The objective of this report is to compare and evaluate paired laboratory and field measurement of BSAFs and to evaluate the extent of their agreement. The peer-reviewed literature was searched for studies that conducted laboratory and field measurements of chemical bioaccumulation using the same or taxonomically related organisms. In addition, numerous Superfund and contaminated sediment site study reports were examined for relevant data. A limited number of studies were identified with paired laboratory and field measurements of BSAFs. BSAF comparisons were made between field-collected oligochaetes and the laboratory test organism Lumbriculus variegatus and field-collected bivalves and the laboratory test organisms Macoma nasuta and Corbicula fluminea. Our analysis suggests that laboratory BSAFs for the oligochaete L. variegatus are typically within a factor of 2 of the BSAFs for field-collected oligochaetes. Bivalve study results also suggest that laboratory BSAFs can provide reasonable estimates of field BSAF values if certain precautions are taken, such as ensuring that steady-state values are compared and that extrapolation among bivalve species is conducted with caution.

Metal Mixture Modeling Evaluation project: 3. Lessons learned and steps forward

A comparison of 4 metal mixture toxicity models (that were based on the biotic ligand model [BLM] and the Windermere humic aqueous model using the toxicity function [WHAM-FTOX ]) was presented in a previous paper. In the present study, a streamlined version of the 4 models was developed and applied to multiple data sets and test conditions to examine key assumptions and calibration strategies that are crucial in modeling metal mixture toxicity. Results show that 1) a single binding site on or in the organism was a useful and oftentimes sufficient framework for predicting metal toxicity; 2) a linear free energy relationship (LFER) for bidentate binding of metals and cations to the biotic ligand provided a good first estimate of binding coefficients; 3) although adjustments in metal binding coefficients or adjustments in chemical potency factors can both be used in model calibration for single-metal exposures, changing metal binding coefficients or chemical potency factors had different effects on model predictions for metal mixtures; and 4) selection of a mixture toxicity model (based on concentration addition or independent action) was important in predicting metal mixture toxicity. Moving forward, efforts should focus on reducing uncertainties in model calibration, including development of better methods to characterize metal binding to toxicologically active binding sites, conducting targeted exposure studies to advance the understanding of metal mixture toxicity, and further developing LFERs and other tools to help constrain the model calibration.

Effect of thioarsenite formation on arsenic(III) toxicity

Soluble arsenic(III)-sulfide complexes (thioarsenites) play a significant role in the chemistry of arsenic in reducing, sulfidic environments at circumneutral pH. Chemical equilibrium calculations using thioarsenite thermodynamic data from the literature indicate that the formation of a dithioarsenite complex, AsS(OH)(SH)(-1), reduces the concentration of the uncomplexed inorganic As(III) species present (defined sigma H3AsO3, where sigma H3AsO3 = AsO3(-3) + HAsO3(-2) + H2AsO3(-1) + H3AsO3). With enough sulfide present, soluble As(III) is dominated by this complex. Therefore, it is of interest to examine the effect of dithioarsenite formation on As(III) toxicity. The Microtox acute toxicity test was used for this purpose. Tests performed on solutions with varying S:As ratios indicate that As(III) toxicity is a function of the uncomplexed As(III) concentration rather than the total As(III) concentration. This suggests that the dithioarsenite species is not bioavailable and that its formation reduces As(III) toxicity. Chemical equilibrium calculations and sediment pore-water field data from various sources indicate that, in many sediments, dithioarsenite formation can reduce toxicity.

Modeling water column partitioning of polychlorinated biphenyls to natural organic matter and black carbon.

High volume in situ surface water samples were collected from a tidal tributary of the Delaware Estuary using an Infiltrex sampling system equipped with a 1 μm particle filter and a XAD-2 resin column. Particulate and dissolved phase polychlorinated biphenyl (PCB) congeners were analyzed using high resolution gas chromatography/high resolution mass spectrometry to obtain detection levels in the femtograms per liter range. The data were fit to a four-phase equilibrium partitioning model including freely dissolved PCB, PCB bound to particulate organic carbon (POC), PCB bound to dissolved organic carbon (DOC), and PCB bound to black carbon (BC). Isotherms were assumed to be linear for POC and DOC and nonlinear for BC. The partition coefficient between BC and dissolved PCB was assumed to depend on the dihedral angle between the phenyl rings. Following parameter optimization, the correlation coefficient between the log of the modeled and measured apparent distribution coefficient Kp,app was 0.94, and the RMSE was 0.189 log units. Including BC in the model reduces the dissolved PCB phase concentration in the water column for all congeners, especially for the non-ortho and mono-ortho substituted congeners.

Specific Methodologies/Tools to Support Assessment

Abstract Several IT tools have been developed to facilitate the evaluation of complex inorganic materials, with the aim to support the assessor at specific steps of the assessment, such as the Metals Classification Tool (MeClas) for hazard identification and classification, or MEASE and specific environmental release categories (SPERCs) that generate exposure estimates. In addition, several tools have been made available by the scientific, regulatory, and industry communities to consider metal specificities such as bioavailability in the environment (Biomet, PNEC.Pro, M-BAT) and fate (TICKET-UWM). All these tools, easily accessible, are based on the current knowledge and are regularly updated to take into account new metal science and user-friendliness. The chapter provides a general overview of these tools as well as their principal features.