Susan Griffin

An In Vitro Method for Estimation of Arsenic Relative Bioavailability in Soil

This report summarizes the results of a study to develop an in vitro bioaccessibility (IVBA) extraction technique for estimating the relative bioavailability (RBA) of arsenic (As) in soil. The study was implemented in several steps. In step 1, key variables in the extraction protocol were identified. In step 2, 21 different extraction conditions were tested on 12 different soils with reliable RBA values measured in swine or monkeys to identify which yielded useful in vivo–in vitro correlations (IVIVC). In step 3, three extraction conditions were evaluated using 39 different test soils to make a final selection of the best IVIVC. In step 4, the within- and between-lab reproducibility of the extraction method was examined. The optimum IVIVC model for swine utilized a pH 1.5 IVBA extraction fluid, with an R 2 value of .723. For monkeys, the optimum IVIVC model was obtained using a pH 7 IVBA extraction fluid that contained phosphate, with an R 2 value of .755. Within-lab precision of IVBA results was typically less than 3%, with an average of 0.8% for all 4 labs. Between-lab variation in mean IVBA values was generally less than 7%, with an overall average of 3%. The principal advantages of this IVBA method compared to other in vitro methods described in the literature are that (1) the fluids and extraction conditions are simple, (2) the results have been calibrated against a larger data set than any other method, and (3) the method has been demonstrated to be reproducible both within and between labs.

Presynaptic malfunction: the neurotoxic effects of cadmium and lead on the proton gradient of synaptic vesicles and glutamate transport.

Exposure to Cd(2+) and Pb(2+) has neurotoxic consequences for human health and may cause neurodegeneration. The study focused on the analysis of the presynaptic mechanisms underlying the neurotoxic effects of non-essential heavy metals Cd(2+) and Pb(2+). It was shown that the preincubation of rat brain nerve terminals with Cd(2+) (200 μM) or Pb(2+) (200 μM) resulted in the attenuation of synaptic vesicles acidification, which was assessed by the steady state level of the fluorescence of pH-sensitive dye acridine orange. A decrease in L-[(14)C]glutamate accumulation in digitonin-permeabilized synaptosomes after the addition of the metals, which reflected lowered L-[(14)C]glutamate accumulation by synaptic vesicles inside of synaptosomes, may be considered in the support of the above data. Using isolated rat brain synaptic vesicles, it was found that 50 μM Cd(2+) or Pb(2+) caused dissipation of their proton gradient, whereas the application of essential heavy metal Mn(2+) did not do it within the range of the concentration of 50-500 μM. Thus, synaptic malfunction associated with the influence of Cd(2+) and Pb(2+) may result from partial dissipation of the synaptic vesicle proton gradient that leads to: (1) a decrease in stimulated exocytosis, which is associated not only with the blockage of voltage-gated Ca(2+) channels, but also with incomplete filling of synaptic vesicles; (2) an attenuation of Na(+)-dependent glutamate uptake.

Application of a Probabilistic Risk Assessment Methodology to a Lead Smelter Site

Exposure of children to lead in the environment was assessed at the Murray Smelter Superfund site using both a deterministic risk assessment approach, the Integrated Exposure Uptake Biokinetic (IEUBK) model, and a probabilistic approach, the Integrated Stochastic Exposure (ISE) model. When site-specific data on lead in environmental media were input as point estimates into the IEUBK model, unacceptable risks were predicted for children living within five of eight study zones. The predicted soil cleanup goal was 550 ppm. Concentration and exposure data were then input into the ISE model as probability distribution functions and a one-dimensional Monte Carlo analysis (ID MCA) was run to predict the expected distribution of exposures and blood lead values. Uncertainty surrounding these predictions was examined in a two-dimensional Monte Carlo analysis (2-D MCA). The ISE model predicted risks that were in the same rank order as those predicted by the IEUBK model, although the probability estimates of exceedin...

Predicting oral relative bioavailability of arsenic in soil from in vitro bioaccessibility

ABSTRACT Several investigations have been conducted to develop in vitro bioaccessibility (IVBA) assays that reliably predict in vivo oral relative bioavailability (RBA) of arsenic (As). This study describes a meta-regression model relating soil As RBA and IVBA that is based upon data combined from previous investigations that examined the relationship between As IVBA and RBA when IVBA was determined using an extraction of soil in 0.4 M glycine at pH 1.5. Data used to develop the model included paired IVBA and RBA estimates for 83 soils from various types of sites such as mining, smelting, and pesticide or herbicide application. The following linear regression model accounted for 87% of the observed variance in RBA (R2 = .87): RBA(%) = 0.79 × IVBA(%) + 3.0. This regression model is more robust than previously reported models because it includes a larger number of soil samples, and also accounts for variability in RBA and IVBA measurements made on samples collected from sites contaminated with different As sources and conducted in different labs that have utilized different experimental models for estimating RBA.

Estimating risk assessment exposure point concentrations when the data are not normal or lognormal.

The U.S. Environmental Protection Agency (EPA) recommends the use of the one-sided 95% upper confidence limit of the arithmetic mean based on either a normal or lognormal distribution for the contaminant (or exposure point) concentration term in the Superfund risk assessment process. When the data are not normal or lognormal this recommended approach may overestimate the exposure point concentration (EPC) and may lead to unecessary cleanup at a hazardous waste site. The EPA concentration term only seems to perform like alternative EPC methods when the data are well fit by a lognormal distribution. Several alternative methods for calculating the EPC are investigated and compared using soil data collected from three hazardous waste sites in Montana, Utah, and Colorado. For data sets that are well fit by a lognormal distribution, values for the Chebychev inequality or the EPA concentration term may be appropriate EPCs. For data sets where the soil concentration data are well fit by gamma distributions, Wongs method may be used for calculating EPCs. The studentized bootstrap-t and Halls bootstrap-t transformation are recommended for EPC calculation when all distribution fits are poor. If a data set is well fit by a distribution, parametric bootstrap may provide a suitable EPC.

The importance of problem formulations in risk assessment: a case study involving dioxin-contaminated soil.

The need to remediate contaminated soils is typically accomplished by applying standard risk assessment methods followed by risk management to select remedial options. These human health risk assessments (HHRAs) have been largely conducted in a formulaic manner that relies heavily on standard deterministic exposure, toxicity assumptions and fixed mathematical formulas. The HHRA approach, with its traditional formulaic practice, does not take advantage of problem formulation in the same manner as is done in ecological risk assessment, and historically, has generally failed to emphasize incorporation of site-specific information. In response to these challenges, the National Academy of Sciences recently made several recommendations regarding the conduct of HHRAs, one of which was to begin all such assessments with problem formulation. These recommendations have since been extended to dose response assessment. In accordance with these recommendations, a group of experts presented and discussed findings that highlighted the importance and impact of including problem formulation when determining the need for remediation of dioxin contamination in soils, focusing in particular on exposure assessment is described.

Effects of Manganese Mining on Water Quality in the Caucasus Mountains, Republic of Georgia

One of the world’s richest manganese (Mn) deposits and largest Mn mining areas lies in the foothills of the Caucasus Mountains, near the city of Chiatura in the Republic of Georgia. This study was an initial evaluation of the effects of Mn mining on water quality in the Chiatura region. Seven river and stream locations (three on the Kvirila River and four on tributaries), five untreated drinking water supplies (four springs and one groundwater well), and one untreated industrial wastewater discharge (Mn processing) were sampled and analyzed for field indicator parameters, anions, cations, and metals. Five river bed sediment sites (co-located with river water sites) were also sampled and analyzed for metals. Three of the public water supplies were contaminated by coliform bacteria, and concentrations of dissolved Mn, Fe, and Ni exceeded Georgian drinking water criteria in the groundwater supply well. The Kvirila River had very high concentrations of total Mn and Fe relative to an upstream location, especially downstream of the industrial discharges. Several tributaries also had elevated concentrations due to nonpoint source pollution from mine waste near the streams. Mn and Fe loads in the Kvirila River and tributaries were primarily in the particulate form. The river bed sediments at all five sampled river sites contained elevated metal concentrations. Mn and Ni, in particular, were very high in the Kvirila River near the discharges compared to background soil levels. Although Mn and Fe oxide solids in sediment can increase adsorption and attenuation of other metals from the water column, the contaminated sediments can also serve as a long-term residual source of metal contamination of river water, with potentially significant adverse ecological and human health effects.

Evaluation of the Contribution of Lead in Soil to Lead in Dust at Superfund Sites

ABSTRACT The concentration of lead in indoor dust is a key parameter in the Integrated Exposure Uptake Biokinetic (IEUBK) model used by the U.S. Environmental Protection Agency (USEPA) to evaluate risks to children from lead in soil. The default assumption is that the concentration of lead in indoor dust is 70% of the concentration of lead in outdoor soil. This report reviews the basis of this assumption, and compares the assumption to data obtained at mining/smelting Superfund sites in USEPA Region 8. Data for lead concentrations measured in both indoor dust and outdoor soil at a number of different properties at nine different Superfund sites were fit to a linear model (Cdust = K0 + Ksd·Csoil). Based on ordinary linear regression, values of Ksd ranged from 0.04 to 0.34. Values of Ksd estimated using a simple method to account for measurement errors yielded values from 0.04 to 0.35. These findings indicate that the concentration of lead in dust at mining/smelting sites in Region 8 is usually not as large as the IEUBK default assumption indicates. Use of the default is likely to be protective, but will likely result in an overestimation of childhood exposure and risk from lead in soil.

A Public Health Approach to Identifying and Reducing Lead Exposures at a Mining Site

Eureka City, Utah, was the site of mining, milling, and smelting activities that resulted in contamination of soil with lead. The lead is mainly in the form of lead carbonate, which is readily bioavailable. Children in Eureka had elevated blood lead levels (24% above 10 µg/dL). Recreational exposures on waste piles were also associated with increased risk of elevated blood lead. To reduce lead exposures, contaminated residential soils were removed or capped, and homes with contaminated dust were provided HEPA vacuums. Education programs, home visits by the community nurse, and regular blood lead testing programs were developed. Blood lead levels in younger children have decreased in recent years.