Bruce K. Hope

A global biogeochemical budget for vanadium.

Evidence that environmental levels of vanadium are increasing has raised concern over the injection of vanadium into the atmosphere from anthropogenic sources. A simple global mass balance model was developed to demonstrate the influence of anthropogenic vanadium on the global distribution of this trace metal. Vanadium in particulate emissions owing to mans industrial activities were estimated to comprise approximately 53% of total atmosphere vanadium loading and exceeded natural continental or volcanogenic dust by only a narrow margin. Oceanic deposition of vanadium adhering to anthropogenic particles was estimated to comprise approximately 5% of total ocean vanadium loading. There is no suggestion that these inputs of anthropogenic vanadium pose a significant global environmental threat. It is entirely possible, however, that anthropogenic vanadium inputs could pose an environmental hazard given a more restricted area and a specific set of unfavorable circumstances.

Generating Probabilistic Spatially‐Explicit Individual and Population Exposure Estimates for Ecological Risk Assessments

Exposure to chemical contaminants in various media must be estimated when performing ecological risk assessments. Exposure estimates are often based on the 95th-percentile upper confidence limit on the mean concentration of all samples, calculated without regard to critical ecological and spatial information about the relative relationship of receptors, their habitats, and contaminants. This practice produces exposure estimates that are potentially unrepresentative of the ecology of the receptor. This article proposes a habitat area and quality-conditioned exposure estimator, E[HQ], that requires consideration of these relationships. It describes a spatially explicit ecological exposure model to facilitate calculation of E[HQ]. The model provides (1) a flexible platform for investigating the effect of changes in habitat area, habitat quality, foraging area, and population size on exposure estimates, and (2) a tool for calculating E[HQ] for use in actual risk assessments. The inner loop of a Visual Basic program randomly walks a receptor over a multicelled landscape--each cell of which contains values for cell area, habitat area, habitat quality, and concentration--accumulating an exposure estimate until the total area foraged is less than or equal to a given foraging area. An outer loop then steps through foraging areas of increasing size. This program is iterated by Monte Carlo software, with the number of iterations representing the population size. Results indicate that (1) any single estimator may over- or underestimate exposure, depending on foraging strategy and spatial relationships of habitat and contamination, and (2) changes in exposure estimates in response to changes in foraging and habitat area are not linear.

Analysis and Portrayal of Uncertainty in a Food-Web Exposure Model

The results of quantitative risk assessments are key factors in a risk managers decision of the necessity to implement actions to reduce risk. The extent of the uncertainty in the assessment will play a large part in the degree of confidence a risk manager has in the reported significance and probability of a given risk. The two main sources of uncertainty in such risk assessments are variability and incertitude. In this paper we use two methods, a second-order two-dimensional Monte Carlo analysis and probability bounds analysis, to investigate the impact of both types of uncertainty on the results of a food-web exposure model. We demonstrate how the full extent of uncertainty in a risk estimate can be fully portrayed in a way that is useful to risk managers. We show that probability bounds analysis is a useful tool for identifying the parameters that contribute the most to uncertainty in a risk estimate and how it can be used to complement established practices in risk assessment. We conclude by promoting the use of probability analysis in conjunction with Monte Carlo analyses as a method for checking how plausible Monte Carlo results are in the full context of uncertainty.

A state-wide survey in Oregon (USA) of trace metals and organic chemicals in municipal effluent

Oregons Senate Bill 737, enacted in 2007, required the states 52 largest municipal wastewater treatment plants (WWTP) and water pollution control facilities (WPCF) to collect effluent samples in 2010 and analyze them for persistent organic pollutants. These facilities are located state-wide and represent a variety of treatment types, service population sizes, geographic areas, and flow conditions. Of the 406 chemicals ultimately analyzed, 114 were detected above the level of quantification (LOQ) in at least one sample. Few persistent pollutants were found possibly because of their diversion from effluent via sorption to sludge (solids phase) or high LOQs for certain chemicals. Several pesticides, as well as benzene and phenol degradation products, all previously unreported in effluent, were detected. Ten polychlorinated biphenyls (PCB) congeners were present at low concentrations in ≤ 10 samples, while polychlorinated naphthalenes and dioxins/furans were not detected at all. Twenty-one polybrominated diphenyl ether (PBDE) congeners were found, nine of which have been reported in Osprey eggs in Oregon and Washington. Methylmercury was present in 65% of samples, with average and maximum concentrations of 0.18 and 1.36 ng/L, respectively. Although they are generally assumed to be innocuous by-products of sewage treatment, additional research is needed on potential impacts to aquatic ecosystems of high loadings of coprostanol and cholesterol. These results suggest that effluent, rather than just receiving waters, should itself be analyzed for a wide range of contaminants in order to understand how upstream sources, conveyed through WWTPs and WPCFs, could be impacting aquatic ecosystems.

Performing Spatially and Temporally Explicit Ecological Exposure Assessments Involving Multiple Stressors

Abstract Ecological risk assessments have traditionally focused on estimating risk associated with a receptors exposure to chemical stressors in abiotic (soil, water, etc.) and biotic (tissues, prey items) media. However, a free-living receptor is also constantly challenged to avoid or minimize adverse effects associated with those physical (e.g., loss of habitat) and biological (e.g., lack of adequate food) stressors that are already a consistent and natural part of its everyday existence. All three stressors, as well as their relative spatial and temporal positions with respect to each other and the receptor, may interact in ways that alter a chemical stressors relative contribution to a receptors overall risk. Evidence suggests that better representations of a chemical stressors true contribution to overall risk would result if spatial, temporal, and multiple stressor interactions were more routinely considered and quantified. However, examples of this occurring in typical ecological risk assessments are rare, due, in part, to a lack of practical and accessible procedures for this purpose. This article outlines a procedure to give ecological risk assessment practitioners greater access to spatial, temporal, and multistressor techniques, describes an implementable spreadsheet-based model for performing calculations associated with this procedure, and discusses the types of ecological, life history, and landscape information needed to parameterize this model.

Exposure Assessment and Risk of Gastrointestinal Illness Among Surfers

Surfing is a unique recreational activity with the possibility of elevated risk for contracting gastrointestinal (GI) illness through ingestion of contaminated water. No prior studies have assessed exposure from ingestion among surfing populations. This study estimated the magnitude and frequency of incidental water ingestion using a Web-based survey and integrated exposure distributions with enterococci distributions to predict the probability of GI illness at six Oregon beaches. The mean exposure magnitude and frequency were 170 ml of water ingested per day and 77 days spent surfing per year, respectively. The mean number of enterococci ingested ranged from approximately 11 to 86 colony-forming units (CFU) per day. Exposure-response analyses were conducted using an ingested dose model and two epidemiological models. Risk was characterized using joint probability curves (JPC). At the most contaminated beach, the annualized ingested dose model estimated a mean 9% probability of a 50% probability of GI illness, similar to the results of the first epidemiological model (mean 6% probability of a 50% probability of GI illness). The second epidemiological model predicted a 23% probability of exceeding an exposure equivalent to the U.S. Environmental Protection Agency (EPA) maximum acceptable GI illness rate (19 cases/1000 swimmers). While the annual risk of GI illness for Oregon surfers is not high, data showed that surfers ingest more water compared to swimmers and divers and need to be considered in regulatory and public health efforts, especially in more contaminated waters. Our approach to characterize risk among surfers is novel and informative to officials responsible for advisory programs. It also highlights the need for further research on microbial dose-response relationships to meet the needs of quantitative microbial risk assessments (QMRA).

Recommendations for the development and application of wildlife toxicity reference values.

Toxicity reference values (TRVs) are essential in models used in the prediction of the potential for adverse impacts of environmental contaminants to avian and mammalian wildlife; however, issues in their derivation and application continue to result in inconsistent hazard and risk assessments that present a challenge to site managers and regulatory agencies. Currently, the available science does not support several common practices in TRV derivation and application. Key issues include inappropriate use of hazard quotients and the inability to define the probability of adverse outcomes. Other common problems include the continued use of no-observed- and lowest-observed-adverse-effect levels (NOAELs and LOAELs), the use of allometric scaling for interspecific extrapolation of chronic TRVs, inappropriate extrapolation across classes when data are limited, and extrapolation of chronic TRVs from acute data without scientific basis. Recommendations for future TRV derivation focus on using all available qualified toxicity data to include measures of variation associated with those data. This can be achieved by deriving effective dose (EDx)-based TRVs where x refers to an acceptable (as defined in a problem formulation) reduction in endpoint performance relative to the negative control instead of relying on NOAELs and LOAELs. Recommendations for moving past the use of hazard quotients and dealing with the uncertainty in the TRVs are also provided.

A basin-specific aquatic food web biomagnification model for estimation of mercury target levels.

In the Willamette River Basin (WRB, Oregon, USA), health advisories currently limit consumption of fish that have accumulated methylmercury (MeHg) to levels posing a potential health risk for humans. Under the Clean Water Act, these advisories create the requirement for a total maximum daily load (TMDL) for mercury in the WRB. A TMDL is a calculation of the maximum amount of a pollutant that a body of water can receive and still meet water-quality standards. Because MeHg is known to biomagnify in aquatic food webs, a basin-specific biomagnification factor can be used, given a protective fish tissue criterion, to estimate total mercury concentrations in surface waters required to lower advisory mercury concentrations currently in fish in the WRB. This paper presents an aquatic food web biomagnification model that simulates inorganic mercury (Hg(II)) and MeHg accumulation in fish tissue and estimates WRB-specific biomagnification factors for resident fish species of concern to stakeholders. Probabilistic (two-dimensional Monte Carlo) techniques propagate parameter variability and uncertainty throughout the model, providing decision makers with credible range information and increased flexibility in establishing a specific mercury target level. The model predicts the probability of tissue mercury concentrations in eight fish species within the range of concentrations measured in these species over 20 years of water-quality monitoring. Estimated mean biomagnification factor values range from 1.12 x 10(6) to 7.66 x 10(6) and are within the range of U.S. Environmental Protection Agency national values. Several WRB-specific mercury target levels are generated, which very by their probability of affording human health protection relative to the federal MeHg tissue criterion of 0.30 mg/kg. Establishing a specific numeric target level is, however, a public policy decision, and one that will require further discussions among WRB stakeholders.

Assessment of Risk to Terrestrial Receptors Using Uncertain Analysis—A Case Study

Former industrial landfills on Crab Orchard National Wildlife Refuge were found to contain polychlorinated biphenyls (Aroclor-1254) at levels posing risks to wildlife. This case study reports on a risk assessment that used both deterministic and probabilistic (Monte Carlo) methods to provide risk managers with point estimates of risk, as well as multiple descriptors of uncertainty and variability around the risk estimate. Problem formulation identified mink (Mustela vison) as the higher trophic level assessment endpoint. Exposure analysis estimated Aroclor 1254 doses to mink using a multi-species food web model with probabilistic exposure parameters. Toxicity reference value point estimates and distributions were obtained from the literature. Risk characterization used quotient and probabilistic methodologies. Deterministic quotient analysis corroborated remedial goals originally established in the Record of Decision. Probabilistic risk analysis estimated projected post-remediation soil conditions as havi...

A Strategy for Using Weight-of-Evidence Methods in Ecological Risk Assessments

ABSTRACT Our review of existing approaches and regulatory uses of weight-of-evidence (WOE) methods suggested the need for a practical strategy for deploying WOE within a predictive ecological risk assessment (ERA). WOE is the process of considering strengths and weaknesses of various pieces of information in order to inform a decision being made among competing alternatives. A predictive ERA uses existing information relating cause and effect to estimate the probability that todays action X will lead to tomorrows adverse outcome Y. There appears to be no practical guidance for use of WOE in predictive assessments. We therefore propose a strategy for using a WOE approach, within an ERA framework, to weigh and integrate outcomes from various lines of evidence to estimate the probability of an adverse outcome in an assessment endpoint. An ERA framework is necessary to connect the results of an assessment to the management goals of concern to decision-makers and stakeholders. Within that framework, a WOE approach provides a consistent and transparent means of interpreting the myriad types of data and information gathered during a complex ecological assessment. Impediments to application of WOE are discussed, including limited regulatory guidance, limited prior regulatory use, and persistent reliance on threshold-based decision-making.