Guide for developing data quality objectives for ecological risk assessment at DOE Oak Ridge Operations facilities

For the past several years the US Environmental Protection Agency (EPA) has been attempting to streamline and increase the efficiency of field data collection programs, especially Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) Remedial Investigation (RI) programs, by encouraging project managers to develop Data Quality Objectives (DQOs) prior to sampling. The DQO process is a strategic planning approach that is used to prepare for a data collection activity. It provides a systematic procedure for defining the criteria that a data collection design should satisfy, including when to collect samples, where to collect samples, the tolerable level of decision errors for study, and how many samples to collect. It is important to note that DQOs are developed through a process that ties data collection to specific problems and decisions

Estimating Fuel Cycle Externalities: Analytical Methods and Issues, Report 2

The activities that produce electric power typically range from extracting and transporting a fuel, to its conversion into electric power, and finally to the disposition of residual by-products. This chain of activities is called a fuel cycle. A fuel cycle has emissions and other effects that result in unintended consequences. When these consequences affect third parties (i.e., those other than the producers and consumers of the fuel-cycle activity) in a way that is not reflected in the price of electricity, they are termed ''hidden'' social costs or externalities. They are the economic value of environmental, health and any other impacts, that the price of electricity does not reflect. How do you estimate the externalities of fuel cycles? Our previous report describes a methodological framework for doing so--called the damage function approach. This approach consists of five steps: (1) characterize the most important fuel cycle activities and their discharges, where importance is based on the expected magnitude of their externalities, (2) estimate the changes in pollutant concentrations or other effects of those activities, by modeling the dispersion and transformation of each pollutant, (3) calculate the impacts on ecosystems, human health, and any other resources of value (such as man-made structures), (4) translate the estimates of impacts into economic terms to estimate damages and benefits, and (5) assess the extent to which these damages and benefits are externalities, not reflected in the price of electricity. Each step requires a different set of equations, models and analysis. Analysts generally believe this to be the best approach for estimating externalities, but it has hardly been used! The reason is that it requires considerable analysis and calculation, and to this point in time, the necessary equations and models have not been assembled. Equally important, the process of identifying and estimating externalities leads to a number of complex issues that also have not been fully addressed. This document contains two types of papers that seek to fill part of this void. Some of the papers describe analytical methods that can be applied to one of the five steps of the damage function approach. The other papers discuss some of the complex issues that arise in trying to estimate externalities. This report, the second in a series of eight reports, is part of a joint study by the U.S. Department of Energy (DOE) and the Commission of the European Communities (EC)* on the externalities of fuel cycles. Most of the papers in this report were originally written as working papers during the initial phases of this study. The papers provide descriptions of the (non-radiological) atmospheric dispersion modeling that the study uses; reviews much of the relevant literature on ecological and health effects, and on the economic valuation of those impacts; contains several papers on some of the more complex and contentious issues in estimating externalities; and describes a method for depicting the quality of scientific information that a study uses. The analytical methods and issues that this report discusses generally pertain to more than one of the fuel cycles, though not necessarily to all of them. The report is divided into six parts, each one focusing on a different subject area

Decomposition analysis of LTREs may facilitate the design of short-term ecotoxicological tests

This study compared two methods, based on re-analyzed data from a partly published life table response experiment (LTRE), to help determine the optimal approach for designing ecotoxicological assessments. The 36-day LTRE data recorded the toxic effects of cadmium (Cd) and imidacloprid, alone and in combination, on the reproduction and survivorship of aphids (Acyrthosiphon pisum Harris). We used this data to construct an age-classified matrix model (six age classes, each 6 days long) to estimate aphid population growth rate (λ) under each treatment. For each treatment, an elasticity analysis and a demographic decomposition analysis were performed, and results were compared. Despite different results expected from the two toxicants, the elasticity values were very similar. The elasticity of λ with respect to survival was highest in the first age class, and that with respect to fertility was highest in the second age class. The demographic decomposition analysis examined how changes in life-history traits contributed to differences in λ between control and treated populations (Δλ). This indicated that the most important contributors to Δλ were the differences in survival (resulting from both demographic sensitivity and toxicity) in the first and the second age classes of aphids and differences in fertility in the third and the fourth age classes. Additionally, the toxicants acted differently. Cd reduced Δλ by impairing fertility at third age class and reducing survivorship from the second to the third age class. Imidacloprid mostly reduced survivorship at the first and second age classes. The elasticity and decomposition analyses showed different results, because these methods addressed different questions about the interaction of organism life history and sensitivity to toxicants. This study indicated that the LTRE may be useful for designing individual-level ecotoxicological experiments that account for both the effects of the toxicant and the demographic sensitivity of the organism

Traits and stress: keys to identify community effects of low levels of toxicants in test systems

Community effects of low toxicant concentrations are obscured by a multitude of confounding factors. To resolve this issue for community test systems, we propose a trait-based approach to detect toxic effects. An experiment with outdoor stream mesocosms was established 2-years before contamination to allow the development of biotic interactions within the community. Following pulse contamination with the insecticide thiacloprid, communities were monitored for additional 2 years to observe long-term effects. Applying a priori ecotoxicological knowledge species were aggregated into trait-based groups that reflected stressor-specific vulnerability of populations to toxicant exposure. This reduces inter-replicate variation that is not related to toxicant effects and enables to better link exposure and effect. Species with low intrinsic sensitivity showed only transient effects at the highest thiacloprid concentration of 100 μg/l. Sensitive multivoltine species showed transient effects at 3.3 μg/l. Sensitive univoltine species were affected at 0.1 μg/l and did not recover during the year after contamination. Based on these results the new indicator SPEARmesocosm was calculated as the relative abundance of sensitive univoltine taxa. Long-term community effects of thiacloprid were detected at concentrations 1,000 times below those detected by the PRC (Principal Response Curve) approach. We also found that those species, characterised by the most vulnerable trait combination, that were stressed were affected more strongly by thiacloprid than non-stressed species. We conclude that the grouping of species according to toxicant-related traits enables identification and prediction of community response to low levels of toxicants and that additionally the environmental context determines species sensitivity to toxicants

Effects of the fungicide metiram in outdoor freshwater microcosms: responses of invertebrates, primary producers and microbes

The ecological impact of the dithiocarbamate fungicide metiram was studied in outdoor freshwater microcosms, consisting of 14 enclosures placed in an experimental ditch. The microcosms were treated three times (interval 7 days) with the formulated product BAS 222 28F (Polyram®). Intended metiram concentrations in the overlying water were 0, 4, 12, 36, 108 and 324 μg a.i./L. Responses of zooplankton, macroinvertebrates, phytoplankton, macrophytes, microbes and community metabolism endpoints were investigated. Dissipation half-life (DT50) of metiram was approximately 1–6 h in the water column of the microcosm test system and the metabolites formed were not persistent. Multivariate analysis indicated treatment-related effects on the zooplankton (NOECcommunity = 36 μg a.i./L). Consistent treatment-related effects on the phytoplankton and macroinvertebrate communities and on the sediment microbial community could not be demonstrated or were minor. There was no evidence that metiram affected the biomass, abundance or functioning of aquatic hyphomycetes on decomposing alder leaves. The most sensitive populations in the microcosms comprised representatives of Rotifera with a NOEC of 12 μg a.i./L on isolated sampling days and a NOEC of 36 μg a.i./L on consecutive samplings. At the highest treatment-level populations of Copepoda (zooplankton) and the blue-green alga Anabaena (phytoplankton) also showed a short-term decline on consecutive sampling days (NOEC = 108 μg a.i./L). Indirect effects in the form of short-term increases in the abundance of a few macroinvertebrate and several phytoplankton taxa were also observed. The overall community and population level no-observed-effect concentration (NOECmicrocosm) was 12–36 μg a.i./L. At higher treatment levels, including the test systems that received the highest dose, ecological recovery of affected measurement endpoints was fast (effect period < 8 weeks)