Biomedical subjects

Donald D MacDonald

Publications and source records attributed to Donald D MacDonald.

3 recordsLinked to original sources

PubMed2006

Calculation and uses of mean sediment quality guideline quotients: a critical review.

Fine-grained sediments contaminated with complex mixtures of organic and inorganic chemical contaminants can be toxic in laboratory tests and/or cause adverse impacts to resident benthic communities. Effects-based, sediment quality guidelines (SQGs) have been developed over the past 20 years to aid in the interpretation of the relationships between chemical contamination and measures of adverse biological effects. Mean sediment quality guideline quotients (mSQGQ) can be calculated by dividing the concentrations of chemicals in sediments by their respective SQGs and calculating the mean of the quotients for the individual chemicals. The resulting index provides a method of accounting for both the presence and the concentrations of multiple chemicals in sediments relative to their effects-based guidelines. Analyses of considerable amounts of data demonstrated that both the incidence and magnitude of toxicity in laboratory tests and the incidence of impairment to benthic communities increases incrementally with increasing mSQGQs. Such concentration/response relationships provide a basis for estimating toxicological risks to sediment-dwelling organisms associated with exposure to contaminated sediments with a known degree of accuracy. This sediment quality assessment tool has been used in numerous surveys and studies since 1994. Nevertheless, mean SQGQs have some important limitations and underlying assumptions that should be understood by sediment quality assessors. This paper provides an overview of the derivation methods and some of the principal advantages, assumptions, and limitations in the use of this sediment assessmenttool. Ideally, mean SQGQs should be included with other measures including results of toxicity tests and benthic community surveys to provide a weight of evidence when assessing the relative quality of contaminated sediments.

Animals↗

PubMed2003

Applications of numerical sediment quality targets for assessing sediment quality conditions in a US Great Lakes Area of Concern.

Contaminated sediments are receiving increasing recognition around the world, leading to the development of various sediment quality indicators for assessment, management, remediation, and restoration efforts. Sediment chemistry represents an important indicator of ecosystem health, with the concentrations of contaminants of potential concern (COPCs) providing measurable characteristics for this indicator. The St. Louis River Area of Concern (AOC), located in the western arm of Lake Superior, provides a case study for how numerical sediment quality targets (SQTs) for the protection of sediment-dwelling organisms can be used to support the interpretation of sediment chemistry data. Two types of SQTs have been established for 33 COPCs in the St. Louis River AOC. The Level I SQTs define the concentrations of contaminants below which sediment toxicity is unlikely to occur, whereas the Level II SQTs represent the concentrations that, if exceeded, are likely to be associated with sediment toxicity. The numerical SQTs provide useful tools for making sediment management decisions, especially when considered as part of a weight-of-evidence approach that includes other sediment quality indicators, such as sediment contaminant chemistry and geochemical characteristics, sediment toxicity, and benthic macroinvertebrate community structure. The recommended applications of using the numerical SQTs in the St. Louis River AOC include: designing monitoring programs, interpreting sediment chemistry data, conducting ecological risk assessments, and developing site-specific sediment quality remediation targets for small, simple sites where adverse biological effects are likely. Other jurisdictions may benefit from using these recommended applications in their own sediment quality programs.

Animals↗

PubMed2002

Predicting amphipod toxicity from sediment chemistry using logistic regression models.

Individual chemical logistic regression models were developed for 37 chemicals of potential concern in contaminated sediments to predict the probability of toxicity, based on the standard 10-d survival test for the marine amphipods Ampelisca abdita and Rhepoxynius abronius. These models were derived from a large database of matching sediment chemistry and toxicity data, which includes contaminant gradients from a variety of habitats in coastal North America. Chemical concentrations corresponding to a 20, 50, and 80% probability of observing sediment toxicity (T20, T50, and T80 values) were calculated to illustrate the potential for deriving application-specific sediment effect concentrations and to provide probability ranges for evaluating the reliability of the models. The individual chemical regression models were combined into a single model, using either the maximum (P(Max) model) or average (P(Avg) model) probability predicted from the chemicals analyzed in a sample, to estimate the probability of toxicity for a sample. The average predicted probability of toxicity (from the P(Max) model) within probability quartiles closely matched the incidence of toxicity within the same ranges, demonstrating the overall reliability of the P(Max) model for the database that was used to derive the model. The magnitude of the toxic effect (decreased survival) in the amphipod test increased as the predicted probability of toxicity increased. Users have a number of options for applying the logistic models, including estimating the probability of observing acute toxicity to estuarine and marine amphipods in 10-d toxicity tests at any given chemical concentration or estimating the chemical concentrations that correspond to specific probabilities of observing sediment toxicity.

Amphipoda↗