Deriving field-based sediment quality guidelines from the relationship between species density and contaminant level using a novel nonparametric empirical Bayesian approach

This paper describes a novel statistical approach to derive ecologically relevant sediment quality guidelines (SQGs) from field data using a nonparametric empirical Bayesian method (NEBM). We made use of the Norwegian Oil Industrial Association database and extracted concurrently obtained data on species density and contaminant levels in sediment samples collected between 1996 and 2001. In brief, effect concentrations (ECs) of each installation (i.e., oil platform) at a given reduction in species density were firstly derived by fitting a logistic-type regression function to the relationship between the species density and the corresponding concentration of a chemical of concern. The estimated ECs were further improved by the NEBM which incorporated information from other installations. The distribution of these improved ECs from all installations was determined nonparametrically by the kernel method, and then used to determine the hazardous concentration (HC) which can be directly linked to the species loss (or the species being protected) in the sediment. This method also enables an accurate estimation of the lower confidence limit of the HC, even when the number of observations was small. To illustrate the effectiveness of this novel technique, barium, cadmium, chromium, copper, mercury, lead, tetrahydrocannabinol, and zinc were chosen as example contaminants. This novel approach can generate ecologically sound SQGs for environmental risk assessment and cost-effectiveness analysis in sediment remediation or mud disposal projects, since sediment quality is closely linked to species density. © 2013 Springer-Verlag Berlin Heidelberg

Deriving sediment quality guidelines from field-based species sensitivity distributions

The determination of predicted no-effect concentrations (PNECs) and sediment quality guidelines (SQGs) of toxic chemicals in marine sediment is extremely important in ecological risk assessment. However, current methods of deriving sediment PNECs or threshold effect levels (TELs) are primarily based on laboratory ecotoxicity bioassays that may not be ecologically and environmentally relevant. This study explores the possibility of utilizing field data of benthic communities and contaminant loadings concurrently measured in sediment samples collected from the Norwegian continental shelf to derive SQGs. This unique dataset contains abundance data for ca. 2200 benthic species measured at over 4200 sampling stations, along with cooccurring concentration data for >25 chemical species. Using barium, cadmium, and total polycyclic aromatic hydrocarbons (PAHs) as examples, this paper describes a novel approach that makes use of the above data set for constructing field-based species sensitivity distributions (f-SSDs). Field-based SQGs are then derived based on the f-SSDs and HCx values [hazardous concentration for x% of species or the (100 - x)% protection level] by the nonparametric bootstrap method. Our results for Cd and total PAHs indicate that there are some discrepancies between the SQGs currently in use in various countries and our field-data-derived SQGs. The field-data-derived criteria appear to be more environmentally relevant and realistic. Here, we suggest that the f-SSDs can be directly used as benchmarks for probabilistic risk assessment, while the field-data-derived SQGs can be used as site-specific guidelines or integrated into current SQGs. © 2005 American Chemical Society.link_to_subscribed_fulltex