Methodology for deriving toxicant guideline values for sediments
You can use default guideline values (DGVs) for toxicants in sediment as an indicator of potential toxicity problems.
The methods we outline to derive toxicant guideline values for sediments are described by Simpson et al. (2013a):
- empirical guideline values based on ranking of biological effects
- mechanistic guideline values based on equilibrium partitioning
- guideline values based on toxicity testing and the use of species sensitivity distributions (SSDs).
Fewer toxicity tests are available for toxicants in sediments than in waters. This is why adequate datasets for sediments were not available for application of SSDs based on whole sediment toxicity testing (the most recently developed method).
Empirical guideline values based on ranking of biological effects
It is possible to derive guideline values for toxicants in sediment by ranking biological-effects data.
As introduced in 2000, a set of sediment quality guideline values was adapted from the effects database developed by Long et al. (1995) and McDonald et al. (2000) in North America. The guideline values were derived from a ranking of measured toxicant concentrations coincident with measurements of biological-effects data for different toxicants assessed in contaminated sediments. The data included effects on toxicity and on populations. The 10th percentile of the biological-effects data (termed the effects range low (ERL) values) were largely adopted for Australia and New Zealand in the ANZECC & ARMCANZ (2000) guidelines.
For organic contaminants, the ERL values appeared to be less reliable than the threshold effect level (TEL) values derived by McDonald et al. (2000). So the TEL values were adopted as the DGVs for many organic toxicants in the latest revision of the sediment quality guideline values (Simpson et al. 2013a).
A second set of guideline values indicates an upper bound in contaminant concentrations, referred to as the GV-high for sediment quality. The GV-high values represent the median of the measured concentrations associated with effects data. GV-high provides a guide as to concentrations at or above which effects on sediment biota are probable. This was termed effects range median (ERM) by Long et al. (1995) and probable effects level (PEL) by MacDonald et al. (2000).
Unlike guideline values for toxicants in water, which are based on effects data for individual toxicants, the majority of the effects data used to derive sediment quality guideline values suffer from co‐occurrence of multiple toxicants. This prevents the observed effects being confidently assigned to any one toxicant. It is the source of the greatest uncertainty in these guideline values for sediment quality.
Mechanistic guideline values
You can use equilibrium partitioning (EqP) theory to derive guideline values for toxicants in sediment.
EqP theory is based on the assumption that the critical factor controlling sediment toxicity is the concentration of contaminant in the sediment pore water.
Water quality guideline values can be applied to pore water contaminants, and the sediment quality guideline value can be defined by the concentration of contaminant in the sediment that is in equilibrium with the water quality guideline value concentration in the pore water. The ratio of the contaminant concentration in the sediment (CS) and its concentration in the surrounding water (CW) is defined as the partition coefficient, KD.
The EqP model predicts that sediments will be toxic when the pore water concentration exceeds the water‐only toxic concentration. If the water quality guideline value (WQGV, in µg/L) is known, then the sediment quality guideline value (SQGV, in µg/kg) is given by the partition coefficient KD (L/kg) between pore water and sediment according to the equation:
SQGV = KD x WQGV
The sediment/pore water partition coefficient, KD, is related to the organic carbon partition coefficient, KOC, and the fraction by weight of organic carbon, fOC:
SQGV = fOC x KOC x WQGV
KOC is empirically related to the readily determined octanol/water partition coefficient KOW.
This approach to deriving sediment quality guideline values is most readily applicable to hydrophobic organic chemicals. Mechanistic guideline values derived in this manner have been used as equilibrium sediment benchmarks (ESBs) by the United States (US) Environmental Protection Agency (EPA).
Equilibrium partitioning sediment benchmarks developed by the USEPA (e.g. USEPA 2008, 2012) represent site‐specific concentrations for non‐ionic organic chemicals in sediments that are protective of the presence of freshwater and marine benthic organisms.
This approach uses EqP relationships to estimate the bioavailability of non‐ionic organic contaminants based on their measured sediment concentrations and sediment organic carbon content. A toxic unit approach (based on final chronic values) is then applied to the calculated pore water concentrations of the individual organics to predict the toxicity of the mixture.
The EqP relationships are intended to account for the influence of different sediments on the observed biological effects. As a consequence, the benchmarks are causally linked to the specific chemical, applicable across sediments, and are considered protective of benthic organisms.
Guideline values based on toxicity testing and the use of SSDs
You can derive guideline values for sediments based on toxicity data — similar to the approach used for the derivation of water quality guideline values — if you have sufficient data from standardised laboratory toxicity testing using benthic biota.
Over the past decade, lack of sufficient toxicity testing datasets has been addressed to some extent, at least for marine sediments.
Many of the toxicity tests available are acute tests (Simpson & Batley 2016). Ideally, we require chronic test data to derive guideline values for toxicants in sediments, and that deficiency is currently being addressed.
You can follow 2 approaches for deriving sediment quality guideline values.
Derive a sediment quality guideline value
You can use data from whole sediment toxicity testing of spiked sediments in an SSD to derive a sediment quality guideline value
This approach has been discussed by Simpson et al. (2011, 2013b) in an application to derive a guideline value for copper in sediment. The situation is more complex than it is for water quality guideline value derivation because the toxicity of sediment contaminants will be a function of both grain size and organic carbon content, both for metals and many organics. Normalising the derived guideline value to both of these parameters enables a sediment property-specific guideline value to be derived. The generic approach outlined by Simpson et al. (2011, 2013b) is readily adaptable to other contaminants but has only been applied to copper, and is a costly exercise.
Derive a guideline value for pore waters that can be related to a sediment quality guideline value
You can use SSDs of data for dissolved contaminant toxicity to benthic organisms to derive a guideline value for pore waters that could be related by EqP to a sediment quality guideline value
Using this approach, it is important that toxicity data to be used in an SSD are relevant only to benthic organisms that will be most impacted by sediment contaminants. Tests are likely to be water-only tests but we know that organisms can obtain contaminants from food, sediment (including directly via ingestion) or water. The use of EqP to compute a sediment concentration from the water concentration could be a questionable approach because it is based on a known or measured partition coefficient that assumes pore water as the major exposure route.
To date, few examples have been discussed where field-effects data have been used in sediment quality guideline value derivation, although field effects including benthic community composition and toxicity formed part of the early effects rankings. Because of the variability in sediment composition, it would be important that such an approach would take into consideration sediment grain size and the presence of contaminant binding phases.
References
ANZECC & ARMCANZ 2000, Australian and New Zealand Guidelines for Fresh and Marine Water Quality, Australian and New Zealand Environment and Conservation Council and Agriculture and Resource Management Council of Australia and New Zealand, Canberra.
Long ER, MacDonald DD, Smith SL & Calder FD 1995, Incidence of adverse effects within ranges of chemical concentrations in marine and estuarine sediments, Environmental Management 19: 81–97.
MacDonald DD, Ingersoll CG & Berger TA 2000, Development and evaluation of consensus‐based sediment quality guidelines for freshwater ecosystems, Archives of Environmental Contamination and Toxicology 39: 20–31.
Simpson SL & Batley GE 2016, Sediment Quality Assessment: A practical handbook, CSIRO Publishing, Clayton.
Simpson SL, Batley GE & Chariton AA 2013a, Revision of the ANZECC/ARMCANZ Sediment Quality Guidelines, CSIRO Land and Water Report 8/07, CSIRO Land and Water.
Simpson SL, Batley GE, Hamilton I & Spadaro DA 2011, Guidelines for copper in sediments with varying properties, Chemosphere 85(9): 1487–1495.
Simpson SL, Spadaro DA & O’Brien D 2013b, Incorporating bioavailability into management limits for copper in sediments contaminated by antifouling paint used in aquaculture, Chemosphere 93(10): 2499–2506.
USEPA 2008, Procedures for the Derivation of Equilibrium Partitioning Sediment Benchmarks (ESBs) for the Protection of Benthic Organisms: Compendium of tier 2 values for nonionic organics, EPA-600-R-02-016, Office of Research and Development, United States Environmental Protection Agency, Washington, DC.
USEPA 2012, Equilibrium Partitioning Sediment Benchmarks (ESBs) for the Protection of Benthic Organisms: Procedures for the Determination of the Freely Dissolved Interstitial Water Concentrations of Nonionic Organics, EPA 600/R-02/012, US Environmental Protection Agency, Washington, DC.
