Accounting for local conditions

​​​​​Default guideline values (DGVs) can provide you with an important starting point for managing water quality but they cannot account for the large spatial or temporal variation in natural water quality. This includes variation in environmental variables that influence the bioavailability and toxicity of contaminants.

Toxicant DGVs are based on data from a specific set of solution parameters, such as pH, hardness, dissolved organic carbon, salinity and temperature. These characteristics vary widely throughout Australian and New Zealand waters.

In addition, DGVs may not protect locally important species because published global data are usually only available for a very limited number of (usually standard) toxicity testing species. Very few of them are Australian or New Zealand species.

This is why you should, wherever possible, tailor DGVs and the types of water chemistry data collected to account for local conditions. The ultimate tailoring of a DGV to local conditions is the derivation of a site-specific guideline value. In addition to the guidance provided in the Water Quality Guidelines, another relevant source of guidance for deriving site-specific guideline values can be found in van Dam et al. (2019), while Huynh & Hobbs (2019) provide specific guidance tailored for the coal resource industry, but which will also have broader applicability.

In some cases, DGVs can be appropriately modified to account for some important local factors. For example:

  • corrections for the effects of hardness for metals
  • corrections for the effects of pH for ammonia
  • dealing with naturally elevated background concentrations (natural toxicant concentrations unrelated to human disturbance).

In addition, or alternatively, direct toxicity assessment (DTA) can be a useful tool to account for local conditions (sections 8.3.5.19 and 8.3.6 of ANZECC & ARMCANZ 2000), and has several applications. We provide some updated guidance on the benefits and uses of DTA.

The process of accounting for local conditions is usually completed in the Water Quality Management Framework at:

Knowing when to refine default guideline values

Note that your local jurisdiction may have its own guidance for applying and refining guideline values, and you should always consult with it on appropriate methods.

For most indicators and issues, you would only refine DGVs after continuous and extensive monitoring showed that test-site data exceedances posed no risk to the ecosystem. This would require hierarchical measurements of the stressor line of evidence, in this case water chemistry, together with other lines of evidence that also demonstrate no ecosystem detriment.

You could also refine guideline values if longer-term monitoring showed that test-site data were consistently below the DGVs.

Evaluating monitoring data against default guideline values

You need to interpret water quality monitoring data correctly to enable effective comparison with guideline values.

To do this, you should apply a decision scheme that provides step-by-step guidance on how to consider and (if necessary) treat water chemistry data obtained for site-specific environmental conditions. Such a scheme would include consideration of:

  • bioavailable fraction
  • background concentrations
  • analytical detection limits.

It is not mandatory to use hierarchical chemical measurements because, in most instances, the chemistry line of evidence will be assessed in parallel with other lines of evidence.

If you want to make meaningful and appropriate comparisons with DGVs or site-specific guideline values, then hierarchical chemical measurements are important. They can greatly assist with interpretation of the chemistry line of evidence where initial measurements (i.e. steps early in the hierarchy) are near or above the guideline value.

The simple adjustments and corrections that we describe here make this a cost-effective and (in practice) rapid exercise when data on key water quality parameters are available.

You should consider the extent to which local factors will influence your sampling and data collection and manipulation requirements when defining the issue and setting objectives for a monitoring or assessment program.

Expand a​ll

Refining default guideline values for metals

When evaluating measured chemistry against DGVs for metals, where possible it will be important to account for the influences of important local water quality variables, such as water hardness, pH, salinity and the associated mix of dissolved salts, and dissolved organic carbon (DOC) on metal bioavailability.

DGVs are typically derived for either low hardness, near neutral pH fresh waters with negligible DOC or pristine marine waters of typical oceanic salinity. Currently, there are no DGVs for estuarine waters or inland saline waters.

Expand a​ll

Refining toxicant guideline values based on other considerations

We describe how guideline values can potentially be refined based on a number of other considerations.

Expand a​ll

Using multiple lines of evidence

So far our discussion concerns the assessment of the chemistry line of evidence for evaluating compliance with guideline values. But it is important that compliance with guideline values is considered as one of a number of lines of evidence that may be used when assessing water quality (as part of a weight-of-evidence process). These lines of evidence could include monitoring or assessment of other pressures, stressors (e.g. habitat, flow) and ecosystem receptors.

Ultimately, it is biological measurement of relevant ecosystem receptors that will provide confirmation that water quality at a site is not being affected.

If the guideline value is exceeded or there is low confidence in desktop assessments (e.g. difficulty in modelling metal speciation), then you should opt for DTA or biodiversity assessment as an additional line of evidence. Additional evidence can be obtained from measurements of bioaccumulation.

The toxicity line of evidence could specifically deal with cases where chemical mixtures modify toxicity from that based on the additivity of individual toxicant effects. This will also be of use where the protection of locally important species is a concern.

References

Altenburger R, Walter H & Grote M 2004, What contributes to the combined effect of a complex mixture? Environmental Science and Technology 38: 6353–6362.

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.

Arnot, J. A. and Gobas, F. A. (2006). A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms. Environmental Reviews 14(4): 257-297.

Backhaus T, Altenburger R, Boedeker W, Faust M & Scholze M 2000b, Predictability of the toxicity of a multiple mixture with combinations of environmental chemicals, Science 272: 489–492.

Bass JA, Blust R, Clarke R, Corbin T, Davison W, De Schamphelaere K, Janssen C, Kalis E, Kelly M, Kneebone N, Lawlor AJ, Lofts S, Temminghoff EJM, Thacker SA, Tipping E, Vincent C, Warnken K & Zhang H 2008, Environmental Quality Standards for Trace Metals in the Aquatic Environment, Science Report – SC030194, Environment Agency, Bristol.

Batley GE, Apte SC & Stauber JL 2004, Speciation and bioavailability of trace metals in water: Progress since 1982, Australian Journal of Chemistry 57: 903–919.

Batley, GE, van Dam, RA, Warne, MStJ, Chapman, JC, Fox, DR, Hickey CW & Stauber, JL, 2018, Technical Rationale for Changes to the Method for Deriving Australian and New Zealand Water Quality Guideline Values for Toxicants, CSIRO Land and Water Report, Lucas Heights.

Bowles KC, Apte SC, Batley GE, Hales LT & Rogers NJ 2006, A rapid Chelex column method for the determination of metal speciation in natural waters, Analytica Chimica Acta 558: 237–245.

Burgess RM, Ho KT, Brack W & Lamoree M 2013, Effects-directed analysis (EDA) and toxicity identification evaluation (TIE): Complementary but different approaches for diagnosing causes of environmental toxicity, Environmental Toxicology and Chemistry 32: 1935–1945.

Burkhard LP & Ankley GT 1989, Identifying toxicants: NETAC's toxicity-based approach, Environmental Science and Technology 23: 1438–1443.

Cedergreen N 2014, Quantifying Synergy: A Systematic Review of Mixture Toxicity Studies within Environmental Toxicology, PLoS ONE 9(5): e96580.

Chevre N, Loepfe C, Singer H, Stamm C, Fenner K & Escher BI 2006, Including mixtures in the determination of water quality criteria for herbicides in surface water, Environmental Science and Technology 40: 426–435.

De Schamphelaere K & Janssen C 2008, Voluntary risk assessment of copper, copper (II) sulphate pentahydrate, copper(I) oxide, copper(II) oxide, dicopper chloride trihydroxide, Chapter 3 — Environmental effects, Appendix U: Modelling copper bioavailability and toxicity in freshwater: Uncertainty reduction for risk assessment (chronic fish-BLM), in: Voluntary Risk Assessment Reports – Copper and Copper Compounds, European Copper Institute, Brussels.

De Zwart D & Posthuma L 2005, Complex mixture toxicity for single and multiple species: Proposed methodologies, Environmental Toxicology and Chemistry 24: 2665-2676.

Environment Canada 2016, Canadian Water Quality Guidelines for the Protection of Aquatic Life: Zinc (Draft), Scientific Criteria Document, Ottawa.

Faust M, Altenberger R, Boedeker W & Grimme LH 1994, Algal toxicity of binary combinations of pesticides, Bulletin of Environmental Contamination and Toxicology 53(1): 134–141.

Ghosh U, Kane Driscoll S, Burgess RM, Jonker MT, Reible D, Gobas F, Choi Y, Apitz SE, Maruya KA, Gala WR, Mortimer M & Beegan C 2014, Passive sampling methods for contaminated sediments: Practical guidance for selection, calibration, and implementation, Integrated Environmental Assessment and Management 10: 210–223.

Gobas, F. A., de Wolf, W., Burkhard, L. P., Verbruggen, E. and Plotzke, K. (2009). Revisiting bioaccumulation criteria for POPs and PBT assessments. Integrated Environmental Assessment and Management: An International Journal 5(4): 624-637.

Harford AJ, Mooney TJ, Trenfield MA & van Dam RA 2015, Manganese toxicity to tropical freshwater species in low hardness water, Environmental Toxicology & Chemistry 34(12), 2856–2863.

Hickey CW, Batley GE & Gadd J 2016, ANZECC Methodology used for Freshwater Copper and Zinc Guideline Determinations, NIWA Report No MFE16205, prepared for Ministry for the Environment, Wellington.

Hogan AC, Trenfield MA, Harford AJ & van Dam RA 2013, Toxicity of magnesium pulses to tropical freshwater species and the development of a duration-based water quality guideline, Environmental Toxicology and Chemistry 32(9): 1969–1980.

Huynh T & Hobbs D. 2019.  Deriving site‐specific guideline values for physico‐chemical parameters and toxicants. Report prepared for the Independent Expert Scientific Committee on Coal Seam Gas and Large Coal Mining Development through the Department of the Environment and Energy. Canberra (AU).

Huynh T & Vink S 2016, Reducing Analytical and Water Quality Monitoring Costs using Diffusive Gradients in Thin Films (DGT) Technique, ACARP, Brisbane.

Kelly, B. C., Ikonomou, M. G., Blair, J. D., Surridge, B., Hoover, D., Grace, R. and Gobas, F. A. (2009). Perfluoroalkyl contaminants in an Arctic marine food web: trophic magnification and wildlife exposure. Environmental Science & Technology 43(11): 4037-4043.

Liu Y, Vijver MG, Pan B & Peijnenburg WJGM 2017, Toxicity models of metal mixtures established on the basis of “additivity” and “interactions”, Frontiers of Environmental Science and Engineering, 11, DOI: 10.1007/s11783-017-0916-8.

Mann, R. M., Vijver, M. G., & Peijnenburg, W. J. G. M. (2011). Metals and metalloids in terrestrial systems: Bioaccumulation, biomagnification and subsequent adverse effects. In F. Sánchez-Bayo, P. J. van den Brink, & R. M. Mann (Eds.), Ecological impacts of toxic chemicals (pp. 43– 62). Sharjah: Bentham.

Ng, C. A. and Hungerbühler, K. (2014). Bioaccumulation of perfluorinated alkyl acids: observations and models. Environmental Science & Technology 48(9): 4637-4648.

Niimi, A. (1983). Biological and toxicological effects of environmental contaminants in fish and their eggs. Canadian Journal of Fisheries and Aquatic Sciences 40(3): 306-312.

Prouse AE, Hogan AC, Harford AJ, van Dam RA & Nugegoda D 2015, Hydra viridissima (green Hydra) rapidly recover from multiple magnesium pulse exposures, Environmental Toxicology and Chemistry 34: 1734–1743.

Rand GM (ed.) 1995, Fundamentals of Aquatic Toxicology: Effects, environmental fate and risk assessment, 2nd Edition, CRC Press.

Sinclair A, Tayler K, van Dam R & Hogan A 2014, Site-specific water quality guidelines: 2. Development of a water quality regulation framework for pulse exposures of mine water discharges at a uranium mine in northern Australia, Environmental Science and Pollution Research 21: 131–140.

van Dam RA & Chapman JC 2001, Direct toxicity assessment (DTA) for water quality guidelines in Australia and New Zealand, Australasian Journal of Ecotoxicology 7: 175–198.

van Dam RA, Hogan AC, McCullough C, Houston M, Humphrey CL & Harford AJ 2010, Aquatic toxicity of magnesium sulfate, and the influence of calcium, in very low ionic concentration water, Environmental Toxicology & Chemistry 29: 410–421.

van Dam RA, Hogan AC, Humphrey CL & Harford AJ 2019, How specific is site-specific? A review and guidance for selecting and evaluating approaches for deriving local water quality benchmarks, Integrated Environmental Assessment and Management 15: 683–702.

van Dam RA, Hogan AC & Harford AJ 2017, Development and implementation of a site-specific water quality limit for uranium in a high conservation value ecosystem, Integrated Environmental Assessment & Management 13(4): 765–777.

Vighi M & Calamari D 1996, Quality objectives for aquatic life: the problem of mixtures of chemical substances, Human and Ecological Risk Assessment 2: 412−418.

Vrana B, Allan IJ & Greenwood R 2005, Passive sampling techniques for monitoring pollutants in water, TrAC Trends in Analytical Chemistry 24: 845–868.

USEPA 1992, Toxicity Identification Evaluation: Characterization of Chronically Toxic Effluents, Phase I, United States Environmental Protection Agency, Washington DC.

USEPA 1993, Methods for Aquatic Toxicity Identification Evaluations: Phase III Toxicity Confirmation Procedures for Samples Exhibiting Acute and Chronic Toxicity, United States Environmental Protection Agency, Washington DC.

USEPA 2007, Aquatic Life Ambient Freshwater Quality Criteria – Copper, 2007 Revision, United States Environmental Protection Agency Report EPA 822-R-07-001, Criteria and Standards Division, Washington DC.

Wang, Q., Lai, N. L.-S., Wang, X., Guo, Y., Lam, P. K.-S., Lam, J. C.-W. and Zhou, B. (2015). Bioconcentration and transfer of the organophorous flame retardant 1, 3-dichloro-2-propyl phosphate causes thyroid endocrine disruption and developmental neurotoxicity in zebrafish larvae. Environmental Science & Technology 49(8): 5123-5132.

Warne, MStJ, Batley GE, van Dam RA, Chapman JC, Fox DR, Hickey CW & Stauber JL 2018, Revised Method for Deriving Australian and New Zealand Water Quality Guideline Values for Toxicants, Australian Government Department of Agriculture and Water Resources, Canberra.

​​​

Last updated: