Registration Dossier

Ecotoxicological information

Endpoint summary

Administrative data

Description of key information

Additional information

The aluminium (Al) toxicity studies compiled in this section represent tests with aquatic organisms conducted over a pH range from 6 – 8 as being representative of conditions in most European surface waters as evaluated in a preliminary exposure assessment (EURAS 2007). Most of the short-term studies included were only those used for purposes of characterization and to evaluate potential effects of water quality on Al toxicity. The long-term chronic studies represent all known studies of sufficient reliability according to Klimisch criteria levels one (reliable without restriction) and two (reliable with restriction; Klimisch et al. 1997). These data are being assessed with a view towards developing a Probable No Effect Concentration (PNEC) principally for use in setting Environmental Quality Standards (EQS) values. We point out that aluminium chloride was not classified for the aquatic environment by the EU Classification and Labeling Committee, therefore other less soluble forms of Al such as the oxides, powders, and massive metal would also not classify for the aquatic environment (see reference below). Therefore, no PNEC is required for REACH purposes. The long term goal of the industry is to develop an aquatic PNEC that covers the pH range of < 6, 6-7, and > 7. These pH ranges reflect that the form of Al present in water changes significantly as a function of pH. PNEC values calculated in this dossier are preliminary and reflect the state of the science to date. Scientists around the world have worked towards a PNEC for Al for the past 30 years. The aluminum industry over the past 3 years has utilized several decades of work to develop a biotic ligand model (BLM) fish, invertebrates and algae for pH values across the range of 5.5-8.0. The state of the model is summarized in this CSR. It is recognized that there is a need to demonstrate that this model applies to a broader range of aquatic organisms. Efforts to date have focused on salmonids, fathead minnows, daphnids and algae. Studies are on-going at pH 6.0 with a wider range of species to achieve a data base that has 8-10 chronic PNEC values as recommended in the London PNEC Workshop. 


For all of the aquatic toxicity studies, endpoints are expressed as a function of total Al, rather than as dissolved or monomeric Al. This is because for most test solutions with pH from 6 – 8, Al will be largely insoluble, and so dissolved and monomeric concentrations remain relatively constant even with large increases in total or nominal Al. Thus, dose-dependent responses observed by aquatic organisms can only be reliably quantified using total Al across the full pH range from 5.5-8.0. 

Review of Data Leading to Development of an Aluminium Chronic BLM: Toxic effects of aluminium have been observed in several types of aquatic organisms under certain exposure conditions. Factors that influence aluminium toxicity are consistent with the factors that influencealuminiumspeciation (discussed in Section 4.2). These factors include pH, dissolved organic matter concentration (DOC), and water hardness (see Roy and Campbell 1997; and Gensemer and Playle 1999 for relevant reviews). Fluoride has also been shown to influencealuminiumtoxicity (Hamilton and Haines 1995), though fluoride is not commonly found at elevated levels in the environment. Several studies have demonstrated that some forms ofaluminiumare only bioavailable and potentially toxic in freshly prepared solutions, and that this toxicity declines or is eliminated after several minutes of aging (e.g. Exely et al. 1996 [others too]; Witters et al. 1996; Teien et al. 2006). Toxicity in these cases may depend on short-lived transient chemical forms of aluminium hydroxide whose environmental relevance would be restricted to mixing zones where aluminium-rich acidic waters mix with a more alkaline water.


A biotic ligand model (BLM) was developed to address the bioavailability and toxicity of dissolved, particulate, and transient forms of aluminium. Application of the BLM framework to understanding aluminium toxicity was reasonable because many of the factors that influence aluminium bioavailability are consistent with the factors that influence aluminium speciation or forms in the environment. As with BLMs for other metals, the Al BLM combines information about chemical speciation and interaction with gill surfaces to explain and predict aluminium bioavailability and toxicity (DiToro et al, 2001; Santore et al 2001; Paquin et al 2002). Factors that affect aluminium bioavailability by altering the chemical speciation of the metal (such as DOC, pH, and fluoride) are directly considered by the speciation model (Tipping 1994; Santore and Driscoll, 1996).  Other factors (such as hardness cations), affect aluminium bioavailability by competing with gill binding sites in a manner similar to what has been observed for other metals (Playle et al 1992; Meyer et al, 1999) and are considered by including interactions for these cations with the BL sites on the gill. The detailed speciation within the aluminium BLM allows the model to predict bioavailability for a number of different aluminium fractions. Depending on available input data, the model can be run with monomeric, dissolved, or total aluminium as the primary input parameter, and the distribution among dissolved species and precipitated forms can be simulated by the model. Comparison of predicted and measured distribution of aluminium fractions in waters where aluminium toxicity has been extensively studied typically shows very good agreement (Figure7.1.1-1).

Factors that are known to affect speciation have also been shown to affect bioavailability and toxicity in both acute and chronic exposures, and the consistency of these affects in different exposure durations allows a common model framework for prediction of both acute and chronic affects. Acute data were useful in model development due in part to the large amount of available data that combined coincident measurement of detailed speciation measurements, measures of Al accumulation in gills, and observation of lethal and sub-lethal effects over wide ranges of water chemistry. Data for development of the Al BLM included Atlantic salmon (Salmo salar) and brown trout (Salmo trutta) from studies performed by NIVA and collaborators from UMB (Kroglund et al. 1997; Kroglund et al. 1998a,b,c,d; Erstad et al. 2002; Teien et al. 2004a,b; Teien et al. 2006; Andren et al. 2006). These studies typically investigated the effects of water chemistry on the accumulation of Al on/in the gills of S. salar and S. trutta, but in some cases, mortality was reported. Many of these studies purposefully investigated the effects of water chemistry on the level of Al accumulation in S. salar and S. trutta gills. The pH conditions varied from approximately pH 5 (Andren et al. 2006) to pH 10 (Erstad et al. 2002). The total organic carbon concentrations (TOC) ranged from approximately 0.5 mg/L to 16 mg/L (Kroglund et al. 1998a,b, and Erstad et al. 2002, respectively). Calcium concentrations ranged from approximately 1 mg/L to 11 mg/L (Kroglund et al. 1998a,b, and Erstad et al. 2002, respectively).

From these data it was clear that observed toxicity was strongly related to aluminium accumulation on the gill (Figure 7.1.1.-2). The calibrated Al BLM was able to reasonably predict the level of Al accumulation on the gills of S. salar and S. trutta, with one consistent set of BLM parameters (Figure 7.1.1.-3) over a range of approximately 2 orders of magnitude. This wide range in gill accumulation was primarily due to the diverse water chemistry conditions tested, and suggests that the BLM is relatively robust over this wide range of conditions. These data were also used to estimate critical Al accumulation levels for those datasets that reported associated mortality data (i.e. Suldal Fall 1997 – Kroglund et al. 1998a). For example, from Figure 7.1.1-2, critical accumulation levels corresponding to the LC10 and LC50 values for mortality could b derived as 2995 and 4225 nmol/g wet weight, respectively.   

Although data from acute exposures were extensively used to parameterize the prediction of gill-accumulation over a wide range of conditions, the goal in model development is the evaluation of the ability of the Al BLM to predict effects in chronic exposures. The use of data from both acute and chronic exposures in model development is justified by the consistency of the observed effects of changing water chemistry (such as pH, NOM, and hardness) in both acute and chronic exposures. Adjustment of the Al BLM for different exposure durations (acute versus chronic) and endpoints (lethal or sub-lethal) is primarily accomplished by adjustment of the critical accumulation level for each endpoint and exposure condition. Application to chronic data will be further discussed in the sections that follow


Andren C.M., Kroglund F., Teien H.C. 2006. Controlled exposure of brown trout to humic water limed to different pH and inorganic aluminium concentrations. Verh. Internat. Verein. Limnol. 29:1548-1552.

Di Toro D.M., Allen H.E., Bergman H.L., Meyer J.S., Paquin P.R., Santore R.C.. 2001. Biotic ligand model of the acute toxicity of metals. 1. Technical basis. Environmental Toxicology and Chemistry 20:2383-2396.

ECO DOC 99/48. 1999.Draft Summary Record Commission Working Group on the Classification  and Labelling of Dangerous Substances, Environment, Ispra 15-17 September 1999.

Erstad K.J., Teien H.C., Kroglund F., Havardstun J., Sorlie L., Salbu B. 2002. Testing av halvbrent dolomittmjol til innsjokalking med saerlig vekt pa aluminiums- og jernkjemi. ISBN 82-7963-006-6. ISSN 1501-2735. Radgivande Agronomar Rapport

EURAS. 2007. Development of a High Quality Aquatic Ecotoxicity Database for Al Metal, Al Oxide, and Al Hydroxyde: Effects Assessment. Report prepared for the European Aluminum Association.

Exley C., Wicks A.J., Hubert R. B., Birchall D. 1996. Kinetic constraints in Acute aluminium toxicity in the rainbow trout (Oncorhynchus mykiss). Journal of Theoretical Biology 179:25-31

Gensemer R. W. and R.C. Playle 1999. The bioavailability and toxicity of aluminum in aquatic environments. Critical Reviews of Environmental Science and Technology 29(4): 315-450.

 Hamilton S. J., Haines T. A. 1995. Influence of fluoride on aluminum toxicity to Atlantic salmon (Salmo salar). Canadian Journal of Fisheries and Aquatic Sciences 52:2432-2444

Klimisch, H.J. M. Andreae, and U. Tillman. 1997.A systematic approach for the evaluating the quality of experimental toxicological and ecotoxicological data. Regulatory Toxicology and Pharmacology 25: 1-5.

Kroglund, F., H.C. Teien, J. Håvardstun, A. Kvellestad, B. Salbu, B.O. Rosseland, B. Finstad. 1997. Betydningen av lave aluminiumskonsentrasjoner for laksesmolt. Norwegian Institute for Water Research, ISBN No.: ISBN 82-577-xxxx-x. Draft Report. NIVA xxxx-xx. 


Kroglund, F; H.C. Teien,E. Lucassen, B.O. Rosseland, B. Salbu, and Å. Åtland.1998a. Detoxification of aluminum in clearwater rivers by use of chemical mitigation methods. Experiments with Atlantic salmon smolts in River Suldalslågen. Norwegian Institute for Water Research, ISBN No.: ISBN 82-577-3564-7. NIVA 3970-98. 

Kroglund, F., H.C.Teien, J.Håvardstun, B.O.Rosseland, B.Salbu and A.Kvellestad. 1998b. The duration of unstable toxic forms of aluminium after liming and the importance of pH in reducing the mixing zone toxicity for parr of Atlantic salmon (Salmo salar). Norwegian Institute for Water Research, ISBN No.: ISBN 82-577-3393-8. NIVA 3815-98.


Kroglund, F., H.C.Teien, E. Lucassen, J.Håvardstun, B.O.Rosseland, B.Salbu,M.N.Pettersen. 1998c.Endring i giftighet til aluminium i en humøs elv ved ulike kjemiske tiltak.Norwegian Institute for Water Research, ISBN No.: ISBN 82-577-xxxx-x. Draft Report. NIVA xxxx-xx.

Kroglund F, H.C. Teien, E. Lucassen, J. Havardstun, B.O. Rosseland, B. Salbu, M.N. Petersen. 1998d. Reetablering av laks i forbindelse med kalking-reetablering av laks i Tovdals og Mandalselva; avgiftingsrater til aluminium i humusrike vannkvaliteter og effecter pa fisk. NIVA report.

Meyer, J. S., R. C. Santore, J. P. Bobbitt, L. D. Debrey, C. J. Boese, P. R. Paquin, H. E. Allen, H. L. Bergman, and D. M. Ditoro. 1999. Binding of nickel and copper to fish gills predicts toxicity when water hardness varies, but free ion activity does not. Environmental Science and Technology 33:913-916.

Paquin, P., J. W. Gorsuch, S. Apte, G. E. Batley, K. C. Bowles, P. G. C. Campbell, C. Delos, D. M. DiToro, R. Dwyer, F. Galvez, R. W. Gensemer, G. G. Goss, C. Hogstrand, C. R. Janssen, J. C. McGeer, R. B. Naddy, R. C. Playle, R. C. Santore, U. Schneider, W. A. Stubblefield, C. M. Wood, and K. B. Wu. 2002. The biotic ligand model: A historical overview. Comparative Biochemistry and Physiology C 133:3-35.

Playle, R. C. (1998). "Modelling metal interactions at fish gills."The Science of the Total Environment 219: 147-163.


Roy R.L., Campbell P.G.C. 1997. Decreased toxicity of Al to juvenile Atlantic salmon (Salmo salar) in acidic soft water containing natural organic matter: a test of free-ion model. Environmental Toxicology and Chemistry 16: 1962-1969

Santore R. C., DiToro D.M., Paquin P.R., Allen H.E., Meyer J.S. 2001. Biotic ligand model of the acute toxicity of metals. 2. Application to acute copper toxicity in freshwater fish and Daphnia. Environmental Toxicology and Chemistry 20: 2397-2402.

Santore R.C., Driscoll C.T. 1995. The CHESS model for calculating chemical equilibria in soils and solutions. In Chemical Equilibrium and Reaction Models. R Loeppert, AP Schwab,S Goldberg, editors. American Society of Agronomy, Madison, WI.

Teien H.C., Salbu B., Kroglund F., Rosseland B.O. 2004. Transformation of positively charged aluminium-species in unstable mixing zones following liming. Science of the Total Environment 330:217-232.


Teien Hans-Christian, Kroglund F., Salbua B., Rosseland B. O. 2006. Gill reactivity of aluminium-species following liming. Science of the Total Environment 358:206-220.

Teien H.C., Kroglund F., Atland A., Rosseland B.O., Salbu B. 2006b. Sodium silicate as alternative to liming-reduced aluminium toxicity for atlantic salmon (Salmo Salar L.) in unstable mixing zones. Science of the Total Environment 358:151-163.


Tipping, E., 1994. WHAM—a chemical equilibrium model and computer code for waters, sediments, and soils incor- porating a discrete siteyelectrostatic model of ion-binding by humic substances. Comput. Geosci. 20, 973–1023.


Witters H.E., Van Puymbroeck S., Stouthart A.J.H.X., and Wenderlaar Bonga S.E. 1996. Physicochemical changes of aluminum in mixing zones: mortality and physiological disturbances in brown trout (Salmo trutta L.). Environmental Toxicology and Chemistry 15: 986-996.