Simplification of biotic ligand models of Cu, Ni, and Zn by 1-, 2-, and 3-parameter transfer functions

• Published in: Integrated environmental assessment and management Vol. 8; no. 4; pp. 738 - 748
• Main Authors: Verschoor, Anja J, Vink, Jos PM, Vijver, Martina G
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2012; Blackwell Publishing Ltd
• Subjects: Biotic ligand models; Carbon - analysis; Copper - toxicity; Environmental Monitoring - methods; Linear Models; Metals; Models, Biological; Netherlands; Nickel - toxicity; Regression analysis; Regulatory tools; Risk Assessment; Speciation; Surface water; Water Framework Directive; Water Pollutants, Chemical - toxicity; Zinc - toxicity; Netherlands
• ISSN: 1551-3777; 1551-3793
• DOI: 10.1002/ieam.1298

Biotic ligand models for calculation of watertype‐specific no effect concentrations are recognized as a major improvement in risk assessment of metals in surface waters. Model complexity and data requirement, however, hamper the regulatory implementation. To facilitate regulatory use, biotic ligand models (BLM) for the calculation of Ni, Cu, and Zn HC5 values were simplified to linear equations with an acceptable level of accuracy, requiring a maximum of 3 measured water chemistry parameters. In single‐parameter models, dissolved organic carbon (DOC) is the only significant parameter with an accuracy of 72%–75% to predict HC5s computed by the full BLMs. In 2‐parameter models, Mg, Ca, or pH are selected by stepwise multiple regression for Ni, Cu, and Zn HC5, respectively, and increase the accuracy to 87%–94%. The accuracy is further increased by addition of a third parameter to 88%–97%. Three‐parameter models have DOC and pH in common, the third parameter is Mg, Ca, or Na for HC5 of Ni, Cu, and Zn, respectively. Mechanisms of chemical speciation and competitive binding to the biotic ligand explain the selection of these parameters. User‐defined requirements, such as desired level of reliability and the availability of measured data, determine the selection of functions to predict HC5. Integr Environ Assess Manag 2012; 8: 738–748. © 2012 SETAC