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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., (Copyright © 2012 SETAC.)

Geographical and temporal variations in metal speciation were calculated and water-type specific sensitivities were derived for a range of aquatic species, using surveillance water chemistry data that cover almost all surface water types in The Netherlands. Biotic ligand models for Cu, Zn, and Ni were used to normalize chronic no-effect concentrations (NOEC) determined in test media toward site-specific NOEC for 372 sites sampled repeatedly over 2007-2010. Site-specific species sensitivity distributions were constructed accounting for chemical speciation. Sensitivity of species as well as predicted risks shifted among species over space and time, due to changes in metal concentrations, speciation, and biotic ligand binding. Sensitivity of individual species (NOEC) and of the ecosystem (HC5) for Cu, Ni, and Zn showed a spatial variation up to 2 orders of magnitude. Seasonality of risks was shown, with an average ratio between lowest and highest risk of 1.3, 2.0, and 3.6 for Cu, Ni, and Zn, respectively. Maximum risks of Cu, Ni, and Zn to ecosystems were predicted in February and minimum risks in September. A risk assessment using space-time specific HC5 of Cu and Zn resulted in a reduction of sites at risk, whereas for Ni the number of sites at risks increased.

Metals in floodplain soils and sediments (deposits) can originate from lithogenic and anthropogenic sources, and their availability for uptake in biota is hypothesized to depend on both origin and local sediment conditions. In criteria-based environmental risk assessments, these issues are often neglected, implying local risks to be often over-estimated. Current problem definitions in river basin management tend to require a refined, site-specific focus, resulting in a need to address both aspects. This paper focuses on the determination of local environmental availabilities of metals in fluvial deposits by addressing both the origins of the metals and their partitioning over the solid and solution phases. The environmental availability of metals is assumed to be a key force influencing exposure levels in field soils and sediments. Anthropogenic enrichments of Cu, Zn and Pb in top layers could be distinguished from lithogenic background concentrations and described using an aluminium-proxy. Cd in top layers was attributed to anthropogenic enrichment almost fully. Anthropogenic enrichments for Cu and Zn appeared further to be also represented by cold 2M HNO3 extraction of site samples. For Pb the extractions over-estimated the enrichments. Metal partitioning was measured, and measurements were compared to predictions generated by an empirical regression model and by a mechanistic-kinetic model. The partitioning models predicted metal partitioning in floodplain deposits within about one order of magnitude, though a large inter-sample variability was found for Pb.