MULTIPLE LINEAR REGRESSION (MLR) MODELS FOR PREDICTING CHRONIC ALUMINUM TOXICITY TO FRESHWATER AQUATIC ORGANISMS AND DEVELOPING WATER QUALITY GUIDELINES

Abstract

The bioavailability of aluminum (Al) to freshwater aquatic organisms varies as a function of several water chemistry parameters, including pH, dissolved organic carbon (DOC), and water hardness. We evaluated the ability of multiple linear regression (MLR) models to predict chronic Al toxicity to a green alga (Pseudokirchneriella subcapitata), a cladoceran (Ceriodaphnia dubia), and a fish (Pimephales promelas) as a function of varying DOC, pH, and hardness conditions. The MLR models predicted toxicity values that were within a factor of two of observed values in 100% of the cases for P. subcapitata (EC10s and EC20s), 91% of the cases for C. dubia (EC10s and EC20s), and 95% (EC10s) and 91% (EC20s) of the cases for P. promelas. The MLR models were then applied to all species with Al toxicity data to derive species and genus sensitivity distributions that could be adjusted as a function of varying DOC, pH, and hardness conditions (the P. subcapitata model was applied to algae and macrophytes, the C. dubia model was applied to invertebrates, and the P. promelas model was applied to fish). HC5s (hazardous concentrations to 5% of the species or genera) were then derived in two ways: (1) fitting a log-normal distribution to species mean EC10s for all species (following the European Union methodology); and (2) fitting a triangular distribution to genus mean EC20s for animals only (following the U.S. Environmental Protection Agency methodology). Overall, MLR-based models provide a viable approach for deriving Al water quality guidelines that vary as a function of DOC, pH, and hardness conditions and are a significant improvement over bioavailability corrections based on single parameters. This article is protected by copyright. All rights reserved

from Enviromental via alkiviadis.1961 on Inoreader http://ift.tt/2xp11Xr