Tags

Type your tag names separated by a space and hit enter

Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines.
Environ Toxicol Chem. 2018 01; 37(1):80-90.ET

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 2 of observed values in 100% of the cases for P. subcapitata (10 and 20% effective concentrations [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). Hazardous concentrations to 5% of the species or genera were then derived in 2 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 US 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. Environ Toxicol Chem 2018;37:80-90. © 2017 SETAC.

Links

Publisher Full Text (DOI)

Authors+Show Affiliations

DeForest DK
Windward Environmental, Seattle, Washington, USA.
Brix KV
EcoTox, Miami, Florida, USA. Department of Marine Biology and Ecology, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA.
Tear LM
Windward Environmental, Seattle, Washington, USA.
Adams WJ
Red Cap Consulting, Lake Point, Utah, USA.

MeSH

AluminumAnimalsAquatic OrganismsChlorophytaCladoceraCyprinidaeFresh WaterGuidelines as TopicLinear ModelsSpecies SpecificityToxicity Tests, ChronicWaterWater Pollutants, ChemicalWater Quality

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28833517

Citation

DeForest, David K., et al. "Multiple Linear Regression Models for Predicting Chronic Aluminum Toxicity to Freshwater Aquatic Organisms and Developing Water Quality Guidelines." Environmental Toxicology and Chemistry, vol. 37, no. 1, 2018, pp. 80-90.
DeForest DK, Brix KV, Tear LM, et al. Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines. Environ Toxicol Chem. 2018;37(1):80-90.
DeForest, D. K., Brix, K. V., Tear, L. M., & Adams, W. J. (2018). Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines. Environmental Toxicology and Chemistry, 37(1), 80-90. https://doi.org/10.1002/etc.3922
DeForest DK, et al. Multiple Linear Regression Models for Predicting Chronic Aluminum Toxicity to Freshwater Aquatic Organisms and Developing Water Quality Guidelines. Environ Toxicol Chem. 2018;37(1):80-90. PubMed PMID: 28833517.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Multiple linear regression models for predicting chronic aluminum toxicity to freshwater aquatic organisms and developing water quality guidelines. AU - DeForest,David K, AU - Brix,Kevin V, AU - Tear,Lucinda M, AU - Adams,William J, PY - 2017/02/08/received PY - 2017/04/03/revised PY - 2017/07/14/accepted PY - 2017/8/24/pubmed PY - 2018/10/23/medline PY - 2017/8/24/entrez KW - Aluminum KW - Bioavailability KW - Multiple linear regression model KW - Water quality guideline SP - 80 EP - 90 JF - Environmental toxicology and chemistry JO - Environ Toxicol Chem VL - 37 IS - 1 N2 - 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 2 of observed values in 100% of the cases for P. subcapitata (10 and 20% effective concentrations [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). Hazardous concentrations to 5% of the species or genera were then derived in 2 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 US 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. Environ Toxicol Chem 2018;37:80-90. © 2017 SETAC. SN - 1552-8618 UR - https://www.unboundmedicine.com/medline/citation/28833517/Multiple_linear_regression_models_for_predicting_chronic_aluminum_toxicity_to_freshwater_aquatic_organisms_and_developing_water_quality_guidelines_ DB - PRIME DP - Unbound Medicine ER -