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Comparison of Multiple Linear Regression and Biotic Ligand Models for Predicting Acute and Chronic Zinc Toxicity to Freshwater Organisms.
Environ Toxicol Chem. 2023 02; 42(2):393-413.ET

Abstract

Multiple linear regression (MLR) models for predicting zinc (Zn) toxicity to freshwater organisms were developed based on three toxicity-modifying factors: dissolved organic carbon (DOC), hardness, and pH. Species-specific, stepwise MLR models were developed to predict acute Zn toxicity to four invertebrates and two fish, and chronic toxicity to three invertebrates, a fish, and a green alga. Stepwise regression analyses found that hardness had the most consistent influence on Zn toxicity among species, whereas DOC and pH had a variable influence. Pooled acute and chronic MLR models were also developed, and a k-fold cross-validation was used to evaluate the fit and predictive ability of the pooled MLR models. The pooled MLR models and an updated Zn biotic ligand model (BLM) performed similarly based on (1) R2 , (2) the percentage of effect concentration (ECx) predictions within a factor of 2.0 of observed ECx, and (3) residuals of observed/predicted ECx versus observed ECx, DOC, hardness, and pH. Although fit of the pooled models to species-specific toxicity data differed among species, species-specific differences were consistent between the BLM and MLR models. Consistency in the performance of the two models across species indicates that additional terms, beyond DOC, hardness, and pH, included in the BLM do not help explain the differences among species. The pooled acute and chronic MLR models and BLM both performed better than the US Environmental Protection Agency's existing hardness-based model. We therefore conclude that both MLR models and the BLM provide an improvement over the existing hardness-only models and that either could be used for deriving ambient water quality criteria. Environ Toxicol Chem 2023;42:393-413. © 2022 SETAC.

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Publisher Full Text (DOI)

Authors+Show Affiliations

DeForest DK
Windward Environmental, Seattle, Washington, USA.
Ryan AC
International Zinc Association, Durham, North Carolina, USA.
Tear LM
Windward Environmental, Seattle, Washington, USA.
Brix KV
EcoTox, Miami, Florida, USA. Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA.

MeSH

AnimalsLinear ModelsLigandsWater Pollutants, ChemicalFresh WaterAquatic OrganismsZincCopper

Pub Type(s)

Journal Article

Language

eng

PubMed ID

36398855

Citation

DeForest, David K., et al. "Comparison of Multiple Linear Regression and Biotic Ligand Models for Predicting Acute and Chronic Zinc Toxicity to Freshwater Organisms." Environmental Toxicology and Chemistry, vol. 42, no. 2, 2023, pp. 393-413.
DeForest DK, Ryan AC, Tear LM, et al. Comparison of Multiple Linear Regression and Biotic Ligand Models for Predicting Acute and Chronic Zinc Toxicity to Freshwater Organisms. Environ Toxicol Chem. 2023;42(2):393-413.
DeForest, D. K., Ryan, A. C., Tear, L. M., & Brix, K. V. (2023). Comparison of Multiple Linear Regression and Biotic Ligand Models for Predicting Acute and Chronic Zinc Toxicity to Freshwater Organisms. Environmental Toxicology and Chemistry, 42(2), 393-413. https://doi.org/10.1002/etc.5529
DeForest DK, et al. Comparison of Multiple Linear Regression and Biotic Ligand Models for Predicting Acute and Chronic Zinc Toxicity to Freshwater Organisms. Environ Toxicol Chem. 2023;42(2):393-413. PubMed PMID: 36398855.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Comparison of Multiple Linear Regression and Biotic Ligand Models for Predicting Acute and Chronic Zinc Toxicity to Freshwater Organisms. AU - DeForest,David K, AU - Ryan,Adam C, AU - Tear,Lucinda M, AU - Brix,Kevin V, Y1 - 2023/01/09/ PY - 2022/07/07/revised PY - 2022/05/27/received PY - 2022/11/14/accepted PY - 2022/11/19/pubmed PY - 2023/1/31/medline PY - 2022/11/18/entrez KW - Ambient water quality criteria KW - bioavailability KW - biotic ligand model KW - multiple linear regression KW - zinc SP - 393 EP - 413 JF - Environmental toxicology and chemistry JO - Environ Toxicol Chem VL - 42 IS - 2 N2 - Multiple linear regression (MLR) models for predicting zinc (Zn) toxicity to freshwater organisms were developed based on three toxicity-modifying factors: dissolved organic carbon (DOC), hardness, and pH. Species-specific, stepwise MLR models were developed to predict acute Zn toxicity to four invertebrates and two fish, and chronic toxicity to three invertebrates, a fish, and a green alga. Stepwise regression analyses found that hardness had the most consistent influence on Zn toxicity among species, whereas DOC and pH had a variable influence. Pooled acute and chronic MLR models were also developed, and a k-fold cross-validation was used to evaluate the fit and predictive ability of the pooled MLR models. The pooled MLR models and an updated Zn biotic ligand model (BLM) performed similarly based on (1) R2 , (2) the percentage of effect concentration (ECx) predictions within a factor of 2.0 of observed ECx, and (3) residuals of observed/predicted ECx versus observed ECx, DOC, hardness, and pH. Although fit of the pooled models to species-specific toxicity data differed among species, species-specific differences were consistent between the BLM and MLR models. Consistency in the performance of the two models across species indicates that additional terms, beyond DOC, hardness, and pH, included in the BLM do not help explain the differences among species. The pooled acute and chronic MLR models and BLM both performed better than the US Environmental Protection Agency's existing hardness-based model. We therefore conclude that both MLR models and the BLM provide an improvement over the existing hardness-only models and that either could be used for deriving ambient water quality criteria. Environ Toxicol Chem 2023;42:393-413. © 2022 SETAC. SN - 1552-8618 UR - https://www.unboundmedicine.com/medline/citation/36398855/Comparison_of_Multiple_Linear_Regression_and_Biotic_Ligand_Models_for_Predicting_Acute_and_Chronic_Zinc_Toxicity_to_Freshwater_Organisms_ DB - PRIME DP - Unbound Medicine ER -