Development of Multiple Linear Regression Models for Predicting Chronic Iron Toxicity to Aquatic Organisms

被引:5
|
作者
Brix, Kevin V. [1 ,2 ]
Tear, Lucinda [3 ]
DeForest, David K. [3 ]
Adams, William J. [4 ]
机构
[1] EcoTox, Miami, FL 33181 USA
[2] Univ Miami, Rosenstiel Sch Marine & Atmospher Sci, Miami, FL 33146 USA
[3] Windward Environm, Seattle, WA USA
[4] Red Cap Consulting, Lake Point, UT USA
关键词
Iron; Bioavailability; Multiple linear regression; Water quality guidelines; WATER-QUALITY CRITERIA; DIVALENT METALS; NICKEL; BIOACCUMULATION; SENSITIVITY; COPPER; TESTS; ZINC; FE; PH;
D O I
10.1002/etc.5623
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
We developed multiple linear regression (MLR) models for predicting iron (Fe) toxicity to aquatic organisms for use in deriving site-specific water quality guidelines (WQGs). The effects of dissolved organic carbon (DOC), hardness, and pH on Fe toxicity to three representative taxa (Ceriodaphnia dubia, Pimephales promelas, and Raphidocelis subcapitata) were evaluated. Both DOC and pH were identified as toxicity-modifying factors (TMFs) for P. promelas and R. subcapitata, whereas only DOC was a TMF for C. dubia. The MLR models based on effective concentration 10% and 20% values were developed and performed reasonably well, with adjusted R-2 of 0.68-0.89 across all species and statistical endpoints. Differences among species in the MLR models precluded development of a pooled model. Instead, the species-specific models were assumed to be representative of invertebrates, fish, and algae and were applied accordingly to normalize toxicity data. The species sensitivity distribution (SSD) included standard laboratory toxicity data and effects data from mesocosm experiments on aquatic insects, with aquatic insects being the predominant taxa in the lowest quartile of the SSD. Using the European Union approach for deriving WQGs, application of MLR models to this SSD resulted in WQGs ranging from 114 to 765 mu g l(-1) Fe across the TMF conditions evaluated (DOC: 0.5-10 mg l(-1); pH: 6.0-8.4), with slightly higher WQGs (199-910 mu g l(-1)) derived using the US Environmental Protection Agency (USEPA) methodology. An important uncertainty in these derivations is the applicability of the C. dubia MLR model (no pH parameter) to aquatic insects, and understanding the pH sensitivity of aquatic insects to Fe toxicity is a research priority. An Excel-based tool for calculating Fe WQGs using both European Union and USEPA approaches across a range of TMF conditions is provided. Environ Toxicol Chem 2023;00:1-15. (c) 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
引用
收藏
页码:1386 / 1400
页数:15
相关论文
共 50 条
  • [1] Multiple Linear Regression Models for Predicting Chronic Aluminum Toxicity to Freshwater Aquatic Organisms and Developing Water Quality Guidelines
    DeForest, David K.
    Brix, Kevin V.
    Tear, Lucinda M.
    Adams, William J.
    ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY, 2018, 37 (01) : 80 - 90
  • [2] Updated Multiple Linear Regression Models for Predicting Chronic Aluminum Toxicity to Freshwater Aquatic Organisms and Developing Water Quality Guidelines
    DeForest, David K.
    Brix, Kevin, V
    Tear, Lucinda M.
    Cardwell, Allison S.
    Stubblefield, William A.
    Nordheim, Eirik
    Adams, William J.
    ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY, 2020, 39 (09) : 1724 - 1736
  • [3] Comparison of Multiple Linear Regression and Biotic Ligand Models for Predicting Acute and Chronic Zinc Toxicity to Freshwater Organisms
    DeForest, David K.
    Ryan, Adam C.
    Tear, Lucinda M.
    Brix, Kevin V.
    ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY, 2023, 42 (02) : 393 - 413
  • [4] Development and Validation of Multiple Linear Regression Models for Predicting Chronic Zinc Toxicity to Freshwater Microalgae
    Price, Gwilym A. V.
    Stauber, Jenny L.
    Jolley, Dianne F.
    Koppel, Darren J.
    Van Genderen, Eric J.
    Ryan, Adam C.
    Holland, Aleicia
    ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY, 2023, 42 (12) : 2630 - 2641
  • [5] Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the Toxicity of Nickel to Aquatic Freshwater Organisms
    Croteau, Kelly
    Ryan, Adam C.
    Santore, Robert
    DeForest, David
    Schlekat, Christian
    Middleton, Elizabeth
    Garman, Emily
    ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY, 2021, 40 (08) : 2189 - 2205
  • [6] QSAR Models for Predicting Aquatic Toxicity of Esters Using Genetic Algorithm-Multiple Linear Regression Methods
    Rajabi, Mehdi
    Shafiei, Fatemeh
    COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING, 2019, 22 (05) : 317 - 325
  • [7] Applicability of Chronic Multiple Linear Regression Models for Predicting Zinc Toxicity in Australian and New Zealand Freshwaters
    Stauber, Jennifer L. L.
    Gadd, Jennifer
    Price, Gwilym A. V.
    Evans, Anthony
    Holland, Aleicia
    Albert, Anathea
    Batley, Graeme E. E.
    Binet, Monique T. T.
    Golding, Lisa A. A.
    Hickey, Chris
    Harford, Andrew
    Jolley, Dianne
    Koppel, Darren
    McKnight, Kitty S. S.
    Morais, Lucas G. G.
    Ryan, Adam
    Thompson, Karen
    Van Genderen, Eric
    Van Dam, Rick A. A.
    Warne, Michael St. J.
    ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY, 2023, 42 (12) : 2614 - 2629
  • [8] Development and Application of a Biotic Ligand Model for Predicting the Chronic Toxicity of Dissolved and Precipitated Aluminum to Aquatic Organisms
    Santore, Robert C.
    Ryan, Adam C.
    Kroglund, Frode
    Rodriguez, Patricio H.
    Stubblefield, William A.
    Cardwell, Allison S.
    Adams, William J.
    Nordheim, Eirik
    ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY, 2018, 37 (01) : 70 - 79
  • [9] Novel QSAR models for predicting toxicity of chemicals to aquatic organisms and identifying the mode of action
    Lanevskij, Kiril
    Juska, Liutauras
    Didziapetris, Remigijus
    Japertas, Pranas
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2011, 241
  • [10] Applications of dynamic models in predicting the bioaccumulation, transport and toxicity of trace metals in aquatic organisms
    Wang, Wen-Xiong
    Tan, Qiao-Guo
    ENVIRONMENTAL POLLUTION, 2019, 252 (1561-1573) : 1561 - 1573