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Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the Toxicity of Nickel to Aquatic Freshwater Organisms
Environmental toxicology and chemistry, 2021-08, Vol.40 (8), p.2189-2205
Croteau, Kelly
Ryan, Adam C.
Santore, Robert
DeForest, David
Schlekat, Christian
Middleton, Elizabeth
Garman, Emily
2021
Details
Autor(en) / Beteiligte
Croteau, Kelly
Ryan, Adam C.
Santore, Robert
DeForest, David
Schlekat, Christian
Middleton, Elizabeth
Garman, Emily
Titel
Comparison of Multiple Linear Regression and Biotic Ligand Models to Predict the Toxicity of Nickel to Aquatic Freshwater Organisms
Ist Teil von
Environmental toxicology and chemistry, 2021-08, Vol.40 (8), p.2189-2205
Ort / Verlag
United States: Blackwell Publishing Ltd
Erscheinungsjahr
2021
Link zum Volltext
Quelle
MEDLINE
Beschreibungen/Notizen
Toxicity‐modifying factors can be modeled either empirically with linear regression models or mechanistically, such as with the biotic ligand model (BLM). The primary factors affecting the toxicity of nickel to aquatic organisms are hardness, dissolved organic carbon (DOC), and pH. Interactions between these terms were also considered. The present study develops multiple linear regressions (MLRs) with stepwise regression for 5 organisms in acute exposures, 4 organisms in chronic exposures, and pooled models for acute, chronic, and all data and compares the performance of the Pooled All MLR model to the performance of the BLM. Independent validation data were used for evaluating model performance, which for pooled models included data for organisms and endpoints not present in the calibration data set. Hardness and DOC were most often selected as the explanatory variables in the MLR models. An attempt was also made at evaluating the uncertainty of the predictions for each model; predictions that showed the most error tended to show the highest levels of uncertainty as well. The performances of the 2 models were largely equal, with differences becoming more apparent when looking at the performance within subsets of the data. Environ Toxicol Chem 2021;40:2189–2205. © 2021 SETAC
Sprache
Englisch
Identifikatoren
ISSN: 0730-7268
eISSN: 1552-8618
DOI: 10.1002/etc.5063
Titel-ID: cdi_proquest_journals_2553729493
Format
–
Schlagworte
Aquatic Organisms
,
Biotic ligand model
,
Calibration
,
Dissolved organic carbon
,
Exposure
,
Fresh Water - chemistry
,
Freshwater organisms
,
Hardness
,
Ligands
,
Linear Models
,
Metal toxicity
,
Multiple linear regression
,
Nickel
,
Nickel - toxicity
,
Organisms
,
Performance evaluation
,
Regression analysis
,
Regression models
,
Toxicity
,
Uncertainty
,
Water Pollutants, Chemical - toxicity
,
Water quality guidelines
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