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Multiple linear and principal component regressions for modelling ecotoxicity bioassay response

Title
Multiple linear and principal component regressions for modelling ecotoxicity bioassay response
Type
Article in International Scientific Journal
Year
2014
Authors
Ana I. Gomes
(Author)
Other
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Sónia A. Figueiredo
(Author)
Other
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Rui A. R. Boaventura
(Author)
FEUP
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Journal
Vol. 35 No. 8
Pages: 945-955
ISSN: 0959-3330
Publisher: Taylor & Francis
Scientific classification
CORDIS: Natural sciences > Environmental science
FOS: Engineering and technology > Environmental engineering
Other information
Authenticus ID: P-009-33G
Abstract (EN): The ecotoxicological response of the living organisms in an aquatic system depends on the physical, chemical and bacteriological variables, as well as the interactions between them. An important challenge to scientists is to understand the interaction and behaviour of factors involved in a multidimensional process such as the ecotoxicological response. With this aim, multiple linear regression (MLR) and principal component regression were applied to the ecotoxicity bioassay response of Chlorella vulgaris and Vibrio fischeri in water collected at seven sites of Leca river during five monitoring campaigns (February, May, June, August and September of 2006). The river water characterization included the analysis of 22 physicochemical and 3 microbiological parameters. The model that best fitted the data was MLR, which shows: (i) a negative correlation with dissolved organic carbon, zinc and manganese, and a positive one with turbidity and arsenic, regarding C. vulgaris toxic response; (ii) a negative correlation with conductivity and turbidity and a positive one with phosphorus, hardness, iron, mercury, arsenic and faecal coliforms, concerning V. fischeri toxic response. This integrated assessment may allow the evaluation of the effect of future pollution abatement measures over the water quality of Leca River.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 11
Documents
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ET_2014_Post-Print Post-Print version 262.07 KB
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