Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5267
Título: Multiple linear and principal component regressions for modelling ecotoxicity bioassay response
Autor: Gomes, Ana I.
Pires, José C.M.
Figueiredo, Sónia Adriana
Boaventura, Rui
Palavras-chave: Chlorella vulgaris
ecotoxicological assessment
multiple linear regression
principal component regression
surface water quality
Vibrio fischeri
Data: 2014
Editora: Taylor & Francis
Relatório da Série N.º: Environmental Technology;Volume 35, Issue 8
Resumo: 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 Leça 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 Leça River.
Peer review: yes
URI: http://hdl.handle.net/10400.22/5267
DOI: 10.1080/09593330.2013.856956
Versão do Editor: http://www.tandfonline.com/doi/abs/10.1080/09593330.2013.856956#.VJgDNfmMA
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