Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data

• Type: Article in International Scientific Journal
• Date: 2014

Authors

Ana I. Gomes (Author) Other; José C. M. Pires (Author) FEUP; Sónia A. Figueiredo (Author) Other; Rui A. R. Boaventura (Author) FEUP

Journal

Title: Water Resources Management

Vol. 28 No. 5

Pages: 1345-1361

ISSN: 0920-4741

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 17 Citations

Scopus - 20 Citations

INSPEC

Scientific classification

CORDIS: Natural sciences > Environmental science

FOS: Engineering and technology > Environmental engineering

Associated Projects

UI0511 - PROJECTO ESTRATÉGICO - UI 511 - 2013-2014

Other information

Authenticus ID: P-009-8R6

DOI: 10.1007/s11269-014-0547-9

Abstract (EN): This study aims to optimize the water quality monitoring of a polluted watercourse (Le double dagger a River, Portugal) through the principal component analysis (PCA) and cluster analysis (CA). These statistical methodologies were applied to physicochemical, bacteriological and ecotoxicological data (with the marine bacterium Vibrio fischeri and the green alga Chlorella vulgaris) obtained with the analysis of water samples monthly collected at seven monitoring sites and during five campaigns (February, May, June, August, and September 2006). The results of some variables were assigned to water quality classes according to national guidelines. Chemical and bacteriological quality data led to classify Le double dagger a River water quality as "bad" or "very bad". PCA and CA identified monitoring sites with similar pollution pattern, giving to site 1 (located in the upstream stretch of the river) a distinct feature from all other sampling sites downstream. Ecotoxicity results corroborated this classification thus revealing differences in space and time. The present study includes not only physical, chemical and bacteriological but also ecotoxicological parameters, which broadens new perspectives in river water characterization. Moreover, the application of PCA and CA is very useful to optimize water quality monitoring networks, defining the minimum number of sites and their location. Thus, these tools can support appropriate management decisions.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17