Suppr超能文献

Temporal characterisation of river waters in urban and semi-urban areas using physico-chemical parameters and chemometric methods.

作者信息

Felipe-Sotelo M, Andrade J M, Carlosena A, Tauler R

机构信息

Department of Environmental Chemistry, IIQAB-CSIC, Jordi Girona 18-26, E-08034 Barcelona, Spain.

出版信息

Anal Chim Acta. 2007 Jan 30;583(1):128-37. doi: 10.1016/j.aca.2006.10.011. Epub 2006 Oct 12.

Abstract

Three sampling campaigns were carried out in rivers located at two hydrographic basins affected by urban and semi-urban areas around the Metropolitan area of A Coruña (ca. 500,000 inhabitants, NW-Spain) to study local and temporal variations of 21 physicochemical parameters (pH, conductivity, Cl-, SO4(2-), SiO2, Ca2+, Mg2+, Na+, K+, hardness, NO3(-), NO2(-), NH4(+), COD, PO4(3-), Zn2+, Cu2+, Mn2+, Pb2+, alkalinity and acidity) in 23 sampling points. The temporal evolution of the water quality was assessed by matrix augmentation principal components analysis (MA-PCA) and parallel factor analysis (PARAFAC). Moreover, classical principal components analysis (PCA) (one per sampling campaign) was applied with exploratory and comparison purposes. The first factor of the different studies comprised variables associated to the mineral content and it differentiated the samples according to their hydrographic basins. The second factor was mainly associated to organic matter, from domestic wastes and decomposition of natural debris. The temporal evolution of the water quality was mostly related to seasonal increments of the physicochemical parameters defining the decomposition of the organic matter. The three models applied (PCA, MA-PCA and PARAFAC) led to similar conclusions, nonetheless, MA-PCA excelled, since the refolding of scores provided more straightforward and convenient overview of sample time and geographical variations than individual PCA and it is more flexible and adaptable to environmental studies than PARAFAC.

摘要

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验