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Surface water quality evaluation using multivariate methods and a new water quality index in the Indian River Lagoon, Florida
Author(s) -
Qian Yun,
Migliaccio Kati White,
Wan Yongshan,
Li Yuncong
Publication year - 2007
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2006wr005716
Subject(s) - water quality , multivariate statistics , environmental science , principal component analysis , dry season , hydrology (agriculture) , cluster analysis , wet season , statistics , geography , mathematics , ecology , cartography , engineering , biology , geotechnical engineering
Appropriate assessment of long‐term water quality monitoring data is essential to evaluation of water quality and this often requires use of multivariate techniques. Our objective was to evaluate water quality in the south Indian River Lagoon (IRL), Florida using several multivariate techniques and a comprehensive water quality index (WQI). Clustering was used to cluster the six monitoring stations into three groups, with stations on the same or characteristic‐similar canals being in the same group. The first five factors from exploratory factor analysis (EFA) explain around 70% of the total variance and were used to interpret water quality characterized by original constituents for the purpose of data reduction. Nutrient species (phosphorus and nitrogen) were major variables involved in the construction of the principal components (PCs) and factors. Seasonal and spatial differences were observed in compositional patterns of factors and principal water quality constituents. Positive or negative trends were detected for different factor at different monitoring groups identified by clustering during different seasons. The composite WQI was developed based on principal water quality constituents greatly contributing to the construction of factors which were derived from EFA. The WQI showed significant difference among the three clustering groups with the greatest WQI median in group 1 stations (C23S48, C23S97, and C24S49). Medians of WQI were significantly greater in the wet than in the dry season, which implied more natural nutrient water status during the dry than the wet season probably due to the different contribution of nonpoint sources between two seasons.