Surface water quality evaluation using multivariate methods and a new water quality index in the Indian River Lagoon, Florida

Abstract

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. Copyright 2007 by the American Geophysical Union.