Yongshan Wan

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

[1] 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.

Linking Spatial Variations in Water Quality with Water and Land Management using Multivariate Techniques

Most studies using multivariate techniques for pollution source evaluation are conducted in free-flowing rivers with distinct point and nonpoint sources. This study expanded on previous research to a managed canal system discharging into the Indian River Lagoon, Florida, where water and land management is the single most important anthropogenic factor influencing water quality. Hydrometric and land use data of four drainage basins were uniquely integrated into the analysis of 25 yr of monthly water quality data collected at seven stations to determine the impact of water and land management on the spatial variability of water quality. Cluster analysis (CA) classified seven monitoring stations into four groups (CA groups). All water quality parameters identified by discriminant analysis showed distinct spatial patterns among the four CA groups. Two-step principal component analysis/factor analysis (PCA/FA) was conducted with (i) water quality data alone and (ii) water quality data in conjunction with rainfall, flow, and land use data. The results indicated that PCA/FA of water quality data alone was unable to identify factors associated with management activities. The addition of hydrometric and land use data into PCA/FA revealed close associations of nutrients and color with land management and storm-water retention in pasture and citrus lands; total suspended solids, turbidity, and NO + NO with flow and Lake Okeechobee releases; specific conductivity with supplemental irrigation supply; and dissolved O with wetland preservation. The practical implication emphasizes the importance of basin-specific land and water management for ongoing pollutant loading reduction and ecosystem restoration programs.

Multivariate Analysis of Temporal and Spatial Variability of Water Quality in the Southern Indian River Lagoon (IRL), Florida

Appropriate assessment of long-term water quality monitoring data is essential to correctly interpret data and this often requires use of multivariate techniques. Our objective was to evaluate the temporal and spatial variations in water quality in the Southern Indian River Lagoon (IRL) watershed, Florida using several multivariate techniques as well as a comprehensive water quality index (WQI) based on exploratory water quality constituents greatly contributing to the construction of exploratory factors (EFs) which were derived from exploratory factor analysis (EFA). Trend analysis was conducted on EFs and WQI based on annual and seasonal data sets to estimate time series trends in water quality. Cluster analysis (clustering) was used to cluster the six monitoring stations into three groups. The first five EFs 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 (P and N) were major variables involved in the construction of the EFs. Seasonal and spatial differences were observed in compositional patterns of EFs. Positive or negative trends were detected for different EF at different monitoring groups during different seasons. The composite WQI showed significant difference among the three clustering groups with the lowest WQI median in station C44S80. At the three monitoring groups, medians of WQI were significantly greater in the wet than in the dry season, which implied that wet season rainfall likely resulted in constituent transport into canals.