Water Quality of Inflows to the Everglades National Park over Three Decades (1985–2014) Analyzed by Multivariate Statistical Methods

Lei Wan and Xiaohui Fan
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Lei Wan: Xuzhou University of Technology, No. 2 Lishui Road, New City District, Xuzhou 22111, China
Xiaohui Fan: Soil and Water Science Department at Tropical Research and Education Center, IFAS, University of Florida, 18905 SW 280th Street, Homestead, FL 33031, USA

IJERPH, 2018, vol. 15, issue 9, 1-12

Abstract: The Everglades, a vast subtropical wetland, dominates the landscape of south Florida and is widely recognized as an ecosystem of great ecological importance. Data from seven inflow sites to the Everglades National Park (ENP) were analyzed over three decades (1985–2014) for temporal trends by the STL (integrated seasonal-trend decomposition using LOESS) method. A cluster analysis (CA) and principal component analysis (PCA) were applied for the evaluation of spatial variation. The results indicate that the water quality change trend is closely associated with rainfall. Increasing rainfall results in increasing flow and thus, decreasing concentrations of nitrogen and phosphorus. Based on 10 variables, the seven sampling stations were classified by CA into four distinct clusters: A, B, C, and D. The PCA analysis indicated that total nitrogen (TN) and total phosphorus (TP) are the main pollution factors, especially TN. The results suggest that non-point sources are the main pollution sources and best management practices (BMPs) effectively reduce organic nitrogen. However, TN and TP control is still the focus of future work in this area. Increasing the transfer water quantity can improve the water quality temporarily and planting submersed macrophytes can absorb nitrogen and phosphorus and increase the dissolved oxygen (DO) concentration in water, continuously improving the water quality.

Keywords: water quality; inflows to everglades; trend analysis; CA; PCA (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
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