Abstract
Identification of the key environmental indicators (KEIs) from a large number of environmental variables is important for environmental management in tidal flat reclamation areas. In this study, a modified principal component analysis approach (MPCA) has been developed for determining the KEIs. The MPCA accounts for the two important attributes of the environmental variables: pollution status and temporal variation, in addition to the commonly considered numerical divergence attribute. It also incorporates the distance correlation (dCor) to replace the Pearson’s correlation to measure the nonlinear interrelationship between the variables. The proposed method was applied to the Tiaozini sand shoal, a large-scale tidal flat reclamation region in China. Five KEIs were identified as dissolved inorganic nitrogen, Cd, petroleum in the water column, Hg, and total organic carbon in the sediment. The identified KEIs were shown to respond well to the biodiversity of phytoplankton. This demonstrated that the identified KEIs adequately represent the environmental condition in the coastal marine system. Therefore, the MPCA is a practicable method for extracting effective indicators that have key roles in the coastal and marine environment.
Highlights
Coastal tidal flats have often been reclaimed to moderate the conflict between population growth and land scarcity [1]
A pool attributes consisting of water variables and seven of attributes consisting of 8 water variables and seven sediment sediment variables variables (Sulphide, (Sulphide, total organic carbon nitrogen (TOC), TOC, PETRO, PETRO, TKN, TKN, total phosphorus (TP), TP, Cd, Cd, Hg)
In the modified principal component analysis approach (MPCA) algorithm, a characteristic space composed of three attribute dimensions of spatial distribution, pollution status, and temporal variation of environmental variables was constructed to embody their integrated environmental characteristics
Summary
Coastal tidal flats have often been reclaimed to moderate the conflict between population growth and land scarcity [1]. In China, about 1.12 million hectare (ha) of coastal flats has been reclaimed since. 1979 [2], and the number will increase another 0.25 million ha by 2020 [3]. Numerous studies have noted that the reclamation has noticeable effects on the coastal marine environment [4,5,6,7]. To characterize the local environment with changes that are caused by reclamation and evaluate the environmental effect of reclamation activity, it is essential to identify the key environmental indicators (KEIs) from a large number of environmental variables. The KEIs provide useful tools for tracking the environmental progress, supporting policy evaluation, and informing the public about coastal and marine governance. Environmental issues often involve analysis of a wide range of variables simultaneously. Principal component analysis (PCA) can effectively reduce the dimension of a multivariate data set by using only the first few principal components (PCs) [8], while still preserving its structure to the extent
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