Using grey clustering to evaluate nitrogen pollution in estuaries with limited data

Abstract

Many techniques exist for the evaluation of nutrient pollution, but most of them require large amounts of data and are difficult to implement in countries where accurate water quality information is not available. New methods to manage subjectivity, inaccuracy or variability are required in such environments so that water managers can invest the scarce economic resources available to restore the most vulnerable areas. We propose a new methodology based on grey clustering which classifies monitoring sites according to their need for nitrogen pollution management when only small amounts of data are available. Grey clustering focuses on the extraction of information with small samples, allowing management decision making with limited data. We applied the entropy-weight method, based on the concept of information entropy, to determine the clustering weight of each criterion used for classification. In order to reference the pollution level to the anthropogenic pressure, we developed two grey indexes: the Grey Nitrogen Management Priority index (GNMP index) to evaluate the relative need for nitrogen pollution management based on a spatiotemporal analysis of total nitrogen concentrations, and the Grey Land Use Pollution index (GLUP index), which evaluates the anthropogenic pressures of nitrogen pollution based on land use. Both indexes were then confronted to validate the classification. We applied the developed methodology to eight estuaries of the Southern Gulf of Mexico associated to beaches, mangroves and other coastal ecosystems which may be threatened by the presence of nitrogen pollution. The application of the new method has proved to be a powerful tool for decision making when data availability and reliability are limited. This method could also be applied to assess other pollutants.

Keywords: Coastal ecosystems; Coastal management; Entropy weighting; Grey clustering; Nitrogen pollution; Water quality.