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

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.