Identifying associations of water quality parameters using cluster analysis: A case study of Biscayne Bay, USA

Correlation and cluster analysis protocols for analyzing multiple-parameter water-quality data sets are presented and demonstrated. A novel cluster analysis methodology is developed that identifies parameters that are either directly or indirectly correlated. Application of these analyses was demonstrated using multi-parameter water-quality measurements in Biscayne Bay and the primary drainage canals in South Florida. The results of these analyses identified the controlling nutrients at various locations within the bay, the probabilities of exceeding chlorophyll a criteria for various imposed nutrient criteria, the locations where nutrient levels are strongly linked to surface runoff, and locations where depressed levels of dissolved oxygen are strongly controlled by nitrogenous biochemical oxygen demand. Overall, the analysis protocols are shown to be effective in isolating specific locations where improved nutrient control measures would likely lead to substantial water-quality improvements in the region.