A regression model using sediment chemistry for the evaluation of marine environmental impacts associated with salmon aquaculture cage wastes

This study was undertaken to develop an approach for modelling changes of sediment chemistry related to the accumulation of aquaculture waste. Metal composition of sediment Al, Cu, Fe, Li, Mn, and Zn; organic carbon and <63 μm particles were used to determine the extent of detectable effects around the cage. This study showed marked differences in the sediment chemistry between aquaculture sites and the natural background: (1) negative correlations between sediment Cu and Zn with Al, (2) poor correlations between metals and Li, and (3) concentrations of Fe and Mn decreased with increased accumulation of organic carbon. There is a trend among normalised metals, organic carbon and particles related to normal, hypoxic and anoxic sediment conditions. The trends are useful for detecting and assessing the cumulative effects from aquaculture wastes to the marine environment. Lithium is less interactive with other metals in aquaculture sediments compared with the natural background sediments. Principal components analysis (PCA) was carried out on the metals, organic carbon, and particles to cluster the similarities of the variables so as to establish the predicted or adjusted environmental monitoring program (EMP) ratings. This approach, using the adjusted EMP rating based on sediment chemistry, yields a regression model with R2=0.945 compared to R2=0.653 for the regression model using unadjusted EMP for assessing the environmental conditions.