Parallel factor analysis and multivariate curve resolution as data fusion tools to supervise a stream

• Combine multivariate data of several orders.
• A new method to monitor superficial waters of a stream.
• Study the distribution of dissolved organic matter with physicochemical variables.
• Measures of fluorescence and other parameters of water through both time and space.
• This method combines parallel factor analysis with multivariate curve resolution.

In this work, a new method is proposed to monitor the distribution, evolution and correlation of dissolved organic matter on the superficial water of a stream with respect to physicochemical variables that characterize the basin and season sampling of each campaign. The method is based on measuring fluorescence emission–excitation matrices and some physicochemical parameters of water samples through both time and space. In a first phase, parallel factor analysis (PARAFAC) or multivariate curve resolution with alternating least-squares (MCR-ALS) were applied to extract the information on the relative proportions of each fluorophore on each sample. Then, MCR-ALS was applied again to the entire database, in order to study the spatial and time distribution. This methodology was used to study the behavior of a basin stream that is significantly modified by anthropic activities.