Methods for unsupervised contribution analysis of raw EEM data in water monitoring. Contaminant identification and quantification.

Fluorescence EEM spectra provide the "fingerprint" of water contamination and is a very efficient way to access the quality of water bodies. These multivariate datasets correspond to complex mixtures and are very rich in information. Graphical approaches have been used for decades to characterize and quantify different contamination sources.

Adaptive monitoring approach to assess dissolved organic matter dynamics during rainfall events.

Rainfall events induce water quality transformation in river systems influenced by the watershed land use and hydrology dynamics. In this context, an adaptive monitoring approach (AMA) is used to assess non-point sources (NPS) of pollution events, through dissolved organic matter (DOM) contribution. The case study is a monitoring site in a semi-urban watershed characterized by NPS contribution.

Applicability of conventional and non-conventional parameters for municipal landfill leachate characterization.

The disposal of municipal solid waste (MSW) in landfills generates leachate, a highly polluting liquid to the aquatic environment. Leachate composition become a challenge to choose the best treatment process. Then, detailed techniques to determine the organic content, in terms of refractability, composition, sources and biodegradability in landfill leachate can help to choose the appropriate treatment and improve landfill management.

Differences in the expression of selected genes associated with cell stress response in a PTSD patient and in healthy individuals - a case study.

The aim of the study was determination of the level in expression genes associated with cell stress response in a patient with PTSD. A 57-year-old PTSD patient, A.P.

Unsupervised component analysis: PCA, POA and ICA data exploring - connecting the dots.

Under controlled conditions, each compound presents a specific spectral activity. Based on this assumption, this article discusses Principal Component Analysis (PCA), Principal Object Analysis (POA) and Independent Component Analysis (ICA) algorithms and some decision criteria in order to obtain unequivocal information on the number of active spectral components present in a certain aquatic system. The POA algorithm was shown to be a very robust unsupervised object-oriented exploratory data analysis, proven to be successful in correctly determining the number of independent components present in a given spectral dataset.