New Insights into the Seasonal Variation of DOM Quality of a Humic-Rich Drinking-Water Reservoir—Coupling 2D-Fluorescence and FTICR MS Measurements

Long-term changes in dissolved organic matter (DOM) quality, especially in humic-rich raw waters, may lead to intensive adaptions in drinking-water processing. However, seasonal DOM quality changes in standing waters are poorly understood. To fill this gap, the DOM quality of a German drinking water reservoir was investigated on a monthly basis by Fourier-transform ion cyclotron resonance mass spectrometry (FTICR MS) measurements and 2D fluorescence for 18 months. FTICR MS results showed seasonal changes of molecular formula (MF) intensities, indicating photochemical transformation of DOM as a significant process for DOM quality variation. For an assessment of the two humic-like components, identified by parallel factor analysis (PARAFAC) of excitation–emission matrices (EEM), their loadings were Spearman’s rank-correlated with the intensities of the FTICR MS-derived MF. One of the two PARAFAC components correlated to oxygenrich and relatively unsaturated MF identified as easily photo-degradable, also known as coagulants in flocculation processes. The other PARAFAC component showed opposite seasonal fluctuations and correlated with more saturated MF identified as photo-products with some of them being potential precursors of disinfection byproducts. Our study indicated the importance of elucidating both the chemical background and seasonal behavior of DOM if raw water-quality control is implemented by bulk optical parameters

Discrimination of biofilm samples using pattern recognition techniques

Biofilms are complex aggregates formed by microorganisms such as bacteria, fungi and algae, which grow at the interfaces between water and natural or artificial materials. They are actively involved in processes of sorption and desorption of metal ions in water and reflect the environmental conditions in the recent past. Therefore, biofilms can be used as bioindicators of water quality. The goal of this study was to determine whether the biofilms, developed in different aquatic systems, could be successfully discriminated using data on their elemental compositions. Biofilms were grown on natural or polycarbonate materials in flowing water, standing water and seawater bodies. Using an unsupervised technique such as principal component analysis (PCA) and several supervised methods like classification and regression trees (CART), discriminant partial least squares regression (DPLS) and uninformative variable elimination–DPLS (UVE-DPLS), we could confirm the uniqueness of sea biofilms and make a distinction between flowing water and standing water biofilms. The CART, DPLS and UVE-DPLS discriminant models were validated with an independent test set selected either by the Kennard and Stone method or the duplex algorithm. The best model was obtained from CART with 100% correct classification rate for the test set designed by the Kennard and Stone algorithm. With CART, one variable describing the Mg content in the biofilm water phase was found to be important for the discrimination of flowing water and standing water biofilms