Fluorescence spectroscopy for tracking microbiological contamination in urban waterbodies - Angelotti de Ponte Rodrigues, Natália; Carmigniani, Rémi; Guillot-Le Goff, Arthur; Lucas, Françoise S.; Therial, Claire; Naloufi, Manel; Janne, Aurélie; Piccioni, Francesco; Saad, Mohamed; Dubois, Philippe; Vinçon-Leite, Brigitte (Frontiers in Water, 2024)
Dissolved organic matter (DOM) plays a crucial role in freshwater ecosystem function. Monitoring of DOM in aquatic environments can be achieved by using fluorescence spectroscopy. Particularly, DOM fluorescence can constitute a signature of microbiological contamination with a potential for high frequency monitoring. However, limited data are available regarding urban waterbodies. This study considers fluorescence data from field campaigns conducted in the Paris metropolitan region: two watercourses (La Villette basin and the river Marne), two stormwater network outlets (SO), and a wastewater treatment plant effluent (WWTP-O). The objectives of the study were to characterize the major fluorescence components in the studied sites, to investigate the impact of local rainfall in such components and to identify a potential fluorescence signature of local microbiological contamination. The components of a PARAFAC model (C1-C7), corresponding to a couple of excitation (ex) and emission (em) wavelengths, and the fluorescence indices HIX and BIX were used for DOM characterization. In parallel, fecal indicator bacteria (FIB) were measured in selected samples. The PARAFAC protein-like components, C6 (ex/em of 280/352 nm) and C7 (ex/em of 305/340 nm), were identified as markers of microbial contamination in the studied sites. In the La Villette basin, where samplings covered a period of more than 2 years, which also included similar numbers of wet and dry weather samples, the protein-like components were significantly higher in wet weather in comparison to dry weather. A positive relationship was obtained between C6 and FIB. In urban rivers, the high frequency monitoring of C6 levels would support the fecal contamination detection in rivers. In addition, it could help targeting specific field campaigns to collect comprehensive dataset of microbiological contamination episodes.