CDOM (coloured or chromophoric dissolved organic matter) is present in all types of natural water and plays a significant role in its optical properties. The humic-type fluorescence band (emission in the blue region with a maximum within 400 to 500 nm) essentially depends both on the CDOM source and on the wavelength of the exciting radiation. Despite the long-term study of the properties of CDOM and humic substances (HS), which make up most of the CDOM, their spectral properties have not yet been explained. Difficulties arise due to the fact that because of the wide variety of these substances and their polydispersity, the exact composition of fluorophores is not known. Currently, there is an active search for individual components in the fluorescence spectra of CDOM fractions, humic preparations of various origin, as well as similar in chemical structure nano-sized particles of graphene oxide or so called carbon dots (CD). Assuming that all these substances could have similar groups of fluorophores, we compared the spectral properties of CDOM, fulvic acid (FA), humic acids (HA) of different genesis, and carbon dots. It has been revealed that the fluorescence properties of all studied samples depend significantly on the excitation wavelength. The analysis of emission/excitation properties allowed us to distinguish the following classes of substances with fluorophores similar in spectral characteristics: (a) CDOM of Karelian freshwater lakes - fulvic acid samples (humic-type fluorescence with a maximum at 440-460 nm and significant blue shift); and (b) CD - HA of coal origin (wavelength of humic-like emission at 500 to 515 nm, no blue shift). We propose the following chain of organic material transformation according to changes in degree of humification and optical properties: biopolymers ? aquatic HS (CDOM and FA) - terrestrial HS (geopolymers) - fractionated carbon nanoparticles.

A method of simultaneous determination of temperature and salinity of water by Raman spectra was tested and validated on natural waters from the White Sea area. The basis of the method is the solution of this multi-parametric inverse problem by a modern technique of pattern recognition: artificial neural networks. Testing of the presented method was carried out on natural waters of unique meromictic lakes of the White Sea coast. The accuracy of determination of natural water parameters is 0.1 degree Celsius for temperature and 0.2 psu for salinity in laboratory conditions in the investigated ranges of variation of parameters.