Spatiotemporal Variability in Microbiological Water Quality of the Białka River and Its Relation to the Selected Physicochemical Parameters of Water

إنجليزي. The aim of this 2-year study was to examine the temporal changes in the concentration of microbiological indicators of water contamination and selected physicochemical parameters within the Białka river and its selected tributaries in the vicinity of the largest ski station in Białka Tatrzańska. The study involved 24 series of sampling in eight sites throughout the Białka river and on its selected tributaries. Temperature, pH, and electrical conductivity (EC25 °C) were measured onsite. The microbiological assays included the numbers of mesophilic and psychrophilic bacteria, Staphylococcus spp. and Salmonella spp., as well as coliforms, thermotolerant (fecal) coliforms, Escherichia coli, Enterococcus faecalis, and sulfate-reducing Clostridium. The chemical analyses were conducted to determine the concentration of NH4+, NO2−, NO3−, and PO43−. The analyses showed that contrary to common opinion, waters of the Białka river are strongly polluted in some sections. Seasonal variation in the prevalence of microbial indicators of fecal pollution was found, and the largest numbers of microorganisms were observed in winter ski season, followed by summer holidays. Similar seasonal pattern was observed in the examined chemical parameters. There was also very strong spatial variation within the tested microbial and chemical parameters, indicating the presence of pollution hotspots in the course of the Białka river. The employed principal component analysis revealed the presence of two main pollution sources, mainly affecting the quality of river water, i.e., fecal contamination of human origin and the natural source in the form of surface runoff and soil leaching. These factors, depending on the location of the sampling site, occurred in different configurations.

إنجليزي. Białka river, Spatiotemporal variability, Microbiological quality, Fecal contamination, Nutrients, Principal component analysis

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