| Summary: | The aim of the study was to determine the impact of natural and anthropogenic factors on the values of 22 quality indicators of surface waters flowing out of two small catchments differing in physiographic parameters and land use, in particular forest cover and urbanization of the area. The research was carried out in the years 2012−2014 at four measurement-control points located on the Chechło river and the Młoszówka stream (Poland), which are the main tributaries of the retention reservoir. Basic descriptive statistics, statistical tests, as well as cluster analysis and factor analysis were used to interpret the research results. The water that outflowed from the forestry-settlement catchment of the Młoszówka stream contained higher concentrations of total phosphorus, phosphates, nitrite, and nitrate nitrogen and salinity indicators than outflow from the Chechło river. Water from the Młoszówka stream was characterized by more favourable oxygen conditions. Higher oxygen concentration in the catchment influenced a large slope of the watercourse and thus higher water velocity, which is promoted by the mixed process. In the case of the forest catchment of the Chechło river, the water quality was generally better than in the Młoszówka stream, mainly in cases of total suspended solids TSS, total phosphorus TP, phosphates PO<sub>4</sub><sup>3−</sup>, total nitrogen TN, nitrite N−NO<sub>2</sub><sup>−</sup>, nitrate N−NO<sub>3</sub><sup>−</sup>, and salinity parameters. Despite it being a short section of the river taken into the study, favourable self-purification processes like mixed, nitrification, and denitrification were observed in its water. The research shows that forest areas have a positive effect on the balance of most substances dissolved in water, and natural factors in many cases shape the quality and utility values of surface waters on an equal footing with anthropogenic factors. In the case of a large number of examined parameters and complex processes occurring in water, the interpretation of the results makes it much easier by applying multivariate statistical methods.
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