There has been analyzed the nature of the distribution of the oxidability of water. It is revealed that the nature of the distribution of indicator values during the year depends largely on the seasonal factor, and therefore the analysis of the distribution of oxidability is proposed to be studied separately for each month. A variation series is constructed and empirical distribution functions of oxidability distribution is derived. It is established that the law of water oxidability distribution differs from the normal and lognormal distributions, but it is described with sufficient accuracy by the gamma distribution or by a cubic polynomial function (being the simplest). The hypothesis about the distribution law is confirmed by the Kolmogorov–Smirnov test. The water oxidability distribution function allows to determine the probability of exceeding the specified values of the indicator and quantitatively assess the risks of exceeding them, which can become the basis for developing solutions for managing water quality and increasing the efficiency of the water treatment process.

The distribution of turbidity values in given sample is analyzed. The results demonstrated that the nature of the distribution of turbidity values during the year largely depends on the seasonal factor, hence the analysis of the distribution of turbidity is performed separately for each month. Order statistic (variation series) is computed and an empirical distribution function of turbidity values is derived. It is concluded, that the distribution of turbidity in given water sample differs from normal, log-normal and gamma distributions. However, it can be described with sufficient accuracy by a cubic polynomial function. The turbidity distribution hypothesis is tested by the Kolmogorov–Smirnov test. The water turbidity distribution function predicts the probability of exceeding the specified values of turbidity and enables numerical assessment of its likelihood.

The method of least squares was used to optimize the theoretical water turbidity distribution function; as a result, the function was derived with all the properties of the distribution function (continuous and monotonically increasing function, values ranging from 0 to 1). It was demonstrated that the optimized polynomials can be used to estimate the probability of any water turbidity-related occurrence, but they cannot be used reliably for all time periods. Thus, the obtained polynomials are ineffective for studying the seasonal nature of changes in the turbidity distribution, which is not entirely convenient for modeling and entails the need to search for other mathematical models.

Risks of organoleptic (taste and odor) effects in drinking water from three water intake facilities are assessed, and research results are presented. The highest risk values for water hardness were identified in samples from infiltration water intake; the value for color-related risks was constant and equal to 0.001. For surface water intake samples, the values of water hardness and associated organoleptic risk are the lowest, compared to other water intakes, and do not exceed 0.008. Risk values of organoleptic effects associated with color at the surface water intake facilities are within the range of 0.001-0.003. The risk values for the taste, and odor effects due to turbidity are constant for all water intakes and equal to 0.002. There is no risk of developing organoleptic-olfactory products associated with the chemical oxygen demand parameter in all samples. The research shows that the overall values of the organoleptic risks are the highest in instances form infiltration water intakes compared with the surface water intake. In addition, the water hardness parameter contributes the most to overall organoleptic risks for all water intakes. The authors conclude that the risks associated with organoleptic (taste and odor) effects do not exceed an acceptable level, both for each indicator considered separately and for their combined effect.