The quality of water transported through freshly cemented water pipes depends not only on the type of cement used. The other factor affecting the degree of leaching pollutants from cement coatings is the chemical parameters of the water. A disinfection process is necessary for maintaining water which is safe for human health and high quality. However, simultaneously, the presence of disinfectant in water may cause some problems. The results of the experiments performed showed that independently of the concentration of sodium hypochlorite in water, rapid changes of water pH soon after cement mortar lining renovation may always be expected. The addition of disinfectant to water causes an increase in pollutants leaching from cement coatings, even in the case of a low disinfectant dose. Although the concentration of disinfectant in water has a differential impact on the degree of pollutants leaching from cement coatings, the increase in the leaching of the pollutants is not proportional to the disinfectant concentration.

The aim of the present work was the modeling of the wastewater treatment plant operation work using Monte Carlo method and different random variables probability distributions modeling. The analysis includes the following pollutants indicators; BOD₅ (Biochemical Oxygen Demand), COD_Cr (Chemical Oxygen Demand), Total Suspended Solids (SS_t), Total Nitrogen (TN), and Total Phosphorus (TP). The Anderson–Darling (A–D) test was used for the assessment of theoretical and empirical distributions compatibility. The selection of the best-fitting statistical distributions was performed using peak-weighted root mean square (PWRMSE) parameter. Based on the performed calculations, it was stated that pollutants indicators in treated sewage were characterized by a significant variability. Obtained results indicate that the best-fitting pollutants indicators statistical distribution is Gauss Mixed Model (GMM) function. The results of the Monte Carlo simulation method confirmed that some problems related to the organic and biogenic pollutants reduction may be observed in the Wastewater Treatment Plant, in Jaworzno.

Among the many factors affecting water supply system failures are the weather conditions that change over the year. Since this is an important research issue, as part of this study, investigation of water supply system failure seasonality by the selected statistical approaches was presented. The basis of the research was monthly number of the pipelines' failures from the multi-year period of 2007–2017 of the municipal water network located in southern Poland. Mann–Kendall test results proved decreasing seasonal trend of the failure rate indexes λ. In turn, the results of the Colwell indexes' calculations allowed it to be stated that seasonal course of the water pipelines' failure events can be relatively easy to predict. As it turned out, it is difficult to determine unambiguously the impact of a given period of the year on the water pipeline failure events' occurrence. However, greater failure-free operation of the water pipelines may be expected in spring and summer months than in autumn and winter months. Because using Colwell indexes for seasonality analysis has no limitations compared to other methods, Colwell indexes may be considered as reliable tools for the assessment of the seasonal course of the water pipelines' failure events.

The paper presents modelling of wastewater treatment plant (WWTP) operation work efficiency using a two-stage method based on selected probability distributions and the Monte Carlo method. Calculations were carried out in terms of sewage susceptibility to biodegradability. Pollutant indicators in raw sewage and in sewage after mechanical treatment and biological treatment were analysed: BOD₅, COD, total suspended solids (TSS), total nitrogen (TN) and total phosphorus (TP). The compatibility of theoretical and empirical distributions was assessed using the Anderson–Darling test. The best-fitted statistical distributions were selected using Akaike criterion. Performed calculations made it possible to state that out of all proposed methods, the Gaussian mixture model (GMM) for distribution proved to be the best-fitted. Obtained simulation results proved that the statistical tools used in this paper describe the changes of pollutant indicators correctly. The calculations allowed us to state that the proposed calculation method can be an effective tool for predicting the course of subsequent sewage treatment stages. Modelling results can be used to make a reliable assessment of sewage susceptibility to biodegradability expressed by the BOD₅/COD, BOD₅/TN and BOD₅/TP ratios. New data generated this way can be helpful for the assessment of WWTP operation work and for preparing different possible scenarios for their operation.