Jarosław Górski

The dynamics of the processes shaping the quality of rainwater discharged by sewer systems is very complex. The use of hydrodynamic models to simulate surface runoff and the dynamics of changes in pollutants, including heavy metal (HM) concentrations, requires the collection of a lot of data that is difficult to obtain, and model calibration is complex and time-consuming. This paper presents a machine learning model and investigates the possibility of applying data mining methods to simulate changes in the concentrations of selected heavy metals (Ni, Cu, Cr, Zn and Pb) based on rainwater quality studies conducted in three urban catchments located in Kielce, southern Poland, with the aim of developing a model with broader applicability. Simulations of HM content in rainwater were performed using regression and classification trees (RF), neural networks (MLP) and support vector machines (SVMs). The MLP (MAPE ≤ 21.6) and SVM (MAPE ≤ 23.5) methods were shown to have the highest accuracy in simulating HM content. These models produced satisfactory simulation results based on rainfall amount and meteorological conditions, and they had relatively simple model structures and short simulation time. The study demonstrated that the proposed approach provides a transferable tool for estimating HM content in rainwater based on air quality, expressed in terms of visibility, and the type of catchment development.

The purpose of this study was to attempt to develop a stochastic model that describes the operation of the stormwater overflow located in the stormwater sewerage system. The model built for this study makes it possible to simulate the annual volume of the stormwater discharge, the maximum volume of the overflow discharge in a precipitation event, and the share of the latter in the total amount of stormwater conveyed directly, without pre-treatment, to the receiver. The dependence obtained with the linear regression method was employed to identify the occurrence of stormwater discharge. The prediction of the synthetic annual rainfall series was made using the Monte Carlo method. This was performed based on the determined log-normal distribution, the parameters of which were specified using 13-year rainfall series. Additionally, simulation of the stormwater overflow operation was performed with the use of a calibrated hydrodynamic model of the catchment. The model was developed using the Storm Water Management Model (SWMM). The results of the hydrodynamic simulations of the volume and number of discharges were within the scope of the probabilistic solution, which confirms the applicative character of the method presented in this study, intended to assess the operation of stormwater overflow.

This paper reports the results of studies on the concentrations of total suspended solids (TSS) and heavy metals (HMs) (Cu, Zn, Cr, Ni, Cd, Pb) in stormwater. Pollutant loads were calculated for the observed high water stages. Pollutographs showing M/M_c = f(V/V_c) relationships were generated. In the description of the relationships, two functions, namely the exponential and the power functions, were employed. These represented the dynamics of the pollutant washout from the surface of the catchment area. The analyses demonstrated that the exponential function provides a slightly better description of the course of the process compared with the power function. In the former case, correlation coefficients (R) ranged from 0.900 to 0.999, whereas in the latter they ranged from 0.864 to 0.999. The analyses of correlations between the characteristics describing discharge hydrographs and the values of pollutant washout coefficients indicate that the strongest statistical relationships were identified for TSS. It was demonstrated that the value of the washout coefficient for total suspended solids (k_TSS) drops with an increase in rain intensity (q). This also depends on the 10-min precipitation (P_td=10). Regarding the studied heavy metals, a statistically significant impact of the dry period (t_bd) on the washout coefficient was observed only for lead (k_Pb). Taking into account the washout coefficient in the first flush model makes it possible to improve the accuracy of calculations. This is important for understanding the studied phenomenon.

One of the advanced oxidation methods is the Fenton’s reaction which uses a mixture of iron(II) and hydrogen peroxide to generate hydroxyl radicals. The Fenton process efficiency is influenced by a number of factors including the pH, the Fe2+:H2O2 relation or the Fe2+ dose. The aim of the study was to optimize the Fenton method in the application for landfill leachate treatment, with the landfill in Janczyce (Świętokrzyskie Voivodeship) as an example. The sequence of determining the most advantageous conditions for the use of Fenton’s reagent was as follows: A – the pH effect; B – the oxidation time effect; C – the Fe2+:H2O2 relation effect; D – the Fe2+ dose effect. The optimum catalyst/oxidant ratio of 0.125 was determined. The optimum process time of 90 minutes was determined. It was demonstrated that the Fenton process can be effectively carried out already at the pH of 4. A reduction by 75% for the TOC and by 89% for the COD, respectively, was obtained.

The dynamics of processes affecting the quality of stormwater removed through drainage systems are highly complicated. Relatively little information is available on predicting the impact of catchment characteristics and weather conditions on stormwater heavy metal (HM). This paper reports research results concerning the concentrations of selected HM (Ni, Cu, Cr, Zn, Pb and Cd) in stormwater removed through drainage system from three catchments located in the city of Kielce, Poland. Statistical models for predicting concentrations of HM in stormwater were developed based on measurement results, with the use of artificial neural network (ANN) method (multi-layer perceptron). Analyses conducted for the study demonstrated that it is possible to use simple variables to characterise catchment and weather conditions. Simulation results showed that for Ni, Cu, Cr, Zn and Pb, the selected independent variables ensure satisfactory predictive capacities of the models (R² > 0.78). The models offer considerable application potential in the area of development plans, and they also account for environmental aspects as stormwater and snowmelt water quality affects receiving waters.