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Designing retention facilities is a complex engineering process that requires the collection of the detailed hydrological data of a catchment and hydraulic sewerage system. The acquired data are necessary to prepare a model of the retention tank in appropriate software for hydrodynamic modelling. The article shows the results of tests concerning the analysis of the sensitivity of a sewerage model of a rainwater retention tank which may be implemented in this software. The results of tests allowed determining the impact of the individual hydraulic characteristics of the catchment and the sewerage system on the required retention capacity of a tank. A planned analysis is performed based on artificial neural networks and the required data are acquired by hydrodynamic simulations in SWMM 5.1.

The article presents the results of research on the development of a simplified dimensioning method for sewage retention tanks equipped with a high-efficiency hydraulic system. The need to develop a new method is associated with the outdated rainfall model by Błaszczyk, which is the most popular model in Poland. According to the research made by numerous scientific centres, this model underrates the values of the maximum intensity of critical rains. As a consequence, its use affects the design of sewage systems and related facilities with insufficient hydraulic capacity.

Hydraulic overload of networks and its related objects is a problem that is more and more common in drainage systems. The most popular way to prevent it from its occurring is to increase the storage capacity of the system. At present, there are numerous solutions allowing to increase retention in drainage systems, with the selection of the most beneficial one being dependent on numerous factors; it must also be preceded by a technical and financial analysis. Financial analysis is of particular importance here, since due to significant dimensions and complicated structure, these objects require high investment outlays for its construction. As a consequence, it is a priority to undertake actions aimed at developing such solutions that will allow for significant increases of retention in the drainage system, and cost reductions at the same time. One of such solutions is the Underground tank for combined sewage and stormwater that is now patent-pending. The application of this solution allows to multiply retention capacity of the drainage system, without the necessity of modernizing the existing sewer system. This paper presents the application potential of the subject method for increasing sewer retention, along with its hydraulic function analysis. The research carried out and presented was based on the analysis of individual phases of functioning of the subject solution.

In this study, an analysis of hydraulic systems with forced retention is presented as an alternative to traditional stormwater retention methods for drainage systems. This study evaluates the hydraulic efficiency of three different geometries of forced-retention hydraulic systems and compares the results with the efficiency of a traditional single-chamber retention tank and a traditional linear system without retention under two operational scenarios. Given the identical capacities of the solutions studied, an evaluation procedure was proposed based on the system's resilience time, which was measured until a set level of stormwater accumulation was reached in the system. Additionally, a sensitivity analysis of the models was performed using statistical tools and machine learning. This also provides a basis for verifying the applicability of artificial neural networks in assessing the efficiency of such solutions. The results of the global sensitivity analysis indicated that the inflow rate and the permissible level of stormwater accumulation in the system were key factors affecting the hydraulic efficiency of forced retention facilities, with high value of impact strengths of 121.651 and 76.035, respectively. In the scenario with a lower accumulation level, forced-retention hydraulic systems, particularly ring-shaped solutions, exhibited higher efficiency, with the hydraulic efficiency coefficient reaching 5.500. In contrast, in the scenario allowing for a higher level of system fill, the efficiency and selection of the most effective solution were closely related to the hydraulic conditions, with the efficiency of the forced retention units, describing the ratio of the analysed retention unit capacity to a conventional retention tank, ranging from 0.300 to 1.140. Based on these findings, it was concluded that forced retention can serve as an alternative to classic network tanks, especially in urban areas with limited available space., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:This publication reflects the implementation of Research Project No. (2020)/04/X/ST8/00,450 funded by Poland's National Science Centre., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The aim of this article is to apply advanced predictive modeling techniques to understand the degradation process of microplastics in aquatic environments. Utilizing a Fractional Factorial Central Composite Experimental Plan, this study seeks to develop precise predictive statistical models that enable forecasting the quantity of pollutants generated during the degradation of microplastics under various environmental conditions. This tool was applied to model changes in DOC (dissolved organic carbon) and DEHP (bis(2-ethylhexyl) phthalate) values during the degradation of microplastics in aquatic ecosystems. The methods were developed using data derived from laboratory tests conducted using the GC-MS technique. The obtained approximating functions, considering factors such as degradation time, water temperature, and particle size, significantly reduced the analysis time. A two-stage verification of the approximating functions was conducted, considering the accuracy of the function form, its adequacy, the statistical significance of input variables, and their correlation with DOC and DEHP. The employed a Fractional Factorial Central Composite Experimental Plan allowed for the simultaneous reduction in the number of experiments and prediction of the influence of variables on the output values. Precise predictive models support understanding of the microplastic degradation process, facilitating the development of effective strategies for managing this pollution., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Financed by the Minister of Science and Higher Education Republic of Poland within the program "Regional Excellence Initiative”. This publication reflects the implementation of Research Project No. 2022/06/X/ST5/00624, ID: 556207 funded by the Polish National Science Center., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The aim of the work was to assess the usefulness of machine learning in predicting the migration of pollutants from microplastics. The search for methods to reduce unnecessary laboratory analyzes is a necessary action both to protect the environment and from an economic perspective. Multiple regression, artificial neural networks, support vector method and random forest regression were used in the study to predict leaching of plasticizers and other contaminants from microplastics. The development of the methods were based on the results of laboratory tests obtained by the GC-MS method. The results obtained confirm the potential of artificial neural networks and the support vector method for effective modelling and prediction of chemical compounds leached from microplastics. Correlation results were obtained for the analyzed parameters between the data obtained in the model and laboratory data in the range of 0.96-0.98 and 0.93-0.99 for artificial neural networks and the support vector method, respectively. Multiple regression showed the lowest performance in all cases in predicting plastic phthalic acid esters (coefficient of determination (R 2 ) in the range of 0.03-0.24). ENVIRONMENTAL IMPLICATION: The results presented in this paper will provide new insight into the influence of different parameters and factors on the leaching of plastic additives. This information is necessary to assess the harmfulness of these materials. The collected data is unique on a global scale. For the first time, machine learning were used to predict the leaching rate of plasticizers from different polymers under different environmental conditions. The use of machine learning allows to reduce unnecessary laboratory tests and reduce costs and protect the environment. Currently, there are no research results in this field in the scientific literature., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This publication reflects the implementation of Research Project No. 2019/03/X/ST10/01557 funded by Poland’s National Science Centre. The research leading to these results has received funding from the commissioned task entitled "VIA CARPATIA Universities of Technology Network named after the President of the Republic of Poland Lech Kaczyński” contract no. MEiN/2022/DPI/2578 action entitled "In the neighborhood - inter-university research internships and study visits.”, (Copyright © 2023 Elsevier B.V. All rights reserved.)

The objective of this article was to assess the effectiveness of simulation models in predicting the emission of additives from microplastics. The variety of plastics, their chemical structure, physicochemical properties, as well as the influence of environmental factors on their decomposition generate countless cases for analysis in the laboratory. The search for methods to reduce unnecessary laboratory analyses is a necessary action to protect the environment and ensure economic efficiency. In this study, machine learning techniques, specifically the methodology of artificial neural networks (ANNs), were employed to predict the leaching of contaminants from microplastics. The network's development was based on laboratory test results obtained using gas chromatography coupled to a mass spectrometer (GC-MS). The conducted research revealed the significant utility of the multilayer perceptron (MLP) - type networks, which exhibited correlation levels exceeding 95 % for various predicted values. One comprehensive ANN was developed, encompassing all the parameters analyzed, alongside individual networks for each parameter. A common network for all factors enabled for satisfactory results. Temperature and holding time had the greatest influence on the values of parameters such as the electrolytic conductivity of water (EC), dissolved organic carbon (DOC), and di(2-ethylhexyl) phthalate (DEHP). Correlation results ranged from 0.94 to 0.99 for EC, DEHP and DOC between the model data and laboratory data in each set of training, test, and validation data. The conducted research demonstrated that ANNs are a valuable machine learning method for analyzing and predicting pollutant emissions during the decomposition of microplastics., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This publication reflects the implementation of Research Project No. 2019/03/X/ST10/01557 funded by the Polish National Science Center. The research leading to these results has received funding from the commissioned task entitled “VIA CARPATIA Universities of Technology Network named after the President of the Republic of Poland Lech Kaczyński” contract no. MEiN/2022/DPI/2578 action entitled “In the neighborhood - inter-university research internships and study visits.”, (Copyright © 2023 Elsevier B.V. All rights reserved.)

The paper presents research on the emission of pollutants from used car tires in the form of microparticles. Due to the large amount of waste generated in the form of used car tires, a method is being sought to utilize this material as secondary raw material. More and more frequently, this waste is used in civil engineering. To do this, it is necessary to determine the environmental impact of this waste. 2 g of microplastic samples with fractions of 3000-8000, 1000-3000, 1000, and 600 µm were incubated in water protected from light. The influence of pH (3, 7, and 10), incubation time (1, 3, 5, 7, and 14 d) and temperature (20, 60, and 90 °C) on the degree of emission of selected phthalic acid esters and other tire components was investigated. Additionally, the influence of the decomposition of the analyzed material on the emission of methane and carbon dioxide at the temperature of 20 °C during 30, 180, and 360 days was investigated. The analysis of the amount of released substances was carried out using a gas chromatograph coupled with a mass spectrometer. Greenhouse gas analysis was performed using a gas chromatograph with a Barrier Discharge Ionization Detector. The research carried out confirmed that, depending on the environmental conditions, tire particles leach out, among others, phthalates, benzenediamine, phenol, benzothiazole, and benzene. Influence of particle size, pH value, incubation time, and temperature on the degree of emission of selected plasticizers was investigated. An innovative aspect of the research was the use of artificial neural networks to identify the validity of using machine algorithms in environmental research. An additional negative aspect of the implementation of tire particles in the water-soil environment is the emission of carbon dioxide and methane that are greenhouse gases., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)