## 1 Introduction  
Urbanisation and increasingly frequent extreme weather events result in higher volumes of stormwater flowing from catchments. That, together with the deterioration of air quality in urban areas, leads to growing amounts of pollutants that are washed off surfaces in rainfall events. Consequently, they negatively affect the aquatic environment of the receiving waters. Various mathematical models are currently developed in order to predict negative stormwater impacts on the natural environment [1; 2; 3]. They are useful when making decisions, in adherence to sustainable development principles, which concern land use and urban space management. To predict stormwater quality, these models primarily take into account deposition of pollutants on surfaces [1], their runoff, and transportation by the drainage system. The Stormwater Management Model (SWMM) software is commonly used; however, simplifications [2] adopted in the model can make it difficult to fit simulation results to measurements. Literature overview [4] shows that, in many cases, unsatisfactory prediction results can be caused by the simplified model of pollutant deposition. The complex process of pollutant accumulation on the catchment surface is influenced by numerous factors, including air quality, weather patterns, and local conditions. Air quality and pollutant deposition conditions vary in time [5; 6]; therefore, their proper modelling is not easy and requires advanced tools. Consequently, statistical models are more frequently used to account for the stochastic character of determinants that affect stormwaterquality. In addition, empirical models are much cheaper and simpler to develop than physical ones [7]. Statistical models (e.g., artificial neural networks (ANNs)) allow simulation of strongly nonlinear phenomena occurring in the natural environment [8]. However, the lack of physical interpretation of the obtained model structure may be a disadvantage, which in some cases can produce errors in computations. Physical and statistical models for stormwater quality prediction developed so far are local in character; thus, they cannot be applied to catchments with different characteristics. In practice, even for a single catchment model, development generates high costs of field studies, and may not be economically viable. Consequently, it is necessary to look for and develop models that would be global in character, and at the same time, account for variation in site factors. The literature reports that artificial neural networks (ANNs) are frequently used to predict phenomena where physical models are too complex. In addition, physical models are often not able to produce reliable simulation results. Therefore, ANNs are generally applied to model phenomena that are nonlinear in character. In addition, depending on their numerical values can have diversified effects on the predicted dependent variable. The benefits offered by ANN have been utilised in many research fields [9]. Among others, ANNs are employed to simulate stormwater quantity and quality [10], and also to design and size sewage systems [11; 12]. Although ANNs were used to predict heavy metal (HM) concentrations in stormwater, the analysis dealt exclusively with small, highly impervious, homogeneous catchments. ANN-based methods are also commonly employed to predict wastewater quality [13; 14] with respect to organic and inorganic compounds and HM. However, the method was mainly applied to simulate HM concentrations in sewage for small urban catchments, where the pattern of HM transportation is different from riverine catchments. This study proposes a methodology for building a versatile ANN model that would capture a range of the catchment's physical and geographical characteristics, and also local weather conditions that indirectly affect air quality. The approach makes it possible to examine interactions between variables included in the model.