<p>Measuring air pollution in urban areas represents a technical challenge due to the complexity of technical components and their integration into a unified system. Nonetheless, such measurements are essential, as air pollution negatively affects human health and, consequently, all aspects of life in urban areas. After establishing air quality measurement systems in urban environments, the next step involves optimizing these systems to effectively monitor pollution while minimizing all possible costs. This paper introduces a simulation environment for minimizing the number of measurement stations in urban areas, where the spatial distribution of measurement stations is modeled as an undirected unweighted graph. In addition to presenting the system architecture, a method for minimizing the number of measurement stations based on graph clustering using the similarity of measured values from neighboring stations is proposed. The simulation results indicate that it is possible to obtain recommendations for determining the minimum number of measurement stations under various scenarios. More precisely, results indicate that the best minimizations of the number of measurement stations of over 70% are achieved when the threshold for similarity for single measured values allows difference of 20% of the measuring range, and that there is no need to run a simulation with a large number of measurement stations at a short distance. Generating datasets based on real values sourced from relevant international organizations significantly reduces the costs associated with implementing pollution measurement solutions while simultaneously shortening the time required to deploy technical solutions. The key advantages of the proposed approach include adaptability in simulation parameters, extensibility through the incorporation of new pollutants or weather parameters, and reduced maintenance costs and energy consumption.</p>