The emissions from urban traffic are becoming an issue related to the drops in the quality of life in cities, which is exacerbated by rapidly changing climate due to global warming. To counter these problems, several measures, such as limiting traffic in sensitive city areas, are being studied and have already been implemented in some cities. However, at this point, it is not possible to completely ban traffic emissions, as economic activities and personal comfort are dependent on mobility. The paper analyzes the situation in the city of Constanta, where there is a history of measurements for air quality, with the sensor positioned in a crowded area. The peculiarity of the city of Constanta lies in the fact that there are two-time intervals in which the traffic is completely different, namely winter-spring, with normal traffic for the city, respectively summer-autumn, with overcrowded traffic due to the summer season. By analyzing the data recorded by the sensors and complemented with the estimated values from Sentinel 5P satellite data, equipped with sensors that allow estimating the concentration of ozone, CO, SO2, NO2, and CH4, an evolution model for air quality is established. To get a model that can be attached to the evolution of real traffic, a correlation with a traffic model is needed, which is possible using a micro-simulator. The initial conditions can be obtained from a macro-simulator, but it will be limited to predetermined areas of the studied area. The proposed algorithm correlates the results from optimized traffic scenarios from SUMO and the emissions from locally measured values and those from satellite data. The traffic model developed for an area of interest can be later extended to adjacent areas, where the environmental issues are studied, including traffic and fleet optimization if necessary.