Due to increasing issues of air pollution in urban areas continuous research is being conducted to upgrade models, which can predict the distribution of pollutants and thus enable timely interventions to mitigate their negative effects. To support these efforts, traffic data from an integrated transport model was used to drive the COPERT traffic emission model and the WRF-Chem atmospheric chemistry model. With reliable macroscopic traffic data from the Budapest region, traffic state estimations were calculated for every fifteen minutes of the day using dynamic assignment with predefined and time-varying static demand matrices. Then the COPERT vehicular emission model of average speeds was applied to provide the emission factors, so that the macroscopic emissions for the traffic network could be calculated. As a next step the WRF-Chem online coupled weather and atmospheric chemistry model was adapted to estimate atmospheric dispersion of pollutants (CO, NO_x, O₃). The coupled models are presented in a 2-day case study with qualitative comparison of obtained results with measurements. As a result, it can be stated that combining macroscopic road traffic modeling with atmospheric models can enhance the estimation efficiency of urban air pollution.

由于城市地区空气污染问题的增加，正在进行持续的研究以升级模型，该模型可以预测污染物的分布，从而能够及时采取干预措施以减轻其负面影响。为了支持这些工作，使用了来自综合运输模型的交通数据来驱动COPERT交通排放模型和WRF-Chem大气化学模型。借助来自布达佩斯地区的可靠宏观交通数据，可使用具有预定义且随时间变化的静态需求矩阵的动态分配，针对每天每15分钟计算一次交通状态估算。然后应用平均速度的COPERT车辆排放模型来提供排放因子，从而可以计算出交通网络的宏观排放。_x，O ₃）。在为期2天的案例研究中介绍了耦合模型，并对获得的结果与测量结果进行了定性比较。结果，可以说，将宏观道路交通模型与大气模型相结合可以提高城市空气污染的估算效率。