Traffic is the major source of pollution in many cities. Despite recent efforts to improve air quality, some issues (usually related to NOX and PM) remain in urban areas worldwide. Ambient air quality standards non-compliance situations are often linked to traffic-related hot-spots. The assessment of potential solutions for such locations requires very high-resolution models that can successfully capture the complexity of the processes that determine emissions from mobile sources and provide accurate inputs for highly detailed microscale air quality models. These estimations are based on acceleration-deceleration processes and speed that are significantly influenced by congestion. There are several approaches that can be used for this purpose. Among them, modal emission models are able to compute emission rates as a function of specific engine or vehicle operating modes with very high temporal resolution (seconds) being suitable for this kind of analysis. The aim of this work is to obtain detailed traffic emissions from simulated driving patterns in different typologies of complex urban network configurations in Australia and Spain. To obtain detailed traffic results, a microscale traffic flow simulation model was fed with measured traffic data. Traffic results were used as input for two microscale modal emission models (the Australian P?P and the European PHEM-light). To understand the response of these models, detailed estimation of NOX emissions and fuel consumption for diverse vehicle types were compared. In addition to differences on vehicle classification and other implementation details, large differences in the results are observed due to Power-to-Mass ratios. Therefore, it is essential to define power and load parameters as accurately as possible for each vehicle class (in addition to realistic driving patterns) to obtain accurate emissions. Our test suggest that satisfactory results can be achieved with any of the models if reliable information of vehicle fleet composition and vehicle characteristics is provided
