Rafael Borge, for trusting me to carry out this work and for his guidance throughout the thesis. Especially for the opportunities that have arisen from this work and that have been possible due to his involvement. Of course, to all the coworkers from the TARIndustrial group and people from the Department of Industrial Chemistry and Environmental Engineering that travelled with me through this journey. With their help and good advice this work was improved. To the whole group of the TECNAIRE-CM project. Their effort and work has leed to very good results and many possibilities have arisen in this research line. To Ángeles Cristóbal from the Madrid City Council, Ana Rosa Llorente and Gregorio Vallejo from the Planning Department in the General Subdirectorate for the Implementation of Mobility and Transport for providing all the necessary permits and traffic data to carry out this work. Especial thanks to Dr. Robin Smit and Dr. Mark Hickman for their kindness by giving me the opportunity to carry out my international research stay at the University of Queensland. Also, for facilitating my professional and personal integration during my stay as well as providing all accesses to the necessary software (PΔP). I cannot forget the colleagues of the Fire and Transport Group of the School of Civil Engineering (AEB) who treated me as one more since the very first day. I would also like to thank Hans-Jurgen Don, Ignacio Galindo and Vidal Roca for supplying PTV software which has been fundamental for the accomplishment of this work. Also to Arjan Eijk from TNO for the help provided with the software VERSIT+ micro /ENVIVER. On the other hand also to Stefan Hausberger and Martin Dippold from TUGraz for providing PHEM and PHEM-light software's. And to Kyung-Hwan Kwak (Kangwon National University) for collaborating in the validation of the simulation system. Last but not least, to my family and all those people who during this time have always shown me their unconditional support, even in the hardest moments. Sincerely, thank you very much to all, I am sure that otherwise it would not have been possible. The value of a person is defined by V = (K + S) x A. Knowledge and Skills add up but, Attitude multiplies.
Traffic-related air quality issues remain in urban areas worldwide. For this reason, there is an increasing need to estimate the contribution of road traffic to atmospheric emissions at local level with high temporal and spatial resolution. Modal models compute emission rates as a function of specific engine or vehicle operating conditions at the highest resolution (seconds). They can be applied for microscale studies being a cost-effective tool to emulate differences in emissions levels in road networks. Two modal emission models, the Australian PΔP (Power-delta-Power) and the simplified version of the European PHEM (Passenger Car and Heavy-duty Emission Model), PHEM-light model, have been used. Also, a comparison to a cycle-variable emission model (VERSIT+micro) has been performed. For the comparison of both modal models, the main variables involved in traffic emission calculation were identified. Driving patterns (i.e. 1 Hz speed-time profiles) for individual vehicles were generated with the traffic microsimulation model VISSIM for different traffic conditions. To understand the response of modal models, detailed estimations of NOx emissions and fuel consumption were compared for different vehicle classes. Instantaneous emission profiles for individual driving patterns are highly sensitive to speed-acceleration profiles, vehicle mass, and road gradient, which are essential variables for the emission calculation. Although there are differences between European and Australian models, engine power and load were used to map vehicle classes for a more consistent comparison. It is essential to accurately define these parameters for each vehicle class in addition to detailed driving patterns to obtain high-resolution emissions estimates. In this sense, a larger number of vehicle classes included in the model provides more flexibility to develop representative emissions estimates. Emission predictions between modal models were reasonably consistent presenting larger differences with the cycle-variable model, despite both modal models being based on different on-road fleet measurements. In conclusion, analyzing emission estimations for different traffic conditions demonstrates the importance of an accurate definition of the model parameters for a specific vehicle fleet.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.