This study shows differences between emission models and identifies key variables for high resolution estimations.
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.
This work aims to (1) examine and clarify the practical implications associated with using different models, and (2) identify key variables for microscale emission calculation in urban areas for the development of local road traffic emission inventories.
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.
One of the outcomes of this research is that it is essential to accurately define engine power and load 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.
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https://iopscience.iop.org/article/10.1088/1748-9326/ac8b21
