AI Summary & Key Takeaways
AI InsightResults indicated that emissions at VSP modes under cruising speeds were 10e12 times less than idling (which is the mode used for emission standard certification), braking and accelerating conditions.
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Özet
This paper presents the characterization and modeling of exhaust emissions released from the passenger cars on urban roads under heterogeneous traffic conditions. Onboard exhaust emissions measurement were made at selected corridors in a populous urban area of India. Exhaust emissions were characterized for different driving modes classified according to vehicle specific power (VSP). Results indicated that emissions at VSP modes under cruising speeds were 10e12 times less than idling (which is the mode used for emission standard certification), braking and accelerating conditions. Also it has been found that more than 20% of time vehicles were in idling conditions at most of the roads. Real-time exhaust emission prediction models for heterogeneous traffic conditions were developed using artificial neural network (ANN) technique. The vehicle characteristics such as revolutions per minute (RPM), speed, acceleration and VSP were used as input to the model. The onboard measurements of CO, HC and NOx concentrations were used to train the ANN based exhaust emission prediction models. Result showed good agreement with onboard measured emissions data (index of agreement ¼ 0.9) of all driving modes. Further, ANN model's emissions were compared with emissions estimated from the COPERT model and emission factors recommended by the Automotive Research Association of India (ARAI). It was found that the ANN model emissions were edge over the ARAI and COPERT model emissions and useful for urban air quality management and traffic planning.