Evaluation, comparison, and analysis of the exhaust pollutant emissions from Peugeot TU5 and XU7 internal combustion engines

Air pollution is one of the most critical environmental crises facing humanity today, with motor vehicles playing a major role. Therefore, assessing the emission levels of air pollutants from vehicles is more important than ever. This study aimed to evaluate the emissions of air pollutants from vehicles equipped with TU5 and XU7 engines through direct sampling of pollutant gases and modeling using the IVE software. Data related to the emission of CO, CO₂, NOx, VOC, and HC gases were collected from 720 vehicles with these two engine types over a four-month period at the Shiraz vehicle inspection center, using a five-gas analyzer. Additionally, the amount of PM10 particulate matter was estimated through modeling in the IVE software, based on fleet data, environmental conditions, and vehicle performance characteristics. Sampling was conducted under two conditions: when the vehicle was idling and when it was moving at an average speed of 10 km/h. The measured values were recorded, and all data were entered into the environment and fleet input files of the IVE software to extract emission factors of pollutants. The results from the vehicle inspection center showed that the TU5 engine, due to its more advanced design, performed better in reducing gaseous pollutants compared to the XU7 engine. Under idling conditions, emissions of CO and HC gases for both engine types were higher than under driving conditions. Moreover, the XU7 engine exhibited higher emissions of CO and NOx compared to the TU5 engine. Modeling the emission factors using the IVE model revealed that the software overestimated CO and CO₂ emissions from gasoline vehicles compared to actual data, while for PM10 and VOC pollutants, the measured values were higher than model predictions. TU5 vehicles performed better in reducing pollutants under driving conditions, but their emissions significantly increased under idling conditions. This study emphasizes the need to optimize and localize the IVE model to improve its predictive accuracy. The findings of this research can contribute to improved engine design, the adoption of advanced technologies, and the implementation of effective environmental policies to reduce air pollution and enhance urban life quality.