Quantification of CO2 Emission from Liquefied Natural Gas Truck Under Varied Traffic Condition via Portable Measurement Emission System

Liquefied natural gas (LNG) container trucks are regarded as clean energy vehicles with the potential to reduce air pollution. However, their CO2 emissions remain relatively high and are not yet well understood. In this study, the actual CO2 emissions of LNG container trucks in Shanghai were measured using a portable emissions measurement system (PEMS). This study quantitatively analyzed the relationship between traffic congestion levels and CO2 emissions on elevated roadways, providing new insights into the impact of urban traffic conditions. In addition, distinct emission patterns were revealed under different uphill, downhill, and level road conditions, highlighting the substantial effects of roadway geometry on vehicle carbon emissions. Based on these findings, engine-related factors were identified as the dominant contributors, explaining 74% of the emission variance, while road slope analysis showed that uphill driving increased emissions by 13.41% compared with flat roads, whereas downhill driving reduced them by 76.22%. Finally, an efficient carbon emission prediction model for LNG container trucks was developed using machine learning methods. This study enriches the understanding of carbon emissions from LNG container trucks and provides theoretical support for their future applications in sustainable freight transportation.