# Characterizing On-Road CO2 and NOx Emissions of LNG and Diesel Container Trucks Using Portable Emission Measurement System

## TL;DR

This study measures real-world CO2 and NOx emissions from LNG and diesel container trucks using portable sensors to evaluate their environmental performance.

## Contribution

The study introduces a PEMS-based approach to characterize real-driving emissions of LNG trucks and compares them with diesel trucks using machine learning.

## Key findings

- High CO2 emissions from LNG trucks occur during low- to medium-speed acceleration and at speeds above 40 km/h on highways.
- The Random Forest model provides superior CO2 emission prediction accuracy for LNG trucks in highway scenarios.
- Engine parameters mainly influence LNG truck emissions, while VSP and acceleration impact diesel trucks.

## Abstract

Heavy-duty vehicles (HDVs) are major greenhouse gas emitters, and liquefied natural gas (LNG)-powered HDVs have emerged as a promising low-carbon alternative. However, their real-world emission performance and mitigation potential remain insufficiently studied, necessitating the characterization of LNG container trucks’ on-road CO2 emissions via advanced sensing technologies. To characterize HDVs’ emission characteristics, real-driving emissions from China VI LNG and diesel-powered container trucks were measured employing portable emissions measurement systems (PEMS). The results reveal that high CO2 emissions predominantly occur during low- to medium-speed acceleration and at speeds above 40 km/h with an acceleration exceeding 0.3 m/s2 on highways, whereas emissions on port roads are more dispersed. A third-degree polynomial function fits emissions well with vehicle-specific power (VSP). Engine parameters mainly influence CO2 emissions for LNG trucks, while VSP and acceleration significantly impact diesel trucks. The Random Forest model achieves superior prediction accuracy, particularly in highway scenarios, and significantly better CO2 forecasting for LNG-powered trucks. These findings validate the effectiveness of PEMS-based sensing in characterizing low-carbon HDVs’ real-world emissions. The integration of multi-source sensor data and machine learning also provides a reference for intelligent sensing in transportation environmental monitoring.