Structural decomposition of heavy-duty diesel truck emission contribution based on trajectory mining

Abstract

Nonlocal heavy-duty diesel trucks (HDDTs) transported across regions cause serious pollution in local atmospheric environments. However, previous studies misestimate the number of nonlocal HDDTs in the region and cannot identify the origin of the HDDTs on unmonitored road segments, resulting in unclear spatiotemporal distribution patterns of the emission contributions. This study therefore inferred the origins of HDDTs using trajectory data mining and obtained an accurate decomposition of the emission contribution from nonlocal HDDTs, from the single-vehicle-based HDDT emission inventory. A case study is conducted in Beijing. The results showed that the emission contribution of HDDTs from other regions to Beijing has a significant aggregation pattern and power function relationship with distance. The majority of nonlocal HDDTs in Beijing originate from Hebei and Tianjin and have the largest interaction intensity with Beijing, contributing 43% of the traffic counts and 37.05% of the emission intensity of HDDTs in Beijing. Temporally, the emission contribution of HDDTs from different regions has daily periodicity and is affected by major festivals. Nonlocal HDDTs dominate night-time emissions, which contributed 69.94% of total emissions at 1:00 a.m. The spatial heterogeneity of the emission contribution structure is mainly attributable to the traffic volume, highway freight ton-kilometers, and tertiary industry proportion. These findings extend the scientific understanding of the emission contributions of HDDTs and provide an important scientific basis for future strategies related to the control and management of emissions from HDDTs.