Spatiotemporal evolution pattern detection for heavy-duty diesel truck emissions using trajectory mining: A case study of Tianjin, China

摘要: Abstract Emissions from heavy-duty diesel trucks (HDDTs) pose a major threat to environment and human health. Understanding the behavior of pollutant emissions HDDTs facilitate formulation traffic-related policy measures mitigate adverse effects. This study proposes new method estimate emission inventory analyze their spatiotemporal evolution characteristics. Multi-source data were fused provide complete picture transport environment. With idea modeling by “single vehicle” “road segment,” inventories constructed with different scales using localized factors. A cube model was introduced represent high-resolution inventory. hot-spot local-outlier analysis conducted explore mechanism emissions. The megacity Tianjin in China taken as area for case study. average daily CO, NOx, PM, VOC are 12,978.18, 48,675.22, 712.6, 1217.72 kg d−1, respectively. Temporally, had significant peak at 06:00, 11:00, 18:00 affected festivals. Spatially, distribution pattern policy-driven closely related its spatial location. It increases radially outward outer-ring road periphery. identified 16 patterns. segment persistent cold spots accounted 48.27% roads, mainly distributed within outer ring road. segments intensifying hot 19.04% on intercity highway. locations outlier values reached 12,027, accounting 31.90%. key time intervals occurrence 11:00–12:00 01:00–02:00. Road showing only low–low cluster is located roads. Those low–high exhibits relatively scattered distribution, which heterogeneous HDDTs.