Identification of Recurrent Urban Vehicle Emission Hotspots Using Repeated GPS-Based Real-Driving Measurements and Vehicle-Specific Power: A Case Study in Quito, Ecuador

Urban vehicle emissions are strongly influenced by transient driving conditions, while route-level emission factors may conceal locations where high-emission events repeatedly occur. This study proposes a spatial recurrence framework integrating repeated real-driving measurements, GPS trajectories, Vehicle-Specific Power (VSP), and instantaneous emissions to identify persistent emission hotspots. Twelve repeated tests conducted along the same urban route in Quito, Ecuador, provided 69,414 synchronized observations of vehicle operation and CO, CO2, and HC emissions. High-power conditions (VSP>5 kW/t) represented 16.67% of observations but accounted for 83.65% of CO, 55.89% of CO2, and 67.61% of HC emissions. Pollutant-specific hotspots were identified using emission intensity, high-load occurrence, and minimum-data criteria, and their recurrence was evaluated across the twelve runs. Using a 100 × 100 m reference grid, 2 CO, 14 CO2, and 19 HC cells exhibited recurrence levels of at least 75%. Grid-size sensitivity analysis (50–200 m) revealed a trade-off between spatial detail and repeated-route coverage, while CO2 hotspot counts remained stable across 100–200 m grids. An additional sensitivity analysis applying an empirically estimated 19 s temporal correction showed relatively high spatial agreement for CO2 and HC hotspots (Jaccard indices of 0.765 and 0.895, respectively), whereas neither of the two original CO hotspots retained its location. The proposed framework distinguishes isolated high-emission events from spatially persistent emission-prone locations while highlighting the pollutant-dependent influence of temporal alignment on hotspot identification.