A multi-source data fusion framework for high-resolution NO_X emission mapping and targeted mitigation of heavy-duty...

Heavy-duty trucks (HDTs) are a dominant source of urban nitrogen oxide (NO_X) emissions, yet their in-use emissions often diverge from regulatory limits. We present a multi-source data fusion framework to map truck NO_X emissions at high resolution and evaluate targeted mitigation strategies for sustainable urban management. Using Shanghai as a case study, we integrate over one billion trajectory records, 2513 plume-chasing tests, and remote on-board diagnostics (OBD) data from 40,726 HDTs, fused to derive emission factor distributions and activity levels. These data feed into a dynamic, road-level, hourly inventory disaggregated by emission standard and usage pattern. Results reveal strong spatial heterogeneity, with freight corridors and port-related links as hotspots, and highly skewed distributions within the fleet: the top 20% of trucks contribute 44% of total NO_X, including not only China IV vehicles but also China V and poorly performing China VI models. We compared two control strategies: phasing out all China IV trucks versus targeted removal of the highest-emitting 20%. Under an idealized high-emitter prioritization scenario, the targeted strategy achieves ∼40% greater overall reduction and delivers larger benefits on major freight corridors. These findings highlight the potential of multi-source big data for targeted emission management, offering an effective pathway toward cleaner, more sustainable cities.