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<p>Road-traffic NO<sub>2</sub> hotspots are still often modelled with static emissions and generic temporal profiles, although near-road concentrations respond strongly to rapidly changing traffic conditions. Here, we test whether detector-informed dynamic traffic emissions improve hyperlocal NO<sub>2</sub> modelling relative to a conventional static baseline. To this end, we couple an online-calibrated mesoscopic traffic model (SUMO) with the LES-based urban dispersion model CAIRDIO in a nested high-resolution framework for Leipzig, Germany. We compare two otherwise identical experimental setups: a static reference simulation and a coupled simulation in which road-traffic emissions within the SUMO domain are replaced by dynamic emissions derived from simulated traffic states. The framework is designed for city-wide high-resolution application, while the present evaluation focuses on two traffic-oriented hotspot settings across three 1-week periods. Compared against hourly NO<sub>2</sub> observations of official air-quality monitoring, the coupled setup performs better overall, with the clearest improvement at the street-canyon hotspot and in the representation of concentration peaks. Dynamic traffic emissions, therefore, provide clear added value for hyperlocal NO<sub>2</sub> prediction where hotspot realism and exposure-relevant peaks matter.</p>

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Hyperlocal urban NO2 hotspot modeling driven by microscopic traffic data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月7日

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  • Michael Weger,
  • Thomas Trabert,
  • Timo Houben,
  • Alexander Sohr,
  • Elmar Brockfeld,
  • Oswald Knoth,
  • Roland Schrödner,
  • Jan Bumberger

摘要

Road-traffic NO2 hotspots are still often modelled with static emissions and generic temporal profiles, although near-road concentrations respond strongly to rapidly changing traffic conditions. Here, we test whether detector-informed dynamic traffic emissions improve hyperlocal NO2 modelling relative to a conventional static baseline. To this end, we couple an online-calibrated mesoscopic traffic model (SUMO) with the LES-based urban dispersion model CAIRDIO in a nested high-resolution framework for Leipzig, Germany. We compare two otherwise identical experimental setups: a static reference simulation and a coupled simulation in which road-traffic emissions within the SUMO domain are replaced by dynamic emissions derived from simulated traffic states. The framework is designed for city-wide high-resolution application, while the present evaluation focuses on two traffic-oriented hotspot settings across three 1-week periods. Compared against hourly NO2 observations of official air-quality monitoring, the coupled setup performs better overall, with the clearest improvement at the street-canyon hotspot and in the representation of concentration peaks. Dynamic traffic emissions, therefore, provide clear added value for hyperlocal NO2 prediction where hotspot realism and exposure-relevant peaks matter.