Hyperlocal urban NO2 hotspot modeling driven by microscopic traffic data

TL;DR

This study demonstrates that incorporating detector-informed dynamic traffic emissions into urban NO2 modeling significantly improves hyperlocal concentration predictions, especially at hotspots, compared to static emission models.

Contribution

It introduces a coupled high-resolution framework combining dynamic traffic emissions with dispersion modeling, enhancing hyperlocal NO2 hotspot prediction accuracy.

Findings

Dynamic emissions improve hotspot modeling accuracy.

Better representation of concentration peaks and street-canyon hotspots.

Coupled model outperforms static baseline in NO2 prediction.

Abstract

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 experiment 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 during two one-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.