Quantification of Multi-Source Road Emissions in an Urban Environment Using Inverse Methods

The spatial quantification of multiple sources within the urban environment is crucial for understanding urban air quality and implementing measures to mitigate air pollution levels. At the same time, emissions from road traffic contribute significantly to these concentrations. However, uncertainties arise when assessing the contribution of multiple sources affecting a single receptor. This study aims to evaluate an inverse dispersion modelling methodology that combines Computational Fluid Dynamics (CFD) simulations with the Metropolis–Hastings Markov Chain Monte Carlo (MCMC) algorithm to quantify multiple traffic emissions at the street scale. This approach relies solely on observational data and prior information on each source’s emission rate range and is tested within the Augsburg city centre. To address the absence of extensive measurement data of a real pollutant correlated with traffic emissions, a synthetic observational dataset of a theoretical pollutant, treated as a passive scalar, was generated from the forward dispersion model, with added Gaussian noise. Furthermore, a sensitivity analysis also explores the influence of sensor configuration and prior information on the accuracy of the emission estimates. The results indicate that, when the potential emission rate range is narrow, high-quality predictions can be achieved (ratio between true and estimated release rates, Δq≤2) even with networks using data from only 10 sensors. In contrast, expanding the allowable emission range leads to reduced accuracy (2≤Δq≤6), particularly in networks with fewer than 50 sensors. Further research is recommended to assess the methodology’s performance using real-world measurements.