Estimating the air quality standard exceedance areas and the spatial representativeness of urban air quality stations applying microscale modelling

This study builds upon the findings of a FAIRMODE intercomparison exercise conducted in a district of Antwerp, Belgium, where a comprehensive dataset of air pollutant measurements (air quality stations and passive samplers) was available. Long-term average NO<sub>2</sub> concentrations at very high spatial resolution were estimated by several dispersion modelling systems (Martín et al., 2024) to investigate the ability of these to capture the detailed spatial distribution of NO<sub>2</sub> concentrations at the microscale in urban environments. In this follow-up research, we extend the analysis by evaluating the capability of these modelling systems to predict the NO<sub>2</sub> annual limit value exceedance areas (LVEAs) and spatial representativeness areas (SRAs) for NO₂ at two reference air quality stations. The different modelling approaches used are based on CFD, Lagrangian, Gaussian, and AI-driven models. The different modelling approaches are generally good at predicting the LVEA and SRAs of urban air quality stations, although a small SRA (corresponding to low concentration tolerances or the traffic station) is more difficult to predict correctly. However, there are notable differences in performance among the modelling systems. Those based on CFD models seem to provide more consistent results predicting LVEAs and SRAs. Then, lower accuracy is obtained with AI-based systems, Lagrangian models, and Gaussian models with street canyon parameterizations. The Gaussian models with street-canyon parametrizations show significantly better results than models using simply a Gaussian dispersion parametrization. Furthermore, little differences are observed in most of the statistical indicators corresponding to the LVEA and SRA estimates obtained from the unsteady full month CFD simulations compared to those from the scenario-based CFD simulation methodologies, but there are some noticeable differences in the LVEA or SRA (traffic station, 10 % tolerance) sizes. The number of scenarios does not seem to be relevant to the results. Different bias correction methodologies are explored.

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Publication Details

Journal
The Science of The Total Environment
Published
2025-06-06
DOI
https://doi.org/10.1016/j.scitotenv.2025.179824
Citations
4
Primary Topic
Air Quality and Health Impacts
Type
article
Field-Weighted Citation Impact
4.80

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article

Estimating the air quality standard exceedance areas and the spatial representativeness of urban air quality stations applying microscale modelling

Marco Villani, Fernando Martín, Jorge Sousa, G. Tinarelli et al.
4 citations
The Science of The Total Environment
Air Quality and Health Impacts
4.80
article

Estimating the air quality standard exceedance areas and the spatial representativeness of urban air quality stations applying microscale modelling

Marco Villani, Fernando Martín, Jorge Sousa, G. Tinarelli, Stijn Janssen, Rose M. Jackson, L. Tarrasón, Esther Rivas, László Környei, Zoltán Horváth, Roberto San José, Jenny Stocker, Vera Rodrigues, Nicolas Reiminger, Ioannis A. Sakellaris, J.G. Bartzis, Gabriela Sousa-Santos, Nicola Masey, Xavier Jurado, J.L. Santiago, P. Thunis, Felicita Russo, Scott Hamilton, C. Cuvelier, Diego Barbero, J. L. Perez-Camanyo
article en
4 citations

Abstract

This study builds upon the findings of a FAIRMODE intercomparison exercise conducted in a district of Antwerp, Belgium, where a comprehensive dataset of air pollutant measurements (air quality stations and passive samplers) was available. Long-term average NO<sub>2</sub> concentrations at very high spatial resolution were estimated by several dispersion modelling systems (Martín et al., 2024) to investigate the ability of these to capture the detailed spatial distribution of NO<sub>2</sub> concentrations at the microscale in urban environments. In this follow-up research, we extend the analysis by evaluating the capability of these modelling systems to predict the NO<sub>2</sub> annual limit value exceedance areas (LVEAs) and spatial representativeness areas (SRAs) for NO₂ at two reference air quality stations. The different modelling approaches used are based on CFD, Lagrangian, Gaussian, and AI-driven models. The different modelling approaches are generally good at predicting the LVEA and SRAs of urban air quality stations, although a small SRA (corresponding to low concentration tolerances or the traffic station) is more difficult to predict correctly. However, there are notable differences in performance among the modelling systems. Those based on CFD models seem to provide more consistent results predicting LVEAs and SRAs. Then, lower accuracy is obtained with AI-based systems, Lagrangian models, and Gaussian models with street canyon parameterizations. The Gaussian models with street-canyon parametrizations show significantly better results than models using simply a Gaussian dispersion parametrization. Furthermore, little differences are observed in most of the statistical indicators corresponding to the LVEA and SRA estimates obtained from the unsteady full month CFD simulations compared to those from the scenario-based CFD simulation methodologies, but there are some noticeable differences in the LVEA or SRA (traffic station, 10 % tolerance) sizes. The number of scenarios does not seem to be relevant to the results. Different bias correction methodologies are explored.

The Science of The Total EnvironmentVol. 988
National Agency for New Technologies, Energy and Sustainable Economic Development (IT), Universidad Complutense de Madrid (ES), Centre National de la Recherche Scientifique (FR), Ricardo (United Kingdom) (GB), Cambridge Environmental Research Consultants (United Kingdom) (GB), Joint Research Centre (IT), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), University of Aveiro (PT), NILU (NO), Flemish Institute for Technological Research (BE), Université de Strasbourg (FR), Széchenyi István University (HU), Universidad Politécnica de Madrid (ES), University of Western Macedonia (GR)
European Commission, Ministério da Ciência, Tecnologia e Ensino Superior, Jacobs University, Centro de Estudos Ambientais e Marinhos, Universidade de Aveiro, Fundação para a Ciência e a Tecnologia, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, European Regional Development Fund
Sustainable cities and communities
Openalex Percentile: Top 5%
Air Quality and Health Impacts
4.80
DOIOpen Access PDFOpenAlex
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