Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp

F Martín; S Janssen; V Rodrigues; J Sousa; J L Santiago; E Rivas; J Stocker; R Jackson; F Russo; M G Villani; G Tinarelli; D Barbero; R San José; J L Pérez-Camanyo; G Sousa Santos; J Bartzis; I Sakellaris; Z Horváth; L Környei; B Liszkai; Á Kovács; X Jurado; N Reiminger; P Thunis; C Cuvelier

doi:10.1016/j.scitotenv.2024.171761

Using dispersion models at microscale to assess long-term air pollution in urban hot spots: A FAIRMODE joint intercomparison exercise for a case study in Antwerp

Sci Total Environ. 2024 May 15:925:171761. doi: 10.1016/j.scitotenv.2024.171761. Epub 2024 Mar 16.

Authors

F Martín¹, S Janssen², V Rodrigues³, J Sousa², J L Santiago⁴, E Rivas⁴, J Stocker⁵, R Jackson⁵, F Russo⁶, M G Villani⁶, G Tinarelli⁷, D Barbero⁷, R San José⁸, J L Pérez-Camanyo⁸, G Sousa Santos⁹, J Bartzis¹⁰, I Sakellaris¹⁰, Z Horváth¹¹, L Környei¹¹, B Liszkai¹¹, Á Kovács¹¹, X Jurado¹², N Reiminger¹³, P Thunis¹⁴, C Cuvelier¹⁴

Affiliations

¹ CIEMAT, Research Center for Energy, Environment and Technology, Avenida Complutense 40, 28040 Madrid, Spain. Electronic address: fernando.martin@ciemat.es.
² VITO NV, Flemish Institute for Research and Technology, Boeretang 200, 2400 Mol, Belgium.
³ CESAM & Department of Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal.
⁴ CIEMAT, Research Center for Energy, Environment and Technology, Avenida Complutense 40, 28040 Madrid, Spain.
⁵ Cambridge Environmental Research Consultants (CERC), UK.
⁶ ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, 40129 Bologna, Italy.
⁷ ARIANET S.r.l., via Crespi 57, 20159 Milano, Italy.
⁸ Computer Science School, Technical University of Madrid (UPM), Campus de Montegancedo, s/n, 28660 Madrid, Spain.
⁹ NILU - The Climate and Environmental Research Institute, Norway.
¹⁰ University of Western Macedonia (UOWM), Dept. of Mechanical Engineering, Sialvera & Bakola Str., 50132 Kozani, Greece.
¹¹ SZE, Széchenyi István University, Győr, Hungary.
¹² AIR&D, Strasbourg, France.
¹³ AIR&D, Strasbourg, France; ICUBE Laboratory, UMR 7357, CNRS/University of Strasbourg, F-67000 Strasbourg, France.
¹⁴ European Commission, Joint Research Centre (JRC), Ispra, Italy.

PMID: 38494008
DOI: 10.1016/j.scitotenv.2024.171761

Abstract

In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO₂ long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m². Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD - Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO₂ concentrations. The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO₂ hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO₂ averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO₂ averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.

Keywords: Air pollution; Long-term concentration; Microscale modelling; Model intercomparison; NO(2); Urban area.