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. Horvath, L. Kornyei, B. Liszkai, A. Kovacs, X. Jurado, N. Reiminger, P. Thunis, C. Cuvelier

Science of The Total Environment, 925, 171761

Thématiques : CFD, air pollution, nitrogen dioxide, validation

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.

https://doi.org/10.1016/j.scitotenv.2024.171761

N Reiminger, X Jurado, L Maurer, J Vazquez, C Wemmert

Sustainable Cities and Society, 103, 105286

Thématiques : CFD, air pollution, nitrogen dioxide, validation

Abstract

Major cities worldwide constantly deal with health hazards caused by air pollution. Modeling this pollution on an urban scale is essential for assessing the impact of local policies and promoting sustainable urban development. However, there are practical difficulties when using microscale modeling in applied context, and particularly for nitrogen dioxide modeling (NO2). In this study, a Computational Fluid Dynamics (CFD) model was employed to assess monthly NO2 concentrations in Antwerp, Belgium, and the results were compared to a one-month measurement campaign using 73 passive samplers. The result showed that using CFD with conventional assumption – such as neutral atmospheric stability consideration and using a turbulent Schmidt number (Sct) set to 0.7 – yield satisfying results according to air quality model acceptance criteria. Optimal outcomes were achieved by considering NO2 background concentration instead of NOx and employing Bachlin et al.’s empirical function to convert modeled NOx concentrations to NO2, dismissing the need for straightforward chemical mechanisms – such as photostationary steady-state equilibrium (PSS) –, or more expensive models in terms of computing resources. This approach yielded an overall error of less than 15 % and a correlation coefficient R of 0.78, affirming its effectiveness in modeling NO2 air quality in applied context.

https://doi.org/10.1016/j.scs.2023.104951

X Jurado, N Reiminger, L Maurer, J Vazquez, C Wemmert

Sustainable Cities and Society, 104951

Thématiques : AI, deep learning, atmospheric pollution, validation

Abstract

Urban areas face a significant health risk due to atmospheric pollution necessitating continuous monitoring to assess and mitigate its impact and achieve a sustainable city. In this study, a deep learning model was developed and trained using Computational Fluid Dynamics (CFD) simulations to predict the dispersion of nitrogen dioxide (NO2) emissions from traffic. The model’s performance was evaluated by comparing it to real-world field measurements conducted in Antwerp, Belgium, throughout 2016. Temporal comparisons with a traffic-influenced air quality station over the entire year yielded an average correlation coefficient (R) of 0.83 and a mean relative error (MRE) of 0.21. Additionally, a spatial evaluation was conducted by comparing the model’s predictions to a measurement campaign involving 73 samplers. The spatial evaluation resulted in an R of 0.72 and a MRE of 0.18 for samplers located near known emission sources. Notably, the deep learning model demonstrated computational efficiency, outperforming CFD simulations by an order of magnitude of 100 to 1000, enabling real-time pollution monitoring and large-scale scenario studies without compromising the consideration of micro-scale effects that are typically overlooked in larger-scale models.

https://doi.org/10.1016/j.scs.2023.104951

N. Reiminger, X. Jurado, L. Maurer, J. Vazquez, C. Wemmert

Journal of Wind Engineering and Industrial Aerodynamics 235, 105361

Themes: CFD, air quality, atmospheric stability, guidances

Abstract

Outdoor air quality is a major concern worldwide, especially in urban areas. In this paper, the influence of depressed roads (specific road designs where the road’s surface is lower than the surrounding ground level) on the downwind pollutant concentration was assessed using a validated Computational Fluid Dynamics (CFD) solver, for eight road depths (D) and thirteen stability conditions (Richardson numbers, Ri). Depressed roads can decrease downwind pollutant concentrations compared to classical roads, but only under neutral and unstable thermal conditions. Under neutral thermal condition, a threshold is reached for D = 0.375, leading to a maximal pollution reduction of around 10% at pedestrian level and around 5% at the first-floor level. In such stability conditions, pollutant concentrations are lower as D increases and Ri decreases. Under stable conditions, such roads lead to higher pollutant concentrations. Four equations allowing to predict the downwind pollutant concentrations are given depending on the distance from the road centerline, the road depth, and the thermal stability condition. The results of this study provide pre-construction guidance on whether a depressed road should be considered to protect human health, as well as predictive tools to assess the beneficial or adverse impact of such structures on air quality.

https://doi.org/10.1016/j.jweia.2023.105361

Pre-print for download

X. Jurado, N. Reiminger, L. Maurer, J. Vazquez, C. Wemmert

Atmosphere 14, 385

Themes: Monitoring, air quality, methodologies, PM10, PM2.5

Abstract

Annual concentration is a key element to assess the air quality of an area for long-time exposure effects. Nonetheless, obtaining annual concentrations from sensors is costly since it needs to have a year of measurements for each required pollutant. To overcome this issue, several strategies are studied to assess annual particulate matter concentration from monthly data, with their pros and cons depending on the risk acceptance and measurement campaign costs. When applied on a French dataset, the error spans from 12-14% with one month of measurement to 4-6% for six months of measurement for PM10 and PM2.5, respectively. A relationship between the mean relative error and 95th percentile relative error is provided with an R 2 of 0.99. The relationship between PM10 and PM2.5 was also investigated and improved compared to previous work by considering the sea-sonality and influence on emission reaching a mean relative error of 12%. Thus, this study provides tools for urban planners, engineers, researchers, and public authorities for improved monitoring of annual air pollution at a lower cost for particulate matter.

https://doi.org/10.3390/atmos14020385

Article available for free download

X. Jurado, N. Reiminger, M Benmoussa, J. Vazquez, C. Wemmert

Expert Systems with Applications 203, 117294

Themes: AI, CFD, deep learning, air quality

Abstract

Air quality is a major health issue for densified cities nowadays. To evaluate and act upon it, modeling alongside sensors has proved to be a powerful tool. Among the different available models, Computational Fluid Dynamics (CFD) has proved to be formidable to evaluate airborne pollutant dispersion locally in urban areas since it is able to consider buildings and others complexes phenomenon at the scale of the meter. Nevertheless, this method has a major drawback, it is computationally expensive and cannot be applied in real time or over large areas. To overcome this issue, several state-of-the-art deep learning methods to treat spatial information have been trained based on CFD results to predict airborne pollutant dispersion. Among these models, multiResUnet architecture was proved to be the best on overall over seven metrics. It managed to have two out of three air quality metrics within satisfactory range for a good air quality model. These results are obtained in a mere matter of tens of seconds against several hours for CFD.

https://doi.org/10.1016/j.eswa.2022.117294

X. Jurado, N. Reiminger, M Benmoussa, J. Vazquez, C. Wemmert

Application in life sciences and beyond, 13

Themes: AI, CFD, deep learning, air quality

Abstract

Air quality is a worldwide major health issue, as an increasing number of people are living in densified cities. Several methods exist to monitor pollution levels in a city, either physical models or sensors. Computational Fluid Dynamics (CFD) is a popular and reliable approach to resolve locally pollutant dispersion in urban context for its capacity to consider complex phenomenon at local scale. Nevertheless, this method is computationally expensive and is not suitable for real time monitoring over large areas and city shape that evolves permanently. To overcome this issue, a deep learning model based on the MultiResUNetarchitecture have been trained to learn pollutant dispersion from precalculated computational fluid dynamics. This model has been used in situ on an area spanning 1km² with real values from traffic and meteorological sensors in the surroundings of Strasbourg (France) and compared against the equivalent CFD results. Classic air quality metrics shows that the Deep Learning model manages to have satisfying results against the CFD model. The similarity index used in the study shows a 62% similarity for a result obtained in minutes against the CFD result obtained in tenth of hours.

X. Jurado, N. Reiminger, J. Vazquez, C. Wemmert

Sustainable Cities and Society 71, 102920

Themes: CFD, air quality, methodologies

Abstract

Computational fluid dynamics has shown a great interest among the scientific community to assess air pollutant concentrations in urban areas and, define strategies to limit air pollution and achieve sustainable cities of the future. Recent studies have given methodologies on how to assess mean annual concentrations based on numerical model results to compare with the annual air quality standards. Nonetheless, these methodologies need many wind directions to be modelled and, therefore, lead to high calculation costs. The purpose of this paper is to present two approaches to decrease the calculation costs when calculating annual concentration from computational fluid dynamics results by (1) ignoring uniformly spaced wind direction and (2) considering the predominant wind directions. According to the results, the first approach is on overall better than the second one for any wind rose or building layout considered. With the first approach, the calculation costs can be reduced up to 50 % without leading to more than 20 % of error, and even less error can be expected for homogeneous wind roses. Finally, a method to finely evaluate errors made when using the first approach versus using the whole wind rose, without computing it, is presented.

https://doi.org/10.1016/j.scs.2021.102920

Pre-print for download

L. Maurer, C. Villette, N.  Reiminger, X. Jurado, J. Laurent, Maximilien Nuel, R. Mosé, A. Wanko, D. Heintz

Water Research 190, 116672

Themes: Micropollutants, water treatment, CFD

Abstract

Conventional wastewater treatment plants are not designed to treat micropollutants; thus, for 20 years, several complementary treatment systems, such as surface flow wetlands have been used to address this issue. Previous studies demonstrate that higher residence time and low global velocities promote nutrient removal rates or micropollutant photodegradation. Nevertheless, these studies were restricted to the system limits (inlet/outlet). Therefore, detailed knowledge of water flow is crucial for identifying areas that promote degradation and optimise surface flow wetlands. The present study combines 3D water flow numerical modelling and liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS/MS). Using this numerical model, validated by tracer experimental data, several velocity areas were distinguished in the wetland. Four areas were selected to investigate the waterflow influence and led to the following results: on the one hand, the number and concentration of micropollutants are independent of the waterflow, which could be due to several assumptions, such as the chronic exposure associated with a low Reynolds number; on the other hand, the potential degradation products (metabolites) were also assessed in the sludge to investigate the micropollutant biodegradation processes occurring in the wetland; micropollutant metabolites or degradation products were detected in higher proportions (both number and concentration) in lower flow rate areas. The relation to higher levels of plant and microorganism metabolites suggests higher biological activity that promotes degradation.

https://doi.org/10.1016/j.watres.2020.116672

N. Reiminger, X. Jurado, J. Vazquez, C. Wemmert, N. Blond, J. Wertel, M. Dufresne

Sustainable Cities and Society 59, 102221

Themes: CFD, air quality, methodologies

Abstract

Numerical models are valuable tools to assess air pollutant concentrations in cities which can be used to define new strategies to achieve sustainable cities of the future in terms of air quality. Numerical results are however difficult to be directly compared to air quality standards since they are usually valid only for specific wind speed and direction while some standards are on annual values. The purpose of this paper is to present existing and new methodologies to turn numerical results into mean annual concentrations and discuss their limitations. To this end, methodologies to assess wind speed distribution based on wind rose data are presented first. Then, methodologies are compared to assess mean annual concentrations based on numerical results and on wind speed distributions. According to the results, a Weibull distribution can be used to accurately assess wind speed distribution in France, but the results can be improved using a sigmoid function presented in this paper. It is also shown that using the wind rose data directly to assess mean annual concentrations can lead to underestimations of annual concentrations. Finally, the limitations of discrete methodologies to assess mean annual concentrations are discussed and a new methodology using continuous functions is described.

https://doi.org/10.1016/j.scs.2020.102221

Pre-print for download

N. Reiminger, X. Jurado, J. Vazquez, C. Wemmert, N. Blond, M. Dufresne, J. Wertel

Journal of Wind Engineering and Industrial Aerodynamics 200, 104160

Themes: CFD, air quality, atmospheric stability, guidances

Abstract

People around the world increasingly live in urban areas where traffic-related emissions can reach high levels, especially near heavy-traffic roads. It is therefore necessary to find short-term measures to limit the exposure of this population and noise barriers have shown great potential for achieving this. Nevertheless, further work is needed to better understand how they can act on pollution reduction. To do this, a Reynolds-Averaged Navier-Stokes model that takes into account thermal effects is used to study the effects of wind speed and atmospheric stability on the concentration reduction rates (CRR) induced by noise barriers. This study shows that the CRR behind the barriers may depend on both wind and thermal conditions. Although only the wind direction, and not the wind speed, has an impact on CRR in a neutral atmosphere, this parameter can be changed by both wind speed and thermal variations in non-neutral atmospheres. Stable cases lead to a higher CRR compared to unstable cases, while the neutral case gives intermediate results. Finally, it is shown that the variation of CRR is negligible for Richardson numbers ranging from −0.50 to 0.17.

https://doi.org/10.1016/j.jweia.2020.104160

Pre-print for download

N. Reiminger, J. Vazquez, N. Blond, M. Dufresne, J. Wertel

Journal of Wind Engineering and Industrial Aerodynamics 196, 104032

Themes: CFD, air quality, guidances

Abstract

Atmospheric pollution became a big issue in densified urban areas where the ventilation in streets is not sufficient. It is particularly the case for street surrounded by high buildings so-called street canyons. The ventilation and, thus, the concentrations in this kind of street are highly relying on geometric properties of the street (width of the street, heights of the buildings, etc.). Reynolds-averaged Navier-Stokes equations are used to investigate the impact of two geometric street ratios on pollutant dispersion: the ratio of the leeward to the windward building height (H1/H2) and the ratio of the street width to the windward building height (W/H2). The aim is to quantitatively assess the evolution of mean pollutant concentrations in the case of step-down street canyons with H1/H2 ranging from 1.0 to 2.0 and street width ratios W/H2 ranging from 0.6 to 1.4. Three types of recirculation regimes could be established, depending on the number and the direction of the vortices occurring inside and outside the canyon. Evolution of pollutant concentrations as a function of both ratios is provided as well as the recommended regimes in the perspective of reducing pollutant concentration in step-down street canyons at pedestrian level and near building faces.

https://doi.org/10.1016/j.jweia.2019.104032

Pre-print for download

X. Jurado, N. Reiminger, J. Vazquez, C. Wemmert, M. Dufresne, N. Blond, J. Wertel

Atmospheric Environment 221, 117087

Themes: Monitoring, air quality, methodologies, NOx, NO2

Abstract

NO₂ is a pollutant harmful to both health and the environment. The European Union and the World Health Organization have developed guidelines in terms of pollutant. The value of 40 μg/m³ is set by both entities as the annual mean NO₂ concentration not to be exceeded to prevent risks for human health. To assess this given value, yearlong in situ measurements are required. However, sometimes only partial data are available, such as having only NO_x (NO + NO₂) information, on the one hand, and, on the other hand, brief NO₂ measurements performed over few months. To overcome the first hurdle, several methods exist in the literature to transform NO_x data into NO₂ data. The method of Derwent and Middleton is the most appropriate for France with less than 8% of deviation and even less deviation when considering rural and urban sites. For all values, NO_x concentrations behave as expected with higher concentrations in autumn and winter than in spring and summer. However, for NO₂ this trend changes around 80 μg/m³ for which the spring and summer values are higher. Therefore, to maximize measurements to assess an upper limit on annual NO₂ concentration over a short period of time, those measurements should be done in winter if an annual concentration of less than 80 μg/m3 is expected, otherwise they should carry out in summer. To tackle the second issue, a second order polynomial approach is built on a Paris dataset covering years between 2013 and 2017 to determine annual mean concentrations with monthly mean concentrations and gives an overall error of 10%. The law built on Paris was then tested on several regions in France for the same period and resulted in predicted values with a mean error of about 15% compared to the measured ones. In the end, the presented methodology allows covering twelve times more ground with a single NO₂ or NO_x sensor with an acceptable error.

https://doi.org/10.1016/j.atmosenv.2019.117087

Pre-print for download