Validation of a Lagrangian particle dispersion model with wind tunnel and field experiments in urban environment

Castelli, Silvia; Armand, Patrick; Tinarelli, G.; Duchenne, Christophe; Nibart, M.

doi:10.1016/j.atmosenv.2018.08.045

Cited by 42 publications

(23 citation statements)

References 20 publications

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“…6. Validation metrics (Chang and Hanna, 2004) were used to quantify the overall accuracy of the CFD simulated concentrations based on OF, compared with the measured values (Ferrero et al, 2019;Trini Castelli et al, 2018). The following metrics were used: fractional bias (FB), geometric mean bias (MG) and normalized mean square error suggested that |FB| < 0.3, 0.7 < MG < 1.3 and NMSE < 4 are acceptable for simulated concentrations (Hanna et al, 2004).…”

Section: Fig 1 Simulation Domain Of Street Canyonmentioning

confidence: 99%

Modelling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD

Lin

Wang

Ooka

et al. 2022

Preprint

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Abstract. In the urban environment, gas such as nitrogen dioxide NO2, and particles impose adverse impacts on pedestrians’ health. The conventional computational fluid dynamics (CFD) methods that regard pollutant as passive scalar cannot reproduce the formation of secondary pollutants, such as NO2 and secondary inorganic and organic aerosols, leading to uncertain prediction. In this study, SSH-Aerosol, a modular box model that simulates the evolution of gas, primary and secondary aerosols, is coupled with the CFD software OpenFOAM and Code_Saturne. The transient dispersion of pollutants emitted from traffic in a street canyon is simulated using unsteady Reynolds-averaged Navier–Stokes equations (RANS) model. The simulated concentrations of NO2, PM10 and black carbon are compared with field measurements on a street of Greater Paris. The simulated NO2 and PM10 concentrations based on the coupled model achieved better agreement with measurement data than the conventional CFD simulation. Meanwhile, the black carbon concentration is underestimated, probably partly because of the underestimation of non-exhaust emissions (tyre and road wear). Vehicles are considered the main source of ammonia (NH3) in urban environments, which may condense with nitric acid (HNO3) to form ammonium nitrate. In the reference simulation with NH3 traffic emissions accounting for 1–2 % of NOx emissions, aerosol dynamics leads to an ammonium nitrate increase of 46 % on average over a 12-hour simulation period (5 a.m. to 5 p.m.) compared to the conventional CFD simulation. Furthermore, an increase in NH3 traffic emissions (to 10 % and 20 % of NOx emissions) may leads to a large increase in ammonium nitrate (35 % and 55 %) compared to the reference simulation. In addition, aerosol dynamics leads to a 52 % increase in 12-hour time-averaged organic matter concentrations compared to the conventional CFD simulation, because of the condensation of anthropogenic compounds from precursor-gas emissions and of background biogenic precursor-gases on the enhance inorganic concentrations.

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Section: Fig 1 Simulation Domain Of Street Canyonmentioning

confidence: 99%

Modelling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD

Lin

Wang

Ooka

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…In this study, Christchurch was selected as a target city to demonstrate MAPM's capability, as Christchurch is New Zealand's third largest city (population of 385 500 as of June 2019) and is one of the most polluted cities in New Zealand, especially during winter. The main source of PM emissions in Christchurch in winter is burning wood and coal for home heating (Scott and Sturman, 2006;Coulson et al, 2017;Tunno et al, 2019). Minor anthropogenic sources of PM 2.5 are industry and transport, along with natural sources including dust and sea salt particles from the nearby ocean.…”

Section: Mapm Measurement Campaignmentioning

confidence: 99%

“…emissions (Maksyutov et al, 2020), there are not many studies that have used LPDMs to infer air pollution sources at city scales. Although not used to infer air pollution sources, Trini Castelli et al (2018) demonstrated the capabilities of a three-dimensional LPDM driven by three-dimensional flow and turbulence input in both idealized and realistic urban mock-ups. Gariazzo et al (2007) used the SPRAY (Tinarelli et al, 1994) LPDM to evaluate the relative impact on air quality of harbour emissions, with respect to other emission sources located in the same area, for the city of Taranto, Italy.…”

Section: Introductionmentioning

confidence: 99%

The MAPM (Mapping Air Pollution eMissions) method for inferring particulate matter emissions maps at city scale from in situ concentration measurements: description and demonstration of capability

et al. 2021

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Abstract. Mapping Air Pollution eMissions (MAPM) is a 2-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. In this proof-of-concept study, we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future developments. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on a city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40 %–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps.

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“…PMSS has then been improved, and parallel versions of Micro-SWIFT and Micro-SPRAY (abbreviated as PSWIFT and PSPRAY) have been developed [7,8]. Recent validations in this scope of application are presented in [9,10].…”

Section: The Pmss Modeling Systemmentioning

confidence: 99%

Optimization of HPC Use for 3D High Resolution Urban Air Quality Assessment and Downstream Services

et al. 2021

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The number of cities, or parts of cities, where air quality has been computed using the PMSS 3D model now appears to be sufficient to allow assessment and understanding of performance. Two fields of application explain the growing number of sites: the first is the long-term air quality assessment required in urban areas for any building or road project. The geometric complexity found in such areas can justify the use of a 3D approach, as opposed to Gaussian ones. However, these studies have constraining rules that can make the modelling challenging: several scenarios are needed (current, future with project, future without project), the long-term impact implies a long physical time period to be computed, and the spatial extension of the domain can be large in order to cover the traffic impact zone of the project. The second type of application is dedicated to services and, essentially, to forecasting. As for impact assessments, the modelling can be challenging here because of the extension of the domain if the target area is a whole city. Forecast also adds the constraint of time, as results are requested early, and the constraint of robustness. The CPU amount needed to meet all these requirements is important. It is therefore crucial to optimize all possible parts of the modelling chain in order to limit cost and delay. The sites presented in the article have been modelled with PMSS for long periods. This allows feedback to be provided on different topics: (a) daily forecasts offer an opportunity to increase the robustness of the modelling chain; (b) quantitative validation at air quality measurement stations; (c) comparison of annual impact based on a whole year, and based on a sampling list of dates selected thanks to a classification process; (d) large calculation domains with widespread pollutant emissions offer a great opportunity to qualitatively check and improve model results on numerous geometrical configurations; (e) CPU time variations between different sites provide valuable information to select the best parametrizations, to predict the cost of the services, and to design the needed hardware for a new site.

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Validation of a Lagrangian particle dispersion model with wind tunnel and field experiments in urban environment

Cited by 42 publications

References 20 publications

Modelling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD

Modelling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD

The MAPM (Mapping Air Pollution eMissions) method for inferring particulate matter emissions maps at city scale from in situ concentration measurements: description and demonstration of capability

Optimization of HPC Use for 3D High Resolution Urban Air Quality Assessment and Downstream Services

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