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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.

Noxious atmospheric releases may originate from both accidents and malicious activities. They are a major concern for public authorities or first responders who may wish to have the most accurate situational awareness. Nonetheless, it is difficult to reliably and accurately model the flow, transport, and dispersion processes in large complex built-up environments in a limited amount of time and resources compatible with operational needs. The parallel version of Micro-SWIFT-SPRAY (PMSS) is an attempt to propose a physically sound and fast response modelling system applicable to complicated industrial or urban sites in case of a hazardous release. This paper presents and justifies the choice of the diagnostic flow and Lagrangian dispersion models in PMSS. Then, it documents in detail the development of the parallel algorithms used to reduce the computational time of the models. Finally, the paper emphasizes the preliminary model validation and parallel performances of PMSS based on data from both wind tunnel (Evaluation of Model Uncertainty) and in-field reduced-scale (Mock Urban Setting Test) and real-scale (Oklahoma City) experimental campaigns.

In 2015, 2016, nine chlorine releases experiments took place at Dugway Proving Ground (Utah, USA). Five trials conducted in 2015 featured a grid of CONEX shipping containers around the release tank to simulate an idealized urban area. The trials were a great occasion to collect data describing the dynamics of “high molecular weight” species in an idealized urban environment. This paper proposes a comparison of the PMSS (Parallel Micro-Swift Micro-Spray) suite against the experimental data from four of the trials, following the choice made by an international model inter-comparison team. PMSS combines two MPI parallelized urban modelling subsystems: (1) PSWIFT phenomenologically accounts for 3D wind flow and turbulence around buildings, and (2) PSPRAY simulates air-based contaminant dispersion in urban areas using a stochastic Lagrangian Particle Dispersion Model. For this comparison, PMSS is deployed first on a hypothetic configuration and compared to the Code_Saturne CFD model, and then using the data of the trials 1, 5, 6 and 7 performed during JRII (Jack Rabbit II) 2015 and 2016. The initial momentum due to the tank discharge and the dense gas and droplet effects of the pressurized chlorine release are modelled using a dedicated radial source term jet and the Glendening equations. In trials 1 and 5, the influence of the CONEX shipping containers on the wind flow and the dispersion is explicitly considered. While spread through a large interval, the chlorine concentrations are computed both in the near field and in the far field (up to 11 km) using PMSS nesting capabilities. The results of the model are compared with the experimental data and sensitivity tests are also presented.

Urban wind flow and dispersion models are needed that can satisfactorily account for the effects of the three-dimensional (3D) building geometries but which run much faster than Computational Fluid Dynamics (CFD) models. With sufficient speed, the models can be used for rapid response and for applications where many simulations must be performed in a short time period. To satisfy this need, several diagnostic wind flow models have been developed for urban areas, where mass-consistent principles are used in combination with local wind observations to solve for the mean wind flow on domains of size ranging from a few hundred meters to several kilometers on a side, within which detailed 3-D building geometries are defined. Simple assumptions about vortex flow structures are parameterized near buildings and in street canyons. The wind flow results are used as inputs to a Lagrangian particle dispersion model (LPDM), where the needed turbulent velocities and time scales are parameterized using standard boundary layer profile formulas combined with special relations around buildings. As part of a collaborative study, with the intent of advancing each model, the developers of four of these models have run their models for two tracer releases (one daytime and one nighttime) during the Joint Urban 2003 (JU2003) field experiment. The four models are: QUIC by Los Alamos National Laboratory (LANL), 3DWF by the Army Research Laboratory, the urban Lagrangian model by the Israel Institute for Biological Research (IIBR), and Microswift/Spray (MSS) by Aria Technologies and SAIC. The comparison uses nearly identical domains and grid systems, and all models use the same input wind profile. The simulated patterns of wind fields and tracer contours are in good qualitative agreement. For wind speed near the surface, the mean model biases are less than about 20% and RMS errors are about 1–2 m s −1 . For tracer concentrations, the four models give similar quantitative results, where the mean relative biases suggest that the individual models can be sometimes as much as a factor of two high or low, and where the scatter suggests that, for all models, about 30 or 40% of the simulations are within a factor of two of observations. In most cases, the observed plume is broader than the simulated plume, and the models are biased toward slight underestimation of the dispersion of the plumes to the tall rooftops.

International audience; In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment.This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence.The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples.

The AIRCITY project, partly funded by the European Union, is now successfully achieved. It aimed at developing a 4D innovative numerical simulation tool dedicated to the dispersion of traffic-induced air pollution at local scale on the whole urban area of PARIS. AIRCITY modeling system is based on PMSS (Parallel-Micro-SWIFT-SPRAY) software, which has been developed by ARIA Technologies in close collaboration with CEA and MOKILI. PMSS is a simplified CFD solution which is an alternative to micro-scale simulations usually carried out with full-CFD. Yet, AIRCITY challenge was to model the flow and pollutant dispersion with a 3 m resolution over the whole city of Paris covering a 14 km × 11,5 km domain. Thus, the choice was to run a mass-consistent diagnostic flow model (SWIFT) associated with a Lagrangian Particle Dispersion Model (SPRAY) on a massively parallel architecture. With a 3 m resolution on this huge domain, parallelization was applied to the computation of both the flow (by domain splitting) and the Lagrangian dispersion (management of particles is split over several processors). This MPI parallelization is more complex but gives a large flexibility to optimize the number of CPU, the available RAM and the CPU time. So, it makes possible to process arbitrarily large domains (only limited by the memory of the available nodes). As CEA operates the largest computing center in Europe, with parallel machines ranging from a few hundred to several thousand cores, the modeling system was tested on huge parallel clusters. More usual and affordable computers with a few tens of cores were also utilized during the project by ARIA Technologies and by AIRPARIF, the Regional Air Quality Management Board of Paris region, whose role was also to build the end-users requirements. These computations were performed on a simulation domain restricted to the hypercenter of Paris with dimensions around 2 km × 2 km (at the same resolution of 3 m). The focus was on the improvements needed to adapt simulation codes initially designed for emergency response to urban air quality applications: • Coupling with the MM5 / CHIMERE operational photochemical model at AIRPARIF (as the forecast background), • Turbulence generated by traffic / coupling with traffic model, • Inclusion of chemical reactions / Interaction with background substances (especially NO / NO2). Finally, in-depth validation of the modeling system was undertaken using both the routine air quality measurements in Paris (at four stations influenced by the road traffic) and a field experiment specially arranged for the project, with LIDARs provided by LEOSPHERE Inc. Comparison of PMSS and measurements gave excellent results concerning NO / NO2 and PM10 hourly concentrations for a monthly period of time while the LIDAR campaign results were also promising. In the paper, more details are given regarding the modeling system principles and developments and its validation. Perspectives of the project will also be discussed as AIRCITY system. The TRL must now be elevated from a demonstration to a robust and systematically validated modeling tool that could be used to predict routinely the air quality in Paris and in other large cities around the world.

International audience

A new version of the Lagrangian particle model MicroSpray is proposed. It simulates the dense gas dispersion in situations characterized by the presence of buildings, other obstacles, complex terrain, and possible occurrence of low wind speed conditions. Its performances are compared to an atmospheric CFD model output and to a field experiment (Thorney Island).

International audience; This paper presents the objectives and the research work carried out in the frame of the ANEMOS project on short-term wind power forecasting. The aim of the project is to develop accurate models that substantially outperform current state-of-the-art methods, for onshore and offshore wind power forecasting, exploiting both statistical and physical modeling approaches. The project focus on prediction horizons up to 48 hours ahead and investigates predictability of wind for higher horizons up to 7 days ahead useful i.e. for maintenance scheduling. Emphasis is given on the integration of high-resolution meteorological forecasts. For the offshore case, marine meteorology is considered as well as information by satellite-radar images. Specific modules are also developed for on-line uncertainty and prediction risk estimation. An integrated software platform, 'ANEMOS', is developed to host the various models. This system will be installed by several end-users for on-line operation at onshore and offshore wind farms for prediction at a local, regional and national scale. The applications include different terrain types and wind climates, on- and offshore cases, and interconnected or island grids. The on-line operation by the utilities will allow validation of the models and an analysis of the value of wind prediction for a competitive integration of wind energy in the developing liberalized electricity markets.

Several procedures have been used in the past 20 years for limiting pollution peaks around industrial sites in the lower SEINE Valley, in France. Some deal with general anticyclonic situations where pollutant dispersion is very slow. Some newer procedures deal with 'plume type' peaks where steady winds direct pollution toward communities nearby. In order to demonstrate improvement possibilities of these procedures, two measurement campaigns have been performed using several techniques including: (1) Dispersion models (both Gaussian and Lagrangian). The dispersion models were used both a predictive tools and a-posteriori to judge the effect of emission reduction. The models were part of the ADSO software suite from Aria Technologies. (2) A long path (integrating) monitor, the SANOA UV-DOAS, in order to assess average pollutant concentrations along a line as opposed to point monitoring. During this program, an original set-up for checking the performances and calibration of DOAS instruments was developed at INERIS. (3) A large set (11) of point monitors for SO2. (4) Several wind sensors, including ultrasonic anemometers, and a SOund raDAR (SODAR). (5) Mesoscale wind forecasts (the ALADIN prediction from METEO France). During the campaign, all data was collected in real time and wind model predictions were input periodically into dispersion models. Measurement and model data have been analyzed a-posteriori to determine a better tailoring of emission reduction to real situations. The European Commission supported this program through the LIFE financial instrument.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In the context of the FEDER AIRCITY project, Paris City is modelled with a 3 m resolution with the purpose of computing pollutant concentration relevant for human exposition. This project involves AIRPARIF (Ile-de-France Air Quality Monitoring Network), for emission and immission data, CEA (French Atomic and Alternative Energy Agency) for HPC availability, National Geographic Institute (IGN) for city 3D data. For pollutant dispersion modelling, PMSS software was selected including a parallelised fast 3D wind field and turbulence code and a parallelised stochastic LPDM. A key point in urban air quality is NO 2 concentration, so the NO/NO 2 transformation at the street scale is a key issue. In this paper, a preliminary independent study will focus on fundamental questions. After theoretical consideration, a practical case is carried on over the Paris Opera district. The results are compared with standard continuous measurements at two air quality stations of the AIRPARIF monitoring network.