An accurate simulation of the short-range plume dispersion of a hazardous pollutant in a geometrically complex urban region is a prerequisite in emergency preparedness and to assist regulators for developing effective policies. This study critically examines the real predictive capability of a three-dimensional Computational Fluid Dynamics (CFD) model, Fluidyn-PANACHE, to apply it in emergency contexts of an accidental or deliberate three cases of the inflow boundary conditions is well achieved within the acceptable standards for air quality applications. The model with three cases 1, 2, & 3 predicts respectively 52.8%, 59.9%, and 67.9% of the total concentrations within a factor of two and shows an overall under-prediction. The sampling line maximum concentrations are better simulated by the CFD model with case-3 (95% within a factor of two) in comparison to other cases 1 & 2. A comparative statistical analysis is also performed with other evaluation studies in the literature for the averaged and sampling line maximum concentrations. The present evaluation of the Fluidyn-PANACHE strengthen the evidence that it is capable of dealing properly with the dispersion phenomena in geometrically complex urban environments.

This study describes a methodology combining a recently proposed renormalization inversion technique with a building‐resolving computational fluid dynamics (CFD) approach for source retrieval in the geometrically complex urban regions. It presents the first application of the renormalization inversion approach to estimate an unknown continuous point release in real situations at an urban scale. The renormalization inversion approach is based on an adjoint source‐receptor relationship and is purely deterministic in nature. The source parameters (i.e., source location and release rate) are reconstructed from a finite set of point measurements of concentration acquired from some sensors and the adjoint functions computed from a CFD model fluidyn‐PANACHE that is able to represent the geometric and flow complexity inherent in the urban regions. The inversion procedure is evaluated for a point source reconstruction using measurements from the Mock Urban Setting Test (MUST) field experiment. Source reconstructions are performed for 20 trials of the MUST experiment of a continuous point release in an idealized urban geometry consisting of a regular array of shipping containers. The steady state flow fields are computed by solving the three‐dimensional Reynolds‐averaged Navier‐Stokes equations by using a finite volume scheme. Then, in each MUST trial adjoint functions are obtained and used for the source identification. Inversion results are presented with both synthetic and real measurements in various atmospheric stabilities varying from neutral to stable and very stable conditions. With real concentration measurements, the point source is retrieved within an average Euclidean distance of 14.6 m from the actual source location. The estimated source intensity is overpredicted by an average factor of 1.37 of the true release rate. In a posterior uncertainty analysis with 10% random noise in measurements, it is demonstrated that standard deviation in the location error and release strength, respectively, varies by 5.22 m and ∼21% from their mean value for all 20 trials. A sensitivity analysis shows that the use of nonzero measurements helps in reducing the uncertainties involved in the source reconstruction. The source reconstruction results in various stability conditions exhibit the reliability of the renormalization inversion methodology coupled with the CFD approach in an urban area. The present methodology can be used by emergency regulators as a tool to detect the unknown accidental or deliberated releases in the complex urban environments.

To obtain, over medium term periods, wind speed time series on a site, located in the southern part of the Paris region (France), where long recording are not available, but where nearby meteorological stations provide large series of data, use was made of ANN based models. The performance of these models have been evaluated by using several commonly used statistics such as average absolute error, root mean square error, normalized mean square error and correlation coefficient. Such global criteria are good indicators of the "robustness" of the models but are unable to provide useful information about their "effectiveness" in accurately generating wind speed fluctuations over a wide range of scales. Therefore a complementary wavelet cross coherence analysis has been performed. Wavelet cross coherence, wavelet cross correlation and spectral wavelet cross correlation coefficients, have been calculated and displayed as functions of the equivalent Fourier period. These coefficients provide quantitative measures of the scale-dependence of the model performance. In particular the spectral wavelet cross coherence coefficient can be used to have a rapid and efficient identification of the validity range of the models. The results show that the ANN models employed in this study are only effective in computing large scale fluctuations of large amplitude. To obtain a more representative time series, with much higher resolution, small scale fluctuations have to be simulated by a superimposed statistical model. By combining ANN and statistical models, both the high and the low frequency segments of the wind velocity spectra can be simulated, over a range of several hours, at the target site.

This study illustrates an atmospheric source reconstruction methodology for identification of an unknown continuous point release in the geometrically complex urban environments. The methodology is based on the renormalization inversion theory coupled with a building resolving Computational Fluid Dynamics (CFD) modelling approach which estimates the release height along with the projected location on the ground surface and the intensity of an unknown continuous point source in an urban area. An estimation of the release height in a three-dimensional urban environment is relatively more difficult from both technical and computational point of view.

Abstract. This study presents a methodology for the optimization of a monitoring network of sensors measuring the polluting substances in an urban environment with a view to estimate an unknown emission source. The methodology was presented by coupling the Simulated Annealing algorithm with the renormalization inversion technique and the Computational Fluid Dynamics (CFD) modeling approach. Performance of a network was analyzed by reconstructing the unknown continuous point emissions using the concentration measurements from the sensors in that optimized network. This approach was successfully emission rates with the 10 and 13 sensors networks were estimated within a factor of two which are also comparable to 75% 10 from the original network. This study presents the first application of the renormalization data-assimilation approach for the optimal network design to estimate a continuous point source emission in an urban-like environment.

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The aim of this study is to optimize sensor networks for fast deployment in order to reconstruct an unknown source of intentional or accidental release in local urban topography. In such emergency circumstances, only the meteorological conditions are available in real time and the network deployed must be efficient enough regardless of a source's position and intensity. To determine the optimal positions to be instrumented by the sensors, an adequate cost function is defined based on the renormalization inversion method. This function, named the entropic criterion, quantifies the amount of information contained in a network of the sensors to estimate the intensity and the location of an unknown source. The optimal design is approached as combinatorial optimization (NP‐Hard) and a stochastic algorithm (simulated annealing, SA) is employed to solve this problem. The computation is performed by coupling the CFD adjoint fields in an urban environment, the renormalization algorithm and the SA. The optimization is evaluated with 20 trials of the Mock Urban Setting Test (MUST) tracer field experiment for the reconstruction of a continuous point release in an idealized urban geometry using optimal networks of sizes 10 and 13 sensors. The process is achieved successfully and the results showed that the reduction of an original network of 40 sensors to one third (13) and one quarter (10) does not degrade the performance of this network. Also, a comparison of the optimal design efficiency based on apriori information and without apriori information about the source showed that the present entropic criterion leads to network design and performance that can accurately retrieve an unknown emission source in an urban environment.