Reconstruction of an atmospheric tracer source in an urban‐like environment

Abstract

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.

Optimization of an urban monitoring network for emergency response applications: An approach for characterizing the source of hazardous releases

“…Here, the superscript p on & represents the number of sensors in an optimal network. For comparison, source estimation results for the original network and the optimal networks from the Normalized Error criterion are taken from Kumar et al () and Kouichi et al (), respectively. The (first) parentheses give the standard deviations of the estimated source parameters with 10% noise in the measurements.…”

“…A two-equation k-turbulence model was used derived from the standard high-Reynolds-number (Re) form with corrections for buoyancy and compressibility (Launder, 2004;Hanjalic, 2005). The details about the inflow profiles (wind, temperature and turbulence), the prediction errors of the forward CFD model, the duality verification, and the sources reconstruction using the original network, are available in Kumar et al (2015a;2015b) . The boundary conditions are now briefly presented.…”

“…This inversion method is operational and efficient for the source estimation at local and continental scale and also in different terrain topography (i.e. domains with and without obstacles) (Issartel, ; Sharan et al ); Kumar et al , ; ). Recently, the renormalization technique was coupled with optimization algorithms (simulated annealing and genetic algorithm) for the optimal design of networks and the results were satisfactory (Kouichi et al ; Kouichi, ).…”

Retrieval of Unknown Number of Source Terms in Dispersion Events Involving Multiple Point Sources

“…), cost function formulations (Ma et al, ; Thomson et al, ; Wang et al, , etc. ), and applications (Chai et al, ; Kumar et al, ; Sharan, Issartel, et al, ; Singh & Rani, ; Singh, Turbelin, et al, , etc. ).…”

Comparative interpretations of renormalization inversion technique for reconstructing unknown emissions from measured atmospheric concentrations

“…The present study explores about an inversion technique, called renormalization , recently proposed for the reconstruction of unknown tracer emissions from their measured atmospheric concentrations [ Issartel et al , ]. The technique requires minimal a priori information about the unknown releases and is shown efficient in reconstructing both point as well as areal distributed emission sources [ Sharan et al , ; Kumar et al , , and others]. A unique feature is the utilization of a weight matrix derived from the geometry of the monitoring network which helps to (i) provide an apparent a priori information about unknown releases to the monitoring network [ Sharan et al , ] and (ii) optimize the localization features of the retrieved source [ Turbelin et al , ; Singh et al , ].…”