The aim of the study is to propose a technique for the retrieval of point sources of atmospheric trace species from concentration measurements. The inverse problem of identifying the parameters of a point source is addressed within the assimilative framework of renormalization recently proposed for the identification of distributed emissions. This theory has been extended for the point sources based on the property that these are associated with the maximum of the renormalized estimate computed from the observations. This approach along with an analytic dispersion model is used for point source identification, and the sensitivity of the samplers is described by the same model in backward mode. The proposed technique is illustrated not only with synthetic measurements but also with seven sets of observations, corresponding to convective conditions, taken from the lowwind tracer diffusion experiment conducted at the Indian Institute of Technology Delhi in 1991. The position and intensity of the source are retrieved exactly with the synthetic measurements in all the sets validating the technique. The position of the source is retrieved with an average error of 17 m, mostly along wind; its intensity is estimated within a factor 2 for all the sets of real observations. From a theoretical point of view, the link established between point and distributed sources clarifies new concepts for the exploitation of monitoring networks. In particular, the influence of the noise on the identification of a source is related to the relative visibility of the various regions described with a renormalizing weight function. The geometry of the environment modified according to the weights is interpreted as an apparent geometry. It is analogous to the apparent flatness of the starry sky in eye's view, usually considered an impression rather than a scientific fact.

The identification of single and multiple-point emission sources from limited number of atmospheric concentration measurements is addressed using least square data assimilation technique. During the process, a new two-step algorithm is proposed for optimization, free from initialization and filtering singular regions in a natural way. Source intensities are expressed in terms of their locations reducing the degree of freedom of unknowns to be estimated. In addition, a strategy is suggested for reducing the computational time associated with the multiple-point source identification. The methodology is evaluated with the synthetic, pseudo-real and noisy set of measurements for two and three simultaneous point emissions. With the synthetic data, algorithm estimates the source parameters exactly same as the prescribed in all the cases. With the pseudo-real data, two and three point release locations are retrieved with an average error of 17 m and intensities are estimated on an average within a factor of 2. Finally, the advantages and limitations of the proposed methodology are discussed.

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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 paper addresses the parametric inverse problem of locating the point of release of atmospheric pollution. A finite set of observed mixing ratios is compared, by use of least squares, with the analogous mixing ratios computed by an adjoint dispersion model for all possible locations of the release. Classically, the least squares are weighted using the covariance matrix of the measurement errors. However, in practice, this matrix cannot be determined for the prevailing part of these errors arising from the limited representativity of the dispersion model. An alternative weighting proposed here is related to a unified approach of the parametric and assimilative inverse problems corresponding, respectively, to identification of the point of emission or estimation of the distributed emissions. The proposed weighting is shown to optimize the resolution and numerical stability of the inversion. The importance of the most common monitoring networks, with point detectors at various locations, is stressed as a misleading singular case. During the procedure it is also shown that a monitoring network, under given meteorological conditions, itself contains natural statistics about the emissions, irrespective of prior assumptions.