Abstract
Abstract. This study presents an optimization methodology for reducing the size of an existing monitoring network of the sensors measuring polluting substances in an urban-like environment in order to estimate an unknown emission source. The methodology is presented by coupling the simulated annealing (SA) algorithm with the renormalization inversion technique and the computational fluid dynamics (CFD) modeling approach. This study presents an application of the renormalization data-assimilation theory for optimally reducing the size of an existing monitoring network in an urban-like environment. The performance of the obtained reduced optimal sensor networks is analyzed by reconstructing the unknown continuous point emission using the concentration measurements from the sensors in that optimized network. This approach is successfully applied and validated with 20 trials of the Mock Urban Setting Test (MUST) tracer field experiment in an urban-like environment. The main results consist of reducing the size of a fixed network of 40 sensors deployed in the MUST experiment. The optimal networks in the MUST urban region are determined, which makes it possible to reduce the size of the original network (40 sensors) to ∼1/3 (13 sensors) and 1∕4 (10 sensors). Using measurements from the reduced optimal networks of 10 and 13 sensors, the averaged location errors are obtained as 19.20 and 17.42 m, respectively, which are comparable to the 14.62 m obtained from the original 40-sensor network. In 80 % of the trials with networks of 10 and 13 sensors, the emission rates are estimated within a factor of 2 of the actual release rates. These are also comparable to the performance of the original network, whereby in 75 % of the trials the releases were estimated within a factor of 2 of the actual emission rates.
Highlights
In the case of an accidental or deliberate release of a hazardous contaminant in densely populated urban or industrial regions, it is important to accurately retrieve the location and the intensity of that unknown emission source for risk assessment, emergency response, and mitigation strategies by the concerned authorities
The computational fluid dynamics (CFD) computations of the flow field presented in Kumar et al (2015a) and retroplumes computed in Kumar et al (2015b) are utilized in the proposed optimization methodology described in this study to obtain optimal monitoring networks
This study describes an approach for optimally reducing the size of an existing monitoring network of sensors in a geometrically complex urban environment
Summary
In the case of an accidental or deliberate release of a hazardous contaminant in densely populated urban or industrial regions, it is important to accurately retrieve the location and the intensity of that unknown emission source for risk assessment, emergency response, and mitigation strategies by the concerned authorities. Kumar et al (2015b, 2016) have extended the applications of the renormalization inversion technique to retrieve an unknown emission source in urban environments, whereby a CFD approach was used to generate the adjoint receptor–source relationship In this process, a coupled CFD–renormalization source reconstruction approach was described for the identification of an unknown continuous point source located at the ground surface or at a horizontal plane corresponding to a known or predefined altitude above the ground surface or an elevated release in an urban area. The concentration measurements obtained from the optimally reduced sensor networks in 20 trials of the Mock Urban Setting Test (MUST) field tracer experiment are utilized to validate the methodology by estimating an unknown continuous point source in an urban-like environment
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