Optimization of an Urban Monitoring Network for Retrieving an Unknown Point Source Emission
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 applied and validated with 20 trials of the Mock Urban Setting Test (MUST) tracer field experiment in an urban-like environment. The optimal networks in the MUST urban region enabled to reduce the size of original network (40-sensors) to ∼1/3rd (13-sensors) and to 1/4th (10-sensors). The 10 and 13 sensors optimal networks have estimated the averaged location errors of 19.20 m and 17.42 m, respectively, which are comparable to 14.62 m from the original 40-sensors network. In 80% trials, 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.