The effects of air pollution sources / sensor array configurations on the likelihood of obtaining accurate source term estimations

Estimating the source term in the case of multiple leaks using a sparse sensor array is a challenging task. Here, the effect of sensor array/leak configurations on the reliability of the source term estimation is studied using two new measures. The first describes the overall change in the sensor array response to different source terms. The second represents the effect of the source term on the readout of each sensor in the array. These measures are subjected to several model cases differing in sensor array/leak configurations. Then, the source term is estimated using a self-adaptive multiobjective evolutionary (MOEA) search algorithm combined with a gas dispersion model. The method searches for a set of leaks, each one of which has a typical emission rate and location that results in a minimal difference between the sensors' actual and computed pollution concentration. This objective, which is often used for source term estimation, is traded off against the second objective of maintaining a minimum number of active sources, which follows Occam's razor principle of parsimony. Analysis of the results obtained for these model cases suggests that the measures can be implemented as a design tool using a combination of computer simulation and field experiments before operational deployment.

Automated summary

In this study, the impact of sensor array/leak configurations on source term estimation reliability was investigated using two new measures. The first measure describes the overall change in sensor array response to different source terms, while the second measure represents the effect of the source term on individual sensor readings. The results suggest that these measures can serve as a design tool for improving source term estimation methods through a combination of computer simulation and field experiments.