Estimation of the point source parameters by the adjoint equation in the time-varying atmospheric environment with unknown turn-on time

Abstract

Bayesian inference coupled with computational fluid dynamics (CFD) has been widely used in the source term estimation (STE). At present, most scholars have studied the pollution source released in the time-invariant flow field and the turn-on time of the source is often regarded as a known parameter which requires no estimation. Nevertheless, it is of greater practical significance to estimate the source parameters by considering the time-varying characteristics of flow field. Besides, estimating the turn-on time is very important as it provides the critical information for post-disaster rescue and accident cause analysis. In this paper, the predicted concentrations are calculated by the adjoint equation in the time-varying flow field, and the turn-on time, taken as an unknown parameter, is estimated together with the source location and the release rate. Accurate estimated results are thereby obtained. Then, how the sensors’ sampling time intervals and the measured data in different dispersion stages influence the estimated results is investigated. It is found that reducing the sampling time interval can decrease the uncertainties of the estimations. In addition, in order to estimate the turn-on time accurately, the measured information in the developing stage of dispersion is indispensable. However, only using the measured information in the stable stage of dispersion cannot predict the turn-on time. Finally, the earliest time when the source parameters can be estimated with accuracy is also explored. The results show that the method of the STE proposed can obtain accurate source information at the early stage of dispersion.