Development of a DREAM-based inverse model for multi-point source identification in river pollution incidents: Model testing and uncertainty analysis

Abstract

Source identification plays a vital role in implementing control measures for sudden river pollution incidents. In contrast to single-point source identification problems, there have been no investigations into inverse identification of multi-point emissions. In this study, an inverse model is developed based on the observed time series of pollutant concentrations and the DiffeRential Evolution Adaptive Metropolis (DREAM) method to identify multi-point sources with uncertainty quantification. We aim to simultaneously determine source mass, release location and time of multi-point sources. The newly developed DREAM-based model has been tested and verified through both numerical and field data case studies in terms of accuracy, reliability, and computational time. Adapted cases with single-point, two-point and three-point sources in the Songhua River are conducted to test the applicability of the modeling approach, respectively. The developed model can correctly quantify source parameters with a relative error that does not exceed ±0.63%, although it shows that an increase of emission sources may slightly increase the identification error. Among the three source parameters, the identification error of the release time tends to rise more obviously in response to the increase in the number of pollution sources. It is also found that the identification accuracy is primarily sensitive to the river velocity, followed by the dispersion coefficient and the river cross-sectional area. Furthermore, good monitoring strategies, including reducing observation errors, shortening monitoring interval time and selecting the proper monitoring distance between the monitoring and the source sites, help to achieve a better application of the developed model in river pollution incidents.