An effective method for point pollution source identification in rivers with performance-improved ensemble Kalman filter

To manage a river sudden pollution incident, a fundamental task is to quickly identify when, where, and how much pollutant was released. The source identification of this kind of pollution incident due to instantaneously spilled pollutant from point source remains a challenging problem because of the limited observations and the ill-posedness. In this paper, the ensemble Kalman filter (EnKF) is coupled with the backward location probability (BLP) to improve its performance in identifying the river pollution source. When applied in practice, the proposed BLP-EnKF method has two important advantages. First, it supports on-line identification and can mitigate the limitation from shortage of observations in the sudden river pollution case. Second, BLP-EnKF performs much better than conventional EnKF in terms of computation time, because it can identify the pollution source without restart forecast process. The effectiveness and efficiency of BLP-EnKF is tested and demonstrated by a synthetic case study. The case results show that the BLP-EnKF can identify all the source parameters with a relative error approximately 1.00% or smaller. Its performance can be further improved with more accurate estimation of observational error or dispersion coefficient. A real-world case in Ganjiang River further demonstrates the applicability of BLP-EnKF in practice. The pollution source is identified successfully with relative errors smaller than 3.0% for all the source parameters, while the computation time is sharply shorted from 8.0 h of conventional EnKF to 22.0 s of BLP-EnKF.