Crucial time of emergency monitoring for reliable numerical pollution source identification - Yang, Ruiyi; Jiang, Jiping; Pang, Tianrui; Yang, Zhonghua; Han, Feng; Li, Hailong; Wang, Hongjie; Zheng, Yi (Water research (Oxford), 2024)
•Crucial time exists in the accumulation of monitoring data for Bayesian inversion.•Section number and location impact crucial time, but frequency and error level do not.•Relative crucial time determined by Peclet number and mainly controlled by dispersion.•Spatial structure of crucial time uncovered and explained by information entropy theory.•Novel design method on emergency monitoring largely improve PSI practicality. The Pollution source identification (PSI) is an important issue on river water quality management especially for urban receiving water. Numerical inversion method is theoretically an effective PSI technique, which employs monitored downstream pollutant breakthrough curves to identify the pollution source. In practice, it is important to know how much monitoring data should be accumulated to provide PSI results with acceptable accuracy and uncertainty. However, no literature reports on this key point and it seriously handers the numerical PSI technology to mature practical applications. To seek a monitoring guideline for PSI, we conducted extensively numerical experiments for single-point source instantaneous release taking Bayesian-MCMC method as the baseline inversion technique. The crucial time (Tc) phenomenon was found during the data accumulation process for Bayesian source inversion. After Tc, estimated source parameters subsequent sustained low error levels and uncertainty convergence. Results shown the presence of Tc impacted by the number and location of monitoring sections, while monitoring frequency and data error do not. Under different river hydrodynamic conditions, relative crucial time (Λ) is determined by the river's Peclet number, and minimum effective Λ was controlled by dispersion coefficient (Dx). Analytic spatial structure of Λ(U, Dx) was uncovered and this relationship successfully explained by the information entropy theory. Based on these findings, a novel design method of PSI emergency monitoring network for preparedness plan and a practical framework of PSI for emergency response were established. These findings fill the important knowledge gap in PSI applications and the guidelines provide valuable references for river water quality management. [Display omitted]