New insights on the practical significance of numerical methods for surface water pollution source identification

The source identification of surface water pollution is the key issue in environmental management and has important practical needs. Model-based numerical inversion methods have received widespread attention that there are many research reports. However, most of the existing research on the pollution source identification (PSI) problem focuses on the combination innovation and theoretical analysis of the inversion algorithm, and does not consider the urgent time constraints of the emergency response process, which has become an important technical bottleneck. To this end, this study focuses on the timeliness and operability of numerical inversion methods in the emergency response process, and makes full use of multi-source information of pollutants to explore robust and fast sampling and numerical inversion methods in practical operations. The study adopts the Adative Metroplis Monte Carlo (AM-MCMC) Bayesian method as the basic source identification inversion framework, and takes the USGS tracer test in Truckee River from 2006 to 2007 as the basic scenario to carry out numerical experiments. Through the data assimilation method, the pollution source information is dynamically updated. With the input of new monitoring data, the accuracy of the inversion results is gradually improved; By integrating multiple pollutants information, greatly improves the robustness and practical ability of the numerical source identification technology. The study establishes the best practice method of parallel sampling, which can achieve reliable numerical inversion accuracy in the early stage of sampling. The quantitative design criteria of the minimum sampling cost required for inversion to meet a certain error limit under different river hydrological conditions are discussed, and the relative critical time Λ of sampling and Peclet number (Pe) that characterizes river hydrodynamics are found as follow relation: Λ=-0.816×Pe^-1/2-0.978×Pe^-1/2lnPe^-1/2+0.554, R²=0.938, and has been proved by information entropy theory. The precise design process of the emergency PSI monitoring scheme with the timeliness as the optimization goal is further proposed. This study provides important theoretical guidance for the innovative application of new monitoring methods in scenarios such as leakage detection and emergency monitoring under the background of environmental Internet of Things.