Water Distribution Networks (WDNs) are critical infrastructures that are exposed to deliberate or accidental chemical, biological or radioactive contamination. The project SMaRT-OnlineWDN aims to develop methods and software solutions 1) to detect contamination from non-specific sensors, 2) to maintain an online water quantity and water quality model that is reliable and 3) to use the past model predictions to backtrack the potential sources of contaminations. This deliverable aims to adapt, integrate and extend existing source identification concepts in the online context. From the deliverable 6.1 and the analysis of existing methods, it was decided that a two-stage method should be adapted for online context. First, an enumeration step for calculating all candidate locations is undertaken. The reaction kinetics of particular substances is not considered. Secondly, an exploration algorithm calculates probabilities for ranking of the candidate solutions. The first step method should be adapted to use more reliable recent hydraulic information stored in a historical database. It is able to treat subsequently released alarms in near real-time. This deliverable details how this method is adapted in the online context. The adaptation consists of acceleration of the first step by an adjoint method and a development of multi-stage refinement to reuse the solution from the previous times. It is developed in the Sir 3S solution with sliding windows for storing past velocity data. The source identification problem in an online context may use new responses (every 5 minutes) from sensors to improve the potential source solution with successive problem runs. As a consequence, it is adaptive and multistage. Then, for exploring solutions and accelerating the identification process, a dynamical graph simplification, both spatially and temporally, is suggested. The time simplification aggregates all the time steps to retain only the potential candidates. The graph simplification consists of applying tree/core decomposition to select best locations, representative of any contamination spreading. Finally, a sensitivity analysis is proposed to explore the influence of the uncertainty in the demand distribution.