In this study, we consider the assimilation problem of subsurface contaminants at the port of Rotterdam in the Netherlands. This involves the estimation of solute concentrations and biodegradation rates of four different chlorinated solvents. We focus on assessing the efficiency of an adaptive hybrid ensemble Kalman filter (EnKF-OI) and the exact second-order sampling formulation (EnKF-ESOS), for mitigating the undersampling of the filter covariance and the observation errors, respectively. A multi-dimensional and multi-species reactive transport model is coupled to simulate the migration of contaminants within a Pleistocene aquifer layer located around 25 m below mean sea level. The biodegradation chain of chlorinated hydrocarbons starting from Tetrachloroethene and ending with Vinylchloride is modeled under anaerobic environmental conditions for five decades. Yearly concentration data is used, in a synthetic setup, to condition the forecast concentration and degradation rates in presence of model and observational errors. Assimilation results demonstrate the robustness of the hybrid EnKF, for accurately calibrating the uncertain biodegradation rates. When implemented serially, the adaptive EnKF-OI scheme efficiently adjusts the weights of the involved covariances for each individual measurement. The relevance of the EnKF-ESOS is emphasized by a better maintaining of the parameters ensemble spread. On average, a well tuned hybrid EnKF-OI and the EnKF-ESOS suggest around 48% and 21% improved concentration estimates and around 70% and 23% improved anaerobic degradation rates, over the standard EnKF respectively.