The quality of groundwater (GW) has deteriorated rapidly, which causes many serious threats owing to the exposure of many contaminants to the environment and living creatures. In the last three decades, Permeable Reactive Barrier (PRB) has been proven an efficient and well-established in-situ GW remediation technology for treating various contaminated sites around the world. In this study, the long-term performance of an in-situ PRB has been evaluated by three-dimensional numerical modelling using Visual MODFLOW for the simultaneous treatment of three different contaminants, nitrate (NO3-), phosphate (PO43-) and hexavalent chromium Cr(VI), from GW. The selected materials for PRB are considered as a mixture of five different low cost-reactive materials, i.e., rise husk (RH), fly ash (FA), quartz sand (QS), activated charcoal (AC), and activated alumina (AA), in an optimized proportion. Initially, the model has been simulated for a period of 10 years to obtain the natural attenuation of contaminant plume, with NO3-, PO43-, Cr(VI) contaminants, with the aquifer. Further, multiple continuous PRBs have been installed with optimized design and selected material properties. The simulation results show that the PRB is able to remediate the continuous multi-contaminant plume effectively. The interference of contaminants in the PRB performance also indicates a significant factor in the decline or enhancement of PRB performance in the long run. Similarly, the pumping rate in the proximity of PRB emplacement and the ratio of PRB to surrounding aquifer hydraulic conductivity also played a significant role in enhancing PRB performance. The higher the ratio, the larger the plume contaminant passes through the aquifer, which increases the overall removal efficiency of the PRB system. Therefore, the three-dimensional numerical modelling simulation results of the flow and solute transport model following the PRB performance for simultaneous removal of multi contaminants pave the way for opting for an efficient PRB design for the effective and sustainable performance for a variety of aquifers remediation. The model also provides support to build the understanding for avoiding any potential failure after the emplacement of PRB in the field.

The human health risk assessment (HHRA) of groundwater system in the vicinity of Chandigarh dumping site was conducted, assuming oral ingestion and dermal contact exposure scenarios. Observed data of lead (Pb) concentration in the leachate was used to compute cancer risk (CR) by integrating unsaturated 1-D leaching model with probabilistic HHRA framework. The 99 percentile and maximum value of lead (Pb) concentration at the water table was estimated as 0.089 mg/L and 0.506 mg/L, respectively, for pre-monsoon season, higher than the safe limit of 0.050 mg/L. In contrast, for the post-monsoon season, only the maximum value of Pb concentration exceeded the safe limit. Results from 10,000 Monte Carlo simulations showed that the 99 percentile and maximum value of CR for all the sub-populations during pre-monsoon exceeded the safe limit (>10 ) via oral ingestion exposure to Pb-contaminated groundwater. The 95 percentile value of CR for adult sub-population was estimated as 1.05 x 10 for premonsoon; however, for the post-monsoon season, only maximum values of CR exceeded the safe limit. The cancer risk estimates for the pre-monsoon and post-monsoon seasons via skin dermal contact exposure were found to be lower than the safe level, posing no danger to human health. Among sub-populations, the order of posing CR was found to be in the order as adults (>18 years) > child I (1-5 years) > teen (11-18 years) > child II (6-10 years). Uncertainty analysis showed that the lead concentration (>95% variance contribution), as a major contributor towards uncertainty in the risk estimates, while event duration (t ), exposure duration (ED), and ingestion rate (IR) were observed as minor contributors. The approach presented in this study considered the uncertainty in the unsaturated leaching model parameters along with uncertainty in the exposure model parameters, thus can help decision-makers in estimating risk from open dumping sites with minimal data availability.