End-to-End Integrated Simulation for Predicting the Fate of Contaminant and Remediating Nano-Particles in a Polluted Aquifer

TL;DR

This paper introduces an integrated simulation framework that models the migration, dissolution, and remediation of DNAPL contaminants in aquifers using nZVI particles, enhancing predictive accuracy for groundwater cleanup.

Contribution

It presents a novel end-to-end modeling framework combining contaminant transport and nZVI reactive behavior for aquifer remediation.

Findings

Framework accurately predicts contaminant plume evolution.

Simulates nZVI mobility and reactive degradation.

Applicable to heterogeneous aquifer scenarios.

Abstract

Groundwater contamination caused by Dense Non-Aqueous Phase Liquid (DNAPL) has an adverse impact on human health and environment. Remediation techniques, such as the in-situ injection of nano Zero Valent Iron (nZVI) particles, are widely used in mitigating DNAPL-induced groundwater contamination. However, an effective remediation strategy requires predictive insights and understanding of the physiochemical interaction of nZVI and contamination along with the porous media properties. While several stand-alone models are widely used for predictive modeling, the integration of these models for better scalability and accuracy is still rarely utilized. This study presents an end-to-end integrated modeling framework for the remediation of DNAPL-contaminated aquifers using nZVI. The framework simulates the migration pathway of DNAPL and subsequently its dissolution in groundwater resulting in an aqueous contaminant plume. Additionally, the framework includes simulations of nZVI mobility, transport, and reactive behavior, allowing for the prediction of the radius of influence and efficiency of nZVI for contaminant degradation. The framework has been applied to a hypothetical 2-dimensional and heterogeneous silty sand aquifer, considering trichloroethylene (TCE) as the DNAPL contaminant and carboxymethyl cellulose (CMC) coated nZVI for remediation. The results demonstrate the framework's capability to provide comprehensive insights into the contaminant's behavior and the effectiveness of the remediation strategy. The proposed modeling framework serves as a reference for future studies and can be expanded to incorporate real field data and complex geometries for upscaled applications.