Quantifying aquifer heterogeneity using superparamagnetic DNA particles

Abstract

Identifying and determining hydraulic parameters of physically heterogeneous aquifers is pivotal for flow field analysis, contaminant migration and risk assessment. In this research, we applied a novel uniquely sequenced DNA tagged superparamagnetic silica microparticles (SiDNAmag) to quantify hydraulic parameters and associated uncertainties of a heterogeneous sand tank. In the sand tank with lens shaped heterogeneity, we conducted three sets of multi – point injection experiments in unconsolidated (1) homogeneous (zone 0), (2) heterogeneous with a no-conductivity-zone (zone 1), and (3) heterogeneous with a high-conductive-zone (zone 2). From the breakthrough curves (BTC), we estimated the parameters distributions of hydraulic conductivity (k), effective porosity (ne), longitudinal dispersivity (αL), transverse vertical (αTV), and transverse horizontal dispersivities (αTH) applying Monte Carlo simulation approach for BTC fitting. The estimated parameters and associated uncertainties for each of the heterogeneous sections were further statistically compared (distribution non-specific Mann Whitney U test) these parameter distributions with parameter distributions estimated from the conservative salt tracer. While the time of arrival and time to peak concentration of SiDNAmag and salt in effluent were comparable, peak concentration of SiDNAmag was 1–3 log reduced as compared to the salt tracer due to first order kinetic attachment. Nonetheless, the parameters and associated uncertainty distributions (5 %–95 %) of K, ne, αL, αTV, and αTH, determined from SiDNAmag BTCs were statistically equivalent to the salt tracer in all three experiment systems. Through our experimental and modelling approach, our work demonstrated that in a coarse to very coarse grain sand medium, with lens shaped heterogeneity, the uniquely sequenced SiDNAmag were a promising tool to identify heterogeneity and determine hydraulic parameters and associated uncertainty distributions.