Synthetic DNA Fragments Enable Ultra-High-Reproducibility Tracer Tomography for Aquifer Time-Lapse Monitoring

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Authors: lin
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Abstract

Long-term groundwater management practices such as contaminated site remediation, managed aquifer recharge, and geological carbon storage increasingly rely on time-lapse monitoring to detect temporal changes in aquifer heterogeneity. Yet, distinguishing the temporal evolution of subsurface properties from baseline measurement variability remains a persistent challenge for tracer-based characterization methods. We demonstrate, for the first time, that synthetic DNA Fragments (DNA tracers) enable reproducible tomographic inversion of hydraulic conductivity fields by conducting six replicate experiments (lasting for nearly 2 years) to systematically compare the usefulness of DNA tracers versus traditionally recognized fluorescent dye tracers across three progressive levels: travel-time consistency, inverted K-field coherence, and forward simulation stability. DNA tracers exhibited ultra-high reproducibility at all levels: travel-time deviations clustered tightly (interquartile range, IQR=0.12 vs. 0.25 for dyes), inverted K fields showed moderate-to-strong correlations (R²=0.32–0.86 vs. 0.02–0.52 for dyes), and predicted hydraulic heads maintained high consistency (R²=0.92–0.98 vs. 0.73–0.91 for dyes). This advantage arises from the physicochemical properties of DNA tracers and their sequence-specific quantitation via qPCR, which significantly reduces background interference and transport-related artifacts. These findings position DNA tracers as robust, reproducible benchmarks for time-lapse aquifer monitoring, addressing a critical gap in tracer-tomography-based dynamic groundwater characterization and enabling more reliable interpretation of subsurface changes during long-term environmental interventions.

Fields of Study & Subjects

Groundwater flow and contamination studies Groundwater and Isotope Geochemistry Geophysical and Geoelectrical Methods

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
7 February 2026
Document type
Dataset