Authors
Mariusz Cycoń
Published in
Frontiers in bioengineering and biotechnology. Volume 14. Pages 1866957. Epub Jul 01, 2026.
Abstract
Field claims about in situ bioremediation often fail because monitoring data are not mapped to the causal process they are intended to prove. This critical review defines decision-grade digital twins as inferential systems, not as visualization tools, data lakes, or calibrated trend models. A decision-grade twin must connect a living conceptual site model with evidence streams that test competing hypotheses, prediction models with stated admissibility limits, uncertainty updates, and auditable trigger logic. The review evaluates how geochemistry, compound-specific isotope analysis, functional genes and transcripts, flux measurements, geophysical imaging, toxicity endpoints, and high-frequency sensing can support claims about pathway activation, treatment limitation, rebound risk, secondary impacts, and closure readiness. It also distinguishes the decision value of reactive transport models, hybrid process-data models, surrogate models, data-driven surveillance, and microbial process representations. Across hydrocarbons and polycyclic aromatic hydrocarbons, chlorinated solvents, pesticides, pharmaceuticals, and per- and polyfluoroalkyl substances, decision value depends on whether the evidence can distinguish among reaction, retention, redistribution, metabolite formation, and durable risk reduction. The review concludes that digital twins deserve decision-grade status only when they reduce ambiguity that can affect intervention choice, operating intensity, switching criteria, monitoring design, secondary-risk management, or closure judgment.
PMID:
42460040
Bibliographic data and abstract were imported from PubMed on 16 Jul 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 3
- Comments 0