ReviewInternational journal of molecular sciences2026
Current Research Advances and Future Prospects on Microbial Consortia for Sustainable PFAS Remediation.
Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Filamentous Algae for Wastewater Circularity: A Review of Wastewater Treatment, Resource Recovery, and Biorefinery Opportunities.Microorganisms · 2026Review
- Molecular Monitoring in Soil Bioremediation: From Genetic Potential to Verified Pathway Operation.International journal of molecular sciences · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Soil contamination by per- and polyfluoroalkyl substances (PFAS) represents a pressing environmental and public health concern due to the exceptional persistence of carbon-fluorine bonds, which prevent natural attenuation and limit the effectiveness of conventional remediation. Agricultural and industrial soils serve as long-term sinks for PFAS, continuously releasing these pollutants into groundwater and facilitating their transfer through the food chain. Conventional chemical and physical remediation methods are often costly, energy-intensive, and yield incomplete removal, underscoring the need for sustainable and biologically driven alternatives. Microbial consortia have emerged as a promising solution due to their metabolic complementarities, cross-feeding interactions, and ecological resilience, which together enable PFAS transformation and partial defluorination under complex soil and subsurface conditions. Key enzymes such as oxygenases, reductive dehalogenases, and hydrolases are often operating within co-metabolic networks, which play central roles in these processes. Advances in metagenomics, CRISPR-based functional screening, and metabolic modelling are rapidly uncovering novel PFAS-degrading microbes and pathways. Integration of machine learning with multi-omics and environmental datasets further enables the prediction of degradation mechanisms, identification of keystone degraders, and rational design of synthetic consortia. Emerging sustainable strategies, including biochar- and nutrient-amended soil microcosms, plant-microbe partnerships for coupled soil-groundwater phytoremediation, and bioelectrochemical systems that offer new avenues for enhancing PFAS biodegradation in situ. This review synthesises recent research progress and provides critical perspectives on the mechanistic, ecological, and engineering dimensions of PFAS bioremediation, proposing an integrated conceptual framework linking microbial consortia dynamics, enzymatic pathways, and environmental engineering interventions to guide scalable field applications and sustainable management of PFAS-contaminated soil-groundwater ecosystems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.