Evidence map›Paper›PMID 42174665›Full record

ArticleEnvironmental microbiome2026

Using feeding regime as a microbial selective pressure to optimise biogas production and digestate sanitisation from slurry-based anaerobic digestion.

S Nolan, A Trego, N Waters, C Thorn, O Fenton, K G Richards, V O'Flaherty, U Z Ijaz, F Abram

Abstract read
In one paragraph

Article in Environmental microbiome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

S Nolan *School of Biological and Chemical Sciences, Ryan Institute, University of Galway, Galway, Ireland.
A Trego *School of Biological and Chemical Sciences, Ryan Institute, University of Galway, Galway, Ireland.
N WatersSchool of Biological and Chemical Sciences, Ryan Institute, University of Galway, Galway, Ireland.
C ThornSchool of Biological and Chemical Sciences, Ryan Institute, University of Galway, Galway, Ireland.
O FentonTeagasc, Environmental Research Centre, Johnstown Castle, Wexford, Ireland.
K G RichardsTeagasc, Environmental Research Centre, Johnstown Castle, Wexford, Ireland.
V O'FlahertySchool of Biological and Chemical Sciences, Ryan Institute, University of Galway, Galway, Ireland.
U Z IjazSchool of Engineering, The University of Glasgow, Oakfield Avenue, Glasgow, G128LT, UK. umer.ijaz@glasgow.ac.uk.
F AbramSchool of Biological and Chemical Sciences, Ryan Institute, University of Galway, Galway, Ireland. florence.abram@universityofgalway.ie.

Funding

Irish Department of Agriculture, Food and Marine 14 F847
6 · The paper itself

Abstract

backgroundThe urgent need to adopt sustainable agricultural practices has positioned anaerobic digestion (AD) as a pivotal technology. Indeed, slurry-based AD can mitigate agricultural pollution by capturing greenhouse gas from stored slurry and converting it into biomethane, a valuable source of renewable energy, while generating digestate that can be used as fertiliser. For such a strategy to be effectively and widely deployed however, AD must be optimised. To this end, efforts have typically focused solely on biogas yields, yet improvements in pathogen load reduction may potentially negate the need for a costly pasteurisation step. Hence, optimisation of AD for sanitisation as well as improved biogas output is desirable. To address this, we set up triplicate 10-L CSTR bioreactors, which were fed with a combination of slurry and fats, oils and grease for 216 days. An organic loading rate (OLR) of 2 g VS L

resultsAfter an initial period of adaptation to the new operating conditions (from day 99-150), the change in feeding regime resulted in improved E. coli removal, achieving consistently the required reduction in numbers to satisfy EU sanitisation standards (< 1000 CFU g

conclusionsTaken together, we demonstrate that feeding regime can function as a microbial selection pressure in anaerobic digestion. The switch from a 5-day to a 3-day feeding regime led to shifts in microbial pathways, underpinning the simultaneous improvement in methane production and E. coli removal. While further research is required to assess the impact of the observed microbial community dynamics on system stability, our findings suggest that full scale on-farm AD operators could explore the effects of feeding intervals on their process performance.

Indexed as

Anaerobic digestionMetagenomicsSanitisationSlurry co-digestion

Identifiers

PMID42174665
PMCPMC13404572

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LicenceCC BY
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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.