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Updated: May 8, 2026

Extraction and Analysis of Microbial Phospholipid Fatty Acids in Soils
Published on: August 26, 2016
Proactive monitoring of changes in the microbial community structure in wastewater treatment bioreactors using
Lawson Mensah1, Elise Cartmell2, Mandy Fletton3
1Kwame Nkrumah University of Science and Technology, College of Science, Kumasi, Ghana.
Abstract:
Diverse microbial community structures (MCS) in wastewater treatment plants (WWTPs) are vital for effectively removing nutrients and chemicals from wastewater. However, the regular monitoring of MCS in WWTP bioreactors remains unattractive owing to the skill and cost required for deploying modern microbial molecular techniques in the routine assessment of engineered systems. In contrast, low-resolution methods for assessing broad changes in the MCS, such as phospholipid fatty acid (PLFA) analysis, have been used effectively in soil studies for decades. Despite using PLFA analysis in soil remediation studies to capture the long-term effects of environmental changes on MCS, its application in WWTPs, where the microbial mass is dynamic and operational conditions are more fluid, remains limited. In this study, microbial communities in a controlled pilot plant and 12 full-scale activated sludge plants (ASPs) were surveyed over a two-year period using PLFA analysis. This study revealed that changes in the MCS in wastewater bioreactors could be detected using PLFA analysis. The MCS comprised 59 % Gram-negative and 9 % Gram-positive bacteria, 31 % fungi, and 1 % actinomycetes. The abundances of Gram-negative bacteria and fungi were strongly inversely correlated, with an R2 = 0.93, while the fatty acids cy17:0 and 16:1ω7c positively correlated (R2 = 0.869). Variations in temperature, solid retention time, and WWTP configuration significantly influenced the MCS in activated sludge reactors. This study showed that WWTP bioreactors can be routinely monitored using PLFA analysis, and changes in the bioreactor profile that may indicate imminent bioreactor failure can be identified.
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