The process of breaking down chicken manure through anaerobic digestion is an effective waste management technology. However, chicken manure can be a challenging feedstock, causing ammonia stress and digester instability. This study examined the impacts of adding wood biochar and acid-alkali-treated wood biochar to anaerobically digest chicken manure under conditions of ammonia inhibition. The results highlighted that only the addition of 5 % acid-alkali-treated wood biochar by volume can achieve cumulative methane production close to the typical methane potential range of chicken manure. The treated wood biochar also exhibited highest total ammonia nitrogen removal compared to the Control treatment. Scanning Electron Microscope revealed growing interactions between biochar and methanogens over time. Real-time polymerase chain reaction showed that treated wood biochar produced the highest number of bacterial biomass. In addition, 16S amplicon-based sequencing identified a more robust archaeal community from treated biochar addition. Overall, the acid-alkali treatment of biochar represents an effective method of modifying biochar to improve its performance in anaerobic digestion.

The molecular detection of microorganisms in environmental samples relies on PCR-associated molecular workflows that typically cannot differentiate live from dead microbes. Understanding the microbial functions of complex communities can be significantly hindered by presence of the dead microbes. Using propidium monoazide (PMA), PMA-based viability PCR, is arguably the most convenient method to differentiate viability status apart. Errors from variabilities of non-standardized practices and a lack of understanding of the PMA mechanism deter the viability PCR approach. This review discusses the sources of variability in each of four key sequential steps: pre-analysis, PMA activation, DNA extraction and PCR. An analysis of previous literature on optimization of PMA-based viability PCR indicates that often only one source of variability is considered. However, all steps are interrelated and should be considered together when understanding and mitigating unwanted variability, especially in the PMA activation and PCR steps. Research gaps in PMA are addressed, such as the chemical mechanisms of PMA and possible by-products interferences, internal standard spiking and recommendations for future research.

DNA-based monitoring of microbial communities that are responsible for the performance of anaerobic digestion of sewage wastes has the potential to improve resource recoveries for wastewater treatment facilities. By treating sludge with propidium monoazide (PMA) prior to amplicon sequencing, this study explored how the presence of DNA from dead microbial biomass carried over with feed sludge may mislead process-relevant biomarkers, and whether primer choice impacts such assessments. Four common primers were selected for amplicon preparation, also to determine if universal primers have sufficient taxonomic or functional coverage for monitoring ecological performance; or whether two domain-specific primers for Bacteria and Archaea are necessary. Anaerobic sludges of three municipal continuously stirred-tank reactors in Victoria, Australia, were sampled at one time-point. A total of 240 amplicon libraries were sequenced on a Miseq using two universal and two domain-specific primer pairs. Untargeted metabolomics was chosen to complement biological interpretation of amplicon gene-based functional predictions. Diversity, taxonomy, phylogeny and functional potentials were systematically assessed using PICRUSt2, which can predict community wide pathway abundance. The two chosen universal primers provided similar diversity profiles of abundant Bacteria and Archaea, compared to the domain-specific primers. About 16 % of all detected prokaryotic genera covering 30 % of total abundances and 6 % of PICRUSt2-estimated pathway abundances were affected by PMA. This showed that dead biomass in the anaerobic digesters impacted DNA-based assessments, with implications for predicting active processes, such as methanogenesis, denitrification or the identification of organisms associated with biological foams. Hence, instead of running two sequencing runs with two different domain-specific primers, we propose conducting PMA-seq with universal primer pairs for routine performance monitoring. However, dead sludge biomass may have some predictive value. In principal component analysis the compositional variation of 239 sludge metabolites resembled that of 'dead-plus-alive' biomass, suggesting that dead organisms contributed to the potentially process-relevant sludge metabolome.