The role of pretreatments in handling antibiotic resistance genes in anaerobic sludge digestion – A review

Sludge is among the most important reservoirs of antibiotic resistance genes (ARGs), which would cause potential environmental risks with the sludge utilization. Currently, anaerobic digestion (AD) is effective to simultaneously realize the resource recovery and pollutants removal, including antibiotic resistance genes (ARGs), and various pretreatments are used to enhance the performance. Recently, plentiful publications have focused on the effects of pretreatment on ARGs removal, but the contradictory results are often obtained, and a comprehensive understanding of the research progress and mechanisms is essential. This study summarizes various pretreatment techniques for improving AD efficiency and ARGs reduction, investigates promising performance in ARGs removal when pretreatments combined with AD, and analyzes the potential mechanisms accounting for ARGs fates. The results showed that although thermal hydrolysis pretreatment showed the best performance in ARGs reduction during the pretreatment process, the significant rebound of ARGs would occur in the subsequent AD process. Conversely, ozone pretreatment and alkali pretreatment had no significant effect on ARGs abundance in the pretreatment stage, but could enhance ARGs removal by 15.6-24.3 % in the subsequent AD. Considering the efficiency and economic effectiveness, free nitrous acid pretreatment would be a promising and feasible option, which could enhance methane yield and ARGs removal by up to 27 % and 74.5 %, respectively. Currently, the factors determining ARGs fates during pretreatment and AD processes included the shift of microbial community, mobile genetic elements (MGEs), and environmental factors. A comprehensive understanding of the relationship between the fate of ARGs and pretreatment technologies could be helpful for systematically evaluating various pretreatments and facilitating the development of emerging and effective pretreatment techniques. Moreover, given the effectiveness, economic efficiency and environmental safety, we called for the applications of modern analysis approaches such as metagenomic and machine learning on the optimization of pretreatment conditions and revealing underlying mechanisms.