The long-term occurrence, dynamics and risk of antibiotic resistance genes (ARGs) in anaerobic digestion (AD) of excess sludge (ES) are not fully understood. Therefore, 13-month metagenomic monitoring was carried out in a full-scale AD plant. The highest ARG abundance and risk scores were observed in spring. AD achieved a 35 % removal rate for the total ARG abundance, but the risk score of AD sludge was not always lower than ES samples, because of the higher proportion of Rank I ARGs in AD sludge. ARGs showed less obvious patterns under linear models compared with microbial community, implying their chaotic dynamics, which was further confirmed by nonlinearity tests. Empirical dynamic modeling performed better than the autoregressive integrated moving average model for ARG dynamics, especially for those with simple and nonlinear dynamics. This study highlighted spring for its higher ARG abundance and risk, and recommended nonlinear models for revealing the dynamics of ARGs.
Keywords: Empirical dynamic modeling; Long-term monitoring; Risk assessment; Seasonal dynamics.
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