Abstract

Aerobic duration control (ADC), whereby aeration is terminated before nitrite is extremely oxidized during the nitrification process, is an effective strategy to achieve partial nitritation (PN) for blackwater. This study evaluated the effects of microbial growth type, influent ammonia-oxidizing organisms (AOO), and comammox bacteria from seeding sludge to ADC-based PN. The long-term operation of lab-scale reactors and model simulations were implemented to select the best growth type. The biofilm formed on the inner wall of the activated sludge reactor decreased the nitrite accumulation ratio (NAR) from 99.2% to 77.2%. Meanwhile, the NAR of the pure-biofilm reactor decreased from 95.9% to 47.8%. The deteriorated PN of the biofilm-related reactors was due to the extended solid retention time and increased substrate saturation constants of AOOs compared with those of nitrite-oxidizing organisms (NOO). Periodic biofilm carrier regeneration and biofilm thickness control can recover PN performance but are difficult to implement. In contrast, the optimized activated sludge reactor exhibited high (NAR >94%) and stable (>3 months) PN performance when treating real blackwater. Nitrifiers were found in blackwater, and chemically enhanced high-rate activated sludge pretreatment removed more NOOs than AOOs (41.8% vs. 24.3%) and increased the influent AOO/NOO ratio. Interestingly, the influent AOOs supported fast PN start-up in the moving-bed biofilm reactor without the initial inoculation of activated sludge. Moreover, model simulations verified that high and stable PN could also be realized in an activated sludge reactor by the continuous inoculation of influent AOOs, which is a novel PN start-up strategy. Metagenomic analyses showed that the comammox bacteria from the seeding sludge eventually disappeared owing to their intrinsic specific growth rates and free ammonia inhibition. The findings of this study will provide insightful guidelines for PN application in decentralized and semi-centralized wastewater treatment systems.

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