The recent discovery of comammox (complete ammonia oxidation) Nitrospira has upended the long-held nitrification paradigm. Although comammox Nitrospira have been identified in wastewater treatment systems, the conditions for their dominance over canonical ammonia oxidizers remain unclear. Here, we report the dominance of comammox Nitrospira in a moving bed biofilm reactor (MBBR) fed with synthetic mainstream wastewater. Integrated 16S rRNA gene amplicon sequencing, fluorescence in situ hybridization (FISH), and metagenomic sequencing methods demonstrated the selective enrichment of comammox bacteria when the MBBR was operated at a dissolved oxygen (DO) concentration above 6 mg O2/L. The dominance of comammox Nitrospira over canonical ammonia oxidizers (i.e., Nitrosomonas) was attributed to the low residual ammonium concentration (0.02–0.52 mg N/L) formed in the high-DO MBBR. Two clade A comammox Nitrospira were identified, which are phylogenetically close to Candidatus Nitrospira nitrosa. Interestingly, cryosectioning-FISH showed these two comammox species spatially distributed on the surface of the biofilm. Moreover, the ammonia-oxidizing activity of comammox Nitrospira-dominated biofilms was susceptible to the oxygen supply, which dropped by half with the DO concentration decrease from 6 to 2 mg O2/L. These features collectively suggest a low apparent oxygen affinity for the comammox Nitrospira-dominated biofilms in the high-DO nitrifying MBBR.

Complete ammonia oxidation (i.e., comammox) is a newly discovered microbial process performed by a subset of the Nitrospira genus, and this unique microbial process has been ubiquitously detected in various wastewater treatment units. However, the operational conditions favoring comammox prevalence remain unclear. In this study, the dominance of comammox Nitrospira in four sponge biofilm reactors fed with low-strength ammonium (NH 4 + = 23 ± 3 mg N/L) wastewater was proved by coupling 16S rRNA gene amplicon sequencing, quantitative polymerase chain reaction (qPCR), and metagenomic sequencing. The results showed that comammox Nitrospira dominated in the nitrifying guild over canonical ammonia-oxidizing bacteria (AOB) constantly, despite the significant variation in the residual ammonium concentration (0.01−15 mg N/L) under different sets of operating conditions. This result indicates that sponge biofilms greatly favor retaining comammox Nitrospira in wastewater treatment and highlights an essential role of biomass retention in the comammox prevalence. Moreover, analyses of the assembled metagenomic sequences revealed that the retrieved amoA gene sequences affiliated with comammox Nitrospira (53.9−66.0% read counts of total amoA gene reads) were always higher than those (28.4−43.4%) related to β-proteobacterial AOB taxa. The comammox Nitrospira bacteria detected in the present biofilm systems were close to clade A Candidatus Nitrospira nitrosa.

Efficient traffic signal control is an important means to alleviate urban traffic congestion. Reinforcement learning (RL) has shown great potentials in devising optimal signal plans that can adapt to dynamic traffic congestion. However, several challenges still need to be overcome. Firstly, a paradigm of state, action, and reward design is needed, especially for an optimality-guaranteed reward function. Secondly, the generalization of the RL algorithms is hindered by the varied topologies and physical properties of intersections. Lastly, enhancing the cooperation between intersections is needed for large network applications. To address these issues, the Option-Action RL framework for universal Multi-intersection control (OAM) is proposed. Based on the well-known cell transmission model, we first define a lane-cell-level state to better model the traffic flow propagation. Based on this physical queuing dynamics, we propose a regularized delay as the reward to facilitate temporal credit assignment while maintaining the equivalence with minimizing the average travel time. We then recapitulate the phase actions as the constrained combinations of lane options and design a universal neural network structure to realize model generalization to any intersection with any phase definition. The multiple-intersection cooperation is then rigorously discussed using the potential game theory. We test the OAM algorithm under four networks with different settings, including a city-level scenario with 2,048 intersections using synthetic and real-world datasets. The results show that the OAM can outperform the state-of-the-art controllers in reducing the average travel time.

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