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Practical application of the partial nitritation–anaerobic ammonium oxidation (anammox) process has attracted increasing attention because of its low operational costs. However, the nitritation process, as a promising way to supply nitrite for anammox, is sensitive to the variations in substrate concentration and dissolved oxygen (DO) concentration. Therefore, a stable supply of nitrite becomes a real bottleneck in partial nitritation–anammox process, limiting their potential for application in mainstream wastewater treatment. In this study, five 18-L sequencing batch reactors were operated in parallel at room temperature (22°C ± 4°C) to explore the nitritation performance with different carrier materials, including sepiolite-nonwoven carrier (R1), zeolite-nonwoven carrier (R2), brucite-nonwoven carrier (R3), polyurethane carrier (R4), and nonwoven carrier (R5). The ammonia oxidation rate (AOR) in R1 reached the highest level of 0.174 g-N L−1 d−1 in phase II, which was 1.4-fold higher than the control reactor (R4). To guarantee a stable supply of nitrite for anammox process, the nitrite accumulation efficiency (NAE) was always higher than 77%, even though the free ammonia (FA) decreases to 0.08 mg-N/L, and the pH decreases to 6.8 ± 0.3. In phase V, the AOR in R1 reached 0.206 g-N L−1 d−1 after the DO content increase from 0.7 ± 0.3 mg/L to 1.7 ± 0.3 mg/L. The NAE in R1 was consistently higher than 68.6%, which was much higher than the other reactor systems (R2: 43.8%, R3: 46.6%, R4: 23.7%, R5: 22.7%). Analysis of 16S rRNA gene sequencing revealed that the relative abundance of Nitrobacter and Nitrospira in R1 was significantly lower than other reactors, indicating that the sepiolite carrier plays an important role in the inhibition of nitrite-oxidizing bacteria. These results indicate that the sepiolite nonwoven composite carrier can effectively improve the nitritation process, which is highly beneficial for the application of partial nitritation–anammox for mainstream wastewater treatment.

In the present paper we propose a new method, the Penalized Adaptive Method (PAM), for a data driven detection of structural changes in sparse linear models. The method is able to allocate the longest homogeneous intervals over the data sample and simultaneously choose the most proper variables with the help of penalized regression models. The method is simple yet flexible and can be safely applied in high-dimensional cases with different sources of parameter changes. Comparing with the adaptive method in linear models, its combination with dimension reduction yields a method which properly selects significant variables and detects structural breaks while steadily reduces the forecast error in high-dimensional data.

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