Unraveling the role of salinity in anammox-based nitrogen removal processes via data analysis from the literature and experimental validation. Issue 12 (13th October 2021)
- Record Type:
- Journal Article
- Title:
- Unraveling the role of salinity in anammox-based nitrogen removal processes via data analysis from the literature and experimental validation. Issue 12 (13th October 2021)
- Main Title:
- Unraveling the role of salinity in anammox-based nitrogen removal processes via data analysis from the literature and experimental validation
- Authors:
- Du, Tingting
Xu, Xinxin
Guo, Du
Jiang, Xinye
Zeng, Ming
Wu, Nan
Wang, Chang
Zhang, Zongpeng - Abstract:
- Abstract : The innovation of this article is to reveal the effect of salinity on the anammox nitrogen removal process through literature data analysis and experimental verification by using kinetic models, the ANN model and correlation analysis. Abstract : As an energy-efficient nitrogen removal process, anaerobic ammonia oxidation (anammox) is now widely used to remove nitrogen in various wastewaters, some of which might contain high salinity. Yet, there are many uncertainties in the treatment of saline wastewater by anammox. In this study, the effect of salinity on the anammox nitrogen removal process was revealed by data analysis from the literature and experimental validation. First, the nitrogen removal performance of freshwater anammox bacteria (FAB) and marine anammox bacteria (MAB) in saline wastewater was different, and MAB can adapt to higher salinity (7.5%). Furthermore, the Edwards model can best describe the effect of salinity on the anammox process, due to its high fitted R 2 and reasonable modeled maximum nitrogen removal rate (NRR) ranging between 0.079 and 4.189 kg m −3 d −1 . In addition, the artificial neural network (ANN) model can simulate and accurately predict the NRR of anammox under different salinity conditions. The importance of environmental factors for the ANN model was lower than that of the effluent parameters. The performance of these models could be well verified by batch tests. Spearman correlation analysis showed that due to the coexistenceAbstract : The innovation of this article is to reveal the effect of salinity on the anammox nitrogen removal process through literature data analysis and experimental verification by using kinetic models, the ANN model and correlation analysis. Abstract : As an energy-efficient nitrogen removal process, anaerobic ammonia oxidation (anammox) is now widely used to remove nitrogen in various wastewaters, some of which might contain high salinity. Yet, there are many uncertainties in the treatment of saline wastewater by anammox. In this study, the effect of salinity on the anammox nitrogen removal process was revealed by data analysis from the literature and experimental validation. First, the nitrogen removal performance of freshwater anammox bacteria (FAB) and marine anammox bacteria (MAB) in saline wastewater was different, and MAB can adapt to higher salinity (7.5%). Furthermore, the Edwards model can best describe the effect of salinity on the anammox process, due to its high fitted R 2 and reasonable modeled maximum nitrogen removal rate (NRR) ranging between 0.079 and 4.189 kg m −3 d −1 . In addition, the artificial neural network (ANN) model can simulate and accurately predict the NRR of anammox under different salinity conditions. The importance of environmental factors for the ANN model was lower than that of the effluent parameters. The performance of these models could be well verified by batch tests. Spearman correlation analysis showed that due to the coexistence of a stimulation period and inhibition period, the relationship between the NRR and salinity was not significant under both short-term and long-term salinity stress. Regarding functional bacteria for the nitrogen removal process, Kuenenia and Nitrosomonas had better salt tolerance than Brocadia and Nitrosospira, respectively. Overall, the effect of salinity on anammox-based nitrogen removal processes was elucidated by model fitting and experimental validation. … (more)
- Is Part Of:
- Environmental science. Volume 7:Issue 12(2021)
- Journal:
- Environmental science
- Issue:
- Volume 7:Issue 12(2021)
- Issue Display:
- Volume 7, Issue 12 (2021)
- Year:
- 2021
- Volume:
- 7
- Issue:
- 12
- Issue Sort Value:
- 2021-0007-0012-0000
- Page Start:
- 2295
- Page End:
- 2306
- Publication Date:
- 2021-10-13
- Subjects:
- Water-supply -- Periodicals
Water security -- Periodicals
Water resources development -- Periodicals
Water chemistry -- Periodicals
553.705 - Journal URLs:
- http://pubs.rsc.org/en/journals/journalissues/ew#!recentarticles&all ↗
http://www.rsc.org/ ↗ - DOI:
- 10.1039/d1ew00571e ↗
- Languages:
- English
- ISSNs:
- 2053-1400
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3791.599150
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 20578.xml