Abstract
Monitoring of nitrite is essential for an immediate response and prevention of irreversible failure of decentralized biological urine nitrification reactors. Although a few sensors are available for nitrite measurement, none of them are suitable for applications in which both nitrite and nitrate are present in very high concentrations. Such is the case in collected source-separated urine, stabilized by nitrification for long-term storage. Ultraviolet (UV) spectrophotometry in combination with chemometrics is a promising option for monitoring of nitrite. In this study, an immersible in situ UV sensor is investigated for the first time so to establish a relationship between UV absorbance spectra and nitrite concentrations in nitrified urine. The study focuses on the effects of suspended particles and saturation on the absorbance spectra and the chemometric model performance. Detailed analysis indicates that suspended particles in nitrified urine have a negligible effect on nitrite estimation, concluding that sample filtration is not necessary as pretreatment. In contrast, saturation due to very high concentrations affects the model performance severely, suggesting dilution as an essential sample preparation step. However, this can also be mitigated by simple removal of the saturated, lower end of the UV absorbance spectra, and extraction of information from the secondary, weaker nitrite absorbance peak. This approach allows for estimation of nitrite with a simple chemometric model and without sample dilution. These results are promising for a practical application of the UV sensor as an in situ nitrite measurement in a urine nitrification reactor given the exceptional quality of the nitrite estimates in comparison to previous studies.
Original language | English |
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Pages (from-to) | 244-254 |
Number of pages | 11 |
Journal | Water Research |
Volume | 85 |
DOIs | |
State | Published - Nov 15 2015 |
Externally published | Yes |
Funding
The authors would like to thank Claudia Bänninger, Alexandra Fumasoli, Adriano Joss, Eberhard Morgenroth, Jörg Rieckermann, Karin Rottermann, and Hansruedi Siegrist (Eawag); Martina Hofer (unimon GmbH); Andreas Weingartner, Florian Edthofer, and colleagues (s::can Messtechnik GmbH) for their support and contributions to the work presented in this paper. The research has been made possible by the Eawag Discretionary Funds (the grant number was 5221.00492.007.10 and the name of the project was DF2013/MAC-Nut).
Keywords
- Nitrification
- Online nitrite measurement
- Principal component regression
- Source-separated urine
- UV spectrophotometry