A. (2019). N2O emission in full-scale wastewater treatment: proposing a refined monitoring strategy. Science of the Total Environment, 134157 (24 pp.).

No abstract

Nitrous oxide (N2O) production pathways in a single stage, continuously fed partial nitritation-anammox reactor were investigated using online isotopic analysis of offgas N2O with quantum cascade laser absorption spectroscopy (QCLAS). N2O emissions increased when reactor operating conditions were not optimal, for example, high dissolved oxygen concentration. SP measurements indicated that the increase in N2O was due to enhanced nitrifier denitrification, generally related to nitrite build-up in the reactor. The results of this study confirm that process control via online N2O monitoring is an ideal method to detect imbalances in reactor operation and regulate aeration, to ensure optimal reactor conditions and minimise N2O emissions. Under normal operating conditions, the N2O isotopic site preference (SP) was much higher than expected - up to 40‰ - which could not be explained within the current understanding of N2O production pathways. Various targeted experiments were conducted to investigate the characteristics of N2O formation in the reactor. The high SP measurements during both normal operating and experimental conditions could potentially be explained by a number of hypotheses: i) unexpectedly strong heterotrophic N2O reduction, ii) unknown inorganic or anammox-associated N2O production pathway, iii) previous underestimation of SP fractionation during N2O production from NH2OH, or strong variations in SP from this pathway depending on reactor conditions. The second hypothesis - an unknown or incompletely characterised production pathway - was most consistent with results, however the other possibilities cannot be discounted. Further experiments are needed to distinguish between these hypotheses and fully resolve N2O production pathways in PN-anammox systems.

No abstract

Today, the development and testing of methods for fault detection and identification in wastewater treatment research relies on two important assumptions: (i) that sensor faults appear at distinct times in different sensors and (ii) that any given sensor will function near-perfectly for a significant amount of time following installation. In this work, we show that such assumptions are unrealistic, at least for sensors built around an ion-selective measurement principle. Indeed, long-term exposure of sensors to treated wastewater shows that sensors exhibit fault symptoms that appear simultaneously and with similar intensity. Consequently, this suggests that future research should be reoriented towards methods that do not rely on the assumptions mentioned above. This study also provides the first empirically validated sensor fault model for wastewater treatment simulation, which is useful for effective benchmarking of both fault detection and identification methods and advanced control strategies. Finally, we evaluate the value of redundancy for remote sensor validation in decentralized wastewater treatment systems.