Exploring data quality and seasonal variations of N₂O in wastewater treatment: a modeling perspective

In this work, operational data collected from four Danish wastewater treatment plants (WWTP) are assessed for quality issues and analyzed to investigate the feasibility of data-driven modeling for control purposes. All plants have permanent N2O sensors installed in the biological reactors, and N₂O data are collected on the same terms as other operational data. We present and deploy a six-dimensional data quality assessment to the operational data evaluating (1) relevance, (2) accuracy, (3) completeness, (4) consistency, (5) comparability, and (6) accessibility. To increase the accuracy and completeness of the stored data, it is suggested that future initiatives are taken toward the collection and storing of metadata in WWTPs. Furthermore, seasonal variations and time-varying relationships between N₂O, nitrogenous variables, and oxygen are investigated and compared across various case plants and process designs. Results show that the quality of the operational data varies substantially between plants. The investigation of time-varying interrelation between N₂O and nitrogenous variables showed no clear pattern within or across different case plants. Furthermore, it is recommended that future research should consider adapting models so that more influence is linked to reliable measurements, contrary to assuming that all variables are of equal quality.