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

Abstract

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 measured operational variables. 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. Seasonal variations and time-varying interrelation between N2O and nitrogenous variables are, furthermore, investigated and our evaluation results are compared across the different 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. To increase the accuracy and completeness of the stored data, it is thus suggested that future initiatives are taken toward the collection and storing of metadata in WWTPs. 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.