A validation workflow for treatment wetland performance data

Abstract

Treatment wetlands (TWs) effectively remove target pollutants and enhance urban water circularity and resilience. They constitute a prominent solution for urban wastewater treatment, thanks to their adaptability across various types of wastewater, scales and climatic conditions. However, the disparity in TW designs and the focus on a restricted set of variables applicable to research studies impede any comprehensive evaluation and comparison of TW performance. Our study introduces a methodology for data validation, in concurrently establishing a workflow specific to TW. This approach is aimed at defining the scope and relationships within the data, implementing checks and concatenating them into a quality flag, as an initial step towards building reliable statistical models. We underscore the importance of both mobilising comprehensive knowledge and identifying customary, yet implicit, choices intertwined in data processing. As for the application workflow, we collected and analysed data sourced from peer-reviewed papers on horizontal and vertical flow TW. Deficiencies were noted in key data elements like dimensions, concentrations and operational conditions. For the data analysis, relationships are highlighted between variables introduced for modelling purposes. These methodologies and workflows assess the quality of the data, in paving the way towards more dependable statistical models for TW design and implementation. HIGHLIGHTS We provide a six-step data validation methodology highlighting the key elements to consider, regardless of the field.; We develop data quality checks, in proposing a probabilistic output that can be used in data-driven modelling.; We analyse a worldwide-scale dataset on treatment wetlands that can serve as a ground reference for data-driven modelling.;

Keywords

• categorisation
• data quality
• methodology
• pollutants
• probabilistic
• wastewater treatment