This poster presentation presents the learnings from two full-scale, long-term N2O monitoring studies at plug flow nitrifying ASPs in Canada and the UK which applied different process analysis approaches to understand N2O production and mitigation potential. Both approaches highlight aeration control a key predictor of N2O production and when data models are iterated, were able to explain up to 72% of the measured N2O data. Simple process analysis and the later developed data model for Plant 1 identified zone 2 aeration issues and inlet load components (flow, ammonia concentration) as predictors of N2O. Both approaches require high quality data and visualisation tools and the initial model and simple visualisation dashboard construction may require significant initial build effort which is often under-estimated. Both approaches require multidisciplinary skillsets to build the required tools and challenges in data handling and standardization. Seasonal N2O insights which were most apparent from the visualisation and simple process analysis, could have been identified by model diagnostics with more evolved approaches. Data modelling allows a greater number of input variables to be considered across the full WWTW and all lanes; supporting gaps in data due to instrument faults. Predictive N2O models may also support N2O understanding in zones not modelled – a topic of ongoing work and of particular importance to plug flow ASPs. Insights from both projects have supported ongoing capital programmes and funding at the WWTWs.