Assessment of environmental impacts and operational costs of the implementation of an innovative source-separated urine treatment

International audienceInnovative treatment technologies and management methods are necessary to valorise the constituents of wastewater, in particular nutrients from urine (highly concentrated and can have significant impacts related to artificial fertilizer production). The FP7 project, ValuefromUrine, proposed a new two-step process (called VFU) based on struvite precipitation and microbial electrolysis cell (MEC) to recover ammonia, which is further transformed into ammonium sulphate. The environmental and economic impacts of its prospective implementation in the Netherlands were evaluated based on life cycle assessment (LCA) methodology and operational costs. In order to tackle the lack of stable data from the pilot plant and the complex effects on wastewater treatment plant (WWTP), process simulation was coupled with LCA and costs assessment using the Python programming language. Additionally, particular attention was given to the propagation and analysis of inputs uncertainties. Five scenarios of VFU implementation were compared to the conventional treatment of 1 m(3) of wastewater. Inventory data were obtained from SUMO software for the WWTP operation. LCA was based on Brightway2 software (using ecoinvent database and ReCiPe method). The results, based on 500 iterations sampled from inputs distributions (foreground parameters, ecoinvent background data and market prices), showed a significant advantage of VFU technology, both at a small and decentralized scale and at a large and centralized scale (95% confidence intervals not including zero values). The benefits mainly concern the production of fertilizers, the decreased efforts at the WWTP, the water savings from toilets flushing, as well as the lower infrastructure volumes if the WWTP is redesigned (in case of significant reduction of nutrients load in wastewater). The modelling approach, which could be applied to other case studies, improves the representativeness and the interpretation of results (e.g. complex relationships, global sensitivity analysis) but requires additional efforts (computing and engineering knowledge, longer calculation time). Finally, the sustainability assessment should be refined in the future with the development of the technology at larger scale to update these preliminary conclusions before its commercialization