The desalination sector adopts Minimal Liquid Discharge (MLD) systems to become more circular, reduce brine discharge and enhance water recovery, which transforms them to multifunctional systems. This multifunctionality requires a methodologically consistent and goal-aligned approach to environmental impact assessment that recognises how different modelling choices are connected with specific decision contexts. A criterion LCA-based framework aligned with the ISO 14044 hierarchy and tailored specifically to desalination has been developed. It guides the selection of allocation approaches based on system characteristics, integration level, and assessment objectives and is applied to assess an MLD system which co-produces desalinated water, sodium chloride, magnesium hydroxide, calcium hydroxide, sodium sulphate and hydrochloric acid. Multifunctionality was handled with system expansion and partitioning (physical and economic) approaches, resulting in different functional units. For physical and economic partitioning, the MLD system is modelled from a process and system perspective. The results indicate that the MLD system has larger environmental benefits than the reference system with system expansion. When physical and economic partitioning under different perspectives are applied, they result in different environmental burdens per co-product. The MLD system performs better than the reference system (0.005 kg CO₂/kg desalinated water) only when process economic partitioning (0.003 kg CO₂/kg desalinated water) is applied. Whereas, the rest co-products perform better than reference products for all partitioning approaches applied. Our results highlight the potential of brine as a secondary source of products. This study underscores the importance of selecting appropriate allocation approaches, contributing to sustainable practices in the desalination sector.

Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N₂O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N₂O emissions, using Binary Segmentation. The dependencies between operating variables and N₂O emissions fluctuated according to Spearman's rank correlation. The correlation between N₂O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N₂O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N₂O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N₂O fluxes. Additionally, the highest ranges of measured N₂O fluxes belonged to clusters corresponding with NO₃-N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N₂O emissions partially depends on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N₂O emissions in each sub-period and can be integrated into N₂O emissions data processing at wastewater treatment plants.