A framework for integrating algal-bacterial mechanistic modelling and Life-cycle Assessment in wastewater treatment processes

Abstract

Algae offer a promising route towards sustainable practices in wastewater treatment applications, but their true environmental performance remains difficult to evaluate using static, empirical data. Here, we introduce a framework that integrates bioprocess modelling with Life-Cycle Assessment (LCA) to improve the robustness of environmental impact evaluations. Experimental data from brewery wastewater treatment were used to train a kinetic model that simulates nutrient uptake and biomass production of algal-bacterial cultures, providing mechanistic insights into system behavior and enabling the compilation of inventories for wastewater treatment and downstream sludge management (anaerobic digestion and land application of digestate). Monte-Carlo simulations and global sensitivity analyses revealed that, although algal ponds outperform activated sludge in terms of Eutrophication Potential (EP; 3.4 and 6.6 g P eq·m⁻³ wastewater, respectively), Global Warming Potential (GWP) predictions are similar (0.61–0.69 kg CO₂ eq. per m³ wastewater), highly uncertain, and strongly influenced by sludge management. Subsets of the parameter space that correspond to state-of-the-art downstream practices lead to significant GWP reductions to 0.16 kg CO₂ eq. per m³ wastewater. By decomposing key uncertainties and providing mechanistically grounded predictions across the entire design space, this framework will accelerate the development of sustainable technologies in the algal biotechnology sector.