Decoding Wolffia: A Predictive Model for Duckweed Growth and Nutrient Uptake for Resource Recovery from Wastewater in Tropical Countries

Duckweed-based wastewater treatment systems offer immense potential for sustainable water pollution control, yet predictive tools to optimise their operation under real-world conditions are lacking. In this work, we developed and validated an empirical kinetic model capable of accurately predicting biomass productivity and nutrient uptake in Wolffia angusta cultures across variable environmental conditions. Our model integrates the combined effects of temperature and nutrient availability, capturing dynamic responses and internal nutrient reserves. This model was used to assess the performance of duckweed ponds to improve the quality of effluents from anaerobic baffled reactors, typically used for domestic wastewater treatment in Indonesia.  Experimental cultivation under controlled conditions (20, 25 and 30°C; varying phosphorus and nitrogen supplies, including nutrient limiting scenarios) revealed that temperature predominantly drives biomass growth, while nutrient availability more strongly governs biological nutrient uptake and removal efficiency. Optimal biomass production was observed between 25 - 30°C, though specific nitrogen uptake rates declined at higher temperatures, indicating metabolic constraints. Nutrient removal is mainly biomass-driven rather than resulting from enhanced metabolic rates, which benefits nutrient recovery and reuse. Model calibration markedly improved predictive accuracy (R² > 0.9), even under nutrient limiting conditions. This work advances the mechanistic understanding of Wolffia's growth and nutrient uptake, and delivers a practical, transferable modelling framework to enhance nutrient control in decentralised wastewater treatment systems, like anaerobic baffled reactors, particularly relevant for tropical regions facing nutrient pollution. This approach represents a significant step towards integrating nature-based biotechnologies into circular, sustainable water management strategies.