Unveiling the factors shaping variability in biomass productivity: Meta-analysis of outdoor pilot-scale microalgal...

Meta-analysis and machine learning is used to investigate factors influencing variation in biomass productivity in outdoor algal systems. Understanding these factors is essential for optimizing algal systems. Mean productivity across the analysed studies is 11 g m^-2day^-1, with 6 % of observations surpassing 25 g m^-2day^-1. Analysis reveals that algal species alone is insufficient to optimize productivity, as indicated by wide intra-species variation (5 to 42 gm^-2day^-1). A negative linear relationship between productivity and culture depth (Pearson correlation coefficient, ρ=0.56), growth period (ρ=0.56), and media salinity (ρ=0.64) is identified. Other variables exhibit non-linear associations. Inoculum density, CO₂ content, sulfate concentration, and growth period can enhance productivity while temperature, pH, photosynthetically active radiation, and photoperiod show limited potential. Targeted optimization of key input variables offers a promising pathway to significantly boost productivity and accelerate the deployment of algal systems for sustainable bioproduction.