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Making waves: Rethinking antiscalants in seawater desalination - Balancing scaling control, biofouling risk, and
Graciela Gonzalez-Gil1, Marian Castrillón-Tobón1, Maria Camila Albarracin Ruiz1
1Biological and Environmental Science & Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Abstract:
Antiscalants used in reverse osmosis (RO) desalination are commonly treated as simple operational additives, yet their chemical properties can significantly influence microbial dynamics, thereby creating a critical operational paradox. While antiscalants inhibit mineral scaling, they may simultaneously promote biofouling through poorly resolved mechanisms. Current vendor-driven selection prioritizes scaling control but overlooks microbial and environmental consequences. This opacity obscures molecular composition, impurity profiles, and transformation pathways, introducing uncertainty across the desalination lifecycle. Undisclosed chemical composition limits risk assessment, while interactions with marine microbial communities that drive biofouling lack mechanistic clarity. Emerging evidence indicates that antiscalants can both stimulate microbial growth via orthophosphate impurities, a readily bioavailable inorganic phosphorus source, and labile organic fractions, but may also inhibit growth through trace metal sequestration, particularly the sequestration of bioavailable iron, a metabolically essential micronutrient. The contribution of this inhibitory mechanism under seawater desalination conditions remains to be quantified. The net outcome is formulation-specific and environmental context-dependent. Beyond the treatment plant, the fate and impacts of discharged antiscalants remain insufficiently constrained, introducing uncertainty for marine ecosystems. We argue that antiscalant selection should shift from empirical practices to a predictive, mechanism-based strategy. Accordingly, we propose a Predictive Ecological Chemistry framework grounded in three principles: transparent chemical characterization, mechanistic evaluation of microbial responses, and environmental accountability. This framework offers a pathway toward data-driven selection that balances operational performance with ecological responsibility.
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