Abstract
Heavy metal discharge in aquatic systems poses risks to public health and ecosystems. Bioremediation using live microalgae is a sustainable solution, but performance under realistic multi-metal conditions remains insufficiently explored. Chlorella vulgaris and Arthrospira platensis were exposed to a five-metal mixture (Pb2+, Zn2+, Cd2+, Ni2+, Cu2+) in three real water matrices (river water, lake water, fish-processing wastewater) at elevated concentrations simulating accidental contamination. Kinetic modeling, Fourier transform infrared (FTIR) spectroscopy and interpretable machine learning (ML) were applied to characterize removal dynamics and associated variables. Chlorella vulgaris demonstrated more stable and progressive removal, while Arthrospira platensis achieved final Day-7 removals of 98.1% for Cd2+ in fish-processing wastewater and 95.1% for Cu2+ in lake water. Non-monotonic apparent uptake required use of the first observed uptake maximum for kinetic fitting. Across four input feature configurations, Gaussian process regression generally provided more consistent predictive performance than XGBoost, while reduced models retained predictive information after contemporaneous target-related variables were excluded or replaced. Shapley Additive exPlanations analysis indicated metal and species-specific contributions from temporal, physicochemical, biomass-related and concentration related variables. Overall, the combined analysis revealed distinct, matrix-dependent removal patterns and supports the potential of living microalgae for rapid multi-metal bioremediation under complex multi-metal contamination.
Acknowledgements
G. Vourlias of the Laboratory of Advanced Materials and Devices (AMDE Lab), School of Physics, is acknowledged for providing access to the FTIR facilities of Large Research Infrastructure and Instrumentation of Aristotle University of Thessaloniki, Greece. The authors would like to thank Nikoletta Kyzaki for technical assistance with the preparation of heavy metal mixtures and AAS measurements and acknowledge Evangelos Pistolas for his technical contribution to the initial implementation of the automated cell-counting service developed within the Athena Research Center (Ref. 39).
Funding
The implementation of this research was financially supported under the Research Project “ALGEBRA—Innovative waste bioremediation practice for the removal of toxic compounds with the use of microalgae in the context of a circular economy”, which is funded by the program “NATURAL ENVIRONMENT & INNOVATIVE ACTIONS 2022”, PRIORITY AXIS 3: RESEARCH & IMPLEMENTATION, total budget: 199,501.23 €, Green Fund, Athena—Research & Innovation Center in Information Communication & Knowledge Technologies.
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Malletzidou, L., Kyratzopoulou, E., Nerantzis, E. et al. Chlorella vulgaris and Arthrospira platensis show distinct removal dynamics for multiple heavy metals in real water matrices.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-71494-2
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DOI: https://doi.org/10.1038/s41598-026-71494-2
Keywords
- Phycoremediation
- Living microalgae
- Kinetic modeling
- Interpretable machine learning
- ATR-FTIR
- Aquatic contamination
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