The use of living Arthrospira platensis (A. platensis) cultures emerges as a promising green solution for the bioremediation of water contaminated by toxic metal waste. The scope of the present study is to evaluate the microalga’s potential in heavy metal remediation, in the case of multi-metal-treated (multi-MT) systems. For this reason, A. platensis cultures were exposed to mono- and multi-metal solutions of Cu, Cd, Ni, Pb, and Zn, and their metal adsorption ability was investigated. The heavy metal removal efficiency of A. platensis cultures was evaluated using atomic absorption spectroscopy (AAS). Additionally, the cultures were examined using Fourier transform infrared (FTIR) spectroscopy, Near-Infrared (NIR) Spectroscopy, UV-Vis spectrophotometry, and optical microscopy, together with pH and electrical conductivity (EC) measurements to evaluate the quality of the cultures and the changes induced by heavy metal stress. The results showed that metal removal is still efficient in multi-MT cultures. In particular, Cu, Cd, Pb, and Zn removal of multi-MT cultures is elevated or relative to the respective removal of the mono-metal-treated (mono-MT) cultures, showing a synergistic or cooperative interaction between the metals, while the removal of Ni of multi-MT cultures decreased compared to Ni of mono-MT cultures, showing an antagonistic interaction to the other metals. The study shows that A. platensis is considered an effective microalga toward the bioremediation of multi-metal polluted cultures.

The occurrence of heavy metals in aquatic ecosystems is a serious environmental hazard, and their effective removal is imperative. In this regard, the feasibility of living microalga Chlorella vulgaris (C. vulgaris) to remove heavy metals (Ni, Pb, Zn, Cd, and Cu) is investigated by using 1, 5, and 10 ppm concentrations of single- and multiple-metal-treated (MT) cultures. Experiments were performed in controlled laboratory conditions, and metal removal analysis was performed through atomic absorption spectroscopy (AAS). The cultures were also examined by means of optical microscopy, UV-Vis spectrophotometry, and Fourier transform infrared (FTIR) spectroscopy to follow the cultures’ pigment content, cell population, and functional group changes during cultivation. The removal efficiency results of both single and multiple MT cultures were evaluated using the Langmuir isotherm model. The results indicate that C. vulgaris presents potential for heavy metal bioremediation, even towards multi-MT conditions, despite the influence of a competitive uptake in multi-MT cultures. In mono-MT cultures, the removal efficiency of C. vulgaris presents values of 65–99% on Day 3 and 72–99% on Day 7 of cultivation, while the results for the multi-MT cultures are 49–99% and 62–99% for Days 3 and 7 of cultivation, respectively. The research illustrates the potential for C. vulgaris as a promising biosorbent for heavy metal remediation along with its post-treatment use in applications supporting the green circular economy.

The present study proposes the use of Near-Infrared (NIR) spectroscopy as a rapid method for estimating the growth of Spirulina platensis cultures, avoiding any sample manipulation or pretreatment. NIR spectroscopy in diffuse reflectance mode was used on culture volumes as received, with Principal Component Analysis (PCA) and Partial Least Squares (PLS) linear regression, for developing the calibration model in the wavelength range of 1000–2500 nm, in order to choose the appropriate wavelength to estimate the growth of the microalga. The local reflectance maximum at 1062.6 nm, connected with reduced water absorption and scattering effects by the microalga, was identified from PCA as the positive peak in the first loading plot, correlating diffuse reflectance with dilution levels. The calibration curve of diffuse reflectance at 1062.6 nm in response to dilution presented strong linearity, supported by a coefficient of determination (R²) of 0.995. Cross-validation of NIR spectra with a S. platensis culture confirmed the method’s reliability, showing that the growth follows an exponential pattern. The study shows that diffuse reflectance NIR spectroscopy can be used for the rapid monitoring of Spirulina platensis growth.

This CIDACC dataset was created to determine the cell population of Chlorella vulgaris microalga during cultivation. Chlorella vulgaris has diverse applications, including use as food supplement, biofuel production, and pollutant removal. High resolution images were collected using a microscope and annotated, focusing on computer vision and machine learning models creation for automatic Chlorella cell detection, counting, size and geometry estimation. The dataset comprises 628 images, organized into hierarchical folders for easy access. Detailed segmentation masks and bounding boxes were generated using external tools enhancing the dataset's utility. The dataset's efficacy was demonstrated through preliminary experiments using deep learning architecture such as object detection and localization algorithms, as well as image segmentation algorithms, achieving high precision and accuracy. This dataset is a valuable tool for advancing computer vision applications in microalgae research and other related fields. The dataset is particularly challenging due to its dynamic nature and the complex correlations it presents across various application domains, including cell analysis in medical research. Its intricacies not only push the boundaries of current computer vision algorithms but also offer significant potential for advancements in diverse fields such as biomedical imaging, environmental monitoring, and biotechnological innovations.

Towards the bioremediation of toxic compounds from aquatic environments using living microalgae, Chlorella vulgaris has emerged as a promising candidate for the removal of heavy metals. The present study advances the scale-up of the microalga’s culture and investigates its efficiency in multi-metal removal (Cu, Cd, Ni, Pb, and Zn at 1 ppm each). Two aeration conditions were investigated: standard/conventional aeration (SA), and an innovative, custom-built micro-bubble aeration (MBA), which optimizes CO₂ residence time to enhance photosynthesis. Conducted in a pilot-scale 30 L photobioreactor (PBR) over a cultivation period of 7 days, control and multi-metal treated cultures were monitored for pH, cell population growth, and pigment content. Heavy metal removal efficiency was evaluated by means of atomic absorption spectroscopy (AAS) on Days 3 and 7 of cultivation. The comparative results reveal that MBA significantly enhances both the population and the photosynthetic pigment content of the cultures. Furthermore, the heavy metal removal efficiency under MBA reached up to 95% even by Day 3 of cultivation, remarkably higher than the 67% of the SA treated culture. These findings not only demonstrate Chlorella vulgaris’s effectiveness in multi-metal treated systems but also highlight the potential of advanced aeration systems to enhance bioremediation efficiency in larger-scale aquatic environments.

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.