Tunisian microalgae are diverse and rarely been studied. This study reports a first investigation of thermophile Chlorophyta isolated from mats community colonizing the geothermal springs in the north of Tunisia at water temperature 60 °C. In the study, the combined effect of temperature and light intensity was investigated on the cell growth, the mother and daughter cells abundance and the extracellular polymeric substances synthesis in batch culture of the isolated species. Three levels were tested for each factor, 20, 30, 40 °C for temperature; and 20, 70, 120 μmol photons m(-2) s(-1) for light intensity, using full factorial design and response surface methodology. The thermophile strain was identified as a genus Graesiella and showed 99.8% similarity with two Graesiella species: Graesiella emersonii and Graesiella vacuolata based on the 18S rDNA molecular identification. The optimal growth condition was found at 30 °C and 120 µmol photons m(-2) s(-1) (7 MC mL(-1) day(-1)), with the abundance of vegetative cells (daughter cells). In contrast, the number of mother cells increased significantly as the growth decreased; consequently, the highest ratio of auto spore mother cells versus daughter cells (19.4) was obtained at 20 °C and 20 µmol photons m(-2) s(-1). The highest yield of EPS production (11.7 mg L(-1) day(-1)) was recorded at the highest temperature (40 °C) and lowest light intensity (20 µmol photons m(-2)s(-1)). These results revealed how the species respond to high and low temperatures and suggest that the species should be considered as facultative thermophile.

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Combined effect of light intensity and glucose concentration on Arthrospira platensis growth and photosynthetic response was evaluated using a 3 2 factorial design. This design was carried out with light levels of 50, 100, and 150 µmol photons m −2 s −1 and glucose concentrations of 0.5, 1.5, and 2.5 gL −1 . Results from the response surface methodology were that the highest level of light intensity and glucose concentration improved biomass (1.33 gL −1 ), maximum specific growth rate (0.49 day −1 ), and net photosynthetic rate (139.89 µmol O 2 mg Chl −1 h −1 ). Furthermore, the interaction of both factors showed that at low light, glucose had a low effect on maximum biomass and maximal net photosynthetic rate. However, at the highest light levels, the effect of glucose was more sensitive and the increase of glucose concentration increased the levels of all responses. The rates of the instantaneous relative growth, net photosynthesis, and dark respiration of growth cultures showed two different phases in mixotrophic condition. The first was distinguished by the preponderance of the photoautotrophic mode; the second was based mainly on photoheterotrophy.

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The Chlorophyta Picocystis sp. isolated from a Tunisian household sewage pond appears promising for effective removal of Bisphenol A (BPA). Efficient and cost-effective technology for contaminants remediation relies on a tradeoff between several parameters such as removal efficiency, micro-organism growth, and its tolerance to contaminant toxicity. This article demonstrates the optimum conditions achieving the highest removal rates and the minimal growth inhibition in batch cul-tures of Picocystis using response surface methodology. A central composite face-centered (CCF) design was used to determine the effects on removal and growth inhibition of four operating parameters: temperature, inoculum cell density, light intensity, and initial BPA concentration. Results showed that the maximal BPA removal was 91.36%, reached the optimal culture conditions of 30.7 C, 25 Â 10 5 cells ml À1 inoculum density, 80.6 mmol photons m À2 s À1 light intensity, and initial BPA concentration of 10 mg l À1 . Various substrate inhibition models were used to fit the experimental data, and robustness analysis highlighted the Tessier model as more efficient to account for the interaction between Picocystis and BPA and predict removal efficiency. These results revealed how Picocystis respond to BPA contamination and suggest that optimization of experimental conditions can be effectively used to maximize BPA removal in the treat-ment process.