Using microalgae to remove pharmaceuticals and personal care products (PPCPs) micropollutants (MPs) have attracted considerable interest. However, high concentrations of persistent PPCPs can reduce the performance of microalgae in remediating PPCPs. Three persistent PPCPs, namely, carbamazepine (CBZ), sulfamethazine (SMT) and tramadol (TRA), were treated with a combination of Chaetoceros muelleri and biochar in a photobioreactor during this study. Two reactors were run. The first reactor comprised Chaetoceros muelleri, as the control, and the second reactor comprised Chaetoceros muelleri and biochar. The second reactor showed a better performance in removing PPCPs. Through the response surface methodology, 68.9% (0.330 mg L⁻¹) of CBZ, 64.8% (0.311 mg L⁻¹) of SMT and 69.3% (0.332 mg L⁻¹) of TRA were removed at the initial concentrations of MPs (0.48 mg L⁻¹) and contact time of 8.1 days. An artificial neural network was used in optimising elimination efficiency for each MP. The rational mean squared errors and high R² values showed that the removal of PPCPs was optimised. Moreover, the effects of PPCPs concentration (0–100 mg L⁻¹) on Chaetoceros muelleri were studied. Low PPCP concentrations (40 mg L⁻¹).

Amongst per- and polyfluoroalkyl substances (PFAS) compounds, perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) have a high persistence in physicochemical and biological degradation; therefore, the accumulation of PFOS and PFOA can negatively affect aquatic organisms and human health. In this study, two microalgae species (Chlorella vulgaris and Scenedesmus obliquus) were exposed to different concentrations of a PFOS and PFOA mixture (0 to 10 mg L⁻¹). With increases in the contact time (days) and the PFAS concentration (mg L⁻¹) from 1 to 7, and 0.5 to 10, respectively, the cell viability, total chlorophyll content, and protein content decreased, and the decrease in these parameters was significantly greater in Scenedesmus obliquus. As another step in the study, the response surface methodology (RSM) was used to optimize the toxicity effects of PFAS on microalgae in a logical way, as demonstrated by the high R² (>0.9). In another stage, a molecular docking study was performed to monitor the interaction of PFOS and PFOA with the microalgae, considering hydrolysis and the enzymes involved in oxidation-reduction reactions using individual enzymes. The analysis was conducted on carboxypeptidase in Chlorella vulgaris and on c-terminal processing protease and oxidized cytochrome c6 in Scenedesmus obliquus. For the enzyme activity, the affinity and dimensions of ligands-binding sites and ligand-binding energy were estimated in each case.

The potential of microalgal photobioreactors in removing total ammonia nitrogen (TAN), chemical oxygen demand (COD), caffeine (CAF), and N,N-diethyl-m-toluamide (DEET) from synthetic wastewater was studied. Chlorella vulgaris achieved maximum removal of 62.2% TAN, 52.8% COD, 62.7% CAF, and 51.8% DEET. By mixing C. vulgaris with activated sludge, the photobioreactor showed better performance, removing 82.3% TAN, 67.7% COD, 85.7% CAF, and 73.3% DEET. Proteobacteria, Bacteroidetes, and Chloroflexi were identified as the dominant phyla in the activated sludge. The processes were then optimized by the artificial neural network (ANN). High R² values (>0.99) and low mean squared errors demonstrated that ANN could optimize the reactors’ performance. The toxicity testing showed that high concentrations of contaminants (>10 mg/L) and long contact time (>48 h) reduced the chlorophyll and protein contents in microalgae. Overall, a green technology for wastewater treatment using microalgae and bacteria consortium has demonstrated its high potentials in sustainable management of water resources.

In the current study, an integrated physiochemical method was utilized to remove tonalide (TND) and dimethyl phthalate (DMP) (as emerging contaminants, ECs), and nickel (Ni) and lead (Pb) (as heavy metals), from synthetic wastewater. In the first step of the study, pH, current (mA/cm²), and voltage (V) were set to 7.0, 30, and 9, respectively; then the removal of TND, DMP, Ni, and Pb with an electro-ozonation reactor was optimized using response surface methodology (RSM). At the optimum reaction time (58.1 min), ozone dosage (9.4 mg L⁻¹), initial concentration of ECs (0.98 mg L⁻¹), and initial concentration of heavy metals (28.9 mg L⁻¹), the percentages of TND, DMP, Ni, and Pb removal were 77.0%, 84.5%, 59.2%, and 58.2%, respectively. For the electro-ozonation reactor, the ozone consumption (OC) ranged from 1.1 kg to 3.9 kg (kg O₃/kg Ecs), and the specific energy consumption (SEC) was 6.95 (kWh kg⁻¹). After treatment with the optimum electro-ozonation parameters, the synthetic wastewater was transferred to a fixed-bed column, which was filled with a new composite adsorbent (named BBCEC), as the second step of the study. BBCEC improved the efficacy of the removal of TND, DMP, Ni, and Pb to more than 92%.