Microplastics (MPs) have become an emerging global environmental pollution issue, while studies on the impact of nanoplastics (NPs) on aquatic environments, especially in freshwater environments, are still limited. In this study, we compared the toxic effects of micro- and nano-sized polystyrene microbeads (PS-MPs and PS-NPs) on a widespread freshwater algal species, Chlamydomonas reinhardtii (C. reinhardtii). The results revealed that both PS-MPs and PS-NPs had negative effects on the growth of C. reinhardtii in the range of 50 to 500 mg/L, reducing its biomass and photosynthetic pigment. The growth inhibitory rate (IR) of C. reinhardtii was 61.7% and 23.3% when exposed to 500 mg/L PS-NPs and PS-MPs for 96 h, respectively, demonstrating a higher toxic effect of the NPs than the MPs. In comparison to the control group, reactive oxygen species (ROS) and malondialdehyde (MDA) levels in algae cells increased, enhancing cell membrane permeability and then accelerating PS-NP internalization. MDA levels increased by 1.3 and 2.2 times under PS-MPs and PS-NPs exposure (500 mg/L, 96 h), respectively. Furthermore, the content of the extracellular polymeric substances and the antioxidant enzymatic (superoxide dismutase, peroxidase, and catalase) activities were all enhanced to resist the adverse effects caused by PS-MPs and PS-NPs. It is interesting that the internalized NPs were found to be clearly encapsulated in the vacuoles of the algal cells. These findings suggest that the ecological effect of NPs might need more attention, especially considering the continuing fragmentation of MPs in the environment.

Comparative toxic effects of microplastics and nanoplastics on Chlamydomonas reinhardtii: Growth inhibition, oxidative stress, and cell morphology

The mixed/mixotrophic nitrogen removal for the effective and sustainable treatment of wastewater: From treatment process to microbial mechanism.

Exopolysaccharides from microalgae: Production in a biorefinery framework and potential applications

Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion.

To provide real-time prediction of wastewater treatment plant (WWTP) effluent water quality, a machine learning (ML) model was developed by combining an improved feedforward neural network (IFFNN) with an optimization algorithm. Data used as input variables of the IFFNN included hourly influent water quality parameters, influent flow rate and WWTP process monitoring and operational parameters. Additionally, input variables included historical effluent water quality parameters for future prediction. The model was demonstrated in a WWTP in Jiangsu Province, China, where prediction of effluent chemical oxygen demand (COD) and total nitrogen (TN) with large variations were tested. Relative to the traditional feedforward neural network (FFNN) model without considering historical effluent water quality parameter input, the IFFNN enhanced prediction performance by 52.3% (COD) and 72.6% (TN) based on the mean absolute percentage errors of test datasets, after its model structure was optimized with a genetic algorithm (GA). The problem of over-fitting could also be overcome through the use of the IFFNN, with the determination of coefficient increased from 0.20 to 0.76 for test datasets of effluent COD. The GA-IFFNN model, which was efficient in capturing complex non-linear relationships and extrapolation, could be a useful tool for real-time direction of regulatory changes in WWTP operations.

/pdf/enhancing-real-time-prediction-of-effluent-water-quality-of-1dqt9x1r.pdf

Enhancing Real-Time Prediction of Effluent Water Quality of Wastewater Treatment Plant Based on Improved Feedforward Neural Network Coupled with Optimization Algorithm

Machine learning in natural and engineered water systems.

Extracellular polymeric substances (EPSs) play a significant role in the growth and survival of microorganisms. They have been applied in various fields, such as animal feed, pharmaceuticals and cosmetics, due to their physicochemical characteristics (e.g., high contents of polysaccharides and proteins, properties of antivirus, antitumor and antioxidant). However, lack of standard evaluation methods for efficient extraction restricts further investigation on the characteristics or functions of EPSs and their commercial applications. In this study, the Bound-EPS (B-EPS) Extraction Efficiency Evaluation Model (B-E4 Model) was developed to assess various extraction methods for two microalgae (i.e., the prokaryotic Microcystis sp. and the eukaryotic Scenedesmus sp.), including heating, centrifugation, ultrasound, ethylenediaminetetraacetic acid, cation-exchange resin or sodium hydroxide. The evaluation parameter k, was introduced to represent the extraction yields of B-EPSs, which was normalized to the per unit mass of released intracellular organics. This study revealed that heating at 70 °C for 10 min led to the highest extraction efficiency for both Microcystis sp. and Scenedesmus sp., with k values of 12.34 ± 1.48 and 6.02 ± 0.14, respectively. Moreover, model predictions for cell rupture were in close agreement with the results of flow cytometry analysis obtained in this study and the literature. Moreover, to test the applicability of B-E4 Model established in this study, it was utilized to evaluate the extraction efficiencies of various methods applied in a previous study on the microalgae Neocystis mucosa SX, and cation-exchange resin treating for 6 h was found to be the optimal method. The B-E4 model developed in this study was proven to be a powerful tool for optimizing the microalgal B-EPS extraction procedures.

Quantitative assessment of extraction methods for bound extracellular polymeric substances (B-EPSs) produced by Microcystis sp. and Scenedesmus sp.

The oxidation of thallium [Tl(I)] to Tl(III) by chlorine (HOCl) is an important process changing its removal performance in water treatment. However, the role of bromide (Br-), a common constituent in natural water, in the oxidation behavior of Tl(I) during chlorination remains unknown. Our results demonstrated that Br- was cycled and acted as a catalyst to enhance the kinetics of Tl(I) oxidation by HOCl over the pH range of 5.0-9.5. Different Tl(I) species (i.e., Tl+ and TlOH(aq)) and reactive bromine species (i.e., HOBr/BrO-, BrCl, Br2O, and BrOCl) were kinetically relevant to the enhanced oxidation of Tl(I). The oxidation by free bromine species became the dominant pathway even at a low Br- level of 50 μg/L for a chlorine dose of 2 mg of Cl2/L. It was found that the reactions of Tl+/BrCl, Tl+/BrOCl, and TlOH(aq)/HOBr dominated the kinetics of Tl(I) oxidation at pH 8.0, respectively. The species-specific rate constants for Tl+ reacting with individual bromine species were determined and decreased in the order: BrCl > Br2 > BrOCl > Br2O > HOBr. Overall, the presented results refine our knowledge regarding the species-specific reactivity of TI(I) with bromine species and will be useful for further prediction of thallium mobility in chlorinated waters containing bromide.

Ruixing Huang

Papers

Machine learning in natural and engineered water systems.

Quantitative assessment of extraction methods for bound extracellular polymeric substances (B-EPSs) produced by Microcystis sp. and Scenedesmus sp.

Kinetics of Thallium(I) Oxidation by Free Chlorine in Bromide-Containing Waters: Insights into the Reactivity with Bromine Species.