Microalgal cultures for the remediation of wastewaters with different nitrogen to phosphorus ratios: Process modelling using artificial neural networks.

Microalgae have remarkable potential for wastewater bioremediation since they can efficiently uptake nitrogen and phosphorus in a sustainable and environmentally friendly treatment system. However, wastewater composition greatly depends on its source and has a significant seasonal variability. This study aimed to evaluate the impact of different N:P molar ratios on the growth of Chlorella vulgaris and nutrient removal from synthetic wastewater. Furthermore, artificial neural network (ANN) threshold models, optimised by genetic algorithms (GAs), were used to model biomass productivity (BP) and nitrogen/phosphorus removal rates (RRN/RRP). The impact of various inputs culture variables on these parameters was evaluated. Microalgal growth was not nutrient limited since the average biomass productivities and specific growth rates were similar between the experiments. Nutrient removal efficiencies/rates reached 92.0 ± 0.6%/6.15 ± 0.01 mgN L-1 d-1 for nitrogen and 98.2 ± 0.2%/0.92 ± 0.03 mgP L-1 d-1 for phosphorus. Low nitrogen concentration limited phosphorus uptake for low N:P ratios (e.g., 2 and 3, yielding 36 ± 2 mgDW mgP-1 and 39 ± 3 mgDW mgP-1, respectively), while low phosphorus concentration limited nitrogen uptake with high ratios (e.g., 66 and 67, yielding 9.0 ± 0.4 mgDW mgN-1 and 8.8 ± 0.3 mgDW mgN-1, respectively). ANN models showed a high fitting performance, with coefficients of determination of 0.951, 0.800, and 0.793 for BP, RRN, and RRP, respectively. In summary, this study demonstrated that microalgae could successfully grow and adapt to N:P molar ratios between 2 and 67, but the nutrient uptake was impacted by these variations, especially for the lowest and highest N:P molar ratios. Furthermore, GA-ANN models demonstrated to be relevant tools for microalgal growth modelling and control. Their high fitting performance in characterising this biological system can contribute to reducing the experimental effort for culture monitoring (human resources and consumables), thus decreasing the costs of microalgae production.

Microalgae systems - environmental agents for wastewater treatment and further potential biomass valorisation.

Water is the most valuable resource on the planet. However, massive anthropogenic activities generate threatening levels of biological, organic, and inorganic pollutants that are not efficiently removed in conventional wastewater treatment systems. High levels of conventional pollutants (carbon, nitrogen, and phosphorus), emerging chemical contaminants such as antibiotics, and pathogens (namely antibiotic-resistant ones and related genes) jeopardize ecosystems and human health. Conventional wastewater treatment systems entail several environmental issues: (i) high energy consumption; (ii) high CO2 emissions; and (iii) the use of chemicals or the generation of harmful by-products. Hence, the use of microalgal systems (entailing one or several microalgae species, and in consortium with bacteria) as environmental agents towards wastewater treatment has been seen as an environmentally friendly solution to remove conventional pollutants, antibiotics, coliforms and antibiotic resistance genes. In recent years, several authors have evaluated the use of microalgal systems for the treatment of different types of wastewater, such as agricultural, municipal, and industrial. Generally, microalgal systems can provide high removal efficiencies of: (i) conventional pollutants, up to 99%, 99%, and 90% of total nitrogen, total phosphorus, and/or organic carbon, respectively, through uptake mechanisms, and (ii) antibiotics frequently found in wastewaters, such as sulfamethoxazole, ciprofloxacin, trimethoprim and azithromycin at 86%, 65%, 42% and 93%, respectively, through the most desirable microalgal mechanism, biodegradation. Although pathogens removal by microalgal species is complex and very strain-specific, it is also possible to attain total coliform and Escherichia coli removal of 99.4% and 98.6%, respectively. However, microalgal systems' effectiveness strongly relies on biotic and abiotic conditions, thus the selection of operational conditions is critical. While the combination of selected species (microalgae and bacteria), ratios and inoculum concentration allow the efficient removal of conventional pollutants and generation of high amounts of biomass (that can be further converted into valuable products such as biofuels and biofertilisers), abiotic factors such as pH, hydraulic retention time, light intensity and CO2/O2 supply also have a crucial role in conventional pollutants and antibiotics removal, and wastewater disinfection. However, some rationale must be considered according to the purpose. While alkaline pH induces the hydrolysis of some antibiotics and the removal of faecal coliforms, it also decreases phosphates solubility and induces the formation of ammonium from ammonia. Also, while CO2 supply increases the removal of E. coli and Pseudomonas aeruginosa, as well as the microalgal growth (and thus the conventional pollutants uptake), it decreases Enterococcus faecalis removal. Therefore, this review aims to provide a critical review of recent studies towards the application of microalgal systems for the efficient removal of conventional pollutants, antibiotics, and pathogens; discussing the feasibility, highlighting the advantages and challenges of the implementation of such process, and presenting current case-studies of different applications of microalgal systems.

Microalgae-Based Remediation of Real Textile Wastewater: Assessing Pollutant Removal and Biomass Valorisation.

The textile industry generates highly contaminated wastewater. It severely threatens local ecosystems without proper treatment, significantly diminishing biodiversity near the discharge point. With rapid growth rates, microalgae offer an effective solution to mitigate the environmental impact of textile wastewater, and the generated biomass can be valorised. This study sets out to achieve two primary objectives: (i) to assess the removal of pollutants by Chlorella vulgaris from two distinct real textile wastewaters (without dilution) and (ii) to evaluate microalgal biomass composition for further valorisation (in a circular economy approach). Microalgae grew successfully with growth rates ranging from 0.234 ± 0.005 to 0.290 ± 0.003 d-1 and average productivities ranging from 78 ± 3 to 112.39 ± 0.07 mgDW L-1 d-1. All cultures demonstrated a significant reduction in nutrient concentrations for values below the legal limits for discharge, except for COD in effluent 2. Furthermore, the pigment concentration in the culture increased during textile effluent treatment, presenting a distinct advantage over conventional ones due to the economic value of produced biomass and pigments. This study underscores the promise of microalgae in textile wastewater treatment and provides valuable insights into their role in addressing the environmental challenges the textile industry poses.

Comparative assessment of microalgal growth kinetic models based on light intensity and biomass concentration.

The comprehensive evaluation and validation of mathematical models for microalgal growth dynamics are essential for improving cultivation efficiency and optimising photobioreactor design. A considerable gap in comprehending the relation between microalgal growth, light intensity and biomass concentration arises since many studies focus solely on associating one of these factors. This paper compares microalgal growth kinetic models, specifically focusing on the combined impact of light intensity and biomass concentration. Considering a dataset (experimental results and literature values) concerning Chlorella vulgaris, nine kinetic models were assessed. Bannister and Grima models presented the best fitting performance to experimental data (RMSE ≤ 0.050 d-1; R2≥0.804; d2≥0.943). Cultivation conditions conducting photoinhibition were identified in some kinetic models. After testing these models on independent datasets, Bannister and Grima models presented superior predictive performance (RMSE = 0.022-0.023 d-1; R2 = 0.878-0.884; d2: 0.976-0.975). The models provide valuable tools for predicting microalgal growth and optimising operational parameters, reducing the need for time-consuming and costly experiments.

Native Microalgae-Bacteria Consortia: A Sustainable Approach for Effective Urban Wastewater Bioremediation and Disinfection.

Urban wastewater is a significant by-product of human activities. Conventional urban wastewater treatment plants have limitations in their treatment, mainly concerning the low removal efficiency of conventional and emerging contaminants. Discharged wastewater also contains harmful microorganisms, posing risks to public health, especially by spreading antibiotic-resistant bacteria and genes. Therefore, this study assesses the potential of a native microalgae-bacteria system (MBS) for urban wastewater bioremediation and disinfection, targeting NH4+-N and PO43--P removal, coliform reduction, and antibiotic resistance gene mitigation. The MBS showed promising results, including a high specific growth rate (0.651 ± 0.155 d-1) and a significant average removal rate of NH4+-N and PO43--P (9.05 ± 1.24 mg L-1 d-1 and 0.79 ± 0.06 mg L-1 d-1, respectively). Microalgae-induced pH increase rapidly reduces coliforms (r > 0.9), including Escherichia coli, within 3 to 6 days. Notably, the prevalence of intI1 and the antibiotic resistance genes sul1 and blaTEM are significantly diminished, presenting the MBS as a sustainable approach for tertiary wastewater treatment to combat eutrophication and reduce waterborne disease risks and antibiotic resistance spread.