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.

Microalgae to Bioenergy: Optimization of Aurantiochytrium sp. Saccharification.

Microalgae are a promising feedstock for bioethanol production, essentially due to their high growth rates and absence of lignin. Hydrolysis-where the monosaccharides are released for further fermentation-is considered a critical step, and its optimization is advised for each raw material. The present study focuses on the thermal acid hydrolysis (with sulfuric acid) of Aurantiochytrium sp. through a response surface methodology (RSM), studying the effect of acid concentration, hydrolysis time and biomass/acid ratio on both sugar concentration of the hydrolysate and biomass conversion yield. Preliminary studies allowed to establish the range of the variables to be optimized. The obtained models predicted a maximum sugar concentration (18.05 g/L; R2 = 0.990) after 90 min of hydrolysis, using 15% (w/v) biomass/acid ratio and sulfuric acid at 3.5% (v/v), whereas the maximum conversion yield (12.86 g/100 g; R2 = 0.876) was obtained using 9.3% (w/v) biomass/acid ratio, maintaining the other parameters. Model outputs indicate that the biomass/acid ratio and time are the most influential parameters on the sugar concentration and yield models, respectively. The study allowed to obtain a predictive model that is very well adjusted to the experimental data to find the best saccharification conditions for the Aurantiochytrium sp. microalgae.

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.

Microalgal Cultures for the Bioremediation of Urban Wastewaters in the Presence of Siloxanes.

Microalgae are widely used in the bioremediation of wastewaters due to their efficient removal of pollutants such as nitrogen, phosphorus, and contaminants of emerging concern (CECs). Siloxanes are CECs that reach wastewater treatment plants (WWTPs), leading to the production of biogas enriched with these compounds, associated with the breakdown of cogeneration equipment. The biological removal of siloxanes from wastewaters could be a sustainable alternative to the costly existing technologies, but no investigation has been performed using microalgal cultures for this purpose. This study evaluated the ability of Chlorella vulgaris to bioremediate primary (PE) and secondary (SE) urban effluents and remove volatile methylsiloxanes (VMSs). C. vulgaris grew successfully in both effluents, and approximately 86% of nitrogen and 80% of phosphorus were efficiently removed from the PE, while 52% of nitrogen and 87% of phosphorus were removed from the SE, and the presence of VMSs does not seem to have a negative influence on nutrient removal. Three out of the seven of the analysed VMSs were detected in the microalgal biomass at the end of the PE assay. However, dodecamethylcyclohexasiloxane (D6) was the one that accumulated to a greater extent, since 48% of the initial mass of D6 was detected in the biomass samples. D6 is one of the most lipophilic VMSs, which might contribute to the higher adsorption onto the surface of microalgae. Overall, the results indicate C. vulgaris' potential to remove specific VMSs from effluents.

Empirical ozone isopleths at urban and suburban sites through evolutionary procedure-based models.

Ozone (O3) is a reactive oxidant that causes chronic effects on human health, vegetation, ecosystems and materials. This study aims to create O3 isopleths in urban and suburban environments, based on machine learning with air quality data collected from 2001 to 2017 at urban (EA) and suburban (CC) monitoring stations from Madrid (Spain). Artificial neural network (ANN) models have powerful fitting performance, describing correctly several complex and nonlinear relationships such as O3 and his precursors (VOC and NOx). Also, ANN learns from the experience provided by data, contrary to mechanistic models based on the fundamental laws of natural sciences. The determined isopleths showed a different behaviour of the VOC-NOx-O3 system compared to the one achieved with a mechanistic model (EKMA curve): e.g. for constant NOx concentrations, O3 concentrations decreased with VOC concentrations in the ANN model. Considering the difficulty to model all the phenomena (and acquired all the required data) that influences O3 concentrations, the statistical models may be a solution to describe this system correctly. The applied methodology is a valuable tool for defining mitigation strategies (control of precursors' emissions) to reduce O3 concentrations. However, as these models are obtained by air quality data, they are not geographical transferable.

Quality assessment of water intended for human consumption from Kwanza, Dande and Bengo rivers (Angola).

Angola is one of the countries with a high rate of waterborne diseases, due to the scarcity and poor quality of water for human consumption. The watercourses are receptors of many effluents, mainly domestic sewage, due to a precarious or inexistent sanitation system and a small number of wastewater treatment plants. Therefore, this study aims: (i) to evaluate the water quality (physicochemical and microbiological parameters) of three Angolan rivers (Kwanza, Bengo and Dande) in locations where water is used as drinking water or abstracted for human consumption; (ii) to develop a new water quality index able to quantitatively express the water quality in those sites; and (iii) to assess the spatial distribution of water pollution through principal component analysis (PCA). Water quality assessment was performed by conducting four field surveys (campaigns I to IV); the first two campaigns took place in the dry season, while the last two ones took place in the rainy season. In the first two campaigns, the water quality was suitable to be treated for the production of drinking water, while in the last two campaigns, the water was unsuitable for that purpose (high levels of faecal coliforms were detected). The water quality index allowed to classify the water as generally excellent (campaigns I and II) and poor (campaigns III and IV). The rudimentary disinfection usually performed by individual water suppliers may improve the water quality, but it was not enough to achieve the parametric values required for human consumption in the rainy season (campaigns III and IV) except for Bengo sites. PCA identified sampling sites with the same water quality patterns, grouping into four groups (Kwanza sites) and two groups (Dande and Bengo sites). Therefore, the results of this study may support decision-makers as regards water supply management in the river stretches under study. The new developed Water Quality Index can support decision-makers in terms of water supply management, especially in countries with a high rate of waterborne diseases (e.g. Angola).

Impact of the implementation of Lisbon low emission zone on air quality.

Air pollution is an increasing concern due to the negative impacts on human health, environment, and patrimony. The implementation of a Low Emission Zone (LEZ) is an important air quality policy action to reduce air pollutant emissions. This study aims to assess the air quality improvements in Lisbon with the LEZ implementation, analysing its impact on the air pollutant concentrations. The analysis performed from 2009 to 2016 showed an improvement in air quality. In the Zone 1, the reduction of PM10 and NO2 annual average concentrations were 29% and 12%, respectively, while, in the Zone 2, the reduction of PM10 and NO2 annual average concentrations were 23% and 22%, respectively. The background pollution analysis showed the LEZ effect on the lowest levels of ambient air pollution to which the population is chronically exposed. The achieved reductions of PM10 and NO2 levels were 30.5% and 9.4% in Zone 1, and 22.5% and 12.9% in the Zone 2, respectively. Concluding, this study evidenced an air quality improvement mainly for PM10 and NO2; however, insignificant reductions were observed for NOx and PM2.5. Therefore, stricter restriction standards should be defined, combining with other air quality policy decisions to reduce the population exposure to air pollutants.

Nutrient recovery from wastewaters by microalgae and its potential application as bio-char.

The intensive agricultural practices are increasing the demand for chemical fertilizers, being currently produced from a non-environmental friendly way. Besides the environmental impacts, the nutrient uptake efficiency by the crops is very low, representing huge losses into the fields. Therefore, it is crucial to study alternatives for the current chemical fertilizers, which simultaneous improve nutrient efficiency and minimize environmental impacts. A sustainable solution is to recover nutrients from wastewater streams with microalgal cultures and the biomass conversion into bio-char for soil amendment. Wastewaters are loaded with nitrogen and phosphorus and can be used as culture medium for microalgae. Thus, nutrients can be recycled, reducing the requirement of chemical fertilizers. This paper aims to review nutrient recovery from wastewater using microalgae and the biomass conversion into bio-char. This process promotes nutrient recycling and the bio-char (when added to soil) improves the nutrient uptake efficiency by crops.

Nitrogen Removal from Landfill Leachate by Microalgae.

Landfill leachates result from the degradation of solid residues in sanitary landfills, thus presenting a high variability in terms of composition. Normally, these effluents are characterized by high ammoniacal-nitrogen (N-NH4⁺) concentrations, high chemical oxygen demands and low phosphorus concentrations. The development of effective treatment strategies becomes difficult, posing a serious problem to the environment. Phycoremediation appears to be a suitable alternative for the treatment of landfill leachates. In this study, the potential of Chlorella vulgaris for biomass production and nutrients (mainly nitrogen and phosphorus) removal from different compositions of a landfill leachate was evaluated. Since microalgae also require phosphorus for their growth, different loads of this nutrient were evaluated, giving the following N:P ratios: 12:1, 23:1 and 35:1. The results have shown that C. vulgaris was able to grow in the different leachate compositions assessed. However, microalgal growth was higher in the cultures presenting the lowest N-NH4⁺ concentration. In terms of nutrients uptake, an effective removal of N-NH4⁺ and phosphorus was observed in all the experiments, especially in those supplied with phosphorus. Nevertheless, N-NO3- removal was considered almost negligible. These promising results constitute important findings in the development of a bioremediation technology for the treatment of landfill leachates.

Atmospheric CO2 capture by algae: Negative carbon dioxide emission path.

Carbon dioxide is one of the most important greenhouse gas, which concentration increase in the atmosphere is associated to climate change and global warming. Besides CO2 capture in large emission point sources, the capture of this pollutant from atmosphere may be required due to significant contribution of diffuse sources. The technologies that remove CO2 from atmosphere (creating a negative balance of CO2) are called negative emission technologies. Bioenergy with Carbon Capture and Storage may play an important role for CO2 mitigation. It represents the combination of bioenergy production and carbon capture and storage, keeping carbon dioxide in geological reservoirs. Algae have a high potential as the source of biomass, as they present high photosynthetic efficiencies and high biomass yields. Their biomass has a wide range of applications, which can improve the economic viability of the process. Thus, this paper aims to assess the atmospheric CO2 capture by algal cultures.

Biotechnological potential of Synechocystis salina co-cultures with selected microalgae and cyanobacteria: Nutrients removal, biomass and lipid production.

Cultivation of microalgae and cyanobacteria has been the focus of several research studies worldwide, due to the huge biotechnological potential of these photosynthetic microorganisms. However, production of these microorganisms is still not economically viable. One possible alternative to improve the economic feasibility of the process is the use of consortia between microalgae and/or cyanobacteria. In this study, Chlorella vulgaris, Pseudokirchneriella subcapitata and Microcystis aeruginosa were co-cultivated with Synechocystis salina to evaluate how dual-species cultures can influence biomass and lipid production and nutrients removal. Results have shown that the three studied consortia achieved higher biomass productivities than the individual cultures. Additionally, nitrogen and phosphorus consumption rates by the consortia provided final concentrations below the values established by European Union legislation for these nutrients. In the case of lipid productivities, higher values were determined when S. salina was co-cultivated with P. subcapitata and M. aeruginosa.

Multiple linear and principal component regressions for modelling ecotoxicity bioassay response.

The ecotoxicological response of the living organisms in an aquatic system depends on the physical, chemical and bacteriological variables, as well as the interactions between them. An important challenge to scientists is to understand the interaction and behaviour of factors involved in a multidimensional process such as the ecotoxicological response. With this aim, multiple linear regression (MLR) and principal component regression were applied to the ecotoxicity bioassay response of Chlorella vulgaris and Vibrio fischeri in water collected at seven sites of Leça river during five monitoring campaigns (February, May, June, August and September of 2006). The river water characterization included the analysis of 22 physicochemical and 3 microbiological parameters. The model that best fitted the data was MLR, which shows: (i) a negative correlation with dissolved organic carbon, zinc and manganese, and a positive one with turbidity and arsenic, regarding C. vulgaris toxic response; (ii) a negative correlation with conductivity and turbidity and a positive one with phosphorus, hardness, iron, mercury, arsenic and faecal coliforms, concerning V. fischeri toxic response. This integrated assessment may allow the evaluation of the effect of future pollution abatement measures over the water quality of Leça River.