The environmental remediation capacity of Ulva lactuca: the potential of macroalgae to reduce the threats caused by Titanium in marine invertebrate species

Impact of climate change on the toxicity of bisphenol A in Mytilus galloprovincialis and assessment of phycoremediation using Nannochloropsis salina via a multi-biomarker strategy and modeling

In the current study, the mussels Mytilus galloprovincialis, exposed to four varying temperatures (17, 20, 23, and 26 °C), were contaminated with 50 μg/L of bisphenol A both with and without Nannochloropsis salina. The toxicity evaluation is determined by quantifying various biomarkers related to oxidative stress, neurotoxicity, and cellular damage. The key findings indicate that the toxicity of bisphenol A is heightened by rising temperature. The impact of bisphenol A is most evident at 26 °C, leading to excessive production of reactive oxygen species, depletion of non-enzymatic antioxidants, and activation of antioxidant enzymes (catalase and glutathione-S-transferase). The rise in malondialdehyde levels confirms lipid peroxidation caused by bisphenol A and intensified by thermal stress. These findings have been supported by strong molecular interactions between bisphenol A and lectin mytilec apo-form and proximal thread matrix protein 1 from M. galloprovincialis following the computational modeling assay. The incorporation of N. salina as a food additive helped, firstly, to mitigate the stress effects and, secondly, resulted in a noticeable enhancement of oxidative balance and filtration ability, along with decreased lipid peroxidation.

The role of the macroalgae Ulva lactuca on the cellular effects of neodymium and mercury in the mussel Mytilus galloprovincialis

Rare earth elements (REEs) are increasingly being studied mainly due to their economic importance and wide range of applications, but also for their rising environmental concentrations and potential environmental and ecotoxicological impacts. Among REEs, neodymium (Nd) is widely used in lasers, glass additives, and magnets. Currently, NdFeB-based permanent magnets are the most significant components of electronic devices and Nd is used because of its magnetic properties. In addition to REEs, part of the environmental pollution related to electrical and electronic equipment, fluorescent lamps and batteries also comes from mercury (Hg). Since both elements persist in ecosystems and are continuously accumulated by marine organisms, a promising approach for water decontamination has emerged. Through a process known as sorption, live marine macroalgae can be used, especially Ulva lactuca, to accumulate potential toxic elements from the water. Therefore, the present study aimed to evaluate the cellular toxicity of Nd and Hg in Mytilus galloprovincialis, comparing the biochemical effects induced by these elements in the presence or absence of the macroalgae U. lactuca. The results confirmed that Hg was more toxic to mussels than Nd, but also showed the good capability of U. lactuca in preventing the onset of cellular disturbance and homeostasis disruption in M. galloprovincialis by reducing bioavailable Hg levels. Overall, the biochemical parameters evaluated related to metabolism, antioxidant and biotransformation defences, redox balance, and cellular damage, showed that algae could prevent biological effects in mussels exposed to Hg compared to those exposed to Nd. This study contributes to the advancement of knowledge in this field, namely the understanding of the impacts of different elements on bivalves and the crucial role of algae in the protection of other aquatic organisms.

Restoration of degraded estuarine and marine ecosystems: A systematic review of rehabilitation methods in Europe

This article provides a comprehensive study of ecosystem rehabilitation methods widely used in the 21st century, focusing on Europe. The review covers the evolution and trends in scientific article publication, identification of European countries demonstrating high publication outputs, collaboration patterns, leading journals, and thematic areas. Additionally, it examines primary stressors in European aquatic ecosystems, and different methods and treatments commonly employed for remediation purposes. The analysis of selected articles revealed a significant increase in studies over time, driven by public awareness and financial incentives from national, European and global organizations. Italy, Portugal and Spain were the leading countries in degraded ecosystem rehabilitation studies, mainly focusing on remediating contaminated areas where metals were identified as the primary stressor (chemical pollution). Chemical remediation method emerged as the most used, closely followed by biological remediation method, which have gained prominence in recent years due to their ecological, economic, and social combined benefits. Furthermore, recent studies demonstrate a growing trend towards the combined use of more than one treatment/method to rehabilitate ecosystems, particularly with biological treatments. This combined approach has the potential for synergistic effects in achieving more effective rehabilitation and their sustainability in the long term, thus, a focus for future research.

Ecotoxicological impacts of metals in single and co-exposure on mussels: Comparison of observable and predicted results

Used in high-tech and everyday products, mercury (Hg), cobalt (Co), and nickel (Ni) are known to be persistent and potentially toxic elements that pose a serious threat to the most vulnerable ecosystems. Despite being on the Priority Hazardous Substances List, existing studies have only assessed the individual toxicity of Co, Ni and Hg in aquatic organisms, with a focus on the latter, ignoring potential synergistic effects that may occur in real-world contamination scenarios. The present study evaluated the responses of the mussel Mytilus galloprovincialis, recognized as a good bioindicator of pollution, after exposure to Hg (25 μg/L), Co (200 μg/L) and Ni (200 μg/L) individually, and to the mixture of the three metals at the same concentration. The exposure lasted 28 days at 17 ± 1 °C, after which metal accumulation and a set of biomarkers related to organisms' metabolic capacity and oxidative status were measured. The results showed that the mussels could accumulate metals in both single- and co-exposure conditions (bioconcentration factors between 115 and 808) and that exposure to metals induced the activation of antioxidant enzymes. Although Hg concentration in organisms in the mixture decreased significantly compared to single exposure (9.4 ± 0.8 vs 21 ± 0.7 mg/kg), the negative effects increased in the mixture of the three elements, resulting in depletion of energy reserves, activation of antioxidants and detoxification enzymes, and cellular damage, with a hormesis response pattern. This study underscores the importance of risk assessment studies that include the effects of the combination of pollutants and demonstrates the limitations of applying models to predict metal mixture toxicity, especially when a hormesis response is given by the organisms.

Removal of mercury by silica-supported ionic liquids: Efficiency and ecotoxicological assessment

Anthropogenic impacts have affected the coastal environment and contributed to its contamination. Mercury (Hg) is widespread in nature and has been shown to be toxic in even the smallest amounts, negatively affecting not only the marine ecosystem but also the entire trophic chain due to its biomagnification. Mercury ranks third on the Agency for Toxic Substances and Diseases Registry (ATSDR) priority list and it is therefore imperative to develop more effective methods than those currently available to avoid the persistence of this contaminant in aquatic ecosystems. The present study aimed to evaluate the effectiveness of six different silica-supported ionic liquids (SIL) in removing Hg from contaminated saline water, under realistic conditions ([Hg] = 50 µg/L), and to ecotoxicologically evaluate the safety of the SIL-remedied water, using as test model the marine macroalga Ulva lactuca. The results revealed that SIL [Si][C3C1im][SCN] (250 mg/L) was the most effective in removing Hg from solution, with a efficiency up to 99 % in just 6 h, that enable to obtain < 1 µg/L Hg (European guideline in drinking water). U. lactuca exposed to either the SIL and/or the remedied water showed no significant changes in relative growth rate and chlorophyll a and b levels, compared to the control condition. Biomarker analysis (LPO, GSH, GSSG, SOD, GPx, CAT and GRed) also showed no significant changes in the biochemical performance of U. lactuca. Therefore, it could be assumed that water treatment with SIL or its presence in an aqueous environment does not pose toxicity levels that could inhibit the metabolism or cause cell damage to U. lactuca.

A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach

Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approach in a circular bioeconomy. Biomass waste (biowaste) is discarded organic materials made of biomass (e.g., agriculture waste and algal residue). Biowaste is widely studied as one of the potential feedstocks in the biowaste valorization process due to its being abundantly available. In terms of practical implementations, feedstock variability from biowaste, conversion costs and supply chain stability prevent the widespread usage of bioenergy products. Biowaste remediation and valorization have used artificial intelligence (AI), a newly developed idea, to overcome these difficulties. This report analyzed 118 works that applied various AI algorithms to biowaste remediation and valorization-related research published between 2007 and 2022. Four common AI types are utilized in biowaste remediation and valorization: neural networks, Bayesian networks, decision tree, and multivariate regression. The neural network is the most frequent AI for prediction models, the Bayesian network is utilized for probabilistic graphical models, and the decision tree is trusted for providing tools to assist decision-making. Meanwhile, multivariate regression is employed to identify the relationship between experimental variables. AI is a remarkably effective tool in predicting data, which is reportedly better than the conventional approach owing to its characteristics of time-saving and high accuracy. The challenge and future work in biowaste remediation and valorization are briefly discussed to maximize the model's performance.