Development of an Innovative Biosensor Based on Graphene/PEDOT/Tyrosinase for the Detection of Phenolic Compounds in River Waters.

Phenolic compounds, originating from industrial, agricultural, and urban sources, can leach into flowing waters, adversely affecting aquatic life, biodiversity, and compromising the quality of drinking water, posing potential health hazards to humans. Thus, monitoring and mitigating the presence of phenolic compounds in flowing waters are essential for preserving ecosystem integrity and safeguarding public health. This study explores the development and performance of an innovative sensor based on screen-printed electrode (SPE) modified with graphene (GPH), poly(3,4-ethylenedioxythiophene) (PEDOT), and tyrosinase (Ty), designed for water analysis, focusing on the manufacturing process and the obtained electroanalytical results. The proposed biosensor (SPE/GPH/PEDOT/Ty) was designed to achieve a high level of precision and sensitivity, as well as to allow efficient analytical recoveries. Special attention was given to the manufacturing process and optimization of the modifying elements' composition. This study highlights the potential of the biosensor as an efficient and reliable solution for water analysis. Modification with graphene, the synthesis and electropolymerization deposition of the PEDOT polymer, and tyrosinase immobilization contributed to obtaining a high-performance and robust biosensor, presenting promising perspectives in monitoring the quality of the aquatic environment. Regarding the electroanalytical experimental results, the detection limits (LODs) obtained with this biosensor are extremely low for all phenolic compounds (8.63 × 10-10 M for catechol, 7.72 × 10-10 M for 3-methoxycatechol, and 9.56 × 10-10 M for 4-methylcatechol), emphasizing its ability to accurately measure even subtle variations in the trace compound parameters. The enhanced sensitivity of the biosensor facilitates detection and quantification in river water samples. Analytical recovery is also an essential aspect, and the biosensor presents consistent and reproducible results. This feature significantly improves the reliability and usefulness of the biosensor in practical applications, making it suitable for monitoring industrial or river water.

Electrochemical Detection of Cd2+, Pb2+, Cu2+ and Hg2+ with Sensors Based on Carbonaceous Nanomaterials and Fe3O4 Nanoparticles.

Two electrochemical sensors were developed in this study, with their preparations using two nanomaterials with remarkable properties, namely, carbon nanofibers (CNF) modified with Fe3O4 nanoparticles and multilayer carbon nanotubes (MWCNT) modified with Fe3O4 nanoparticles. The modified screen-printed electrodes (SPE) were thus named SPE/Fe3O4-CNF and SPE/Fe3O4-MWCNT and were used for the simultaneous detection of heavy metals (Cd2+, Pb2+, Cu2+ and Hg2+). The sensors have been spectrometrically and electrochemically characterized. The limits of detection of the SPE/Fe3O4-CNF sensor were 0.0615 µM, 0.0154 µM, 0.0320 µM and 0.0148 µM for Cd2+, Pb2+, Cu2+ and Hg2+, respectively, and 0.2719 µM, 0.3187 µM, 1.0436 µM and 0.9076 µM in the case of the SPE/ Fe3O4-MWCNT sensor (following optimization of the working parameters). Due to the modifying material, the results showed superior performance for the SPE/Fe3O4-CNF sensor, with extended linearity ranges and detection limits in the nanomolar range, compared to those of the SPE/Fe3O4-MWCNT sensor. For the quantification of heavy metal ions Cd2+, Pb2+, Cu2+ and Hg2+ with the SPE/Fe3O4-CNF sensor from real samples, the standard addition method was used because the values obtained for the recovery tests were good. The analysis of surface water samples from the Danube River has shown that the obtained values are significantly lower than the maximum limits allowed according to the quality standards specified by the United States Environmental Protection Agency (USEPA) and those of the World Health Organization (WHO). This research provides a complementary method based on electrochemical sensors for in situ monitoring of surface water quality, representing a useful tool in environmental studies.

Micro and nano plastics in fruits and vegetables: A review.

Plastics are becoming common environmental pollutants. Plants behave as access routes for plastics in the trophic chain since they can adsorb particles through their roots or on their surfaces. In this review, various methods for sample preparation and analytical methods for plastic isolation and identification from vegetables, fruits, and their seedlings were discussed. The effects that plastic particles have on them were also addressed. All of the studies offer convincing proof that micro and nano plastics already exist in fruits and vegetables, or can easily enter into their seedlings and have a variety of effects. Since most studies have been conducted under strictly controlled conditions using standard plastics, more tests under more environmentally realistic conditions are required to ensure that literature studies are applicable. Also, more fruits and vegetables need to be tested to identify the number of plastics currently there that, when consumed, could harm human health.

The influence of climate changes on heavy metals accumulation in Alosa immaculata from the Danube River Basin.

This research report provides a comprehensive overview of the historical trends in heavy metal concentrations in the Pontic shad (Alosa immaculata) populations from both the Danube River and the Black Sea, while also exploring the potential influence of global warming on metal accumulation. Through bibliometric modeling analysis, it reveals significant limitations in existing international research, particularly the lack of comprehensive data on the impact of hydroclimatic changes on heavy metal accumulation in Alosa immaculata. Recognizing the critical importance of studies on heavy metal bioaccumulation in Danube shad, this research underscores their significance in defining tolerance thresholds, quantifying the impact of toxic elements along the aquatic food chain, and enhancing the economic sustainability of ichthyofauna monitoring efforts. Furthermore, these studies contribute invaluable insights into the complex dynamics of aquatic ecosystems, offering essential decision-making support for optimizing commercial fishing management practices on the Danube and ensuring robust support systems for industrial fishing endeavors.

Human health risk assessment of potentially toxic elements and microplastics accumulation in products from the Danube River Basin fish market.

The present study aimed to quantify the concentration levels of potentially toxic elements (PTEs) such as aluminum, arsenic, cadmium, chromium, copper, nickel, lead, zinc, and mercury, as well as microplastics occurrence in various tissues of fish and seafood species, commercialized in the Lower Danube River Basin. A health risk assessment analysis was performed based on the PTEs concentration levels in the muscle tissue. Estimated daily intake (EDI), target hazard quotient (THQ), hazard index (HI), and target cancer risk (TR) of PTEs were calculated. It was observed that the species within the seafood category registered the highest levels of PTEs. For instance, in the muscle tissue of bivalve Mytilus galloprovincialis (from the Black Sea), the highest value was observed in the case of Zn (37.693 mg/kg), and the presence of polystyrene polymer was identified. The values associated with EDI, THQ, HI, and TR of PTE exposure were significantly lower than 1.

Effects of cooking methods and co-ingested foods on mercury bioaccessibility in pontic shad (Alosa immaculata).

Human mercury (Hg) exposure is mostly caused by eating fish. However, there are major differences between the measured and predicted mercury concentration on Hg bioavailability. This study investigated the effects of cooking (steaming, baking, frying, marinating, and smoking) and selected components' co-ingestion on Hg bioaccessibility. Baking and frying reduced Hg bioaccessibility compared to the raw sample. The bioaccessible Hg fraction in fish was assessed through in vitro digestion method. Hg bioaccessibility varied from 4.31 to nearly 24.95% and the Hg recovery rate varied from 63.44 to 78.74%. Co-ingested garlic and broccoli with pontic shad had a positive effect on decreasing fish Hg bioaccessibility. The antioxidant activity of co-ingested food items was also calculated and correlated with mercury bioaccessibility. These results highlighted a possible positive role of plant-based foods and other food processing techniques in the bioaccessibility reduction of other chemical contaminants found in food sources.

Assessing and forecasting water quality in the Danube River by using neural network approaches.

The health and quality of the Danube River ecosystems is strongly affected by the nutrients loads (N and P), degree of contamination with hazardous substances or with oxygen depleting substances, microbiological contamination and changes in river flow patterns and sediment transport regimes. Water quality index (WQI) is an important dynamic attribute in the characterization of the Danube River ecosystems health and quality. The WQ index scores do not reflect the actual condition of water quality. We proposed a new forecast scheme for water quality based on the following qualitative classes very good (0-25), good (26-50), poor (51-75), very poor (76-100) and extremely polluted/non-potable (>100). Water quality forecasting by using Artificial Intelligence (AI) is a meaningful method of protecting public health because of its possibility to provide early warning regarding harmful water pollutants. The main objective of the present study is to forecast the WQI time series data based on water physical, chemical and flow status parameters and associated WQ index scores. The Cascade-forward network (CFN) models, along with the Radial Basis Function Network (RBF) as a benchmark model, were developed using data from 2011 to 2017 and WQI forecasts were produced for the period 2018-2019 at all sites. The nineteen input water quality features represent the initial dataset. Moreover, the Random Forest (RF) algorithm refines the initial dataset by selecting eight features considered the most relevant. Both datasets are employed for constructing the predictive models. According to the results of appraisal, the CFN models produced better outcomes (MSE = 0.083/0,319 and R-value 0.940/0.911 in quarter I/quarter IV) than the RBF models. In addition, results show that both the CFN and RBF models could be effective for predicting time series data for water quality when the eight most relevant features are used as input variables. Also, the CFNs provide the most accurate short-term forecasting curves which reproduce the WQI for the first and fourth quarters (the cold season). The second and third quarters presented a slightly lower accuracy. The reported results clearly demonstrate that CFNs successfully forecast the short-term WQI as they may learn historic patterns and determine the nonlinear relationships between the input and output variables.

Regional Regression Correlation Model of Microplastic Water Pollution Control Using Circular Economy Tools.

Water pollution caused by microplastics represents an important challenge for the environment and people's health. The weak international regulations and standards in this domain support increased water pollution with microplastics. The literature is unsuccessful in establishing a common approach regarding this subject. The main objective of this research is to develop a new approach to necessary policies and ways of action to decrease water pollution caused by microplastics. In this context, we quantified the impact of European water pollution caused by microplastics in the circular economy. The main research methods used in the paper are meta-analysis, statistical analysis and an econometric approach. A new econometric model is developed in order to assist the decision makers in increasing efficiency of public policies regarding water pollution elimination. The main result of this study relies on combining, in an integrated way, the Organisation for Economic Co-operation and Development's (OECD) data on microplastic water pollution and identifying relevant policies to combat this type of pollution.

To a Green Economy across the European Union.

"Is the green economy a real solution to the present economic development?" represents the main question of the present research. The paper deals with assessing the impact of innovation on the green economy and quality of life through policies promoted at the European Union (EU) level. The objectives of the paper are to assess the impact level of the policy implementation across the Member States through the research and development (R&D) process and to identify models for the development of the green economy in Europe. The applied methods are empirical and analytical based on the study of literature, econometric modeling, pivoting econometric models, trend synthesis, prioritization, and staging of policy experimentation in the Member States through data processing and statistical programs. The results consist in obtaining development models for the green economy at the EU and national levels. In conclusion, the paper contributes to the early identification of vulnerabilities that may affect the development of European strategic projects in relation to events and security breaches occurring at the EU level at any given time.

New Approach to Modelling the Impact of Heavy Metals on the European Union's Water Resources.

The present research aims to address the highly topical issue of heavy metal water pollution from an integrated European perspective, i.e., to quantify through modelling a general model of water pollution reduction in the EU. The objectives of the study are mainly aimed at identifying effective solutions to reduce heavy metal water pollution and providing supranational decision-makers with public policy directions in the field. The research methods consist of the foundation of working hypotheses based on the study of the literature, the consolidation of official statistical databases in the field, econometric modelling and the conceptualisation of a general model and its testing and validation by statistical methods. The results of the analysis consist of the following marginal contributions: the identification of a general model for combating heavy metal pollution; the calculation of the degree of contribution of regional policies to the general model; and the identification of effective solutions to improve the combating of heavy metal water pollution in Europe. The main conclusion of the analysis shows that significant progress has been achieved at the EU level in the field of combating heavy metal water pollution. However, the level of disparity and poor policy coordination are real vulnerabilities for the EU.

Multisensory System Used for the Analysis of the Water in the Lower Area of River Danube.

The present paper describes the development of a multisensory system for the analysis of the natural water in the Danube, water collected in the neighboring area of Galati City. The multisensory system consists of a sensor array made up of six screen-printed sensors based on electroactive compounds (Cobalt phthalocyanine, Meldola's Blue, Prussian Blue) and nanomaterials (Multi-Walled Carbon Nanotubes, Multi-Walled Graphene, Gold Nanoparticles). The measurements with the sensors array were performed by using cyclic voltammetry. The cyclic voltammograms recorded in the Danube natural water show redox processes related to the electrochemical activity of the compounds in the water samples or of the electro-active compounds in the sensors detector element. These processes are strongly influenced by the composition and physico-chemical properties of the water samples, such as the ionic strength or the pH. The multivariate data analysis was performed by using the principal component analysis (PCA) and the discriminant factor analysis (DFA), the water samples being discriminated according to the collection point. In order to confirm the observed classes, the partial least squares discriminant analysis (PLS-DA) method was used. The classification of the samples according to the collection point could be made accurately and with very few errors. The correlations established between the voltammetric data and the results of the physico-chemical analyses by using the PLS1 method were very good, the correlation coefficients exceeding 0.9. Moreover, the predictive capacity of the multisensory system is very good, the differences between the measured and the predicted values being less than 3%. The multisensory system based on voltammetric sensors and on multivariate data analysis methods is a viable and useful tool for natural water analysis.