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Prof. Stefan Mihai Petrea
''Dunărea de Jos'' University of Galați

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0 Aquaculture
0 Aquaponics
0 Hydroponics
0 fish migration
0 fish ecology

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Journal article
Published: 10 August 2021 in Sustainability
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Heavy metal pollution is still present in the Danube River basin, due to intensive naval and agricultural activities conducted in the area. Therefore, continuous monitoring of this pivotal aquatic macro-system is necessary, through the development and optimization of monitoring methodologies. The main objective of the present study was to develop a prediction model for heavy metals accumulation in biological tissues, based on field gathered data which uses bioindicators (fish) and oxidative stress (OS) biomarkers. Samples of water and fish were collected from the lower sector of Danube River (DR), Danube Delta (DD) and Black Sea (BS). The following indicators were analyzed in samples: cadmium (Cd), lead (Pb), iron (Fe), zinc (Zn), copper (Cu) (in water and fish tissues), respectively, catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx), malondialdehyde (MDA) (in fish tissues). The pollution index (PI) was calculated to identify the most polluted studied ecosystem, which revealed that Danube River is seriously affected by the presence of Fe (IP = 4887) and strongly affected by the presence of Zn (IP = 4.49). The concentration of Cd in fish muscle tissue was above the maximum permitted level (0.05 µg/g) by the EU regulation. From all analyzed OS biomarkers, MDA registered the highest median values in fish muscle (145.7 nmol/mg protein in DR, 201.03 nmol/mg protein in DD, 148.58 nmol/mg protein in BS) and fish liver (200.28 nmol/mg protein in DR, 163.67 nmol/mg protein, 158.51 nmol/mg protein), compared to CAT, SOD and GPx. The prediction of Cd, Pb, Zn, Fe and Cu in fish hepatic and muscle tissue was determined based on CAT, SOD, GPx and MDA, by using non-linear tree-based RF prediction models. The analysis emphasizes that MDA in hepatic tissue is the most important independent variable for predicting heavy metals in fish muscle and tissues at BS coast, followed by GPx in both hepatic and muscle tissues. The RF analytical framework revealed that CAT in muscle tissue, respectively, MDA and GPx in hepatic tissues are most common predictors for determining the heavy metals concentration in both muscle and hepatic tissues in DD area. For DR, the MDA in muscle, followed by MDA in hepatic tissue are the main predictors in RF analysis.

ACS Style

Ira-Adeline Simionov; Dragoș Sebastian Cristea; Ștefan-Mihai Petrea; Alina Mogodan; Roxana Jijie; Elena Ciornea; Mircea Nicoară; Maria Magdalena Turek Rahoveanu; Victor Cristea. Predictive Innovative Methods for Aquatic Heavy Metals Pollution Based on Bioindicators in Support of Blue Economy in the Danube River Basin. Sustainability 2021, 13, 8936 .

AMA Style

Ira-Adeline Simionov, Dragoș Sebastian Cristea, Ștefan-Mihai Petrea, Alina Mogodan, Roxana Jijie, Elena Ciornea, Mircea Nicoară, Maria Magdalena Turek Rahoveanu, Victor Cristea. Predictive Innovative Methods for Aquatic Heavy Metals Pollution Based on Bioindicators in Support of Blue Economy in the Danube River Basin. Sustainability. 2021; 13 (16):8936.

Chicago/Turabian Style

Ira-Adeline Simionov; Dragoș Sebastian Cristea; Ștefan-Mihai Petrea; Alina Mogodan; Roxana Jijie; Elena Ciornea; Mircea Nicoară; Maria Magdalena Turek Rahoveanu; Victor Cristea. 2021. "Predictive Innovative Methods for Aquatic Heavy Metals Pollution Based on Bioindicators in Support of Blue Economy in the Danube River Basin." Sustainability 13, no. 16: 8936.

Journal article
Published: 18 May 2021 in Land Use Policy
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This study aims to evaluate the opportunity of integrating aquaponics as an environmentally preferable procurement (EPP) solution in Romania, meant to improve productivity and sustainability of both agriculture and aquaculture and to reduce conflicts between these two sectors. The Romanian production of plant species is evaluated and forecasted considering only the main species that can be commonly produced in aquaponics conditions, as follows: coriander, spinach, lettuce, tobacco, garlic, cucumber, tomatoes, and strawberries. The evaluation of the opportunity for integrating aquaponics into Romania, at a large scale, is evaluated using forecast analysis comparison between Romania and the European Union (EU), by applying six methods (exponential smoothing, Holt, Holt exponential, Holt damped, ARIMA, LSTM). Thus, comparative forecast analysis between Romania and the EU average values were developed considering four dimensions, as follows: aquaculture production, agriculture plant production for main species suitable to be cultivated in aquaponics conditions, organic agriculture surface and the amount of pesticides and insecticides used. Romania’s aquaculture production forecast indicates a superior upward trend compared with the EU average. Also, the agriculture production of the main plant species suitable for cultivation in aquaponics conditions records an inconsistent decreasing trend in Romania, compared with the upward trend recorded by analyzing the EU average value. The forecast of organic agriculture surface in Romania emphasizes a higher increasing trend compared to the EU average, situation which reveals a sustainable development direction, confirmed also by the decreasing forecasted trend of the amount of pesticides and insecticides used in agriculture. The registered results recommend the integration of aquaponics production systems into the Romanian green procurement network, considering also the EU context.

ACS Style

Mioara Costache; Dragos Sebastian Cristea; Stefan-Mihai Petrea; Mihaela Neculita; Maria Magdalena Turek Rahoveanu; Ira-Adeline Simionov; Alina Mogodan; Daniela Sarpe; Adrian Turek Rahoveanu. Integrating aquaponics production systems into the Romanian green procurement network. Land Use Policy 2021, 108, 105531 .

AMA Style

Mioara Costache, Dragos Sebastian Cristea, Stefan-Mihai Petrea, Mihaela Neculita, Maria Magdalena Turek Rahoveanu, Ira-Adeline Simionov, Alina Mogodan, Daniela Sarpe, Adrian Turek Rahoveanu. Integrating aquaponics production systems into the Romanian green procurement network. Land Use Policy. 2021; 108 ():105531.

Chicago/Turabian Style

Mioara Costache; Dragos Sebastian Cristea; Stefan-Mihai Petrea; Mihaela Neculita; Maria Magdalena Turek Rahoveanu; Ira-Adeline Simionov; Alina Mogodan; Daniela Sarpe; Adrian Turek Rahoveanu. 2021. "Integrating aquaponics production systems into the Romanian green procurement network." Land Use Policy 108, no. : 105531.

Journal article
Published: 12 May 2021 in Energies
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The work at hand assesses several driving factors of carbon emissions in terms of urbanization and energy-related parameters on a panel of emerging European economies, between 1990 and 2015. The use of machine learning algorithms and panel data analysis offered the possibility to determine the importance of the input variables by applying three algorithms (Random forest, XGBoost, and AdaBoost) and then by modeling the urbanization and the impact of energy intensity on the carbon emissions. The empirical results confirm the relationship between urbanization and energy intensity on CO2 emissions. The findings emphasize that separate components of energy consumption affect carbon emissions and, therefore, a transition toward renewable sources for energy needs is desirable. The models from the current study confirm previous studies’ observations made for other countries and regions. Urbanization, as a process, has an influence on the carbon emissions more than the actual urban regions do, confirming that all the activities carried out as urbanization efforts are more harmful than the resulted urban area. It is proper to say that the urban areas tend to embrace modern, more green technologies but the road to achieve environmentally friendly urban areas is accompanied by less environmentally friendly industries (such as the cement industry) and a high consumption of nonrenewable energy.

ACS Style

Florian Nuţă; Alina Nuţă; Cristina Zamfir; Stefan-Mihai Petrea; Dan Munteanu; Dragos Cristea. National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO2 Emissions in Central–Eastern European Countries by Using Machine Learning Algorithms and Panel Data Analysis. Energies 2021, 14, 2775 .

AMA Style

Florian Nuţă, Alina Nuţă, Cristina Zamfir, Stefan-Mihai Petrea, Dan Munteanu, Dragos Cristea. National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO2 Emissions in Central–Eastern European Countries by Using Machine Learning Algorithms and Panel Data Analysis. Energies. 2021; 14 (10):2775.

Chicago/Turabian Style

Florian Nuţă; Alina Nuţă; Cristina Zamfir; Stefan-Mihai Petrea; Dan Munteanu; Dragos Cristea. 2021. "National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO2 Emissions in Central–Eastern European Countries by Using Machine Learning Algorithms and Panel Data Analysis." Energies 14, no. 10: 2775.

Journal article
Published: 14 October 2020 in Molecules
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Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. Demersal fish species, such as turbot (Psetta maxima maeotica), are accepted by the scientific communities as suitable bioindicators of heavy metal pollution in the aquatic environment. The present study uses a machine learning approach, which is based on multiple linear and non-linear models, in order to effectively estimate the concentrations of heavy metals in both turbot muscle and liver tissues. For multiple linear regression (MLR) models, the stepwise method was used, while non-linear models were developed by applying random forest (RF) algorithm. The models were based on data that were provided from scientific literature, attributed to 11 heavy metals (As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Zn) from both muscle and liver tissues of turbot exemplars. Significant MLR models were recorded for Ca, Fe, Mg, and Na in muscle tissue and K, Cu, Zn, and Na in turbot liver tissue. The non-linear tree-based RF prediction models (over 70% prediction accuracy) were identified for As, Cd, Cu, K, Mg, and Zn in muscle tissue and As, Ca, Cd, Mg, and Fe in turbot liver tissue. Both machine learning MLR and non-linear tree-based RF prediction models were identified to be suitable for predicting the heavy metal concentration from both turbot muscle and liver tissues. The models can be used for improving the knowledge and economic efficiency of linked heavy metals food safety and environment pollution studies.

ACS Style

Ștefan-Mihai Petrea; Mioara Costache; Dragoș Cristea; Ștefan-Adrian Strungaru; Ira-Adeline Simionov; Alina Mogodan; Lacramioara Oprica; Victor Cristea. A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues. Molecules 2020, 25, 4696 .

AMA Style

Ștefan-Mihai Petrea, Mioara Costache, Dragoș Cristea, Ștefan-Adrian Strungaru, Ira-Adeline Simionov, Alina Mogodan, Lacramioara Oprica, Victor Cristea. A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues. Molecules. 2020; 25 (20):4696.

Chicago/Turabian Style

Ștefan-Mihai Petrea; Mioara Costache; Dragoș Cristea; Ștefan-Adrian Strungaru; Ira-Adeline Simionov; Alina Mogodan; Lacramioara Oprica; Victor Cristea. 2020. "A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues." Molecules 25, no. 20: 4696.

Journal article
Published: 08 June 2020 in Sustainability
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Moldova possesses the largest area of farmland as a share of its total land surface, an advantage which should encourage economic development strategies oriented towards the agriculture sector. Government subsidies and agriculture loans have been used as tools for developing the Moldavian agriculture. However, considering the challenges generated by both climate change (the drought from year 2012 that affected 80% of farmland) and a difficult political situation (restrictions imposed by the Russian Federation on the Republic of Moldova’s agri-food imports and exports between 2013 and 2014), the country’s agricultural system ranks very low when it comes to agricultural production efficiency. The present paper analyses the performances of the agricultural sector and its impact on the Moldavian economy over a nine-year period (between 2008 and 2016), by using a custom-developed analytical framework based on a dataset containing 21 relevant indicators. The analytical framework generates various perspectives that can be used to elaborate an economic sustainable development strategy of the Moldavian agriculture sector. The development of the analytical framework is based on the dynamics of agriculture subsidies, agricultural loans, the agricultural sector’s gross domestic product (GDP) and gross value added (GVA), as well as the dynamics of agricultural production and production value, also considering the main crops belonging to the Moldavian agriculture sector. The results are presented as sets of mathematical regression models that quantify the relationships found between the relevant agricultural parameters and their impact on the economics of the agricultural sector. It has been identified that the agriculture sector has a considerable impact on the Moldavian economy, a fact revealed by the significant model between the agriculture GVA and total GVA and GDP. A significant, negative correlation model was identified between agriculture subsidies and agriculture loans, although a small percentage of Moldavian agriculture farms were subsidized. Strong correlation models were also identified between wheat and maize production and total agriculture production, emphasizing the importance of these two crops for the Moldavian agricultural economy. Grape and maize production values also generated a correlation model, emphasizing the market interconnection between these crops It can be concluded that the increase in value of governmental agriculture subsidies, as well as expanding their addressability in order to maximize the access possibility for a higher number of agriculture farms, are essential for the Moldavian agriculture sector’s future development, since considering the limiting value of and accessibility to subsidies, a direct correlation model was identified between governmental agriculture subsidies and agriculture GVA.

ACS Style

Ștefan-Mihai Petrea; Dragos Sebastian Cristea; Maria Magdalena Turek Rahoveanu; Cristina Gabriela Zamfir; Adrian Turek Rahoveanu; Gheorghe Adrian Zugravu; Dumitru Nancu. Perspectives of the Moldavian Agricultural Sector by Using a Custom-Developed Analytical Framework. Sustainability 2020, 12, 1 .

AMA Style

Ștefan-Mihai Petrea, Dragos Sebastian Cristea, Maria Magdalena Turek Rahoveanu, Cristina Gabriela Zamfir, Adrian Turek Rahoveanu, Gheorghe Adrian Zugravu, Dumitru Nancu. Perspectives of the Moldavian Agricultural Sector by Using a Custom-Developed Analytical Framework. Sustainability. 2020; 12 (11):1.

Chicago/Turabian Style

Ștefan-Mihai Petrea; Dragos Sebastian Cristea; Maria Magdalena Turek Rahoveanu; Cristina Gabriela Zamfir; Adrian Turek Rahoveanu; Gheorghe Adrian Zugravu; Dumitru Nancu. 2020. "Perspectives of the Moldavian Agricultural Sector by Using a Custom-Developed Analytical Framework." Sustainability 12, no. 11: 1.

Journal article
Published: 17 December 2019 in Journal of Marine Science and Engineering
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This study investigates the influence of gender in the bioconcentration of essential and nonessential elements in different parts of Black Sea turbot (Psetta maxima maeotica) body, from an area considered under high anthropogenic pressure (the Constanta City Black Sea Coastal Area in Romania). A number of 13 elements (Ca, Mg, Na, K, Fe, Zn, Mn, Cu, Ni, Cr, As, Pb and Cd) were measured in various sample types: muscle, stomach, stomach content, intestine, intestine content, gonads, liver, spleen, gills and caudal fin. Turbot adults (4–5 years old) were separated, according to their gender, into two groups (20 males, 20 females, respectively), and a high total number of samples (1200 from both groups) were prepared and analyzed, in triplicate, with Flame Atomic Absorption Spectrometry and High-Resolution Continuum Source Atomic Absorption Spectrometry with Graphite Furnace techniques. The results were statistically analyzed in order to emphasize the bioconcentration of the determined elements in different tissues of wild turbot males vs. females, and also to contribute to an upgraded characterization of the Romanian Black Sea Coast, around Constanta City, in terms of heavy metals pollution. The essential elements Mg and Zn have different roles in the gonads of males and females, as they were the only elements with completely different patterns between the analyzed groups of specimens. The concentrations of studied elements in muscle were not similar with the data provided by literature, suggesting that chemistry of the habitat and food plays a major role in the availability of the metals in the body of analyzed fish species. The gender influenced the bioaccumulation process of all analyzed elements in most tissues since turbot male specimens accumulated higher concentration of metals compared to females. The highest bioaccumulation capacity in terms of Ca, Mg, Na, Ni, As, Zn and Cd was registered in caudal fin, liver and intestine tissues. Also, other elements such as K, Fe, Cu and Mn had the highest bioaccumulation in their muscle, spleen, liver and gills tissues. The concentrations of toxic metals in Black Sea turbot from this study were lower in the muscle samples compared with the studies conducted in Turkey, suggesting that the anthropogenic activity in the studied area did not pose a major impact upon the habitat contamination.

ACS Style

Ira-Adeline Simionov; Victor Cristea; Stefan-Mihai Petrea; Alina Mogodan; Mircea Nicoara; Emanuel Stefan Baltag; Stefan-Adrian Strungaru; Caterina Faggio. Bioconcentration of Essential and Nonessential Elements in Black Sea Turbot (Psetta Maxima Maeotica Linnaeus, 1758) in Relation to Fish Gender. Journal of Marine Science and Engineering 2019, 7, 466 .

AMA Style

Ira-Adeline Simionov, Victor Cristea, Stefan-Mihai Petrea, Alina Mogodan, Mircea Nicoara, Emanuel Stefan Baltag, Stefan-Adrian Strungaru, Caterina Faggio. Bioconcentration of Essential and Nonessential Elements in Black Sea Turbot (Psetta Maxima Maeotica Linnaeus, 1758) in Relation to Fish Gender. Journal of Marine Science and Engineering. 2019; 7 (12):466.

Chicago/Turabian Style

Ira-Adeline Simionov; Victor Cristea; Stefan-Mihai Petrea; Alina Mogodan; Mircea Nicoara; Emanuel Stefan Baltag; Stefan-Adrian Strungaru; Caterina Faggio. 2019. "Bioconcentration of Essential and Nonessential Elements in Black Sea Turbot (Psetta Maxima Maeotica Linnaeus, 1758) in Relation to Fish Gender." Journal of Marine Science and Engineering 7, no. 12: 466.