**2. Results**

*2.1. Distribution of Children Descending from Normal Pregnancies into Groups Based on Clinical Examination and Consequent Findings*

Children descending from normal pregnancies were divided into two groups based on the results of examination and clinical findings. Children already dispesarized in the department of pediatric cardiology (*n* = 8) and those ones indicated by the sonographer to have valve problems and heart defects tricuspid valve regurgitation (*n* = 8), mitral valve regurgitation (*n* = 1), pulmonary valve regurgitation (*n* = 2), bicuspid aortic valve regurgitation (*n* = 1), ventricular septum defect (*n* = 1), atrial septum defect (*n* = 1), foramen ovale apertum (*n* = 5), arrhythmia (*n* = 1) constituted particular group together with children confirmed over several visits to have a high BP (*n* = 16) (systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) ≥ 90th percentile evaluated by the Age-Based Pediatric Blood Pressure Reference Charts calculator) and/or high BMI (*n* = 9) (BMI 85th percentile evaluated by the BMI Percentile Calculator for Child and Teens). Overall, this group consisted of 38 children (43.18%). The second group consisted from 50 children with normal anamnesis, normal BP, normal BMI, and normal reference values of echocardiographic measurements.

#### *2.2. Up-Regulation of miR-21-5p in Children Descending from Normal Pregnancies that are Overweight/Obese, Prehypertensive/Hypertensive and/or have Abnormal Echocardiogram Findings*

Since we identified within the group of children descending from normal pregnancies those ones with cardiac findings who were already dispesarized in the department of pediatric cardiology, or those ones who were overweight/obese, had prehypertension/hypertension, and/or abnormal echocardiogram findings, we compared the microRNA profile of this group to that one consisting of children with normal anamnesis, normal BP, normal BMI and normal reference values of echocardiographic measurements. The performance of receivers operating characteristic (ROC) curve analysis revealed that only miR-21-5p differentiated children descending from normal pregnancies in dependence on the presence or absence of postnatal abnormal clinical findings with a sensitivity of 28.95% at a specificity of 90.0% (Figure 1).

**Figure 1.** Up-regulation of miR-21-5p in children descending from normal pregnancies that are overweight/obese, prehypertensive/hypertensive and/or have abnormal echocardiogram findings. GH: gestational hypertension; PE: preeclampsia; FGR: fetal growth restriction; ROC: receivers operating characteristic; AUC: Area under the curve; +LR: likelihood ratio positive; –LR: likelihood ratio negative.

### *2.3. Dysregulation of Cardiovascular/Cerebrovascular Disease Associated microRNAs in Children Descending from Complicated Pregnancies*

MicroRNA gene expression was compared between children descending from normal and complicated pregnancies (GH, PE, and FGR). MicroRNA gene expression was analyzed in relation to both aspects, the current presence of cardiovascular risk factors (overweight/obesity and/or prehypertension/hypertension) and cardiovascular complications (valve problems and heart defects) and the previous occurrence of pregnancy-related complications with respect to clinical signs (mild versus severe preeclampsia), dates of delivery (< and > 34 weeks in case of PE, < and > 32 weeks in case of FGR, respectively). The association between microRNA gene expression and Doppler ultrasonography parameters (pulsatility index (PI) in the umbilical artery, PI in the middle cerebral artery, the cerebroplacental ratio, PI in the uterine artery, PI in the ductus venosus, and the presence of unilateral or bilateral diastolic notch in the uterine artery) was analyzed in the group of complicated pregnancies (PE and/or FGR). Just the results that reached a statistical significance or displayed a trend toward aberrant microRNA expression profile in complicated cases are presented below.

#### *2.4. Multiple microRNAs are Up-Regulated in Children Descending from GH Pregnancies*

The ROC curve analysis revealed a significant up-regulation of miR-1-3p, miR-17-5p, miR-20a-5p, miR-21-5p, miR-23a-3p, miR-26a-5p, miR-29a-3p, miR-126-3p, miR-133a-3p, miR-146a-5p, and miR-181a-5p for children descending from GH pregnancies when the comparison to the controls was performed (Figure 2).

**Figure 2.** *Cont*.

**Figure 2.** *Cont*.

**Figure 2.** *Cont*.

**Figure 2.** Postnatal microRNA expression profile in children descending from GH pregnancies. (**a**–**k**) Up-regulation of miR-1-3p, miR-17-5p, miR-20a-5p, miR-21-5p, miR-23a-3p, miR-26a-5p, miR-29a-3p, miR-126-3p, miR-133a-3p, miR-146a-5p, and miR-181a-5p was observed in children descending from GH pregnancies when the comparison to the controls was performed.

The sensitivity at 10.0% false positive rate (FPR) for miR-1-3p (46.3%), miR-17-5p (29.63%), miR-20a-5p (20.37%), miR-21-5p (29.63%), miR-23a-3p (27.78%), miR-26a-5p (16.67%), miR-29a-3p (35.19%), miR-126-3p (29.63%), miR-133a-3p (37.04%), miR-146a-5p (18.52%), and miR-181a-5p (31.48%) was found (Figure 2).

#### *2.5. Up-Regulation of miR-21-5p, miR-23a-3p, miR-26a-5p, miR-103a-3p, miR-125b-5p, miR-195-5p, and miR-342-3p in Children with Normal Postnatal Clinical Findings Descending from GH Pregnancies*

Concurrently, it was observed that the expression of miR-21-5p, miR-23a-3p, miR-26a-5p, miR-103a-3p, miR-125b-5p, miR-195-5p, and miR-342-3p differed significantly between the groups of children affected with GH with normal postnatal clinical findings and the controls (Figure 3). The sensitivity of individual microRNAs at 10.0% FPR was the following: miR-21-5p (39.13%), miR-23a-3p (34.78%), miR-26a-5p (21.74%), miR-103a-3p (30.43%), miR-125b-5p (47.83%), miR-195-5p (34.78%), and miR-342-3p (21.74%) (Figure 3).

**Figure 3.** *Cont*.

**Figure 3.** *Cont*.

**Figure 3.** Postnatal microRNA expression profile in children with normal postnatal clinical findings descending from GH pregnancies. (**a**–**g**) Up-regulation of miR-21-5p, miR-23a-3p, miR-26a-5p, miR-103a-3p, miR-125b-5p, miR-195-5p and miR-342-3p was observed in children with normal postnatal clinical findings descending from GH pregnancies.

Screening based on the combination of miR-26a-5p and miR-195-5p showed the highest accuracy for children with normal clinical findings with a prior exposure to GH (AUC 0.717, *p* = 0.001, sensitivity 86.96%, specificity 52.0%, cut off > 0.246824). It was able to identify 34.78% children with an increased cardiovascular risk at 10.0% FPR (Figure 4).

**Figure 4.** Combined postnatal screening of microRNAs in the identification of children with normal postnatal clinical findings descending from GH pregnancies. Postnatal combined screening of miR-26a-5p and miR-195-5p showed the highest accuracy for the identification of children with normal clinical findings with a prior exposure to GH at a higher risk of later development of cardiovascular/cerebrovascular diseases.

#### *2.6. Up-Regulation of miR-20a-5p in Children with Abnormal Postnatal Clinical Findings Descending from GH Pregnancies*

Overall, the group with abnormal postnatal clinical findings consisted of 31/54 children (57.41%) exposed to GH, 5 children already dispesarized in the department of pediatric cardiology, 12 children with abnormal echocardiographic findings (6 tricuspid valve regurgitation, 1 mitral valve regurgitation, 3 pulmonary valve regurgitation, 1 bicuspid aortic valve regurgitation, 1 ventricular septum defect, 2 foramen ovale apertum), 17 children with prehypertension/hypertension, and 3 children with high BMI. miR-20a-5p expression differed between the groups of children affected with GH with abnormal postnatal clinical findings and the controls. miR-20a-5p differentiated between the children descending from pregnancies affected with GH with abnormal postnatal clinical findings and the controls with a sensitivity of 25.81% at 10.0% FPR (Figure 5).

**Figure 5.** Increased expression of miR-20a-5p in children with abnormal postnatal clinical findings descending from GH pregnancies. Increased expression of miR-20a-5p was found in children with abnormal postnatal clinical findings descending from GH pregnancies.

#### *2.7. Up-Regulation of miR-1-3p, miR-17-5p, miR-29a-3p, miR-126-3p, miR-133a-3p, miR-146a-5p, and miR-181a-5p in Children Descending from GH Pregnancies Irrespective of Postnatal Clinical Findings*

The ROC curve analysis showed the difference in gene expression of miR-1-3p, miR-17-5p, miR-29a-3p, miR-126-3p, miR-133a-3p, miR-146a-5p, and miR-181a-5p between the controls and the group of children exposed to GH with postnatal normal clinical findings or children with a prior exposure to GH that already developed any cardiovascular complication (valve problems and heart defects) or were identified to be overweight/obese and/or prehypertensive/hypertensive (Figure 6). The sensitivity at 10.0% FPR for miR-1-3p (47.83% versus 45.16%) and miR-17-5p (30.43% versus 29.03%) for children descending from GH pregnancies with postnatal normal and abnormal clinical findings was approximately equal (Figure 6). The sensitivity at 10.0% FPR for miR-29a-3p (39.13% versus 32.26%), miR-126-3p (39.13% versus 22.58%), miR-133a-3p (43.48% versus 32.26%), miR-146a-5p (26.09% versus 12.9%), and miR-181a-5p (39.13% versus 25.81%) was slightly higher for children descending from GH pregnancies with postnatal normal clinical findings when compared to those ones with postnatal abnormal clinical findings (Figure 6).

**Figure 6.** *Cont*.

**Figure 6.** *Cont*.

**Figure 6.** *Cont*.

**Figure 6.** Postnatal microRNA expression profile in children descending from GH pregnancies irrespective of postnatal clinical findings. (**a**–**g**) Increased expression of miR-1-3p, miR-17-5p, miR-29a-3p, miR-126-3p, miR-133a-3p, miR-146a-5p, and miR-181a-5p was observed in children descending from GH pregnancies with normal or abnormal postnatal clinical findings. The ROC curve analysis showed the difference in microRNA gene expression between the controls and the group of children exposed to GH with postnatal normal clinical findings or children with a prior exposure to GH that already developed any cardiovascular complication (valve problems and heart defects) or were identified to be overweight/obese and/or prehypertensive/hypertensive.

Combined screening of miR-1-3p, miR-29a-3p, miR-126-3p, miR-133a-3p, and miR-181a-5p showed the highest accuracy for children with a prior exposure to GH with normal clinical findings (AUC 0.803, *p* < 0.001, sensitivity 82.61%, specificity 74.0%, cut off > 0.224754). It was able to identify 47.83% children with an increased cardiovascular risk at 10.0% FPR (Figure 7).

In addition, the combination of all seven microRNA biomarkers (miR-1-3p, miR-17-5p, miR-29a-3p, miR-126-3p, miR-133a-3p, miR-146a-5p, and miR-181a-5p) may be used to identify children with a prior exposure to GH with abnormal clinical findings (AUC 0.801, *p* < 0.001, sensitivity 70.97%, specificity 76.0%, cut off > 0.353483). It was able to identify 38.71% children with an increased cardiovascular risk at 10.0% FPR (Figure 8).

#### **Figure 7.** Combined postnatal screening of microRNAs in the identification of children with normal postnatal clinical findings descending from GH pregnancies. Postnatal combined screening of miR-1-3p, miR-29a-3p, miR-126-3p, miR-133a-3p and miR-181a-5p showed the highest accuracy for the identification of children with normal clinical findings with a prior exposure to GH at a higher risk of later development of cardiovascular/cerebrovascular diseases.

**Figure 8.** Combined postnatal screening of microRNAs in the identification of children with abnormal postnatal clinical findings descending from GH pregnancies. Postnatal combined screening of miR-1-3p, miR-17-5p, miR-29a-3p, miR-126-3p, mir-133a-3p, miR-146a-5p, and miR-181a-5p showed the highest accuracy for the identification of children with abnormal clinical findings with a prior exposure to GH at an increased risk of later onset of cardiovascular/cerebrovascular diseases.

### *2.8.  Abnormal clinical findings were found in 16/27 children affected with mild PE (59.3%), 47/106 children exposed to severe PE (44.3%), 26/49 children exposed to early PE (53.1%), and 37/84 children affected with late PE (44.0%).

#### *2.9. Increased Expression of miR-133a-3p in Children Descending from PE Pregnancies*

The ROC curve analysis was able to identify up-regulated expression profile of miR-133a-3p in 22.56% children affected with PE regardless of the severity of the disease and the delivery date at 10.0% FPR (Figure 9a).

**Figure 9.** *Cont*.

**Figure 9.** *Cont*.

**Figure 9.** Increased expression of miR-133a-3p in children descending from PE pregnancies. (**a**–**c**) Increased expression of miR-133a-3p was observed in children descending from PE pregnancies regardless of the severity of the disease and delivery date, severe PE and late PE; (**d**,**e**) Increased expression of miR-133a-3p was found in children with both normal and abnormal postnatal clinical findings previously exposed to severe PE and late PE; (**f**) Increased expression of miR-133a-3p was found in children with normal postnatal clinical findings previously exposed to early PE.

Subsequently, miR-133a-3p differentiated between children affected with severe PE and controls with a sensitivity of 23.58% at a specificity of 90.0% (Figure 9b).

Parallel, miR-133a-3p was able to identify children exposed to late PE with a sensitivity of 21.43% at a specificity of 90.0% (Figure 9c).

In addition to that, the consecutive analysis showed that the accuracy of miR-133a-3p biomarker for severe PE affected children identified to have normal or abnormal clinical findings was 25.42% and 21.28% sensitivity at 10.0% FPR (Figure 9d).

Similarly, postnatal screening of miR-133a-3p was able to identify children with a history of late PE with either normal or abnormal clinical findings with a sensitivity of 19.15% and 24.32% at a specificity of 90.0% (Figure 9e).

Parallel, ROC curve analysis of miR-133a-3p identified a significant proportion of children with normal clinical findings exposed to early PE during gestation (a sensitivity of 39.13% at 10.0% FPR) (Figure 9f).

#### *2.10. Increased Expression of miR-1-3p, miR-20a-5p, miR-103a-3p, and miR-342-3p in Children with Abnormal Clinical Findings Descending from PE Pregnancies*

The sensitivity at 10.0% FPR for miR-1-3p for children exposed to late PE was 27.38% and miR-1-3p showed even a higher performance for children with abnormal postnatal clinical findings that were exposed to late PE (a sensitivity of 35.14% at a specificity of 90.0%) (Figure 10a).

Furthermore, at 10.0% FPR, miR-103a-3p was up-regulated in 12.77% and 13.51% children with abnormal clinical findings that were exposed to severe PE or late PE (Figure 10b,c).

In addition, miR-20a-5p was up-regulated in 13.51% children with abnormal clinical findings with a prior exposure to late PE (Figure 10d).

MiR-342-3p represented the unique marker, which was able to differentiate between the group of children with abnormal clinical findings that were exposed to early PE and the controls (a sensitivity of 26.92% at 10.0% FPR) (Figure 11).

**Figure 10.** *Cont*.

**Figure 10.** Postnatal microRNA expression profile in children with abnormal postnatal clinical findings descending from PE pregnancies. (**a**) Increased expression of miR-1-3p was found in children with abnormal postnatal clinical findings exposed to late PE; (**b**,**c**) Increased expression of miR-103a-3p was observed in children with abnormal postnatal clinical findings exposed to severe PE or late PE; (**d**) Increased expression of miR-20a-5p was found in children with abnormal postnatal clinical findings with a prior exposure to late PE.

**Figure 11.** Decreased expression of miR-342-3p in children with abnormal postnatal clinical findings descending from early PE pregnancies.

The combination of miR-103a-3p and miR-133a-3p (AUC 0.637, *p* = 0.015, sensitivity 70.21%, specificity 56.0%, cut off > 0.429300) was superior over using only individual microRNA biomarkers, since it was able to identify at 10.0% FPR within the group of children with abnormal clinical findings previously exposed to severe PE 21.28% children with an increased cardiovascular risk (Figure 12).

**Figure 12.** Combined postnatal screening of microRNAs in the identification of children with abnormal postnatal clinical findings descending from severe PE pregnancies. Postnatal combined screening of miR-103a-3p and miR-133a-3p showed the highest accuracy for the identification of children with abnormal clinical findings with a prior exposure to severe PE at a higher risk of later development of cardiovascular/cerebrovascular diseases.

Parallel, combined screening of four microRNAs (miR-1-3p, miR-20a-5p, miR-103a-3p, and miR-133a-3p) showed the highest accuracy for children with abnormal clinical findings with a prior exposure to late PE (AUC 0.701, *p* < 0.001, sensitivity 59.46%, specificity 72.0%, cut off > 0.391116). It was able to identify 32.43% children with an increased cardiovascular risk at 10.0% FPR (Figure 13).

**Figure 13.** Combined postnatal screening of microRNAs in the identification of children with abnormal postnatal clinical findings descending from late PE pregnancies. Postnatal screening based on the combination of miR-1-3p, miR-20a-5p, miR-103a-3p, and miR-133a-3p showed the highest accuracy for the identification of children with abnormal clinical findings with a prior exposure to late PE at a higher risk of later development of cardiovascular/cerebrovascular diseases.

#### *2.11. Up-Regulation of miR-20b-5p in Children with Normal Clinical Findings Descending from Mild PE Pregnancies*

MiR-20b-5p was the only biomarker that differentiated between the group of children with normal clinical findings that were exposed to mild PE and the controls (a sensitivity of 36.36% at 10.0% FPR) (Figure 14).

**Figure 14.** Increased expression of miR-20b-5p in children with normal postnatal clinical findings descending from mild PE pregnancies.

*2.12. Dysregulation of Cardiovascular/Cerebrovascular Disease Associated microRNAs in Children Descending from FGR Pregnancies*

Overall, the group with abnormal postnatal clinical findings consisted of 22/34 children (64.7%) exposed to FGR (9 children already dispesarized in the department of pediatric cardiology, 9 children with abnormal echocardiographic findings (4 tricuspid valve regurgitation, 3 pulmonary valve regurgitation, 1 atrial septum defect, 6 foramen ovale apertum), 9 children with prehypertension/hypertension and 1 child with high BMI). Abnormal clinical findings were found in 7/13 children affected with early FGR (53.85%), and in 15/21 children exposed to late FGR (71.43%).

*2.13. Increased Expression of miR-17-5p, miR-126-3p and miR-133a-3p in Children with Abnormal Clinical Findings Descending from FGR Pregnancies*

MiR-17-5p, miR-126-3p, and miR-133a-3p differentiated between the group of children with abnormal clinical findings that were affected with FGR and the controls with a sensitivity of 22.73 %, 31.82% and 31.82% at a specificity of 90.0% (Figure 15a–c).

The combination of all examined microRNAs (AUC 0.710, *p* = 0.002, sensitivity 63.64%, specificity 78.0%, cut off > 0.305781) was superior over using only individual microRNA biomarkers, since it was able to identify at 10.0% FPR within the group of children with abnormal clinical findings affected with FGR 40.91% children with an increased cardiovascular risk (Figure 16).

**Figure 15.** Postnatal microRNA expression profile in children with abnormal postnatal clinical findings descending from FGR pregnancies. (**a**–**c**) Increased expression of miR-17-5p, miR-126-3p, and miR-133a-3p was observed in children with abnormal postnatal clinical findings descending from FGR pregnancies.

**Figure 16.** Combined postnatal screening of microRNAs in the identification of children with abnormal postnatal clinical findings descending from FGR pregnancies. Postnatal screening based on the combination of miR-17-5p, miR-126-3p and miR-133a-3p showed the highest accuracy for the identification of children with abnormal clinical findings with a prior exposure to FGR at a higher risk of later development of cardiovascular/cerebrovascular diseases.

#### *2.14. The Association between Postnatal Expression of miR-210-3p and the Severity of PE and/or FGR with regard to Doppler Ultrasonography Parameters*

The index of pulsatility in the ductus venosus (DV) showed a strong positive correlation with miR-210-3p gene expression in children with a history of PE and/or FGR (Figure 17). That means that children with prior findings of DV dilatation indicating poor outcome in severe FGR demonstrated increased postpartum levels of miR-210-3p.

**Figure 17.** Association between postnatal miR-210-3p expression and the pulsatility index in the ductus venosus in PE and/or FGR patients. The pulsatility index in the ductus venosus showed a strong positive correlation with miR-210-3p gene expression in patients with a history of PE and/or FGR.

The results of aberrant expression profile of microRNAs in children descending from pregnancy-related complications are summarized in Table 1.

*Int. J. Mol. Sci.* **2019**, *20*, 654 **Table 1.** Aberrant expression profile of microRNAs in children descending from pregnancy-related complications.

#### *2.15. The Effect of Children Age on Particular microRNA Expression in Children Descending from Normal and Complicated Pregnancies*

The effect of children age on particular microRNA expression in children descending from normal and complicated pregnancies is discussed in supplementary file (Figures S1–S4).


*Supporting open minds since 2005*

Multimedia Information Retrieval http://dx.doi.org/10.5772/intechopen.83217 Edited by Eduardo Quevedo

#### Contributors

Lee Mariel Heucheun Yepdia, Alain Tiedeu, Zied Lachiri, Abdullah I. Al-Shoshan, Amr Adel, Brian Cusack, Eduardo Quevedo, Samuel Ortega, Martin Halicek, Himar Fabelo, Gustavo Marrero Callico, Baowei Fei, Saliha Mezzoudj, Surya Prasada Rao Borra, Appari Geetha Devi, Kalapala Vidya Sagar

#### © The Editor(s) and the Author(s) 2021

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**1. Introduction**

The paper industry needs to reduce the consumption of freshwater resources due to the requirements of environmental protection. It is of great significance to increase the utilization of recycled white water and purposely convert it into a totally effluent-free papermaking process [1]. This will lead to an accumulation of pollutants in the system as the white water reuse rate increases. The accumulated pollutants in the water recycling system are called dissolved and colloidal substances (DCS) [2]. The composition of DCS, which mainly comes from pulp, filler, recycled water and the chemicals added during the papermaking process, is very complex [3]. DCS are also known as "anionic waste" because they are generally negatively charged substances in water [4]. Polygalacturonic acid (PGA)

**Citation:** Yao, L.; Zou, X.; Zhou, S.; Zhu, H.; Chen, G.; Wang, S.; Liu, X.; Jiang, Y. Cationic Lignocellulose Nanofibers from Agricultural Waste as High-Performing Adsorbents for the Removal of Dissolved and Colloidal Substances. *Polymers* **2022**, *14*, 910. https://doi.org/10.3390/ polym14050910

Academic Editors: Wei Wu, Hao-Yang Mi, Chongxing Huang, Hui Zhao and Tao Liu

Received: 4 February 2022 Accepted: 22 February 2022 Published: 24 February 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

is one of the main sources of anionic waste in white water [5,6]. PGA was often selected as a model contaminant of DCS due to its relatively high content in DCS [7]. Excessive buildup of the DCS in process water stream may decrease the functioning of the paper machine and increase corrosion, foaming, pitch, precipitation, scaling, consumption of chemicals and the poor physical properties of the paper produced [2,8]. Therefore, it is necessary to remove these harmful substances or reduce their negative impact in order to obtain a completely closed loop water system.

The traditional method of controlling and reducing DCS in paper mills is to use cationic polyelectrolytes for neutralization, but this leads to a high consumption of hazardous chemicals [9]. As technology continues to improve, some new environmentally friendly methods have been invented. For example, membrane filtration treatment [10], dissolved air flotation [11], membrane reactor [12] and biological enzymes [13] have been proposed during recent years, but adaptability and costs have limited their application. Apart from these methods, adsorption methods gained increasing attention due to the advantages of simple and safe operations, low cost, no secondary pollution and the ability to treat water with high concentrations of waste. In current times, some adsorbents have been designed to reduce DCS content in white water through electrostatic interactions [1,8,14]. However, the current technology is still unsatisfactory, so it is necessary to find new adsorbents with larger capacity, higher adsorption efficiency and lower cost.

The vision has shifted to bio-based adsorbents, such as cellulosic nanomaterials, a promising class of adsorbents in the field of environmental remediation. Cellulosic nanomaterials are generally obtained from pretreated cellulose followed by nanofibrillation. So far, cellulosic nanomaterials have been used to adsorb and remove many types of water contaminants, such as natural organic matter [15], dye [16], heavy metals [17,18], fluoride [19], pharmaceutical agents [20] and viruses [21]. Cellulosic nanomaterials have proven to be an ideal sorbent for water contaminants. In contrast to other materials, they have the characteristics of high specific surface area, versatile surface chemistry, environmental inertness and renewability [22,23]. Conventional adsorbents usually have limited low adsorption efficiency and adsorption capacity due to the limited surface area or active sites for adsorption [24]. When the size of adsorbents is reduced to nanoscale, high specific surface area [25] and short intraparticle diffusion distance are expected to improve the situation. At the same time, its strong potential for surface chemical modification [26] means that a large number of active sites can be added. Cellulosic nanomaterials are renewable, widely sourced and environmentally inert biomaterials, and therefore pose little threat to the environment. However, cellulosic nanomaterials have not yet been investigated as a DCS adsorbent.

In general, cellulosic nanomaterials can reduce cost and improve performance by adjusting raw materials and pretreatment methods. In most cases, cellulosic nanomaterials used for water purification are obtained from purified cellulose sources, i.e., cellulose fibers where noncellulosic components (mainly lignin) have been removed [17]. This usually requires a complex and hazardous bleaching process. Therefore, a more beneficial way to produce water purification nanomaterials is directly from lignocellulosic raw materials without or with mild chemical treatment while achieving full lignocellulose utilization. Moreover, there is a preference for agricultural by-products rather than wood as lignocellulosic raw materials due to the lack of forest resources. Over 32 billion kilograms of high volume, low value and underutilized lignocellulosic biomaterial are produced from agricultural by-products annually, creating significant disposal problems [27]. In terms of pretreatment methods, deep eutectic solvents (DES) have been heralded as the most promising environmentally benign solvents to replace volatile organic solvents due to their almost null toxicity and total biodegradability [28]. DES are a fluid obtained by simply mixing two or three cheap and safe components with lower melting point than any of the original components [29]. Recently, deep eutectic solvents (DES) have been used as pretreatment mediums for production of functionalized lignocellulosic nanofibers [30,31].

The current study aims to use a green and simple strategy to prepare functionalized nanofiber adsorbents with high adsorption efficiency, large adsorption capacity and low cost from bagasse for the efficient removal of DCS model contaminant PGA. Concretely, cationic lignocellulose nanofibers (CLCNF) with different contents of quaternary ammonium groups have been prepared via cationic modification of bagasse in a DES followed by mechanical disintegration. DES is composed of aqueous tetraalkylammonium hydroxide and 1,3-dimethylurea, and Glycidyltrimethylammonium chloride was chosen as the cationization agent. The structure of CLCNF has been characterized using transmission electron microscopy, Fourier transform infrared, elemental analysis, X-ray diffraction, and thermogravimetric analysis. On the other hand, the effects of quaternary ammonium group contents, pH, contact time and initial concentration of PGA on the adsorption were investigated in a batch adsorption study. Moreover, the kinetics of adsorption and adsorption isotherms were performed to analyze the adsorption mechanism and predict adsorption capacity.


**3. Data and Methodology**

The main analysis focused on the share of each renewable energy source in gross electricity production from renewable sources, which can be written as follows:

$$X\_{ijt} = \frac{GEP\\_RES\_{ijt}}{GEP\\_RES\_{jt}} \cdot 100\% \tag{1}$$

where *GEP*\_*RESijt* is the amount of electricity production from the *i*-th renewable source or biofuel in the *j*-th country in the period *t*, (GWh); and *GEP*\_*RESjt* is the total amount of electricity production from renewable sources and biofuels in the *j*-th country in the period *t* (GWh), where *GEP*\_*RESjt* = ∑*n i*=1 *GEP*\_*RESijt*.

We use publicly available Eurostat's data in the presented study [81]. In the analysis the EU countries (2020 composition) and United Kingdom are included. United Kingdom is counted since this state is the EU member until the end of 2019 and the analysis cover the period 2005–2019.

The Gini coefficient ( *G*) is applied to the concentration analysis in Reference [80]:

$$G\_{jt} = \frac{\sum\_{i=1}^{n} (2i - n - 1) \, GEP\\_RES\_{ijt}}{n^2 \overline{GEP\\_RES}\_{jt}} \tag{2}$$

where *GEP*\_*RESijt* is the amount of electricity production (GWh) from *i*-th renewable source in *j*-th country in period *t*, and *n* = 7; and *GEP*\_*RESjt*= 1 *n*∑*n i*=1*GEP*\_*RESijt*.

The considered sources of renewable energy are as follows: *i* = 1 (hydro), 2 (wind), 3 (solar), 4 (biofuels), 5 (biogases), 6 (renewable municipal waste), and 7 (other: geothermal and tide, wave, and ocean).

The k-means is the research tool applied for data clustering. This algorithm is introduced by Reference [82], (see also the description of the algorithm presented in References [83,84]). A procedure scheme for the application of the k-means is presented by Reference [85], among others. The calculations are prepared by using STATISTICA 13 software (TIBCO Software Inc. Palo Alto, CA, USA). In the first stage, all variables are standardized. Then Euclidean distance is used as a distance measure. The clustering is conducted for a different number of clusters, *k* = 2, ... , 12. The number of clusters is selected by using the silhouette index (SI index [86]; see also References [87,88]. The highest value of SI index indicates the best division. In turn Reference [89] or [88] reports that acceptable divisions are characterized by the values of the SI index at least 0.5 (then the structure of the clustering is considered reasonable).

In the data clustering, seven variables constructed according the Formula (1) are considered. The list of variables is as follows:

*X*1*jt*—the share of electricity production in hydro power plants in total electricity production from renewables and biofuels (*GEP*\_*RESjt*) in *j*-th country in period *t*;

*X*2*jt*—the share of electricity production in wind power plants in total electricity production from renewables and biofuels (*GEP*\_*RESjt*) in *j*-th country in period *t*;

*X*3*jt*—the share of electricity production from solar power (solar thermal and solar photovoltaic) in total electricity production from renewables and biofuels (*GEP*\_*RESjt*) in *j*-th country in period *t*;

*X*4*jt*—the share of electricity production from biofuels (primary solid biofuels, pure biodiesels, and other liquid biofuels) in total electricity production from renewables and biofuels (*GEP*\_*RESjt*) in *j*-th country in period *t*;

*X*5*jt*—the share of electricity production from biogases in total electricity production from renewables and biofuels (*GEP*\_*RESjt*) in *j*-th country in period t;

*X*6*jt*—the share of electricity production from renewable municipal waste in total electricity production from renewables and biofuels (*GEP*\_*RESjt*) in *j*-th country in period *t*;

*X*7*jt*—the share of electricity production from other sources in total electricity production from renewables and biofuels (*GEP*\_*RESjt*) in *j*-th country in period *t* (other sources are geothermal and tide wave, ocean).

### **4. Use of Renewable Energy Sources and Biofuels in the Electricity Production in the European Union**

This section presents selected issues related to the use of renewable sources for electricity production in the European Union. In the first part, we present the share of electricity production from renewables and biofuels in the total electricity production. This part of the analysis covers the European Union (EU27, for the period 1990–2019) and the individual EU countries, including the UK (for the years 2005 and 2019). In the second part, we describe the types of renewable sources (RESs, according to the Eurostat's classification) used for the electricity production in the EU and characterize the changes that have occurred in the shares of the five most popular RES in renewable energy production from RES (*GEP\_RES*). In the third part, we report an analysis of changes in the level of concentration (measured by the Gini coefficient) of individual RES in *GEP\_RES* production. We conduct this part of the analysis for the EU area (EU27) for the period 1990–2019 and for the individual EU countries, including the UK, for the years 2005 and 2019).

### *4.1. Renewable Energy in the Electricity Production in the EU*

The main determinant of electricity production is the demand for electricity created by consumers. According to Eurostat data [90], in 2019, final energy consumption in the EU

accounted for 10,879,807.319 GWh and was 5.1% lower than in 2005. The share of electricity in total final energy consumption is 22.8%, which is higher than the corresponding rate from 2005 by 1.6 percentage point (pp). In general, the electricity final consumption increased by 2.1% in the analyzed period. Increasing the share of electricity in the EU's total energy consumption is included in the Clean energy for all Europeans package [11].

Figure 1 shows the gross electricity production (GEP), GEP from renewables and biofuels (*GEP\_RES*) and share of gross electricity production from RES in total GEP in the EU (EU-27). As the presented data dates back to the year 1990, it can be noted that until around year 2005, gross electricity production in the EU had been steadily growing. During that period (1990–2005) GEP increased by 28.2%. As in the period of 2005–2019, one can observe a relatively constant level of GEP in the EU. In 2019, there was even a slight decrease in GEP compared to 2005—by 0.5%. However, analyzing the changes of the production of electricity from renewable sources (*GEP\_RES*) shows that, in the period 1995–2005, there was an increase in its production by 49.2%, while, in the period between 2005 and 2019, there was an intensification and increase amounted by 110.8%. The total increase in the production of *GEP\_RES* in the extended period (1990–2019) accounts for 214.4%. The vast development is also visible from the share of RES in GEP production (columns in Figure 1). In 1990, this share was 14.1%; in 2005, it slightly increased to 16.3%, but in 2019, it was already 34.6%. One of the reasoning behind such increase is the fact that the new member states joining the EU in its largest expansions in 2004 undertook many actions to adopt the guidelines related to the transition to low-emission economies (e.g., reduction of CO2 and other harmful substances emissions and the use of renewable sources for energy production to a greater extent).

**Figure 1.** Gross electricity production (GEP), gross electricity production from renewables and biofuels (*GEP\_RES*), and share of gross electricity production from RES in the total gross electricity production (%RES) in 1990–2019 in the European Union (EU-27). Source: Reference [81].

> Table 1 shows the shares *GEP\_RES* in the total GEP in individual EU countries (including UK) in 2005 and 2019. In 2005, these shares ranges from 0% in Malta to 69.6% in Latvia. In 17 out of 28 analyzed countries, in 2005, the share of RES in electricity production was below the EU level (16.3%). The RES shares in GEP ranges from 10% in Cyprus to 85.9% in Luxembourg in 2019. In three more countries, the share of RES in GEP is greater than 70%; Lithuania (81.9%), Denmark (78.2%), and Austria (77.8%). Numerous countries have recorded a significant increase in the share of RES in GEP. In 11 countries, it is higher by

20 pp, and in three by as much as 50 pp: in Denmark (by 51.1 pp), Lithuania (by 76.3 pp), and Luxembourg (by 61.8 pp). This means that the policy of increasing RES for energy production has brought visible effects, especially in the case of electricity production. As a result, there is a noticeable reduction in the differentiation between EU countries in terms of this feature.

**Table 1.** Share of electricity produced from renewable sources and biofuels in the total electricity production in individual EU countries in 2005 and 2019.

Source: Own study based on data [81].

### *4.2. Types of Renewable Energy Sources Used for Electricity Production in the EU*

Energy data are collected by Eurostat according to a strictly defined methodology [91]. Data are collected in areas that allow to assess, firstly, the origin of energy, secondly, the degree of dependence on energy imports, and thirdly, the types and costs of energy consumed. A key element of the EU's energy policy is increasing the use of renewable sources, in particular regarding electricity production. As previously mentioned, the Clean energy for all Europeans package [11] assumes that, by 2050, electricity will account for over 50% of the energy consumption in the EU, with a significant share of renewable energy sources. Reliable and comparable statistics are therefore essential to be able to evaluate activities and progress in this area. In Table 2, we present the types of renewable sources and biofuels used for electricity production listed by Eurostat. While in 2019 the amount of electricity produced from all sources decreased slightly compared to 2005 (by 0.5%), the production of electricity from renewable sources and biofuels increased by 100.8% and exceeded 1 M GWh. This stands for an increase in the share of RES in the total electricity production by 18.3 pp (from 16.3% in 2005 to 34.6% in 2019).

Table 2 also shows the shares of individual sources used in gross electricity production from renewables and biofuels ( *XEU i* ) in 2005 and 2019 (columns three and five) in the EU-27 area. Figure 2, additionally, presents the changes in the shares of selected sources in an extended period of 1990–2019. In 2005, hydropower constituted the largest share of the RES\_GEP (71.4%). Hydropower [92] noted a great decrease compared to 1990 (by 22.9 pp), when hydropower was responsible for over 94% of electricity generated from renewable sources (see Figure 2). Continuously, this share significantly decreased by 2019— by 37 pp. (to the level of 34.3%). Even if the actual amount of the electricity produced from hydropower has slightly increased since 2005 (by 1.4%), the highly decreased trend is due to other emerging technologies enabling the use of other renewable sources. In the study period (2005–2019), energy produced from wind; kinetic energy of wind exploited for electricity generation in wind turbines [93] gained a lot of importance, and increased by 439.1%. Its share in the production of electricity from renewable sources in 2019 was 36.5% and is higher by 22.2 pp from that in 2005. In 2019, both wind and hydro were responsible

for 70.9% of electricity produced from renewable sources. Thus, these two sources are currently the main RESs used for electricity production. Another source that has gained in importance in recent years is solar energy. Eurostat distinguishes two types of solar power; solar photovoltaic (sunlight converted into electricity employing solar cells which exposed to light will generate electricity [94]) and solar thermal (heat from solar radiation (sunlight) exploited for useful energy purposes [95]). The second type of energy is produced by using, for example, solar thermal–electric plants, and its technology for the production of electricity is currently under development. According to Eurostat data, in 2005 this source was not used, and in 2019 it accounted for 0.6% of electricity production. In total, in 2019, solar energy was responsible for 12.5% of electricity produced from renewable sources. This indicator was higher than in 2005 by 12.2%. Since 2007, which is the year of the technology development, there has been an increase in the share of this type of energy (see Figure 2). Among the other technologies for the production of electricity from renewable sources, biofuels (solid and liquid biofuels) and biogases are a significant source. Electricity production from solid and liquid biofuels increased by 102.9% in the period 2005–2019, and the share of GEP production from RES slightly decreased (from 8.9% in 2005 to 8.5% in 2019). Furthermore, biogas significantly increased its importance in the production of electricity. In their case, the *XEUi* ratio increased by 3.8 percentage points in the analyzed period, to the level of 5.5%, while the production of electricity from this source increased by 581.5%. RES of minor importance in the entire EU-27 are renewable municipal waste, which in 2019 was responsible for about 2% of electricity produced from RES and geothermal and tide, wave, and ocean. The latter two sources are used by only a few countries. Geothermal is most used in Italy; and tide, wave, and ocean are used in France. While in 2005 their share in *GEP\_RES* was 2%, in 2019, it was only 0.7%. Thus, it is not a technology of strategic importance in the production of electricity, and its importance is marginalized in the scale of the entire EU.

Source: Own elaboration based on Reference [81]; *dGEP*\_*RESEUi* = *GEP*\_*RESEUi*,2019 *GEP*\_*RESEUi*,2005 − 1·100% —means a change in the production of electricityfromthe*i*-thsourceintheperiod2005–2019.

**Figure 2.** Shares of selected renewable sources (*XEUi* ) in the production of electricity from renewable sources (*GEP\_RES*). Source: Own calculation based on Reference [81].

### *4.3. Concentration of Renewable Sources in Electricity Production*

In our analysis, we determine the Gini coefficient (see Formula (2)) by dividing renewable sources into seven categories (see Section 3, Data and Methodology). Figure 3 shows the evolution of this coefficient for the EU-27 area in 1990–2019. In 1990, the value of this coefficient was 0.83. This means that, in the EU, there was a high concentration of renewable sources used to produce electricity, and hydroelectric power plants were mainly used during this period. The energy produced by this method accounted for almost 95% of electricity production from renewable sources. In the following years, we observe a decrease in the value of the Gini coefficient. The pace of its changes is firstly slow, till around 2001, and then it accelerates. This is the result of measures taken to use more diverse sources of renewable energy. After 2001, we observe the use of wind energy and biofuels to a greater extent. In turn, after 2007, we can see that the importance of solar energy was increasing. In 2005, the concentration of renewable fuel sources was 0.704 and was lower than in 1990 by about 15%. In the following years, an even greater decline in the Gini coefficient occurred. In 2019, it was 0.512 and was lower than in 2005 by over 27%. This was influenced by several factors. Firstly, it refers to the largest enlargement of the EU in 2004. The EU-27 area for which we calculate the Gini coefficient includes the countries currently constituting the EU. Before accession, they were not obliged to implement measures for low-carbon economies on the scale that followed. The newly admitted member states had to comply with the introduced rules concerning the use of renewable sources for energy production. It is worth noting that, in the years 1990–2005, the average change in the concentration coefficient of the use of renewable sources for electricity production was higher in the EU-15 countries, and it was 0.11 (refer to the formula from the Methodology section), and for the new coming countries in 2004 or later, this change was only 0.02. In the period 2005–2019, the situation was different. It is in the new member states that the changes intensified (the Gini coefficient dropped by 0.16 on average, and for the EU-15 countries decreased by 0.08). Therefore, it is visible that the greatest progress in this area was recorded by the states of the EU-15 before 2005, and the newly admitted states only after joining the EU structures.

**Figure 3.** Changes in concentration of analyzed types of renewable sources in the production of electricity from renewable sources (*GEP\_RES*) in the EU-27 in 1990–2019. Source: Own calculation based on Reference [81].

Table 3 presents the values of the Gini coefficients for 2005 and 2019 for the EU and UK. In 2005, the highest concentration of RES appeared in Bulgaria, Croatia, Lithuania, Romania, and Slovakia (in their case, the G coefficient exceeded 0.85), as well as France, Latvia, Austria, Slovenia, and Sweden (0.85 > G > 0.8). These are the countries that used mainly hydroelectric power at that time, and the share of this type of source in the generation of electricity from RES was about 90% or more. In 2005, only in eight countries, the concentration level was lower than 0.7. During this period, we notice the greatest diversity in the use of renewable sources in the case of the UK (0.51), Germany (0.562), Estonia (0.583), Belgium (0.584), the Netherlands (0.626), Portugal (0.664), Spain (0.685), and Ireland (0.697). In the UK, the distribution of renewable energy use was as follows: hydro (40%), biogases and biofuels (41%), wind (14%), and other (5%). Germany, on the other hand, made the greatest use of wind (39.6%), hydro (37.6%), biofuels (10.8%), and all others (12%). It is also worth taking a closer look at the diversity of the use of individual sources for the production of electricity. Assuming that the i-th source can be considered significant in the production of renewable electricity, we see that the limit of *Xi* > 1% is set, then in 2005 for two countries the limit of 1% was exceeded in the case of six sources (Germany and Italy). Furthermore, for four countries, *Xi* > 1% was recorded for five sources (Belgium, Luxemburg, Netherlands, and the UK). For 14 countries, there were only three or fewer sources. In 2019, the concentration level above 0.8 was recorded only in Malta (0.849), which mainly uses solar energy (97%), and Slovenia (0.807), where hydro is mainly used (89.3%). In nine countries, the concentration level was below 0.6. The lowest values of the Gini coefficients were recorded for the Czech Republic (0.409) and Italy (0.448). The Czech Republic used mostly hydro (28.3%), biogases and biofuels (43.9%), and solar (20.6). On the other hand, in Italy, hydro (40.9%), solar (20.1%), and winds (17.2%) are used the most. Considering the diversity of the use of sources, the shares of *Xi* > 1% for each of the seven sources, this was the case only for Italy. For seven other countries (Belgium, Germany, France, Lithuania, Luxembourg, Hungary, and the UK), *Xi* > 1% were recorded for six sources. For only two countries (Malta and Cyprus), the number of valid sources was three (Cyprus) or less (two—Malta). In Cyprus, mainly 46.3% winds and 42.4% solar and 11.3% biogases were used, while in Malta, the main source of renewable electricity was solar energy (97.04%) and, to a much lesser extent, biogases (2.93%). Looking at the changes in the concentration factor, it is clear that the concentration of renewable sources in electricity production increased in the analyzed period (2005–2019) in three countries. It is

most noticeable in Estonia, where the value of the Gini coefficient increased by 0.104, due to an increase in the use of biofuels by 58.8%. An increase in concentration coefficient was also recorded in the case of Ireland ( Δ *G* = 0.074) and the UK ( Δ *G* = 0.057). The largest drops in the concentration level were recorded in the Czech Republic ( Δ *G* = −0.373), Bulgaria (Δ *G* = −0.342), Hungary ( Δ *G* = −0.274), Italy ( Δ *G* = −0.265), and Lithuania ( Δ *G* = −0.244). The Czech Republic has significantly reduced the share of hydropower (to 28.3% in 2019) in favor of solar energy and biofuels and biogases. In Hungary, in 2005, the largest share was recorded for energy produced from biofuels; in 2019, the importance of this source was reduced in favor of a greater use of solar and winds.

**Table 3.** Concentration of types of sources in the production of electricity from renewable sources in the EU countries—the values of Gini coefficients.

Source: Own calculation based on Reference [81]. Δ*G* = *G*2019 − *G*2005.

In summary, we note that there has been a significant reduction in the concentration of renewable source types used for electricity production in almost all EU countries over the period analyzed. In those countries with slightly higher levels of concentration, windgenerated electricity in particular has gained in importance. In general, we are now seeing trends across the EU where two sources in particular are gaining in importance: wind and solar. Supporting these sources is part of the EU's energy policy.

### **5. Classification of the EU Countries by the Usage of Renewable Sources for Electricity Production**

To examine similarities and differences in the use of renewables for electricity generation, we conducted the classification of the EU countries (including the UK) by applying the k-means algorithm. As in previous parts of the paper, the year 2019 was set as the reference year. The selection of the number of clusters was made based on the values of the silhouette coefficient (SI) presented in Table 4. The highest value of SI = 0.603 in the 2019 classification was obtained for 10 groups, and thus it was adopted as final. This is a satisfactory result because, with SI > 0.5, it is considered that the obtained division is characterized by a strong class structure. In the 2005 classification, the SI value for 10 groups is 0.81 and is slightly lower than the highest score for 12 groups (0.852). With an SI score > 0.7, the obtained division is considered to have a strong class structure. In addition, for the classification of data from 2011, the best division turns out to be the one into 12 groups (SI = 0.832). However, to ensure the comparability of the results, further analysis considered the division into 10 clusters, which is considered satisfactory, because the value of SI = 0.554 exceeds the limit of 0.5.

**Table 4.** Silhouette coefficients for the 2005, 2011, and 2019 classifications and the selected number of clusters.

Source: Own calculations.

The breakdown for 2005 (see Table 5) shows, first, numerous of one-object (oneelement) clusters—as many as 7 out of 10. These are the following groups: 1 (UK), 3 (Hungary), 4 (Denmark), 5 (Netherlands), 8 (Malta), 9 (Cyprus), and 10 (Italy). Those clusters constitute countries classified as standing out from the others in terms of the use of renewable sources for the production of electricity.

**Table 5.** The results of the classification of EU countries according to the shares of individual renewable energy sources in the production of electricity (clusters averages, %)—data from 2005.

Source: Own calculation in STATISTICA based on Reference [81]. Country abbreviations refer to those used by Eurostat; see Table 3.

> In 2005, Malta did not use renewable sources to produce electricity. Therefore, this country is naturally classified as a separate group. However, to maintain the consistency of the samples with the samples used in the other classifications (for 2011 and 2019), it is also included in the analysis for 2005. Italy in 2005 is distinguished primarily by the fact that it used sources that for the purposes of the presented classifications are categorized as other (variable *X*7). In Italy, geothermal is a popular source of energy. In 2005, this source contributed to the generation of almost 10% of renewable electricity. In the case of Cyprus, the main characteristic is that, in 2005, solar energy was mainly used to produce renewable electricity (over 61%). Moreover, in 2005, only two renewable sources were used in Cyprus—apart from solar energy, Cyprus used wind energy (over 38% share). Hungary is distinguished as a single-element group due to the fact that biofuels (over 84%) has a significant share in the production of renewable electricity. On the other hand, in the case of Denmark, the distinguishing factor is the share of wind energy (over 67%). The Netherlands and the UK are distinguished from other EU countries by the considerable variety of renewable sources they use. In the case of the Netherlands, these are biofuels

(almost 50%), wind (over 27%), and waste (17%). The Netherlands is the only country where waste is classified as meaningful. In turn, the UK uses mainly hydro (almost 40%), as well as wind, biofuels, and biogases, the share of which in the production of renewable electricity is greater than 10% (for each of the sources). The UK is distinguished by the share of biogases (almost a quarter of renewable electricity generated). Great diversification of renewable sources in the case of the UK is confirmed by the low value of the Gini coefficient (see Table 3).

The most numerous cluster is cluster #2. The algorithm classified 13 countries into it (46% of the analyzed objects). This cluster is distinguished by a high share of energy produced in hydroelectric plants. The group mean for this feature is *X*2005 1 = 93.3%, and the group included countries for which these shares ( *X*1) are at least 80%.

Cluster number six joins four countries with large share of wind energy: Germany, Estonia, Ireland, and Spain. The group mean for this feature is *X*2005 2 = 45.844, and the individual values of this coefficient ( *X*2) for these countries ranged from 39% to 51%. The algorithm assigns Belgium, Poland, Portugal, and Finland to Cluster #7. These countries are characterized by a similar level of hydropower consumption ( *X*1 between 46% and 70%) with a simultaneous significant consumption of biofuels (group average *X*2005 4 = 27%, and the individual values of the *X*4 feature are between 15% and 40%).

In the 2011 classification (Table 6), only four clusters are single-object. As in the previous classification (from 2005), Malta (#5), Italy (#10), and the Netherlands (#9) are classified as single-object clusters. In Malta, in 2011, two sources of renewable electricity were used: solar ( *X*3 = 50.4%) and biogases ( *X*5 = 49.6%). It is worth noting that, compared to other countries, Malta has the largest share of solar energy use. Italy, as in the previous classification, is distinguished due to the high level of use of other sources ( *X*7 = 6.66%) compared to other countries. However, this share is lower than in 2005, as, at that time, Italy began to use wind and solar panels on a larger scale. In the case of the Netherlands, there is a significant share of waste ( *X*6 = 16.5%), comparable to the previous classification. Other sources with a high share of renewable electricity production are wind and biofuels, but in 2011 their proportions changes in favor of greater use of the wind. Finland is also classified in a separate cluster, which is distinguished by the fact that the main sources used in the production of renewable electricity are hydro ( *X*1 = 51.48%) and biofuels ( *X*4 = 44.75%), totaling 96.23%.

In the 2005, Finland is classified together with Belgium, Poland, and Portugal. In the case of Belgium and Poland, in 2011, a much smaller share of renewable electricity production in hydroelectric plants is recorded, and in the case of Portugal, the importance of using biofuels decreased. The values of the coefficients have changed so significantly that these countries are no longer characterized as similar. This time Poland joined the group together with Estonia and Hungary (cluster #6). This cluster is distinguished by the significant use of biofuels (average *X*2011 4 = 58%). Furthermore, Belgium is classified together with the Czech Republic (#3). Characteristic for this cluster is the use of various sources. Cluster means greater than 10% are observed for the following traits: *X*1 (hydro), *X*2 (wind), *X*3 (solar), and *X*4 (biofuels). Due to the increased production of renewable electricity from wind in Ireland (the share increased from 50% to 80%), the algorithm classifies it together with Denmark (#4). The clusters' mean of this coefficient ( *X*2011 2 ), in this case, accounted for almost 75%. Ten countries remain classified in the largest cluster (#8). Their main source of renewable electricity production is hydro, with the mean is *X*2011 1 = 84.5% and range from 71% to over 95%. Cluster #7 (Greece, Spain, Portugal, and Lithuania) is distinguished by the largest shares of two sources: hydro ( *X*2011 1 =49.78%) and wind ( *X*2011 2 = 38.11%). The last cluster (#2) includes Germany, UK, and Cyprus, with the main sources in the production of renewable electricity being wind ( *X*2011 2 = 48.20%), biogases ( *X*2011 5 = 20.64%), and hydro ( *X*2011 1 = 13.55%). It is worth adding that, in the case of Cyprus, the role of the solar source has significantly decreased, from 61.45% in

2005 to 6.7% in 2011, with a simultaneous large increase in energy production from these two sources.

**Table 6.** Results of the classification of EU countries according to the shares of individual renewable energy sources in the production of electricity (clusters averages, %)—data from 2011.

Source: Own calculation in STATISTICA based on Reference [81]. For country abbreviations, refer to those used by Eurostat (see Table 3).

> The compositions of clusters change again for the 2019 classification (see Table 7). Five countries are classified into single-object clusters. As in the previous classification, these are Malta (#8) and Italy (#1), as well as the Czech Republic (#3), Hungary (#5), and Estonia (#6). Italy, as in the previous cases, is distinguished primarily by a high share of other sources (*X*7 = 5.16%) compared to other countries. Although it decreased compared to 2011, the amount of electricity generated with this method has increased. It is also worth noting that, in Italy, the importance of the use of solar and wind energy has increased. In 2019, in Malta, solar is the dominant source used for the production of renewable electricity, with the share of *X*3 = 97.04%. This is a significant increase compared to 2011, by over 45 pp. Estonia is distinguished by a high consumption of biofuels, with a significant consumption of wind energy and a significant reduction in the share of hydropower compared to that in 2011. In Hungary, significant shares of biofuels ( *X*4 = 37.74%) and solar energy ( *X*3 = 31.94%) are recorded. On the other hand, the Czech Republic still stands out due to the significant— compared to other countries—use of biogases in the mix of renewable sources ( *X*5 = 22.54%) and the burden of electricity production being distributed among four sources: in addition to the mentioned biogases, hydro ( *X*1 = 40.92%), biofuels ( *X*4 = 21.38), and solar energy (*X*3 = 20.13%). Thus, a low level of concentration of RES in the production of electricity.

> The cluster with the highest share of hydro is Cluster #4: Croatia, Austria, Romania, and Slovenia. The clusters' mean for this variable was as high as *X*1 = 74.81%. Another cluster with high hydro consumption is Cluster #2 (Bulgaria, France, Latvia, Luxembourg, Slovakia, Finland, and Sweden). At the same time, in this cluster, there is a significant consumption of biofuels ( *X*4 = 16.7%), which distinguished it from #4. Eight countries are classified to the largest Cluster #9, distinguished by the significant use of wind energy (*X*1 = 57.35%). The other two clusters are Clusters #8 (Belgium and the Netherlands) and #10 (Germany and Cyprus), which are also characterized by significant use of wind energy (group averages for this variable being, respectively, 49.16% and 48.52%). However, significant use of other sources is also important for the breakdown. In the case of Belgium and the Netherlands, these are solar ( *X*3 = 22.15%) and biofuels ( *X*4 = 14.49%). Menawhile, in the case of Germany and Cyprus, these are solar ( *X*3 = 30.54%) and biogases ( *X*5 = 12.25%).

**Table 7.** Results of the classification of EU countries according to the shares of individual renewable energy sources in the production of electricity (clusters averages, %)—data from 2019.

Source: Own calculation in STATISTICA based on Reference [81]. Country abbreviations refer to those used by Eurostat (see Table 3).