**5. Nonconventional tools and techniques**

Recent advances in next-generation sequencing technologies (NGS) have encouraged rapid development of bioenergy crops. Different new "omics" technologies (genomics, transcriptomics, metabolomics, and metagenomics), marker-aided selection (MAS), and genetic engineering approaches have been considered and are widely used as the most promising solutions to improve agronomic traits in bioenergy oil plants. "Omics" technologies can accelerate and be used for the identification of novel and useful genes responsible for oil production, to discover oil production pathways including mapping of significant QTLs in the family [33] and also to improve oil content and composition [34]. The tremendous efforts in terms of genome analysis have been done in Crucifereae family [35]. To facilitate such efforts, whole genomes of some oil plants including soybean [36], rapeseed [37] sunflower [38], castor [39], Jatropha [40], oil palm [41, 42], Camelina [43] have been already sequenced. Such huge deal of data has opened a new high-throughput way to discover

#### **Table 1.**

*Nonconventional/biotechnological efforts.*

*Nature of Importance of Various Parameters for Ideal Biofuel Crops: Special Reference… DOI: http://dx.doi.org/10.5772/intechopen.107102*

**Figure 2.**

*Conventional and nonconventional approaches to improve fatty acid profile in oilseeds.*

genes and pathways involved in oil production as well as the genes affecting oil quality in these plants. Moreover, by using omics technologies, such as quantitative trait locus (QTL) mapping, single-nucleotide polymorphism (SNP), and expressed sequence tag (EST)-based molecular markers, cDNA libraries, and RNA-seq analysis, researchers could identify and characterize genes involved in lipid synthesis pathways, tolerance to biotic and abiotic stresses, and the other important traits (Tang et al., 2017) [27], and similar few nonconventional efforts are summarized in **Table 1**.

This chapter creates the two paths that cross each other while selection and development of ideal biofuel crops in conventional and nonconventional ways. The strategies are not only based upon the extraction and utilization of oil but first going into the system of plants using genetic dissection methods that can delineate the mechanisms of gene responsible for the development of useful fatty acid profile. The triggers can be developed to identify useful genes, thereby understanding their expression profiling in **Figure 2**.


*2.2. Participants*

Approximately 1850 university employees or service providers were potentially eligible for research and were invited to participate in the online survey. No inclusion criteria other than working for the university were imposed and there were no exclusion criteria. In the first data collection, in May 2020, 407 responses were received. The participants had a mean age of 37.5 years, and the majority were female (68%) and had been, on average, social distancing for 59 days. In the second stage of data collection, in June, 258 responses were received. These participants had a mean age of 40.9, 72% were female, and they had a mean time in social isolation of 92 days. Finally, 207 participants responded to the third stage in August, who had a mean age of 41 years, 72% were female, and had a mean of 130 days in isolation. Except for the time in isolation, which predictably increased, there were no statistically significant differences (*p* > 0.05) between the proportion by gender and the average age of the participants in the three stages.

The selection criterion for inclusion in this analysis of change was having responded to at least two stages of data collection (*n* = 256). Only 36 participants responded in all three stages, 87 participated in the first and second stages, 70 in the second and third stages, and 63 participants in the first and third stages.


*Edited by Hassan A. Yasser*

This book reviews current research, including applications of matrices, spaces, and other characteristics. It discusses the application of matrices, which has become an area of great importance in many scientific fields. The theory of row/column determinants of a partial solution to the system of two-sided quaternion matrix equations is analyzed. It introduces a matrix that has the exponential function as one of its eigenvectors and realizes that this matrix represents finite difference derivation of vectors on a partition. Mixing problems and the corresponding associated matrices have different structures that deserve to be studied in depth. Special compound magic squares will be considered. Finally, a new type of regular matrix generated by Fibonacci numbers is introduced and we shall investigate its various topological properties.

ISBN 978-1-78923-466-4

Matrix Theory - Applications and Theorems

Published in London, UK © 2018 IntechOpen © wacomka / iStock


**1. Introduction**

The term energy harvesting is the process of capturing or harvesting wasted or unused ambient energy, such as temperature gradients, mechanical vibrations, radio frequencies, etc., and converting it into useable electrical energy. This differs from powering a system using traditional energy sources such as batteries, fuel cells, etc. Energy harvesting has received much attention in the last two decades from various disciplines, mainly due to its potential impact as a key technology enabling autonomous ultra-low-power electronics operating in remote and harsh environments without the need for large energy storage elements. Energy harvesting thus replaces conventional batteries, enhances the environmental friendliness of the system, and lowers the maintenance costs. In other words, it is a promising technology to power various applications ranging from structural health monitoring to medical implants.

Energy harvesting from mechanical sources including industrial machines, human activity, vehicles, building structures, and other environmental sources can be achieved through different conversion methods. Such approaches include piezoelectric, electromagnetic, electrostatic, and

magnetostrictive e ffects. Among the most promising sources for recovering energy are periodic vibrations generated by rotating machinery or environmental sources such as wind or ocean waves.

The present work addresses vibration-based energy harvesting systems based on the piezoelectric effect. Up to now, the piezoelectric e ffect has been extensively investigated and integrated in di fferent applications, such as sensors and actuators. Vibration energy harvesting converts vibration energy into electrical energy. The large availability of vibrations in industrial environments and the simultaneous need for sensing applications in such locations motivated the research in recent time.

Most often, a mechanical resonator is used to amplify the low vibration levels into usable deflections. Such systems consist usually of a tip-loaded clamped-free cantilever [1–3], which we refer to as 'first generation harvester concepts'. The drawback of such harvesters is that they operate efficiently only if the harvester's resonance frequency coincides with the dominant ambient vibration frequency. Any di fference between these frequencies will lead to a decrease in the power output. As realistic ambient vibration spectra exhibit multiple frequencies and vary over time as the vibration source is aging or changing in temperature for instance, 'first generation' energy harvesting schemes fail. In order to overcome this limitation, many research groups are addressing new resonator designs with an optimized active bandwidth, which enables the harvester to collect power on a broader frequency range. This research can be categorized in two groups. On one hand, numerous multimodal resonator designs, which are able to operate resonantly at multiple frequencies, have been introduced in [4–7]. Wu et al. [8] investigated a compact piezoelectric energy harvester, comprising of one main cantilever beam and an inner secondary cantilever beam. The system harvests power at two distinct frequencies. A novel trident (three-pronged spear) shaped piezoelectric energy harvester has been proposed by Upadrashta and Yang in [9] to collect power from wideband, low frequency, and low amplitude ambient vibrations. Lamprecht et al. [10] investigated how multiple vibration harvesters can be combined to a macroscopic array configuration by aiming not on high resonance peak powers, but on close spectral overlaps in lower power regions with a bandwidth 500 Hz. In [11] a multiresonant structure comprising a clamped–clamped piezoelectric fiber composite generator has been proposed by Qi et al., with side mounted cantilevers, which are tuned by added masses to resonate at individual frequencies, resulting in a wider harvesting bandwidth. Other works [12–14] proposed multiple concepts of multiresonant piezoelectric energy-harvesting devices, capable of harvesting power on a wider frequency range using both translational and rotational degrees of freedom.

On the other hand, other groups proved that integrating nonlinearity in the harvesting device broadens the operative bandwidth compared to the standard linear harvesters. The authors of [15–21] demonstrated the usability of bistable resonators for harvesting over a wider operational frequency range, by integrating permanent magnets positioned with respect to another permanent magnet on the resonator. In [22] a compact nonlinear multistable energy harvester array has been presented by Lai et al., for harvesting energy at low frequencies. Ho ffmann et al. [23,24] developed a harvesting system capable to autonomously adapt the magnetic field strength, acting on the resonator, by adjusting the orientation of a diametrically polarized permanent magnet. Another bistable rotational energy harvester operating at low frequencies has been proposed by Fu and Yeatman in [25,26]. The harvesting is achieved by magnetic plucking of a piezoelectric cantilever using a driving magnet mounted on a rotating platform. In [27–29] an autonomous tuning mechanism has been developed, allowing a compensation of the hysteresis, as well as maintaining the optimal working point. It allows the use of both coupling modes (attractive and repulsive), which enables the harvester to adapt its operating frequency to the dominant vibration frequency of the environment. Nammari et al. [30] proposed an enhanced novel design of a nonlinear magnetic levitation-based energy harvester, where the tuning effect is achieved by the magnetic and the oblique springs. In [31–34] we studied a dual-frequency piezoelectric energy harvester incorporating permanent magnets for bidirectional frequency tuning and presenting an increased useable frequency range compared to standard harvesters. Other groups used di fferent bandwidth broadening strategies, for example, shunted piezoelectric control systems have been proposed in [35,36]. Zhang and Afzalul [37] analyzed a broadband energy harvester with an array of piezoelectric bimorphs mechanically connected through springs. The operative bandwidth broadening can be achieved by carefully selecting the masses and adjusting the spring sti ffness.

In the present paper, we propose a so-called 'folded beam' resonator design for energy harvesting. It consists of an outer tip-loaded beam, mechanically connected to a pair of inner beams, which extend towards the fixed end. In contrast of [8], the resonator is fabricated from a single steel sheet. It uses permanent magnets at the free ends of the cantilevers for higher frequency agility, by making use of the nonlinearity provided for the nonlinear magnetic forces. It resonates at two distinct frequencies, spaced 10 Hz apart, in the range (50,100) Hz. Bidirectional frequency tuning was achieved using a similar approach as presented in [34]. It has been demonstrated that both modes can be tuned independently, together with the dual frequency feature, can provide a superior frequency agility and increase the operational bandwidth of the system compared to existing approaches. Furthermore, an optimized version of the resonator is presented in this paper, where the two power peaks could be nearer to each other and provide the same power levels. This feature further enhances the performances of such a harvester and shows the benefit of such a design compared to an array of simple harvesters. Due to the close resonances enabling the resonator to amplify the deformation of the outer beam and leading to a higher strain distribution on the inner beam compared to the aforementioned simple design, where the second cantilever will be directly connected to the same excitation. A control scheme, which uses an energy e fficient maximum- amplitude tracking algorithm, is optimized compared to [34]. A reduced model of the harvester has been developed and enabled us to fully simulate the behavior of the harvester in question. The position-dependent magnetic force obtained from magnetostatic simulations presented in [33] have been implemented together with the reduced model in ANSYS twin builder. The system is able to autonomously choose and tune the closest mode to the dominant vibration frequency to maintain maximum possible oscillation amplitude. Furthermore, we dropped the self-adaptive step size tuning control scheme due to the potential of higher power requirements needed for the fine-tuning operation. Finally, we experimentally characterized the behavior of the first prototype of such a piezoelectric harvester design and the result matched with the simulation results in terms of frequency and expected voltage output.

#### **2. Dual Frequency Piezoelectric Energy Harvester**

This section reviews shortly the dual-frequency harvester proposed in [32–34] together with its tuning approach. Continuing our modelling e ffort from [34] we advance the finite element (FE) model into a system-level model. For this purpose, we derive a reduced order model, integrate nonlinear tuning and damping forces, a control algorithm, and electrical circuitry into a more complex model.


Net-Zero Energy Buildings: Principles and Applications

*Maher Shehadi*

## **Abstract**

Global warming and climate change are rising issues during the last couple of decades. With residential and commercial buildings being the largest energy consumers, sources are being depleted at a much faster pace in the recent decades. Recent statistics shows that 14% of humans are active participant to protect the environment with an additional 48% sympathetic but not active. In this chapter, net-zero energy buildings design tools and applications are presented that can help designers in the commercial and residential sectors design their buildings to be netzero energy buildings. Case studies with benefits and challenges will be presented to illustrate the different designs to achieve a net-zero energy building (NZEB).

**Keywords:** energy consumption, energy conservation, net-zero energy, building performance, sustainable development, sustainable energy sources


**1. Introduction**

Worldwide and implicitly at the European level, the issue of waste reduction is increasingly being addressed in the context of sustainable economic and social development of any country. This aspect takes into account the fact that sustainability is an integral component in the development strategy of each individual company and society as a whole, because it manifests itself in the interrelated plan between the three parts: economic, social and environmental. Closely interdependent with the environment, the evolution of waste is also closely linked to the strategic policy of preventing its generation and favoring recycling, so that the decision of prescriptive regulations or educational campaigns could be taken [1].

The concept of "waste" is oriented, on the one hand, to products that are at the end of the production and utility life cycle, and on the other hand to construction and demolition, production and trade processes. Manufacturing each product includes a significant amount of waste during the life cycle, highlighting all the impacts and types of pressures exerted: thus, the impact on soil degradation, emissions, etc. occurs in the procurement of raw materials; the impact of production is signaled through the use of materials and energy, air and water emissions, solid waste, etc., and the impact of transport—through raw materials and products directed to the final markets [2].

The education of young people in the field of environmental protection both in family and in schools is a priority, representing an efficient way to reduce the amount of waste [3]. In this direction, studies have been carried out [4–6] certifying the importance of education and training in the field of environmental protection, raising public awareness of environmental responsibility, minimally invasive environmental lifestyles based on recycling and waste reduction, essential components for sustainability.

Liu and Wu [7] identified three main components in the waste management process: urban economy and urban development (where the urban population has the highest share), energy consumption and urban scale. At the same time, other important studies have shown that the amount of waste is directly influenced by the residential environment of households, with significant differences according to the location of the household (in rural or urban areas) [8–10].

In this context, the present research was designed to find out whether there is a long-standing relationship between the amount of waste in each country—as an endogenous variable—and the exogenous variables represented by the population of each country by urbanization degree (cities, towns and suburbs, rural areas), pupils and students by education level and classroom teachers and academic staff by education level, respectively, thus representing a strategic point of using and modeling the evolution of the waste amount, in the direction of its diminishing.

Taking into account that education on waste management starts from the first years of life, inside the family, and continues to be enhanced with each level of school education, two variables have been introduced into the study reflecting how education in pre-university and university field influences the fluctuation of the amount of waste in the direction of its reduction: Pupils and students by educational level and Classroom teachers and academic staff by education level. At the same time, the existence of significant differences in the behavior of generated waste in relation to the location of the rural or urban household has led to the need to consider the population by degree of urbanization (cities, rural areas) as exogenous variables.

The paper is organized in six sections. The first section—Introduction—presents briefly aspects of the context of addressing the impact of education and demographic factors by residential areas on the long-term waste behavior, in the context of sustainability. This was completed by the second section, which includes the literature with the presentation of many results of studies, through which several points of view of both the interested persons and the specialists dealing with waste management were highlighted in the context of the sustainability phenomenon. Section 3 describes the methodology used to identify the influence of the level of education and the degree of urbanism on the long-term waste evolution, a methodology that includes building and validating cointegration models.

The analysis—whose results are presented in Section 4—is based on statistical data on waste evolution, population by degree of urbanisation (cities, rural areas), pupils and students, respectively classroom teachers and academic staff by education level, in the EU states and Iceland.

The paper finally includes two sections (Discussion and Conclusions) summarizing the results of the analysis and outlining aspects of managerial possibilities and proposals regarding the impact of factors in reducing the amount of waste in the future.

The main objective of the analysis was to identify how the studied variables can lead to an improved waste management in the context of sustainable development at European level, given that the integrated and sustainable waste management capacity must be a top priority and overcome technical issues to embrace the various vital elements of sustainability [11].

#### **2. Literature Review**

According to Directive 2008/98/EC of the European Parliament, waste is "any substance or object which the holder discards or intends or is required to discard". The Directive establishes and regulates the ways of reducing or preventing possible adverse impacts from waste generation and management [12]. However, since it is necessary to increase the sustainability of the waste management process by promoting the principles of the circular economy, this directive has been modified by the adoption of Directive 851/2018. It aims at adopting additional measures on sustainable production and consumption, covering the whole life cycle of products and setting new, more ambitious, objectives for European countries to move towards a circular economy [13]. A correct and effective approach to waste management can lead to progress in meeting the Millennium Development Goals [14].

There is a lot of research highlighting the aggravation of the problem of generated waste, a problem that threatens the very human-environment relationship itself. A solution to this issue, provided by the studies, is to set up a rigorous waste management plan as an important tool for developing environmental policies by different countries, in order to reduce greenhouse gas emissions [15] towards a transition to a more circular economy [16,17]. One component of this plan is to predict as much as possible the quantities of municipal waste generated [18]. The authors of this study have used multiple regression models applied to panel data on 38 urban and rural localities in the New York area to estimate the amount of residential municipal solid waste, based on independent variables in the climatological, demographic, socio-economic, cost and distance dimensions.

Kolekara et al. [19] have shown some negative aspects in the efficient and accurate determination of the amount of waste generated, including the lack of sufficient data, especially in developing countries and especially in rural areas, as well as the high degree of uncertainty. They present the main existing models used in estimating the amount of waste generated on the basis of economic, socio-demographic or management factors. Thus, as we will see below, many studies identify as a factor with significant action on waste generation a number of households' characteristics: household size, household income and the level of education. Often the application of the identified models is limited to certain regions and depends on the way the waste flows are selected.

Kawai et al. [20] assessed the confidence level and comparability of the various models used in estimating the quantities of municipal solid waste generated per capita. The level of socio-economic development and the environmental policies of some regions generate differences in the amount of municipal solid waste per capita. A low degree of comparability of per capita waste estimation models may come from the use of definitions of the concept of solid municipal waste, different from country to country. The low efficiency of waste data collection or migration flows between urban and rural areas can also affect the confidence of these models, especially in developing countries.

Another direction on which many specialized studies have been focused is the identification of the determinants, of the main factors affecting the waste generation process. Thus, an important share of the total waste generated by the population is the food waste [21,22]. Chalak et al. [23] analyzed the impact of legislation and economic incentives on the amount of food waste generated by households, based on data covering 44 countries.

Significant variations in the amount of food waste between countries with different levels of development and incomes were noted. The results of the analysis highlighted the higher impact of legislative regulations, policies and strategies in the field on the improvement in the amount of waste generated, compared to fiscal measures.

Significant regional differences in the amount of waste generated per capita are addressed and explained by Saladia [24] in terms of the seasonal population which is normally not taken into account in determining the amount of waste in a given region. The author's analysis reveals that per capita waste is positively correlated with the relative contribution of the tertiary sector to GDP creation and negatively correlated with the population over 64 years. The analysis does not certify that there is a significant correlation between waste generated and per capita income. The demographic factor itself is, however, an important one in modeling the behavior of the waste generation process [25], the applied model highlighting a direct correlation between the amount of waste and the population density.

Another category of studies addresses different components of waste generation (institutional, residential and commercial) [26–31]. Thus, the analysis by Hockett et al. [32] highlighted that retail sales (including restaurant, grocery and clothing sales) and disposal taxes have a significant impact on waste generation. However, the research did not reveal a significant link between industry, construction, personal income, urbanization and waste generation.

Among the determinants of waste generation, urbanization is the one around which many studies have focused, analyzing its impact on the amount of waste generated [33]. According to Ugwuanyi and Isife [34], the weaknesses of the waste management process are: infrastructure precariousness, legal and political framework, environmental issues management, budgetary constraints, overcapacity, environmental education insufficiency, issues which are—to a large extent—related to the degree of urbanization.

The significant direct impact of the increase in the demographic factor and the rapid development pace on the efficiency of the municipal solid waste management system are addressed by Pai et al. [35]. The results of the study show that an increase in the population leads to an exponential increase in the amount of municipal waste generated.

Another category of studies and research focuses on the importance and impact of education on the waste generation process. Thus, Fredrick et al, Sinthumule and Mkumbuzi, and Al-Khatib, et al. [36–40] identify some of the means of educating and raising awareness among urban communities about the waste management process: involving active organizations in education, NGOs and private companies, public meetings, media use, household head training. Analysis based on a cross-sectional, multistage survey has demonstrated the positive effect of public education on optimizing and streamlining urban waste management, but also draws attention to the insufficiency of education provided in the field of waste separation and organic waste management, which represents more than 50% of the total amount of waste generated in major cities.

Knowledge acquisition in the field of waste recycling, reuse, recovery and composting can be achieved even at younger age at school. Therefore, Rada et al. [3] illustrates the role of education provided to young people in the field of environmental protection, amplified by the example they can offer to their families as a way to optimize household waste management. The study covered educational units at different levels of education (primary, secondary, high school) and analyzed the influence of youth age and typology of educational and informational activities on household behavior in waste management. Research results show that waste production depends on the size of the educational institution (expressed as the number of pupils and teachers), on the types of activities carried out outside the teaching hours, and on the habits of the household members.

Also, research has highlighted the way in which households and their socioeconomic characteristics influence the waste management system [41–48]. Using probit regression models, Handayani et al. [49] showed that the level of education and knowledge gained, as well as the income level have a significant influence on household waste management behavior. At the same time, the authors have demonstrated the existence of regional disparities between urban and rural areas in waste management practices. Households whose members have a higher education level and income and are located in the urban area are more likely to increase the amount of waste generated, but also have a higher probability of managing their waste amount, compared to households in rural areas which, generally, have a lower level of education and income. The positive correlation between the educational level of the household members and the generation of waste within it is in line with the results of some studies, like: Sujauddin et al., Thi Thu Nguyen et al., Fang et al., Jörissen et al. [50–53]. Age and gender also generate significant differences in households' behavior regarding waste management, with women and older people displaying greater responsibility than men and young people. Similar results were also obtained by Li et al. [54] and Limbu [9], which also identified a negative correlation between the educational level of family members and the amount of waste generated within the household. A positive correlation

between household income and the amount of waste generated was identified by Zia et al. [55], who also studied the influence of seasons on waste generation. The conclusion was that this process is more pronounced, more intense in spring and winter [56,57].

The residential area of households has a significant direct influence on waste generation (Skourides et al. [8]; Liao [58]), so changing the household location (rural and urban) leads to a significant increase in the amount of waste generated [9,10]. Other variables positively correlated with the amount of waste generated by households are: household size, family members' availability to spend their leisure time outside the household, consumption pattern and household income. Based on the application of a logistic regression model to the data obtained from a sample of 402 respondents, Afroz et al. [5] concluded that household size and income are significant determinants of the amount of waste generated by households, while the age and education of family members can significantly influence their availability to waste recycling. Emerging from the need to improve negative pressures exerted by human activity on the environment, researchers' studies focused on the differences in the waste management system by the location of the household (urban or rural area). Thus, Han et al. [59] and Marshall and Farahbakhsh [60] study the factors influencing waste generated in rural areas and the peculiarities of this process in developing countries. The explanatory variables included in the study were of economic nature (household income and expenditure, types of energy and fuels used and types of existing industries in the rural area), social (population, education and culture) and natural (temperature, precipitation, humidity, harvesting periods). These factors determine the waste content and consumption patterns and can be used as ways in the optimization and improvement of the waste management system.

Daban Astane and Hajilo [61] carried out a quantitative and qualitative analysis of the waste generated in rural Iranian areas on a sample of 318 households, pointing out that about 70% of the average amount of waste generated per person per day is organic waste, 30% being solid waste. It is noted that there are patterns in the waste spatial distribution, with significant differences in the amount of waste generated in the north and northwest areas, areas with significantly higher rates of waste generation than others. The most important determinants of waste generation are household income, assets, the age and attitude of household members towards environmental issues, while behavior and knowledge related to resource efficiency are significant factors in reducing the amount of waste generated.

#### **3. Methodology and Data**

Taking into account the objective of the study—identifying the long-term relationship between the amount of waste generated per capita and a number of factors influencing their evolution in 29 European countries during 2013–2017, as well as highlighting the similarities and the differences between them, regarding the indicators analyzed—four explanatory variables were selected. These variables, together with the explained variable, represent the five sets of data on which the study is based (Table 1). The datasets were provided by EUROSTAT and processed with Eviews program.

**Table 1.** List of variables used in the analysis.

Source: Authors' selection, based on EUROSTAT data.

The methodology applied in the study started with the analysis of the main characteristics of the variables included, as well as the identification of the correlation matrix between them.

In analyzing the dynamics of economic processes and systems, frequently occur situations in which the endogenous variables are placed both on the left and right side of the equations of the respective economic models. In these cases a possibility of analysis is the use of Vector Autoregressive Models (VAR), as well as Vector Error Correction (VEC).

Given the complexity of the interdependencies between the variables included in the analysis and the necessity of highlighting the cointegration relationships between them, starting from the matrix *V* ∈ *Rk*×*n* (where *k* represents the number of endogenous variables and *n*—the lengths of the data series), the following model were generated: VAR models—for the analysis of the interdependent time series and the effects of perturbations on the values of the variables involved, and VEC models—for identifying the cointegration relationships between the variables.

The general form of a VAR model is:

$$y\_t = \sum\_{i=1}^p A\_i \cdot y\_{t-i} + B \cdot x\_t + \varepsilon\_t \tag{1}$$

where *yt* is a vector of *k* endogenous variables, *xt* is a vector of *m* exogenous variables, and *Ai* ∈ *Rk*×*p*, *B* ∈ *Rk*×*m* are coefficients matrices to be estimated.

VEC models are derived from VAR models and are designed to restrict the long-term behavior of endogenous variables by incorporating cointegration relationships in VEC models, so as to converge to them by allowing short-term adjustments. The term "*cointegration*" is called "Error Correction Term" (ECT), as it allows estimating and correcting short-term deviations from the long-term equilibrium of the studied phenomenon.

One of the most commonly used methods for estimating VEC models is Generalised Method of Moments (GMM), the Arellano, M. and Bond, S. (1991) [62] estimator being applied in the analysis of various growth models.

In the research we started from a VEC model whose general form is:

$$
\Delta y\_t = \beta \cdot ECT\_{t-1}^y + \sum\_{i=1}^p A\_i \cdot \Delta y\_{t-i} + u\_t \tag{2}
$$

In Equation (2) Δ*yt* = *yt* − *yt*−1 is a vector of *k* endogenous variables, *Ai* ∈ *Rk*×*p* are coefficients matrices to be estimated and β ∈ *Rk* is the vector of ECT coefficients.

Taking into account the relation (2) and the data series included in the matrix V, the corresponding VEC model has the following form:

$$\begin{aligned} \Delta W\\_POP\_t &= \beta\_1 \cdot \text{ECT}\_{t-1} + \sum\_{i=1}^p a\_{1,j}^{W\\_POP} \cdot \Delta W\\_POP\_{t-i} + \sum\_{i=1}^p \delta\_{1,j}^{P\underline{S}\underline{S}} \cdot \Delta P\underline{S}t\_{t-i} \\ &+ \sum\_{i=1}^p \delta\_{1,j}^{T\underline{S}\underline{P}OP} \cdot \Delta TS\\_POP\_{t-i} + \sum\_{i=1}^p \phi\_{1,j}^{P\underline{P}\underline{P}\underline{C}} \cdot \Delta POP\\_{t-i} + \sum\_{i=1}^p \gamma\_{1,j}^{P\underline{P}\underline{P}\underline{R}} \cdot \Delta P\underline{P}P\underline{R}t\_{t-i} + u\_{1,t} \end{aligned} \tag{3}$$

$$\begin{aligned} \Delta PSE\_t &= \beta\_2 \cdot ECT\_{t-1} + \sum\_{i=1}^p a\_{2,i}^{W\_t \text{POP}} \cdot \Delta W\_- \text{POP}\_{t-i} + \sum\_{i=1}^p \delta\_{2,i}^{PSE} \cdot \Delta PSE\_{t-i} \\ &+ \sum\_{i=1}^p \delta\_{2,i}^{TS\text{POP}} \cdot \Delta TS\_- \text{POP}\_{t-i} + \sum\_{i=1}^p \phi\_{2,i}^{POP\\_C} \cdot \Delta POP\_-\text{C}\_{t-i} + \sum\_{i=1}^p \gamma\_{2,i}^{POP\\_R} \cdot \Delta POP \text{R}\_{t-i} + u\_{2,t} \end{aligned} \tag{4}$$

$$\begin{aligned} \Delta TS\_{-}POP\_{t} &= \beta\_{3} \cdot ECT\_{t-1} + \sum\_{i=1}^{p} \alpha\_{3,i}^{W\_{-}POP} \cdot \Delta W\_{-}POP\_{t-i} + \sum\_{i=1}^{p} \delta\_{3,i}^{PSE} \cdot \Delta PSE\_{t-i} \\ &+ \sum\_{i=1}^{p} \delta\_{3,i}^{TS\\_POP} \cdot \Delta TS\_{-}POP\_{t-i} + \sum\_{i=1}^{p} \phi\_{3,i}^{POP\\_C} \cdot \Delta POP\_{-}C\_{t-i} + \sum\_{i=1}^{p} \gamma\_{3,i}^{POP\\_R} \cdot \Delta POPR\_{t-i} + u\_{3,t} \end{aligned} \tag{5}$$

$$\begin{aligned} \Delta \text{POP\\_C}\_{t} &= \beta\_{4} \cdot \text{ECT}\_{t-1} + \sum\_{i=1}^{p} a\_{4,i}^{W\\_POP} \cdot \Delta \text{W\\_POP\\_t-i} + \sum\_{i=1}^{p} \delta\_{4,i}^{PSE} \cdot \Delta \text{PSE}\_{t-i} \\ &+ \sum\_{i=1}^{p} \delta\_{4,i}^{T\\_POP} \cdot \Delta \text{TS\\_POP\\_t-i} + \sum\_{i=1}^{p} \phi\_{4,i}^{POP\\_C} \cdot \Delta \text{POP\\_C}\_{t-i} + \sum\_{i=1}^{p} \gamma\_{4,i}^{POP\\_R} \cdot \Delta \text{POP\\_R}\_{t-i} + u\_{4,t} \end{aligned} \tag{6}$$

$$\begin{aligned} \Delta \text{POP\\_R}\_t &= \beta\_5 \cdot \text{ECT}\_{t-1} + \sum\_{i=1}^p \alpha\_{5,i}^{W\\_POP} \cdot \Delta W\\_POP\_{t-i} + \sum\_{i=1}^p \delta\_{5,i}^{PSE} \cdot \Delta PSE\_{t-i} \\ &+ \sum\_{i=1}^p \delta\_{5,i}^{TS\\_POP} \cdot \Delta TS\\_POP\_{t-i} + \sum\_{i=1}^p \phi\_{5,i}^{POP\\_C} \cdot \Delta POP\\_C\_{t-i} + \sum\_{i=1}^p \gamma\_{5,i}^{POP\\_R} \cdot \Delta POP\\_{t-i} + \mathfrak{u}\_{5,t} \end{aligned} \tag{7}$$

A first condition in modeling and testing the causal relationship between the five endogenous variables involves testing the stability of the data series. For this, Unit Root Test of Augmented Dickey-Fuller test was used. The Null Hypothesis states that X (the analyzed variable) has a unit root. The validation condition is to reject the Null Hypothesis for 5% significance level (α = 0.05).

Testing the existence of cointegration relationships was carried out with the Johansen Cointegration Test [63], (for which the Null Hypothesis states that there are no cointegration relationships). If this second Null Hypothesis is also rejected, the VEC model (or models) is generated and the statistical significance of the parameter values is tested. In (3) the conditions are α*W*\_*POP* 1,*j* , δ*PSE* 1,*j* , μ*TS*\_*POP* 1,*j* ,φ*POP*\_*C* 1,*j* , γ*POP*\_*R* 1,*j* - 0, (∀)*j* - 0 and they are similar to the conditions for Equations (4)–(7).

A second important condition for validating the model is to ensure the system convergence to balance. For this, the ECT coefficient should be negative (β1 < 0) and statistically significant for the significance level chosen. For this, the t-statistics test was used.

After identifying the general form of the ECT, its parameters have to be statistically significant. This condition is also checked using the t-statistics test. Confidence level used was 95% (α = 0.05). In some situations, a 90% confidence level (α = 0.10) was also accepted.

For checking the stability conditions, autoregressive roots graph, as well as the model response to unit impulses applied to its variables are displayed and analyzed.

The methodology used throughout the paper produced results that have led to a clearer picture of the impact of the urbanization process and institutional education on past, present and future evolution of waste amount, in the countries included in the analysis.

### **4. Results**

Achieving the objective of finding a way in which the management of the analyzed variables can lead to sustainable waste management and address sustainable European development, requires an approach based on the main features of the variables included in the analysis, as well as the correlation matrix, presented in Table 2.

The results of partial correlations show that the W\_POP variable is correlated with the other variables (all correlation coefficients being statistically significant), although the correlation between W\_POP and TS\_POP is poor. Taking this into account, we will consider W\_POP as the main variable in the analysis performed. At the same time, it can be inferred that the PSE, TS\_POP and POP\_C variables can be considered as independent variables in the first phase, which can influence W\_POP. As for the POP\_R variable, although the results may indicate a possible endogenousness between POP\_C and POP\_C, it can be solved by generating the VEC model.

Using VEC models requires prior determination of the integration order. The results obtained by applying the Augmented Dickey-Fuller (ADF) test are presented in Table 3. These lead to the conclusion that series are 1st order integrated and suitable for further cointegration testing.

**Table 2.** Main characteristics of the analyzed variables and the correlation matrix between them.

**Table 3.** Augmented Dickey-Fuller test statistics.

\*\*\* 1% level, \*\* 5% level, \* 10% level

In order to determine the co-integration order of VAR (p)—type process between the series in the level, there have been used the information criteria of Akaike (AIC), Schwarz (SC) and Hannan-Quinn (HQ), as well as Final Prediction Error and sequential modified LR statistical test (LR). All the other criteria (Table 4) suggest the order of the VAR process q = 1. Taking into account that the stationarity of the series was obtained by a 1st order differentiation, the chosen process will be VAR (2).

**Table 4.** Results regarding the establishment of the order in the VAR process.

Considering the results obtained above and using Johansen's (1988) approach, the existence of three cointegration equations (Table 5) for the chosen significance level was highlighted. At the same time, however, the Max-Eigen Statistic values for 5% significance level indicate the existence of a single regression equation, which includes the five variables considered as endogenous ones.

Taking into account the fact that the Max-Eigen Statistic is more restrictive, the intention was to identify the model that co-integrates all five variables included in the analysis. Testing their endogeneity was performed with the Pairwise Granger causality test (Null Hypothesis: the variables analyzed cannot be exogenous variables in the model) and their belonging to the model was verified using Lag Excluding Tests (Null Hypothesis: the analyzed variable is not excluded from the model).

**Table 5.** The results of Cointegration Rank Test.

The results of Pairwise Granger causality (Table 6) lead to accepting the Null Hypothesis for all five variables. Thus, in the case of the dependent variable D(W\_POP) that reflects the change in the volume of waste per capita per time unit (year), as a result of the change in one time unit of the Pupils and Students as % of total age population, Classroom teachers and academic staff, Population total median equivalised income—Cities, Population total median equivalised income—Rural Areas, the individual probabilities corresponding to the values of the χ2 statistic (Chi-square) and the value corresponding to the model as a whole (Prob = 0.7894) are higher than the 5% significance level (α = 0.05). The same conclusion is reached for the other four variables.

**Table 6.** The results of VEC Granger Causality test.

The above tests highlighted the existence of a model in which all five variables are considered endogenous. To exclude or include them from the VEC model, the critical value of the Chi-squared statistic, for 5% significance level (α = 0.05) and 5 degrees of freedom whose value is 11.070498 was taken into account.

The results of the Lag Exclusion Wald Tests (Table 7) show that all Chi-squared test statistics for both DLag\_1 and DLag\_2 are strictly lower than the critical value (11.0705). Consequently, the Null Hypothesis is accepted. It follows that none of the variables will be excluded from the model. The same conclusion is reached by analyzing *p\_value* values, which are significantly higher than the chosen significance level.

**Table 7.** VEC Lag Exclusion Wald Tests.

Numbers in [ ] are *p*-values

Taking into account the obtained results, the VEC model, which includes all five analyzed variables, was generated (Table 8). Testing the model validity and the statistical significance of its parameters was performed with the Student (t) test. In the case of this model, for the significance level chosen, the critical test value is 1.98238. Since the value of CointEq1 constant (coefficient β1 in Equation (3)) is negative (−0.007086) and statistically significant (t-statistics = −2.33718 > t-critic = 1.98238), the VEC model is statistically significant. Also, since all the t-statistic values of the model coefficients are higher than the critical value, it follows that the Null Hypothesis is rejected and the Alternative Hypothesis is accepted. All coefficients are statistically significant.

**Table 8.** The estimation of VEC coefficients and their Standard errors and t-statistics values.

Regarding the stability of the VEC model, this is highlighted on the one hand by how the analyzed model reacted to the application of a unit impulse on its variables, and on the other hand that all the roots of the characteristic polynomial are included in the unit circle (Figure 1).

**Figure 1.** *Cont*.

**Figure 1.** Response of W\_POP (**a**), PSE (**b**), TS\_POP (**c**), POP\_C (**d**), POP\_R (**e**) to Cholesky One S.D. Innovations, and Inverse Roots of AR characteristic polynomial (**f**).

The long-term relation, corresponding to the Error Correction Term (ECTt+1), based on Equations (3)–(7) has the following form:

$$\begin{array}{llll}d(W\_{-}\text{POP}) = & -0.007 \cdot (W\_{-}\text{POP}\_{l-1} + 0.649 \cdot PS\_{l-1} + 4.077 \cdot TS\_{-}\text{POP}\_{l-1} - 0.135 \cdot POP\_{-l-1} \\ & - 0.135 \cdot POP\_{-l}R\_{l-1} + 19.936 \cdot () + 0.107 \cdot W\_{-}\text{POP}\_{l-1} - 0.142 \cdot W\_{-}\text{POP}\_{l-2} - 0.005 \cdot PSE\_{l-1} \\ & - 0.005 \cdot PSE\_{l-2} + 0.004 \cdot TS\_{-}\text{POP}\_{l-1} + 0.012 \cdot TS\_{-}\text{POP}\_{l-2} - 0.003 \cdot POP\_{-l-1} \\ & - 0.0005 \cdot POP\_{-l-2} - 0.002 \cdot POP\_{-l}R\_{l-1} - 0.0009 \cdot POP\_{-l-2} \end{array} \tag{8}$$

The long-term relationships of Error Correction Term (ECT1), based on Equations (3)–(7) is:

$$\begin{array}{l}\text{W\\_POP} = & -0.6489 \cdot \text{PSE} - 4.0769 \cdot \text{TS\\_POP} \\ & + 0.1351 \cdot \text{POP\\_C} + 0.1351 \cdot \text{POP\\_R} - 19.9356 \end{array} \tag{9}$$

The regression coefficients show the long-lasting reverse connection between the amount of waste and Pupils and students—as % of the total age population, respectively Classroom teachers and academic staff per 100 inhabitants, meaning that an increase in the education level leads to a reduction in the waste amount. As for the other two factors (Population total median equivalised income—Cities, Population total median equivalised income—Rural Areas), their influence on the amount of waste is direct, without a significant long-term difference between urban and rural areas. It follows that the education level is a primordial factor, which can act favorably to sustainable development.

#### **5. Discussions**

Considering that, in time, limitations exist regarding the complete and detailed analysis of the amount of waste generated, mainly due to difficulties in data collection [19], the paper addressed the issue of waste quantity behavior, by studying the interdependence level of two categories of factors: educational and demographic, by degree of urbanization, at the level of 29 European countries.

In this context, an autoregressive vector model, followed by a Vector Error Correction model have been applied, on 2013–2017 data, referring to pupils and students, classroom teachers and academic staff by educational level and the population by degree of urbanization (cities, rural areas).

Analyzing the long-term relationship between the amount of waste, education and the demographic factor by residence area, it is pointed out that the amount of waste is reduced as the level of education increases and is increased with the demographic factor, taking into account the existence of insignificant difference by the two types of residence areas.

Regarding the first factor (the educational one), it is noted that at institutional level sustainable development can be addressed through the upgrading of the educational waste recycling-oriented process, in order to improve the protection and the quality of the environment, through increasing the competence of the services provided, resulted in well-trained graduates, both theoretically and practically.

Currently, more and more employers are looking for graduates with sustainable education. They need to know the aspects of sustainability and to have the skills to use them in their professional work.

At both institutional and social levels, universities can be seen as having an important role in the community. At the same time, students can demonstrate they are the most important agents of change, both in the environment and in society, that they have environmental, social and economic knowledge about sustainability and that they have a new system of values, motivation and other abilities to produce change.

Regarding the second factor (the demographic one, by residence area) it is noted that in many low-urbanization areas there is no uniform, equal distribution of solid waste collection services, but there is a hierarchy of rural localities according to their financial strength to provide solid residential waste collection services, as well as some informal features of governance (such as involving local wealthy families in local governance) [64,65].

The solid municipal waste recycling system in developing countries highlights that sustainable solid waste management is a concern for the mankind as a whole and is also addressed in the United Nations Millennium Development Goals, as a way of reversing the negative impact of human activity on environment [66]. Therefore, adequate management of solid waste should be addressed in order to reduce poverty, reduce child mortality and improve the population health. While large-scale sustainable solid waste management methods are used in developed countries, it is advisable in developing countries to take over and use these methods on an increasing scale.

The system may include an economic and financial mechanism that respects the general principles, especially "the polluter pays" principle and the subsidiarity principle. In addition, the integrated waste management system should also consider promoting information and awareness system for all actors involved in the action, as well as a modern system for obtaining complete and accurate data and information, adequate to national and European reporting requirements.

#### **6. Conclusions**

A major challenge for sustainable development is how to encourage activities that positively influence the environment and discourage those which cause environmental damage [67]. As waste is

an important component of the environmental pressure exerted by humans, the capacity to manage the activities of waste amount reduction is a priority task in managerial strategies.

In this context, in order to capture future waste behaviour (as a measure of sustainability), starting from the 2013–2017 data, two factorial plans were addressed: education (pupils and students by education level and classroom teachers and academic staff by education level) and demographic, by degree of urbanization (cities, rural areas). The research was conducted at the level of 29 EU countries. The innovative contribution of the article consists of combining the educational and demographic factors by residential areas, as well as the statistical indicators selected for the quantification of the two factors. At the same time, originality also consists in the fact that the applied model analyzes the long-term implication of these factors on the quantity of waste generated, a less discussed aspect in other specialized studies.

Applying the Vector Autoregressive Model and Vector Error Correction Model led to a long-term relationship between waste quantity, education and demographic factor by residence. At the same time, this relationship shows the reduction of the waste quantity is significantly influenced by the increase of the educational level, while the demographic factor, due to the insignificant difference between the two types of residence environments, determines the increase of the waste amount.

In this context, it is possible to highlight the way in which we can manipulate each factor so that to encourage an improvement in the waste management process.

The educational factor puts the spotlight on the educational institutions. Thus, they must have the capacity to allocate resources intelligently on the one hand to become sustainable, and on the other hand to provide learners (pupils, students) with a living experience in a sustainable environment. At the same time, they must be the catalyst for the changes that are expected and demanded at the society level as a whole, but also by their graduates in order to become tools for the society transformation towards sustainable development.

As a conclusion, it can be said that both pupils and students must have many of the attributes needed to act as agents of change and need to be trained as active citizens to boost and sustain the global economy.

Regarding the demographic factor by urbanization degree, the comparative analysis of the data shows the waste management system in low and middle income countries is less developed in rural areas than in urban areas, a fact also confirmed by other studies in the literature [68,69]. The main problems of the waste management system in rural areas in less developed countries are: lack of political and financial tools that can be applied to improve waste management, lack of long-term vision of waste management policy, lack of elementary measures, which have long been applied in developed countries, lack of funds, lack of environmental responsibility, poor quality of waste management services [70]. There are significant differences in the coverage of waste collection services between major cities and rural areas in less developed countries [71], while in developed countries these services cover entirely the rural areas.

In this context, it is necessary to act in the future on the main factors with a significant influence on the waste management system: the existence of legislative regulations in the field, the application of incentive schemes, the cost of waste management procedures, the budgets allocated for this purpose, education and knowledge of the importance of waste collection, household income, availability of local population to pay waste collection and evacuation taxes, existence of markets based on recycled materials, involvement of companies and small businesses in the waste management process. It is also recommended to conduct awareness and accountability programs for urban population, focused on the need to reduce the amount of waste, by periodically evacuating and recycling the waste generated.

Designing and applying innovative solutions that should be both resource efficient and waste management systems add value to business and contribute to sustainability. In this way, we can talk about an improvement in operations management and environmental management, that can be integrated at operational level and include the waste management supply chain.

The implementation of an integrated waste management system requires the adaptation and development of an institutional and organizational framework, for the introduction of national requirements and harmonization with European structures.

The authors intend to extend the analysis by identifying other variables with significant impact on the evolution of the amount of waste generated, as well as forecasting it for a future period, by considering other typologies of econometric models. As the model is limited to European countries, the authors wish to analyze the pattern of the relationship between the amount of waste generated, the level of education and the environment of residence also in non-European countries.

**Author Contributions:** All the authors were equally involved in the documentation phase, in choosing the research methodology, in data analysis, as well as in results analysis and in discussions. All the authors have equally participated to the manuscript preparation and have approved the submitted manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.


**11.1 Optimization of antimalarial therapy with available drugs**

This technique involves mixing existing medications with new dosing regimens or formulations, as well as optimising treatment with currently available drugs. Other non-artemisinin-based combination therapies, in addition to the already proven and recommended artemisinin-based combination therapy, are intended to prevent the development of resistance, improve effectiveness, and tolerability. The idea is to combine two or more drugs with different mechanisms of action, resulting in different molecular targets in the parasite. In an area where *P. falciparum* transmission is endemic and Chloroquine resistance is high, chloroquine combined with sulfadoxine-pyrimethamine demonstrated a large therapeutic return over sulfadoxine-pyrimethamine alone.


**5. Conclusions**

There is a great need for new approaches targeting EBV, against which there are no licensed vaccines or immunotherapies available. Acute infection can lead to infectious mononucleosis, and the risk of autoimmune diseases such as multiple sclerosis is increased following symptomatic infection. Immunotherapy targeting conserved, expressed, and oncogenic viral genes has the potential to drive immunity that impacts EBV-associated cancers. Here we generated synthetic DNA immunogens targeting the EBV latent proteins EBNA1, LMP1, and LMP2. These engineered SynCon DNA vaccines were delivered by Cellectra EP into mice to study their immune responses. The combination of immunogens generated significant CD8 T cell responses. In addition, these responses impacted tumor growth in a mouse challenge model. Further study of this combination synthetic DNA approach in EBV-driven disease is warranted.

**Author Contributions:** Conceptualization, A.P.-P., D.B.W. and K.W.; methodology, A.P.-P. and K.W.; validation, A.P.-P. and K.W.; formal analysis, A.P.-P. and K.W.; investigation, A.P.-P. and K.W.; data curation, A.P.-P. and K.W.; Writing—Original Draft preparation, K.W.; Writing—Review and Editing, A.P.-P., D.B.W. and K.W.; visualization, A.P.-P. and K.W.; supervision, D.B.W.; project administration, A.P.-P. and K.W.; funding acquisition, D.B.W.

**Funding:** This work was supported by a University of Pennsylvania/Wistar Institute NIH Special Program of Research Excellence grant (P50 CA174523 to D.B.W.), the Wistar National Cancer Institute Cancer Center (P30 CA010815), the W.W. Smith Family Trust (to D.B.W.), a grant from Inovio Pharmaceuticals (to D.B.W.), and T32 CA 9171-41 (K.W.).

**Acknowledgments:** We would like to thank the Wistar Flow Cytometry Facility and Animal Facility for their technical assistance.

**Conflicts of Interest:** D.B.W. has received an SRA, has an ownership interest including IP, and performs Board service for Inovio, has received an SRA from and consulted for GeneOne; has received an SRA from and consulted with Geneos; and has served on advisory boards for AstraZeneca and Sanofi, among others. A.P.-P. is an employee at Inovio Pharmaceuticals. The other authors declare no conflicts of interest.


**5. Conclusions**

The present study evaluated the potential of DInSAR techniques for detecting LS in the TCP. Coherence degradation, due to temporal decorrelation caused by vegetation, which is

the dominant land cover in the AOI, and due to recurrent floods, as well the degradation of the precision of DInSAR measurements due to APS effects, affected the effectiveness of conventional DInSAR application. However, advanced differential SAR interferometry, e.g., the DIS approach tested in this study, could be efficiently used to investigate the LS in the AOI. Using the DIS approach, average LOS displacement rates were obtained for the 2018–2019 period and several subsiding zones were identified. The subsiding zones are located in Paraíso and Comalcalco, at the limits of the Villahermosa urban area and its outskirts, such as Pomaca and Saloya 2nd, as well as in other localities, such as Nacajuca, Soyateco, and Jalpa de Méndez. The subsiding zone at the south limits of the Villahermosa urban area has a spatial correlation with the area recurrently affected by floods, indicating the possible influence of LS on the flood vulnerability of this zone. Three of the detected subsiding areas have a spatial correlation with hydrocarbon extraction areas, suggesting a possible relationship between the hydrocarbon extraction and surface deformation. However, more detailed investigations are required for more precise determination of the origin of subsidence in these, and the other subsiding zones identified in this study. This work represents the first effort to address the topic of subsidence in the TCP and could be used as a reference in future investigations.

**Author Contributions:** Conceptualization, G.M.-F. and O.S.; formal analysis, Z.P.-F.; funding acquisition, G.M.-F.; investigation, O.S. and G.M.-F.; methodology, O.S.; resources, O.S. and G.M.-F.; software, O.S., Z.P.-F. and G.M.-F.; supervision, G.M.-F. and O.S.; visualization, Z.P.-F.; writing— original draft, Z.P.-F.; writing—review and editing, G.M.-F. and O.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** The Consejo Nacional de Ciencia y Tecnología de México awarded a doctoral scholarship to Z.P.-F. This work has been supported by the Centro Interdisciplinario de Ciencias Marinas, Instituto Politécnico Nacional (CICIMAR-IPN) within the framework of the project SIP-20195187. The APC was funded by Secretaría de Investigación y Posgrado (SIP-IPN).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Sentinel-1 data are available at Copernicus Sentinel data 2018–2019, processed by the ESA.

**Acknowledgments:** The authors would like to thank the CICIMAR-IPN and Centro de Estudios Superiores de Ensenada (CICESE) for the support to carry out this work. We thank the anonymous reviewers for their insightful comments and suggestions.

**Conflicts of Interest:** The authors declare no conflict of interest.


**4. Case study: continental shelf of the Campos Basin (SE, Brazil)**

The continental shelf of Campos Basin is located on the southeast coast of Brazil between latitude 21° and 23°S, the southwest margin of the Atlantic Ocean, which comprises part of the area occupied by the Marine Sedimentary Campos Basin between the 25 and 150 m isobaths (**Figure 3**). The sedimentary contribution to this region is restricted to the rivers Itapemirim, Paraíba do Sul, Macaé, and São João, and most of the material derived from rivers and coastal erosion seems to be retained in coastal waters; what exceeds in this region is readily carried by oceanic currents. Detrital materials derived from the river discharge of the Paraíba do Sul River are distributed throughout the inner shelf by developing small muddy zones and large mud accumulations in an area adjacent to Búzios and Cabo Frio, where the currents have energy close to zero, 150 km south to the mouth of the river [64].

The South Atlantic Central Waters (SACW), a colder, nutrient-rich water mass, enters the continental shelf increasing local primary production and associated secondary productivity. Many studies have been carried out to investigate aspects related to the coastal upwelling of Cabo Frio, south of the Campos Basin [65].

#### **4.1 Methods applied in Continental Shelf of the Campos Basin**

Sampling methods, preparation of samples, control of quality, and methods of data analysis are described in detail in [66]. Living benthic foraminifera of the Campos Basin continental shelf were studied in 239 samples collected in nine transects (A–I) perpendicular to the coast line. The sampling was performed during the dry season of 2008 and the rainy season of 2009. The dry season, in this region, occurs in winter and corresponds to lowest precipitation and less frequent upwelling events. By contrast, the rainy season takes place in summer and corresponds to the period with higher precipitation and with more frequent upwelling events.

At each transect, five isobaths (25, 50, 75, 100, and 150 m) were sampled with a very large and modified (with an upper opening) Van Veen, which functions as a box corer. In each isobath, three independent samples successfully taken with a 10 cm × 10 cm × 2 cm, a "quadrat" yields samples with 200 cm3 . A fixative solution (4% formalin buffered with sodium borate) with Rose Bengal stain was immediately added to fix and evidence the protoplasm of living foraminifera. In the laboratory, 20 cm3 of sediment from each sample was separated for analysis of living foraminifera.

 The foraminiferal samples were washed trough a 63 μm sieve, dried in an oven (<60°C), and then picked under a stereomicroscope. Density values are equivalent to the total number of living individuals in 20 cm3 (volume) or 10 cm2 (area). Although the 63 μm mesh size was used as the size limit in the washing and screening of samples, many individuals smaller than 63 μm were found alive adhered to the grains; they were removed and incorporated into the slides for study. Careful quality control ensured similar patterns of screening and identification of living foraminifera, making the differences between pickers minimized. The species identification was based on [67, 68], and other specific references, as well as by the analysis of museum collections. The biomass was calculated by the volumetric method [33, 69].

*Response of Benthic Foraminifera to Environmental Variability: Importance of Benthic… DOI: http://dx.doi.org/10.5772/intechopen.81658*

**Figure 3.**

*Continental shelf of Campos Basin area. The transects A–I and their sampling points 1–5, according to the isobaths (25 m = 1; 50 m = 2; 75 m = 3; 100 m = 4; and 150 m = 5).*

According to Pianka [70], k-strategist or conservative species have greater body size, longer life cycle, and population size largely constant in time, being close to the capacity of support of the environment; r-strategist species are known for their opportunistic behavior, small size, short life cycle, and very variable population size without adjustment balance in relation to available resources (mainly space and food). R-strategists can proliferate opportunistically and vary considerably its absolute abundance. According to these characteristics, Warwick [71] developed

 a method based on the abundance and biomass curves (ABC) for the detection of perturbation and to analyze the species response to environmental changes. The ABC curves are compared together with the W statistic calculation [71]. According to Magurran [36], in undisturbed environments, one or two species dominate the biomass and this has the effect of raising the biomass curve in relation to the abundance curve. On the contrary, in highly disturbed environments, a few species have very large number of individuals of small size, but since these species have small size, they do not dominate the biomass and so, the abundance curve is above the biomass curve. In intermediate conditions, the biomass and abundance curves cross each other several times. In this work, the ABC curves were generated for each group in both periods with all the species in which it was possible to calculate the biomass.

#### **4.2 Discussion of results of continental shelf of the Campos Basin**

The composition of the foraminifera assemblages allowed to detect areas under upwelling influence on the continental shelf. *Epistominella exigua*, *Adercotryma glomeratum*, *Bulimina marginata*, *Pappina compressa*, *Angulogerina angulosa* s.l., *Nonionella stella*, *Nonionella opima*, *Hopkinsina pacifica, Bolivina fragilis, Bolivinellina translucens, Fursenkoina pontoni*, and *Stainforthia complanata* can be considered as indicators of seasonality-enriched areas with phytodetrital material signaling the upwelling events in the study region of Campos Basin. The continental shelf presents many encrusting foraminifera, both large and small adults, as well as juveniles of larger species that are adhered to the sediment grains, indicating that it is a region with a predominance of high hydrodynamic energy near the bottom. Small individuals play an important role in the ecological characterization of oligotrophic areas where organic carbon is scarce and rapidly consumed, transported, or oxidized.

The increase of the continental discharge during rainy season brings more inorganic and organic nutrients to the coastal system, increasing the primary production. The high supply of continental nutrients in addition to the nutrients of the coastal upwelling of Cabo Frio and adjacent areas disturbs the natural equilibrium of the biological communities of this area.

 The results of the ABC curves by isobaths evidence that in the 25 m isobath of the dry season of 2008, there was some disturbance (W = 0.0332), but in the rainy season of 2009, the disturbance was much high (W = − 0.0071), so that the abundance curve overlaps with that of biomass (**Figure 4**). The ABC curves indicate the 25 m isobath as the most disturbed one within the continental shelf and the upwelling/organic enriched group as an area of moderated disturbance. During the rainy season, the 25 m isobath is disturbed by natural eutrophication phenomena and it may be significantly amplified by anthropogenic activities in the catchment area of Paraiba do Sul river and other anthropogenic disturbances, once this segment of the Brazilian coast is strongly influenced by agricultural, industrial, and urban activities as well as by fishery and many other coastal activities. In the southern region, the disturbed patterns seem to be strongly related to natural phenomena due to the presence of many species who indicate upwelling events, which are well known near the Cabo Frio area.

In his final remarks, Warwick [71] does not believe that this method can be applied to meiobenthic species, because according to him, there are no obvious size differences between k species and meiobenthic r-strategists. He mentioned Oncholaimidae nematodes and Tisbidae copepods as dominant species in polluted environments and reminds that they are often larger. However, among the foraminifers, there are species known as k-strategists that correspond to the characteristics mentioned by Pianka [70], and therefore, the application of the ABC curves to assess environmental disturbance could be successful. Although the foraminifera belong to both micro- and meiofauna, the results obtained with the ABC curves in

*Response of Benthic Foraminifera to Environmental Variability: Importance of Benthic… DOI: http://dx.doi.org/10.5772/intechopen.81658*

**Figure 4.**

*ABC curves of the continental shelf assemblages of foraminifers. Groups 1–4 of the cluster analysis from rainy period (2009).*

this work evidence they can be used to trace environmental disturbance. In fact, it is expected that the ABC curves can be used to identify both natural and anthropogenic stressors. However, without complementary studies that allow to qualify and quantify the disturbance origin by applying the ABC curves, we will have only the sign of the environmental disorder. On the other hand, we still have little information on the response of some species to natural and anthropogenic stressors, but this integrated study will allow the construction of a more effective monitoring plan, which can provide subsidies for preserving the integrity of the environment.