*1.3. Objective*

Though both seabed and water column data have been collected for the Waipaoa River continental shelf, knowledge of sediment transport mechanisms is benefited by development of a three dimensional hydrodynamic-sediment transport numerical model providing spatial coverage unattained by observational efforts. This paper describes the implementation of the ROMS-CSTMS numerical model for the Waipaoa continental shelf and examines the sensitivity of sediment flux estimates to model nesting and seabed erodibility parameterizations.

**Figure 1.** Study site on North Island, New Zealand. (**A**) Waipaoa Shelf map showing locations of Poverty Bay, Poverty Gap, shelf depocenters (Dep), and the Lachlan and Ariel Anticlines (L.A. and A.A., respectively). Red arrow indicates river mouth. Grey bathymetric contours drawn every 10 m up to 70 m depth, while dashed grey line indicates shelf break at 150 m. Inset shows location of study site within the larger-scale model. (**B**) Waipaoa Shelf map showing shelf bathymetry up to 80 m water depth (shading; in meters), tripod locations (brown dots) and multi-core stations (black dots) from the January 2010 research cruise. Grey and black bathymetric contours drawn every 10 m to 100 m depth, then at 150 m depth. (**C**) Waipaoa model grid showing bathymetry, where each box shown encompasses 25 model cells. Model boundaries labeled "NW", "SW", "SE", "NE". (**D**) Bathymetric map of larger-scale model Regional Ocean Modeling System (ROMS)-NZ covering the eastern half of the North Island, New Zealand. Black box indicates location of Waipaoa shelf model.

#### **2. Model Development**

This section describes the equations and numerical schemes used to specify hydrodynamic and sediment transport processes within the model and at the boundaries of the grid. Table 1 lists symbols for all equations.

#### *2.1. Hydrodynamic Model and Numerical Schemes*

ROMS-CSTMS, a community-developed numerical circulation and sediment transport model, solves the equations for Reynolds-averaged Navier-Stokes, tracer advection-diffusion, and continuity using the hydrostatic and Boussinesq assumptions as described in [3]. Tracer concentrations could represent an array of different tracers, which included temperature, salinity, and seven sediment classes. River discharge was treated as a point source for momentum, temperature, salinity and suspended sediment. Sources and sinks within the governing equations included, but were not limited to, bottom friction, wind stress, and nudging to match the regional grid (see section 2.5). The density equation of state accounted for temperature, salinity, and sediment concentrations [1,40].

ROMS distinguishes itself from other community hydrodynamic models by its model grid, and time-stepping and advective schemes. It uses a curvilinear orthogonal grid in the horizontal and a stretched, terrain-following grid in the vertical which allows it to carry high resolution in both the surface and bottom boundary layers [2]. The numerical schemes in ROMS include split barotropic-baroclinic modes (Leap-Frog—Adams-Moulton predictor-corrector scheme; [2–5]) and reduce the pressure-gradient truncation error [41–44] by redefining the pressure-gradient term [3,45,46]. ROMS also provides high-order schemes for estimating both vertical and horizontal advection [3,47]. For advection of sediment and other tracers, ROMS provides the MP-DATA scheme (Multidimensional Positive Definite Advection Transport Algorithm; [48]) to avoid numerical oscillations and negative concentrations, and reduce numerical dispersion, e.g., [1,49]. For vertical advection of sediment, the ROMS framework implemented the PPM (Piecewise Parabolic Method) so that relatively large timesteps can be used for faster settling sands without the introduction of instabilities [1,50,51]. These numerical schemes and reasonably high spatial resolution are important for representing the high gradients typical of coastal and estuarine settings without sacrificing computational efficiency, e.g., [49]. For application of ROMS to the Waipaoa shelf, numerical schemes and the model grid (Figure 1) were chosen to reduce error in numerical computations without sacrificing model efficiency. A timestep of 15 s, and the numerical schemes listed in Table 2 were used.

#### *2.2. Surface Boundary Formulation*

The surface boundary formulation in ROMS was adopted from the physically-based COARE (Coupled Ocean Atmosphere Response Experiment) framework [52,53]. In this one layer boundary model, wind and rain transfer momentum from the atmosphere to the ocean.

**Table 1.** Model parameters as described in this paper.

**Table 2.** Numerical Schemes for Waipaoa Shelf model.

#### *2.3. Bottom Boundary Layer Formulation*

This implementation of ROMS used the Sherwood, Signell and Warner [1] bottom boundary layer parameterization, a physics-based approach that could account for form drag and ripples. This formulation, defined as "SSW" within ROMS, was based on [54] that divided the bottom boundary layer into two sections: a thin combined wave-current boundary layer, and a current boundary layer. For this study, the hydraulic roughness represented the grain size roughness, which was set equal to 0.30 mm based on estimates of bed shear stress from acoustic Doppler velocimeters (ADVs) provided by [30]. The model used the bottom roughness, the eddy viscosity profiles, wave orbital velocities provided by input files, and currents 20 cm above the bed, estimated by the hydrodynamic model, to calculate the total maximum current-wave-induced bed shear stresses, , following [54]. W *cw*,max r

#### *2.4. Seabed Model*

As summarized in section 2.1, the model accounted for suspended transport, erosion and deposition. Both fluvial discharge and seabed erosion provided sediment to the water column. To calculate erosion and deposition, Equations (1) and (2) were calculated for multiple sediment types, each having assigned values for settling velocity and diameter. Other values (e.g., erosion rate parameter, critical shear stress, and seabed porosity) were identical for all sediment classes. Processes not explicitly represented in the model include flocculation [55], seabed consolidation [56], and bioturbation [57].

Erosion was calculated following the Ariathurai and Arulanandan formulation [58] for each sediment class with index *ised*:

$$F\_{cs,load} = E\_{load} - \frac{\partial \mathcal{W}\_{s,load} C\_{s,1,load}}{\partial \mathbf{z}\_s} \tag{1}$$

$$E\_{load} = \begin{cases} M(\mathbf{l} - p) f\_{load} \left| \frac{\mathbf{r}\_{cve,\max}}{\mathbf{r}\_{cve}} \right| \cdot \tau\_{cvi} & \text{when } \left| \frac{\mathbf{r}}{\mathbf{r}\_{cve,\max}} \right| > \tau\_{cvi} \\ \mathbf{0} & \text{when } \left| \frac{\mathbf{r}}{\mathbf{r}\_{cve,\max}} \right| < \tau\_{cvi} \end{cases} \tag{2}$$

As indicated in Equation (1), the model assumed continuous deposition so that *Fcs,ised*, the net entrainment of suspended sediment for each class, was calculated as the difference between the erosion of each sediment class, *Eised*, and estimated settling to the bed based on settling velocity of each sediment class, *ws,ised*, and on *Cs,1,ised*, the mass of suspended sediment per unit area of the bed in the bottommost grid cell for each sediment class. In Equation (2), *p* was porosity, or void fraction of the seabed, *fised* was the fraction of the seabed composed of sediment class *ised*, was the magnitude of the total wave-current-bed shear stress, and *IJcrit* was a constant critical bed shear stress. Erosion during any time step was limited to the amount of each size class available within a thin active layer whose thickness, *za*, was specified as: *cw*,max W r

$$\tau\_a = \max\left[k\_1(\left|\tau\_{c\circ,\max}\right|-\tau\_{c\circ i}), 0\right] + k\_2 D\_{\circ 0} \tag{3}$$

where *k1* and *k2* were constants set equal to 0.007 m2 s2 kgí1 and 6.0, respectively, and *D50* was the median grain size on the seabed [59]. As implemented for the Waipaoa shelf, active layer thicknesses on the mid-shelf rarely exceeded ~5–10 mm and increased in shallow areas and near the anticlines due to the relatively high bed stress and the larger sediment grains found there (Figure 2).

Consistent with observations of erodibility on the Waipaoa shelf, the model formulation was modified to encourage erosion of sediment from shallow areas by varying the erosion rate parameter, *M*, with water depth, *h*. Choice of *M* used for a given water depth depended on parameters including the maximum and minimum erosion rate parameters (*Mmax* and *Mmin*), and a transitional water depth (*htransition*):

$$M = \left| \left[ \left( M\_{\text{min}} - M\_{\text{max}} \right) \Big| \left( 1 + \exp \left( 0.2 \bullet \left( h - h\_{\text{reaction}} \right) \right) \right) \right] + M\_{\text{min}} \right| \tag{4}$$

Based on observations, *Mmax*, *Mmin*, and *htransition*, were set to 4.5 × 10í4 kg mí2 sí1 , 0.1 × 10í4 kg mí2 s í1 , and 30 m, respectively (see section 3.6; [36]). Sections 4.1 and 4.3 evaluate the erodibility parameterization by comparing model results that used Equation (4) with two cases that used spatially-uniform *M* (see section 3.7). Both the active layer thickness and spatial variation in the erosion rate parameter influenced seabed erodibility. Other parameters such as critical shear stress likely also varied spatially and affected erodibility. However, estimations of parameters from erodibility experiments and tripod data can carry substantial uncertainty, and so this paper focused on a single variable, the erosion rate parameter, because it required no additional information about the seabed critical stress profiles and was computationally efficient. Use of a spatially-varying critical shear stress would likely also encourage erosion of sediment from shallow areas, similar to the parameterization used here. Alternate parameterizations from the literature are discussed in section 4.3.

Sediment bed properties such as grain size distribution were stored for eight seabed layers that each initially represented 20 cm of sediment. Erosion and deposition of multiple sediment classes modified the thickness of seabed layers and the grain size distributions stored for the sediment bed, as described in [1]. These changes impacted the upper few layers of the sediment bed, while the deeper layers served as a repository of sediment. The surficial seabed layer was ~1 cm across the shelf on average, but was thinner in areas of low deposition and where the active layer was thin (Figure 2).

**Figure 2.** Shows (**a**) time-averaged active layer thickness in the standard model, and (**b**) the spatially-varying erosion rate parameter, *M*, from Equation (4).

#### *2.5. Open Boundary Conditions*

The Waipaoa shelf model grid was bounded by land on the northwestern side (Figure 1), so a free-slip wall condition was used there which specified a zero gradient condition for tracers and sea surface elevation, set water velocities normal to land equal to zero, and used a free-slip condition for tangential velocities. Along the other three edges, open boundary conditions (OBCs) for sea surface height, barotropic and baroclinic velocities, and tracer concentrations accounted for tides, shelf waves, and the transient behavior of the river plume. Radiation conditions along the southwest, southeast, and northeast boundaries allowed waves to propagate through them without reflecting. Specifically, the Chapman [11] and Flather [60,61] conditions were applied there for the free surface and barotropic momentum, respectively, to account for tides [62–64]. Velocity and sea surface height at the boundary were required as input and were specified using data from ROMS-NZ, a larger-scale hydrodynamic model ([65]; model used a similar setup to [66]; see Figure 1; section 3.5). Sediment concentrations, which were not provided by ROMS-NZ, were nudged toward zero at the boundaries. This parameterization implied that external sediment sources were negligible, a common assumption in sediment budget calculations, e.g., [38,39], and in many models of riverine-dominated systems, e.g., [6,15]. Nudging sediment concentrations toward zero also assumed that material leaving the grid did not reenter the model domain, which was a reasonable expectation because the largest fluxes occurred during floods, when the river plume carried sediment off of the proximal

shelf [30,35]. Using the oblique radiation condition for baroclinic velocities and tracer concentrations reduced artificial reflections at the boundaries [11,13,67].

Similar to other studies [13,68], we nudged baroclinic current velocities, salinity, temperature, and suspended sediment concentrations within 30 grid cells of the open boundaries toward values specified from ROMS-NZ, or zero, as described above:

Nudging, evaluated within grid interior:

,

*R b*

*T*

*RO*

*RO OBC*

$$
\xi = \xi' + \frac{F\_{gvd}}{T\_{R,l}} \left(\xi\_{ohc} - \xi'\right) \Delta t \tag{5}
$$

Nudging-Radiation OBC, evaluated at model boundary:

$$\frac{\partial \mathcal{L}}{\partial t} + c\_x \frac{\partial \mathcal{L}}{\partial x} + c\_y \frac{\partial \mathcal{L}}{\partial y} = \left(\frac{1}{\mathcal{T}\_{\mathbf{x}, \phi}}\right) \tilde{\varphi}\_{\text{ac}}(t) \tag{6}$$

$$c\_x = -\frac{\partial \mathcal{L}}{\partial t} \frac{\partial \tilde{\varphi}\_{\text{ax}}}{\partial \tilde{\varphi}\_{\text{Ox}}^2 \dot{\lambda}^2 + (\tilde{\mathcal{C}} \frac{\partial \mathcal{L}}{\partial \mathbf{y}})^2}$$

$$c\_y = -\frac{\partial \tilde{\varphi}}{\partial t} \frac{\partial \tilde{\varphi}\_{\text{Ox}}}{\partial \tilde{\varphi}\_{\text{Ox}}^2 \dot{\lambda}^2 + (\tilde{\mathcal{C}} \frac{\partial \mathcal{L}}{\partial \mathbf{y}})^2}$$

$$= \begin{cases} T\_{RO} & \text{current velocities directed} & \text{out of} & \text{grid} \\ & T\_{RO} \text{ current velocities dominated} & \text{current width} \end{cases}$$

Here, *ȗƍ* was the variable of interest (e.g., velocity or tracer concentrations) before nudging, *TR,i* and *TR,b* were the relaxation timescales in the interior of the grid and at the open boundaries, and *ǻt* was the timestep. The larger-scale model, ROMS-NZ provided *ȗobc(t)*. The open boundary relaxation timescale, *TR,b*, decreased with decreasing *TRO* and increasing *FOBC*, enhancing nudging when currents flowed into the model grid. Based on sensitivity tests, *TR,i*, *TRO* and *FOBC* were set to 2.5 days, 2.5 days and 2.5. Section 4.2 presents results for "moderately-nudged" and "weakly-nudged" sensitivity tests where *TR,i* and *TR0* were doubled and multiplied by a factor of 10, respectively, increasing the influence of larger-scale currents. The parameter *Fgrid* specified spatial variability for nudging so that it was non-zero only for locations within 30 grid cells of open boundaries, and weakened sinusoidally with distance from the open boundary:

*T F current velocities directed in to grid*

° ° ¯ ¿

$$F\_{grid} = \begin{cases} 1 - \sin\left(\frac{\pi}{2}\frac{j}{30}\right) & for \quad j \in [1, 30] \\ 1 - \sin\left(\frac{\pi}{2}\frac{J - j + 1}{30}\right) & for \quad j \in [J - 29, J] \\ 1 - \sin\left(\frac{\pi}{2}\frac{I - i + 1}{30}\right) & for \quad i \in [I - 29, I] \\ 0 & otherwise \end{cases} \tag{7}$$

where *I* and *J* were the total number of grid cells in the NW-SE and SW-NE directions and coordinates *(i, j)* indicate location within the grid. Note that since ROMS-NZ did not include sediment, this meant that suspended sediment concentrations within 30 grid cells of the boundaries were nudged toward zero. Nesting not only enabled the model to account for larger-scale currents, but also reduced reflections of river plume salinity and suspended sediment concentrations at the boundary, which increased model stability.


*Edited by Robert J. Reynolds and Steven M. Day*

This edited book, *"Aging - Life Span and Life Expectancy"*, is a collection of reviewed and relevant research chapters offering a comprehensive overview of recent developments in this field. The book comprises chapters authored by various researchers and edited by an expert active in the research area. All chapters are individually complete but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors and opening new possible research paths for further novel developments.

ISBN 978-1-78984-081-0

Aging - Life Span and Life Expectancy

Published in London, UK © 2020 IntechOpen © Yiran Ding / iStock


**1. Introduction**

The structure of the article is as follows: we first describe the framework of spatial disparities, the role of work, and the development of remote work in Section 1. In Section 2 we formulate and focus the research question. In Section 3 we describe the methodology through which we address the research question. In Section 4 we display the results of the survey, in Section 5 we discuss the findings from the survey with the literature and finally in Section 6 we formulate conclusions on the findings, limitations and an outlook.

The research on why, where and how urban and rural disparities exist addresses different causes and impacts, such as the differences in health [1,2], education [3] and social services. Most prominently in the urban-rural divide discourse is however the demographic disparity [4,5]. Processes of urbanization in developed and developing countries are showing more or less the same trend, characterized by massive urban growth, rural exodus, urban sprawling—the consumption of land or change of land use [6–9], traffic jams and many other problems, in different stages [10–12]. Such demographic changes result in significant problems in allocations and conversions of land use and land cover, and ultimately land rights and restrictions. Cities and urban agglomerations are growing and the number of their inhabitants are increasing since the first wave of industrialization, faster and faster in the last decades in nearly every part of the world—the so called urbanization process [13]. This leads to a higher demand of land for housing [14] on one hand and a shrinking proportion of living space per person [15,16], due to the rising rental and purchase prices, on the other hand. Meanwhile the living space per person in rural region is higher [17] and growing.

Stimulated by advancements in agricultural science, mechanization and industrialization, the workforce which is needed to feed the population, has been shrinking [18] since decades, especially in developed countries. This reflects the "push" for people to leave

**Citation:** Hölzel, M.; de Vries, W.T. Digitization as a Driver fur Rural Development—An Indicative Description of German Coworking Space Users. *Land* **2021**, *10*, 326. https://doi.org/10.3390/ land10030326

Academic Editor: Ruishan Chen

Received: 19 February 2021 Accepted: 16 March 2021 Published: 21 March 2021

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

**Copyright:** © 2021 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/).

rural regions alongside beside the "pull" of urban regions and major cities [19]. Small rural towns and villages are also suffering from automobile traffic, the growth of commercial and residential areas on the outskirts of towns [20], and the simultaneous vacancy and closure of stores and pubs in inner-city areas—the so called Donut-Effect [21–24].

Germany is a country in an advanced stage of development [25,26]. Yet, in several fields of society (economy, democracy, science etc.) it exhibits an advanced stage in rural-urban disparity as well [27]. From the perspective of spatial planning, the problem of demographic disparities between rural and urban region has led to heated debates and contested assessment frameworks. The indicators which are used and the results related to spatial development vary, but the general insight is that spatial inequality is the manifestation of the disparity [28,29]. There is both spatial inequality between German regions [30] and there is a lack of spatial justice [31]. Other countries, such as France or the U.S.A. exhibit similar spatial disparity challenges [32,33], which leads internationally to polarization among EU-member states for example [34]. In Germany, political actors have gradually become aware of these trends and have therefore formulated a policy of "equal living conditions in urban and rural areas", which needs to be implemented at different administrative and institutional levels of the federal states. At a highest administrative level, the target "to preserve coherent living conditions" is formulated in the constitution of the German Federal Republic (GG Art. 72 Abs. 2 Nr. 3) from 1949 [35]. In 1994 the formulation was modified to "production of equivalent living conditions" [36]. In the coalition agreement of the present German administration the target of equivalent living conditions is articulated as well [37]. It led to a report of a government commission with programmatic recommendations for action [38] by the Federal Ministry of the Interior, Building and Community; the Federal Ministry of Food and Agriculture and Family Affairs, Senior Citizens, Women and Youth. At the level of the European Union, support programs for rural development such as LEADER were introduced in the 1990s [39].

Taking into account that local economic strength, the labor market and incomes contribute to the local economy by the generation tax yields, the spending capacity for the people who live there, is the main driver for disparities and divide, the spatial distribution of local economic strength and work is essential for development and equivalent living conditions.

A key to bridge the urban rural divide could be the allocation of work from urban regions to rural regions. The possibility of remote work means that knowledge-based work is no longer necessarily tied to a specific location. A relatively new phenomenon is coworking spaces in which teleworking is performed.

This research aims to detect the habits and demands of users of rural coworking spaces and to estimate and assess the influence on local offers and services for mobility, consumption, shopping, catering, social and cultural. Based on these aims we formulate the following research questions:

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

Drivers of spatial disparities in Germany constitute the variations in regional economic strength, availability and spatial distribution of jobs [40], wages and corporate earnings which contribute to the local economy [41]. They entail prosperity, tax yields for the local and regional authorities and spending capacity for the people who live there [42]. Depending on the tax system regional authorities and municipalities receive financial inflows [43,44]. It seems that there is a general link between regional disparities caused by economic strength or weakness, jobs, income and tax yields [45].

Reasons for migration and the decision where to life rely on a complex decisionmaking process. From a conceptual standpoint, Ravenstein developed the concept of "push and pull" [46] as a trigger for migration. Widely referred are the 4 classes or types structured by Petersen [47], i.e., Primitive, Forced/Impelled, Mass, Free. In this case it can be focused on "Free" migration forced by higher aspirations or a rational choice, because migration due to natural disaster, war or collective behavior could be unconsidered do not take place at the moment in Germany [48].

These base factors play an important role in peoples' decisions where to live. Especially in Germany, rational choices in life and career planning are often made [49,50]. At crucial turning points in life, such as choices for education, in the professional domains and careers, types of jobs or founding a family [51] they make a decision with spatial impact [52]. The conditions in such rational choices are that before a family is founded, an income has to be ensured [53] and a professional career needs to be established [50]. Due to the availability of birth control pills and better education of women, women have become more independent and the decision to found a family has become a more 'rational choice' as opposed to discretionary or random choice or fate (going with the flow) [49]. Many of the complex decisions about the place where to live are directly or indirectly related or influenced by job or educational opportunities [54]. "Work as the basis of human existence" is examined and considered by sociologists and philosopher like Habermas and Marcuse [55]. From an economical perspective, especially classic economists such as Adam Smith and Karl Marx have considered such work decision rationalities [56,57].

In case of better education and job opportunities in cities und agglomerations— Ravenstein would call it a "pull" [46]—people moving there from rural, less dense and less promising—referring to Ravenstein a "push"—regions [51]. This leads to a kind of vicious circle of depopulation, mainly by people who aspire better job opportunities to combat their declining spending capacity, decreasing entrepreneurship opportunities and growing lack of public services in regions and municipalities outside of major and/or university cities [58]. Declining number of inhabitants in this regions could fall below the technical function threshold of facilities of general interest [59] and jeopardize their economic viability [48,60].

The growing rural urban disparities are described is various publications [27,61]. At the same time a majority of people answer the question where they want to live with "villages, small towns" and the "country side" as a survey on behalf of the BHW Bausparkasse (Building and loan association) [62,63] and the German public television and radio broadcaster ARD [64]. Some studies predict a trend that more people want to and will live in small towns and rural regions. This trends are titled e.g., "Progressive Provinzen" [65,66], "KoDorf" [67] or "Urbane Dörfer" [68].

With the contact limitation due to the Covid-19 pandemic the amount of work which is done remotely was increasing rapidly [69–71], in relation to moderate growth in the past years [72,73]. People indicate that they want to continue working remote [74]. That could amplify the trend people moving from major cities to more rural towns as it is indicated in reporting for Germany [75,76] and for North-America too [77,78]. However, some authors are still skeptical if that will baffle the main trend of urbanization [79].

Wouldn't that be an opportunity to combine the wish of more country life and rurality on the one hand and the need of educated, well paid people in rural regions on the other hand to reduce or turn around the trend of rural exodus? So-called "knowledge workers" [80,81], usually an academically educated professional with specific knowledge and skills, combined with a professional social network are currently demanded at the labor market [82]. Richard Florida describes this group of human resources as the 'creative class' [83]. Knowledge, thought or brainpower is mostly non-physical. Without physical demand this kind of work is easy to transfer to another place and transmit the result to another. Thought work or knowledge work often requires access to knowledge repositories, such as libraries, archives, etc. Knowledge management deals with the nature, management, distribution and generation of knowledge, among other things from an entrepreneurial perspective [84,85].

Before the internet was established and before an increasing amount of information was provided by websites, database, virtual libraries etc. accessing information and knowledge required a physical presence or process by entering a library or an archive, finding and reading the desired Information in a book, report or document—or obtain the requested information in some lists, schedules, directories or the like. Today, a huge amount of knowledge and information is accessible via the internet on server, databases, virtual and digital libraries [86] and through social knowledge networks [87].

The idea of remote work was already practiced in the early 1960s, be it however in an analog manner using paper and pencil from home [72]. In the late 1960s the concept of communication substitute transportation was developed [88] and expanded in the 1970s as a substitute for the increasing business travel [89] and eminently by the oil crises [90]. Remote work or Telecommuting [91], which in German is often called "Home Office", could be operated from home. Workers save time by avoiding the daily commute or also just for a single day per week or month by working from home. However, that kind of remote work entail some negative effects or problems. Homes often are missing a separate room equipped with ergonomic office furniture for remote work from home [92]. Beside this, working from home could stress people, due to the double task of work and family, if they live in a family home. If they live alone the can feel lonely and disregarded [93–95]. Coworking spaces as a place between home and work could be a "third place" as described by Oldenburg [96] enlarging social contacts.

An alternative to operate the remote work from home is to perform in a coworking space. The term "coworking" was invented in 1999 by Bernard DeKovan and the first "coworking space" was opened 2005 in San Francisco by Brat Neuberg [97,98]. A coworking space is a kind of office space where people work at the same location on their own project or tasks and have the opportunity to network, socialize or cooperate with their "space mates". The concept of coworking was well described by Clay Spinuzzi in 2012 [99]. Coworking spaces provides options of collaboration and knowledge sharing, brainstorming and creativity [100]. Janet Merkel described coworking as "Coworking is hence not just about working 'alone together' or 'alongside each other' in a flexible and mostly affordable office space. It is also underpinned by a normative cultural model that promotes five values: community, collaboration, openness, diversity, and sustainability." [101]. Coworking spaces were mostly founded by free lancers, but nowadays this form of work is increasingly used by employees as well [102]. While founder of early coworking spaces were often locally engaged people, in recent years large, professional operators such as WeWork and Mindspace have discovered the market for themselves and offer desks in coworking spaces [103].

The social isolation, which comes along with working remote from home, also underlined by research regarding the impact of Covid-19 pandemic [104], could be tackled by working together in a common space, such as a coworking spaces [105]. At this point we should clarify that in Germany working remote from home is called "homeoffice", "home office" or "Tele-Heimarbeit" [90]. Just working not at the employers office is called "Telearbeit" or in recent years "Mobiles Arbeiten" [106]. In recent years the size and number of coworking spaces is increasing [107,108], not only in cities in rural regions in Germany as well [109,110]. The trend of rural coworking spaces is taken up by policies and some promotion initiatives are introduced [111,112].

Distance and occupied time for the daily commute is increasing since years [113,114]. However, generally remote work save time by avoiding the commute which can be used for other purpose, as spending time with family, friends or for sports, being active in a local club. These benefits lead to different preferable results from the perspective of spatial planning. The main benefit could be a declining mobility demand and a reduction of traffic. The commute is increasing since years [115] an reached an average of 10.5 km [116] one way each day, notable the amount of a commute over 50 km increase more strongly [116]. Reducing the demand of mobility for work, the traffic systems (roads, public transport etc.) could be relieves by avoiding the daily commute, reduce it to occasional or periodical commute. If a presence for a meeting is need the employee can travel for this specific to be there in time e.g., 13:00 o'clock and avoid the rush hour in the morning. This could spread the traffic volume over time and diminish the pressure on the traffic systems [117,118].

Further the saved time could be spent with the family or with friends and improve the work-life-balance (WLB), but could also generate stress [106]. "Entgrenzung der Arbeit" delimitation of work is a broader topic of changes in the field of work to which remote work contributes substantial [94,119].

Not only because of online shopping, but also because of the high commuting share to distant office districts and the paths traveled, most of which do not lead past traditional village or small-town retail locations, town and city centers suffer from a lack of liveliness, a shrinking range of products and services, and consequently from vacancies [120–123]. Due to increasing distances between home and work, mobility in rural areas is dominated by the use of motor vehicles [124–126]. Another effect of the increasing distance between the location of residence and the location of work leads to multi-local lifestyle [127,128].

Recent publications see an opportunity for the revitalization of rural areas through coworking spaces [112], what probably support an allocation of knowledge work to small towns.

Coworking spaces, whether in inner cities or rural areas, usually offer different types of workplaces. The majority, however, are mostly open office structures, with different desks in an open space setting. Just a small amount of desks are located in smaller units as private, single or double offices [129]. The open structure encourage contact, exchange between the users, what is a major opportunity of coworking spaces related to working from home. However, this open structure have difficulties regarding the acoustic [130–132], if people want to work concentrated and undisturbed, make a phone call or participate in a videoconference [132,133].


**2. Main electrochemical phenomenon**

subjectivity appears in any modeling work, the approach of a mechanic/energy specialist, an electrochemist, or a physicochemist will differ mainly in the basic assumptions of modeling (model simplifications). However, whatever the cultural origin of the modeler, the numerical resolution of a multiphysical problem makes it

The modeling of an electrochemical system involves the mathematical expressions of the physical phenomena that take place there (a priori). Obviously, all model representations only offer a fragmentary assessment of the real systems. These various representations are distinguishable by their scales of time and space. However, the notion of adapted or appropriate modeling remains subjective. Indeed, as described in the literature about fuel cell models [6, 7], each of the approaches has limitations of description or prediction, and their main interest is to highlight one specific process. Despite this subjectivity, the model must prove

The validity of the model could be named external, i.e., related to theories, concepts, assumptions, and experimental data. Thus, the model is theoretically valid if it accepts theories or models already validated. In addition, if the model well matches to its potential of scientific explanation (the state of the art), one will qualify its heuristic validity. However, building a model cannot be done without solving it in all its intended range. Consequently, it is also necessary to define criteria of internal validity, which are criteria of evaluation of the model independent of the theories, results, and hypotheses. The algorithm (solver) must be appropriate, and the evaluation errors must remain within "valid" limits.

External validations that may be acceptable include empirical validity (the model corresponds to the available data) or pragmatic validity (the model satisfies

A fuel cell is a nonlinear and strongly coupled dynamic system. It is a multiinput multi-output system based on multiphase flow, electrochemical reactions, and heat transfer. For example, the control strategies of PEMFC can be built on a prediction of the future output of the system to compute the current control action [8]. In practice, the current control action is obtained by solving online an optimization problem. The aim of the optimization problem is to find the optimum of a cost function that minimizes the mean squared difference between predicted

To spare computation time due to computing of multiphysic fuel cell models, artificial intelligence (AI) techniques are useful as alternate approaches to conventional multiphysic modeling: e.g., artificial neural network (ANN) simulator could be employed to predict the fuel cell behavior [9–12]. The ANN could be trained with a reduced amount of data generated by a validated cell model [13]. Once this network is trained, it can predict different operational parameters of the fuel cell reducing the computation time [14]. This strategy has many possibilities [15]: spectroscopic analysis, prediction of reactions, chemical process control, and the analysis of electrostatic potentials. The ANN is trained to learn the internal relationships from data. These data may be taken from the real process even if there are noisy. The database should have a significant size and contain the maximum

possible to assure three major functions in the phase of development [5]:

• Assistance with the understanding of experimental results

• Study and optimization of design

*Thermodynamics and Energy Engineering*

• The prediction of performances

its validity.

the intended use).

outputs and target values.

**80**

An electrochemical cell is characterized by the I-V (current–voltage) behavior: the current that passes across the cell to the applied cell voltage. The I-V relation depends on various physical phenomena and is fundamental to achieve efficiently electrochemical conversion. When current is drawn using computational tools, the current density may not be uniformly distributed on the electrode surfaces. The performance and lifetime of electrochemical cells, such, is often improved by a uniform current density distribution. Therefore, it is necessary to optimize the current distribution. The electric current is a flow of electric charges: through the electrolyte between the anode and the cathode in the ions form and within the wires and current collectors/electrode materials in the electrons form. When the overall current of the cell is equal to zero, for example, on the disconnection of an electrode from the power supply or the load, the cell voltage is equal to UOCV the open circuit voltage:

$$I = 0; U\_{cell} = U\_{OCV} = E\_{a,0} - E\_{c,0} \tag{1}$$

where *Ea*,0 *and Ea*,0 are the potential of each electrodes at OCV. It is shown that the *UOCV* is related to the difference of free enthalpy differences of each reaction, involving the number n of electrons exchanged and the Faraday constant:

$$U\_{OCV} = \frac{\Delta G\_i}{nF} \tag{2}$$

where *ΔGi* is the Gibbs energy of the species i with specified cell temperature (T) and cell pressure (P). At a non-zero current, the cell voltage of an electrochemical reactor (electrolyzer) is greater than *UOCV*, and electrochemical generator (fuel cell or battery) is smaller than *UOCV* due to the various irreversibilities standing in the electrochemical conversion. The electrode potentials differ from the equilibrium values *Ea*,0 and *Ea*,0, and this difference is called overvoltage:

$$
\eta\_a = E\_a - E\_{a,0} \tag{3}
$$

$$
\eta\_c = E\_c - E\_{c,0} \tag{4}
$$

According to this description, an anode overvoltage is positive, while the cathode overvoltage is negative in all cases. The overvoltages depend on the current density at the electrode, depending on the involved electrochemical reactions, the electrode materials, and several operating conditions: concentration species, flow rate, etc. The current density is an extensive quantity that can be defined in any point of the electrochemical device, i.e., at the surface of the electrodes and through the electrolyte. In addition Ohm's law expresses the current density according to the local potential gradient, using the conductivity as follows:

$$
\overrightarrow{I} = -\sigma \overrightarrow{\nabla} \overrightarrow{E} \tag{5}
$$

(MEA), placed between two graphite bipolar plates, which feed the device with gases and cool it down. At the anode, fuel H2 is oxidized, liberating electrons and producing protons. The electrons flow to the cathode via an external circuit, where they combine with the proton and the dissolved oxidant O2 to produce water

> ψ UOCV Ucell

Proton transfer from the anode to the cathode via the membrane closes the electrical circuit. A careful water management is required to ensure that the membrane remains fully hydrated in order to improve the ionic conductivity and to avoid the electrode flooding (which occurs when an excess of liquid water restrains the active species access to the active layer). The gas diffusion electrodes (GDEs) are made up of two distinct areas: an inactive area (backing layer) and an active area (active layer) which is a place of the electrochemical and chemical reactions (**Figure 1**). Therefore, in the first approach it is possible to observe separately secondary current distribution (only in the active layer) and tertiary current

PEMFC and all solid electrolyte cells are the model devices in order to sketch the primary (in the polymeric membrane), the secondary (in active layer), and tertiary (in backing layer) current distributions. In addition, operating cell potential and each overvoltage could be compared to thermodynamic potential (Gibbs energy) to

**3. One-dimensional modeling of electrochemical membrane cell**

A single cell can be described schematically as an assembly of several layers constituting four distinct areas. A fuel cell is a composite structure of anode, cathode, and electrolyte. Good electrochemical performance of the cell requires effective electrocatalysts. On both sides of the cell, the interconnect plates cumulate three functions: current collector, gas feeding via the gas channels, and thermal control thanks to cooling water channels. Flow field is used to supply and distribute the fuel and the oxidant to the anode and cathode electrocatalysts, respectively. The distribution of flow over the electrodes should ideally be uniform to try to ensure a uniform performance of each electrode across its surface. Thus, it is possible to develop a single-cell 1D model. Although most of the models used are one-dimensional, they correctly predict the electrochemical behavior of membrane

In order to build a 1D model, a particular attention is required on water flux. The water managements is the key issue in single-cell modeling. In order to predict cell performance, the single-cell model must take into account gas diffusion in the porous electrodes, water diffusion, and electroosmotic transport through the polymeric membrane. Ramousse et al. [16] have developed one-dimensional coupled charge and mass transfer model in the electrodes. The three main types of model

can be employed to evaluate the overvoltages at both electrodes:

The efficiency of fuel cell is easily computed according to:

*How to Build Simple Models of PEM Fuel Cells for Fast Computation*

*DOI: http://dx.doi.org/10.5772/intechopen.89958*

distribution (only in the backing layer).

access enthalpic balance.

electrode assembly.

**83**

H2 ⇆ 2Hþ þ 2e� (8)

(10)

2Hþ þ 2e� þ ½O2 ⇆ H2O (9)

and heat:

where *σ* is the ionic conductivity of an electrolyte. In the case of a onedimensional system where the electrolyte is confined in a finite space between two surface electrodes S and separated from each other by the distance e, this potential difference (called ohmic drop *ηohm*) can be expressed as follows:

$$
\eta\_{ohm} = -\frac{e}{\sigma \mathcal{S}} I \tag{6}
$$

where J is the total current through electrochemical device: J is the average current density I multiplied by the electrode surface S. For other geometries, it will be necessary to calculate *ηohm* from the relation (Eq. 5) by integration. The ohmic drop in the electrolytic solution of an electrochemical cell is one of the parameters to be evaluated to optimize the cell efficiency. This limitation is also called primary current distribution. Thus, the cell potential is calculated from following expression:

$$\mathbf{U\_{cell}} = \mathbf{U\_{OCV}} - \eta\_{\mathbf{a}} + \eta\_{\mathbf{c}} - \eta\_{\text{ohm}} \tag{7}$$

Inside the nonaqueous electrochemical device (**Figure 1**), the primary current distribution is well controlled, because a solid electrolyte is confined in a finite space between two surface electrodes and only the misalignment of both electrodes could affect the current distribution through the electrolyte. In nonaqueous case, the optimization endeavor is devoted to secondary (electrochemical activation) and tertiary (mass transport limitation) current distributions. In this context, fuel cells are the best example to scrutinize energetic balance of electrochemical devices.

The main advantage associated with fuel cells is that they are not limited by Carnot efficiency. Besides, no moving parts are required to convert thermal energy into mechanical energy. The energy release from interatomic bonds of the reactants is converted efficiently into electrical energy. In this document, we consider protonexchange membrane fuel cells (PEMFCs): a PEMFC consists of a polymer electrolyte sandwiched between two electrodes to form a membrane electrode assembly

**Figure 1.** *Schematic description of a MEA.*

*How to Build Simple Models of PEM Fuel Cells for Fast Computation DOI: http://dx.doi.org/10.5772/intechopen.89958*

(MEA), placed between two graphite bipolar plates, which feed the device with gases and cool it down. At the anode, fuel H2 is oxidized, liberating electrons and producing protons. The electrons flow to the cathode via an external circuit, where they combine with the proton and the dissolved oxidant O2 to produce water and heat:

$$\text{H}\_2 \oplus 2\text{H}^+ + 2\text{e}^- \tag{8}$$

$$\text{\textbullet2H}^+ + 2\text{e}^- + \text{\textbulletO}\_2 \oplus \text{H}\_2\text{O} \tag{9}$$

The efficiency of fuel cell is easily computed according to:

$$
\Psi \frac{\mathbf{U}\_{\text{OCV}}}{\mathbf{U}\_{\text{cell}}} \tag{10}
$$

Proton transfer from the anode to the cathode via the membrane closes the electrical circuit. A careful water management is required to ensure that the membrane remains fully hydrated in order to improve the ionic conductivity and to avoid the electrode flooding (which occurs when an excess of liquid water restrains the active species access to the active layer). The gas diffusion electrodes (GDEs) are made up of two distinct areas: an inactive area (backing layer) and an active area (active layer) which is a place of the electrochemical and chemical reactions (**Figure 1**). Therefore, in the first approach it is possible to observe separately secondary current distribution (only in the active layer) and tertiary current distribution (only in the backing layer).

PEMFC and all solid electrolyte cells are the model devices in order to sketch the primary (in the polymeric membrane), the secondary (in active layer), and tertiary (in backing layer) current distributions. In addition, operating cell potential and each overvoltage could be compared to thermodynamic potential (Gibbs energy) to access enthalpic balance.


**2. Mathematical Model**

The heat transfer process for a granular bed with buried tubes is complex [14]. This process includes the convection heat transfer between high-temperature flue gas and filled particles, heat transfer within filled particles, heat transfer between gas films on a particle surface, heat transfer between contact particles and air film, heat transfer between particles and tube walls, heat transfer within the tube wall, and the convection heat transfer of the cooling water inside a tube wall. To simplify the calculation process, an equivalent heat transfer coe fficient method of a particle bed is used on the basis of logarithmic mean temperature di fference formula. Macroscopically, the heat in high-temperature gas is exchanged with cooling water through the filling particles and heat exchange tubes. This formula is expressed as follows [15]:

$$Q = h\_{ht} A\_{ht} \Delta T = \text{cinc}(t\_{w,out} - t\_{w,in}).\tag{1}$$

The total heat transfer coe fficient of the granular bed refers to the comprehensive heat transfer coe fficient between high-temperature flue gas and cooling water. The influencing factors include the convective heat transfer coe fficient of the inner and outer surfaces of the buried tube, the thermal conductivity of the buried tube, and the heat transfer characteristics.

The logarithmic mean temperature di fference [16] is defined as follows:

$$t\_{\text{max}} = t\_{\text{g,in}} - t\_{w,in} \tag{2}$$

$$t\_{\rm min} = t\_{\rm \%out} - t\_{\rm uv\beta wt} \tag{3}$$

$$
\Delta T = \frac{(t\_{\text{max}} - t\_{\text{min}})}{\ln \left(\frac{t\_{\text{max}}}{t\_{\text{min}}}\right)} \tag{4}
$$

The total heat exchange area is the sum of the total surface area of the heat exchange tubes and the surface area of the filled particles; this area can be expressed as follows:

$$A\_{ht} = n\pi dl + \frac{m}{m\_1} \times 4\pi r^2. \tag{5}$$

The formula for the total heat transfer coe fficient of the granular bed is presented as follows:

$$d\eta\_{lt} = \frac{\dot{\text{cm}} (t\_{w,out} - t\_{w,in})}{\left[\pi n dl + \frac{m}{m\_1} \times 4\pi r^2\right] \Delta T} \tag{6}$$


*Article* **Pollution and Health Risk Assessments of Potentially Toxic Elements in the Fine-Grained Particles (10–63** *µ***m and <10** *µ***m) in Road Dust from Apia City, Samoa**

**Hyeryeong Jeong 1,2 and Kongtae Ra 2,3,\***

**Abstract:** Fine road dust is a major source of potentially toxic elements (PTEs) pollution in urban environments, which adversely affects the atmospheric environment and public health. Two different sizes (10–63 and <10 µm) were separated from road dust collected from Apia City, Samoa, and 10 PTEs were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Fine road dust (<10 µm) had 1.2–2.3 times higher levels of copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), antimony (Sb), lead (Pb), and mercury (Hg) than 10–63 µm particles. The enrichment factor (EF) value of Sb was the highest among PTEs, and reflected significant contamination. Cu, Zn, and Pb in road dust were also present at moderate to significant levels. Chromium (Cr), cobalt (Co), and nickel (Ni) in road dust were mainly of natural origins, while Cu, Zn, Sb, and Pb were due to traffic activity. The levels of PTEs in road dust in Samoa are lower than in highly urbanized cities, and the exposure of residents in Samoa to PTEs in road dust does not pose a noncarcinogenic health risk. Further studies of the effects of PTEs contamination in road dust on the atmosphere and living organisms are needed.

**Keywords:** potentially toxic elements; source identification; risk assessment; Samoa

#### **1. Introduction**

There have been many studies on metal contamination in road dust in association with increasing transportation activities worldwide, due to urbanization and industrialization [1–5]. In urban environments, road dust is highly contaminated with various potentially toxic elements (PTEs) of natural origin, as well as PTEs derived from human activities, including industrial and traffic emissions [6,7]. These PTEs do not decompose in the environment and accumulate in living organisms. Some metals in road dust, including chromium (Cr), copper (Cu), zinc (Zn), antimony (Sb), cadmium (Cd), and lead (Pb), are derived from the corrosion of plated parts of vehicles and wear of brake pads, discs, tires, and asphalt [8–11]. Although some metals, such as Cu and Zn, are classified as essential, high concentrations in the environment have toxic effects on living organisms [12,13]. Some metals (Cr, Cd, and Pb) are classified as toxic elements and can present a carcinogenic risk to humans [14–16].

Fine road dust particles generally have relatively high PTEs concentrations compared to coarse particles due to their large surface area, and PTEs concentrations tend to increase as the particle size becomes smaller [17–19]. Fine particles of road dust are resuspended by wind and rainfall runoff, transported into the atmosphere and surrounding aquatic environments, and eventually deposited in coastal areas [20–24]. Therefore, road dust is a

**Citation:** Jeong, H.; Ra, K. Pollution and Health Risk Assessments of Potentially Toxic Elements in the Fine-Grained Particles (10–63 µm and <10 µm) in Road Dust from Apia City, Samoa. *Toxics* **2022**, *10*, 683. https://doi.org/10.3390/ toxics10110683

Academic Editor: Ilaria Guagliardi

Received: 5 October 2022 Accepted: 8 November 2022 Published: 11 November 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/).

major source of PTEs contamination in urban environments, as well as the atmosphere and coastal environments. PTEs are of interest due to concerns about the detrimental effects of contaminated road dust on the atmosphere, living organisms, environment, and human health. Many countries are working to reduce the harmful effects of road and street dust on the environment and public health by regularly cleaning road surfaces using sweeping machines and vehicles to manage atmospheric pollution and stormwater quality [25–29]. These efforts have reduced the amount of road dust and metal contamination, but are limited to urban areas with large traffic volumes and high population density.

The fine particles in road dust are not removed efficiently, so they accumulate on the road surface over long periods. PTEs concentrations in road dust have been reported to be relatively high in areas of heavy traffic, including industrial, residential, and commercial areas [30–32]. Relatively high concentrations of PTEs were found in river sediments near urban and commercial regions, and PTEs-contaminated road dust is considered as a cause of contamination to river and coastal environments [24,33]. As more than half of the world's population lives in urban cities, residents living in areas with heavy traffic and high population densities are at higher risk of exposure to PTEs present in road dust [34,35]. Especially, road dust with a diameter <10 µm can enter the human body through inhalation, ingestion, and dermal contact [36–38]. Long-term exposure adversely affects human health, and may even have carcinogenic effects.

Studies on PTEs in road dust are concentrated in large cities and industrial regions, and the human health risk of residents and employees are evaluated. In Samoa, the tourism industry is making an important economic contribution. Traffic activities related to tourism is one major cause of PTE contamination in road dust and can have a detrimental effect on residents as well as tourists. However, research on PTE contamination and risk assessments in road dust from Pacific Island countries, including Samoa, is very limited. Therefore, this study was performed to evaluate PTE contamination and health risks posed by fine road dust (10–63 µm, <10 µm) collected in Apia City, Samoa.

#### **2. Materials and Methods**

#### *2.1. Study Area*

Samoa is located in the Polynesian region of south-central Pacific Ocean and consists of the two large island of Upolo and Savai'i and eight small islands. Our study area, Upolo island, is a volcanic island formed by a massive basaltic shield volcano rising from the seafloor of the Pacific Ocean. The volcanic rocks of Upolo Island include basaltic lavas, scoria cones, tuffs, and pyroclastic [39,40]. There is mountainous interior covered with dense rain forests with a height about 1100 m of volcanic origin. The length and area of Upolo Island are 75 km and 1125 km2 , respectively, and Apia, the capital of Samoa, is located in the northern part of the Upolu Island. The climate of study area is oceanic tropical characterized by high humidity and temperature.

#### *2.2. Sampling and PTE Analysis*

Eighteen road dust samples were collected using a vacuum cleaner (DC35; Dyson, Wiltshire, UK) from Apia City, Samoa, in July 2014 (Figure 1). To prevent cross-contamination during road dust sampling, the vacuum cleaner was disassembled and cleaned after sampling.

Four surface soil samples were also collected near the R2, R11, R12, and R16 sites to provide background values for pollution assessment. In the laboratory, particles of two size classes (10–63 and <10 µm) were separated using a vibratory sieve shaker (Analysette 3 Pro; Fritsch Co., Idar-Oberstein, Germany) with a nylon sieve. For PTE analysis, all samples were ground and homogenized with a planetary mono mill (Pulverisette 6; Fritsch Co.).

**Figure 1.** Map of sampling sites for road dust from Apia city of Samoa (base map from Google Earth). **Figure 1.** Map of sampling sites for road dust from Apia city of Samoa (base map from Google Earth).

After weighing about 100 mg of sample in a Teflon vessel, high-purity acid mixture (HF, HNO3, HClO4 = 4:3:1 *v*/*v*) was added and heated at 180 °C for 24 h on a hot plate for complete digestion. The decomposed samples were evaporated to dryness and dissolved in 2% HNO3. PTEs other than Hg were analyzed by inductively coupled plasma mass spectrometry (ICP-MS; i-CAPQ; Thermo Fisher Scientific, Dreieich, Germany) at the Korea Institute of Ocean Science and Technology (KIOST). Hg concentrations were deter-After weighing about 100 mg of sample in a Teflon vessel, high-purity acid mixture (HF, HNO3, HClO4 = 4:3:1 *v*/*v*) was added and heated at 180 ◦C for 24 h on a hot plate for complete digestion. The decomposed samples were evaporated to dryness and dissolved in 2% HNO3. PTEs other than Hg were analyzed by inductively coupled plasma mass spectrometry (ICP-MS; i-CAPQ; Thermo Fisher Scientific, Dreieich, Germany) at the Korea Institute of Ocean Science and Technology (KIOST). Hg concentrations were determined using a direct mercury analyzer (DMA-80; Milestone Inc., Sorisole, Italy).

mined using a direct mercury analyzer (DMA-80; Milestone Inc., Sorisole, Italy). Two certified reference materials (CRMs), MESS4 and BCR723, were decomposed together with the samples and analyzed to verify the reliability of PTE analysis. The recovery of CRMs was 97.6–103.3% for MESS-4 (*n* = 6) and 96.2–106.1% for BCR723 (*n* = 6), Two certified reference materials (CRMs), MESS4 and BCR723, were decomposed together with the samples and analyzed to verify the reliability of PTE analysis. The recovery of CRMs was 97.6–103.3% for MESS-4 (*n* = 6) and 96.2–106.1% for BCR723 (*n* = 6), which were highly consistent with the certified values.

#### *2.3. Pollution Assessment*

erence element.

*2.3. Pollution Assessment*  Many pollution indices are widely used to evaluate the contamination levels of single or multiple elements. In this study, the enrichment factor (EF) and pollution load index Many pollution indices are widely used to evaluate the contamination levels of single or multiple elements. In this study, the enrichment factor (EF) and pollution load index (PLI) were used to evaluate PTE contamination in road dust.

(PLI) were used to evaluate PTE contamination in road dust. EF was calculated by the following equation, and aluminum (Al) was used as a ref-EF was calculated by the following equation, and aluminum (Al) was used as a reference element.

$$|EF| = |\frac{(metal/Al)\_{sample}}{(metal/Al)\_{background}}|$$

 = (⁄)௨ௗ where (metal/Al)sample and (metal/Al)background are the ratios of PTE and Al concentrations in two different size classes of road dust and background soil, respectively. The concentrations of Cr, Co, Ni, Cu, Zn, As, Cd, Sb, Pb, and Hg in surface soil, as background values, were 519, 96.3, 346, 37.9, 109, 7.8, 0.33, 0.50, 13.1, and 0.05 mg/kg, respectively (Table 1).

which were highly consistent with the certified values.

The calculated EF values were classified into five categories as follows [41]: EF < 2 (depletion to minimum enrichment), 2 < EF < 5 (moderate enrichment), 5 < EF < 20 (significant enrichment), 20 < EF < 40 (very high enrichment), EF > 40 (extremely high enrichment).

PLI was evaluated using the following equation, reflecting the overall contamination level of 10 PTEs in road dust analyzed in this study [42].

$$|PLI| = |\left(\frac{\mathbf{C}\_{i1}}{\mathbf{C}\_{b1}} \vert \times \vert \frac{\mathbf{C}\_{i2}}{\mathbf{C}\_{b2}} \vert \times \vert \frac{\mathbf{C}\_{i3}}{\mathbf{C}\_{b3}} \vert \times \vert \cdot \cdots \frac{\mathbf{C}\_{in}}{\mathbf{C}\_{bn}}\right)^{\frac{1}{n}}$$

where Ci and Cb are the concentrations of element *i* in road dust and background soil, respectively.

The calculated PLI values were divided into four categories, as follows [43,44]: PLI < 1 (unpolluted), 1 < PLI < 2 (low polluted), 2 < PLI < 3 (moderately polluted), and PLI > 3 (heavily polluted).

#### *2.4. Health Risk Assessment*

The noncarcinogenic health risks to adults and children were evaluated considering three exposure pathways, i.e., ingestion (hand to mouth), inhalation (mouth and nose), and dermal contact. The level of PTE exposure by fine road dust particles (<10 µm) was calculated by the following equation using the chronic daily intake [45].

$$\text{ADD}\_{\text{ing}} \left| = \left| \mathbf{C}\_{\text{i}} \right| \times \left| \left( \frac{\text{Ing} \mathbf{R} \right| \times \left| \text{EF} \right| \times \left| \text{ED}}{\text{BW} \left| \times \left| \text{AT} \right|} \right) \right| \times \left| 10^{-6} \right| $$

$$\text{ADD}\_{\text{inh}} \right| = \left| \mathbf{C}\_{\text{i}} \right| \times \left( \frac{\text{Inh} \mathbf{R} \parallel \times \left| \text{EF} \right| \times \left| \text{ED}}{\text{PEF} \left| \times \left| \text{BW} \right| \times \left| \text{AT} \right|} \right. \right) $$

$$\text{ADD}\_{\text{dem}} \right| = \left| \mathbf{C}\_{\text{i}} \right| \times \left| \left( \frac{\text{SL} \mid \times \left| \text{SA} \right| \times \left| \text{ABS} \right| \times \left| \text{EF} \right| \times \left| \text{ED}}{\text{BW} \left| \times \left| \text{AT} \right|} \right. \right) \right) \times \left| 10^{-6} \right| $$

Here, Ci is the concentration of PTE i in road dust (<10 µm).

The noncarcinogenic health risk for each PTE was calculated using the following equation.

$$|\text{HQ}\_{\text{ing}}| = |\frac{\text{ADD}\_{\text{ing}}}{\text{RfD}\_{\text{ing}}}; \text{HQ}\_{\text{inh}}| = |\frac{\text{ADD}\_{\text{inh}}}{\text{RfD}\_{\text{inh}}}; \text{HQ}\_{\text{dem}}| = |\frac{\text{ADD}\_{\text{dem}}}{\text{RfD}\_{\text{dem}}}|$$

The hazard index (HI) was calculated as the sum of the HQ values for the three exposure pathways.

HI values > 1 indicate potential chronic effects, and HI < 1 indicates no risk of adverse health effects [46].

RfD is the reference dose for ingestion, inhalation, and dermal contact, and is an estimate of daily exposure in residents [47,48]. The values for each parameter in the ADD calculation were obtained from Adamiec and Jarosz-Krzeminska [49] and are presented by Jeong et al. [4].

#### *2.5. Statistical Analysis*

The mean, minimum, maximum, and coefficient of variation (CV) values were carried out using Microsoft Excel program based on the PTE concentration. The Pearson's correlation analysis was applied to understand the potential source of each PTE in road dust (<10 µm) through the correlation between elements and was performed using PASW Statistics 18.0.

10–63 μm

mean min. max. CV (%) 5.8 4.9 6.6 8

#### **3. Results and Discussion** road dust of 10–63 and <10 μm. In road dust with particle size 10–63 μm, Cr had the highest mean concentration and the PTE concentrations decreased in the order Cr (739 mg/kg)

**3. Results and Discussion**  *3.1. PTE Concentration*

*Toxics* **2022**, *10*, x FOR PEER REVIEW 5 of 12

#### *3.1. PTE Concentration* > Zn (351 mg/kg) > Ni (272 mg/kg) > Cu (82.8 mg/kg) > Co (66.7 mg/kg) > Pb (35.3 mg/kg)

The minimum, maximum, mean, and coefficient of variation (CV) PTE levels in road dust are presented in Table 1. Figure 2 shows 10 PTE concentrations for particle sizes in road dust of 10–63 and <10 µm. In road dust with particle size 10–63 µm, Cr had the highest mean concentration and the PTE concentrations decreased in the order Cr (739 mg/kg) > Zn (351 mg/kg) > Ni (272 mg/kg) > Cu (82.8 mg/kg) > Co (66.7 mg/kg) > Pb (35.3 mg/kg) > As (4.5 mg/kg) > Sb (2.1 mg/kg) > Cd (0.31 mg/kg) > Hg (0.03 mg/kg) (Table 1) [47–49]. The CV represents the spatial variation according to sampling site for the PTE concentrations analyzed in this study. A high CV reflects heterogeneity of the PTE concentration among environmental samples, indicating the presence of hotspots of PTE contamination from specific anthropogenic sources [50,51]. The CVs for Cr, Ni, and Zn were <20%, indicating low regional variability, while the other PTEs showed moderate regional variability (20% < CV < 50%). > As (4.5 mg/kg) > Sb (2.1 mg/kg) > Cd (0.31 mg/kg) > Hg (0.03 mg/kg) (Table 1) [47–49]. The CV represents the spatial variation according to sampling site for the PTE concentrations analyzed in this study. A high CV reflects heterogeneity of the PTE concentration among environmental samples, indicating the presence of hotspots of PTE contamination from specific anthropogenic sources [50,51]. The CVs for Cr, Ni, and Zn were <20%, indicating low regional variability, while the other PTEs showed moderate regional variability (20% < CV < 50%). In road dust with particle size <10 μm, Zn showed the highest mean concentration, at 494 mg/kg (Figure 2). The mean concentrations of Cr, Co, and Ni in road dust with particle size 10–63 μm were higher than those in fine road dust (<10 μm). However, the fine road dust had 1.2–2.3 times higher levels of Cu, Zn, As, Cd, Sb, Pb, and Hg than road dust with particle size 10–63 μm.

The minimum, maximum, mean, and coefficient of variation (CV) PTE levels in road dust are presented in Table 1. Figure 2 shows 10 PTE concentrations for particle sizes in

**Figure 2.** Comparison of PTEs concentrations with particle size of 10–63 μm (left) and <10 μm (right) in road dust. **Figure 2.** Comparison of PTEs concentrations with particle size of 10–63 µm (**left**) and <10 µm (**right**) in road dust.

**Table 1.** Comparison of mean values (minimum and maximum value in parenthesis) and coefficient of variation (CV; %) for Al and PTEs concentration in the fine particles of road dust from Samoa and those in the other reported data (mean value).硬回车 **Size Al Cr Co Ni Cu Zn As Cd Sb Pb Hg References**  % mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg mg/kg In road dust with particle size <10 µm, Zn showed the highest mean concentration, at 494 mg/kg (Figure 2). The mean concentrations of Cr, Co, and Ni in road dust with particle size 10–63 µm were higher than those in fine road dust (<10 µm). However, the fine road dust had 1.2–2.3 times higher levels of Cu, Zn, As, Cd, Sb, Pb, and Hg than road dust with particle size 10–63 µm.

739 395 1078 26 66.7 53.0 81.6 11 272 184 364 16 82.8 55.5 123 21 351 288 520 18 4.49 2.92 5.99 21 0.31 0.25 0.58 25 2.08 1.10 3.76 36 35.3 17.7 52.6 33 0.03 0.01 0.05 40 This study **Table 1.** Comparison of mean values (minimum and maximum value in parenthesis) and coefficient of variation (CV; %) for Al and PTEs concentration in the fine particles of road dust from Samoa and those in the other reported data (mean value).

The highest concentrations of Cu (163 mg/kg), Zn (677 mg/kg), and Sb (11.69 mg/kg) were observed at site R17, which had a high traffic volume. Pb (85.0 mg/kg) and Hg (0.15 mg/kg) concentrations were highest at site R5, which is near the intersection in the downtown direction of Apia City. PTE concentrations tended to be relatively high at sampling sites with high traffic volume. The CV of Sb was 53%, which was higher than those of other PTEs. Most PTEs showed low CVs and the spatial distributions in fine road dust were not different. These results suggest that PTEs in road dust may be affected by human activities, such as transportation of residents and tourists, rather than by specific contamination sources, such as industrial emissions. The highest concentrations of Cu (163 mg/kg), Zn (677 mg/kg), and Sb (11.69 mg/kg) were observed at site R17, which had a high traffic volume. Pb (85.0 mg/kg) and Hg (0.15 mg/kg) concentrations were highest at site R5, which is near the intersection in the downtown direction of Apia City. PTE concentrations tended to be relatively high at sampling sites with high traffic volume. The CV of Sb was 53%, which was higher than those of other PTEs. Most PTEs showed low CVs and the spatial distributions in fine road dust were not different. These results suggest that PTEs in road dust may be affected by human activities, such as transportation of residents and tourists, rather than by specific contamination sources, such as industrial emissions.

#### *3.2. Pollution Assessment of PTEs in Road Dust*

*Toxics* **2022**, *10*, x FOR PEER REVIEW 6 of 12

The mean EF values for 10 PTEs are shown in Figure 3. Sb had the highest mean EF value among the PTEs examined in both fractions of road dust. The mean EF values of PTEs in road dust with a particle size of 10–63 µm decreased in the order Sb > Zn > Pb > Cu > Cr > Cd > Ni > Co > As > Hg, whereas those in fine road dust (<10 µm) decreased in the order Sb > Zn > Pb > Cu > Cd > Hg > As > Cr > Co > Ni; the EF values for Cr, Co, and Ni, which are of natural origin, were relatively low. For 10–63-µm road dust particles, the mean EF value of Sb was 6.1, corresponding to significant contamination. Cu, Zn, and Pb had mean EF values between 2 and 5, indicating moderate contamination. For Zn, the EF values exceeded 5 at five sampling sites with high traffic volume. *3.2. Pollution Assessment of PTEs in Road Dust*  The mean EF values for 10 PTEs are shown in Figure 3. Sb had the highest mean EF value among the PTEs examined in both fractions of road dust. The mean EF values of PTEs in road dust with a particle size of 10–63 μm decreased in the order Sb > Zn > Pb > Cu > Cr > Cd > Ni > Co > As > Hg, whereas those in fine road dust (<10 μm) decreased in the order Sb > Zn > Pb > Cu > Cd > Hg > As > Cr > Co > Ni; the EF values for Cr, Co, and Ni, which are of natural origin, were relatively low. For 10–63-μm road dust particles, the mean EF value of Sb was 6.1, corresponding to significant contamination. Cu, Zn, and Pb had mean EF values between 2 and 5, indicating moderate contamination. For Zn, the EF values exceeded 5 at five sampling sites with high traffic volume.

**Figure 3.** Comparison of enrichment factor (EF) for PTEs with particle size 10–63 μm (left) and <10 μm (right) in road dust. **Figure 3.** Comparison of enrichment factor (EF) for PTEs with particle size 10–63 µm (**left**) and <10 µm (**right**) in road dust.

For fine road dust (<10 μm), the mean EF of Sb was 11.2, corresponding to significant contamination. At sampling site R17, which had the widest road and dense traffic, the EF value of Sb was 26.8, indicating very high contamination. The mean EF of Zn, contamination with which is mainly due to tire wear in urban areas, indicates significant contamination. The contamination level of road dust in Samoa was lower than in large cities with high population density and traffic activity, but high levels of Cu, Zn, Sb, and Pb contamination were observed in relation to traffic activity (Table 1). Notably, Sb, which is present in high levels in brake pads [9,55], was a significant contaminant. For fine road dust (<10 µm), the mean EF of Sb was 11.2, corresponding to significant contamination. At sampling site R17, which had the widest road and dense traffic, the EF value of Sb was 26.8, indicating very high contamination. The mean EF of Zn, contamination with which is mainly due to tire wear in urban areas, indicates significant contamination. The contamination level of road dust in Samoa was lower than in large cities with high population density and traffic activity, but high levels of Cu, Zn, Sb, and Pb contamination were observed in relation to traffic activity (Table 1). Notably, Sb, which is present in high levels in brake pads [9,55], was a significant contaminant.

The PLI value of road dust with particle size of 10–63 μm ranged from 0.9 to 1.6 and (mean value = 1.3), indicating low pollution levels for 10 PTEs (Figure 4). The mean PLI value of fine road dust (<10 μm) was 1.6, corresponding to low pollution. However, two sampling sites (R5 and R17) had higher PLI values (exceeding 2) than the other sampling sites 2, indicating that that fine road dust in Samoa was moderately polluted with PTEs. The PLI value of road dust with particle size of 10–63 µm ranged from 0.9 to 1.6 and (mean value = 1.3), indicating low pollution levels for 10 PTEs (Figure 4). The mean PLI value of fine road dust (<10 µm) was 1.6, corresponding to low pollution. However, two sampling sites (R5 and R17) had higher PLI values (exceeding 2) than the other sampling sites 2, indicating that that fine road dust in Samoa was moderately polluted with PTEs.

**Figure 4.** Spatial distribution of pollution load index (PLI) in road dust at sampling site. **Figure 4.** Spatial distribution of pollution load index (PLI) in road dust at sampling site.

Table 2 shows the relationship between the concentration of PTEs in <10 μm of road dust of this study. The concentrations of Cr, Ni, and Co in road dust were closely related. The mean concentrations of Cr, Ni, and Co in the surface soil were 519, 96.3, and 346 mg/kg, respectively, which were very high compared to road dust in this study and the upper continental crust [56]. In a study of heavy metals in soils of a volcanic island (Réunion) located in the western part of the Indian Ocean, Dœlsch et al. [57] reported that the Cr and Ni contents in soil were closely related to volcanic rocks. In this study, no clear correlations were found between Cr, Ni, or Co, and other PTEs (Cu, Zn, Sn, and Pb), mainly arising from nonexhaust emission sources, such as the wear of tires, brake pads, and asphalt due to traffic activity. However, there were significant correlations among Cu, Zn, Sb, and Pb. Therefore, the high Cr, Ni, and Co concentrations in road dust in Samoa, and their high correlations, were strongly influenced by natural processes, such as the weathering of volcanic parent rocks. Table 2 shows the relationship between the concentration of PTEs in <10 µm of road dust of this study. The concentrations of Cr, Ni, and Co in road dust were closely related. The mean concentrations of Cr, Ni, and Co in the surface soil were 519, 96.3, and 346 mg/kg, respectively, which were very high compared to road dust in this study and the upper continental crust [56]. In a study of heavy metals in soils of a volcanic island (Réunion) located in the western part of the Indian Ocean, Dœlsch et al. [57] reported that the Cr and Ni contents in soil were closely related to volcanic rocks. In this study, no clear correlations were found between Cr, Ni, or Co, and other PTEs (Cu, Zn, Sn, and Pb), mainly arising from nonexhaust emission sources, such as the wear of tires, brake pads, and asphalt due to traffic activity. However, there were significant correlations among Cu, Zn, Sb, and Pb. Therefore, the high Cr, Ni, and Co concentrations in road dust in Samoa, and their high correlations, were strongly influenced by natural processes, such as the weathering of volcanic parent rocks.

**Table 2.** Results of Pearson's correlation matrix between PTEs in <10 μm of road dust. Bold indicates that correlation is significant at the 0.05 level (2-tailed).加粗 **Table 2.** Results of Pearson's correlation matrix between PTEs in <10 µm of road dust. Bold indicates that correlation is significant at the 0.05 level (2-tailed).

*3.3. Non-Carcinogenic Risk of PTEs in Fine Road Dust (10 μm) 3.3. Non-Carcinogenic Risk of PTEs in Fine Road Dust (10 µm)*

Table 3 presents the mean HQ and HI values for adults and children, and three exposure pathways (in descending order): HQing > HQderm > HQinh. These observations indicated that the pathway of ingestion from hand to mouth was the main exposure route, having a detrimental effect on human health compared to other processes. The contribution of ingestion was 76% for adults and 79% for children, but the sum of the HQ values for PTEs was 0.62 for children, which was higher than that for adults (0.07). HQ and HI values for all PTEs were <1 at all sampling sites, indicating no noncarcinogenic health risk for adults or children. The mean HI values for adults and children were 0.09 and 0.78, respectively. Therefore, the noncarcinogenic health risk of exposure to PTEs in road dust was about nine times higher in children than adults. Table 3 presents the mean HQ and HI values for adults and children, and three exposure pathways (in descending order): HQing > HQderm > HQinh. These observations indicated that the pathway of ingestion from hand to mouth was the main exposure route, having a detrimental effect on human health compared to other processes. The contribution of ingestion was 76% for adults and 79% for children, but the sum of the HQ values for PTEs was 0.62 for children, which was higher than that for adults (0.07). HQ and HI values for all PTEs were <1 at all sampling sites, indicating no noncarcinogenic health risk for adults or children. The mean HI values for adults and children were 0.09 and 0.78, respectively. Therefore, the noncarcinogenic health risk of exposure to PTEs in road dust was about nine times higher in children than adults.

**Table 3.** The median values of hazard quotient (HQ) and hazard index (HI) of non-carcinogenic hazards for PTEs with particle size of less than 10 µm in this study.

The HI value of Cr was higher than that of other PTEs, and the order of HI values was Cr > As > Sb > Pb > Ni > Co > Cu > Zn > Cd > Hg in adults. These results suggest that Cr and As may be major contributors to noncarcinogenic risk among PTEs in road dust accumulated on road surfaces in Samoa.

Cr is widely used in many industries, including metallurgy, steel manufacturing, automobile industry, electroplating, and wood preservation, because of its anticorrosive properties [58–61]. Cr is highly mobile and has extremely toxic effects even at very low concentrations; it is classified as a Group A carcinogen element in humans [62]. Cr is used for the wheels of vehicles, and many parts of vehicles are plated with Cr as it has an aesthetic appearance and is not easily corroded. Many studies have reported that Cr contamination in road dust and soil is due to traffic and industrial activities [63–66].

Although the noncarcinogenic risk of Cr is higher than those of other PTEs, the mean EF value of Cr in road dust (<10 µm) was 0.9 (range 0.6–2.2) in this study, and most of the sampling sites showed depletion to minimal enrichment. The contamination level of Cr in road dust was lower than those of Cu, Zn, Sb, and Pb, which are mainly derived from traffic activity. High concentrations of Cr and Ni have been reported in the soil of islands formed by volcanic activity [57,67].

The mean concentrations of Cr in road dust (<10 µm) and surface soil were 395 and 519 mg/kg, respectively, in this study, which were higher than those in the upper continental crust (92.0 mg/kg) [55]. Our results indicated that Cr concentration in road dust in Samoa is derived from natural sources, mainly due to the weathering of volcanic parent materials. Therefore, the noncarcinogenic risk of Cr in road dust on Samoa seemed to be much lower than in highly urbanized cities.

PTEs such as Cu, Zn, Sb, and Pb have a negligible health risk on the human body, but road dust is contaminated with these elements which come from traffic activities. Pacific Island countries, including Samoa, import and use used cars for the tourisms industry. The activation of the tourism industry and the environmental factors (high humidity and heavy precipitation) can increase the contamination levels of PTEs in road dust, as these accelerate the corrosion of vehicles. In addition, road dust contaminated with PTEs is easily resuspended via stormwater runoff, transport to rivers and streams, and is finally deposited in the coastal environments. Road dust in urban environments is one of the major sources of PTEs contamination that contaminates marine sediments and has a detrimental effect on marine organisms. Efficient management by road cleaning is necessary to prevent the impact on the environments and ecosystem caused by road dust contaminated with PTEs.

#### **4. Conclusions**

In this study, road dust was collected in Apia City, Samoa, and divided into two fractions (10–63 and <10 µm). Analyzing road dust is important for public health, and for

understanding environmental PTE contamination. Pollution and health risk assessments were performed for 10 PTEs in road dust. Cr, Co, and Ni concentrations in road dust with a particle size of 10–63 µm were higher than in fine road dust (<10 µm), while Cu, Zn, As, Cd, Sb, Pb, and Hg were present at relatively high concentrations in fine road dust. The PTE concentration in road dust was lower than in large cities with high levels of human activity. The CVs of PTEs in road dust suggested that anthropogenic sources related to traffic activity rather than industrial emissions affect road dust in Apia City, Samoa. Both particle sizes of road dust had significant levels of Sb, and the levels of Cu, Zn, and Pb, which are derived from nonexhaust emission arising from traffic activity, were also moderately high. The results of correlation analyses and pollution assessments showed that Cr, Co, and Ni are greatly affected by natural sources due to the weathering of volcanic parent rocks. There were significant correlations among Cu, Zn, Sb, and Pb. In addition, relatively high concentrations were observed in sampling sites with high traffic volume, suggesting that contamination with these PTEs (Cu, Zn, Sb, and Pb) was mainly due to traffic activity. The ingestion exposure route posed a greater health risk than inhalation and dermal contact. The mean HI values for adults and children were 0.09 and 0.78, respectively, indicating that the noncarcinogenic health risk in children was about nine times higher than that in adults. However, HI values for adults and children were <1 at all sampling sites, suggesting no noncarcinogenic risk from exposure to PTEs in road dust. Among the PTEs, Cr had a higher HQ value than the other elements, and posed a major noncarcinogenic health risk. The Cr in road dust in Samoa was mainly derived from natural sources, i.e., the weathering of volcanic parent rocks rather than anthropogenic sources (traffic activity). Therefore, our results suggest that the health risks posed by PTEs in road dust in Samoa are negligible compared to highly urbanized cities. However, further studies regarding the effects of PTE-contaminated road dust on the atmosphere and living organisms, including humans, are needed.

**Author Contributions:** Conceptualization, H.J. and K.R.; investigation, K.R.; methodology, H.J. and K.R.; formal analysis, H.J. and K.R.; data curation, H.J.; visualization, K.R.; writing—original draft preparation, H.J.; writing—review and editing, K.R.; funding acquisition, K.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by grants (PEA0012) from the Korea Institute of Ocean Science and Technology (KIOST).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All data for this study are available from the first or corresponding authors upon request.

**Acknowledgments:** We gratefully appreciate the assistance of Ministry of Natural Resources and Environment of Samoa for helping with this research.

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

#### **References**

MDPI St. Alban-Anlage 66 4052 Basel Switzerland Tel. +41 61 683 77 34 Fax +41 61 302 89 18 www.mdpi.com

*Toxics* Editorial Office E-mail<EMAIL_ADDRESS>www.mdpi.com/journal/toxics

MDPI St. Alban-Anlage 66 4052 Basel Switzerland

Tel: +41 61 683 77 34

www.mdpi.com

ISBN 978-3-0365-6238-4


**6. Conclusions**

When the road is constructed by the development of the urban, it is essential to cut-slope. However, the slope has gradually degraded the strength of the soil due to the time-dependent deterioration, and progressive slope failure is induced. This study developed an integrated analysis method for stability analysis and maintenance of cut-slopes in urban. The slope stability analysis was performed using the finite element model, and the progressive slope failure by time-dependent deterioration was quantified by using the strength parameters of soil applying the SRF. The displacements until the slope failure by slope stability analysis were quantified by cumulative displacement curve, velocity curve, and inverse velocity curve and, applied to the slope maintenance method. The inverse-velocity curve applied to the prediction of the time of slope failure was regressed to the 1st linear equation in the brittle material and the 3rd polynomial equation in the ductile material. This is consistent with the proposed formula of Fukuzono [1] and also shows similar behavior to the existing failure case.

Recently, sensors and communication technologies for measuring the behavior of slopes have been dramatically developed. However, there is no technique or management method to interpret it. This study aims to solve these problems.

The proposed method integrates the existing slope stability analysis and maintenance techniques. It is difficult to obtain the measurement data of the collapsed slope, but the proposed technique should be verified through the failure of the model slope. And the behavior of ductile and brittle behavior appeared depending on the characteristics of the materials constituting the slope. Research on this part is also necessary. In the future, integrated analysis method should be improved through additional research. And it should be applied to cut-slope of urban to prevent disasters.

**Author details**

Mincheol Park1

**175**

\*, Heuisoo Han2 and Yoonhwa Jin2

*Integrated Analysis Method for Stability Analysis and Maintenance of Cut-Slope in Urban*

*DOI: http://dx.doi.org/10.5772/intechopen.94252*

2 Kumoh National Institute of Technology, Gumi, Republic of Korea

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

1 Seoul Institute of Technology, Seoul, Republic of Korea

\*Address all correspondence to<EMAIL_ADDRESS>
provided the original work is properly cited.

In this study, the proposed method was carried out only on homogeneous slopes consist of soil. In the future, research on the slope with the heterogeneous and anisotropic is needed. Also, the proposed method should be verified by applying it to the 3D rock slope which causes collapse along the joint of rock.


**5. Conclusions**

That drama and religion connect through ritual must be qualified by the observation that theatrical drama comes into its own only when it is no longer ritual but, say, entertainment or education.56 Even when 'to entertain' deepens to 'discern attentively,' the ritual-drama relation can be a puzzle. Does ritual discern? Does entertainment ritualize? Well, 'both,' or 'sometimes,' no doubt. That issue aside, this essay proposes to understand religious dimensions in theatrical drama through concepts of limit. Limit experiences and questions speak to orientations and disorientations toward 'the real' as they become pertinent to theatre, ritual, and religious traditions. Metatheatrical relations can lead us to explore where embodied worlds and horizons meet, differentiate, overlap, and make claims on us. Theatrical drama invites, even impels, us to discern limits and 'otherwise' possibilities of life, together.

Metatheatre-3 is probably the hardest case for my claim, even though the mode registers the "histrionic sensibility"57 of human experience. Namely, that (1) we 'play' or are 'played' by multiple social-personal roles and parts; (2) we enjoy imitating, for others, such play; and (3) we especially enjoy witnessing and learning from theatrical imitations. The word *histrionic* signals the problem. As with much theatre lingo transferred to life, it is pejorative, suggestive of feigned hyperbolic emotion, insincerity, or inauthenticity. The opposite insight, though equally old—that by rehearsing social parts to the point of in-habiting them we become who we are with and for one another—does not seem so quickly to come to mind. I sense antitheatrical bias in some performance theory that would expose a cultural achievement or some purported universal as being *merely* a power-inflected construct or illusion. Universals, however, straddle the difference between limit-to and limit-of. As Plato and Aristotle differently argued, universals are immanent in our particular constructs of experience, yet also transcend them, making our meanings finitely shareable. Metatheatricality explores this straddling of the limits 'to' and 'of' shared experience. Yet an unacknowledged preference for solitary authenticity is detectable in "Putting It Together." Most of its characters not only assume we should have no illusions, but that it would be better if our lives, somehow, were *not* contingently and artificially constructed, even though we know better. Ambivalent critiques of this sort neglect to explore possibilities of honestly collaborative constructs, in social life or in art. A construct can point beyond itself and give rise to feeling and thought.

Metatheatricality in *Our Town* can also seem antitheatrical in its minimalism. ("There's some scenery for those of you who think they have to have scenery.") Wilder's claim, however, is that by minimizing the smoke and mirrors and allowing us to see how they actually work, the play substantiates the universals it hopes we acknowledge but cannot 'see.' That is, life-transcending value—immanent "way down deep" in every moment and nexus of life—is made real through discernment and bodily proximity. We are to feel the weight of universals pressing on our skin as we are side by side with people constructing and querying them. In *Sunday in the Park*, the emphases on constructing and deconstructing are more elaborate; the play's bravura design both minimizes and maximizes the 'magic of theatre' and risks 'shorting out.' But in so doing, it reveals and values limits and possibilities in different ways than *Our Town*.

*Our Town* was so metaphysically confident in inherent, quotidian value that it could leave the ultimate limit-of unarticulated. We know only that as we wean from the cares of this world (limit-to), we await and prepare for "something" eternal. If, like Emily, the dead in their in-between state choose to see their finite interrelationships *sub specie aeternitatis*, it can be devastating, unless with a saint's eye or poet's. By contrast, Act II of *Sunday in the Park* has little metaphysical confidence as it exposes the pragmatics that puts art together. Yet Act I, which also shows art to be socially as well as aesthetically constructed,58 does have confidence in ordinary perception. It is in the eye and ear (limit-to) that

56 See Friedrich 1983.

57 Driver's phrase (1970), p. 262; his point is that technological modernity loses this impulse, to the detriment of drama.

58 McLaughlin 2016, p. 155.

the performative universals—design, tension, harmony—mix to manifest the plethora of possibilities (limit-of). There, as the play ends on the island, a simpler magic of theatre allows us see, with George, a transcending 'more': Dot enters, *there* and *now*, across times and spaces. *Sunday in the Park* is confident that those among the living will perceive possibilities of love and art in the moving 'now,' *sub species aeternitatis et theatrum*.

Oh—just as I had a notion for restaging the *Grand Jatte* tableau at the end of Act II, I have one for the Chromolume. Be apophatic. Do not show the machine, not even its piercing lasers or dancing dots.59 Just show the faces of the guests being illuminated by light—I suggest a wash of bright but calm light, with quieter music—as they make of the Chromolume what they will, differently. Let their faces show the limits and possibilities of that new story.

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

**Conflicts of Interest:** The author declares no conflict of interest.


**Author details**

Shaojun Yuan1,2\*, Gordon Xiong2 , Ariel Roguin3 , Swee Hin Teoh4 and Cleo Choong2\*

\*Address all correspondence to<EMAIL_ADDRESS>
\*Address all correspondence to<EMAIL_ADDRESS>
1 Multi-phases Mass Transfer & Reaction Engineering Lab, College of Chemical Engineer‐ ing, Sichuan University, Chengdu, China

2 School of Materials Science and Engineering, Nanyang Technological University, Singa‐ pore

3 Department of Cardiology, Rambam Medical Center, B. Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Israel

4 School of Chemical and Biomedical Engineering, Nanyang Technological University, Sin‐ gapore


**Conduit Selection for Improved Outcomes in Coronary Artery Bypass Surgery**

Zane B. Atkins1,2,\*, Kristine V. Owen3 andWalter G. Wolfe1,2

*1Department of Surgery, Veterans Affairs Medical Center Durham, NC 2Department of Surgery, Duke University Hospital Durham, NC 3Department of Medicine, Charles George Veterans Affairs Medical Center Asheville, NC USA*


Stagno d'Alcontres Francesco

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/64133

### **Abstract**

Critical wounds are well known to develop in elderly people and in other conditions where inflammation, vascular, and nervous disease lead to chronical inefficiency in running up healing processes. Recent researches have been focusing on microenviron‐ ment, and specific technologies have contributed to design and produce new materials (the era of biomaterials and devices in wound healing).

At present, regenerative medicine and surgery have introduced a new approach, based on cells' transplantation producing specific cytokines and stimulating healing. This is the role played by fat transplantation combining both stromal and vascular cellular progenitor lines, monocyte products, platelet‐rich plasma, and glues.

New tools, such as templates and materials (comprising micro‐ and nanoparticles), as well as technologies, such as cell seeding and gene therapy, revealed promising in this direction. The authors report their experimental evidences and clinical experiences with lipografting and peripheral blood mononuclear cells in critical wounds, focusing on how to treat monocyte/macrophage cell depletion as well as insufficient vascular supply.

**Keywords:** adipose‐derived stem cells, angiogenesis, difficult wounds, lipografting, monocytes, vascular supply


**ADVANCEMENT IN MICROSTRIP ANTENNAS WITH RECENT APPLICATIONS**

Edited by **Ahmed Kishk**

#### **Advancement in Microstrip Antennas with Recent Applications**

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