**Abstract**

Physicochemical interaction with micro plastic at River Delimi represents one of the most pressing threats to water and plant resources as it is a challenge to human health. Micro plastics in the food chain constitutes a major threat to water, soil and plants which affect food safety, it affects public health when consuming products that have this pollutant, such as fruits, and vegetables. Micro plastics (MPs) are tiny particles broken down from larger pieces of plastics, accumulated in River Delimi. This study aimed at assessing the physicochemical parameters (Cd, Zn, Co, Pb and Ni) in water and (Ba, Ce, Rb, La, Nd, Ta, Sm, Sc and Th) while mercury and silver show high concentration (Hg, Ag) plants as it interaction with micro plastics (majorly polyethylene terephthalate (PET) concentration and plasticized polyvinylchloride (PVC both dry and rainy season)in the study area, along Rivers Delimi in Jos North LGA of Plateau State. Water and plant samples were collected from six (6) different irrigation sites in the area, namely Delimi village, Delimi area, Marhaba Masjd, Malam Adamu and Tudun Wada along the River Delimi, Barakin Kogi, and Baraki Naraguta along River Bonga, contain micro particles in edible plants due to the contamination of water-irrigated soils and methods to determine them. The negative effect of micro plastics on various food products and their interaction with physicochemical properties especially heavy metals impact on the environment is determined. Samples were analyzed to determine the concentration of the physicochemical parameters using the Atomic Absorption Spectrometer. Micro plastic are carriers for physicochemical parameters and exhibit diverse interactive effects, these interactions are poorly understood especially how they pose risks on living organisms. The challenges of their combined toxic effects and the potential hazards to human health were also discussed. Results show that the concentration of the physicochemical parameters in water as electrical conductivity, hydrocabonate, chloride, sodium carcium, chromium and lead while plasticized polyvinylchloride (PVC) both dry and rainy season, in river Delimi and river Bonga were pronounce. In plants potassium, phosphorus, molybdenum, manganese, mercury and silver were also high.

**Keywords:** micro plastics, heavy metals, interactions, microbe, aquatic environment

## **1. Introduction**

#### **1.1 Micro plastics**

Additives increase specific properties of plastic polymers; those called butyl tins stabilize polyvinyl chloride polymers (butyl tins)are found also in animal and human livers due to diet uptake [1]. Plastic items, which can be of different polymers or shape, can be classified according to size, and in particular, items ranging within 1 μm and 5 mm are called micro plastics (MPs) [2, 3]. Micro plastics (MPs) are small pieces of plastic, less than 5 mm (0.2inch) in length, that occur in the environments resulting to plastic pollution., Physicochemical factors accelerate the degradation time of plastics by mechanical stimuli, biological, thermal, and photo-oxidative degradation [4]. Micro plastic are varied contaminant suite originated from different product types, composed of various polymers and chemical additives, characterized by a broad range of colors and shape [5].

Micro plastic consist of carbon and hydrogen atoms bond together in polymer chain, other chemicals such as phthalates, polybrominated diphenyleither's (PBDEs) and tetrabromobisphenol A (TBBPA), are present in micro plastic and many of these chemical additive leach out of the plastic after entering the river**.** They are divided into primary or secondary, according to their origin. Primary micro plastic (fMPs) are the final products of industrial activities used in cosmetic [6].

Primary micro plastic: include micro bends (personal care products), plastic pellets (used in manufacturing), and plastic fibers (used in synthetic textiles or nylon). Secondary micro plastics (sMPs) are products of plastics litter/degradation. Exposure of plastic waste in the environment causes deterioration in its mechanical and physicochemical properties, leading to the formation of plastic fragments, which are considered as micro plastics (MPs) when their size is less than (<5 mm). Delimi river is an important natural drainage and irrigation system, ensuring water flows through is of paramount important, as the cultivation of vegetable crops in Jos, depend on it. The presence of micro plastic in River Delimi is a health risk. The primary challenges of MPs to ecosystems are their ubiquity and bioavailability for ingestion, entanglement or inhalation [7]. MPs can be accumulated in the environment due to their inert nature [8]. They have been found in table salts and potable water [9]. The health implications of MPs may be physical by blocking the digestive system due to particle localization, chemical with associated toxic chemical effects and biological involving toxins [10]. The exposure of aquatic organisms to MPs has been associated with short- and longterm adverse effects on organism's health, including biological feeding, reproduction, antioxidant defense and innate immunity [11–14]. A contaminant that is widespread in the environment, heavy metals can enter water bodies continuously due to their non-degradable nature and are recycled and enriched in the aqueous environment. MPs and heavy metals are not only acting as persistent pollutants, their combined pollution poses a new threat to the world. Due to the large surface area, MPs can act like magnets for toxic pollutants and concentrate them to a very high level [15, 16]. The need to reduce the amount or quantity of plastic waste emitted to safeguard environmental and human health is a task to be involved every stakeholder in the environment. Heavy metals are present in the environment from both naturally occurring and anthropogenic sources. As a contaminant that is widespread in the environment, Aquatic ecosystems contain a great diversity of microorganisms, which play critical roles in many biogeochemical processes. Their existence makes the interaction between MPs and heavy metals more complicated [16]. There is no existing

baseline study concerning the micro plastic challenges of River Delimi in Jos Plateau hence this study seek to provide the baseline.

#### **1.2 The chemical composition of micro plastics**

Aquatic ecosystems contain a great diversity of microorganisms, which play critical roles in many biogeochemical processes. Their existence makes the interaction between MPs and heavy metals more complicated. MPs provide an emergent ecological niche for microorganisms by the formation of microbial biofilms, named plastisphere [17, 18]. Primary micro plastics are intentionally manufactured in small sizes for different industrial purposes, such as housing and transportation applications and are generally made of polyethylene or polystyrene [19–21].

#### **1.3 The boundaries of the study**

The general aim of this study is to investigate the interaction between micro plastic and physicochemical parameters with challenges of micro plastic in River Delimi, Plateau State, Nigeria. River Delimi is within urban and commercial centre of Jos Plateau State capital, the investigated areas include; Delimi village, Delimi area, Congo Road, Tudun Wada, Anguwan Rogo, and BarakinNaraguta.

## **2. Materials and methods**

#### **2.1 Study area**

Rive Delimi covers a distance of about 15 km as it passes through Jos metropolis. It originates from Delimi village moves through Delimi area, Anguwan Rogo, Yelwan Mista Bow, Toro Bauchi, meeting up with the Rafin Zaki and extending to the Hadeja/ Jamaare River Basin and Lake Chard. It shares a boundary to the North with Toro Local Government Area of Bauchi State; to the South with Jos-South Local Government area; to the North-East with Jos-East Local Government Area; and to the West with Bassa Local Government Area [22]. Jos enjoys a temperate climate with average temperatures of between 280C (81.70F) maximum and 110C (51.70F) minimum. It covers a total land area of 291 km2 (112 sq mi) with a 2006 census of 429,300 people. The warmest temperatures usually occur in the dry season months of March and April Jos is characterized by a mean annual rainfall of between 1317.5 mm (131.75 cm) and 1460.00 mm (146.0 cm), mostly from May to August The Onset and Cessation of rainfall in Jos are experienced in April (15 days in April), and October (15 days in October). The relative humidity is characterized by a marked seasonal variation [23]. The Jos Plateau is a high land region in North Central Nigeria. River Delimi is the major drainage system in Jos Metropolis. The river serves as a source of water for domestic use, fishing and irrigation. The River Delimi in Jos Plateau is the source of many Rivers in northern Nigeria including the Kaduna Gongola, Hadejia and Yobe River [23].

Plates 1, 2 and 3, show the type of menace that the disposal of solid wastes in water resources with cumulative effect micro plastic challenges which affect the water of the urban centers and the health hazards to the people who are exposed. The waste collection containers that were seen to be available at most locations in Jos are no longer in place at most of these locations.

#### **2.2 Data collection**

#### *2.2.1 Sampling methods and analysis*

Water samples were collected from 6 different sampling points marked WT1-WT6 (plate 1–3). Bulk water sampling, which facilitates sampling of smaller micro plastics but it, is limited by the size of the area.. The collection of samples from surface water was done using bulk water technique. Temperature, electrical conductivity (EC) and total dissolved solute (TDS) were measured in-situ during the sampling process. Water samples were filtered through Millipore filter paper of reweighed 0.45 μm pore size. The filtered water samples were acidified with 0.2%(v/v) concentration HN03 and kept in glass bottles. For anions analysis the sample were not acidify and titration carried out to ascertain the concentrations of the Anions. The filter papers containing the suspended solids were air-dried and reweighd. They were digested with 4/I (v/v) HN03/HCl mixture using a microwave device. After cooling, digestions were diluted to 30 ml with mili-Q water. At each of the sampling stations three samples of plants were collected close to water sampling station while water were collected using 1 liter polyethylene bottles with screw caps which were acid washed and rinsed with distilled water prior to the sampling... A total of 16 samples comprising of different types of plants alongside water samples each from the six sites along River Delimi and River Bonga were taken for laboratory analysis (see **Tables 1** and **2**). Specimens were labeled and transported to the laboratory in temperature - controlled boxes for analysis. An ambient temperature of 28° was maintained during transportation. Samples were digested with aqua regia solutions 3/I (v/v) HCl/HNO3 in microwave device. After cooling, digestions were diluted to 30 ml with mili-Q water.

#### *2.2.2 Microplatics analysis*

The different instrumental methodologies for determining and quantifying micro plastics in different matrices such as water, fruits and vegetables (see **Table 1**). The

#### **Table 1.**

*Identification and quantification micro plastics in river Delimi water, fruits and vegetables.*

*Micro Plastic Challenges in River Delimi Due to Its Interaction with Physicochemical… DOI: http://dx.doi.org/10.5772/intechopen.107150*

#### **Table 2.**

*Concentration of physical parameters and anions in water from all the sampling sites.*

techniques are focused on a physical type (non-destructive) determination; the composition of the micro plastic of the polymer that was separated from the sample by different analytical procedures is visualized. Reagents for separating the micro plastics from the samples [3, 19, 24, 25] with a high concentration of organic matter, where their action is to destroy the organic matter and release the micro plastics from the matrix in order to later be separated by filtration processes [7, 26–30]. Mesoplastic (MEP,>5 mm) and coarse micro plastic (Cmp,>2 mm) were visually identified from sieving fractions using a stainless-steel bowl with an imprinted grid (1x1 cm grid size) and inspected under stereomicroscope. Large micro plastics (L-Mp,>1 mm) and medium micro plastics (M-Mp,>0.3 mm) were analyzed after density separation micro plastics ZnCl2 and NaI. Separating micro plastics from water samples was done either by direct filtration [7, 31, 32] or organic materials such as dried leaves which showed recovery rate of 82% [33]. For density separation, solution of chemicals such as NaCl, NaI, NaCl, and ZnCl2 are used. After density separation, sieving, and filtration of floating sample material [30, 34]. Stained filters were visually detected under a stereomicroscope. Concentrated samples retained on the filters were identified and quantified. In the identification and quantification of micro plastics in samples, light microscopes have been applied in studies conducted in laboratories. Final identification of each potential plastics particle within the MEP, CMp, and L-Mp and M-Mp sizes was performed.

### **3. Results and discussion**

#### **3.1 Concentration of physical parameters (tem, pH, TDS and EC)**

**Figure 1** illustrates the concentration of Physical parameters in all sample locations. The highest concentrations of electrical conductivity (EC) are in sample locations WT1 (near Delimi street Bridge) along Delimi river and WT2 (katako) also along the Delimi river. Concentrations were lower in WT5-WT6 (AngwanRogo) (Naraguta) and along the Delimi, both along River Bonga.

The characteristic of physical parameters of samples from six samples that were analyzed for physical parameters in River Delimi (see **Table 2**).

**Figure 1.** *Concentration of physical parameters in water from the entire sampling site.*

Variation of physical parameters in River Delimi showed EC has highest concentration at WT1-WT2. Value of electrical conductivity EC of the water sample at WT1 (435 ohm/m), while WT5 exhibits lowest value of EC (210 ohm/m). Temperatures, pH and TDS have lower values (presented in **Figure 1**).

#### **3.2 Concentration of anions (S042, Cland HC03)**

The concentration of Anions in water from all sample location. The concentration of hydrocarbon ate was high in sample WT2 –WT6 (Congo) along river Bonga and chloride at sample and WT5 –WT6 (Anguwan Soya) along river Bonga. However, the lowest concentration was recorded at sample station WT1 (Delimi street Bridge) for hydrocarbon ate and WT4 (Anguwan Rogo) for chloride.

The highest (see **Table 2** and presented in **Figure 2**) concentration of hydrocarbonate 98.98 (WT2) while the lowest concentration 38.38 (WT1), chloride highest concentration 99.26 (WT5) while the lowest concentration 28.36 (WT4) and sulfite which the least,highest concentration 35.5 (WT2) while lowest concentrations 23.5 (WT1). Hydrocarbon ate and chloride has higher values in River Delimi.

**Figure 2.** *Concentration of anions in water from the entire sampling site.* *Micro Plastic Challenges in River Delimi Due to Its Interaction with Physicochemical… DOI: http://dx.doi.org/10.5772/intechopen.107150*

**Table 3.**

*Concentration of cations and heavy metals in water from all the sampling sites.*

#### **3.3 Concentration of cations (Na+ , K+ , Mg2+ and Ca2+)**

The concentration of cations in all sample locations. The concentration of **Sodium (Na+ ) and calcium (Ca2+)** are higher in the sample locations from WT1-WT6 both along river Delimi and Bonga. Highest (see **Table 3** and presented in **Figure 3**) concentration of sodium in river Delimi 18.50 (ST6) mg/l while the lowest 14.48 (WT6), calcium highest concentration 14.50 (WT2) while the lowest concentration 14.32 (WT6).

#### **3.4 Concentration of heavy metals (Cr, Ni, Pb and Fe)**

The concentration of heavy metals in all sample locations. The concentration of chromium is higher in all the sample locations from WT1-WT6 both along river Delimi and Bonga. Lead is higher in the sample locations WT4-WT6. **Figure 4**

**Figure 3.** *Concentration of cations in water from the entire sampling site.*

**Figure 4.** *Concentration of heavy metals in water from the entire sampling site.*

revealed high concentration of chromium 3.62 (WT1) and lowest value of 2.77 (WT6) while lead higher value 2.36 (WT1), least value 2.28(WT2) (see **Table 3** and presented in **Figure 4**).

Micro plastics in River Delimi.

Concentration o f micro plastics (MPs) in river Delimi both dry and rainy season were abundant in color and dominated by fibrous items. Polyethylene terephthalate (PET) concentration 22.65% and Plasticized polyvinylchloride (PVC) concentration 50% while others 27.55% (dry season). Polyethylene terephthalate (PET) concentration 18.20%(and Plasticized polyvinylchloride (Plasticized (PVC) concentration 80.50% while others 11.70% (rainy season) Plasticized polyvinylchloride (Plasticized (PVC) were the predominant accounting for 50% and 80.50% in both dry and rainy season (presented in **Figures 5** and **6**).

#### **3.5 Concentration of metals (Ca2+, Mg2+ and K+ )**

The concentration of potassium (K+ ) in all sample locations. The concentration of potassium is higher in all the sample locations from WT1-WT16 both along river Delimi and Bonga (see **Tables 4** and **5** and presented in **Figure 7**).

**Figure 5.**

*Concentration of micro plastic in river Delimi water from the entire sampling site (dry season).*

*Micro Plastic Challenges in River Delimi Due to Its Interaction with Physicochemical… DOI: http://dx.doi.org/10.5772/intechopen.107150*

#### **Figure 6.**

*Concentration of micro plastics in river Delimi water from the entire sampling site (rainy season).*

#### **3.6 Concentration of nonmetals (S and P)**

The concentration of sculpture and phosphorus in PT4-PT16 sample locations. The concentration of sulfur and phosphorus in sample locations PT4-PT16 are higher both along river Delimi and Bonga (presented in **Figure 8**).

#### **3.7 Concentration of metals and nonmetals**

The concentration of metals and nonmetals in all sample locations. The concentration of metals is higher in all the sample locations from PT1-PT16 both along river Delimi and Bonga (Presented in **Figure 9**).

#### **3.8 Concentration of heavy metals (Mo, Cu, Pb and Zn)**

The concentration of heavy metals n all sample locations. The concentration of molybdenum is higher in all the sample locations from PT1-PT16 both along river Delimi and Bonga (see **Table 6** and presented in **Figure 10**).

#### **3.9 Concentration of heavy metals (Ni, Co, Mn, Cr, Ba, Ti and B)**

The concentration of heavy metals in all sample locations. The concentration of Mn is higher in all the sample locations from W T1-WT16 both along river Delimi and Bonga. The concentration of B, Ti and Ba also show high concentration (presented in **Figure 11**).

#### **3.10 Concentration of heavy metals (W, Sc, Tl, Se and Ga)**

The concentration of metals in all sample locations. The concentration of metals that are less than or equal to one in all the sample locations from WT1-WT16 both along river Delimi and Bonga..Ba, Ce, Rb, La, Nd, Ta, Sm, Sc and Th were high (presented in **Figure 12**).

### **3.11 Interaction between MPs and heavy metals in plants irrigated with river Delimi water**

Micro plastics particles can interact with biological and synthetic systems due to their small size, enormous specific surface area, and high functionalization capacity,

#### **Table 4.**

*Concentration of metals and non metals in plants irrigated with river Delimi water from all the sampling sites.*

capable of even permeating biological membranes [3] Various sources of water and soils contaminated with micro plastics, are the source from where plants absorb water and nutrients.

The consumption of vegetable crops with a high level of heavy metals (Ba, Ce, Rb, La, Nd, Ta, Sm, Sc and Th) while mercury and silver show high concentration (Hg, Ag) (see **Tables 6** and **7**) due to high content of PVC in both dry and rainy season which is a challenge to humans. This showed that the use of water from river Delimi for irrigation may cause danger to the crops growing. Micro plastics increase cations of

*Micro Plastic Challenges in River Delimi Due to Its Interaction with Physicochemical… DOI: http://dx.doi.org/10.5772/intechopen.107150*

#### **Table 5.**

*Concentration of heavy metals in plants irrigated with river Delimi water from all the sampling sites.*

**Figure 7.** *Concentration of metals in plants irrigated with river Delimi water from the entire sampling site.*

Na+ , Ca2+ and decrease K+ , Mg2+, and Fe concentrations. Fragment, fiber shapes were identified in the surface water of the rivers, with fragment shape having the highest occurrence. The distribution of the plastics was as follows: polyethylene terephthalate (PET) – 22.65%, polyvinyl chloride (PVC) – 50%, others – 27.35% (Dry season), (PET) – 18.20%, polyvinyl chloride (PVC) – 80,50%, others – 11.70% (Rainy season) [35]. Estimated that humans are exposed to about 27 types of micro pollutants by consuming fruits and vegetables derived from irrigation water contaminated with

**Figure 8.** *Concentration of nonmetals in plants irrigated with river Delimi water from the entire sampling site.*

**Figure 9.**

these compounds [36], indicates that the concentration of micro plastics. Environmental factors may indirectly exert influence on MPs by changing biofilm structures on the MPs surface [10] found that nutrient salts, total nitrogen, total phosphorus, and pH have a greater influence on colony structure, while MP physical and chemical properties such as particle size and contact angle have less influence. Both MPs and heavy metals can accumulate at high level in the environment and consequently contaminate the food. Many studies have shown that MPs can absorb and release heavy metals, and their combined exposure may pose a potential threat to ecological system and human being [37].


*3.2. Simulation Experiment Description*

In this study, the independence and interaction of individual pig farmers as an actor were simulated in a computer-generated environment when considering the influences of their knowledge and actor-to-actor information exchange on their VA use. The simulation was performed using Matlab based on Wu's and Zhou's research [22,24], and is described as follows:

(1) The simulation area is a 20 × 20 square area. At the start of the simulation, 100 farmers were randomly distributed in this area. Specific parameters are listed in Table 1 below.

**Table 1.** Settings of experimental parameters.

penalties), take a value in [1,5] in the simulation, respectively, where 1 means no knowledge and 5 means complete knowledge. Based on the behavior probability model, *ϕi*1, *ϕi*1, and *ϕi*3 are the coefficient part of proper use, and *ϕi*4, *ϕi*5, and *ϕi*6 are the coefficient part of improper use. As proper and improper VA uses are two opposite behaviors, when a farmer has a high willingness to perform one behavior, the willingness to perform the other behavior will be relatively low. Therefore, it is assumed that the relationship between the two sets of coefficients is as follows:

$$\begin{cases} \begin{aligned} \varphi\_{i1} + \varphi\_{i4} &= 5 \\ \varphi\_{i2} + \varphi\_{i5} &= 5 \\ \varphi\_{i3} + \varphi\_{i6} &= 5 \end{aligned} \tag{4} \end{aligned} \tag{5}$$

To ensure scientific rigor and practical relevance of the simulation, the 100 farmers were assumed to have a lower-middle level of knowledge at the start of the simulation. It was assumed that *ϕi*0 = 2 and *ϕi*1 = *ϕi*2 = *ϕi*3 = 3, that is, the farmers' three categories of knowledge fluctuated in the range of [1,3].

(4) Farmers' expected return. The farmers' expected return can be calculated by Equations (1) and (2). Farmers' normal return, *G*, follows uniform distribution in [5,9] (in ten thousand yuan). *θ* is the ratio of farmers' increased return from improper VA use to that from proper usage. In general, the higher the knowledge level regarding VA use specification, the lower the probability of an improper return. Therefore, *θ* is correlated with *ϕi*1. To ensure that *θ* is nonnegative, it was assumed that *θ* + *ϕi*1 = 5. Based on the finding of field interview regarding spot checks for pig farmers that were conducted by government regulators each year, the initial value of *q* was set to 0.3. According to the Regulations on Administration of Veterinary Drugs in China, the penalty for improper VA use was set to 30,000 yuan considering the various forms of improper use. Hence, *C*1 = 3. The higher the farming return, the higher the pressure from public opinion and moral pressure when the misconduct is disclosed and sanctioned. Hence, it is assumed that *C*2 = 2 × *G*.

#### **4. Results and Discussion**


**5. Conclusions**

In this work, two-dimensional Saint-Venant equations were implemented by using the leapfrog-style finite difference method for the purpose of studying flood behavior. Largescale heterogeneous parallel solution is implemented using MPI, OpenMP, Pthread, and OpenCL runtime libraries. The heterogeneous strategy is optimized by overlapping communication calculation, setting up work groups, etc., and performance tests from various perspectives are also carried out. Finally, a large-scale heterogeneous parallel solution for 2D SV equations with good performance is obtained. In the future, we hope that our work can become a large-scale heterogeneous parallel solution framework capable of solving SV equations -like PDE. For example, the heat conduction equation as an important PDE should also be able to use our computational framework. It describes how the temperature in a region changes with time, and like the Saint-Venant equation, it is difficult to obtain analytical solutions, and numerical methods are usually used to obtain numerical solutions. There are also some other problems that may also be able to use our computational framework, such as phase transitions, elasticity, electrical potential, etc. The purpose of our current research is to propose a massively heterogeneous parallel framework based on Saint-Venant's equations in domestic advanced computing systems. Therefore, a simple model is used to facilitate heterogeneous parallel implementation. In the future work, we will consider more complex and more types of models to simulate various actual situations (such as floods, tsunamis, dam failures, etc.).

**Author Contributions:** Conceptualization, Q.L. and N.N.; methodology, Q.L. and N.N.; software, Y.Q.; validation, L.G., W.Y. and Z.L.; formal analysis, Q.L.; investigation, Y.Q.; resources, Q.L.; data curation, Y.Q. and X.S.; writing—original draft preparation, Y.Q.; writing—review and editing, Q.L., Z.Z. and J.Z.; visualization, Y.Q. and S.T.; supervision, Q.L.; project administration, N.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key R&D Program of China (Grant No. 2020YFB1709501), GHFund A (No. 20210701) and the Shandong Province Natural Science Foundation (Grant No. ZR201910310143).

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

**Informed Consent Statement:** Not applicable.

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


*4.3. Prediction of Effective Length Considering Pullout Force*

The pullout force distribution in the geosynthetic strip occurred within a limited reinforcement length range. Therefore, it will be possible to achieve a more economical design if the effective length at which the pullout resistance is induced by the pullout force is calculated. The relationship between the pullout force and Li/L (as shown in Figure 10) can be simplified as shown in Figure 11, and it is possible to be used to reflect the experimental results. As mentioned previously, in the cases of GS50W and GS70W, the pullout forces of the reinforcement at normal stress conditions became almost similar at a length ratio (Li/L) of approximately 0.5 L. Subsequently, the induced pullout force decreased and then hardly changed after 0.75 L, at which the strain became approximately 1%. Therefore, the effective length of the geosynthetic strips used in this study was determined as follows: the effective length (LE = 0.5 L) was defined as the length at which the pullout force became almost similar, regardless of the normal stress, and the maximum effective length (LE(max) = 0.75 L) as the distance at which the pullout force hardly changed.

**Figure 11.** Prediction of effective length using the pullout force generated in reinforcement cases.

#### *4.4. Evaluation of Pullout Resistance Considering the Prediction of Effective Length*

To evaluate the effective length prediction method presented in Section 4.3, the pullout strength was evaluated according to the effective length (LE) and maximum effective length (LE(max)), respectively, based on the use of the average resistance method proposed by [23]. The pullout strength was evaluated using the total and effective area methods based on consideration of the extensibility of the geosynthetic strips, and the results are shown in Figure 12 and Table 3.

**Figure 12.** Relationship between normal stress and pullout strength: (**a**) GS50W; (**b**) GS70W.

Regarding the pullout strength of GS50W based on considerations of the maximum effective length (LE(max)), the results of the effective area method and total area method were similar at low normal stress conditions because the pullout force was transmitted to the maximum effective length (LE(max)). However, as the normal stress increased, the pullout strength gradually increased because the pullout force transmitted to the maximum effective length (LE(max)) decreased. For GS50W, the effective area method that considered the effective length (LE) yielded a higher pullout strength in normal stress conditions compared with the total and the effective area methods that considered the maximum effective length (LE(max)) because the distance from the front decreased. The same tendency was observed for GS70W.

**Table 3.** Summary of pullout parameters.

The pullout strengths of GS50W and GS70W with respect to the reinforcement width were compared. GS50W and GS70W exhibited similar pullout strengths based on the total area method. The pullout strength obtained by the effective area method, however, showed that the reinforcement width had an influence on the soil-reinforcement interface adhesion. This was more obvious when the effective length (LE) was applied.

Figure 13 shows the calculation results of the bond coefficient of the geosynthetic strips using the shear strength of the soil and the pullout strength ratio of the soil-reinforcement interface. For all evaluation methods, the bond coefficient slowly decreased and then exhibited a tendency to converge as the normal stress increased. Tatlisoz et al. [33] reported that the soil-reinforcement interface adhesion was sufficient when the bond coefficient was 1.0, and the adhesion was low when the bond coefficient was 0.5 or less. For GS50W and GS70W, the bond coefficient was the lowest when the normal stress was 150 kPa; the respective values were 0.88 and 0.80 in the case of the total area method, respectively. These values are higher than the friction coefficient of soil (0.71) and are, thus, considered stable. Therefore, for a more efficient design of geosynthetic strips, the effective length (LE) and maximum effective length (LE(max)) can be considered. However, it is reasonable to apply the effective length (LE) because there is no significant difference in the pullout force between the effective length (LE) and maximum effective length (LE(max)).

**Figure 13.** Relationship between normal stress and bond coefficient: (**a**) GS50W; (**b**) GS70W.


**Contents**

## **About the Editors**

#### **Sang Heon Lee**

Professor Sang Heon Lee received his doctoral degree in oceanography from the University of Alaska, Fairbanks, US. He worked as a senior research scientist at the Korea Polar Research Institute from 2007 to 2010. Currently, he has been working as a full professor at Pusan National University since September 2010. His major research interest is on marine ecosystems, mainly phytoplankton in various oceans from the Antarctic Ocean to the Arctic Ocean.

#### **P.S. Bhavya**

Dr. Bhavya received her doctoral degree in marine biogeochemistry from the Physical Research Laboratory, Ahmedabad, India. She was a recipient of the Technical University of Munich Foundation Fellowship 2020–2021. Currently, she has been working as a research scientist at Leibniz Institute for Baltic Sea Research since March 2021. Her major research interest is on primary production and nitrogen fixation rate estimation using 13C and 15N tracer techniques, respectively. She also worked on the impacts of anthropogenic interferences and climatological variations on the marine carbon and nitrogen cyclings.

#### **Bo Kyung Kim**

Dr. Bo Kyung Kim has been working as a Post-Doc of the Division of Polar Ocean Sciences, Korea Polar Research Institute, South Korea, since 2017. She received her doctoral degree in Oceanography from Pusan National University, South Korea. Her area of expertise is in the field of primary production by phytoplankton using stable isotopes and biomolecules (amino acids, protein, carbohydrate, and lipid) under various environmental conditions in Polar Oceans.

### *Editorial* **Marine Nitrogen Fixation and Phytoplankton Ecology**

**Sang Heon Lee 1,\*, Panthalil S. Bhavya 2 and Bo Kyung Kim 3**

#### **Marine Nitrogen Fixation and Phytoplankton Ecology**

Many oceans are currently undergoing rapid changes in environmental conditions such as warming temperature, acidic water condition, coastal hypoxia, etc. Obvious warming and acidification in various oceans, from polar oceans to tropical oceans, was well reported in the fifth Assessment Report (AR5) by the Intergovernmental Panel on Climate Change [1]. These climate-driven environmental changes could lead to dramatic alterations in the biology and ecology of phytoplankton as major primary producers and biogeochemical drivers and subsequently impact the growth and survival of other marine organisms [2–5]. Consequently, the entire marine ecosystem and global biogeochemical cycles would be very different from what we have now.

Marine phytoplankton are an important indicator of marine ecosystem changes in response to climate-induced environmental change [2,4–6], since they are major primary producers that consolidate solar energy into organic matter and transfer it to marine ecosystems throughout the food web. Recently, increasing numbers of roles of small phytoplankton as a major contributor to primary production have been reported in various oceans, and it has been found that small phytoplankton could become more prominent under an ocean warming scenario [2,4,6–9]. The ecological and biogeochemical traits of small phytoplankton are very different from those of large phytoplankton [3,4,9]. Therefore, it is urgent to verify the different biological and chemical properties of small phytoplankton and understand their ecological roles under ongoing environmental changes.

It is widely known that nitrogenous nutrients are key components of primary production in the ocean, and the only biological source of such nutrients is diazotrophic N2 fixation. Similar to primary producers, N2 fixers (diazotrophs) are also vulnerable to changing environmental conditions. It was found that the polar regions can be introduced to diazotrophic activity under warming conditions, and the increased N availability can lead to elevated primary productivity [10–12]. However, if ocean acidification continues in the future, the diazotrophic activity is likely to decrease [13]. The documentation and processing of information on N2 fixation is highly important as its role in the N cycle of the oceans is critical for preparing future projections on the effects of global environmental changes on the biogeochemical balance of the ecosystems. The employment of enriched isotopic tracers of dinitrogen (15N2) [14], natural abundance studies of the N2 isotopes in particulate and dissolved forms of N, and the introduction of a simple enzyme-based assay, the acetylene reduction method, have opened possibilities expand our knowledge of biological N2 fixation in the global oceans. The measured N2 fixation rates in a *Trichodesmium* bloom in the Arabian Sea showed the highest depth-integrated values, ranging from ∼0.1 to 34 mmol N m−2 d−1 [15]. The highest depth-integrated N2 fixation rates in non-bloom conditions are obtained in the western tropical South Pacific (638 ± 1689 μmol N m−2 d−1), which are higher than those of the subtropical North Atlantic (182 ± 479 μmol N m−2 d−1) and North Pacific (118 ± 101 μmol N m−2 d−1). N2

**Citation:** Lee, S.H.; Bhavya, P.S.; Kim, B.K. Marine Nitrogen Fixation and Phytoplankton Ecology. *Water* **2022**, *14*, 1638. https://doi.org/ 10.3390/w14101638

Received: 16 May 2022 Accepted: 18 May 2022 Published: 20 May 2022

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**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/).

fixation rates in the eastern South Pacific are measured to be 86 ± 99 μmol N m−2 d−1, whereas the southern Indian Ocean (<20 μmol N m−2 d−1, [12]) rates are low.

Recent genetic studies on microbial communities report niches of diazotrophic activities which were previously unknown. Most measurements on N2 fixation rates are from bulk samples, which might have possibly comprised cyanobacteria and other diazotrophs. The infusion of the cell-specific N2 fixation method using a nanoscale mass spectrometer gave a high-definition perspective of cellular-level N2 fixation; more importantly, it provides knowledge about diazotrophy on an individual-species basis [16]. This novel method helps to identify the species that are capable of N2 fixation, and to determine their role in transferring fixed N2 to the autotrophs in association with them. However, information on global N2 fixation rates and cell-specific N2 fixation so far is scant, and inconsistent in different spatio-temporal scales due to a lack of sufficient measurements. To tackle the perplexing response of diazotrophs, a detailed assessment of the diazotrophic community response toward the changing environmental conditions needs to be recorded thoroughly. Considering the fundamental roles of phytoplankton in marine ecosystems and global biogeochemical cycles, it is important to understand phytoplankton ecology and N2 fixation as a potential N source in various oceans.

This Special Issue covers a wide range of geographic study regions from pole to pole and from coastal systems to open oceans, including Terra Nova Bay, Ross Sea in the Antarctic Ocean, Northern Bering Sea, Chukchi Sea, Canada Basin, and Kongsfjorden, Svalbard in the Arctic Ocean, Western South China Sea, Northern East China Sea, Northwestern Pacific Ocean, and Jaran Bay in South Korea. In this Special Issue, we present a total of 11 articles offering ecological and biogeochemical baselines as indicators for the changes in marine environments and ecosystems driven by global climate changes. In particular, articles on the compositions of intracellular biochemical components such as proteins, lipids, and carbohydrates of phytoplankton could provide important information for their physiological conditions and the nutritional value of organic matter available to grazers [17,18]. Recently, phytoplankton-derived transparent exopolymer particles (TEPs) are known for making a considerable contribution to the organic matter pools and thus marine biogeochemical cycles in aquatic environments [19]. Ref. [20] investigated monthly TEPs concentration and particulate organic carbon (POC) concentration in Jaran Bay, a large shellfish aquaculture site in a southern coastal region of Korea. They found that the contribution of TEPs ranged from 2.4% to as high as 78.0% of the POC concentration, which indicated that TEPs-C could be a significant contributor to the POC pool in a coastal bay. Since little information on the monthly variation in TEPs is available, their investigation on the TEPs could be a very important baseline in a coastal bay system. Moreover, in Jaran Bay, ref. [21] also observed the seasonal and spatial variations in the biochemical compositions of phytoplankton. They found that the dominant biochemical component was carbohydrates (51.8 ± 8.7%), followed by lipids (27.3 ± 3.8%) and proteins (20.9 ± 7.4%). Large phytoplankton and the P × (PO4 3−1/16 × NO3 −) and NH4 + concentrations were identified as major controlling factors for food material (FM) in Jaran Bay. Over a year at Jang Bogo Station (JBS) in Antarctica, ref. [18] measured bi-weekly biochemical compositions of particulate organic matter (POM) and concentrations of TEPs. The high composition of lipids and proteins indicated a good food source in summer, whereas stably low concentrations of carbohydrates and lipids were utilized for long-term energy storage in the survival of phytoplankton in winter. They found that TEPs have a longer residence time than POC, and the contribution of TEPs-C to the POC pool could be important in the Ross Sea. The biochemical composition of POM deriving mainly from phytoplankton in the Chukchi Sea, Arctic Ocean were presented by [22]. They investigated the biochemical components of phytoplankton and their spatial pattern. Carbohydrates were the predominant macromolecules, accounting for 42.6% in the Chukchi Shelf and 60.5% in the Canada Basin, followed by lipids and proteins. Based on their study, the biochemical compositions of phytoplankton could be considerably different in the regions of the Arctic Ocean. In a similar region, ref. [23] estimated the bioavailable fraction of POM through enzymatic hydrolysis that can be utilized by higher trophic levels. Based on their results, nutrient, temperature, meltwater and different size classes of phytoplankton (micro and picophytoplankton) were the main factors of the compositional variations and the spatial distributions. More studies on the changes in the biochemical compositions of phytoplankton should be conducted under future environmental changes.

In terms of elemental composition and primary productivity driven by phytoplankton, ref. [24] determined the combined physiological–elemental ratio changes in two phytoplankton species, *Scrippsiella trochoidea* (Dinophyceae) and *Heterosigma akashiwo* (Raphidophyceae). They found higher average ratios of particulate organic nitrogen (PON) to chlorophyll-a (Chl-*a*) and POC to Chl-*a* in *S. trochoidea* than those of *H. akashiwo.* However, the authors observed similar ratios of POC/PON of the two microalgae. These results can be used to develop physiological models for phytoplankton, with implications for the marine biogeochemical cycle. In Kongsfjorden's high-latitude open fjord systems, ref. [25] found that the turbidity associated with glacier meltwater impacted the penetration depth of light and that nutrients could cause the lower productivity rates of phytoplankton. They found that picophytoplankton was largely based on regenerated nutrients, even more productive than that suggested by their biomass contribution and their nitrogen uptake. For a better understanding of the biochemical traits of small phytoplankton, ref. [26] conducted field measurements in the biologically productive northern Bering and Chukchi seas. The contributions of small phytoplankton to the total primary production were 38.0% (SD = ±19.9%) and 25.0% (SD = ±12.8%) in 2016 and 2017, respectively. They found that small phytoplankton synthesize different biochemical compositions with nitrogen-rich POC from large phytoplankton.

The three articles below on phytoplankton communities and cyanobacterial contributions provide significant ecological predictions under expected warming ocean conditions. In the North Pacific Ocean, ref. [27] determined the picocyanobacterial contribution and the total primary production. The average picocyanobacterial contributions to the carbon uptake rates were 45.2% in the tropical Pacific region and 70.2% in the subtropical and temperate Pacific region, respectively. In addition, their contributions to the nitrogen uptake rates were significantly higher than those of carbon uptake rates. Based on high-performance liquid chromatography (HPLC) pigment analysis, ref. [28] investigated spatiotemporal variations in phytoplankton community compositions in the northern East China Sea (ECS), the largest marginal sea in the north-western Pacific Ocean. Overall, the two major phytoplankton groups were diatoms (32.0%) and cyanobacteria (20.6%) in the northern ECS, and the two groups were negatively correlated. In the western South China Sea, ref. [29] investigated the distinct seasonal variation in phytoplankton community structure related to different oceanographic conditions and observed a major shift from a diatom-dominated regime in winter to a cyanobacteria-dominated system in summer. The authors found that the increased overall abundance of phytoplankton and cyanobacteria during the summer was caused by upwelling and enriched eddy activity, whereas the abundant symbiotic cyanobacteria–diatom association during the winter was mainly due to the influence of the cool temperature. Long-term monitoring of the phytoplankton communities and the picocyanobacterial contributions should be conducted for a better understanding of the ecological impacts of the global warming scenario, with a focus on the ecological roles of picocyanobacteria.

This Special Issue covers various articles on N2 fixation and aspects of marine phytoplankton ecology, such as biodiversity, distribution, biomass, photosynthetic traits, biochemical compositions, productivity, etc., in various oceans, including polar oceans. Finally, we hope that this Special Issue provides ecological and biogeochemical baselines that broaden our existing knowledge on the current and ongoing changes in marine ecosystems in response to global climate change.

**Author Contributions:** Conceptualization, S.H.L., P.S.B. and B.K.K.; writing—review and editing, S.H.L., P.S.B. and B.K.K.; supervision, S.H.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** Sang Heon Lee was financially supported by the project titled "Improvements of ocean prediction accuracy using numerical modeling and artificial intelligence technology" funded by the Ministry of Oceans and Fisheries, Korea (PM62850). P.S. Bhavya was supported by Leibniz Institute for Baltic Sea Research and N-Amazon project (DFG 08/2020-07/2022) by German Research Foundation (Deutsche Forschungsgemeinschaft). Support for Bo Kyung Kim was provided by the Korean government (MOF;<PHONE_NUMBER>).

**Acknowledgments:** We thank all the authors who submitted their papers to this Special Issue. We are especially indebted to all reviewers for providing their valuable review comments for individual articles of this volume.

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

#### **References**

### *Article* **Seasonal Variations in the Biochemical Compositions of Phytoplankton and Transparent Exopolymer Particles (TEPs) at Jang Bogo Station (Terra Nova Bay, Ross Sea), 2017–2018**

**Sanghoon Park 1, Jisoo Park 2, Kyu-Cheul Yoo 2, Jaeill Yoo 2, Kwanwoo Kim 1, Naeun Jo 1, Hyo-Keun Jang 1, Jaehong Kim 1, Jaesoon Kim 1, Joonmin Kim 1 and Sang-Heon Lee 1,\***

**Abstract:** The biochemical composition of particulate organic matter (POM) mainly originates from phytoplankton. Transparent exopolymer particles (TEPs) depend on environmental conditions and play a role in the food web and biogeochemical cycle in marine ecosystems. However, little information on their characteristics in the Southern Ocean is available, particularly in winter. To investigate the seasonal characteristics of POM and TEPSs, seawater samples were collected once every two weeks from November 2017 to October 2018 at Jang Bogo Station (JBS) located on the coast of Terra Nova Bay in the Ross Sea. The total chlorophyll-*a* (Chl-*a*) concentrations increased from spring (0.08 ± 0.06 μg L−1) to summer (0.97 ± 0.95 μg L−1) with a highest Chl-*a* value of 2.15 μg L−1. After sea ice formation, Chl-*a* rapidly decreased in autumn (0.12 ± 0.10 μg L−1) and winter (0.01 ± 0.01 μg L−1). The low phytoplankton Chl-*a* measured in this study was related to a short ice-free period in summer. Strong seasonal variations were detected in the concentrations of proteins and lipids (one-way ANOVA test, *p* < 0.05), whereas no significant difference in carbohydrate concentrations was observed among different seasons (one-way ANOVA test, *p* > 0.05). The phytoplankton community was mostly composed of diatoms (88.8% ± 11.6%) with a large accumulation of lipids. During the summer, the POM primarily consisted of proteins. The composition being high in lipids and proteins and the high caloric content in summer indicated that the phytoplankton would make a good food source. In winter, the concentrations of proteins decreased sharply. In contrast, relatively stable concentrations of carbohydrates and lipids have been utilized for respiration and long-term energy storage in the survival of phytoplankton. The TEPS values were significantly correlated with variations in the biomass and species of the phytoplankton. Our study site was characterized by dominant diatoms and low Chl-*a* concentrations, which could have resulted in relatively low TEP concentrations compared to other areas. The average contributions of TEP-C to the total POC were relatively high in autumn (26.9% ± 6.1%), followed by those in summer (21.9% ± 7.1%), winter (13.0% ± 4.2%), and spring (9.8% ± 3.1%).

**Keywords:** phytoplankton; macromolecular composition; transparent exopolymer particles; Ross Sea; polar night

#### **1. Introduction**

The Ross Sea is one of the most productive regions in the Southern Ocean, with strong seasonal variations in the biomass and production of phytoplankton [1]. The high phytoplankton biomass commonly observed in coastal areas during spring is linked to the stabilization of the water column and the release of micronutrients during ice melting [2]. However, phytoplankton growth is limited by micronutrient availability in ice-free waters

**Citation:** Park, S.; Park, J.; Yoo, K.-C.; Yoo, J.; Kim, K.; Jo, N.; Jang, H.-K.; Kim, J.; Kim, J.; Kim, J.; et al. Seasonal Variations in the Biochemical Compositions of Phytoplankton and Transparent Exopolymer Particles (TEPs) at Jang Bogo Station (Terra Nova Bay, Ross Sea), 2017–2018. *Water* **2021**, *13*, 2173. https:// doi.org/10.3390/w13162173

Academic Editor: Oscar Romero

Received: 10 June 2021 Accepted: 5 August 2021 Published: 8 August 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/).

under conditions of high irradiance in summer [3,4]. Furthermore, the changes in complex hydrographic features such as sea ice formation during the transition between late autumn and winter are likely to substantially influence the distribution of phytoplankton [5] and consequently the biochemical processes in the water columns [6–8]. Recently, sea ice concentration has increased significantly in the Ross Sea [9] and the annual ice-free period has shortened [10], which could potentially reduce water stratification and irradiance throughout the surface water. These changes can influence the composition and biomass of phytoplankton [11,12] and consequently the biogeochemical cycles and the food web structure in the Southern Ocean [13].

Through photosynthesis, phytoplankton synthesize inorganic nutrients into various organic compounds, which mainly consist of carbohydrates, proteins, and lipids [14]. The macromolecular composition of phytoplankton can change depending upon various environmental factors, such as nutrient concentrations, light intensity, and the species composition of the phytoplankton [14–16]. In the Southern Ocean, phytoplankton growth is generally limited by irradiance in spring [17] and by iron in summer [3,4,18]. Previous studies on the macromolecular composition of phytoplankton in the Ross Sea have been conducted primarily during spring and summer seasons [19–22]. Only a few studies [23] on the biochemical characteristics of phytoplankton have been previously conducted throughout a single year in the Southern Ocean. The Jang Bogo Station (JBS), located on the coast of Terra Nova Bay (TNB) in the Ross Sea, is one of the overwintering science stations in Antarctica. This study was conducted for approximately one year from November 2017 to October 2018 to monitor seasonal variations in the biochemical compositions of particulate organic matter (POM) and transparent exopolymer particles (TEPs) strongly related to phytoplankton.

TEPs are carbon-rich gel organic particles stainable with Alcian Blue, a specific dye for acidic polysaccharides, and phytoplankton are considered to be the main source of TEPs and their precursors [24,25]. TEPs are formed naturally by self-assembly of dissolved precursors, which appreciably contribute to the dissolved organic matter pool in the water column [26]. The formation of TEPs is critical as a significant pathway in which dissolved organic matter is transformed into POM [26]. Furthermore, TEPs are considered to play an important role in carbon cycling [27]. Because of their great stickiness, TEPs increase the aggregation rate of particles, forming marine snow, and enhance the carbon sinkage into deep waters in the marine system [28,29]. The distribution of TEPs is affected by environmental factors such as nutrient conditions [30,31] and the species and growth conditions of phytoplankton [32–34]. However, little information on TEPs' abundances and regulating ecological factors is currently available for the Southern Ocean [25,35,36]. Most previous studies on TEPs have described their formation and dynamics either under experimental conditions [25,37] or during phytoplankton bloom seasons [36,38]. Our study is the first to show seasonal variation in TEPs at the JBS in the Ross Sea. The present study aimed to (1) identify the seasonal characterization of phytoplankton, (2) investigate the seasonal variations in the macromolecular composition of phytoplankton and TEPs concentrations at the JBS throughout the year, and (3) understand the roles of macromolecules and TEPs' contributions to the organic carbon pool in Antarctic coastal environments.

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

#### *2.1. Study Area and Water Sampling*

The JBS is located on the coast of TNB in the Ross Sea, Antarctica (74◦37 39.59 S, 164◦14 25.75 E) (Figure 1). Surface seawater samples for biochemical parameters from a continuously flowing water tank at the station were collected biweekly from 3 November 2017 to 26 October 2018. In situ seawater temperature and salinity were measured by YSI professional plus. To verify seasonal differences, our entire study period was divided into four seasons: spring (November to December), summer (January to February), autumn (March to April), and winter (May to October).

**Figure 1.** Location of the Jang Bogo Station (JBS) in Terra Nova Bay, Ross Sea.

#### *2.2. Nutrient Analysis*

Water samples for major inorganic nutrients (nitrite + nitrate, ammonium, silicate, and phosphate) were filtered through a 47 mm GF/F (0.7 μm pore size), and the filtered water samples (45 mL) were stored frozen at −80 ◦C in a 50 mL conical tube until analysis. The concentrations of inorganic nutrients were analyzed using an automated nutrient analyzer (Quaatro, Seal Analytical, Norderstedt, Germany) based on the manufacturer's instructions at the National Institute of Fisheries Science (NIFS).

#### *2.3. Chlorophyll-a Analysis*

Seawater samples (0.5 L) for the total chlorophyll-*a* (Chl-*a*) concentration were filtered through 0.7 μm pore size glass fiber filters (GF/F; Whatman, Maidstone, UK, 25 mm). Samples (1 L) for size-fractionated Chl-*a* concentration were passed sequentially through 20 and 2 μm Nuclepore membrane filters (20 μm; GVS, Sanford, Sioux Falls, SD, USA, 47 mm; 2 μm; Whatman, Maidstone, UK, 47 mm) and 0.7 μm pore size Whatman GF/F filters (47 mm). Chl-*a* was extracted with 90% acetone for 24 h in the dark at 4 ◦C following [39], and the Chl-*a* concentrations were measured using a Trilogy fluorometer (Turner Designs, San Jose, CA, USA) after calibration.

#### *2.4. Particulate Organic Carbon and Nitrogen Analysis*

For the particulate organic carbon (POC), nitrogen (PON), and δ13C analyses of POM, 0.5 L of each seawater sample was filtered through Whatman GF/F filters (25 mm). The GF/F filters were kept frozen at −80 ◦C until mass spectrometric analysis in the stable isotope laboratory of the University of Alaska Fairbanks after HCl fuming overnight to remove carbonate.

#### *2.5. Phytoplankton Community*

Water samples (2–3 L) for photosynthetic pigment analysis were filtered through Whatman GF/F filters (47 mm). The GF/F filters were wrapped in aluminum foil to prevent photooxidation and stored at −80 ◦C until the extraction and analysis of pigments at the home laboratory in Pusan National University, Korea. The phytoplankton pigments were extracted in 100% acetone (5 mL) with canthaxanthin (100 μL) as an internal standard for 24 h in the dark at 4 ◦C and analyzed using a high-performance liquid chromatography (HPLC- Agilent Infinite 1260, Santa Clara, CA, USA) system. The contribution of each phytoplankton class to the total Chl-*a* concentration was estimated from the ratio of the different pigments to Chl-*a* based on the CHEMTAX program [40–42]. The ratio of initial pigment to the total Chl-*a* concentrations for the determination of the phytoplankton classes at the JBS was based on diverse phytoplankton groups obtained around the Ross Sea [43].

#### *2.6. Macromolecular Compositions*

Seawater samples (0.5 L) for macromolecular components (carbohydrates, proteins, and lipids) of total phytoplankton were filtered through Whatman GF/F filters (47 mm). The filters were immediately frozen and stored at −80 ◦C until analysis at the home laboratory. The content of total carbohydrates was determined by the phenol–sulfuric acid method from [44] with a glucose standard (1 mg mL−1, 108 Sigma, St. Louis, MO, USA), and the protein concentration of POM was analyzed according to [45] with a protein standard solution (2 mg mL−1, Sigma). For lipid extraction and assay, the methods described by [46] and [47] were used with the chloroform–methanol mixture (1:2 *v*/*v*). Tripalmitin solution was used as a standard for the protein concentration. The detailed method for the determination of macromolecular compositions was described by [48].

The sum of carbohydrate, protein, and lipid concentrations of POM was referred to as the food material (FM) [49]. The calorific value (Kcal g−1) and contents (Kcal m−3) for each macromolecule component were calculated using the equation from [50] with conversion factors of 0.041, 0.055, and 0.095 for carbohydrates, protein, and lipid, respectively.

#### *2.7. TEP Analysis*

TEPs sampling began on 17 December, which was later than the other samplings because Alcian Blue solution, which is prohibited for air shipping, was needed for the TEP sampling but was delayed by a research vessel, *Araon*. The missing TEP data would not have affected our results in this study since the sampling time before 17 December was biologically stagnant with a low Chl-*a* biomass. Seawater samples (0.3 L) for TEPs were filtered at a low constant vacuum onto 0.4 μm pore sized polycarbonate membrane filters (ADVANTEC; 25 mm, Toyo Roshi Kaisha, Tokyo, Japan) and stained with Alcian Blue solution. The concentrations of TEPs were estimated according to the method of [35], and their units were used as gum xanthan equivalents (μg Xeq. L−1). The filters for TEP concentration were extracted with 80% sulfuric acid for 3 h, and the absorbance of the TEPs was measured by a spectrophotometer at 787 nm using Xanthan gum as a standard. Estimations of TEP carbon (TEP-C) for converting from micrograms of gum xanthan equivalents to micrograms of carbon were obtained from the conversion factor of 0.51 μg Xeq. L−1 reported by [51].

#### *2.8. Data Treatment and Statistical Analyses*

All statistical analyses were performed using the statistical software SPSS 12.0 K for Windows. Pearson's correlation analysis at an alpha value <0.01 was used to estimate the correlation between Chl-*a*, TEP concentrations and nutrients measured in the water samples. One-way ANOVA at an alpha value <0.05 was applied to identify differences in macromolecular concentrations, phytoplankton compositions of different sizes, and TEP-C contributions for each season. Principal component analysis (PCA) was performed using the prcomp function in the R stats package (R studio, version 1.1.463, Inc., Boston, MA, USA) to evaluate the relationship among the environmental factors (e.g., salinity, temperature, nitrite + nitrate, ammonium, and species of phytoplankton) and biochemical variables (e.g., POC, Chl-*a*, POM originating from phytoplankton, and TEP) and to identify relatively significant factors during the study period. Eigenvalues (>1.0) were considered to determine the number of principal components and the rotated eigenvectors were the results using the Varimax method with Kaiser normalization.

#### **3. Results**

#### *3.1. Physical and Chemical Conditions in the Study Area*

The landfast sea ice in our study area completely melted on 13 February 2018 and formed again on 24 February 2018. During the overall study period, surface water temperature and salinity ranged from 0.74 to −1.12 ◦C (mean ± S.D. = −0.69 ± 0.51 ◦C) and 30.88 to 32.51 (31.91 ± 0.38), respectively. Temperature increased and salinity decreased toward summer, largely caused by melting sea ice. Little seasonal variation in temperature (−1.06 ± 0.06 ◦C) and salinity (32.06 ± 0.03) was detected from May to October (Figure 2).

**Figure 2.** Salinity and temperature variations at the JBS during our sampling period.

Seasonal variations in major inorganic nutrient concentrations are shown in Figure 3. The concentrations of nitrate + nitrite, silicate, and phosphate were lowest in summer (11.0, 39.2, and 0.9 μM for nitrate + nitrite, silicate, and phosphate, respectively), whereas the ammonium concentration was lowest in winter 2018 (0.5 μM). In our study, all major inorganic nutrients had strong relationships with each other except for ammonium (r = 0.97, phosphate vs. nitrate + nitrite; r = 0.98, phosphate vs. silicate; r = 0.98, and nitrate + nitrite vs. silicate, *p* < 0.01, n = 24).

#### *3.2. Chl-a Concentrations*

The total Chl-*a* concentrations ranged from 0.01 to 2.15 μg L−1 (0.21 ± 0.50 μg L−1) at the JBS during our study period from November 2017 to October 2018. The total Chl-*a* concentrations increased from early December 2017 and were highest (2.15 μg L−1) in February 2018 (Figure 4). The concentration decreased rapidly as sea ice started to form between February and March, 2018. The average total Chl-*a* concentrations were 0.08 ± 0.06, 0.97 ± 0.95, 0.12 ± 0.10, and 0.01 ± 0.01 μg L−1 during spring, summer, autumn, and winter, respectively. The seasonal variations in the contributions of different size-fractionated Chl-*a* levels to the total Chl-*a* concentration are shown in Figure 5. The average contribution of micro-sized phytoplankton (>20 μm) to the total Chl-*a* in November 2017 to October 2018 was 39.7% ± 10.7%, and those of nano-sized (2–20 μm) and pico-sized (<2 μm) phytoplankton were 39.5% ± 10.5% and 20.8% ± 5.9%, respectively. Based on statistical analysis (one-way ANOVA test, *p* > 0.05), there were no significant differences in the average contributions of each size-fractionated Chl-*a* class over different seasons.

#### *3.3. POC and PON*

The POC and PON concentrations were 49.3–239.5 μg L−1 (92.5 ± 46.6 μg L−1) and 7.0–42.7 μg L−1 (14.0 ± 10.4 μg L−1), respectively. A strong linear relationship was found between POC and PON concentrations (PON = POC × 0.2192, r2 = 0.98, *p* < 0.01). Unlike the Chl-*a* concentration, the POC concentration did not largely decrease after bloom (45.6% and 89.5% for POC and Chl-*a*, respectively) and was persistent throughout the winter period

(Figure 4). The average C/N ratios of POM were 7.1 (±1.0), 5.9 (±0.4), 8.3 (±0.7), and 10.6 (±0.9) during spring, summer, autumn, and winter, respectively.

**Figure 3.** Variations in each major inorganic nutrient concentration at the JBS during our sampling period.

**Figure 4.** Variations in POC and total Chl-*a* concentrations at the JBS during our sampling period.

**Figure 5.** Contributions of different size-fractionated Chl-*a* concentrations to the total Chl-*a* concentration (**a**) and trans parent exopolymer particles (TEPs) concentrations (**b**) at the JBS during our sampling period.

#### *3.4. Phytoplankton Community Composition*

A seasonal variation in the phytoplankton community is shown in Figure 6. Seasonally, diatoms accounted for 95.2% ± 6.6% of the phytoplankton community during winter from early July to late October in 2018, whereas dinoflagellates were mostly the second dominant group in autumn (14.9% ± 5.9%). *Phaeocystis antarctica* was dominant during spring (10.9% ± 12.4%), and cryptophytes were observed to be relatively high in summer (5.2% ± 5.3%). Other phytoplankton communities were rarely observed in this study.

**Figure 6.** Contribution of phytoplankton communities at the JBS during our sampling period.

#### *3.5. Macromolecular Composition of Phytoplankton*

Strong seasonal variations in the concentrations of proteins and lipids (one-way ANOVA test, *p* < 0.05) were found, whereas no significant difference in carbohydrate concentrations was observed during the season (one-way ANOVA test, *p* > 0.05) in this study (Figure 7a). During the observation period, the overall carbohydrate, protein, and lipid concentrations of POM were 50.3–167.5 μg L−1 (123.7 ± 31.7 μg L−1), 0–226.1 μg L−1 (39.3 ± 68.9 μg L−1), and 51.1–141.9 μg L−1 (73.8 ± 27.1 μg L−1), respectively (Figure 7a). The average contributions of carbohydrates, proteins, and lipids to the total POM during spring were 53.0% ± 11.0%, 9.6% ± 11.5%, and 37.3% ± 8.4%, respectively (Figure 7b). The average contributions of each component in summer were 33.3% ± 5.8%, 39.1% ± 5.2%, and 27.6% ± 2.2%, respectively. In autumn and winter, the carbohydrate, protein, and lipid compositions were 58.7% ± 15.0%, 10.5% ± 12.2%, and 30.8% ± 5.1% and 67.4% ± 5.5%, 0.1% ± 0.3%, and 32.5% ± 5.4%, respectively. The contribution of proteins was observed to be high in summer but decreased sharply after autumn. Based on the sum of each macromolecular composition, the highest averaged FM concentration was observed in summer (437.8 ± 76.0 μg L−1), followed by autumn (239.6 ± 45.4 μg L−1), spring (203.3 ± 84.8 μg L−1), and winter (181.2 ± 34.5 μg L−1) (Figure 7a). In comparison, the averaged caloric contents of FM had the highest value in summer (2.7 ± 0.5 Kcal m−3) followed by autumn (1.4 ± 0.4 Kcal m−3), spring (1.3 ± 0.5 Kcal m−3), and winter (1.1 ± 0.2 Kcal m−3) (Figure 8). Proteins were rarely detected whereas lipids and carbohydrates remained relatively constant during winter.

**Figure 7.** Variations in concentrations (**a**) and relative contributions (**b**) of carbohydrate (CHO), protein (PRT), and lipid (LIP) concentrations (a) at the JBS during our sampling period.

**Figure 8.** Seasonal variations in the caloric content of FM at the JBS during our sampling period.

#### *3.6. TEP Concentration*

The TEP concentration ranged from 10.2 to 96.1 μg Xeq. L−1 (32.7 ± 24.6 μg Xeq. L−1) during our observation period from December 2017 to October 2018 (Figure 5b). The highest TEP concentration was observed during February, and the TEP concentrations were nearly stable during the winter period after April (18.3 ± 6.9 μg Xeq. L−1). Overall, the TEP concentrations were significantly correlated with Chl-*a* and fucoxanthine concentrations in this study (Figure 9).

**Figure 9.** Correlations between log TEP concentrations and log Chl-*a* (**a**) and between log TEP concentrations and log fucoxanthine (**b**) at the JBS during our sampling period.

The concentrations of TEP-C ranging from 7.6 to 62.8 μg Xeq. L−1 (24.5 ± 18.4 μg Xeq. L−1) were significantly different among each season (one-way ANOVA test, *p* < 0.05). The average

contributions of TEP-C to the total POC were relatively higher in autumn (26.9% ± 6.1%), followed by summer (21.9% ± 7.1%), winter (13.0% ± 4.2%), and spring (9.8% ± 3.1%) (Figure 10).

**Figure 10.** Seasonal variations in percent TEP-C of total POC at the JBS during our sampling period. *3.7. PCA*

PCA was conducted to determine major environmental factors controlling macromolecular concentrations, and TEPs primarily originated from phytoplankton during our sampling period (Figure 11). As a result of the PCA, the first and second principal components explained 64.8% and 16.8%, respectively, of the biochemical variables relative to the environmental factors during our research period. The first principal component was positively correlated with total Chl-*a* concentrations, POC, TEPs, lipids, proteins, diatoms, and dinoflagellates (eigenvectors of 0.957, 0.869, 0.911, 0.854, 0.894, 0.975, and 0.881, respectively) but negatively correlated with NO3 + NO2 (eigenvectors of −0.773). The second principal component was positively correlated with NH4, water temperature, cryptophytes, and *P. antarctica* (eigenvectors of 0.909, 0.704, 0.851, and 0.828, respectively) but had negative loadings for salinity (eigenvectors of −0.719). However, we did not find a correlation between carbohydrates with either the first or second principal components.

**Figure 11.** Principal component analysis based on environmental factors and biochemical variables.

#### **4. Discussion**

#### *4.1. Environmental Conditions*

The seasonal variations in temperature and salinity at the JBS were lower than those previously reported from other studies in the Antarctic coastal environment [52,53]. In spring and summer, relatively large variations in temperature and salinity were observed with the melting of sea ice, but they were nearly stable during the winter period, as expected. The ice-free period in our study was only 12 days, which is a relatively shorter period compared with a previous study in which the ice-free period lasted more than a month [23].

The concentrations of major inorganic nutrients were generally abundant throughout the study period, although relatively lower concentrations were found during the phytoplankton growth season in summer. Because of the consistently high concentrations, the growth of phytoplankton in Antarctic coastal waters is not limited by major inorganic nutrients [54,55]. We estimated nutrient utilization following [55] by subtracting the average concentration in summer from the average concentration in winter, which was largely not affected by sea ice melting or biological uptake. Although nitrate and phosphate in our study (14.7 and 1.0 μM, respectively) were within the range of net nutrient utilization values (8.9–23.0 and 0.9–1.6 μM, respectively) measured in a previous study in TNB [55], the utilization of silicate was noticeably higher in our study (29.0 μM) than in [55] (12.8–23.0 μM). Silicate is necessary for the growth of diatoms [55], which is consistent with our pigment results for diatom-dominant phytoplankton communities observed at JBS in our study.

#### *4.2. Seasonal Variations in Biomass and Major Species Compositions of Phytoplankton*

Large variations in both Chl-*a* and POC concentrations were observed in spring and summer, but they were nearly stable in winter. Generally, Chl-*a* concentrations in the Ross Sea reach their maximum in spring and decline thereafter, and then the second peak can be observed again in summer [17,56]. In this study, one short peak of Chl-*a* concentration was distinct in summer. This difference could be due to different locations between this and other studies. This study was conducted at the JBS, which is in a coastal area. Consistently, the authors of [57] reported that a phytoplankton bloom appeared in coastal waters of the Ross Sea in late summer. In addition, the maximum biomass of phytoplankton was reported in February, although interannual variability in biomass was observed in the Ross Sea according to [58]. The maximum concentration of Chl-*a* at the JBS in 2018 was relatively low compared to those from previous studies in other Antarctic coastal regions in summer [52,53,59,60]. Consistently, the maximum concentration of POC in summer also showed a relatively lower value compared with those of previous studies conducted in coastal Antarctica [20,61,62]. Our maximum Chl-*a* concentration (2.15 μg L−1) was also relatively lower than that of [23], which was previously conducted in 2015 (4.29 μg L−1). In addition, the average total Chl-*a* concentrations were lower for all seasons during this study compared with those in 2015 [23]. This could have been due to different growing season periods between the two studies at the JBS. This study had a relatively shorter icefree growing season (approximately 12 days) than that in 2015 (>1 month). The considerable contribution of nano-sized phytoplankton (>40%) in spring and summer persisted through winter in this study whereas micro-sized phytoplankton (>40%) continued to dominate in the autumn and winter in 2015 [23]. These results could support the results in a previous study reporting that the phytoplankton community structure in winter could be determined in a previous season [5].

Seasonal variations in Chl-*a* concentrations were also reflected in the POC and PON concentrations in this study, suggesting that the majority of POM in Antarctic coastal waters originated directly from phytoplankton [20]. Similarly, Fabiano et al. [61] found that the sea ice melting process in TNB was a key factor influencing the distribution and composition of POM originating from phytoplankton. The C/N ratio in summer in this study (6.02 ± 0.48) was very similar to that in previous studies (6.2–6.5) conducted in TNB during summer [20,61]. This indicates a typical POM dominated by fresh algal material [63]. Furthermore, Fabiano and Pusceddu [20] found that the C/N ratio continued to increase until the end of their study period in middle February. We also found an increasing C/N ratio from autumn to winter, which suggests that N-based materials decreased more than C-based materials in POM. This is consistent with the macromolecular compositions of POM with dominant carbohydrates and lipids in the winter season, as we discuss later.

According to Smith and Asper [64], major phytoplankton communities include diatoms and *P. antarctica* in the Ross Sea. In particular, both groups occur in the spring, whereas diatoms dominate summer phytoplankton assemblages [65]. During the entire study period in 2018, the phytoplankton community was mostly composed of diatoms (88.8% ± 11.6%), followed by dinoflagellates (5.2% ± 6.3%), *P. antarctica* (4.2% ± 8.1%), and cryptophytes (1.6% ± 3.6%). Although the composition of *P. antarctica* was highest in spring in this study, it accounted for only 10.9% of the phytoplankton community. The proportion of dinoflagellates increased in autumn and winter during this study. This was similar to previous results, which accounted for approximately 10%–20% of the composition of dinoflagellates during summer in TNB [66]. Arrigo et al. [67] found different N/P ratios depending on the major phytoplankton community in the Ross Sea. The N/P ratios were 9.7 and 19.2 for diatom- and *P. antarctica*–dominated communities, respectively [67]. The average N/P ratios were 12.7 (±0.9) and 12.2 (±1.6) in spring and summer at the JBS, respectively, which were similar to the value (11.0 ± 0.9) from a previous study with diatom blooms during late summer in the Ross Sea [68].

#### *4.3. Seasonal Variations of Macromolecular Composition*

The macromolecular compositions of POM mainly originating from phytoplankton are affected by various factors, such as nutrients [15,69,70], light conditions [16,71], species composition [14,72,73], and different growth phases of phytoplankton [74,75]. Considering the incomplete removal of macronutrients (e.g., nitrate) at the end of the growth phase, iron and irradiance could be assumed to exert major controls on the productivity of phytoplankton in the Ross Sea [4,76–79]. The light intensity is sufficiently strong in spring [80], but POM cannot be synthesized by phytoplankton in the water column due to the blocking of light by sea ice. This could cause the low composition of proteins and high composition of lipids during spring in our study. The highest contribution of proteins observed in summer represented high growth rates and physiologically healthy conditions of phytoplankton [16]. The assemblages and species of phytoplankton could also have significant impacts on seasonal variations in macromolecular composition [81,82]. Previous studies have claimed that the dominance of *P. antarctica* biomass could be a key reason for the high carbohydrate concentrations [83,84]. In contrast, polar diatoms have been observed to have a large accumulation of lipids, partially related to temperature effects by altering the allocation of carbon into storage products, such as cytoplasmic lipid droplets [85,86]. This may explain the relatively higher contents of lipids in this study with diatom-predominant communities compared with those in other studies [84,87] during the spring and summer seasons. Indeed, the PCA results showed that lipids had the highest correlations with diatoms (r = 0.87, *p* < 0.01) (Table 1).

In addition, proteins were positively correlated with diatoms in our study (r = 0.94, *p* < 0.01), which is consistent with previous results from the Ross Sea (r = 0.59, *p* < 0.01) [88]. These results suggest that polar diatoms in the Southern Ocean have physiological mechanisms to increase protein concentrations to compensate for slow enzyme rates at cold temperatures [89].

In addition, carbohydrates linked to *P. antarctica* rapidly decrease during the degradation process, while carbohydrates linked to diatoms are retained more within their cellular matrix [90]. This indicates that carbohydrates remain persistent throughout the winter season at our study site. Ahn et al. [75] linked the growth phases of phytoplankton with seasonal variations in macromolecular composition. During the exponential phase corresponding to the summer in our study, the POM was mainly composed of proteins (39.1% ± 5.2%) as a functional reservoir of cell growth and division [91]. In contrast, cell storage compounds such as carbohydrates and lipids tended to increase in the stationary periods [92], corresponding to the autumn and winter in our study, with high carbohydrate (58.7% ± 15.0% and 67.4% ± 5.5%, respectively) and lipid contents (30.8% ± 5.1% and 32.5% ± 5.4%, respectively).

*Water* **2021**, *13*, 2173

**Table 1.** Pearson's correlation analysis for the environmental factors and biochemical variables.

"-" indicates not significant, \* *p* < 0.05, \*\* *p* <

 0.01.

The relative contributions of different macromolecule compositions in phytoplankton can be an indicator of the quality of phytoplankton as primary food resources [75]. Proteins have a relatively higher efficiency in carbon transfer to herbivores than other macromolecular components [16,93]. Carbohydrates and lipids are necessary components for all cell membranes as well as major energy reservoirs in phytoplankton bodies [94,95]. In particular, glucan in carbohydrates is mobilized during respiration to maintain growth in the dark [94]. In terms of quality aspects, the calorie content of POM represents the condition of energy sources. Although the overall Chl-*a* concentrations were lower than those in other studies, the overall calorie content (1.42 ± 0.68 Kcal m−3) in this study was similar to that from previous studies measured primarily in spring or summer [21,63,87]. The calorie content was high even during the polar night period, when photosynthesis was assumed to rarely occur. Although phytoplankton had a very low productivity in polar dark winters, they could maintain high caloric contents by having higher carbohydrate and lipid contents (Figure 8). Carbohydrates and lipids are maintained by respiration and long-term energy storage [96] for the survival of phytoplankton in winter.

#### *4.4. Seasonal Variations of TEPs*

Several previous studies on the distribution of TEPs in the Southern Ocean have previously been conducted in the Antarctic Peninsula [29,35,97], Drake Passage [97], and Ross Sea [36]. However, these previous studies were conducted during spring or summer. This is the first report of the annual variability in TEP concentrations in the Southern Ocean. The TEP concentrations obtained at the JBS during this study ranged from 10.2 to 96.1 μg Xeq. L−1, which generally falls within the range observed in other studies from various oceans around the world [98–101]. However, the overall TEP concentrations obtained in this study were relatively lower than those previously reported for the Southern Ocean (Table 2).

**Table 2.** Comparison of the TEP concentrations between this and previous studies from the Southern Ocean.

The species composition, biomass, and growth conditions of phytoplankton and environmental factors could have a great influence on the concentrations of TEPs [25,32,34]. Based on the PCA results (Figure 11), the seasonal variation in TEPs was primarily affected by diatoms and dinoflagellates in our study period. The TEP concentrations were positively correlated with the abundance of diatoms (r = 0.89, *p* < 0.01) and dinoflagellates (r = 0.95, *p* < 0.01) (Table 1). Similarly, previous studies reported that TEP concentrations in the Southern Ocean were significantly related to the abundance of diatoms (r = 0.67, *p* < 0.01) and dinoflagellates (r = 0.67, *p* < 0.01) [34]. In contrast, we found that TEP concentrations had no significant relationship with *P. antarctica* in our study, which conflicts with a previous finding that *P. antarctica* is a major producer of TEPs in the Southern Ocean [36], probably because the phytoplankton community was not dominated by *P. antarctica* in this study. In fact, TEP production by phytoplankton depends on the species composition of phytoplankton [25,32]. Hong et al. [36] observed that the concentrations of TEPs measured during a *P. antarctica*–dominant bloom were higher than those produced by coastal diatom blooms in the Ross Sea. The relatively lower TEP concentrations in this study could have resulted from the diatom-dominant phytoplankton community at the JBS. Additionally, the lower Chl-*a* concentrations observed in this study could be another reason for the lower TEP concentration since a significant relationship was found between the TEPs and Chl-*a* concentrations during this study period (Figure 10a). The relationship between TEPs and Chl-*a* can be expressed as TEPs = *a*(Chl-*a*) b [97]. The slope, *a*, of the log-converted TEP and Chl-*a* relationship in our study (0.33) is comparable to the previously reported values of 0.32, 0.38, and 0.35 for the Antarctic Peninsula, Bransfield Strait, and the Northern Weddell Sea in the Southern Ocean, respectively [29,34,97]. In comparison, Hong et al. [36] reported an extremely high value (3.63) when *P. antarctica* was dominant in the Ross Sea.

TEPs frequently make up a substantial fraction of the POC in the seawater column [100]. To estimate TEP-C, we used the lowest conversion factor (0.51 μg Xeq. L−1) commonly used for the diatom-dominated phytoplankton community [51,102]. Considerably large variations in the contribution of TEP-C to the POC stocks were observed seasonally during our observation period (Figure 9), which was different from the seasonal pattern in the TEP concentrations. TEP concentrations were highest in summer, but the contribution of TEP-C to the POC was highest in autumn (26.9% ± 6.1%). This suggests that TEPs could have a longer residence time than POC in the water column [29]. Previous studies have shown relatively faster degradation of protein compared to other components in the Antarctic Ocean [23,84]. Indeed, a decrease in the protein components of POC from the spring growth season to the summer and winter seasons was observed in this study (Figure 7). Therefore, TEP-C could stay longer and accumulate in comparison to phytoplankton or bacterial cells in the water column [29]. Although there have been few studies on TEP-C in the Southern Ocean, our average contribution of TEP-C to the total POC concentration in summer (21.9% ± 7.1%) was lower than that in the northern Weddell Sea (38.8% ± 12.3%) [34] but comparable to that on the Antarctic Peninsula (18%) [29]. The lower TEP-C contributions to the total POC suggest that relatively more particulate-based organic carbon materials were available in the water column during our observation period.

**Author Contributions:** Conceptualization, S.P., J.P., K.-C.Y., and S.-H.L.; methodology, S.P., K.K., N.J., and H.-K.J.; software, S.P.; validation, S.P. and S.-H.L.; formal analysis, S.P and S.-H.L.; investigation, S.P., K.-C.Y., and J.Y.; writing—original draft preparation, S.P.; writing—review and editing, S.-H.L.; visualization, S.P., K.K., N.J., H.-K.J., J.K. (Jaehong Kim), J.K. (Jaesoon Kim), and J.K. (Joonmin Kim); supervision, S.-H.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT; NRF-2019R1A2C1003515) and the Korea Polar Research Institute (KOPRI; PE21110).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We sincerely thank members of the fifth overwintering team at the JBS for collecting seawater samples safely for 1 year. We would like to thank the anonymous reviewers and the handling editors for their constructive and valuable recommendations.

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

#### **References**


2.5.1. Espinosa's Method (EM)

Based on empirical data for many systems featuring intermolecular hydrogen bonds of the H··· O type, Espinosa et al. [115] proposed the following relationship between the energy of an intermolecular hydrogen bond and the local electronic potential energy density that is determined at the bond critical point of a given hydrogen bond

$$E\_{\rm HB}^{\rm EM} = \frac{1}{2} V\_{\rm BCP} \tag{32}$$

It should be noted that both the simplicity of the formula (32) and the easy availability of the *V*BCP value (QTAIM calculations) resulted in a rather uncritical acceptance of this expression for determining not only the energy of intermolecular hydrogen bonds, but also the energy of intramolecular interactions. The latter, however, must be firmly criticized [44]. Since *V*BCP is a negatively defined quantity [94], the energy of any hydrogen bond (or other interaction) determined by formula (32) will always give a negative *E*EMHB value. Therefore, according to EM, any interaction will be stabilizing. However, as shown [44], many C-H··· O contacts, which many would probably consider weak hydrogen bonds, are, in fact, destabilizing, i.e., repulsive in nature. Indeed, *V*BCP, which is crucial in formula (32), was interpreted [115] as the pressure exerted by the system on the electrons in the closest vicinity of BCP. Therefore, it is easy to imagine a situation that the short distance H··· Y is merely forced, e.g., by the stiffness of the molecular skeleton or some steric interactions, which lead to a high value of *V*BCP and, consequently, to a large value of *E*EMHB , suggesting strong hydrogen bond, though in reality the interaction may be locally repulsive in nature. For this reason, the use of EM in the cases of intramolecular interactions is not recommended [44].

It should be added that some concerns regarding EM have also been pointed out by Gatti et al. [118] and more recently by Nikolaienko et al. [119]. For example, the latter authors complained that the expression (32) was obtained while using data relating to crystallographic structures in which, as is known, the distances H··· Y are often much shorter due to lattice forces. Moreover, this expression was obtained for X-H··· O (X = C, N, O) hydrogen bonds only and its use for hydrogen bonds of other types is unreliable. They also refer to the example of hydrogen bonds of the H··· F type, for which the Espinosa formula (32) should rather have a factor of 0.31 [120]. To all of these allegations [119], it can be added that the hydrogen bond energies were obtained [115] with a real mixture of theoretical methods. Therefore, it seems necessary to revise the derivation of formula (32). In fact, some modifications to the orginal Espinosa's formula (Equation (32)) have been proposed [44,118,120,121]. For example, Afonin et al. [121] obtained the formula

$$E\_{\rm IIB} = 0.277V\_{\rm BCP} + 0.45\tag{33}$$

in which the slope value of 0.277 is very close to the mentioned 0.31 value that was obtained by Mata et al. [120]. Even a little earlier, Jabło ´nski and Monaco proposed correcting the Espinosa's formula (32) by adding a constant *k* of 3.4 kcal/mol, thus to use the expression *E*HB = *E*EMHB + 3.4 [44]. Importantly, this expression was specifically dedicated to intramolecular hydrogen bonds.

## 2.5.2. Interacting Quantum Atoms (IQA)

As already mentioned, the Interacting Quantum Atoms (IQA) approach [116,117], which is based on QTAIM, allows for the total energy of a system to be divided into mono- and polyatomic components. Among many energy terms available by means of IQA, the most important one in the context of this article is the interatomic interaction energy defined as follows

$$E\_{\rm int}^{\rm E\_1\rm E\_2} = V\_{\rm nn}^{\rm E\_1\rm E\_2} + V\_{\rm en}^{\rm E\_1\rm E\_2} + V\_{\rm ne}^{\rm E\_1\rm E\_2} + V\_{\rm ee}^{\rm E\_1\rm E\_2} \qquad (E\_1 \neq E\_2) \tag{34}$$

where *V*E1E2 nn is the repulsion energy between nuclei of atoms E1 and E2, *V*E1E2 en is the attraction energy between electrons of the atom E1 and the nucleus of the atom E2, *V*E1E2 ne is the attraction energy between the nucleus of the atom E1 and the electrons of the atom E2, and *V*E1E2 ee is the interatomic two-electron repulsion energy. Because the *E*1 and E2 atoms may be e.g., the H and Y atoms from the X-H··· Y hydrogen bridge, it is evident that IQA via Equation (34) can be a suitable tool for calculating the energy of inter- and, more importantly, intramolecular hydrogen bonds. In this case, Equation (34) takes the following form

$$E\_{\rm int}^{\rm H\cdots Y} = E\_{\rm HB}^{\rm IQ} = V\_{\rm nn}^{\rm HY} + V\_{\rm en}^{\rm HY} + V\_{\rm ree}^{\rm HY} + V\_{\rm ee}^{\rm HY} \tag{35}$$

It should be emphasized that the determination of the interaction energy of H··· Y using formula (35) does not require assuming any reference system or referring to empirical data, and from this point of view the IQA-based approach is absolutely unique and, therefore, also worth a wider study of its applicability. It should also be added that *E*1 and *E*2 of Equation (34) can be any atoms, and therefore the interaction energy of any interatomic contact, not just hydrogen bonding, can be determined in a similar way. Moreover, these atoms do not need to be linked to each other by a bond path, nor do they need be in a close proximity to each other.

Unfortunately, as compared to intermolecular hydrogen bonds [122–126], the IQA-based estimates of the energy of intramolecular hydrogen bonds are relatively rare [127–130]. It is worth noting here that the list of IQA applications given most recently by Guevara-Vela et al. [117] can be easily supplemented with various repulsive interactions [105–108,112–114], which are often related to the presence of an appropriate bond path. Therefore, these cases are important in the very discussion on the interpretation of a bond path and the earlier connection of its presence on molecular graphs with the stabilizing nature of interactions, as stated in the orthodox QTAIM [94].

It should be emphasized that, as it seems, the interatomic interaction energy itself (i.e., *E*E1E2 int ) is currently not as popular quantity as the exchange-correlation component (*E*E1E2 ee,xc) of the interelectron interaction energy (*E*E1E2 ee = *E*E1E2 ee,C + *E*E1E2 ee,xc). This, in turn, results from the fact that *E*E1E2 ee,xc was associated with the presence of bond path via the concept of so-called privileged exchange channels [131]. Moreover, even more importantly, at short distances [113] *E*E1E2 ee,xc is related to the strength of a given bond [123]. Therefore, it turns out that, despite the possibility of determining the interatomic interaction energy and thus also of an intramolecular hydrogen bond, which is significant for the theory of intramolecular interactions, this quantity in IQA has become less important than dimensionally much lower exchange energy.


*2.2.2 Legal marriage and partnership*

CHIS asked all participants the standard marital question, "Are you now married, living with a partner in a marriage-like relationship, widowed, divorced, separated, or never married?" The response options do not include same-sex marriage or legal partnership. Previous research has shown that lesbian/gay women and gay men under-report marriage and legal partnership when responding to standard marital status questions [19]. To address under-report of marriage and to reflect the 2008 CSCD, CHIS asked all participants who reported having sex with someone of the same sex within the preceding 12 months whether that sexually-active respondent had a legal same-sex spouse or domestic partner. In addition to legallypartnered and married, we consolidated remaining status into *other*, a category including unmarried people who may be divorced, widowed, never-married, or living with a partner without legal recognition. We also constructed a binary variable for married/legally-partnered to increase the power of the data to find statistically significant results. There is no way to separate married and legally-partnered for data prior to 2009 because of questionnaire wording. In 2009, revised question wording distinguished married from legally-partnered. We compared percent of moderate psychological distress between married and legally-partnered lesbian women and gay men and found no statistical difference. Therefore, we collapsed married/legally-partnered as one group for our analyses in order to compare pre to post CSCD.

#### *2.2.3 Statistical analyses*

We performed all statistical analyses using CHIS data pooled from survey years 2005–2015 and weighted to the California population. Lesbian/gay women and gay men were compared to their heterosexual counterparts on sociodemographic variables. Data collected before 2008 were considered prior to the CSCD and data collected in 2008 and later were considered after the legal decision. The proportion of the sample experiencing moderate mental distress was plotted over time by gender, sexual identity, and couple status. Joinpoint analysis tested if trends in moderate psychological distress changed at specific years. Joinpoint uses weighted least squares to fit the trend model, using the inverse of the standard error as the weight variable.

Bi-variate and logistic regression analyses compared psychological distress using the K6 scale between the periods before and after the CSCD. Bi-variate analyses were replicated only for lesbian/gay women and gay men and compared stress levels between those legally married or partnered as compared with those not. Independent variables of the logistic regression included the main effects of sexual identity, marriage/legal partnership and the timing of CSCD (before or after) and all the two-way interaction effects (i.e., sexual identity x marriage/legal partnership, sexual identity x timing of CSCD, marriage/legal partnership x timing of CSCD) and the three-way interaction effect of the three variables (i.e., sexual identity x marriage/legal partnership x timing of CSCD) while adjusting for the following sociodemographics: Race, marital/partnered status, children in home, education, work status, income, geography, age. The conditional adjusted odds ratios compare the odds of reporting moderate mental health distress for that specific group pre- versus after-CSCD while holding all other variables constant.