*4.4. COVID-19 Pandemic Period*

Analyzing the results presented in the Pearson and Spearman correlation matrices for the pandemic period (Figures 15 and 16) and the values of correlation coefficients collected in tabular form (Tables 3 and 4), it should be noted that in relation to the period before the pandemic, a slight increase in the number of significant relationships between the variables was noted. The highest value of the correlation coefficient occurred again in the case of the R1 and R5 restaurants in the Pearson linear correlation (0.82), as well as R4-R5 (0.81) and R1-R4 (0.80). The dependencies identified by the Spearman's rho test were much weaker—a maximum of 0.70 in the case of the R1-R2 pair. Next in terms of the strength of the relationship is the pair of hotels H2-H3 (r = 0.77) and H2-H4 (rho = 0.76). It is worth noting the negative Spearman correlation between H1-H3 at −0.52. The C1–C3 cafes again showed an average correlation at the level of r = 0.61 and rho = 0.54.

In the linear Pearson correlation of electricity consumption profiles in enterprises with the profile of power demand in the National Power System, only the cases of C2 café and H1 hotel showed a value above 0.4 (moderate dependence). It is worth noting, however, that at the same time the Spearman's rho-statistic showed the existence of a much stronger non-linear relationship in the case of the C2 cafe with the Spearman correlation coefficient at the level of rho = 0.70.

With regard to the insolation conditions, that is, the productivity of a potential PV installation, a decrease in the correlation power was noted, and the highest non-linear correlation value at the level of 0.5 was achieved by the C3 café. The R5 hotel reported a slightly worse result (0.43).

However, in the case of the analysis of the correlation with the demand profile in the transmission network, the existence of an average to strong correlation occurring between approx. 70% of the analyzed cases (values of Pearson's and Spearman's coefficients ranging from 0.7 to 0.82 in the period before and during the COVID-19 pandemic) entitles one to attempt to draw conclusions about the restaurant industry on a national scale. Similarly, there is a moderate Pearson and Spearman correlation with coefficient values of 0.6–0.7 for 75% of the analyzed hotels and 0.5–0.6 for 66% of the analyzed cafes.

A high positive correlation of the energy consumption profile in relation to the profile of power demand in the pandemic period was observed in cafe C2, although its volume of energy consumed is several times lower than that of other representatives of the sector. Thus, the significance of inference based on its example in the context of the impact on the stability of the power grid may be flawed.

The strength of this dependence in the pandemic period, and thus the destabilizing effect on the distribution network, was increased by the H1 hotel and the R6 restaurant, which are in an intermediate position among the analyzed cases in terms of the amount of energy demand. The case of the R6 restaurant shows that it is a facility that has successfully recovered from the unprecedented COVID-19 situation. The average consumption during the pandemic decreased in the case of the R6 restaurant by less than 9% compared to the same period 2 years earlier. This may indicate a strong market position and an established brand that previously successfully offered a kitchen for delivery, which thus constituted a market advantage in the new reality.

On the other hand, a decrease in the value of correlation coefficients of 25–50% was recorded by: café C3, hotel H4 or restaurant R5. Other enterprises noticed multiple decreases in the value of the correlation power to the demand profile in the energy system, at the same time showing the average value of energy consumption two or even three times lower than in the corresponding period in previous years. This proves the problems of those entities that encountered difficulties in functioning in the new reality. The gap between restaurants with lower energy consumption and those with a stronger market position is particularly visible. The same applies to cafes. The four analyzed hotel cases recorded a decrease in the volume of electricity consumption of approx 100%.

In the context of the change in the correlation of the electricity demand profiles in the analyzed enterprises in relation to the insolation conditions, it is worth noting that the average value of correlation coefficients during the pandemic decreased compared to the comparative period by 19% in the case of cafes—64% in the case of hotels and 18% in the case of restaurants.

Only in two cases, that is, the R1 and R5 restaurants, an increase in the value of correlation coefficients was recorded in the pandemic period. On the other extreme, however, there is the case of the R6 restaurant whose demand profile is characterized by a peak coinciding with the evening peak of demand in the distribution network and in the hours of the highest availability of solar radiation, the level of electricity consumption is relatively low. The highest, an almost six-fold decrease in value, was recorded in the case of the H1 hotel, the energy demand profile of which results directly from the nature of the accommodation facility—daily peaks occur before checking out and at dinner time.

To sum up, it should be stated that among the analyzed enterprises, the greatest potential for using photovoltaics for the purposes of self-consumption of electricity was retained by entities from the catering industry.


*Article* **Proppant Transportation in Cross Fractures: Some Findings and Suggestions for Field Engineering**

#### **Yan Zhang 1,2, Xiaobing Lu 1,2, Xuhui Zhang 1,2,\* and Peng Li 1,2**

Received: 10 August 2020; Accepted: 18 September 2020; Published: 19 September 2020

**Abstract:** The proppant transportation is a typical two-phase flow process in a complex cross fracture network during hydraulic fracturing. In this paper, the proppant transportation in cross fractures is investigated by the computational fluid dynamics (CFD) method. The Euler–Euler two-phase flow model and the kinetic theory of granular flow (KTGF) are adopted. The dimensionless controlling parameters are derived by dimensional analysis. The equilibrium proppant height (EPH) and the ratio of the proppant mass (RPM) in the secondary fracture to that in the whole cross fracture network are used to describe the movement and settlement of proppants in the cross fractures. The main features of the proppant transportation in the cross fractures are given, and several relative suggestions are presented for engineering application in the field. The main controlling dimensionless parameters for relative EPH are the proppant Reynolds number and the inlet proppant volume fraction. The dominating dimensionless parameters for RPM are the relative width of the primary and the secondary fracture. Transportation of the proppants with a certain particle size grading into the cross fractures may be a good way for supporting the hydraulic fractures.

**Keywords:** proppant transportation; cross fractures; CFD simulation; dimensional analysis; equilibrium proppant height


**Author details**

Neva Bezeljak1 and Željka Večerić-Haler1,2\*

1 Department of Nephrology, University Medical Centre Ljubljana, Slovenia

2 Medical Faculty, University of Ljubljana, Slovenia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Pathophysiological Changes and Systemic Inflammation in Brain Dead Organ Donors… DOI: http://dx.doi.org/10.5772/intechopen.94360*


*2.5. YGS Treatment Shows Better Climbing Index for Aβ42 Flies*

Figure 4 shows a quantified climbing index of mCD8 and Aβ42 flies at 1, 5, and 10 days after eclosion under sham and YGS treatments. Our results show a quantified climbing index of Aβ42 flies at 5 and 10 days after eclosion, and under YGS treatment was significantly greater than those Aβ42 flies under sham treatment (*N* = 30 for each group, *p* < 0.01–0.05, Figure 4). In other words, YGS treatment should be able to enhance the climbing index for Aβ42 flies.

**Figure 4.** Climbing index of mCD8 and Aβ42 flies at 1, 5, 10 days after eclosion under sham and YGS treatments. The quantified climbing index of Aβ42 flies at 5 and 10 days after eclosion under YGS treatment was significantly greater than those Aβ42 flies under sham treatment (*N* = 30 for each group). In other words, YGS treatment should be able to enhance the climbing index for Aβ42 flies. Values are mean ± SEM (\*\* *p* < 0.01, \* *p* < 0.05, two-way ANOVA followed by a Student–Newman– Keuls multiple comparisons post-test). Abbreviations: YGS, Yi-Gan-San; Aβ, amyloid-beta; SEM, standard error of the mean; ANOVA, analysis of variance.

### *2.6. YGS Treatment Shows Greater Green Fluorescent Protein (GFP) Fluorescence in the External Eyes for GFP-Aβ42 Flies*

We modeled GFP fluorescence in Aβ42-expressing flies and observed that it was more sensitive and suitable for analyzing Aβ42 toxicity. As shown in Figure 5, GFP fluorescence in the external eyes of GFP-Aβ42 flies was weaker than those of GFP-WT flies. When GFP-Aβ42 flies were treated with 0.1% and 1% YGS treatments, GFP fluorescence in the external eyes was stronger than those of GFP-Aβ42 flies with sham treatment (Figure 5A). We quantified and then compared that GFP fluorescence in the external eyes of GFP-mCD8 flies was significantly stronger than those of GFP-Aβ42 flies with sham, 0.1% and 1% YGS treatments (*N* = 30 for each group, *p* < 0.01, Figure 5B); while GFP fluorescence in the external eyes of GFP-Aβ42 flies with 0.1% and 1% YGS treatments was significantly stronger than those of GFP-Aβ42 flies with sham treatment (*N* = 30 for each group, *p* < 0.01, Figure 5B). In addition, GFP fluorescence in the external eyes of GFP-Aβ42 flies with 1% YGS treatment was significantly stronger than those of GFP-Aβ42 flies with 0.1% YGS treatment (*N* = 30 for each group, *p* < 0.05, Figure 5B). The results revealed that GFP-Aβ42 flies showed increasing GFP fluorescence in the external eyes with an increasing dose of YGS treatment.

**Figure 5.** Eye GFP expressions of mCD8-GFP and Aβ42-GFP flies under sham and YGS treatments. (**A**) GFP expressions of external eyes in mCD8-GFP (normal control) flies under sham and YGS treatments were obviously greater than those of Aβ42-GFP under sham and YGS treatments, while GFP expressions of external eyes in the Aβ42-GFP flies under YGS treatment were obviously greater than those of Aβ42-GFP flies under sham treatment. (**B**) Completeness of external eyes in mCD8-GFP flies under sham and YGS treatments was obviously better than those of Aβ42-GFP under sham and YGS treatments, while completeness of external eyes in the Aβ42-GFP flies under YGS treatment was obviously better than those of Aβ42-GFP flies under sham treatment. (**C**) Quantified GFP fluorescence of external eyes of mCD8-GFP flies under sham and YGS treatments were significantly greater than those of Aβ42-GFP under sham and YGS treatments, while quantified GFP expressions of external eyes in the Aβ42-GFP flies under YGS treatment was significantly greater than those of Aβ42-GFP flies under sham treatment (*N* = 30 for each group). Values are mean ± SEM (\*\*\* *p* < 0.001, two-way ANOVA followed by a Student–Newman–Keuls multiple comparisons post-test). Abbreviations: YGS, Yi-Gan-San; Aβ, amyloid-beta, GFP, green fluorescent protein; SEM, standard error of the mean; ANOVA, analysis of variance.


*7.1. General Principles*

The two original validation studies of the MuscleSound® technique [4,5] used protocols that allowed the biopsy to be taken from the identical site on which the image was captured. Meanwhile, as previously identified (Table 1), the outlying studies ([9] and the Bone study presented here) took care to standardise the sites from which both ultrasound images and biopsy samples were collected, but used different sites from the same muscle between and within treatments to accommodate best practice associated with the collection of sequential muscle biopsies. A key premise of the MuscleSound® protocol, at least in research scenarios, was that the location of the image for sequential assessments or comparison with biopsy assessments must be identical. However, much of the extended commentary about the protocol [10], and the specific criticisms of studies which found it did not provide a valid assessment of muscle glycogen [12], misunderstood or misrepresented the larger literature on the assessment of muscle glycogen. Specifically, comments about the variability of glycogen within muscle [10,12] demonstrated a failure to understand the capability of various assessments techniques.

This review has identified that all studies of muscle glycogen in humans utilised indirect and sampling techniques. The basis of such sampling, which occurred in both of the original MuscleSound® validation studies, is that a small piece of muscle collected in a biopsy needle or captured in an ultrasound image includes sufficient muscle fibres to represent the aggregated features of individual fibres. Indeed, both the MuscleSound® score and the chemical analysis of a biopsy sample represent the characteristics of "mixed muscle". Enhanced techniques of analysing biopsy samples include histochemical staining techniques to identify differences in the storage and utilisation of glycogen between different types of muscle fibres [50], and, more recently, electron microscopy of single fibres identified different sub-cellular locations of glycogen particles [21,22,51,52]. Such techniques have helped to understand exercise metabolism and mechanisms of fatigue during exercise. However, ultrasound techniques, just like chemical assessments of homogenates or mixed muscle samples, cannot achieve such a granular assessment. Rather, they provide an overarching, yet still valuable, perspective of muscle fuel stores. In the case of muscle biopsies, there is a specific reason to require and validate the use of different sites for sequential biopsy samples; subsequent biopsies need to be taken from muscle that has not had its glycogen storage capacity impaired by trauma from the first biopsy [19]. However, it was shown that differences in the glycogen content of mixed muscle samples,

representing the average of a large number of individual muscle fibres from a number of individual sites across a muscle, are minor [23]. We acknowledge that the collection of biopsy samples from adjacent, but non-identical sites, or sites from contra-lateral limbs in scenarios involving symmetrical exercise protocols, may contribute to the technical error of measurement involved with chemical determination of muscle glycogen stores. Nevertheless, it is the basis of a robust literature involving many hundreds of studies, which have determined resting muscle glycogen concentrations in different populations [36], glycogen utilisation during exercise [1,53], and glycogen synthesis in response to diet [19,54,55].

Although the size of a biopsy sample can be measured (typically, 20–200 mg), the size and location of the actual site captured in the ultrasound image is uncertain. Since the analysis of the scan is undertaken via proprietary cloud-based software, the precise size and location of the sample and site, and its ability to represent the total muscle, ultimately lies with the company software, rather than the scan technician. Nevertheless, even if there are concerns about the validity of MuscleSound® glycogen assessments, there is some evidence of its reliability in estimating glycogen content across a muscle site. Indeed, the first study of the technique noted a significant correlation between the MuscleSound® glycogen content and its changes due to an exercise bout between two *separate* muscles. Here the correlation between glycogen stores of the rectus femoris and vastus lateralis were *r* = 0.93 (*p* < 0.0001), *r* = 0.91 (*p* < 0.0001), and *r* = 0.76 (*p* < 0.0001) for the pre-exercise, post-exercise, and exercise change scores, respectively [4]. Furthermore, in the Bone study reported in this review, we found a significant correlation between MuscleSound® scores at a single site and a shifting site within the same muscle, across a range of treatments (Figure 3). Therefore, in theory and in practice, there is evidence that in the absence of muscle damage, changes in glycogen in response to diet and exercise are similarly expressed across the gross aspect of a muscle.

#### *7.2. Specific Criticism of Studies That Fail to Support the MuscleSound® Technique*

The major rebuttal of data sets that have found that the MuscleSound® technique was unable to provide a valid measurement of muscle glycogen [10,12] is that "since glycogen is stored in different pools within a same muscle and therefore not uniformly stored, technically it is not possible to correlate the glycogen content from a very small portion of a muscle (1–2 cm2) with the glycogen content of an entire muscle" [10]. We have identified that this criticism of mixed muscle samples was confused with findings of sub-pools of glycogen within a single muscle fibre or between fibre types, and does not provide a legitimate understanding of broader muscle glycogen assessment. It is not necessary to make further comment on this issue. Nevertheless, if differences in the glycogen content of different sites within the same muscle do exist, this might provide an explanation for the lack of correlation between the biopsy site and ultrasound site in the dissenting studies discussed within this review. However, it fails to explain the failure of the MuscleSound® protocol to detect changes in its glycogen assessment metrics when across ultrasound scans taken at the same site on subsequent occasions. That such differences were both logical, based on knowledge of supervised diet and exercise treatments, and easily detected from chemical analysis of biopsy samples creates legitimate concern about the validity of the MuscleSound® technique.


**Kang He 1,†, Jinxi Wang 2,†, Juan Zou 1, Jichun Wu 1, Shaojie Huo 1 and Jiang Du 1,\***

Received: 13 April 2018; Accepted: 10 May 2018; Published: 15 May 2018

**Abstract:** Three new steroidal alkaloids with an unusual 3*α* tigloylamide group, named sarchookloides A–C (**1**–**3**), were isolated along with four known compounds (**4**–**7**) from the roots of *Sarcococca hookeriana*. Their structures and relative configuration were elucidated on the basis of spectroscopic methods including MS, UV, IR, 1D, and 2D NMR data. The isolated compounds were evaluated for their cytotoxicity against five human cancer cell lines: Hela, A549, MCF-7, SW480, and CEM in vitro. All three amide substituted steroidal alkaloids exhibited significant cytotoxic activities with IC50 values of 1.05–31.83 μM.

**Keywords:** *Sarcococca hookeriana*; sarchookloides A–C; steroidal alkaloid; cytotoxicity


*2.1. Sample Synthesis and Characterization*

A total of five coesite samples were synthesized at the *P-T* conditions of 5 and 7.5 GPa and 1273 K with heating durations of 9–12 hours (Table 1), using welded Pt capsules in sintered MgO octahedron assemblies in the 1000-ton multi-anvil press at China University of Geosciences (Wuhan). The corner truncation of the 25.4-mm tungsten carbide cubes was 12 mm for synthetic experiments at 5 GPa and 8 mm for the runs conducted at 7.5 GPa, respectively. Temperature was monitored with a W5Re95–W2Re74 (C-type) thermocouple, and a graphite furnace was used in our experiments. Analytical reagent SiO2, Al(OH)3, B(OH)3 (purity of >99.99%) were adopted as the starting materials to synthesize hydrous coesite samples with different compositions: Si-pure (Run 503), B-doped (R663), Al-doped (R749), and B,Al-doped (R694 and R712). Excess liquid water (1 μL) was added in each capsule to guarantee the water fugacity. Single crystals up to 300 μm were recovered from these synthetic experiments, while no other crystallized phases were detected (by Raman spectra) in the run products.

In-situ analyses of the trace elements of B and Al in these synthetic coesite samples were conducted on an Agilent 7900 inductively coupled plasma mass spectrometry (ICP-MS) (Agilent Technology, Tokyo, Japan) combined with a Yb femtosecond laser ablation (fs-LA) system (GeoLas 2005, Lambda Physik, Göttingen, Germany), without applying an internal standard [29]. The ICP-MS works at a power of 1350 W with a plasma and an auxiliary gas flow rate of 15.0 and 1.0 L/min, respectively; while the fs-LA system (λ = 257 nm) is operated at a repetition rate of 8 Hz and a pulse length of 300 fs. The spot size is 24 μm with an energy density of 2.8 J/cm2, and a mixture of He and Ar is used as the carrier gas. The element contents of B and Al were calibrated against multiple-reference materials (BCR-2G, BIR-1G, and BHVO-2G) using the 100% oxide normalization method [30] with the detection limits of 0.1 ppmw for B and 0.8 ppmw for Al, and the determined average B and Al concentrations in these reference materials show relative deviations of −5 to about −10% from the recommended values [31]. The derived B and Al concentrations are listed in Table 1.

Because the Al concentration in R749 is as high as about 400 ppmw as indicated by fs-LA–ICP-MS, we further checked the Al composition by a JEOL JXA-8100 electron probe micro analyzer (EPMA) (JEOL Ltd., Akishima, Japan), which is equipped with four wavelength-dispersive spectrometers (WDS). The EPMA system is operated at an accelerating voltage of 15 kV and a beam current of 5 nA, while the spot size is reduced to 10 nm to minimize the fluctuations of X-ray intensity as well as sample damage [32]. The certified mineral standards of pyrope garnet (for Al) and olivine (for Si) were adopted for quantification using ZAF wavelength-dispersive corrections. Totally, twelve points were selected for measurements on the sample of R749, and the derived Al2O3 content is 0.0742 ± 0.0096 wt.% with a detection limit of 100 ppmw (corresponding to 393 ± 51 ppmw for the Al element), which is consistent with the result from fs-LA–ICP-MS within the experimental uncertainties.

**Table 1.** Starting composition and synthetic condition for each run. The B and Al element concentrations are mainly measured by femtosecond laser ablation

measured by ICP-MS; *b*: determined by an electron probe micro analyzer (EPMA).

 5

> *a*:

 1273

 9

 —

 —

392.7 ± 50.8 *b*

1166 ± 151 *b*

7.2 ± 2.9

 48 ± 19

**R749:**

SiO2 (96) + Al(OH)3 (4) + 1 μL H2O

#### *Crystals* **2019** , *9*, 642


4. Model development of the study area

A 10 by 10 gridded model of the study area, shown in Figure 3, includes precipitation, overland flow, unsaturated zone computations using the 1D Richards equation, ET using the Penman-Monteith Equation [39, 40], and 1D soil thermal computations using GIPL.

### 4.1 Initial soil moisture and soil temperature conditions

The initial temperature condition which is from CPEAK ground temperature thermistors at several depths on 2002-5-1, 1 AM, is shown in Figure 5. The numerical solution of the soil thermal state using quasi-linear heat conduction equation (Eq. (4)) employs this initial temperature condition of the soil profile.

Process Modeling of Soil Thermal and Hydrological Dynamics DOI: http://dx.doi.org/10.5772/intechopen.84414

The initial soil moisture condition obtained from Caribou-Poker Creeks Research Watershed for 2002-5-1, 1 AM [41], is shown in Figure 6. This initial soil moisture condition of the soil profile is employed by the numerical solution of the soil moisture, Richards equation, in the unsaturated vadose zone (Eq. (1)).

#### 4.2 Model parameter values

The model parameter values, distributed on grids horizontally and on soil layers vertically, for processes, such as overland flow, infiltration, evapotranspiration, and

Figure 5. Initial soil temperature condition.

Table 2. Soil thermal parameter values.

August [38]. Annual snowfall averages 1692 mm and commonly covers the ground from October to April [37]. The model employed CPEAK hydrometeorological data of the year 2002 included relative humidity (%), wind speed (kts), air temperature

Fairplay Silt loam and gravelly silt loam Moderately well drained

Soil series USDA texture Drainage

Olnes Silt loam and very gravelly silt loam Well drained

A 10 by 10 gridded model of the study area, shown in Figure 3, includes precipitation, overland flow, unsaturated zone computations using the 1D Richards equation, ET using the Penman-Monteith Equation [39, 40], and 1D soil thermal

The initial temperature condition which is from CPEAK ground temperature thermistors at several depths on 2002-5-1, 1 AM, is shown in Figure 5. The numerical solution of the soil thermal state using quasi-linear heat conduction equation

(Eq. (4)) employs this initial temperature condition of the soil profile.

), and tipping bucket

(°F), barometric pressure (in Hg), solar radiation (W h m2

4.1 Initial soil moisture and soil temperature conditions

4. Model development of the study area

Soil types and vegetation types of the study area.

Hydrology - The Science of Water

computations using GIPL.

Soil properties of the study area.

Figure 4.

Table 1.

112

rainfall rates and are available at http://www.lter.uaf.edu/data.

thermodynamics, are assigned based on the soil texture, shown in Figure 4a, and land use, shown in Figure 4b.

parameter values, representing the vegetation type of Figure 4(b), employed in the

All the parameter values defined in Table 5 were taken from the literature, which are also defined in the GSSHA wiki https://www.gsshawiki.com. The literature values for albedo and vegetation height are defined in Eagleson [47]. The literature values for canopy resistance are defined in Monteith [39]. The literature

The simulation period was from May 1st to May 31st of 2002, a period during which air temperatures are beginning to rise above freezing. The daily maximum soil temperature obtained from the simulation is compared with the observed one in Figure 7. Both the observed and simulated temperatures in Figure 7 is effective at a depth of 10 cm in the soil profile. The root-mean-square error for this daily maximum soil temperature was 4.7°C. It was found that the soil thermal

values for the transmission coefficient are defined in Sutton [48].

Process Modeling of Soil Thermal and Hydrological Dynamics

DOI: http://dx.doi.org/10.5772/intechopen.84414

Comparison of the time series of observed and simulated temperature.

Hydraulic conductivity under freezing and thawing soil active layer.

evapotranspiration process.

5. Result and discussion

5.1 Soil thermodynamics

Figure 7.

Figure 8.

115

Table 2 shows the soil thermal parameters with values, representing the silt loam [31, 42, 43] of Alaskan woodland and tundra ecosystem sites in permafrostactive region, employed in the thermodynamics process.

Table 3 shows Manning's roughness parameter values, representing the vegetation type of Figure 4(b) [44, 45], employed in the routing process.

Table 4 shows the soil hydraulic parameters with values, representing the silt loam ([46], https://www.gsshawiki.com) of Figure 4(a), employed in the infiltration and soil water retention process.

In this study, Penman-Monteith method was employed as evapotranspiration process. Table 5 shows the Penman-Monteith parameters that includes vegetation transmission coefficient (light penetration through canopy), values of land surface albedo, vegetation height (for aerodynamic resistance term), and vegetation canopy resistance (for stomatal control of the loss of water). Table 5 shows the ET

#### Table 3.

Manning's roughness values.

#### Table 4.

Soil parameter values for the Richards infiltration scheme.


3.2.1. Unfiltered Data

After running the program for the 20 sets, a total of 23,117 paired points were used to calculate the error values. Figure 16 shows a screenshot of the program after the calculation with the reconstruction of the point cloud for both methods, with the TCP method on the left and the cuboid on the right side.

**Figure 16.** GUI showing the resulting 3D reconstruction using unfiltered data.

Table 2 summarises the results, showing a smaller error for the TCP method.

**Table 2.** Summary results for cuboid and TCP reconstruction with MAE, MSE, and RMSE for unfiltered data.

#### 3.2.2. Filtered Data

For the filtered data, a total of 23,603 paired points were used to calculate the error values. Figure 17 shows a screenshot of the program after the calculation.

**Figure 17.** GUI showing the final result of the error measurement of the 3D reconstruction using filtered data.

Table 3 summarises the results obtained, again showing a smaller error for the TCP method.

**Table 3.** Summary results for cuboid and TCP reconstruction with MAE, MSE, and RMSE for filtered data.

#### *3.3. Charuco Cuboid*

The reconstruction using the charuco cuboid had a low RMSE of 2.9837 mm for the filtered set, and an RMSE of 2.6417 mm for the unfiltered set, showing a better result for the unfiltered dataset.

#### *3.4. Robot's TCP Transformation*

The robotic arm ABB IRB 4600 had a payload of 40 kg and a reach of 2.55 m. It had a position repeatability of 0.06 mm, a path repeatability of 0.28 mm, and an accuracy of 1 mm [15]. With this precision, the reconstruction showed the best performance with an RMSE of 1.7432 mm for the filtered dataset and an RMSE of 1.9651 mm for the unfiltered. In this method, the filtered dataset showed an improved result in relation to the unfiltered dataset.

#### **4. Discussion**

The accuracy of the reconstruction is vital to robotics tasks when using depth information to generate trajectories for a robot in a large scene. The usage of depth stream to reconstruct the scene, as in Newcombe et al. [42], may not be appropriate if the system is time-sensitive. It is faster to move the robotic arm to positions at a high speed and then capture a frame than it is to cycle through at a slower speed to capture the data.

The algorithms currently available for point cloud registration, such as iterative closest point (ICP) [16,43] and its variants, as well as other methods that try to find the transformation between point clouds, use overlapping, which in this case is minimal due to the fact that the observed scene is large. Moreover, they are too slow to be used in a time-sensitive system.

The ABB IRB 4600 has a preset maximum speed of 7000 mm/s, and the axis speed varies from 175◦/s (axis 1) to 500 ◦/s (axis 6). With these high speeds, the data acquisition and reconstruction can be sped up through a straightforward mathematical approach, such as the TCP transformation method. The idea is to have a fast and reliable transformation for the camera positions.

Further investigations could involve the study of how the trajectory generated for the TCP is affected by the errors in the reconstructions. Understanding this impact could help in developing a faster method without having repercussions on the desired output.

This work will assist in developing new designs and understanding one's options when responding to reconstruction challenges using multi-camera views and a robotic arm. It casts light on two different approaches and how to evaluate the reconstruction of the 3D scene. However, it is not an exhaustive study on the topic.

#### **5. Conclusions**

In this study, we evaluated three pose estimation methods for RGB-D (3D) cameras in the context of 3D point cloud reconstruction. We proposed a method to identify the point pairs, and performed error measurements. Using a charuco cuboid, we identified and labeled the corner of the ArUco markers in order to create a pairwise set of points to measure the error values.

Two of our developed methods used the charuco board to estimate the pose of the cameras, whereas the third method used the robots' TCP position to calculate and transform the point clouds to reconstruct the scene. The data were captured with a fixed position and at a distance of around 1 m from the board in each direction.

This distance reduces the resolution of the charuco board and consequently reduces the camera's ability to recognise markers on it. To improve the recognition of markers, a sharpening filter was applied. Further filters were used to mitigate artefacts in the point cloud, due to the stereo vision system used by the camera.

Two methods, the cuboid and TCP methods, provided a good reconstruction with low RMSE values, taking into account the often noisy nature of point clouds derived from RGB-D cameras. The double-sided angled board had the highest RMSE, and it was excluded from further assessments. The increased error was assumed to be a result of the combination of the angle and distance from the camera. The robot's TCP position had a high accuracy, exhibiting the best overall performance.

**Author Contributions:** Conceptualisation, I.d.M.E., M.M., O.S. and A.P.; methodology, I.d.M.E.; software, I.d.M.E.; validation, A.M.; formal analysis, I.d.M.E.; investigation, I.d.M.E.; writing original draft preparation, I.d.M.E.; writing—review and editing, A.M.; visualisation, I.d.M.E.; supervision, A.M. and P.J.F.; project administration, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was partially supported by the Research Council of Norway through the funding to the "MeaTable—Robotised cells to obtain efficient meat production for the Norwegian meat industry" project no. 281234. The work is also, in part, supported by the EC H2020 project "RoBUTCHER" grant agreement no. 871631.

**Data Availability Statement:** The source code can be downloaded from https://github.com/imesper/ Calibration.git (accessed on 10 March 2022). For the data please contact the author at<EMAIL_ADDRESS>
**Acknowledgments:** We wish to acknowledge the help provided by Tommy Jonsson and Haris Hadzic from Byte Motion, and Håvard Bakke from RobotNorge for providing us with very valuable knowledge on the matter.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Abbreviations**

The following abbreviations are used in this manuscript:


The current study was funded by NORAD through the Ministry of Natural Resources and Tourism, Tanzania and was a part of the Tanzania Wildlife Research Institute (TAWIRI), Norwegian Institute for Nature Research (NINA) and Norwegian University for Science and Technology (NTNU) project called Biodiversity and Human Wildlife Interface in Western Serengeti. We acknowledge the financial support from the Norwegian Peace Corps and the Quota Programme Scheme (NORAD). We thank Commission for Science and Technology (COSTECH) and Tanzania National Parks (TANAPA) for granting permissions to the foreign researchers to work in Serengeti National Park. We thank T. Holmern and B. Stokke for their valuable comments during the preparation of the manuscript. Finally, we thank village leaders and our field assistants who in one way or the other contributed to this

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### *Edited by Abiud Kaswamila*

Natural resources conservation is one of the dilemmas currently facing mankind in both developed and the developing world. The topic is of particular importance for the latter, where the majority depend on terrestrial ecosystems for livelihood; more than one billion people live in abject poverty earning less than a dollar per day; more than 3.7 billion suffer from micronutrient deficiency and more than 800 million suffer from chronic hunger. Population increase, resource use conflicts, technological advancements, climate change, political doldrums, and unsustainable use and harvesting of resources have all put more pressure on natural resources leading to land degradation and poverty. To achieve a win-win situation, we need to change our mindset by thinking outside the box through advocating integrated and holistic approaches in managing our natural resources. This book presents a variety of sustainable strategies and/or approaches including use of GIS and Remote Sensing technologies, decision support system models, involvement of stakeholders in major decisions regarding use of natural resources, community level initiatives, and use of surveillance and monitoring mechanisms.

Sustainable Natural Resources Management

Sustainable Natural

Resources Management

*Edited by Abiud Kaswamila*

Photo by natasaadzic / iStock


**Chapter 4**

*Noise and Vibration Control - From Theory to Practice*

[14] Yazawa T, Shimoda Y. Detrended fluctuation analysis: An experiment about the neural-regulation of the heart and motor vibration. In: Kim H, Ao SI, Amouzegar M, Rieger B. editors IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical

Engineering Book Series Vol. 247. Springer: Dordrecht; 2014. pp. 665-682. DOI: 10.1007/978-94-007-6818-5\_47

**54**


We would like to thank the captains and crew of RV Lance, and fellow scientists on N-ICE2015 for assistance. We acknowledge Jozef Wiktor, Magdalena Rózanska-Pluta, and Agnieszka Tatarek from the Institute of Oceanology of the Polish Academy of Sciences for species identification and enumeration, N-ICE2015 snow/ice team for ice temperature data, Anja Rösel for organizing the ice stratigraphy work, Torbjørn Taskjelle and Stephen Hudson for work with the irradiance data, and Max König for providing the map. Data used in the study is available through the Norwegian Polar Data Centre (Assmy et al., 2017a; Olsen et al., 2017a).

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars. 2018.00199/full#supplementary-material

and growth in a changing Arctic. Glob. Chang. Biol. 16, 3154–3163. doi: 10.1111/j.1365-2486.2010.02175.x

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Kauko, Olsen, Duarte, Peeken, Granskog, Johnsen, Fernández-Méndez, Pavlov, Mundy and Assmy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Organic Matter Controls of Iron Incorporation in Growing Sea Ice

Julie Janssens 1,2,3 \*, Klaus M. Meiners 2,3, Ashley T. Townsend4 and Delphine Lannuzel 1,2

*1 Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia, 2 Antarctic Climate and Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, TAS, Australia, 3 Australian Antarctic Division, Department of the Environment and Energy, Kingston, TAS, Australia, 4 Central Science Laboratory, University of Tasmania, Hobart, TAS, Australia*

This study presents the first laboratory-controlled sea-ice growth experiment conducted under trace metal clean conditions. The role played by organic matter in the incorporation of iron (Fe) into sea ice was investigated by means of laboratory ice-growth experiments using a titanium cold-finger apparatus. Experiments were also conducted to understand the role of extracellular polymeric substances (EPS) in the enrichment of ammonium in sea ice. Sea ice was grown from several seawater solutions containing different quantities and qualities of particulate Fe (PFe), dissolved Fe (DFe) and organic matter. Sea ice and seawater were analyzed for particulate organic carbon and nitrogen, macro-nutrients, EPS, PFe, and DFe, and particulate aluminum. The experiments showed that biogenic PFe is preferentially incorporated into sea ice compared to lithogenic PFe. Furthermore, sea ice grown from ultra-violet (UV) and non-UV treated seawaters exhibits contrasting incorporation rates of organic matter and Fe. Whereas, the effects of UV-treatments were not always significant, we do find indications that the type or organic matter controls the enrichment of Fe in forming sea ice. Specifically, we come to the conclusion that the incorporation of DFe is favored by the presence of organic ligands in the source solution.

Keywords: sea ice, iron, EPS, organic matter, organic ligands, incorporation, Antarctica

## INTRODUCTION

Sea ice is an important reservoir of iron (Fe) in the Southern Ocean (e.g., Sedwick and DiTullio, 1997; Lancelot et al., 2009; Lannuzel et al., 2010; Wang et al., 2014). Every spring, Fe contained in sea ice is released into surface waters and can trigger phytoplankton blooms in the marginal ice zone. This seasonal event represents an important source of food for higher trophic levels and potentially contributes to the biological pump in the Southern Ocean (Sullivan et al., 1993; Lannuzel et al., 2007, 2010). Climate predictions suggest sea-ice extent and volume will decrease by 24 and 34%, respectively by 2100 (Arzel et al., 2006). The effect of these changes on sea-ice biogeochemical processes, and most importantly on the uptake of carbon dioxide (CO2) in the Southern Ocean, remains enigmatic. It is therefore of primary importance to gain a mechanistic understanding of the processes leading to Fe enrichment in sea ice. This step is necessary to adequately represent ice-associated iron biogeochemistry in numerical models.

The co-occurrence of enrichment of organic matter and Fe in sea ice suggests that they are coupled (Grotti et al., 2005; Lannuzel et al., 2007, 2008, 2014a; van der Merwe et al., 2009, 2011a,b; de Jong et al., 2013, 2015; Janssens et al., 2016). It has been proposed that the Fe

#### Edited by:

*Benjamin Allen Lange, Fisheries and Oceans Canada, Canada*

#### Reviewed by:

*Maria A. Van Leeuwe, University of Groningen, Netherlands Andrea Niemi, Fisheries and Oceans Canada, Canada*

> \*Correspondence: *Julie Janssens<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Cryospheric Sciences, a section of the journal Frontiers in Earth Science*

Received: *17 September 2017* Accepted: *26 February 2018* Published: *15 March 2018*

#### Citation:

*Janssens J, Meiners KM, Townsend AT and Lannuzel D (2018) Organic Matter Controls of Iron Incorporation in Growing Sea Ice. Front. Earth Sci. 6:22. doi: 10.3389/feart.2018.00022* concentration in the ice depends on the Fe concentration in the parent seawater during sea-ice formation (van der Merwe et al., 2009). Dissolved organic ligands, potentially in the form of extracellular polymeric substances (EPS), are thought to control the distribution of dissolved Fe (DFe) in sea ice and might be a crucial vector of Fe enrichment in forming sea ice (Lannuzel et al., 2015; Genovese et al., in review).

EPS are produced by algae and bacteria (Meiners et al., 2004; Mancuso Nichols et al., 2005b; Meiners and Michel, 2017) and have been found to be enriched in sea ice compared to seawater (e.g., Krembs et al., 2002a, 2011; Meiners et al., 2003, 2004; Riedel et al., 2006; van der Merwe et al., 2009; Underwood et al., 2010; Ewert and Deming, 2011; Aslam et al., 2012; Janssens et al., 2016). EPS are a good candidate for metallic anion binding (Croot and Johansson, 2000; Verdugo et al., 2004) given their high stickiness and negatively charged surfaces (Decho, 1990; Underwood et al., 2010). EPS exist in a continuum of sizes from dissolved and colloidal to particulate fractions (Verdugo et al., 2004), and therefore may control the enrichment of all Fe size fractions in sea ice.

Janssens et al. (2016) showed that sea-ice formation leads to concomitant physical enrichment of EPS, particulate organic matter and Fe, with larger particles being preferentially enriched compared to smaller particles during an in situ ice-growth experiment. Nonetheless the simultaneous investigation of physical, chemical and biological processes in field conditions is extremely difficult given the complex interactions between them. In this context, laboratory ice-growth experiments are valuable to isolate specific processes. Laboratory experiments allow the formation of sea ice in a controlled environment and from chosen source solutions.

Ice tank experiments and laboratory ice-growth experiment are numerous (e.g., INTERICE experiments in Germany—e.g., Weissenberger and Grossmann, 1998; Krembs et al., 2002b; Geilfus et al., 2012; Zhou et al., 2014; CRREL experiments in the United State of America—e.g., Kovacs, 1996; Loose et al., 2009; or SERF experiments in Canada—e.g., Galley et al., 2013; Else et al., 2015). To our knowledge, none of these studies have been conducted underthe trace metal clean conditions needed to study Fe-associated processes. This study aims at understanding the basic processes leading to the incorporation of Fe into growing sea ice, and specifically the role of organic matter as a carrier of Fe. Experiments were performed using a titanium (Ti) coldfinger apparatus (Kuiper et al., 2003; Ewert and Deming, 2011) tailored for use under carbon and trace metal non-contaminating conditions. Experiments were conducted to understand the role of EPS in the enrichment of ammonium (NH+ 4 ) in sea ice (Zhou et al., 2013) and the role of organic matter in the enrichment of Fe in sea ice. We first hypothesize that the quality and quantity of EPS control the rate of incorporation of particulate organic carbon (POC) and NH+ 4 into sea ice. We tested this hypothesis by undertaking a set of experiments using surface water (assumed freshly formed EPS) and deep water (assumed old/preformed EPS), where we quantified the incorporation of EPS, organic matter and inorganic macro-nutrients [hereafter referred as macro-nutrients: nitrate+nitrite: NO2+NO3 =NOx, silicic acid: Si(OH)− 4 , phosphate: PO3− 4 , and ammonium: NH+ 4 )] into sea ice. Our second hypothesis was that organic matter is required to incorporate Fe into sea ice. We tested this by undertaking a second set of experiments targeting the role that organic matter plays in the enrichment of Fe (DFe and particulate Fe, PFe) into sea ice using UV and non-UV treated seawater. The addition of desert dusts and sea-ice algae in the source solutions was also carried out to investigate the difference between lithogenic and biogenic PFe incorporation in sea ice. To our knowledge, this work constitutes the first laboratorybased ice-growth experiments conducted under trace metal clean conditions.

## METHODS

## Experiments

### Ice Texture Experiments

The first set of experiments, Ice texture Exp., was conducted onboard RV Polarstern, and aimed at identifying the effects of different ice growth rates on ice crystal structure, i.e., ice texture. Seawater used for these experiments was collected during a winter cruise in the Weddell Sea onboard the RV Polarstern (ANT-XXIX/6, Lemke and Participants, 2014), using the ship water intake line. The ice was grown at −10, −15, and −20◦C, for 24, 8, and 6.45 h respectively, and processed for ice texture analysis onboard the ship (**Table 1**).

#### Extracellular Polymeric Substances, POC, PON, and Macro-Nutrient Experiments

A second set of experiments, EPS, POC, PON, and macro-nut. Exp., was run during the same voyage. Seawater was collected using the ship water intake line, a CTD or a peristaltic pump (E/S Portable Sampler, Masterflex) at different locations and different water depths (**Table 1**) to study the incorporation of EPS, POC, and particulate organic nitrogen (PON) and inorganic macronutrients (NO2+NO3 =NOx, Si(OH)− 4 , PO3− 4 , and NH+ 4 ) into sea ice. The first source solution was surface seawater (Surface SW, ship water intake line). The second source solution was deep seawater (Deep SW) collected with the CTD at 4,300 m depth. In the third source solution (Surface SW + XG), 5 mg L −1 of xanthan gum (XG) from Xanthomonas campestris (Sigma-Aldrich) was added to the surface seawater sampled with a peristaltic pump (E/S Portable Sampler, Masterflex) at the icewater interface. To achieve this, a stock solution of 1,000 mg L −1 XG was made and sonicated for 60 min using a Bandelin Sonopuls sonicator. The stock solution was then diluted 10 times in Ultra High Purity (UHP) water (Millipore, Gradient A10) and sonicated for 30 min before being added to the surface seawater.

**Abbreviations:** DFe, dissolved iron; EI, enrichment indices; EPS, extracellular polymeric substances; EPS, POC, PON and macro-nut. Exp., extracellular polymeric substances, POC, PON, and macro-nutrients experiments; Fe, iron; FEP, teflon fluorinated ethylene propylene; Ice texture Exp., ice texture experiment; Iron Exp., iron experiment; NOx, nitrate+nitrite; NH+ 4 , ammonium; PAl, particulate aluminum; PFe, particulate iron; PO3− 4 , phosphate; POC, particulate organic carbon; PON, particulate organic nitrogen; SiOH− 4 , silicic acid; SW, seawater; UHP, ultra high purity; UV, ultraviolet; XG, xanthan gum; DOM, dissolved organic matter; DOC, dissolved organic carbon; CO2, carbon dioxide.

TABLE 1 | Summary of the different cold-finger experiments.

*In each experiment seawater was collected at different locations and different treatments were used as source solution. SW, seawater; XG, xanthan gum; POC and PON, particulate organic carbon and nitrogen; EPS, extracellular polymeric substances, FSW, filtered seawater; UV, ultra-violet; DFe, dissolved Fe; PFe, particulate Fe; PAl, particulate aluminum.*

#### Iron Experiments

A third set of experiments, Iron Exp., aimed at assessing the role of organic matter in the incorporation of Fe into growing sea ice. Experiments were conducted under trace metal clean conditions at the University of Tasmania. Surface seawater was collected on the 9th of October 2015 from Trumpeter Bay, (Tasmania: 43◦ 16 S, 147◦ 39 E) using a shoreline, and stored in acid-clean lowdensity polyethylene (LDPE, Nalgene) carboys. The sampling depth was approximately 2.5 m. In the home laboratory, seawater was filtered through polycarbonate (PC) membranes (Sterlitech, pore size: 0.4µm) at low vacuum (<0.13 bar) using an acid-clean PC filtration apparatus (Sartorius) under a class-100 laminar flow hood (AirClean 600 PCR workstation, Model 300 Controller, AirClean System).

Filtered seawater (FSW) was divided into separate bottles for separate sets of treatments. Triplicate bottles were then spiked with different types of Fe. Added concentrations were selected to reach detectable Fe signals during analyses, despite the small volume of sea ice formed and remaining seawater. The first source solution, "FSW + PFe," was obtained by adding 30µM of lithogenic PFe (PFe > 0.4µm) to the FSW. Dust particles < 20µm were used, collected in the Atacama desert, Chile (European Southern Observatory, Paranal), assuming a composition of 5% (w:w) of Fe, similar to the Earth's Crust (Taylor, 1964).

A second source solution, "FSW + DFe," was obtained by adding 30µM of DFe (DFe < 0.4µm) into the FSW using a commercial solution of 1,000 ppm of FeCl3 (Merck).

Two complementary sets of experiments were run after UVtreatment of the FSW. This UV exposure step ensured the destruction of any dissolved organic matter (DOM) present in the solution (Queroue et al., 2014). Filtered seawater was dispensed into acid-clean 0.5 L Teflon fluorinated ethylene propylene (FEP) bottles (Nalgene) and placed between ultraviolet lamps inside a black PVC chamber for 15 min. After UV exposure, PFe or DFe was added to the source solution following the steps adopted in treatments "FSW + PFe" and "FSW + DFe." The latter treatments are referred to as "UV-FSW + PFe" and "UV-FSW + DFe."

A final experiment was carried out using FSW to which 44 mL of melted bottom sea ice containing algae was added. The ice sample was collected at Davis station, Antarctica in November 2015 and maintained under optimum growth conditions in the IMAS culture room until the start of the experiment. This treatment, named "FSW + Algae," aimed at studying the incorporation of biogenic PFe in sea ice. A list of the treatments is given in **Table 1**.

#### Sea-Ice Growth Set-Up

The experimental set-up and manipulations were conducted in a custom-made clean plastic bubble in a 4◦C cold room onboard RV Polarstern (Ice texture Exp., EPS, POC, PON, and macronut. Exp.); and under a class-100 laminar flow hood (AirClean 600 PCR workstation, Model 300 Controller, AirClean System) in a 4◦C cold room in the home-laboratory for experiments on the incorporation of Fe and organic matter (Iron Exp.). Ice was grown using a cold-finger apparatus first described by Kuiper et al. (2003) and Ewert and Deming (2011), modified for use under carbon and trace metal non-contaminant conditions. The cold-finger was fully made of titanium (Ti) and cooled with a circulating bath (Wise Circu, WCR-P22) using ethanol as coolant (**Figure 1**).

The cold-finger was immersed straight in a custom-cut square acid-clean 2 L PC container (Nalgene) filled with different source solutions of seawater (**Table 1**). The PC container was placed into an insulated box made of foam insulation sheet (TechLite R ) and placed on a magnetic stirrer to insure the homogeneity of the source solutions during the experiments.

At the end of each 8-h experiment, the ice was carefully removed from the remaining seawater and extracted from the

bubble (*Ice texture Exp.* and *EPS, POC, PON,* and *macro-nut. Exp.*), or under a laminar flow hood (*Iron Exp.*). The cold-finger was connected to a circulating water bath with ethanol (not shown). Experiments were run in triplicate. For each experiment the volume at T0 was 1,350 mL. The ice was formed at −10, −15, and −20◦C for 24, 8, and 6.45 h respectively for the *Ice texture Exp*., and −15◦C for 8 h for the *EPS, POC, PON,* and *marco-nut. Exp.* and the *Iron Exp*. Once formed, the ice was removed from the cold-finger, and processed for ice texture (*Ice texture Exp.*), or allowed to melt in the dark at room temperature before further processing (*EPS, POC, PON,* and *macro-nut. Exp.* and *Iron Exp.*). Both remaining seawater (Rem. SW) and ice were processed for parameters described in the method section.

cold-finger. Ice was allowed to melt at room temperature in an acid-clean PC container, following methods from Rintala et al. (2014). A diagram of the experimental set-up and workflow is shown in **Figure 1**. Both remaining seawater and melted ice were analyzed for the list of parameters described below. Prior to each treatment a T0 sample was collected for measurements of the same parameters in the source solution.

## Physical Variables: Salinity and Ice Texture

Salinities (practical salinity, SP) of seawater, bulk ice (melted bulk-ice sample) and remaining seawater were measured using a portable salinity probe (YSI Incorporated model 30, precision ± 0.1). Vertical and horizontal thin sections of the ice were prepared following the method of Langway (1958). Thin sections were observed through 2 cross-polarized filters and photographed following methods described in detail in Janssens et al. (2016).

## Biogeochemical Variables Extracellular Polymeric Substances

Melted ice and seawater samples were homogenized and filtered onto 25 mm diameter PC membranes (0.4µm, Millipore) under low vacuum (<0.13 bar) to avoid cell lysis. Filters were stained with 500 µL of a solution of Alcian Blue (AB, GX8 Sigma, 0.02% AB in 0.06% acetic acid). Excess dye was removed by rinsing the membrane with 2 mL of UHP water (Millipore, Gradient A10). Membranes were then stored individually at −20◦C in the dark until analysis. Concentrations were determined colorimetrically using the method of Passow and Alldredge (1995) and modified by van der Merwe et al. (2009). Concentrations were computed using the filter capture efficiency of 4% from van der Merwe et al. (2009).

#### Dissolved and Particulate Organic Material and Macro-Nutrients

All glassware used for POC and PON analysis were soaked in a 2% (v:v) HCl solution (analytical grade Merck EMSURE Germany), rinsed with UHP water, wrapped in aluminum foil and combusted at 450◦C for 4 h. In between triplicates and treatments, glassware was rinsed with UHP water, soaked in a 2% (v:v) HCl solution (analytical grade, Merck EMSURE Germany) for 8 h and rinsed thoroughly with UHP water. Samples for POC and PON were filtered onto pre-combusted 25 mm quartz filters (Sartorius). A 20 mL subsample of the filtrate was collected and stored at −20◦C in the dark until analysis for macro-nutrient concentrations (NOx, Si(OH)− 4 , PO3− 4 , and NH+ 4 ). POC and PON samples were stored at −20◦C in the dark until analysis. After acidification of the filters [30 µL of 10 % (v:v) HCl, Ajax Finichem] to remove inorganic carbon, POC and PON concentrations were determined using a Thermo Finnigan EA 1112 Series Flash Elemental Analyzer (precision 1%) at Central Science Laboratory (CSL, University of Tasmania). Silicic acid was analyzed using a photometric analyzer (Aquakem 250) and PO3− 4 , NOx, and NH+ 4 were analyzed with a Lachat Flow injection analyzer following the methods of Grasshoff et al. (2009). Detection limits were 0.1 and 0.002 mg L−1 for each method, respectively.

#### Iron and Aluminum

All sampling LDPE bottles, PC containers, PC filtration sets, petri-dishes and other equipment in contact with the samples were cleaned following GEOTRACES recommendations (Cutter et al., 2017). Polycarbonate membranes for PFe and PAl were soaked in 10% (v:v) ultrapure HCl for a week, thoroughly rinsed with UHP water (Barnstead International, NANOpure Diamond polisher) and stored in UHP water until use. Before and between treatments and triplicates, the cold-finger was thoroughly rinsed with UHP water. Between triplicates, equipment used for Fe filtrations was thoroughly rinsed with UHP water, soaked in a 10% (v:v) HCl solution for 16 h and rinsed 5 times with UHP water.

Immediately after removing the ice from the PC bottle, the remaining seawater was filtered onto 0.4µm pore size 47 mm diameter PC membrane filters (Sterlitech) using an acid-clean PC filtration set (Sartorius) under gentle vacuum (<0.13 bar). The filter was collected for measurements of PFe and PAl (particles > 0.4µm) concentrations, and placed in acid-clean polystyrene petri-dishes. 60 mL of the filtrate was collected in LDPE bottles (Nalgene) and acidified to pH 1.8 with ultrapure HCl (Seastar from Choice Analytics) for DFe analysis. Once fully melted, the same process was applied to the melted sea ice. Filters were stored individually in acid-clean petri-dishes, triple bagged and kept at −20◦C in the dark until analysis. The LDPE bottles containing the dissolved fraction were triple bagged and kept at ambient temperature until analysis.

Filters for PFe and PAl determination were digested using a mixture of strong acids following the method described in Bowie et al. (2010). Dissolved Fe samples were diluted 10 times to reduce sea salt matrix interferences effects during analysis. Particulate metals and DFe concentrations were then measured at the Central Science Laboratory (University of Tasmania) using a Sector Field Inductively Coupled Plasma Mass Spectrometer (ICP-MS, Element 2), following methods described in Bowie et al. (2010).

## Segregation Coefficient and Enrichment Index

The partition coefficient, Keff is used to describe the proportion of solute that is retained in sea ice.

$$\mathcal{K}\_{\mathcal{eff}\mathcal{X}} = [X]\_{ice} / [X]\_{source}$$

where [X]ice and [X]source is the concentration of the component X in the ice and in the source solution, respectively.

To assess the enrichment of each component in the ice compared to sea salt, the enrichment index was calculated (EIx, Gradinger and Ikävalko, 1998).

$$EI\_{\mathfrak{X}} = [\mathrm{Sal}]\_{source} / [\mathrm{Sal}]\_{ice}. \ [X]\_{ice} / [X]\_{source}$$

where [Sal]source is the salinity of the source solution, and [Sal]ice is the salinity of the ice. Enrichment index of 1, <1 or >1 will correspond to conservative, depletion or enrichment, respectively, of the component X as compared to bulk salinity.

## Statistical Analysis

All statistical analyses were conducted using the statistical software R (version 3.3.3, R development Core Team, 2017). Differences in treatments were tested with standard analysis of variance (ANOVA) after log-transforming the response parameters to stabilize the variance (graphical diagnosis: Residuals vs. Fitted values, Normal quantiles vs. Standardized residuals, Scale location and Constant leverage). ANOVA was followed by a single-step multiple comparison Tukey-Honest Significant Differences (Tukey-HSD) procedure to determine which treatments differ. When the ANOVA showed no evidence of treatments differences for EIs, a t-test was performed to assess if the overall mean (of all treatments) of the EI was different from 1.

## RESULTS

## Ice Texture and Salinity

The ice texture was dominated by columnar ice (**Figures 2A,B**). The texture reflected the geometry of the cold-finger. Except for small differences in the size of the ice crystals, temperature had no effect on the geometry of the ice crystals.

Salinities of bulk-ice samples, as well as in the remaining seawater, were consistent throughout the experiments, with a mean bulk-ice salinity of 14.5 ± 1.3 SP (mean ± standard deviation (SD), n = 24), and a mean salinity of 39.6 ± 1.3 (n = 24) for remaining seawater. The different growth temperatures applied to the samples resulted in similar partition coefficients (mean Keff = 0.32 ± 0.07, n = 3).

## Extracellular Polymeric Substances, POC, PON, and Macro-Nutrients Experiments Enrichment Indices of Macro-Nutrients and EPS

**Figure 3** shows the enrichment indices for EPS and macronutrients in sea ice grown from Surface SW, Deep SW and Surface SW + XG. The horizontal line marks the EI = 1 (i.e., conservative incorporation). Results show that Si(OH)− 4 , NOx, and PO3− 4 behave conservatively during sea-ice formation, whatever the source solution may be. Enrichment indices were close or equal to one for the macro-nutrients in Surface SW, Deep SW and Surface SW + XG (mean ± SD = 1.0 ± 0.09, n = 9), except NH+ 4 that was enriched in the ice in each treatment. The largest signal was observed for NH+ 4 , which is clearly enriched in sea ice compared to seawater, especially in the deep samples (**Figure 3**). We observed moderate evidence of a difference in incorporation of NH+ 4 between treatments (ANOVA F = 5.28, P = 0.082). A Tukey-HSD multiple comparison procedure showed that only the Deep SW and Surface SW + XG treatments could be distinguished (P = 0.07). EPS enrichment was variable and showed no significant differences (ANOVA F = 2.03, P = 0.212) between treatments. The highest EIEPS was observed in the Surface SW treatment and the lowest EIEPS was observed in the Deep SW treatment (**Figure 3**). The Deep SW experiment had the highest EINH+ 4 and also the lowest EIEPS.

#### Enrichment Indices of POC and PON

All EIPOC and EIPON were well above 1 indicating an enrichment of POC and PON in newly-formed ice (**Figure 4**). Mean EIPOC and EIPON were 50.5 ± 35.7 (n = 9) and 8.9 ± 6.4 (n = 9), respectively. There is no evidence of treatment difference for EIPOC (ANOVA F = 1.99, P = 0.186), and strong evidence of treatment difference for EIPON (ANOVA F = 11.23, P = 0.005; Tukey-HSD Surface SW + XG and Deep SW, P = 0.038; Tukey-HSD Surface SW + XG and Surface SW, P = 0.005).

## Iron Experiments

#### Enrichment Indices of PFe and DFe

**Figure 5** shows the enrichment indices for DFe and PFe in sea ice grown from the five different source solutions. The horizontal line marks the EI = 1 (i.e., conservative incorporation). Results show that DFe and PFe do not behave conservatively during sea-ice formation in any of the source solutions. Dissolved Fe was slightly enriched in sea ice compared to the source seawater solution in each treatment (EIDFe > 1, t-test confidence level 95%, P = 0.002) and relatively stable between treatments (mean EIDFe = 2.2 ± 0.9, **Figure 5**) with no evidence that EIDFe differed across treatment groups (ANOVA F = 1.85, P = 0.196).

We observed a strong evidence of difference in incorporation of PFe between treatments (ANOVA F = 5.17, P = 0.035). The UV-FSW + DFe and FSW + Algae treatments groups could be distinguished at the 0.05 level (Tukey-HSD, P = 0.02). Ice was enriched in PFe in the treatments where lithogenic or biogenic PFe was added: FSW + PFe (EIPFe = 1.3 ± 1.41, n = 3) and FSW + Algae (EIPFe = 4.7 ± 2.8, n = 3; **Figure 5**). When lithogenic PFe was added and UV treatment applied, less PFe was incorporated in sea ice (UV-FSW + PFe, EIPFe = 0.5 ± 0.3, n = 3) than without UV-treatment. The treatment where biogenic PFe was added (FSW + Algae) showed the highest EIPFe of all treatments (EIPFe = 4.7 ± 2.8, n = 3).

#### Enrichment Indices of POC and PON

Particulate organic matter (POC and PON) was more enriched in sea ice in treatments where PFe was added than in treatments where DFe was added (EIPOC: ANOVA, F = 23.14, P = 0.003; and EIPON: ANOVA, F = 24.57, P = 0.000). Unlike other parameters, POC was the only compound showing a higher EI in UV-treated than in its non-UV treated equivalent. The enrichment index of PON was always lower than EIPOC (ANOVA, F = 4.48, P = 0.040; **Figure 6**).

Overall, the FSW + DFe and UV-FSW + DFe treatments had the lowest EIs and the lowest variability with EIPON of 1.1 ± 0.7, n = 3 and 0.8 ± 0.5, n = 3, respectively.

The treatments with addition of lithogenic and biogenic PFe, and no UV treatment (FSW + PFe and FSW + Algae) were the only ones where enrichment in all compounds (DFe, PFe, POC, and PON) was observed relative to the source solution (**Figures 5**, **6**). This was not the case for the treatments where DFe was added (FSW + DFe and UV-FSW + DFe), or when UV light was applied, where, depending on the compound, enrichment or depletion was observed.

#### Particulate Fe to Particulate Al Ratios

Aluminum can be used as a tracer of lithogenic Fe inputs (Lannuzel et al., 2014a). The PFe/PAl molar ratio in the ice was elevated in all treatments compared to the Earth crustal ratio of 0.33 (Taylor, 1964). Two different patterns were observed: In the treatments with addition of PFe (FSW + PFe, UV-FSW + PFe and FSW + Algae), PFe/PAl was higher in the ice than in the remaining seawater (**Figure 7A**), however, this was not statistically significant (ANOVA F = 0.68, P = 0.423). In these treatments, the PFe/PAl ratios in the ice were up to 2 times higher than the crustal ratio (**Figure 7A**). In the treatment with DFe addition (FSW + DFe and UV-FSW + DFe) the PFe/PAl ratio in the ice was lower than in the remaining seawater (ANOVA F = 9.60, P = 0.011). In these experiments with addition of DFe, the PFe/PAl ratios were two to three orders of magnitudes higher than the crustal ratio (**Figure 7B**).

(PO3− 4 ) in the *EPS, POC, PON, and macro-nut. Exp*. The error bars represent the standard deviation between triplicates (*n* = 3). The black line shows the limit between enrichment (EI>1) or depletion (EI<1) of each compound.

## DISCUSSION

The similarity in bulk ice and remaining seawater salinities between the different sets of experiments shows the reproducibility of our experimental set-up. Despite the unusual ice texture of the samples (horizontal ice crystals), representative processes of the natural environment are shown with the conservative behavior (i.e., relative to salt) of macro-nutrients,

except NH+ 4 as previously observed in natural young ice samples (Janssens et al., 2016) and sea-ice tank experiments (Zhou et al., 2014). In this study, the geometry of the ice crystal grown at 3 different temperatures in the Ice texture Exp. do not show any difference. This indicates that selection of the freezing temperature for the next set of experiments did not influence estimated EIs. The freezing temperature was chosen to resemble a realistic temperature encountered in the field in winter, but

was somewhat constrained by the minimum temperature of the circulating water bath.

## Extracellular Polymeric Substances, POC, PON, and Macro-Nutrients Experiments POC, PON, and EPS

EPS from deep seawater were the least enriched in the ice, although it had the highest initial concentration (0.10 µg xeq

L −1 ). Also, the addition of XG, a reference standard for EPS in marine environments (Passow and Alldredge, 1995), did not show a preferential enrichment compared to the natural EPS in other treatments. Deep-sea bacteria have been found to produce EPS (Mancuso Nichols et al., 2005b), and it could be that deep-water EPS have different surface properties (e.g., number of anionic groups) or molecular weights than EPS produced in surface waters. Decho (1990) reported that physicochemical characteristics of EPS are influenced by their tertiary structure, which is dependent on the frequency and type of functional groups presence on the EPS. This could also affect the incorporation efficiency, with stickier EPS being more enriched (Meiners and Michel, 2017). These divergences in surface properties of EPS could also play an important role in the incorporation of NH+ 4 in this experiment. The highest NH+ 4 enrichment was found when EIEPS was the lowest (**Figure 3**). EPS have a negatively charged surface due to the high content of poly-anionic sugars. They are stabilized by the presence of cations (such as e.g., K+ and Ca2+) (Kloareg and Quatrano, 1988; Verdugo et al., 2004). Also, the sticky properties of EPS determine the interaction of EPS with other components of the marine environment (Passow, 2002). Some authors (e.g., Krembs et al., 2002a; Meiners and Michel, 2017) have suggested that the negatively charged EPS would complex with NH+ 4 , and helped by their stickiness, lead to the enrichment of NH+ 4 and EPS in the ice (Fripiat et al., 2017). However, the lack of correlation between EPS and NH+ 4 in our study does not support this hypothesis, and further study is needed to decipher this process. Microbial regeneration of NH+ 4 has been previously observed in young sea ice (Riedel et al., 2007). However, due to the short duration of our experiment we believe microbial regeneration alone cannot fully explain the enrichment of NH+ 4 in the ice. In an ice tank study, Krembs et al. (2011) found that concentrations of EPS were higher in the ice when Melosira arctica, a diatom found in Arctic sea ice, was added to the source solution compared to ice

grown from saline solution containing different concentrations of XG, even if the concentration of XG was higher than the concentration of EPS produced by the diatom. Similarly, using a cold-finger apparatus similar to ours, Ewert and Deming (2011) showed that only native EPS produced by a cold-adapted marine bacterium (commonly found in sea ice) was preferentially incorporated in sea ice. Our results indicating that EPS from surface seawater are enriched in sea ice when compared to deep seawater are consistent with these previous findings, suggesting that the quality of EPS is a key parameter for its entrapment into sea ice.

Higher EIPOC than EIPON are in line with previous reports of high POC and PON concentrations in young Antarctic sea ice (Janssens et al., 2016). In our laboratory study, POC and PON were more enriched in experiments using Antarctic unfiltered seawater compared with experiments conducted with filtered source solutions from Tasmania (Iron Exp.). The presence of biogenic material (phytoplankton, bacteria and EPS) in the unfiltered Antarctic sample could explain this higher enrichment compared to the filtered Tasmanian seawater sample. Also, similar to EPS, the quality of POC may control the degree of enrichment in sea ice. Due to its location, the coastal Tasmanian sampling site is under influence of terrestrial inputs bringing large amounts of allochtonous material to the pool of organic matter. This is in contrast to the Antarctic sampling sites, where the organic matter in the water column is mainly autochtonous. Zhou et al. (2014) showed that riverine DOM was less enriched in sea ice compared to autochtonous DOM, due to different molecular compositions, different affinity with other compounds, and with sea ice. Similarly, Müller et al. (2013)showed that the degree of dissolved organic carbon (DOC) enrichment depends on the chemical characteristics of the DOM. Our results suggest that the POC of potential terrestrial origin is less enriched in sea ice than the autochtonous Antarctic POC, by an order of magnitude. The reason why the POC is preferentially

incorporated in the ice compared to PON remains unclear at this stage and this needs further investigations. One idea is EPS might have a higher POC/PON ratio or the organic matter in the ice comprises a mixed composition of live and dead material, with a higher carbon content relative to nitrogen.

## Iron Experiments

#### Lithogenic vs. Biogenic Iron: Role of EPS Produced by Ice Algae and Bacteria

The EIPFe in FSW + Algae was found to be up to 4 times higher than in any other treatment, suggesting a better incorporation of the biogenic PFe compared to the lithogenic PFe. The bottom ice sample added to the FSW + Algae treatment was dominated by pennate diatoms such a Nitzschia stellata, N. lecointei, Fragillariopsis spec., and Entomoneis kjellmanii (Andreas Seger and Fraser Kennedy, personal communication). The large size of algal cells compared to the dust sample, may have lead to the preferential enrichment of biogenic PFe relative to lithogenic PFe. The added dusts were sieved through 20µm nylon mesh, while mentioned diatom species can be as large as 150µm in cell size (Scott and Marchant, 2005), and can also form colonies and ribbon-chain like assemblages (Scott and Marchant, 2005; Aslam et al., 2012). Better incorporation of larger cells and impurities has been previously observed in natural young sea ice (Gradinger and Ikävalko, 1998; Riedel et al., 2007; Rózanska et al., 2008; Janssens et al., 2016).

Algal-bound EPS could also play a role in the incorporation of PFe into sea ice. Assemblages of diatoms are predominant in autotrophic-dominated sea-ice habitats and known to be associated to, and the main producers of, EPS (Krembs and Engel, 2001; Meiners et al., 2003, 2004; Mancuso Nichols et al., 2005a,b; Underwood et al., 2010, 2013). Particulate EPS have been shown to be likely of algal origin and quickly enriched into sea ice (Meiners et al., 2003; Riedel et al., 2007). EPS enrichment has been previously observed in cold-finger (Ewert and Deming, 2011) and ice tank experiments (Krembs et al., 2011). Negatively charged EPS (Decho, 1990; Mancuso Nichols et al., 2005a) are considered to be important in binding cationic metals like Fe3+ and Fe2+. In return, Fe provides some stability to the EPS network by acting as bridging ions (e.g., Brown and Lester, 1982; Decho, 1990).

Finally, EPS are two to four orders of magnitude stickier than other particles (Passow, 2002), therefore potentially further helping the adhesion of PFe to ice surfaces. One can therefore assume that EPS were an important component of the added melted bottom ice sample to the source solution in the FSW + Algae treatment, contributing to the preferential enrichment in biogenic PFe compared to lithogenic PFe. Krembs et al. (2011) showed that the presence of EPS in the ice changes the microstructure of the ice and alters it physical properties. Thus, it is also possible that these modified properties are beneficial for PFe incorporation (more adapted shape of the pores and an increased internal sea ice surface area) but this requires further investigation.

The DFe fraction was less affected by the addition of algae than the PFe fraction. EPS range in size from sub-microns to hundreds of microns in the marine environment (Passow and Alldredge, 1995). EPS produced by sea-ice algae are known to be in the colloidal and soluble form (Aslam et al., 2012). In this study, a size distinction of 0.4µm was used to define the dissolved and particulate fractions. This definition is however operationally defined and organic matter actually consists of a size continuum spanning across the soluble, colloidal and particulate size fractions. The colloidal material is operationally in the dissolved fraction as it is defined as the fraction that can pass through a filter with pore size of 0.1 to 0.46µm (Chin et al., 1998). van der Merwe et al. (2009) suggested that organicallybound DFe in the ice shows reduced loss to the water column when brine drainage occurs by being associated with particles or cells (Sunda, 2001). The retention of DFe by EPS could explain the lower EIDFe in the UV-treated FSW + DFe experiment.

#### Effect of UV Treatment on Fe Incorporation Efficiency: Importance of Organic Ligands

When subject to high UV exposure, DOM is degraded and ultimately transformed into carbon dioxide (CO2) and water (H2O). The UV- and non UV-treated experiments aimed at understanding the role of organic matter, including organic ligands such as EPS, in the incorporation of Fe into sea ice. It has been shown that photochemical reactions alter the concentration and reactivity of the organic ligands involved in the complexation/solubilisation of trace metals (Moffett, 1995). More specifically, photochemical reduction of organically bound Fe decreases ligand-binding strength, rendering the complexed Fe more labile and increasing its bioavailability (Barbeau et al., 2001).

In this study, Fe was added to seawater solutions after UV treatment. While results between UV and non-UV treatments were not statistically significant, different enrichment behaviors for Fe and organic matter were observed between UV-treated and non-UV treated source solutions. The lack of statistical significance is a result of the large variance of the data set compared to the limited size of the data set. Further studies are needed to elucidate the full impact of UV treatment on the incorporation of organic matter into sea ice.

Nevertheless, as expected, DFe (and PFe) enrichment decreased when UV treatment was applied to FSW + DFe. This experiment suggests that the presence and quality of organic matter influences the incorporation of DFe into growing sea ice. However, DFe enrichment increased when UV treatment was applied to FSW + PFe. In the former treatments, the DFe was not present in excess; as such precipitation of DFe into PFe was not observed (see section Conversion of Dissolved Fe to Particulate Form). It is suggested that, in the latter case, the UV treatment resulted in the release of dissolved organic ligands from the degradation of particulate organic matter. These "newly-formed" ligands were then able to complex DFe and carry DFe into sea ice leading to higher EIDFe in UV-FSW + PFe than in FSW + PFe, where dissolved organic ligands were more sparse. The difference in enrichment efficiency of DFe in (UV-)FSW+ PFe and (UV-)FSW + DFe by a combined effect of "ligand saturation" and precipitation of DFe into PFe can be explained in this way (see section Conversion of Dissolved Fe to Particulate Form). The UV-broken bound effect might have been compensated by the production of new dissolved organic ligands available for DFe. However, not enough dissolved organic ligands were present in solution to keep the added 30µM of DFe in solution in the FSW + DFe treatment. Free DFe remaining in seawater therefore precipitated into PFe (see section Conversion of Dissolved Fe to Particulate Form), leading to the high PFe/PAl ratios observed.

As observed in the case of DFe incorporation in the (UV-)FSW + DFe treatments, PFe enrichment decreased when UV treatment was applied to FSW + PFe and FSW + DFe. This indicates the role of organic matter in the incorporation of PFe in growing sea ice. Unlike EIPFe and EIDFe, EIPOC increased when UV treatment was applied to FSW + PFe. It has been proposed that UV-B radiation breaks down not only DOC but also POC (UNEP, 1999), although the effect of UV on particulate organic matter is not clearly defined. Results here show that UV could impact on the quality of POC (including EPS) leading to different incorporation behaviors. It is likely that the POC becomes smaller when UV is applied and is then more easily incorporated into the ice. This argument however contradicts what has been previously observed in the field, where large particles are preferentially incorporated in sea ice (Gradinger and Ikävalko, 1998; Riedel et al., 2007; Lannuzel et al., 2014b; Janssens et al., 2016).

One explanation is that UV-radiation changes the physicochemical properties of organic matter in a way that the PFe-organic matter binding capacity would be reduced, but the enrichment of POC would be favored. Organic-free PFe would therefore be hardly incorporated, while the UVinduced modifications in the POC facilitate its incorporation. It has been shown that the binding potential of EPS is greatly influenced by their physico-chemical properties (Krembs and Deming, 2008; Verdugo, 2012), which are likely to be altered by UV irradiation. Therefore, more than the size, the important factor for enrichment of particles would be their physico-chemical properties and the tertiary structure/molecular composition/shape of the particles. Zhou et al. (2014) suggested that organic matter is initially incorporated as particulate organic matter and then converted into DOM once in the ice. This process may be observed in the FSW + Algae treatment, with the transformation of POC into DOC by heterotrophic organisms, leading to lower EIPOC than in (UV-)FSW + PFe. However, we note that 8 h of incubation time may not be sufficient for significant transformation. Zhou et al. (2014) also showed that different quality of DOC impacts the incorporation efficiency. It is suggested that this mechanism is also applicable to particulate constituents as discussed in section Lithogenic vs. Biogenic Iron: Role of EPS Produced by Ice Algae and Bacteria.

#### Conversion of Dissolved Fe to Particulate Form

The PFe/PAl ratios measured in the samples can give an indication on the level of conversion of DFe into PFe. The addition of DFe into seawater led to very high PFe/PAl ratios in the FSW + DFe and UV-FSW + DFe experiments (**Figure 7B**). A significant amount of DFe (30µM) was added to these 2 solutions, without addition of organic ligands (e.g., EDTA) to balance the addition of DFe and keep it in solution. Organic ligands can be produced in situ by sea-ice algae and bacteria (Lannuzel et al., 2015) or supplied externally from e.g., sediment resuspension (Croot and Johansson, 2000; Buck et al., 2007). EDTA is synthetically produced and is therefore not an adequate model ligand of the natural environment.

The conversion observed here of the non-organically bound excess of DFe into the PFe pool (Lannuzel et al., 2014a, 2015), leads to the high PFe/PAl ratios observed. This process was even more expressed in the UV-FSW + DFe treatment, where the UV exposure likely broke the bounds of the Fe-ligand complexes already present in the source solution before addition of DFe. The newly formed PFe and remaining DFe could be then less prone to incorporation into sea ice, as observed by lower EIPFe and EIDFe. Two explanations are possible: (1) As previously mentioned in the preceding section, biogenic PFe (low in these experiments) is preferentially enriched in the newly-formed ice compared to lithogenic PFe. A decoupling between biogenic and lithogenic PFe incorporation could be observed. EPS have been proposed to be acting as a "plug," retaining salinity in the ice (Krembs et al., 2011). If the biogenic PFe is incorporated first, helped by the association to, and then retained in the ice by EPS, the incorporation of lithogenic PFe might not be as efficient and could reach a concentration threshold in the ice. The association of Fe with EPS, as well as the space available in the brine channel system would therefore determine the incorporation, retention, and threshold of PFe in the ice.

(2) Organic ligands may play a key role in transferring DFe from seawater to sea ice. Organically-bound Fe would take advantage of the properties of the particles it is attached to, to get trapped in the ice as discussed previously (section Effect of UV Treatment on Fe Incorporation Efficiency: Importance of Organic Ligands).

It is difficult to distinguish the contribution of each process in these experiments and it is likely a combination of both. We note that Fe concentrations in these experiments were above the range of concentrations encountered in the Southern Ocean, although they are in the range of concentration found in Antarctic fast ice (Grotti et al., 2001; van der Merwe et al., 2011a,b; Noble et al., 2013). All conclusions in this study are derived from a high concentration scenario in regards to added DFe and PFe, but nonetheless, our results provide key information on the general chemistry of the involved processes.

## Limitations of Laboratory Ice-Growth Experiments

Laboratory experiments offer a unique opportunity to study ice growth from its initial stage of ice formation while the history of a natural ice floe is difficult to assess. Laboratory experiments allow the isolation of processes occurring at the onset of ice formation, and the study of the influence of specific parameters, while controlling others. Most importantly, replicate measurements are possible in the laboratory while field sampling of a natural ice floe always carries the uncertainty of high spatial heterogeneity (e.g., Meiners et al., 2012, 2017; Williams et al., 2015).

However laboratory based ice-growth experiments also present some drawbacks. First, ice growth is limited in space (a few cm3 ) and time (a few hours). In the cold-finger case, the geometry of the system is different from natural ice. Ice crystals are mainly growing horizontally (**Figure 2**), while natural columnar ice exhibits vertical ice crystals. Nonetheless, the good agreement between laboratory-based results and field studies shows that the initial incorporation of solutes and particles over a short period of time is not affected by the geometry of the ice. The cold-finger set-up is, therefore, a useful tool to complete field data and isolate processes to gain a mechanistic understanding of sea-ice biogeochemical processes.

## CONCLUSION

This study represents the first study of processes of Fe enrichment in sea ice under laboratory conditions. The investigation suggests

## REFERENCES

an important role of EPS and POC in the enrichment of PFe in sea ice. Results also indicated that large particles of biogenic PFe derived from sea ice algae are preferentially enriched compared to smaller particles of lithogenic PFe derived from continental dust. Combined these results indicate that rather than the quantity, it is the quality of EPS and POC that determines their rate of entrapment in the ice, with autochtonous POC being more enriched than allochtonous POC. The mechanisms remain unclear, but characterizations of these particles would be a step forward to understand the complex links between Fe and organic matter incorporation into sea ice. The quality of particulate organic matter determines its association with PFe, and ultimately the efficiency of PFe incorporation in sea ice. UV treatment likely alters organic matter and modifies its association with Fe (PFe and DFe). This ultimately affects their degree of enrichment. In the context of changing sea-ice environments, it is important to clearly identify the drivers and to quantify the processes that affect ice-associated Fe and carbon biogeochemical cycling in polar waters.

## AUTHOR CONTRIBUTIONS

JJ, KM, and DL: contributed to conception and design; JJ, KM, AT, and DL: contributed to acquisition of data; JJ, KM, AT, and DL: contributed to analysis and interpretation of data; JJ, KM, AT, and DL: drafted and revised the article; JJ, KM, AT, and DL: approved the submitted version for publication.

## FUNDING

This work was funded by the Australian Research Council (LE0989539 and DE120100030), the Australian Government's Cooperative Research Centres Programme through the Antarctic Climate & Ecosystems Cooperative Research Centre (ACE CRC) and the Australian Antarctic Science (AAS) projects 4051 and 4298. JJ was supported by the Quantitative Antarctic Science Program jointly led by the University of Tasmania and the Australian Antarctic Division.

## ACKNOWLEDGMENTS

The authors thank the officers and crew of RV Polarstern for their logistical support, as well as the colleagues from the biogeochemical group during the AWECS voyage. We thank Rob King and Tasha Waller from the Australian Antarctic Division for assistance with the collection of the seawater used in the Iron Experiment. We also thank Simon Wotherspoon for assistance with the statistical analysis.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer, MV, declared a past co-authorship with one of the authors, KM, to the handling editor.

The reviewer, AN, and handling editor declared their shared affiliation.

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