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Article

Stable Carbon Isotopes of Phytoplankton as a Tool to Monitor Anthropogenic CO2 Submarine Leakages

1
National Institute of Oceanography and Applied Geophysics—OGS, 34010 Sgonico (TS), Italy
2
Department of Life Science, University of Trieste, 34127 Trieste, Italy
3
Department of Environmental Sciences, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
4
Center for Marine Research, Ruder Boskovic Institute, 52210 Rovinj, Croatia
*
Author to whom correspondence should be addressed.
Water 2020, 12(12), 3573; https://doi.org/10.3390/w12123573
Received: 29 September 2020 / Revised: 13 December 2020 / Accepted: 16 December 2020 / Published: 19 December 2020
(This article belongs to the Section Hydrology)

Abstract

This study aims to validate the stable carbon isotopic composition (δ13C) of phytoplankton as a tool for detecting submarine leakages of anthropogenic CO2(g), since it is characterised by δ13C values significantly lower than the natural CO2 dissolved in oceans. Three culture experiments were carried out to investigate the changes in δ13C of the diatom Thalassiosira rotula during growth in an artificially modified medium (ASW). Three different dissolved inorganic carbon (DIC) concentrations were tested to verify if carbon availability affects phytoplankton δ13C. Simultaneously, at each experiment, T. rotula was cultured under natural DIC isotopic composition (δ13CDIC) and carbonate system conditions. The available DIC pool for diatoms grown in ASW was characterised by δ13CDIC values (−44.2 ± 0.9‰) significantly lower than the typical marine range. Through photosynthetic DIC uptake, microalgae δ13C rapidly changed, reaching significantly low values (until −43.4‰). Moreover, the different DIC concentrations did not affect the diatom δ13C, exhibiting the same trend in δ13C values in the three ASW experiments. The experiments prove that phytoplankton isotopic composition quickly responds to changes in the δ13C of the medium, making this approach a promising and low-impact tool for detecting CO2(g) submarine leakages from CO2(g) deposits.
Keywords: diatoms; CO2 leakage; 13C; carbon fixation; Carbon Capture and Storage; Phytoplankton; CCS diatoms; CO2 leakage; 13C; carbon fixation; Carbon Capture and Storage; Phytoplankton; CCS

1. Introduction

The 2015 Paris UNFCCC (United Nations Framework Convention on Climate Change) agreement has set the maximum global temperature increase to 2 °C to significantly reduce the risks and impacts of climate change [1]. To achieve this challenging goal, global emissions need to be reduced at about 3% year−1, corresponding to 37 Gt CO2(g) year−1 [2]. Several strategies need to be adopted to decrease atmospheric CO2(g) concentration, such as containing the rate of emissions, developing zero-carbon energy technologies, fuel-switching, improving efficiency, implementing negative emissions technologies and developing and improving carbon capture and storage (CCS) [2,3,4]. CCS has emerged as one of the most promising options among the mitigation technologies by storing carbon dioxide in geological reservoirs, such as depleted oil and gas fields and deep, saline formations [5], either on-shore or off-shore [4]. The International Energy Agency has assessed that, to cope with the purpose of 2 °C warming limitation, CCS should capture about 6000 Mt CO2 year−1 by 2050, contributing 15–20% to global CO2(g) emissions reduction [6]. Thus far, nineteen CCS sites are already operational [7], and, among these, six off-shore sites are currently active [8]. Although a well-engineered storage site is not expected to leak, the success and future of CCS depend on the demonstrable effectiveness of storage integrity and, therefore, on the implementation of reliable monitoring strategies for the detection of leakages and their quantification to an acceptable degree of confidence [3,9]. Geological discontinuities, abandoned exploratory boreholes and operational malfunction have been identified among the mechanisms that could potentially facilitate CO2(g) leakage [10]. The dissolution of the released CO2(g) in seawater leads to lower pH, and thus, it could critically affect the surrounding marine environment. Among the main adverse effects, shell formation in calcifying organisms could be inhibited, the dissolution and desorption of metals could be enhanced and, once mobilised, they can accumulate in marine organisms, interfering with physiological processes, inhibiting growth or even causing death [11,12,13,14]. Concerning primary producers, except calcareous organisms that are detrimentally affected by the dissolution of CO2(g), the contradictory effects of high dissolved CO2 concentration have been reported [14,15,16,17,18]. Phytoplankton and seaweed species seem to benefit from high CO2(aq) levels by enhancing the carbon and nitrogen fixation and stimulating photosynthesis [11,19,20]. However, pH decrease could affect the physiological processes of some phytoplankton species, including photosynthesis, uptake rates of trace metals and toxin production of some harmful algal species. Moreover, natural phytoplankton assemblages and community composition could be modified by high dissolved CO2 concentrations [14]. Therefore, effective management of storage sites is required to early detect leakages and prevent risks for the surrounding environment.
Monitoring strategies have been established to detect leakages from storage sites using surface and downhole seismic methods, direct downhole measurements and seabed and water column monitoring [8,21]. Concerning the environmental monitoring, water column and sediment sampling is crucial for the chemical and geochemical characterisation of spatial and temporal dynamics of the natural baseline, thus allowing the identification of anomalies and avoiding false positive and/or negative results [21,22]. However, the temporal sampling frequency may not permit the detection of small leakages depending on plume dispersion and carbonate buffering or may not be able to cover the entire possible affected area [8]. The use of a multidisciplinary approach is encouraged; however, biological monitoring is still underutilised, though it is a low-cost effective tool [12].
Stable isotopes have been largely applied to estimate the effects of anthropogenic pressures on natural ecosystems [23]. Carbon stable isotope analysis is considered a suitable tool for tracing the fate of CO2(g) in the reservoir and for detecting leakages from storage sites, provided that the isotopic composition (δ13C, sensu Coplen [24]) of injected CO2(g) and its dissolution products is different from that of the surrounding environment [25,26]. The isotopic composition of CO2(g) (δ13CCO2(g)) stored in CCS is predominantly controlled by the δ13C of the feedstock (e.g., fossil fuels, biomass and limestone) used in the CO2(g)-generating process (e.g., combustion, gasification, cement and steel manufacture and fermentation) [5,27], and the δ13C of most of captured CO2(g) is predicted to range between −60‰ and −20‰. Despite some overlap, this range is rather distinct from the expected natural baselines; thus, δ13C represents a valid geochemical tracer [27]. In particular, CO2(g) derived from fossil fuels is generally characterised by δ13C values similar to the isotopic composition of bulk coal (−28.46 to −23.86‰ [28]) or of methane (from −60 to −20‰ [5]) and is thus easier to be distinguished from baseline conditions.
The δ13C of atmospheric CO2(g) has changed significantly since the 19th century, from a value of about −6.5‰ to a present-day value below −8.0‰ as a consequence of the addition of 13C-depleted carbon from fossil fuel and land-use changes [29] (the so-called Suess effect [30]). δ13C of the total dissolved inorganic carbon (DIC) in the ocean ranges, on average, from 0 to −2‰ [31]; however, substantial spatial-temporal variations have been detected [32,33]. Physical and biological processes affect the δ13C of DIC (δ13CDIC), resulting in relatively low values in areas of CO2(g) invasion and in 13C-enriched DIC in areas of outgassing [33]; thus, δ13CDIC could range from −6.56‰ to +3.10‰ [34]. At the present marine pH, bicarbonate (HCO3) is the dominant species composing DIC; therefore, its isotopic composition (δ13CHCO3) is generally very close to δ13CDIC, whereas CO2(aq) is 13C-depleted by about ~10‰ [35], depending on the temperature isotopic fractionation and the concentrations of the carbonate species dissolved in seawater that are driven mainly by temperature, pH and salinity. Thus, the seasonal and spatial variability described for δ13CDIC also applies to both δ13CHCO3 and δ13CCO2(aq).
DIC uptake by primary producers represents one of the biological processes that mainly contribute to the modifications of δ13CDIC. Indeed, during carbon uptake for photosynthesis, isotopic fractionation (ε, sensu Coplen [24]) occurs because of the isotopic discrimination operated by the enzyme involved in carboxylation (ribulose-1,5-bisphosphate carboxylase/oxygenase—RuBisCO). The lighter isotope (12CO2) is fixed at a rate 1.03 times faster than the heavier (13CO2), leading to 13C-depleted values in phytoplankton in respect to the inorganic carbon source [36] and, consequently, enriching the residual DIC. The enzyme RuBisCO discriminates carbon by about 22‰ to 31‰, depending on the different enzyme forms that express a variable affinity for CO2(aq) [37]. These maximum ε values, however, are rarely observed because several factors affect the CO2(aq) concentration at the active site of the enzyme. These factors include the dissolved CO2 concentration [38], phytoplankton growth rates [39,40], cell size or geometry [40], temperature and nutrient availability [41], light intensity [39,42,43], day length [44], temperature effects on enzyme kinetics [37], utilisation of HCO3 through CO2-concentrating mechanisms (CCMs) [45,46] and species variability [44,47]. Therefore, phytoplankton isotopic composition directly depends on δ13CDIC and its spatial and temporal variations. Phytoplankton δ13C values globally range between −31.0‰ and −16.5‰, displaying higher values in the equatorial upwelling regions and lower values at latitudes >60° in the northern hemisphere, and exhibit a high variability in the temperate regions due to seasonal dynamics in climatology and phytoplankton life cycle [32].
The difference between the isotopic composition of captured CO2(g) and the natural dissolved CO2 baseline makes δ13C the most widely analysed tracer, as it allows leakage monitoring with minimal economic and environmental impacts compared to other tracers [5]. Most of the applications of the carbon stable isotope analysis are related to monitoring of the movement of injected CO2(g) inside the reservoir [25,48,49,50]. The direct monitoring of the changes in δ13CDIC could result not suitable to detect transient leaks because of the rapid mixing in the surrounding environment of CO2(g) leaking from a storage site. During an experimental CO2(g) leak into the sediments of North Sea [10,51], the analysis of pore-water δ13CDIC allowed the identification of the leak earlier than the detection of significant changes in bicarbonate concentration [10], confirming the reliability of carbon stable isotope analysis as an inherent tracer of CO2(g) leakage. However, it has also been highlighted that low levels of leakage may be hardly detected and quantified because of carbonate buffering and CO2(g) transport. Phytoplankton, instead, could represent a record of the changes induced by CO2(g) leakage, because their isotopic composition directly depends on the δ13C of the available DIC pool. A considerable fraction of phytoplankton can vertically migrate along the water column to acquire multiple resources and moves towards the bottom to access the higher availability of nutrients regenerated at the sediment–water interface [52]. In a storage site, this active movement towards depth allows therefore the uptake of the CO2(aq) that eventually leaks out. If stored CO2(g) is characterised by δ13C different than natural CO2(aq), the phytoplankton isotopic composition is expected to significantly change from the baseline of the area that needs to be defined to take into account for the natural seasonal variability. Since stored CO2(g) is generally characterised by δ13C lower than natural δ13CDIC, phytoplankton δ13C is thus expected to change towards more negative values. An efficient CCS monitoring should therefore include, besides the geochemical analysis, the evaluation of the biological parameters that could give an integrated contribution to the detection of CO2(g) leakages.
This study aims to explore the feasibility of using the phytoplankton stable carbon isotopic composition as a tool for detecting anthropogenic CO2(g) leakage from the seabed. Through isotopic fractionation during photosynthetic DIC uptake, phytoplankton δ13C is expected to change, thus representing an important contribution to the identification of sporadic leakages that could not be detected by the direct analysis of the geochemical properties of the water column, provided that the isotopic composition of injected CO2(g) and its dissolution products is different from that of the surrounding environment. Three pairs of culture experiments were designed in a model case approach, reproducing the isotopic composition of the carbonate system modified by an anthropogenic CO2(g) submarine leakage but maintaining pH levels in the typical marine range to allow phytoplankton optimal growth. A single diatom species was grown in an artificially modified medium characterised by strongly 13C-depleted dissolved CO2, and three different DIC concentrations were tested to verify if inorganic carbon availability would affect the phytoplankton algal metabolism. Simultaneously, at each experiment, the same diatom species was cultured under natural carbonate system and δ13CDIC conditions to better interpret the isotopic results considering this condition as the reference baseline.

2. Materials and Methods

2.1. Experimental Setup

Three culture experiments (EXP-1, EXP-2 and EXP-3) with the diatom Thalassiosira rotula were conducted within two 2L-photobioreactors (Kbiotech®, Lugano, Switzerland) under controlled conditions of light (light:dark cycle: 14:10 h, intensity 100 µE m−2 s−1) and temperature (20 ± 0.02 °C) (Figure 1 and Table 1). During each experiment, diatoms were simultaneously grown in the two bioreactors filled respectively with different media (Table 1). In one of the two bioreactors, microalgae were grown in a natural seawater medium (NAT), whereas in the second one an artificial seawater medium (ASW) was used.
The ASW medium was prepared to reach a modified isotopic composition of DIC. Since CO2(g) leakages from storage sites induce variations in DIC concentration, for each experiment, ASW medium was prepared differently to test whether altered DIC levels would affect the diatoms δ13C in the isotopically modified environment. The different DIC concentrations tested were not coupled with pH modification; indeed, typical marine pH values (8.0–8.2) were preserved to avoid alterations in phytoplankton physiology (Table 1). DIC concentration in the ASW medium for EXP-1 (1-ASW) was comparable to natural marine levels (~2000 µmol L−1), and during EXP-2 (2-ASW), about half the concentration was tested (~900 µmol L−1), whereas, in EXP-3 (3-ASW), more than twice the natural DIC concentration was reached (~5000 µmol L−1). On the other hand, the DIC concentration was maintained in the natural range (~2000 µmol L−1) for the three NAT experiments.
For NAT medium, a sterile-filtered (0.2-µm pore size) Adriatic Sea water was used, whereas the artificial seawater for ASW was prepared based on the artificial seawater medium ESAW (enrichment solution with artificial water) designed for coastal and open ocean phytoplankton [53], modified as follows. The solutions for the ASW medium were prepared with ultrapure Milli-Q water degassed by bubbling pure nitrogen (N2(g)) to remove the natural dissolved CO2. The required inorganic carbon concentrations were not achieved by the addition of sodium bicarbonate (NaHCO3) as described in Andersen [53] recipe, because a 13C-depleted DIC source needed to be reached. NaHCO3 was replaced by a stoichiometric quantity of NaOH, and the final concentration of DIC was achieved by bubbling CO2(g) (as a mixture of CO2-free air and compressed CO2(g)) into medium solution after the addition of NaOH, until the typical marine pH value was reached (8.0–8.3). Bubbling was performed with an industrial compressed CO2(g) to achieve a modified DIC source after verifying the different isotopic composition of the gas from the current atmospheric δ13CCO2(g) (~−8‰). Since no changes in δ13CCO2(g) due to Rayleigh fractionation occurred during cylinder utilisation, the same CO2(g) cylinder was used for the preparation of the three ASW media experiments (δ13C of CO2(g) used was −48.2‰ ± 0.2‰ as the average of the measurements performed during the preparation of the three different ASW media using the same gas cylinder).
Both media were supplied with nutrients and microelements, as proposed by Andersen [53] for the ESAW medium and were introduced into the photobioreactors by filtration (hydrophilic PTFE sterile filter, nominal pore size 0.2 µm, Merck KGaA, Darmstadt, Germany), with the purpose of minimising bacterial interference and to prevent gas exchange with the atmosphere.
Before the beginning of the experiments, an equal quantity of diatoms was inoculated into the two media (Table 1). A diatom species was selected, since diatoms represent the most abundant microalgae taxa worldwide due to their ability to grow in a wide range of environments and account for about 40% of marine primary production [54,55]. Moreover, carbon isotopic fractionation in diatoms is not affected by the precipitation of calcite that instead occurs in calcifying organisms [56]. The aliquot of microalgae for the inoculation was collected from clonal cultures of Thalassiosira rotula (species identity was confirmed by genetic analysis, Appendix A) established in September 2012 from a sample collected in the Gulf of Trieste (North Adriatic Sea) (45°42.06’ N, 13°42.60’ E). The strain was maintained in 100-mL glass Erlenmeyer flasks filled with modified L1 medium obtained from sterile-filtered (0.2-µm pore size) Adriatic Sea water and supplemented with nutrients, metals and vitamins. The culture was grown at 15 ± 2 °C and at a light intensity of about 53 μE m−2 sec−1 under a controlled light:dark regime (14:10 h). Cells were transferred within sterilised 2-L glass Erlenmeyer flasks to allow growth until the exponential phase was reached for the inoculation in the ASW and NAT media.
Periodic samplings were performed during each culture experiment through a peristaltic pump to minimise the atmospheric gas exchange. During the whole duration of the experiments, the variation of pH, temperature and dissolved oxygen was continuously monitored and recorded by online probes. Intermittent CO2-free air flow was maintained (on:off cycle: 10:50 min), and a continuous gentle stirring prevented stratification and favoured medium mixing (Figure 1).
The duration of each experiment depended on the beginning and persistence of the stationary phase of microalgae (Table 1).

2.2. Sampling and Analysis for Phytoplankton Abundance and Media Characterisation

For phytoplankton abundance (cells mL−1), an aliquot of 10 mL of culture was sampled every day and immediately fixed with prefiltered and neutralised formaldehyde (1.6% final concentration) [57]. Cell counts were performed in triplicates in a Sedgewick Rafter counting chamber at a light microscope (Leica DM2500, Leica Microsystems Srl, Wetzlar, Germany) at a magnification of 100–200×, counting a minimum of 200 cells in random fields or transects. Growth rate of the diatoms was calculated in the exponential phase of the culture [53] as
µ = ( ln cell tf ln cell ti ) / ( t f t i )
where µ (d−1) is the specific growth rate, cell ti represents the abundance of phytoplankton (cells mL−1) at the beginning of the time interval ( t i ; days) in which the culture exhibited exponential growth and cell tf the abundance (cells mL−1) at the end of the time interval analysed ( t f ; days).
The pH (National Bureau of Standards, NBS, scale) was continuously recorded by online probes (EasyFerm BIO Arc 225, Hamilton Bonaduz AG, Bonaduz, Switzerland) previously calibrated on the National Bureau of Standards (NBS) scale with certified buffer solutions of pH 7 ± 0.02 and 9 ± 0.03 (at 25 °C, Merck KGaA, Darmstadt, Germany).
To measure total alkalinity (AT, µmol kgSW−1), samples of 100-mL culture suspension were filtered over precombusted (450 °C for 4 h) Whatman (Cytiva, Global Life Sciences Solutions USA LLC, Marlborough, MA, USA) GF/F filters (nominal porosity 0.7 µm) to remove phytoplankton cells [58]. Each bottle was poisoned with saturated mercuric chloride (HgCl2) to halt biological activity and stored at 4 °C in the dark until analysis. Total alkalinity was determined by potentiometric titration in an open cell (SOP 3b [59]), as described by Ingrosso et al. [60].
Samples for dissolved inorganic nitrogen (DIN, as the sum of nitrite—NO2, nitrate—NO3 and ammonium—NH4); phosphate (PO4) and silicate (Si(OH)4) were filtered over precombusted (450 °C for 4 h) Whatman (Cytiva, Global Life Sciences Solutions USA LLC, Marlborough, MA, USA) GF/F filters (nominal porosity 0.7 µm) in acid-washed polyethylene vials and kept frozen (−20 °C) until laboratory analysis. Inorganic nutrient concentrations were determined calorimetrically with a QuAAtro (Seal Analytical Inc., Mequon, WI, USA) autoanalyser according to Hansen and Koroleff [61]. Detection limits reported by the analytical methods were 0.01 μmol L−1, 0.02 μmol L−1, 0.02 μmol L−1, 0.01 μmol L−1 and 0.01 μmol L−1 for NO2, NO3, NH4, PO4 and Si(OH)4, respectively. The accuracy and precision of the analytical procedures are annually checked through the quality assurance program (AQ1) QUASIMEME Laboratory Performance Studies (Wageningen, The Netherlands), and the relative coefficient of variation for five replicates was less than 5%. Internal quality control samples were used during each analysis.
Concentrations of total dissolved inorganic carbon (DIC) and the other carbonate system parameters, including dissolved CO2 (CO2(aq)) and bicarbonate (HCO3), were calculated using the CO2SYS program [62] through in situ AT, medium pH (pHNBS at 20 °C), temperature, salinity, phosphate and silicate data. Carbonic acid dissociation constants (i.e., pK1 and pK2) of Mehrbach et al. [63] refitted by Dickson and Millero [64] were used for the computation, as well as the Dickson constant for the ion HSO4 [65] and borate dissociation constant of Uppström et al. [66].
The abundance of prokaryotes (cells mL−1) was estimated by a FACSCanto II (Becton Dickinson, Franklin Lakes, NJ, USA) flow cytometer, equipped with an air-cooled laser at 488 nm and standard filter setup. Samples were treated according to Marie et al. [67], as described by Celussi et al. [68].

2.3. Sampling and Analysis of Medium and Phytoplankton Carbon Stable Isotopic Composition and Calculation of Isotopic Fractionation

Samples for the determination of the stable carbon isotope ratio of CO2(g) of the cylinder used for ASW media preparation and the stable carbon isotope ratio of dissolved inorganic carbon (DIC) were collected in 12-mL Exetainer® (Labco Limited, Ceredigion, UK) evacuated glass tubes, preventing gas exchange with the atmosphere.
Stable carbon isotopic composition of CO2(g) (δ13CCO2(g)) was determined with an isotope ratio mass spectrometer (IRMS) Europa Scientific 20-20 (Crowe, UK) with trace gas preparation module ANCA-TG for trace gas samples.
Samples for stable isotopic composition of dissolved inorganic carbon (δ13CDIC) were filtered over precombusted (450 °C for 4 h) Whatman GF/F filters (nominal porosity 0.7 µm) to remove phytoplankton cells, and the analysis was performed by injecting aliquots of the sample into evacuated septum tubes with phosphoric acid. Released CO2(g) was then analysed with continuous-flow IRMS Europa 20-20 (Crowe, UK) with an ANCA-TG trace gas separation module. In order to determine the optimal extraction procedure for water samples, two standard Na2CO3 solutions were prepared with a known δ13C value of −10.8‰ ± 0.2‰ and −4.12‰ ± 0.2‰, respectively.
The carbon isotopic composition of the phytoplankton was determined as bulk particulate organic carbon (δ13CPOC) by filtering aliquots of culture suspension (~20 mL) over precombusted (450 °C for 4 h) Whatman (Cytiva, Global Life Sciences Solutions USA LLC, Marlborough, MA, USA) GF/F filters (nominal porosity 0.7 µm, ø 25 mm). To assess the potential influence of prokaryotes on the bulk δ13CPOC, other aliquots of culture suspension (~20 mL) were filtered over precombusted Whatman (Cytiva, Global Life Sciences Solutions USA LLC, Marlborough, MA, USA) GF/F filters after a prefiltration on a 10-µm nylon mesh to remove phytoplankton cells (δ13CPOC<10µm). The dimension of the mesh for prefiltration was chosen according to the average Thalassiosira rotula cell dimension of 20 µm [69]. Samples for δ13CPOC and δ13CPOC<10µm analysis were collected in duplicate, rinsed with deionised water, oven-dried at 60 °C and stored in desiccators until analysis. Before the analysis, filters for δ13CPOC and δ13CPOC<10µm were treated with 1-M HCl to remove carbonates and inserted into tin capsules for further analysis. Measurements were performed on a Europa Scientific 20-20 IRMS (Crowe, UK) with an ANCA-SL preparation module for solid and liquid samples.
All stable carbon isotope values are given conventionally in δ-notation in per mil deviation (‰) from the Vienna Peed Dee Belemnite (VPDB) standard. IRMS was calibrated against reference materials: IAEA-CH-6 and IAEA-CH-7 (International Atomic Energy Agency, Vienna, Austria) for δ13CPOC analysis and “CO2 ISO-TOP, High” (Messer Group GmbH, Bad Soden, Germany) with a δ13C value of −4.3 ± 0.2‰ for δ13CCO2(g) and δ13CDIC. The precision based on replicate analyses was ±0.2‰ for all stable isotope analyses (δ13CCO2(g), δ13CDIC, δ13CPOC and δ13CPOC<10µm).
A mass balance relation was used to calculate the isotopic composition of dissolved CO2(aq) (δ13CCO2(aq)) and HCO3 (δ13CHCO3) from δ13CDIC by the temperature-fractionationation relationships from Rau et al. [70], based on Mook et al. [35]:
δ 13 C CO 2 ( aq ) = δ 13 C DIC + 23.644   9701.5 T K ,
δ 13 C HCO 3 = δ 13 C CO 2 24.12 9866 T K ,
where TK is the absolute temperature in Kelvin.
Diatom isotopic fractionation associated with photosynthetic CO2(aq) fixation was reported as the isotopic enrichment factor (εCO2-POC) [71] and was calculated relative to the isotopic composition of dissolved CO2 in the bulk medium according to Farquhar et al. [72] and Freeman and Hayes [73]:
ε CO 2 POC = 1000   ( δ 13 C CO 2 ( aq ) δ 13 C POC ) ( 1000 + δ 13 C POC ) .
Despite the presence of CO2-concentrating mechanisms that allow diatoms to use both HCO3 and CO2(aq), the isotopic fractionation was calculated based on δ13CCO2(aq) since all inorganic carbon is converted to CO2(aq) before being fixed and to allow for comparison with literature data.
Considering the experimental set-up, the closed-system Rayleigh-type fractionation relationships [74] were investigated to discuss the data from the natural seawater experiments by applying the following equation [71]:
δ 13 C DIC r = δ 13 C DIC r 0 × f ( α 1 ) ,
where δ 13 C DIC r is the isotopic composition of the residual fraction ( f ) of unutilised substrate remaining (0 ≤ f ≤ 1), δ 13 C DIC r 0 is the isotopic composition of DIC at the beginning of the three NAT experiments (when f = 1 and corresponds to −2.3‰) and α is the fractionation factor and was calculated as (ε/1000) + 1 using the theoretical value of RuBisCO isotopic fractionation (ε) of 20‰ [75].
To verify the possible application of the methodological approach described to CCS sites where stored CO2(g) is characterised by different δ13C values, the equation by Zhang et al. [76] for carbon isotopic fractionation during CO2(g) dissolution in seawater was applied:
ε DIC CO 2 = 0.0144   ×   T × f CO 3 2 0.107 × T + 10.53 ,
where T is the temperature in °C and f CO 3 2 is the carbonate fraction of total DIC measured at the beginning of the experiments. For this calculation, the carbonate fraction measured at the beginning of the ASW experiments was used.

2.4. Statistical Analysis

The Spearman rank correlation test was used to investigate the time variation of the variables and the relationships between phytoplankton abundance, abiotic parameters and isotopic composition.
In order to verify the differences between natural and artificial seawater media and among the three experiments, the Kruskal-Wallis ANOVA test was applied to the variables. Kruskal-Wallis ANOVA and Spearman rank tests were conducted using STATISTICA 7 (StatSoft, Inc., Tulsa, OK, USA), and only significant data (p < 0.05 and p < 0.01) are presented. A multivariate analysis was performed to verify the overall difference between the two media applying a non-metric multidimensional scaling analysis (nMDS) ordination model. Phytoplankton and prokaryotes abundance data were previously square root-transformed to avoid a skewness of data that could have affected the analysis. An nMDS ordination was performed based on the triangular resemblance matrix constructed on z-normalised data using the Euclidean distance method. The analysis of similarity (one-way ANOSIM) between the ASW and NAT was also performed, and a similarity profile (SIMPROF) analysis was applied on the cluster analysis. The nMDS, ANOSIM and SIMPROF analyses were performed using PRIMER 7 (PRIMER-E Ltd., Plymouth, UK) software [77].

3. Results

3.1. Phytoplankton Growth under Different Conditions and Media Characterisation

The Thalassiosira rotula abundance significantly increased (p < 0.05) during the three experiments and nearly followed a typical exponential growth in both natural (NAT) and artificial (ASW) medium (Figure 2). The highest abundance was measured at the end of the experiments, and the maximum value was reached in 2-ASW at 144 h (4.17 × 104 ± 5.42 × 102 cells mL−1). Despite the equal diatom inoculation in the two media at each experiment (250 cells mL−1 in EXP-1, 1000 cells mL−1 in EXP-2 and EXP-3; Table 1), in the ASW, the diatom growth was generally slower than in the NAT. Consequently, the exponential phase was not coincident in the two conditions, and the growth rates (µ) were not calculated in the same time interval. The stationary phase of the culture was generally achieved in the NAT (Figure 2b) within the first 48 h of the culture, and the average growth rate for the three experiments resulted in 1.1 ± 0.3 d−1. On the other hand, stationary conditions in the ASW (Figure 2a) were generally reached after 72 h from the inoculation, and the average growth rate for the three experiments was 1.0 ± 0.2 d−1. Despite the slight dissimilarities in growth rates, the diatom abundances between the two conditions were not significantly different. At the end of the 1-NAT experiment (96 h), the diatom abundance decreased to 1.96 ± 0.22 × 102 cells mL−1, indicating the beginning of the senescent phase. The data from this last sampling, however, were included in the discussion, since the δ13CPOC values detected a fall in the range of the 2-NAT and 3-NAT experiments and still derived from fresh phytoplanktonic organic matter.
At the beginning of the experiments, the pH levels (Figure 3a,b and Table 1) in the ASW (8.17 ± 0.03) fell within the natural marine pH range, as in the NAT (8.34 ± 0.03), suggesting that the modification in the DIC concentration induced for the ASW medium preparation did not alter pH from the natural seawater levels. A significant increase (p < 0.05) was observed in pH values in both media (up to 9.50; the maximum measured at 96 h in 2-NAT; Figure 3a,b). A different pattern was observed in 2-ASW (Figure 3a). In this experiment, the pH values only increased after 72 h, but the high values observed in the other two ASW experiments were not reached (2-ASW maximum pH: 8.32 at 140 h, 1-ASW maximum pH: 8.83 at 96 h and 3-ASW maximum pH: 9.12 at 96 h). Considering the whole experiments duration and excluding 2-ASW, no significant differences were detected in pH values between the NAT and ASW.
Dissolved inorganic carbon concentration in the NAT after diatom inoculation (2272 ± 68 µmol L−1; Figure 3d and Table 1) fell within the typical range for the Adriatic Sea [60,78,79]. On the other hand, the DIC concentration measured in the ASW (Figure 3c and Table 1) at the beginning of the experiments confirmed the effectiveness of the modification imposed on carbonate system during the ASW medium preparation. Indeed, in 1-ASW, a typical marine DIC concentration was measured (1952 µmol L−1), whereas lower (893 µmol L−1) and higher (5098 µmol L−1) concentrations were detected at the beginning of 2-ASW and 3-ASW, respectively. The DIC concentration significantly decreased (p < 0.01) in the NAT during the three experiments, reaching the minimum value at 96 h in 2-NAT (1059 µmol L−1; Figure 3d). A decrease in DIC concentration was also detected in the three ASW experiments 48 h after the inoculation, reaching the lowest DIC value at 72 h in 2-ASW (566 µmol L−1).
The dissolved CO2 concentration at the beginning of the three ASW experiments depended on DIC concentration (Figure 3e). Indeed, the lowest initial concentration was detected in 2-ASW (5.81 µmol L−1), the highest in 3-ASW (29.71 µmol L−1) and a natural marine CO2(aq) concentration (13.94 µmol L−1) was measured in 1-ASW. On the other hand, for the three NAT experiments, the initial CO2(aq) concentration (Figure 3f) fell within the typical marine range (9.09 ± 0.76 µmol L−1). The dissolved CO2 concentration significantly decreased (p < 0.01) in both the ASW and NAT during the three experiments. However, considering only 2-ASW, CO2(aq) concentration remained almost constant and decreased only after 72 h. In the ASW, the minimum concentration was detected at the end of EXP-1 (1.34 µmol L−1), whereas in the NAT, concentrations lower than 0.20 µmol L−1 were detected after 96 h in EXP-2 and EXP-3. Despite the different initial CO2(aq) concentrations, considering the whole duration of the experiments, no significant differences were detected between the ASW and NAT.

3.2. Isotopic Characterisation of the Media and Phytoplankton

The initial isotopic composition of dissolved inorganic carbon (δ13CDIC) was significantly different (Kruskal-Wallis ANOVA, p < 0.01) between the two conditions tested. Indeed, the initial ASW δ13CDIC exhibited very low values in the three experiments (−44.2 ± 0.9‰; Figure 4a), whereas, in the NAT, the DIC isotopic composition (−2.3‰ ± 1.4‰; Figure 5b) fell within the typical range for the Adriatic Sea [79]. During diatom growth, δ13CDIC in the ASW (Figure 4a) significantly increased (p < 0.01). The minimum values were recorded at the beginning of each experiment and the maximum on the last day of the culture, reaching the highest value (−30.1‰) at 96 h in 1-ASW. No significant differences were detected among the three ASW experiments, though, in 3-ASW, δ13CDIC exhibited a slight increase, reaching the maximum value of −41.3‰ at the end of diatom culture (120 h). On the contrary, no significant time variations during diatom growth were detected in δ13CDIC in the NAT experiment (Figure 4b), with values ranging from −3.8‰ (1-NAT at 7 h) to 1.6‰ (2-NAT at 96 h).
At the beginning of the culture experiments, the phytoplankton isotopic composition (δ13CPOC) did not show significant differences between the two conditions tested (−25.2‰ ± 1.3‰; Figure 4c,d); however, microalgae δ13C exhibited an opposite trend in the ASW than NAT during the diatom growth. In the ASW (Figure 4c), the δ13CPOC values significantly decreased (p < 0.01), reaching the lowest values at the end of culture experiments, until the minimum detected in 2-ASW (−43.4‰ at 140 h). On the other hand, in the NAT (Figure 4d), δ13CPOC values slightly increased after the inoculation (p < 0.01), reaching the highest values at the end of the diatom culture (maximum −17.7‰ at 72 h in 3-NAT).
In the artificial seawater condition, the highest isotopic enrichment factor (εCO2-POC, Figure 5a) associated with CO2(aq) uptake by T. rotula was detected at the beginning of the experiments (−29.2‰ ± 1.0‰). The enrichment values in the ASW significantly changed during phytoplankton growth (p < 0.01), reaching 0.9‰ at 140 h in 2-ASW. Slighter variations were detected in carbon isotopic enrichment in the NAT (Figure 5b), with values ranging from 13.8‰ ± 2.7‰, calculated at the beginning of the diatom cultivation, to 6.7‰, detected on the last day of the longest experiment (2-NAT at 140 h).

4. Discussion

4.1. Validation of the Experimental Set-Up and Diatom Growth in the Natural and Modified Media

The isotopic composition of DIC measured in ASW confirms that the experimental set-up succeeded in modifying the isotopic composition of the carbonate species dissolved in water. Indeed, δ13CDIC values at the beginning of ASW experiments (−44.2‰ ± 0.9‰) approached the isotopic composition of the strongly depleted anthropogenic CO2(g) used for media preparation (δ13CCO2(g): −48.2‰ ± 0.2‰). Such low δ13CDIC values are hardly detected in natural systems, even in lakes where a broad range of δ13CDIC values is expected to be found (down to −31.1‰ [80]). On the other hand, δ13CDIC in the NAT (−2.3‰ ± 1.4‰) fell within the typical marine range [79] and resulted significantly higher (Kruskal-Wallis ANOVA, p < 0.01) than values detected in the ASW.
Both the ASW and NAT were characterised by initial pH values typical for the marine environment. pH may play a significant role in limiting the rate of primary production, growth and total abundance of phytoplankton [81]. Moreover, the medium pH also affects the relative concentration of the dissolved carbonate species, thus finally conditioning the isotopic composition of CO2(aq). It was therefore crucial to maintain comparable pH values in both the ASW and NAT in order not to affect microalgae growth and isotopic response during diatom cultivation.
Thalassiosira rotula abundances significantly increased (p < 0.01) in both the ASW and NAT during the three experiments (Figure 2). A general delay in microalgae growth was observed in the ASW, probably due to the acclimation time needed after the inoculation in the modified medium. However, no significant differences in terms of cell abundance were detected between the ASW and NAT.
For the ASW experiments, three different initial DIC concentrations were tested to verify the effects of lower (2-ASW; 893 µmol L−1) and higher (3-ASW; 5098 µmol L−1) DIC availability with respect to the typical marine concentration (1-ASW; 1952 µmol L−1). The diatom abundance was not affected by the different DIC concentrations, as no differences were detected among the three ASW experiments, and, in addition, the highest T. rotula abundance was detected at the end of 2-ASW, suggesting that the low DIC concentration tested during this experiment should not be limiting. These results are consistent with the experimental findings of Clark and Flynn [82] and Clark et al. [83], which showed that, at low DIC concentrations and maintaining initial pH levels in the natural range, the phytoplankton growth was not carbon limited. On the other hand, the higher availability of DIC in 3-ASW neither stimulated microalgae growth, therefore confirming that diatoms are capable of growth over a wide range of DIC availability [83]. Similar results were obtained from the mesocosm experiments performed by Maugendre et al. [84] and Esposito et al. [85], who observed no increase in phytoplankton abundance at high CO2(aq) concentrations. Several studies indicated contrasting effects of high CO2(aq) concentrations on the phytoplankton; indeed, the growth rates of the diatoms resulted in being stimulated, not affected or inhibited [17]. Phytoplankton growth, however, should be considered as the result of the interaction of several factors, such as pH variation, temperature, nutrients and/or light limitation, acting in synergy with the changes in the carbonate system [86]. The experiments performed by Li et al. [87] also proved that elevated CO2(aq) has no detectable effects on the diatom T. weissflogi but showed limited growth and photosynthesis at lowered CO2(aq) levels (0.8~3.4 µmol L−1). However, the experiments performed by Li et al. [87] were conducted at CO2(aq) concentrations lower than 2-ASW (5.81 µmol L−1 at the beginning of the cultivation, Figure 3e) and at an initial higher pH (Li et al. [87]: 8.63~9.02, 2-ASW: 8.19). Li et al. [87] addressed the suppression of diatom growth to the shift in carbonate system composition towards higher concentrations of HCO3 because of the high pH, resulting in higher energy demand for inorganic carbon uptake via concentrating mechanisms. Indeed, diatoms, as many other phytoplankton species, have developed CO2-concentrating mechanisms (CCMs) for actively taking up inorganic carbon and for increasing CO2(aq) concentration around RuBisCO [88]. These mechanisms have evolved to prevent carbon limitations. The high pH of the modern sea and the slow CO2(aq) diffusion rate in water are limiting factors for the dissolved CO2 availability, usually making CO2(aq) concentrations in water not sufficient to saturate RuBisCO fixation rates [88,89], representing less than 1% of total DIC [58]. CCMs increase the available inorganic carbon by active CO2(aq) and HCO3 transport and by the reversible dehydration of bicarbonate to CO2(aq) mediated by intra- and extracellular carbonic anhydrase (CA) enzymes, being CO2(aq) the carbon species ultimately used for photosynthesis. Experimental and genetic data confirmed that both HCO3 and CO2(aq) are possible substrates for diatom photosynthesis [43,88,89,90] and that CCMs grant full-carbon saturation of RuBisCO catalytic sites, supplementing inorganic carbon by direct HCO3 uptake even at high CO2(aq) concentrations [91]. Diatoms are thus able to maintain high photosynthetic rates in response to large variations in the marine carbonate system [91]. The strain of T. rotula used for the experiments was cultivated from a sample collected in the Gulf of Trieste. This semi-enclosed basin is characterised by an elevated variability in its oceanographic properties due to fluctuations in riverine inputs and climatological forcing factors that strongly affect inorganic nutrient concentrations, salinity, pH and the carbonate system [92]. Thus, the microalgae used for the experiments are adapted to a wide range of pH and variations in the carbonate system, as already reported for other coastal phytoplankton communities [17,93,94].
The inorganic carbon uptake for photosynthesis increased pH in both the ASW and NAT, and the experiments were stopped before reaching high pH levels (>9.50) that could have inhibited microalgae growth [81]. Only the 2-ASW experiment differed in pH variation, since the values detected were always lower than 8.50, and pH values increased only after the third day of diatom cultivation. This difference in pH variation could be addressed to the variation in CO2(aq) concentration. In 2-ASW, indeed, the CO2(aq) concentration remained almost constant for the first 72 h of the T. rotula cultivation. Since the diatom growth was comparable to the other experiments, it results that the carbon supply was supported by CCMs by the uptake of HCO3, thus not significantly affecting the medium pH.
Diatoms were cultivated in nutrient-replete conditions (Figure S1) to avoid stress due to nutrient limitations. The DIN and PO4 initial concentrations were comparable between the ASW and NAT, and, during diatom growth, their concentrations significantly decreased (p < 0.05) but were not completely consumed.
Based on the above considerations, the differences observed in phytoplankton isotopic composition between the ASW and NAT should not depend on dissimilarities in the growth conditions. Indeed, T. rotula should not have suffered any stress for carbon or nutrient availability during the three experiments performed. Similarly, the diatoms were not influenced by the two conditions tested, as no significant differences were detected in terms of cell abundance and growth between the NAT and ASW. Thus, the ASW and NAT δ13CPOC values should be considered as driven mainly by the different δ13CDIC values.

4.2. Time Variation of Phytoplankton Isotopic Composition as a Result of Different DIC Sources and the Inorganic Carbon Uptake

The isotopic composition of phytoplankton (δ13CPOC) at the beginning of the experiments did not differ between the ASW and NAT, resulting, on average, in −25.2‰ ± 1.3‰. The aliquots of T. rotula for inoculation in the two media were sampled from the same strain of microalgae culture—thus, the similar δ13CPOC in the two conditions at the beginning of the three experiments. The initial δ13CPOC values in both the ASW and NAT are comparable to previous measurements performed on samples collected in the Gulf of Trieste [95,96], since the culture was prepared from a sample collected in this area and are typical for marine phytoplankton in estuarine environments.
After the inoculation in the two media, the diatoms began to uptake the DIC available, with a consequent gradual modification of the carbon isotopic composition. In the ASW, the δ13CPOC values significantly decreased (p < 0.01) and reached strongly 13C-depleted values at the end of the cultivation (−43.4‰ at 140 h in 2-ASW). On the other hand, in the NAT, the isotopic composition of the POC reached at the end of the experiments (−17.7‰, the maximum measured at 72 h in 3-NAT) remained in the natural marine range and of the typical phytoplankton values measured in the open Northern Adriatic Sea (−20.6‰ [97]).
The variations of the δ13CPOC values observed in both media depend on the isotopic fractionation due to the preferential uptake of 12C for photosynthesis. If no other DIC source is available or supplied, as in the case of the experiments here described, the residual DIC pool gradually enriches in 13C during the photosynthetic uptake. As also observed in mesocosm experiments [85], this typical residual source enrichment (i.e., increasing the δ13CDIC values) naturally occurs during intense bloom events as a consequence of the slow CO2(g) equilibration compared to the fast biological uptake [81,86]. Both the ASW and NAT experiments reproduce a blooming condition. The diatom growth, indeed, increased the medium pH by the DIC consumption and decreased the nutrient concentration. Moreover, in the ASW, the δ13CDIC values increased (Figure 6a), whereas the isotopic composition of the DIC in the NAT (Figure 6b) was not significantly modified by the diatoms carbon uptake, though the δ13CDIC values slightly increased (Figure 4b) during diatom cultivation. On the other hand, a clear different pattern was observed in the δ13CPOC values between the ASW and NAT (Figure 6a,b).
With the proceeding of diatom growth, in both the ASW and NAT, the fraction of available 12C in the DIC pool gradually decreased because of the preferential uptake for photosynthesis. This process in the NAT did not change significantly δ13CDIC values, since the microalgae were in isotopic equilibrium with the medium. However, considering the fast growth of microalgae, the gradual reduction of the 12C in the available DIC resulted in an evident 13C-enrichment of the phytoplankton with increasing δ13CPOC values during diatom growth (Figure 6a). In the ASW, despite the significant increase (p < 0.01) in δ13CDIC values, as the result of the preferential uptake of the 12C by the diatoms, the δ13CPOC values showed an opposite behaviour than in the NAT (Figure 6a). This misleading isotopic anomaly depended on the source of DIC provided in the ASW and on the experimental set-up. The experiments aimed to verify how the isotopic composition of the diatoms changed during the growth in a modified medium; therefore no acclimation time followed the inoculation. The Thalassiosira rotula strain was grown under natural marine δ13CDIC until inoculation, and once transferred in the ASW medium, microalgae started to take up the modified DIC available. As discussed in the previous section (Section 4.1), the ASW was characterised by low δ13CDIC values; therefore, the lightest isotope (12C) was much more abundant than in the NAT. The diatoms had therefore a higher availability of 12C for photosynthesis, and this resulted in the significant (p < 0.01) and quick decrease in δ13CPOC values (Figure 6a) and the following increase in δ13CDIC. Notwithstanding that the variation of δ13CDIC during diatom growth was similar in all ASW experiments, in 3-ASW, a slighter increase was observed. The culture medium for 3-ASW was prepared to obtain high DIC concentration (Figure 3c); consequently, 12C was even more abundant than in 2-ASW and 1-ASW. Since the diatom growth and δ13CPOC followed the same trend as 1-ASW and 2-ASW, the residual DIC in 3-ASW maintained a relatively higher amount of 12C than the other ASW experiment during the T. rotula culture; therefore, a slighter variation was observed in the δ13CDIC.
Esposito et al. [85] found a similar decrease in δ13CPOC values during mesocosm experiments after the addition of seawater bubbled with 13C-depleted CO2(g). The POC isotopic composition reflected the trend of δ13CDIC, and the minimum δ13CPOC was reached at the end of the experiments, although higher values than those measured in our experiments were reached. To our knowledge, extremely low δ13CPOC values, similar to those detected at the end of our experiments, were only measured by Poulain et al. [98] (−58‰ ± 4‰) in a mixed-species phytoplankton culture (used as a zooplankton food source) grown in a medium where 13C-depleted industrial CO2(g) was bubbled, even though additional medium information (e.g., pH, DIC concentration and CO2(g) bubbling duration) was not provided to allow additional consideration.
The isotopic composition of T. rotula in the ASW resulted significantly different (Kruskal-Wallis ANOVA, p < 0.01) from that of diatoms grown in the NAT. The inverse correlation (p < 0.01) of δ13CPOC with both δ13CDIC and diatom abundance in the ASW supports the hypothesis that the isotopic composition of T. rotula depends mostly on the isotopic composition of the available DIC source. The different DIC concentrations in the ASW experiments seem not to affect the final phytoplankton δ13C, as no significant differences were detected in δ13CPOC among the three ASW experiments, confirming that the DIC concentration is not becoming a limiting factor and does not affect the δ13CPOC [99,100].
The presence of prokaryotes was monitored to consider its potential effect on the bulk POC isotopic composition, since the prokaryotes abundance increased (p < 0.05, Figure S2) in both media throughout the duration of the experiments. The isotopic composition of the fraction of POC smaller than 10 µm (δ13CPOC<10µm), assumed to depend mostly on the prokaryotes biomass, followed the same trend as δ13CPOC (p < 0.01) in both the ASW and NAT. Previous studies [101,102] revealed that the heterotrophic prokaryotes isotopic composition is generally close to or similar to that of the organic substrate provided. This is consistent with the results here discussed, because, during algal growth in bioreactors, the only substrate for prokaryotes was the phytoplankton-derived organic matter [103]. Slighter variations, however, were observed in δ13CPOC<10µm compared to δ13CPOC during phytoplankton growth as the result of a delay in the incorporation of the freshly produced phytoplankton material, as already discussed by De Kluijver et al. [104] in labelling experiments. Considering the strong positive correlation between δ13CPOC<10µm and δ13CPOC in both the ASW and NAT, it could be therefore assumed that the increase in prokaryotes biomass might have been not sufficient to modify the bulk POC isotopic composition.
A multivariate analysis (nMDS; Figure 7) was performed to investigate the global difference between the two conditions tested. The isotopic composition of POC, DIC and δ13CPOC<10µm, phytoplankton and prokaryotes abundances, pH and inorganic nutrients were the parameters included in the analysis. Considering the time variation of δ13CPOC and the comparability of the diatom abundance in the three ASW experiments, it was concluded that the different DIC concentrations tested did not affect the phytoplankton carbon uptake; therefore, neither DIC nor CO2(aq) concentrations were included in the nMDS analysis to avoid the strong effect of the different concentrations tested in the analysis. Two groups can be clearly distinguished (Figure 7), and the separation corresponds to the two different media in which the diatoms were grown (ASW and NAT). The strongest separation of the groups was evident at the final sampling times, when the highest difference in δ13CPOC values was detected between the ASW and NAT. The analysis of similarity (one-way ANOSIM) supported the significant global differences between ASW and NAT (p < 0.05), even if with a certain degree of overlapping of the data (R = 0.358) probably related to the common starting condition of the microalgae culture. Indeed, the SIMPROF analysis highlighted as a unique group the data collected within the first day of cultivation, whereas the separation of the NAT and ASW became gradually wider following the phytoplankton growth in the two culture media.
The multivariate analysis confirmed that carbon stable isotopes could be considered as valid tracers of the origin of dissolved inorganic carbon. Indeed, both the δ13CDIC and δ13CPOC values significantly depend on the different origin of dissolved CO2, and phytoplankton δ13C quickly responded to the change of growth medium. The time variation of phytoplankton δ13C in the ASW experiments resulted in a decrease of ~3.1‰ (calculated from the regression line, Figure 6a) in only one day of cultivation, corresponding to an average 13C-depletion of ~18‰ in less than one week. Conversely, the derived increase in δ13CDIC corresponded to ~1.7‰ d−1 (Figure 6a), which is comparable to the increasing trend identified for δ13CPOC in the NAT (~1.4‰ d−1, Figure 6b). The modification of the phytoplankton isotopic composition in the diatoms grown in the ASW is, therefore, faster and broader than the variation of medium δ13CDIC. These results are consistent with the mesocosm experiments performed by Esposito et al. [85] that highlighted a significant drop of δ13CPOC values in the 13C-depleted mesocosm about five days after the bloom phase. The δ13CPOC decrease observed by Esposito et al. [85] is comparable to the POC 13C-depletion in the ASW after ~1.5 days, confirming that our experimental approach is able to fast detect changes in the phytoplankton isotopic composition.
This result should be considered when dealing with CO2(g) leakages from storage sites. In a real CCS site, the chemical composition of the water column quickly changes after a leakage, and δ13CDIC is modified depending on the CO2(g) concentration released and on the environmental conditions. However, the δ13CDIC values return to background levels due to mixing currents that rapidly disperse CO2(g) both horizontally and vertically [11]. Therefore, a CO2(g) leakage could be detected by δ13CDIC analysis only if the sampling is close enough in time. On the other hand, considering the rapid variation of diatom δ13C, the assessment of CO2(g) leakages through the analysis of the isotopic composition of phytoplankton gives the opportunity to gather time-integrated information, and thus, even low and sporadic emissions, that otherwise would be hardly detectable from the seawater analysis, could be identified.

4.3. Phytoplankton Isotopic Fractionation

The isotopic composition of phytoplankton depends on the fractionation occurring during inorganic carbon uptake [73] and was described as isotopic enrichment factor (εCO2-POC) [71]. Isotopic fractionation is mainly the result of the activity of the enzyme RuBisCO during photosynthetic CO2(aq) fixation, which discriminates carbon by about 22‰ to 31‰, depending on the CO2(aq) affinity of the different RuBisCO forms [37], thus resulting in 13C-depleted values in the phytoplankton. However, these maximum εCO2-POC values are rarely observed, because several physiological and environmental factors affect CO2(aq) concentration at the active site of the enzyme [37,38,39,40,41,42,43,44,45,46,47].
For the experiments here discussed, a single diatom species—Thalassiosira rotula—was grown in two different media (ASW and NAT) under equal conditions of light availability, temperature and nutrient concentration. Since T. rotula growth was similar in the ASW and NAT (Section 4.1), the significant differences (Kruskal-Wallis ANOVA, p < 0.01) of the εCO2-POC values detected between the two conditions tested should be addressed to the different isotopic composition of the phytoplankton and DIC and, eventually, to the availability of CO2(aq), even if the DIC concentration seemed not to affect either the diatom growth or its isotopic composition (Section 4.1 and Section 4.2).
Extremely negative εCO2-POC values were calculated in the artificially modified medium (−29.2‰ ± 1.0‰) at the beginning of the experiments, and an increasing trend (p < 0.01) was observed during diatom growth. These low values depend on the experimental setup, since aliquots of T. rotula were inoculated into a strongly 13C-depleted medium without an acclimation period; thus, after inoculation, the diatom δ13C values were higher than δ13CDIC. The largest availability of 12C and the quick utilisation of DIC in the ASW experiments resulted in negative εCO2-POC values that progressively increased during algal growth as a consequence of the gradual reduction in the difference between the source (DIC) and product (POC) isotopic compositions. A switch to positive εCO2-POC values was observed after 96 h of cultivation due to the modifications induced in δ13CDIC and δ13CPOC because of the DIC uptake (Figure 5a).
The isotopic enrichment in the natural medium, on the contrary, was characterised by decreasing values (p < 0.05), following the gradual 13C-enrichment of the DIC pool because of the uptake of 12C for photosynthesis [105]. The εCO2-POC values in the NAT (13.8~6.7‰) were lower than the in vitro RuBisCO ε-values calculated for the marine diatom Skeletonema costatum (18.5‰ [106]) but fell in the range of the measurements performed in a mesocosm experiment (9.6~16.5‰ [85]) where diatom was the predominant phytoplankton taxon.
As previously discussed, DIC concentration does not affect the phytoplankton isotopic composition; however, the observed decrease in εCO2-POC values in the NAT are consistent with previous studies demonstrating that phytoplankton 13C-discrimination is lower the less DIC is available [105,107]. On the other hand, a higher carbon supply is expected to induce more pronounced isotopic fractionation (i.e., an increase in εCO2-POC values) [85,107,108]. The results from the 3-ASW experiment, in which a higher DIC concentration was tested, are in contrast with this assumption. Indeed, no differences were detected in the εCO2-POC values in the ASW experiments due to the different DIC availability, showing that the only change in the DIC concentration did not have a large effect on the δ13CPOC, as also recently reported by Tuerena et al. [99]. In both the ASW and NAT, the εCO2-POC values were strongly correlated with δ13CPOC (p < 0.01) and diatom abundance (p < 0.05); thus, the microalgae growth and the carbon isotopic fractionation occurring during photosynthetic DIC uptake should be considered the main drivers for the variations observed in the phytoplankton δ13C.
The theoretical relationship between the isotopic composition of the phytoplankton and DIC in the natural seawater experiments was investigated by applying the closed-system Rayleigh-type fractionation model [109]. The isotopic fractionation associated with the DIC uptake was assumed to correspond to the theoretical value of RuBisCO isotopic fractionation (ε) of 20‰ [75]. The resulting relationships between the δ13C of the cumulative POC (green curve), source carbon (DIC, blue curve) and incremental POC (red curve) are shown in Figure 8, together with the experimental δ13CPOC (green circles) and δ13CDIC (blue circles) data from the three NAT experiments. According to the literature data, the enzymatic isotopic fractionation can vary between 22‰ and 31‰ [37]; however, by applying any other isotopic fractionation value included in this range the shape of the lines does not significantly change.
The experimental results of the δ13CPOC (green circles) plot in-line with the cumulative product (green line), while the δ13CDIC data (blue circles) plot out of the source carbon curve (blue line); thus, the processes occurring in the system cannot be described only by the closed-system Rayleigh-type fractionation model. Indeed, the literature data indicate that the photosynthetic 13C fractionation can be described as a passive-diffusion phytoplankton photosynthesis, where the isotopic fractionation is associated with diffusive transport of CO2(aq) and enzymatic, intracellular carbon fixation [45,70]. Furthermore, the phytoplankton δ13C values are not solely controlled by the DIC/CO2(aq) concentrations, but several factors both physiological (e.g., phytoplankton growth rates and cell dimension) and environmental (e.g., temperature, nutrient concentration and light intensity) [39,42,43,44,70] affect the isotopic fractionation. Indeed, during the experiments, the εCO2-POC values were not constant either in the ASW or in the NAT. Moreover, the presence of CCMs in diatoms allows the uptake of both CO2(aq) and HCO3 [88,89]. Intra- and extracellular carbonic anhydrase (CA) enzymes have been recognised to operate in the reversible dehydration of bicarbonate to CO2(aq), since it is the species ultimately used for photosynthesis. Bicarbonate is relatively 13C-enriched in respect to CO2(aq) by about ~10‰ [35], but a comparable kinetic fractionation occurs during the dehydration of bicarbonate by CA, making CA-mediated HCO3 uptake indistinguishable from direct CO2(aq) uptake [111] from an isotopic point of view. However, if no HCO3 leaks out of the cell and all the bicarbonate taken up is converted into CO2(aq), no fractionation occurs during the process of intracellular dehydration, making the substrate for photosynthesis 9–10‰ enriched than CO2(aq), thus affecting the resulting phytoplankton isotopic εCO2-POC values [112]. In this process, pH was identified as an important controlling factor, since it regulates the level of CA activity, the induction of CCMs and affects carbon leakage from the cell in relation to the CO2(aq) and HCO3 uptake and conversion [112]. The increase in pH detected in both conditions during diatom growth has clearly moved the carbonate system equilibrium towards higher concentrations of bicarbonate; thus, both in the ASW and NAT, HCO3 should have played an important role as a photosynthetic substrate. No differences were encountered in the pH trend between the ASW and NAT; hence, the bicarbonate contribution should have been comparable and, thus, should not cover a significant role in the interpretation of the differences detected in δ13CPOC. Moreover, as discussed in Section 4.1, in 2-ASW, the pH variation was slighter, and a lower DIC concentration was tested, implying that the contribution of HCO3 was reduced compared to the other experiments performed. However, the δ13CPOC, δ13CDIC and, thus, εCO2-POC values followed the same trend as 1-ASW and 3-ASW, confirming that the different origin of CO2(aq) (i.e., the different isotopic composition) was the main factor affecting the phytoplankton isotopic composition and that the variation in DIC concentration and of the contribution of the different carbonate species did not significantly affect the diatom isotopic composition.

4.4. Application of the Method to CCS Sites: Limitations and Future Perspectives

The available DIC pool for diatoms grown in the artificially modified medium was characterised by δ13CDIC values significantly different from the typical marine range. Through photosynthetic isotopic fractionation, the microalgae isotopic composition rapidly changed, reaching significantly 13C-depleted values (decrease of ~3.1‰) in just one day. Moreover, the modification of diatom δ13C resulted not affected by the different concentrations of dissolved inorganic carbon, exhibiting the same trend in δ13CPOC values for all the DIC concentrations tested.
The results of the experiments described are encouraging for the future application of this method as a monitoring approach at CCS sites. However, the condition tested represents an extreme case, and further experiments should be performed. The δ13CCO2(g) used for the ASW experiments simulates the leakage of stored CO2(g) derived from fossil fuels combustion. However, the different feedstocks and CO2(g)-generating processes are expected to expand the range of stored δ13CCO2(g) values [5,27]. The first requirement for the application of the approach described is that δ13CDIC values after CO2(g) leakage are significantly different from the natural baseline. To test whether different δ13CCO2(g) leaking would result in detectable differences, the equation for carbon isotope fractionation during CO2(g) dissolution in seawater by Zhang at al. [76] (Equation (6), Section 2.3) was applied using different δ13CCO2(g) values and assuming the atmospheric δ13CCO2(g) corresponding to −8.0‰. The different δ13C of leaking CO2(g) tested were −15.0‰, −20.0‰, −25.0‰ and −35.0‰. The equation was also applied to the δ13C value of CO2(g) used for the three ASW media preparations (−48.2‰) (Figure 9).
To assess more confidently whether this approach would be useful on specific CCS sites, the natural baseline must be defined in detail to verify if the variations in δ13C of the DIC and phytoplankton would overlap with the natural seasonal variations. The detection of leakages would be more difficult if δ13CDIC values resulting from leaking CO2(g) dissolution were similar to the natural baseline. Among the different δ13CCO2(g) values tested, leaking CO2(g) characterised by a δ13C of −15‰ represents the worst scenario to be distinctly detected by isotopic analysis. In that case, if the natural δ13CDIC in the CCS area can reach the lowest value of −4‰, the leakage would be detectable if its concentration would finally correspond to at least 70% of the total DIC (Figure 9). On the other hand, if δ13CDIC naturally never reaches values lower than −1‰, the leakage might be detectable if it corresponds to 40% of the total DIC (Figure 9). The resulting differences in phytoplankton isotopic composition are much more complicated to be modelled, since several factors affect fractionation during photosynthesis [37,38,39,40,41,42,43,44,45,46,47,70]. Obviously, if the leaking CO2(g) dissolution results in significant differences in δ13CDIC values in respect to the natural baseline, also significant differences in δ13CPOC are expected accordingly. Therefore, for the correct implementation of this method, it is important to know the natural variations of δ13CDIC and δ13CPOC at the storage site and the isotopic composition of stored CO2(g) at the CCS.
The variation of δ13CDIC calculated might slightly overestimate the final δ13CDIC in case the whole DIC derives from the leaked CO2(g). Indeed, assuming that the DIC completely derives from the dissolution of CO2(g) used for the experiments (−48.2‰), the calculated δ13CDIC result is −39.8‰ (Figure 9), which is higher than the measured δ13CDIC at the beginning of the three ASW experiments (−44.2‰ ± 0.9‰), but it still falls within the limits of the expected range. The equation by Zhang et al. [76], however, assumes that pH is constant and represents the results of the complete mixing of the water mass after leaking CO2(g) dissolves and reaches equilibrium; thus, it does not reproduce the leakage conditions.
As highlighted by the release experiment by Blackford et al. [10], the porewater δ13CDIC values are modified earlier than the detection of significant changes in the bicarbonate concentration and more consistently than the water column δ13CDIC by CO2(g) leakages. The direct monitoring of the changes in the δ13CDIC could therefore result not sufficient in detecting transient leaks because of carbonate buffering and of the rapid mixing in the surrounding environment of CO2(g) leaking from a storage site. The primary producers’ isotopic composition, instead, could represent a record of the changes induced by CO2(g) leakage, because their isotopic composition directly depends on the δ13C of the available DIC pool. Despite that phytoplankton could vertically migrate along the water column [52] and, thus, represents an important record of the modifications occurring in δ13CDIC after CO2(g) leakages, these organisms are not able to contrast the strong currents that eventually can transport them horizontally far from the leakage site. Moreover, our experiments were conducted in a model case approach, where the whole carbonate system isotopic composition was modified and a single diatom species was tested, maintaining the pH levels within the natural range to allow phytoplankton optimal growth. Since storage sites will be predominantly hosted in coastal and shelf seas [11], and simulation results showed that the highest variations due to CO2(g) leakages are predicted to affect the seabed [113], further experiments and investigations should be performed focusing on tycopelagic and benthic primary producers (microalgae and/or seagrass) that are less prone to water currents. Moreover, benthic primary producers represent reliable biomonitoring because of the low spatial movement ability and would allow easier detection of transient and small leakages, since they live attached to the seafloor through which CO2(g) is leaking.
For the future applications of this method, transient leakages characterised by different δ13C of injected CO2(g) should be explored, both by culture and release field experiments, and a multispecies approach should also be tested to consider the interspecific variability of carbon isotopic fractionation. As already highlighted in other studies (e.g., [5,10,21,22]), it is critical to extensively define the natural baseline at the storage site from both a geochemical and biological perspective. The seasonal changes that naturally characterise phytoplankton and DIC isotopic composition [32,33] and seabed community cycles [10] should be deeply understood to allow the correct identification of leakages and not misinterpret the observed deviations. Furthermore, for the coastal storage sites, the definition of the natural baseline would be even more challenging and important, since the anthropogenic and riverine inputs and the higher productivity of these areas represent additional factors that strongly affect the phytoplankton and DIC δ13C [10,33,114]. Moreover, the isotopic baseline should be defined, including the recent monitoring data, to take into account the decrease induced on the δ13CDIC and δ13CPOC by the Suess effect [99]. It has been demonstrated that the decrease in δ13CDIC because of the invasion of anthropogenic CO2(g) from the atmosphere is already responsible for the decrease in δ13CPOC by an extra 0.5–0.8‰ consistently for every cell size [99].

5. Conclusions

The experiments presented here reveal that the primary producer’s isotopic composition quickly responds to changes in the seawater δ13CDIC; thus, this approach represents a valuable contribution for ocean management in the evaluation of the impacts of CO2(g) released from carbon storage sites. These results highlight the relevance of the application of a multidisciplinary approach that includes biological monitoring for the identification of CO2(g) leakages, since the phytoplankton isotopic composition provides time-integrated information on the dissolved inorganic carbon pool, allowing the identification of sporadic emissions that could be hardly detected by seawater sampling. The definition of a detailed baseline is a primary requirement in this field to allow the identification of the seasonal changes that could interfere with the observed variations in the isotopic composition of both the seawater and primary producers.
Considering the importance and the urge to reduce climate change effects, research on CCS monitoring strategies represents a primary field of study in which the biological approach using inherent tracers is still underused and under-investigated. The results of the experiments described are encouraging for the future application of the analysis of the primary producer’s carbon stable isotopic composition for monitoring CCS sites, and more effort deserves to be dedicated in this direction.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4441/12/12/3573/s1: Figure S1: Time variation of inorganic nutrients (DIN and PO4) during the three T. rotula culture experiments. Figure S2: Prokaryotes abundance and isotopic composition during the three T. rotula culture experiments. Data are available at PANGAEA®, doi: https://doi.org/10.1594/PANGAEA.925185.

Author Contributions

Conceptualisation, N.O., M.G. and C.D.V.; data curation, F.R., N.O. and M.G.; formal analysis, F.R., F.C., M.S.T., A.B., L.U. and B.K.; funding acquisition, P.D.N. and C.D.V.; investigation, F.R., M.G., F.C., M.S.T., A.B. and L.U.; methodology, N.O., M.G., F.C. and C.D.V.; project administration, C.D.V.; resources, N.O., M.G., F.C., M.S.T., A.B., P.D.N. and C.D.V.; supervision, N.O., M.G., P.D.N. and C.D.V.; validation, F.R., N.O., M.G. and C.D.V.; visualisation, F.R.; writing—original draft, F.R. and writing—review and editing, F.R., N.O., M.G., F.C., M.S.T., A.B., L.U., B.K. and C.D.V. All authors have read and agreed to the published version of the manuscript.

Funding

This work was conducted using the ECCSEL RI funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 675206. It also represents a part of the project J1-8156: “Stable isotopes in the study of the impact of increasing CO2 levels on C and Hg cycling in coastal waters” financially supported by Slovenian Research Agency and EU project MASSTWIN—Spreading excellence and widening participation in support of mass spectrometry and related techniques in health, the environment and food analysis (H2020, grant agreement No. 692241). The photobioreactors used for the experiments were acquired in the framework of the Acid.it project: “Science for mitigation and adaptation policies to ecological and socio-economic impacts of acidification on Italian seas” financially supported by the Italian Ministry of Education, University and Research. Part of the data presented in the paper have been included in the Ph.D. thesis of Federica Relitti.

Acknowledgments

We are grateful to Alfred Beran for providing the algal culture and for the support in the preparations of the experiments; Sergio Predonzani and Matteo Bazzaro for the valuable help in the experiments set-up and sampling; Martina Kralj for the nutrient analysis and Mauro Celussi for the prokaryotes analysis. The two anonymous reviewers are warmly acknowledged for their valuable and constructive comments that greatly helped to improve our manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

For molecular phytoplankton species identification, 25 mL (~40,000 cells mL−1) of the cell culture from the experiment was filtered on a 1.2-µm cellulose filter and frozen on −80 °C. Genomic DNA was isolated with the DNeasy Plant Mini Kit (Qiagen) according to the manufacturer’s instructions. The 5’ end region of the ribulose bisophosphate carboxylase large subunit (rbcL) gene was amplified using primer pair rbcL66+ (5’-TTAAGGAGAAATAAATGTCTCAATCTG-3’) and DtrbcL3R (5’-ACACCWGACATACGCATCCA-3’) [115,116]. The reaction tubes contained a 25-µL mixture of 200 µmol L−1 of each deoxynucleotide, 0.3 µmol L−1 of each primer, 4-mM MgCl2, 1X DreamTaq green buffer, 0.2 U of DreamTaq DNA polymerase (Thermo Fisher Scientific Inc., Waltham, MA, USA) and 0.5–1 ng of genomic DNA. PCR conditions were as follows: an initial denaturation step of 10 min at 95 °C, 35 cycles of 30s at 95 °C, 30s at 47 °C, 1 min at 72 °C and final extension step of 7 min at 72 °C. PCR-amplified products were purified with a MinElute PCR Purification Kit (Qiagen) according to the manufacturer’s instructions. Purified PCR products were sequenced at Macrogen Europe (Amsterdam, The Netherlands). The resulting sequences (from both ends) were aligned using Geneious 7.1.7. software [117]. BLAST [118] was used for the searches and comparisons of the GenBank database [119].
Using the rbcL gene as a DNA barcode, we confirmed the species identity as Thalassiosira rotula. A new Thalassiosira rbcL nucleotide sequence was deposited in GenBank under accession number: MG214782.

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Figure 1. Schematic description of photobioreactors used for the three T. rotula culture experiments. NAT: natural seawater medium and ASW: artificial seawater medium. During algal growth, only CO2-free air was used (on:off cycle: 10:50 min) in both media. Note that CO2(g) from the cylinder was only supplied during ASW medium preparation. Dissolved oxygen (DO; % sat), temperature (T; °C) and medium pH (pHNBS) were recorded by online probes. Light:dark regime: 14:10 h, continuous gentle stirring. Photobioreactor outline modified from Kbiotech®, Lugano, Switzerland.
Figure 1. Schematic description of photobioreactors used for the three T. rotula culture experiments. NAT: natural seawater medium and ASW: artificial seawater medium. During algal growth, only CO2-free air was used (on:off cycle: 10:50 min) in both media. Note that CO2(g) from the cylinder was only supplied during ASW medium preparation. Dissolved oxygen (DO; % sat), temperature (T; °C) and medium pH (pHNBS) were recorded by online probes. Light:dark regime: 14:10 h, continuous gentle stirring. Photobioreactor outline modified from Kbiotech®, Lugano, Switzerland.
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Figure 2. Time variation of T. rotula abundance during the three experiments of phytoplankton culture in (a) artificial seawater medium (ASW; represented by circles) and (b) natural seawater medium (NAT, represented by triangles). Blue symbols represent the first experiment (EXP-1), red the second experiment (EXP-2) and green the third experiment (EXP-3). Phytoplankton abundance is shown in a logarithmic scale.
Figure 2. Time variation of T. rotula abundance during the three experiments of phytoplankton culture in (a) artificial seawater medium (ASW; represented by circles) and (b) natural seawater medium (NAT, represented by triangles). Blue symbols represent the first experiment (EXP-1), red the second experiment (EXP-2) and green the third experiment (EXP-3). Phytoplankton abundance is shown in a logarithmic scale.
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Figure 3. Time variation of the (a,b) pHNBS, (c,d) dissolved inorganic carbon (DIC) and (e,f) dissolved CO2(aq) during the three T. rotula culture experiments. Circles represent the artificial seawater medium (ASW) and triangles the natural seawater medium (NAT). Blue symbols represent the first experiment (EXP-1), red the second experiment (EXP-2) and green the third experiment (EXP-3).
Figure 3. Time variation of the (a,b) pHNBS, (c,d) dissolved inorganic carbon (DIC) and (e,f) dissolved CO2(aq) during the three T. rotula culture experiments. Circles represent the artificial seawater medium (ASW) and triangles the natural seawater medium (NAT). Blue symbols represent the first experiment (EXP-1), red the second experiment (EXP-2) and green the third experiment (EXP-3).
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Figure 4. Time variation of the isotopic composition of (a,b) dissolved inorganic carbon (δ13CDIC), and (c,d) phytoplankton (δ13CPOC) in the three T. rotula culture experiments. Circles represent the artificial seawater medium (ASW) and triangles the natural seawater medium (NAT). Blue symbols represent the first experiment (EXP-1), red the second experiment (EXP-2) and green the third experiment (EXP-3).
Figure 4. Time variation of the isotopic composition of (a,b) dissolved inorganic carbon (δ13CDIC), and (c,d) phytoplankton (δ13CPOC) in the three T. rotula culture experiments. Circles represent the artificial seawater medium (ASW) and triangles the natural seawater medium (NAT). Blue symbols represent the first experiment (EXP-1), red the second experiment (EXP-2) and green the third experiment (EXP-3).
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Figure 5. Time variation of the isotopic enrichment factor (εCO2-POC) of T. rotula in the three culture experiments calculated on δ13CCO2(aq). Circles (a) represent the artificial seawater medium (ASW) and triangles (b) the natural seawater medium (NAT). Blue symbols represent the first experiment (EXP-1), red the second experiment (EXP-2) and green the third experiment (EXP-3).
Figure 5. Time variation of the isotopic enrichment factor (εCO2-POC) of T. rotula in the three culture experiments calculated on δ13CCO2(aq). Circles (a) represent the artificial seawater medium (ASW) and triangles (b) the natural seawater medium (NAT). Blue symbols represent the first experiment (EXP-1), red the second experiment (EXP-2) and green the third experiment (EXP-3).
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Figure 6. Comparison between the time variation of the isotopic composition of DIC (δ13CDIC, in blue), and phytoplankton (δ13CPOC, in red) during the three T. rotula culture experiments in (a) artificial seawater medium (ASW) and (b) natural seawater medium (NAT). Linear regression (dashed lines) equations and correlation significance are provided in each box for both the δ13CDIC and δ13CPOC.
Figure 6. Comparison between the time variation of the isotopic composition of DIC (δ13CDIC, in blue), and phytoplankton (δ13CPOC, in red) during the three T. rotula culture experiments in (a) artificial seawater medium (ASW) and (b) natural seawater medium (NAT). Linear regression (dashed lines) equations and correlation significance are provided in each box for both the δ13CDIC and δ13CPOC.
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Figure 7. Nonmetric multidimensional scaling analysis (nMDS) of the T. rotula culture experiments. The first experiment (EXP-1) is represented by triangles, the second (EXP-2) by circles and the third (EXP-3) by squares. Blue symbols represent the artificial seawater medium (ASW), and red symbolises the natural seawater medium (NAT). Numbers above the symbols represent the sampling time (h). The green lines represent the groups identified by the similarity profile (SIMPROF) analysis.
Figure 7. Nonmetric multidimensional scaling analysis (nMDS) of the T. rotula culture experiments. The first experiment (EXP-1) is represented by triangles, the second (EXP-2) by circles and the third (EXP-3) by squares. Blue symbols represent the artificial seawater medium (ASW), and red symbolises the natural seawater medium (NAT). Numbers above the symbols represent the sampling time (h). The green lines represent the groups identified by the similarity profile (SIMPROF) analysis.
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Figure 8. Closed-system Rayleigh-type fractionation relationship calculated for the three T. rotula culture experiments in the natural seawater medium (NAT). The isotopic composition of the residual fraction (f; 0 ≤ f ≤ 1) of unutilised substrate remaining (DICr) is represented by the blue line (δ13CDICr), and the isotopic composition of the cumulative (POCC) and instantaneous (POCi) products are shown by the green (δ13CPOCc) and red (δ13CPOCi) lines, respectively. The experimental data of the δ13CPOC and δ13CDIC from the three NAT experiments are also plotted as green circles and blue circles, respectively. The mathematical formulae for reactants and products are shown [110].
Figure 8. Closed-system Rayleigh-type fractionation relationship calculated for the three T. rotula culture experiments in the natural seawater medium (NAT). The isotopic composition of the residual fraction (f; 0 ≤ f ≤ 1) of unutilised substrate remaining (DICr) is represented by the blue line (δ13CDICr), and the isotopic composition of the cumulative (POCC) and instantaneous (POCi) products are shown by the green (δ13CPOCc) and red (δ13CPOCi) lines, respectively. The experimental data of the δ13CPOC and δ13CDIC from the three NAT experiments are also plotted as green circles and blue circles, respectively. The mathematical formulae for reactants and products are shown [110].
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Figure 9. Predicted variation of δ13CDIC values according to the different δ13C of leaking CO2(g) from carbon capture and storage sites (CCS) and to the different proportions of DIC composed by leaking or atmospheric CO2(g). The plots were produced applying the equation by Zhang et al. [76] assuming an atmospheric δ13CCO2(g) corresponding to −8.0‰ and using different leaking δ13CCO2(g) values: −15.0‰ (blue line), −20.0‰ (orange line), −25.0‰ (purple line) and −35.0‰ (green line). The δ13C value of CO2(g) used for the ASW experiments (−48.2‰) was also included (red line).
Figure 9. Predicted variation of δ13CDIC values according to the different δ13C of leaking CO2(g) from carbon capture and storage sites (CCS) and to the different proportions of DIC composed by leaking or atmospheric CO2(g). The plots were produced applying the equation by Zhang et al. [76] assuming an atmospheric δ13CCO2(g) corresponding to −8.0‰ and using different leaking δ13CCO2(g) values: −15.0‰ (blue line), −20.0‰ (orange line), −25.0‰ (purple line) and −35.0‰ (green line). The δ13C value of CO2(g) used for the ASW experiments (−48.2‰) was also included (red line).
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Table 1. Description of the experiments. Initial cell abundance (cells mL−1) and dissolved inorganic carbon (DIC) concentration (µmol L−1); initial medium pH (pHNBS) and experiment duration (h) for the three experiments (EXP-1, EXP-2 and EXP-3) carried out in two bioreactors, one filled with natural seawater (NAT) and the other with artificial seawater medium (ASW).
Table 1. Description of the experiments. Initial cell abundance (cells mL−1) and dissolved inorganic carbon (DIC) concentration (µmol L−1); initial medium pH (pHNBS) and experiment duration (h) for the three experiments (EXP-1, EXP-2 and EXP-3) carried out in two bioreactors, one filled with natural seawater (NAT) and the other with artificial seawater medium (ASW).
ExperimentMedium TestedExperiment CodeInitial Cell Abundance
(Cells mL−1)
Initial DIC Concentration
(µmol L−1)
Initial pH
(pHNBS)
Experiment Duration (h)
EXP-1ASW1-ASW25019528.1496
NAT1-NAT25022228.3296
EXP-2ASW2-ASW10008938.19140
NAT2-NAT100022438.37140
EXP-3ASW3-ASW100050988.19120
NAT3-NAT100023508.3296
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