We present a cost model for implementing a deployment scale effort for conducting ocean iron fertilization (OIF) for marine-based carbon dioxide removal (CDR). The model incorporates basic oceanographic parameters critical for estimating the effective export of newly fixed CO₂ into biomass that is stimulated by Fe addition to an Fe-limited region of the Southern Ocean. Estimated costs can vary by nearly 100-fold between best-case and worst-case scenarios, with best-case values of $7/net tonne C captured versus worst-case $1,500/net tonne C captured, without accounting for verification costs. Primary oceanographic factors that influence cost are the net primary productivity increases achieved via OIF, the amount of C exported into the deep ocean, and the amount of CO₂ ventilated back to the atmosphere. The model compares ship-based versus aerial delivery of Fe to the ocean, and estimates aerial delivery can be 30%–40% more cost effective; however, the specific requirements for aerial delivery require additional research and development. The model also estimates costs associated with verification and environmental monitoring of OIF. These costs increase $/net tonne C captured by 3–4-fold. Best, intermediate, and worst cases for aerial delivery and ship delivery are $21, $83, $2,033, and $24, $94, $4,691, respectively, inclusive of verification costs. The primary goal of this model is to demonstrate the variability in cost of OIF as a CDR method, and to better understand where additional research is needed to determine the major factors that may make OIF a tractable, nature-based CDR method.

Key Points

Variability in key oceanographic parameters can impact predicted costs of ocean iron fertilization by 100-fold
The model finds aerial-based delivery of iron may reduce costs by 30%–40% compared to ship-based delivery
The cost of verification and environmental monitoring may increase overall costs of ocean iron fertilization by 3–4 fold

Plain Language Summary

This paper presents a basic cost model for undertaking ocean iron fertilization (OIF) as a means of removing carbon dioxide from the atmosphere. It presents a simple application scenario that compares two different modes for delivering iron to the ocean, plane-based and ship-based, and estimates costs associated with verification of carbon export into the deep ocean, as well as assessing the environmental changes that may occur as result. Key findings are that, in terms of USD/tonnes carbon exported, OIF can be at the lower cost end (<S10/tonne) of methodologies for carbon dioxide removal, and that aerial delivery is likely to be at least 30% less than ship-based delivery. However, uncertainties pertaining to how quickly this carbon dioxide may return to the atmosphere, and the impacts of other off-setting greenhouse gases could push these costs up by 50-fold or more. Furthermore, verification costs may increase the costs substantially. The primary goal of this work is to place some dollar values on the uncertainties that underlie OIF as a means of marine carbon dioxide removal that can help guide much-needed research into its ultimate efficacy.

1 Introduction

The latest IPCC projections for 1.5°C–2°C global warming by 2100 are now based on an overshoot in greenhouse gas (GHG) emissions that will require implementation of negative emission technologies (NETs) to actively remove and store excess GHGs to achieve a <2°C trajectory (H.-O. Pörtner et al., 2022). Estimates for the amount of annual CO₂ removal (CDR) required by 2100 range from 8.5–19 Gt/yr (equivalent to 2.3–5.2 Gt C/yr) based on one recent analysis (Strefler et al., 2021). NETs fall into two broad categories: (a) novel technical approaches, like direct air capture, or (b) enhancement of natural (CDR) processes. The latter processes may be terrestrial- or marine-based, and generally fall into either enhanced chemical weathering, or promoting photosynthetically-driven CO₂ fixation, coupled to a means for permanent or semi-permanent removal of newly fixed-CO₂ from the atmosphere (F. Wang et al., 2021). Ocean iron fertilization (OIF) is a marine-based NET in this latter class that proposes to add an iron source to parts of the ocean that are iron-limited (GESAMP, 2019). Alleviating this iron limitation results in increased primary production by marine phytoplankton that increases their fixation of CO₂ that is ultimately drawn from the atmosphere. A portion of the biomass is exported to the ocean depths where the newly fixed carbon is removed from the climate system for 10s to 100's to 1,000's of years, based on the biomass export depth in the ocean. Since nearly one-third of the global ocean is iron-limited, the potential for OIF for atmospheric CO₂ drawdown is significant, on the order of a gigaton or more per year when integrated globally (NAS, 2022; Williamson et al., 2022).

The recognition of iron's role as an important limiting nutrient and thus controlling factor in rates of CO₂ uptake and fixation in the global ocean is relatively new. It was first proposed in the 1980s (Martin & Fitzwater, 1988; Martin & Gordon, 1988). Coincident with the recognition of iron as a limiting nutrient in the modern ocean was paleoclimate analysis indicating that iron supply, primarily as wind-blown aeolian dust delivered from the continents to the open ocean, was one of several factors that controlled atmospheric CO₂ concentrations during the glacial-interglacial periods of the past several hundred thousand years (Lamy et al., 2014; Martin, 1990; Martínez-García et al., 2014; Struve et al., 2022). Combined, these factors led to the hypothesis that active addition of Fe to Fe-limited regions of the ocean, often referred to as high nitrate, low chlorophyll (HNLC), could stimulate enough additional phytoplankton growth that the resultant export of newly fixed CO₂ into the deep ocean could be an effective means of CDR for the mitigation of anthropogenic GHG-induced global warming.

A series of 12 meso-scale Fe additions starting in 1993 (IronEX1) and ending in 2009 (LOHAFEX) were undertaken to first test the idea of Fe as an important limiting nutrient, and secondarily, to understand the effects of Fe fertilized phytoplankton blooms on carbon export. These experiments have been reviewed previously (Boyd et al., 2007; Yoon et al., 2018), and will not be addressed in detail here. Suffice it to say, most Fe additions resulted in substantial phytoplankton blooms whose chlorophyll production could be tracked from space; however, the amount of carbon export to ocean depths was variable, or poorly constrained due to lack of measurements. Nonetheless, these experiments corroborate results from natural Fe fertilization events (Blain et al., 2007; Schine et al., 2021), volcanos (Duggen et al., 2010; Hamme et al., 2010), iceberg melting (Koffman et al., 2021; Schroth et al., 2011), or forest fires, demonstrating that Fe added to HNLC oceanic regions stimulates substantial bloom events and may lead to significant carbon export.

There has been a more than decade long cessation of research into OIF as a NET. In part, this was due to the ambiguous carbon export results from meso-scale Fe addition experiments, and debate over the potential negative impacts of OIF. Early efforts at commercialization of OIF to acquire carbon credits were met with skepticism from the oceanographic community due to concern that the inherent ocean processes involved were not well enough understood to safely commercialize OIF (Strong et al., 2009). An ill-advised attempt to improve fisheries by unlicensed dumping of Fe in the Northeast Pacific in 2012 led to establishment of legal norms through the Law of the Sea that further dampened efforts at developing OIF as a NET (Gambardella, 2019). This controversial background for OIF is, however, set against the accumulating evidence for the negative consequences of climate change on human civilization, the lack of progress in curbing anthropogenic emissions of GHGs, and recognition that NETs, in addition to substantial reductions in GHG emissions, are necessary to maintain global temperature increases at levels not considered catastrophic. It is increasingly important to consider multiple CDR approaches. A recent study from the US National Academy of Science on CDR approaches in the ocean reported that research into OIF is a worthy goal in light of new understanding of the ocean's role in controlling atmospheric CO₂ levels (NAS, 2022).

A feasible CDR method must be economically viable in terms of its USD per tonne of C removed, and not emit more CO₂ or other GHG equivalents during delivery and monitoring, than will be sequestered due to the OIF treatment (Güssow et al., 2015). The goal of this work is to develop a simple cost model for OIF. In addition to being a basic economic analysis, a cost model serves several purposes. It puts a tangible value or cost on uncertainty, and indicates where the costs associated with uncertainty are greatest. This can provide a framework for guiding additional research aimed at constraining this uncertainty. A cost model requires a systematic evaluation of the different logistical processes associated with an approach, and uses a common metric of USD per tonne of C sequestered to evaluate these processes. In addition, legal precedence prefers cost-benefit type analysis that puts a dollar value on an “entity” in terms of creating or amending regulatory laws (Frantzeskaki et al., 2019). This work builds from a similar cost model developed by Harrison (2013) for OIF. The work here presents new information on aerial versus ship delivery, accounts for verification costs and environmental impact assessment, and is updated in terms of our continually improved understanding of the iron cycle in the ocean.

2 Model Development

2.1 Model Considerations

Scenario. This cost model presents scenarios for a hypothetical OIF effort in the Southern Ocean aimed at seeding a 200,000 km² region (1% of the entire area of the Southern Ocean). The model assumes a square 450 km (240 nautical miles) on a side will be fertilized uniformly, Figure 1. This scale is significantly greater than any previous OIF experiments, and more in line with an actual OIF-based CDR deployment. It's important to point out that research-based applications required to further evaluate the efficacy of OIF will, at least initially, be substantially smaller in size than the scenario proposed here, and may incur significantly higher costs driven by the specific objectives of a given research effort. It is also important to emphasize that this cost model is not a business model, and does not take into account overhead costs, administrative costs, and other business associated costs.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

Diagram of deployment scenario for applying iron to a 200,000 km² area of ocean. This assumes a uniform distribution pattern of 90 swaths with a 5 km distance between swaths. For a base model projection of 10 days for one OIF application, ship delivery would occur at 15 knots, equivalent to 1.5 swaths/d; aircraft delivery would be 3 swaths/flight with a 4.5 hr total flight time. The target area is assumed to be 1,500 km from land (nearest port/airport) with a 4 days transit time, to and from, for a ship, and 3h transit time, to and from, for a plane.

The oceanographic processes that contribute to exported carbon (C_exp) are summarized as follows: addition of a limiting micro-nutrient, Fe, to an optimal concentration that stimulates photoautotrophy (Fe_opt) leading to a net increase in net primary productivity (NPP_stim) that drives uptake of CO₂ to produce new biomass, defined as particulate organic carbon (POC), a proportion of which is exported below the pycnocline that is determined by the mixed layer depth (MLD) of the ocean during a phytoplankton bloom period of finite length. The amount of Fe required is a function of the optimal Fe concentration required to overcome Fe limitation within the MLD of the surface ocean area being fertilized, and the capacity of that Fe source to be utilized by phytoplankton. The following equation summarizes the amount of Fe-required (Fe_req) in terms of total amount of Fe in tonnes.

{\text{Fe}}_{\text{req}}=\left(\left({\text{Fe}}_{\text{opt}}/{\text{Fe}}_{\text{sol}}\right)\ast \text{MLD}\right)\ast {\mathrm{A}}_{\text{fert}}

(1)

In this case Fe_opt is the targeted Fe seawater concentration (nmol Fe ^. L⁻¹) and Fe_sol is a unitless solubility fraction that estimates the amount of added Fe that is available for biological uptake.

The total amount of C_exp is governed by the export efficiency (C-Exp_eff) of newly produced biomass or NPP, the total area being fertilized (A_fert), as well as the number of OIF applications (App_num) during the growing season based on the following equation.

{\mathrm{C}}_{\mathrm{exp}}=\left({\mathrm{A}}_{\text{fert}}\ast {\text{NPP}}_{\text{stim}}\ast \mathrm{C}-{\mathrm{Exp}}_{\text{ef}\mathrm{f}}\right)\ast {\text{App}}_{\text{num}}

(2)

These processes underlie the cost model developed here that is based on a simple linear model developed by Harrison (2013).

{\mathrm{C}}_{\text{seq}}={\mathrm{C}}_{\mathrm{exp}}\ast \left[1-\left({\mathrm{L}}_{\text{vent}}\right)\right]

(3)

{\mathrm{C}}_{\text{net}}={\mathrm{C}}_{\text{seq}}\mbox{--}\left({\text{Off}}_{\mathrm{N}2\mathrm{O}}+{\text{Off}}_{\text{process}}+{\text{Off}}_{\text{verif}}\right)

(4)

This has two terms: C_seq (tonnes) which is the OIF-stimulated sequestration of C into the ocean taking into account losses in C export (C_exp) due to export inefficiencies driven by ventilation (L_vent) of CO₂ due to remineralization of POC below the MLD. C_net represents the net amount of C (tonnes) sequestered on decadal to millenial timescales, accounting for reductions in C_seq due to factors that offset C_net. These offsets are due to nitrous oxide production (Off_N2O; converted to CO₂-equiv) and CO₂-based GHG emissions (Off_process) resulting from production and delivery of iron for OIF, as well as ship-based verification of carbon export from OIF treatments (Off_verif).

To relate C_net to an actual cost of OIF in USD per tonne C removed ($C), the term Fe_req is multiplied by the USD cost of producing and delivering the Fe (Fe_cost), plus the USD cost of verifying the carbon export from OIF (Fe_verif), to determine USD per tonne C that is net sequestered.

\mathrm{\$}\mathrm{C}=\left({\text{Fe}}_{\text{req}}\ast \left({\text{Fe}}_{\text{cost}}+{\text{Fe}}_{\text{verif}}\right)\right)/{\mathrm{C}}_{\text{net}}

(5)

A full set of terms and definitions are included in Text S1 in Supporting Information S1. Details for the terms used in the model, including verification requirements, are described below, as well as the additional underlying assumptions in the model that are outlined above. We then use the model to compare ship-based delivery to plane-based delivery of Fe for OIF. It’s important to point out that we have chosen to report all costs as USD/tonne of C exported, rather than USD/tonne of CO₂ exported. Our rationale is that the product of OIF will be organic C produced by photosynthetic plankton, and it will be organic C that is exported and stored in the deep ocean, and that is what will be verified. The CO₂ equivalent of a tonne of organic C is 3.67 tonnes of CO₂. The model itself is available through Zenodo, Sofen et al. (2023).

3 Description of Model Components

This section covers some of the primary components and related assumptions that go into the model.

3.1 Fe Availability, Processing, and Delivery

3.1.1 Fe Solubility

The fraction of Fe that can be readily taken up by phytoplankton and other organisms is defined as bioavailable. Bioavailability is a function of the solubility and retention time in the photic zone of the water column of a given Fe source. The solubility is important, since the amount of truly soluble Fe(II) in ocean water is vanishingly small due to thermodynamic and kinetic factors. It is generally accepted that dissolved Fe (dFe), defined as any Fe that passes through a 0.2 μm filter, is the most bioavailable form, and may consist of Fe(II) or Fe(III) bound to a ligand, or Fe(III) as a fine-particulate iron oxyhydroxide or in some other colloidal form Croot and Heller (2012) and Tagliabue, Buck, et al. (2023). In the case of Fe-oxides, the crystallinity of the oxide will affect its bioavailability, with more crystalline forms like hematite or magnetite being less available than ferrihydrite (Huang et al., 2021). Aeolian dust, a primary source of Fe to the open ocean, generally contains more crystalline, fine particles of hematite with reduced bioavailability (Duce et al., 2009). Sources of Fe used for OIF should maximize bioavailability to maximize phytoplankton uptake efficiency. The exact uptake mechanism(s) for Fe acquisition, and form of Fe that is most easily acquired, are relatively poorly understood for many pelagic marine phytoplankton.

3.1.2 Fe Production and Processing

The source of Fe for previous ship-based OIF applications was acidified ferrous sulfate produced by mixing powdered FeSO₄ · 7H₂O with industrial grade HCl, yielding a solution of approximately 30% Fe by weight (300 g/kg) (Boyd et al., 2007). The estimated cost for these materials is $700/tonne. Pigment grade finely powdered Fe-oxide can be purchased for around $1,000 per tonne (https://www.novapolychem.in/hyrox-iron-oxide-pigments.html). Biogenic Fe-oxides have been proposed as another alternative Fe-source (Emerson, 2019); however, these have not been produced at industrial scale, thus it is difficult to assign a cost. The form of iron used will impact solubility. Iron sources also come with a CO₂ offset due to their production. Commercially available sources of iron are derived from mined iron ores. Estimates for the raw materials extraction of iron ore are 80 kgCO₂e/tonne of iron produced; further processing costs are difficult to assess for the types of iron proposed here, but iron agglomeration costs, which is the first processing step in converting iron ore to steel, range from 235 kgCO₂e/tonne to 40 kgCO₂e/tonne, depending on whether coal or natural gas, respectively, is used for this step (IEA, 2020). Taking a more conservative approach we estimate 305 kgCO₂eq/tonne (83 kgC_eq/ton) produce for each tonne of iron used in an OIF application, as an offset to CO₂ captured. Because it is not well constrained, and is a relatively small number, we have not incorporated this offset in the model.

3.1.3 Fe Delivery

For this model we propose two different delivery options for iron to the fertilized area, either ship or plane. Previous mesocosm-based iron additions have been done by ships, using fully research-capable vessels. For the large-scale delivery proposed in this model, we assume that more standard, commercial vessels with significantly lower-cost day rates would be used for open ocean delivery. The model scenario assumes a 10 days delivery period over the fertilized area with a swath width of 5 km (Figure 1), projecting a need for six ships operating at 15 knots to cover the entire area in 10 days, with an additional 6 days of transit to and from the site. Fuel consumption costs in CO_2equiv, for ships is a known calculable cost, and we assume the Fe-delivery methodology used for earlier mesocosm experiments will be used here (Yoon et al., 2018). This scenario calls for three separate Fe additions during the course of a 120 days growing season for phytoplankton in the Southern Ocean.

An alternative to ship-based delivery is aerial application. Aerial application has yet to be tried for OIF. Nonetheless, a major source of iron to the open ocean is air-borne aeolian dust (Hooper et al., 2019; Jickells et al., 2005; Moore & Braucher, 2008; Struve et al., 2022), thus aerial delivery better mimics natural processes of iron delivery than ship-based methods. Recent well-documented examples of large scale phytoplankton blooms from natural Fe-fertilization events due to volcanos (Duggen et al., 2010; Hamme et al., 2010; Watson, 1997) and wild-fires also resulted in delivery of iron from the atmosphere. To cite one specific example, it is estimated that a large fraction of CO₂ released by the 2019 Australian forest-fires was re-adsorbed into the ocean as a result of phytoplankton bloom, although actual C_exp values were difficult to quantify (Tang et al., 2021; Wang et al., 2022). Technologies for the aerial application of agricultural chemicals and fertilizers, or fire-fighting suppressants are well developed, and could be modified for aerial delivery of iron, although this will require at least a moderate amount of R&D to optimize this approach. For the projected OIF scenario, an average of 27 flights at 4h per flight are used to distribute iron over the fertilized area (Figure 1) for each of three total deployments. However, because planes are weight-limited in how much Fe they can carry, in scenarios that require more Fe, for example, lower Fe solublity, more flights are necessary, while the opposite can be true, for example, with greater Fe solublity, see plane costs in Supporting Information S1. This assumes a 1h transit each way to the site and 2.5 hr for each application. Aircraft usage costs that could be adapted for OIF are quite well known (Gentile et al., 2022). The primary unknowns are the effectiveness of different forms of iron that could be used, and the specific logistics of dispersion, see Discussion for more considerations around aerial delivery.

3.2 Negative Offsets

3.2.1 Ventilation

Microbially driven remineralization of exported organic matter back to CO₂ may result in ventilation of CO₂ back to the atmosphere, thus reducing permanence of CO₂ initially captured via photosynthesis. While ventilation is dependent on export efficiency of newly produced particulate organic carbon (POC), we include it as a separate term in the model. The major determinant of ventilation is the rate of re-mineralization that occurs below the mixed layer depth. POC is continually re-mineralized to CO₂ as it sinks, and the deeper the depth of remineralization the longer the re-mineralized CO₂ is retained in the ocean interior and out of atmospheric circulation (Kwon et al., 2009). Martin et al. (1987) using a small set of flux measurements established biomineralization rate curves for the open ocean, and estimated globally that 75% of sinking POC was mineralized back to CO₂ by 500 m depth, and 90% by 1,500 m depth. Siegel et al. (2021) used a global circulation inverse model (OCIM) to model CO₂-sequestration for the global ocean that compared different CO₂ injection depths to assess how effectively CO₂ was sequestered by the biological pump. This exercise revealed CO₂ injection depth made a large difference in C-sequestration, and estimated that biological pump activity, while inherently leaky, led to approximately 75% of CO₂ ventilated back to the atmosphere in 100 years and 67% in 50 years. This work also showed significant differences based on geographical location in the ocean, with the eastern Pacific and northern Indian Oceans having greater retention, and the Atlantic Ocean having lower retention times. Estimating the amount of CO₂ ventilation is confounded by several factors (Buesseler et al., 2020; Morrison et al., 2022). First, the MLD in the ocean, especially in the higher latitude HNLC regions can increase substantially in the winter. POC that does not sink below the MLD is assumed to be re-mineralized in the same season that it was fixed, that is, if it does not sink below the winter MLD, then it has no permanence. Second, advective ocean currents can move POC laterally, as well as vertically, and confound estimates of permanence of CO₂ removal, making it difficult to track sequestered CO₂. Third, direct measurements of POC export and re-mineralization in the deep ocean are technically challenging due the high variability of these processes over space and time. Together these factors lead to a substantial amount of uncertainty in how large a factor remineralization and ventilation is in controlling overall export of POC to the ocean interior.

3.2.2 N₂O Production

The export of increased amounts of organic matter below the MLD in the ocean due to OIF stimulates enhanced oxic microbial respiration. Due to limited transport of O₂ from the surface ocean across the pycnocline, this increased respiration will result in deoxygenation at depth (Fu & Wang, 2022). As O₂ becomes limiting, nitrate-respiring microbes will increasingly contribute to organic matter consumption resulting in the release of nitrous oxide (N₂O), a GHG with nearly 300 times the potency of CO₂, and an atmospheric residence time of 116(±9) years (Tian et al., 2020). Actual measurements of N₂O production in response to OIF field experiments are limited in number, and only two field studies followed N₂O production during an OIF experiment. These studies have come to opposite conclusions as to the importance of N₂O production (Law & Ling, 2001; Walter et al., 2005). Nonetheless, the general principle of N₂O production in response to organic loading in the deep ocean is established (Landolfi et al., 2017), and given the potential for N₂O production to be directly linked to OIF, it is important to include N₂O production as a negative offset in the cost model. We have followed the same rationale as outlined by Harrison (2013) for coupling net C export to N₂O production.

3.3 Verification Costs

We include two separate but integrated costs in the verification offset (Offset_verif) term in Equation 4. Verification costs are those associated with verifying transport of C into the ocean interior, that is, verifying that an OIF application is resulting in C export and effective capture of CO₂ in the deep ocean. Environmental costs are those associated with assessing larger scale changes to the ecosystem, and especially those that could be damaging to ocean ecosystem health. Because there is substantial overlap in the technologies and efforts required to measuring either verification or environmental monitoring, as explained below, they are combined in of Offset_verif term.

3.3.1 Verification Costs

It will be essential to verify the effectiveness of CO₂ removal via OIF, yet verification poses a number of challenges. For the purpose of this modeling exercise, estimated verification costs are integrated into the term $C_net for the monitoring of carbon export. For the model, the costs of verification are based on the day rate for ocean monitoring vessels or aircraft, while others, for example, bio-Argo floats represent additional fixed costs. In addition to these actual costs an additional offset (Off_verif) for the estimated CO₂ usage required for the ocean-based verification process is included. For the purposes of the model, we assume two ships equipped for verification are deployed for consecutive 70 days periods each that will encompass an entire 120 days growing season for phytoplankton in the Southern Ocean, see Table 2. These cruises will provide verification data from the three OIF applications via in-situ analysis across a set of stations inside the fertilized area, and a smaller set of control sites outside the fertilized area. These ships could also deploy long-term autonomous monitoring floats, as well as autonomous undersea gliders to collect data from a wider area. For the present iteration of the model we are only including ship day rate estimates as the cost parameter for the model, see Table S1 in Supporting Information S1 for a breakdown of verification tasks and costs.

Table 1. Values for Major Model Parameters

Parameter	Units	Intermediate	Best	Worst
Fe Solubility	unitless	0.67	0.91	0.57
NPP Stimulation	mgC/m²/d	1,000	1,500	500
Export Efficiency	%Corg@100m	10	15	5
CO₂ Ventilation	%CO₂ re-released	75	65	85
N₂O Emission	fraction N₂O emitted	0.04	0.02	0.06
Fe Concentration	nmol/L	0.6
Mixed Layer Depth	meters	60
Length of Bloom	Days	20
Area Fertilized	km²	200,000

Note. Parameters in italics are those that are varied in scenarios between intermediate, best, and worst estimates for values. See Section 3.4 for more description of these parameters and relevant citations.

Table 2. Base Cost Estimate and Comparison for Aerial- and Ship-Based Delivery of Fe for an OIF Application

Aerial delivery	Parameter	Ship delivery	Parameter
Fertilized area	200,000 km(2)	Fertilized area	200,000 km(2)
C exported	1,200,000 tonnes	C exported	1,200,000 tonnes
Total Amount of Fe	1,809 tonnes	Total Amount of Fe	1,809 tonnes
Reagent cost @$1,250/ton	$2,261,250	Reagent cost @$750/ton	$1,356,750
Aircraft and payload	Boeing 737/22.7 tonnes	Ship requirement	1000 DWT
Operational Cost hourly	$10,000	Day Rate per ship	$25,000
Flight duration^a	4 hr	Total cruise duration/ship	60 days
Total flight #/hrs	81/321	Total ship days/6 ships	360
Total Delivery Cost	$3,211,885	Total delivery cost	$5,399,642
Total cost delivery + reagent	$5,473,972	Total cost (delivery + reagent)	$7,661,729
Cost per tonne C export	$4.56	Cost per tonne C export	$6.38
Fuel usage in CO₂ equiv	2,538 tonnes	Fuel usage in CO₂ equiv	8,071 tonnes
%Offset for aerial delivery	0.22%	%Offset for ship delivery	0.67%

Note. This assumes a total of the three individual applications to the same region during one growing season, and is based on intermediate value oceanographic parameters shown in Table 3. These values do not account for offsets or loss, or include verification costs.
^a This is the average flight duration.

3.3.2 Environmental Impact Assessment (EIA) Costs

Another important monitoring and cost aspect of the project is to track environmental changes associated with an OIF deployment. Many of the parameters important for environmental impact overlap with verification, for example, measurements of changes in nutrients, O₂ concentrations, and pH. Specific EIA-related measurements will focus on more detailed analysis of changes in microbial and phytoplankton community composition, impacts on marine macrobiota, as well as assess toxin-production by phytoplankton blooms. In terms of infrastructural requirements, EIA is projected to utilize similar assets and be of similar duration as the verification efforts, see Table S1 in Supporting Information S1.

3.4 Additional Oceanographic or Biogeochemical Factors

Target Fe concentrations: The concentration of dFe that needs to be added in a specific oceanic region to make it iron replete during a phytoplankton bloom cycle. While phytoplankton taxa vary in the concentration of dFe needed to support maximum growth, a concentration of 0.6 nM is sufficient for most coastal and oceanic species (Sunda and Huntsman, 1995; Twining et al., 2021).

Net increase in PP (NPP): The fundamental mechanism for carbon capture via OIF is the increase in the amount of phytoplankton-driven primary production (PP) stimulated by Fe-addition to HNLC regions of the ocean. Estimates of Fe-stimulated NPP are guided by the previous meso-scale Fe-addition experiments (Yoon et al., 2018).

Export efficiency (Exp_eff): C export is influenced by a complex set of interactions (Boyd et al., 2019; Buesseler et al., 2020; Henson et al., 2012). For simplicity, we refer to the fractional amount of newly fixed organic carbon exported below the mixed layer depth as particulate organic carbon (POC).

Mixed layer depth (MLD): This is a fixed value, determined by the depth of pycnocline that limits vertical mixing and nutrient supply of surface waters with the deeper ocean waters below. It can be accurately measured, but is variable depending on oceanic region and season (Dong et al., 2008; Yoon et al., 2018).

Bloom length: The number of days during which a bloom is in its active growth phase, with average daily NPP informed by previous OIF experiments (Yoon et al., 2018).

Length of growing season: The period most amenable to phytoplankton growth, driven by day length, available nutrient concentrations, and temperature. This model assumes that three successive blooms are stimulated during the growing season.

Number of applications: This is a function of Fe bioavailability, especially residence time, related to bloom length and growing season. For the purposes of this modeling exercise, we assume three applications, evenly spaced apart over a presumed phytoplankton growth season and each resulting in 40 days bloom. The 40 days bloom is composed of a 20 days growth phase of the bloom and 20 days death phase. The 20 days growth phase is what used for quantifying a total amount of new carbon fixed due to OIF.

4 Results

4.1 Base Estimates

Table 2 presents a cost comparison for plane (aerial) versus ship-based delivery of Fe in the OIF scenario outlined above. This scenario assumes an OIF-based stimulation of the primary production rate of 1,000 mgC/m²/d, and an Exp_eff of 10%. Based on three 10 days delivery periods during the growing season this yields a total carbon export of 1,200,000 tonnes (equivalent to 4,400,000 tonnes of CO₂, Figure 2). Based on this scenario, aerial delivery reduces $C_net by approximately 30% over ship-based delivery ($4.98/tonne C_exp vs. $7.99/tonne C_exp). In both cases, the fuel usage for delivery, in CO₂equiv is <1% of the C_exp.

An estimation of the verification and environmental monitoring costs associated with this OIF scenario is shown in Table 2. For the model, we assumed that verification would start within 10 days of the end of the first application and continue through the additional two applications and for at least 60 days following the final application, including transit times this was two 70 days cruises for two ships. This estimate adds an additional $12/tonne C_exp for verification. This higher cost is driven in part by significantly higher costs for monitoring ships than for delivery ships based on the requirement that verification/monitoring ships will require additional instrumentation and technical personnel to carry out their mission. The other factor that impacts verification costs is the offsets due to fuel usage in CO_2eqiv. These offsets become proportionally higher in worst case scenarios where C_exp decreases as a result of higher ventilation losses or offsets due to N₂O production, or reduced export efficiency.

4.2 Estimates Including Losses and Offsets

This base comparison does not take into account losses in C_exp due to ventilation or the offsets due to N₂O production or delivery and verification offsets. To account for these, a series of different scenarios are calculated for best and worst cases for Fe solubility, stimulation of NPP, export efficiency, CO₂ ventilation, and N₂O offset using the values shown in Table 1. The costs for these scenarios in USD/tonne C exported for either plane- or ship-based OIF delivery are shown in Table 4, along with intermediate estimates, and best case (lowest cost) and worst case (highest cost) estimates for the different parameters. The net C exported for different scenarios is shown in Figure 2. The USD/tonne C exported values for the different scenarios are plotted as multivariate plots in Figure 3., that show the range of values if all parameters are set to intermediate-(solid lines), worst-, or best-case scenarios, illustrating the range of values that result.

Table 3. Estimate of Verification/Environmental Monitoring Costs Based on Base Export Model Not Accounting for Losses or Offsets

Verification	Parameter
Fertilized area	200,000 km(2)
C exported	1,200,000
Ship requirement	Research Vessel
Day Rate per ship	$50,000
No of Ships/cruises	2/2
Ship days/cruise	70
Total ship days	280
Total Cost	$14,000,000
Cost per tonne C export	$12.00
Fuel usage in CO₂ equiv	14,230
Offset for aerial delivery	1.2%

Table 4. Values Calculated in USD/Tonne C ($C_net) for Different Scenarios

	Plane delivery		Ship delivery
	No verification	Verification	No verification	Verification
Intermediate Estimate	22	83	31	94
Best Case	5	21	8	24
Worst Case	257	2,033	425	4,691
Solubility best case	18	78	29	92
Solubility worst case	25	86	33	96
Export efficiency best case	15	54	21	61
Export efficiency worst case	45	179	65	209
Ventilation best case	15	55	21	62
Ventilation worst case	43	169	62	197
N₂O Production best case	20	75	29	85
N₂O Production worst case	24	92	35	105
Net PP increase best case	15	54	21	61
Net PP decrease worst case	45	179	65	209

For ship-based delivery of iron, there is nearly a 53-fold difference in the range of the $C sequestered between the overall best ($8/tonne) and worst ($452/tonne) case scenarios, while the best case and intermediate scenarios vary by about a factor of 3. The costs for plane-based delivery of Fe are uniformly less than for ship-based delivery, and there is a nearly 51-fold difference between best ($5/tonne) and worst ($257/tonne) case scenarios. Variation in ventilation losses due to mineralization of C_seq and differences in the total net primary production each accounted for approximately 3-fold differences between best- and worst-case scenarios, while differences in Fe solubility, C export efficiency, and offsets due to N₂O production accounted for smaller differences using the boundaries that were set in Table 1.

5 Discussion

It is our hope that the results of this cost modeling exercise can help guide research to better understand the major uncertainties that could limit the effectiveness of a large-scale OIF approach to CDR. As expected, the values for USD/tonne C sequestered ($C) show a large range (>100-fold) depending upon the values used for the underlying oceanographic parameters. The greatest impacts on cost are estimates for increases in NPP due to OIF, export efficiency, and ventilation. Our mechanistic understanding of both NPP and POC export are relatively well developed (Boyd et al., 2019; Iversen, 2023). NPP can be measured with accuracy using both space-based remote sensing and in situ sampling. Export efficiency is more challenging to measure accurately, especially over large spatial scales, but ship-based and autonomous platforms for this purpose are well developed for deployment. By comparison, the magnitude of the ventilation effect on OIF is also large, but substantially more challenging to quantify due to an incomplete understanding of the mechanistic factors that control ventilation (Morrison et al., 2022). This model thus affirms that research and testing the ways to quantify both export and especially ventilation are essential to determining the overall efficacy of OIF as a means of CDR. Overall, the results from this model are in concordance with an earlier cost model developed by Harrison. This is not surprising given that the base assumptions for this model are derived from Harrison's model, but now add in additional estimates for plane versus ship delivery, and some estimated costs for verification of CO₂ drawdown and environmental monitoring of OIF ecosystem impacts.

5.1 Iron Processing and Delivery

Ship versus Plane. In this cost model, we have purposefully devised a scenario for patch size, and delivery parameters that make cost comparisons between ship and aerially-based delivery as direct as possible. Based on this analysis, the cost per tonne C-sequestered ($C_net) is approximately 30% less for aircraft compared to ship. The carbon offset for aerial delivery is less than ship-based delivery (0.22% vs. 0.67%) for a best estimate, and relatively small in either case. The carbon offset for production of the iron used for OIF is difficult to assess precisely, as discussed above, since it will depend on the form of iron deployed, but due to the relatively small total iron requirement, the processing C offset is likely to be <1% of export.

While the cost model intentionally makes the most direct comparison possible between ship-based and plane-based delivery, in actuality, there are substantial differences in these delivery modes, as well as their associated Fe-seeding strategies. Previous ship-based delivery has used acidified ferrous sulfate injected at the sea surface (Boyd et al., 2007). This delivers a concentrated form of iron effectively as a point source that is diluted and moved initially by vessel propwash, and then by sea-surface currents, wind, and wave action to a wider swath of the ocean. Plane-based delivery would deliver a more dilute iron source over an initially wider swath of ocean. The swath size will depend on aircraft altitude and speed, wind conditions, as well as reagent concentration and matrix. For this scenario, we projected that a dried powdered iron oxide will be used, and delivered from a Boeing 737, or equivalent jet aircraft, flying swath lines at 5 km intervals. It is likely aerial delivery will have more immediate impact on a larger area of the ocean than delivery from a ship. Aerial delivery can also be much faster, depending on the relative number of ships versus planes used, thus the same region could be seeded faster by plane, allowing for faster, or perhaps more synchronized, bloom development. Nonetheless, planes are weight-limited in how much Fe they can deliver per flight, and this can impact their efficiency. In the model, we have set the target Fe concentration (0.6 nM) as fixed, thus at lower Fe-solubilities, more flights are required to deliver enough Fe to achieve this concentration, due to this weight limitation. In addition, planes are also more likely to be subject to weather delays than ships.

The potential advantages of aerial delivery need to be balanced by the fact that aerial delivery of iron has not actually been done and will require a research and development phase to determine the most effective dispersal method(s). There is significant technical development around the delivery of agricultural chemicals and fire suppressants via aircraft, and aircraft-based cloud seeding efforts (Gentile et al., 2022). It is likely some of these technologies could be adapted for aerial delivery of iron; however, these are currently unknowns. The form of iron will also be important. It would be possible to use the same acidified iron solutions as have been used from ships; however, these do incur a significant weight penalty for aircraft, since the solution is only approximately 30% Fe. Alternatively powders of mineral iron oxide could also be used that would have higher Fe yields; however, it would need to be determined if these can be delivered as dried materials, similar to what is done for some fertilizers, or whether a wet slurry would be most effective. Any wetting agent used for aerial delivery will incur an additional weight penalty that increases delivery costs and reduces C_net. Other aspects of aerial delivery that need to be considered are photochemical reactions of the iron reagent in the atmosphere that could change its properties, this will be largely dependent on delivery height and residence time in the atmosphere (Ming et al., 2021). Another consideration is transfer of the material across the air-sea interface, which could be impacted by sea surface tension. Despite these caveats, aerial delivery has the potential to not only be less costly than ship-based delivery, but also generate less CO₂ offsets thus increasing C_net. Modeling efforts that specifically address critical factors associated with aerial delivery of iron, as well as experimental work on different matrices and photochemical reactions would be useful.

5.2 Verification and Environmental Impact Monitoring

The substantial monetary cost and CO₂ offset for verification is an important component of this cost model. Based on the model estimates, verification increases total costs for different scenarios by 3- to 4-fold. The verification strategy for this model exercise relies on a combination of ship-based verification measurements extending 60 days beyond the end of the last OIF application, as well as the use of autonomous devices. The first verification parameter is confirmation and tracking of bloom dynamics to assess overall increases in primary production. Surface characteristics of bloom dynamics, both intensity and duration, can be monitored by satellite, with ship-based measurements and Bio-Argo floats used to confirm satellite-based measurement and track chlorophyll with depth, as well as determine mixed layer depths. Verification of carbon export needs to take into account a dynamic process that is dependent on sinking depth of POC, remineralization processes, and ocean currents. Specific sets of Bio-Argo floats could be deployed that can monitor aspects of export, changes in pCO₂, O₂ and pH with depth, and other parameters for extended periods (Johnson et al., 2022). Ship-based surveys can deploy sediment traps to measure POC export, track Fe and macro-nutrient concentrations, measure N₂O production, as well as document changes in planktonic communities. A set of aerial overflights of the OIF fertilized patch could measure localized changes in air-sea gas exchange for CO₂, N₂O, methane, and dimethylsulfide (DMS); however, these have not been factored into the cost model. A significant challenge to verification is accounting for longer-term ventilation of CO₂ back to the atmosphere that occurs as a result of POC exported to shallow depths, for example, <500 m, being carried well away from the area being monitored, and then mineralized and re-equilibrated with the atmosphere within months to a few years. Specific to environmental monitoring, the emerging field of environmental DNA (eDNA) will be an invaluable tool (Taberlet et al., 2018), as well as use of autonomous devices that collect genetic and phenotypic data (Olson & Sosik, 2007; Ottesen et al., 2011) Continued development of autonomous sensors and sensor platforms could significantly reduce the amount of ship-time required for verification and environmental monitoring, which would likely reduce the relative cost.

5.3 Additional Implications and Impacts

We have not included the cost of modeling efforts that are tied to OIF applications. It will be essential to have a well constrained coupled physical - biogeochemical–atmospheric model of the chosen fertilized region that is run in real time. In the scenario envisioned, where three successive iron applications are proposed, the model can improve the timing and quantities of Fe used in successive applications, and help guide monitoring efforts, and likely reduce application costs. In addition, the model can provide estimates of carbon export and net CO₂ drawdown that can be physically verified. This linkage between model prediction and field measurement will make the model especially useful in refining further OIF applications. Such a model could be especially instructive regarding aerial applications since little is currently known about which key parameters, for example, delivery altitude, swath width, or speed, are most critical. Additionally, a well-constrained, field-tested model will be essential for verifying CO₂ drawdown, and predicting the overall success of continued deployment of OIF as means of mCDR.

One of the most significant negative impacts of large scale and long duration OIF efforts is the potential for nutrient stealing or nutrient robbing. Unused macronutrients in sub-Antarctic and Antarctic waters are exported to lower latitudes via Subantarctic Mode Water (SAMW) and Antarctic Intermediate Water (AAIW) (Marinov et al., 2006; Sarmiento & Orr, 1991; Sarmiento et al., 2004). Thus, more complete utilization of Southern Ocean nutrients resulting from OIF is likely to subsequently reduce productivity in lower latitudes (particularly equatorial waters). Due to the slow speed of the subsurface currents (decades to traverse from sub-Antarctic to equatorial waters (DeVries, 2014)) estimates of this process come from models (e.g., Gnanadesikan et al., 2003; Oschlies et al., 2010; Sarmiento & Orr, 1991) and estimate that productivity in waters north of 30˚S is decreased about 10% after implementation of long-term OIF at high latitudes. Tagliabue, Twinning et al. (2023) also predict a drop in low latitude productivity and suggest that these OIF-driven reductions in remote productivity may exacerbate productivity declines caused by climate change. The earlier cost model of Harrison (2013) included a nutrient steal parameter that resulted in a 10%–20% decrease in the effectiveness of OIF; however, due to the projected impacts of nutrient stealing being far removed, in time and space, as well as lack of empirical evidence, we have not included it in this cost model.

This cost model has not taken into account any potential positive GHG offsets, or beneficial aspects, of OIF. One provocative idea is that OIF carried out in the Southern Ocean could help restore the phytoplankton/krill/whale balance that is speculated to have been a significant contributor to marine CDR in this region as little as a century ago (Pearson et al., 2022; Smetacek, 2021.). This balance was upset due to humans harvesting most of the large baleen whales in the Southern Ocean. Contrary to expectations, the reduction in grazing pressure due to reduced whale populations has not resulted in a rebound in krill populations. Recent empirical data on large baleen whale feeding habits in the Southern Ocean, combined with estimates of how much iron these animals may have contributed to the surface ocean through defecation, make a compelling case that this so called “whale pump” helped maintain greater phytoplankton productivity and krill populations than exist today (Savoca et al., 2021). Determining if it is possible to artificially stimulate the whale pump through specific OIF efforts, and monitoring if there is a direct response of increased whale populations could be an excellent test case for OIF related to both ecosystem restoration, and nature-based CDR.

OIF-induced changes in primary productivity and planktonic community composition, are also likely to alter the extent and composition of trace gases and primary aerosols, including DMS, that exchange between the ocean and atmosphere. This could influence aerosol formation and development, cloud formation and albedo and the oxidative capacity of the atmosphere.

Finally, aerial delivery of Fe, depending upon how it is done, could induce photochemical reactions that enhance methane breakdown in the atmosphere (Ming et al., 2021; Oeste et al., 2017). At the present time, we consider any of these potential positive offsets or feedbacks to lack enough empirical evidence for inclusion in a cost model.

An additional limitation of this cost model is that it does not include any expenses related to meeting the regulatory/legal requirements that will need to be addressed to carry out application-based OIF operations (Scott, 2019). The costs necessary to comply with current legal norms associated with OIF will require preparing regulatory documentation, and instituting appropriate oversight for the effort. It is likely these compliance efforts will require a similar timeframe as the fundamental R&D development and planning for an OIF deployment, and will themselves incur a significant cost that is beyond the scope of this model to estimate.

6 Conclusions

This cost model estimates export costs of carbon in USD per tonne of C exported to depths in the ocean where it is stored for decadal to centennial timescales. Due to the relatively small amounts of Fe that are required to substantially increase net primary production in Fe-limited oceanic regions, costs can be <$10/tonne C exported; however, losses due to decreased export efficiency, increased remineralization of exported C, or offsets due to other greenhouse gas production, and other factors can increase costs >100-fold. This model directly compares ship-based Fe delivery to aerially-based Fe delivery to the ocean, and estimates aerial delivery can be 30%–40% less costly. We have also made initial estimates of the cost of verifying C export, and show that ship-centric verification may increase base costs by 3–4-fold, in most cases. The primary aim of this model is to provide direct cost estimates that can aid in development of a research agenda into the efficacy of OIF as a means of CDR. Based on our findings, the magnitude of losses in carbon export efficiency due to ventilation or remineralization of newly fixed biomass, as well as offsets due to N₂O production, are important oceanographic parameters that need to be better understood. Aerial delivery methods for iron maybe beneficial, but due to their novelty for OIF, need specific research and development efforts. Verification methods that primarily utilize autonomous sensors could reduce the need for expensive ship-based methods, but require further development and testing. Finally, oceanographic models localized to the area of OIF application could provide feedback that would maximize effectiveness, and lower the CDR costs of OIF.

Acknowledgments

We thank Dan Whaley for helpful discussions and providing access to a business model developed in the early 2000 s for Climos, Inc, that helped assess ship-costs and Fe-reagent costs related to OIF. We thank Dr. Ken Buesseler for comments on an earlier draft of this manuscript. We also appreciate the helpful comments of two anonymous reviewers. We are grateful for funding from the Grantham Trust for support for this effort.

Open Research

Data Availability Statement

The Excel file used for the model is available at Sofen et al. (2023).

Supporting Information

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Citing Literature

Volume12, Issue4

April 2024

e2023EF003732

A Cost Model for Ocean Iron Fertilization as a Means of Carbon Dioxide Removal That Compares Ship- and Aerial-Based Delivery, and Estimates Verification Costs

Abstract

Key Points

Plain Language Summary

1 Introduction

2 Model Development

2.1 Model Considerations

3 Description of Model Components

3.1 Fe Availability, Processing, and Delivery

3.1.1 Fe Solubility

3.1.2 Fe Production and Processing

3.1.3 Fe Delivery

3.2 Negative Offsets

3.2.1 Ventilation

3.2.2 N2O Production

3.3 Verification Costs

3.3.1 Verification Costs

3.3.2 Environmental Impact Assessment (EIA) Costs

3.4 Additional Oceanographic or Biogeochemical Factors

4 Results

4.1 Base Estimates

4.2 Estimates Including Losses and Offsets

5 Discussion

5.1 Iron Processing and Delivery

5.2 Verification and Environmental Impact Monitoring

5.3 Additional Implications and Impacts

6 Conclusions

Acknowledgments

Open Research

Data Availability Statement

Supporting Information

References

Citing Literature

Figures

References

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PUBLICATION INFO

3.2.2 N₂O Production