Guidance on integrating marine environmental impacts of marine technologies into life cycle assessment — Application to ocean alkalinity enhancement

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Delval, Patrik J.G. Henriksson, Paul Behrens, Laura Scherer, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9573704/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Human activities in the ocean are putting growing pressure on marine ecosystems. Life cycle assessment (LCA) is used to assess these activities environmentally but faces limitations in capturing marine impacts. Improving LCA requires a detailed understanding of the marine environment impact pathways of these technologies to develop sub-compartmentalised and regionalised characterisation factors (CFs). We demonstrate how such pathways can be identified, illustrating with ocean alkalinity enhancement (OAE). Methods: We build on Woods et al. (2021), who propose a qualitative framework to identify key components of impact pathways, and Richter et al. (2024), who provide guidance on framework development in a multidisciplinary context. We develop a methodological approach that allows to qualitatively identify the marine environmental impact pathways of marine technologies and determine which components are integrated in LCIA models or missing, as an initial phase toward developing CFs for life cycle impact assessment (LCIA). We then apply the approach to OAE as an illustrative case. Results and Discussion: Our methodological approach includes: (1) the selection of literature on a studied marine technology and its impacts on marine ecosystems; (2) the identification of the marine elementary flows in the life cycle inventory and the fate, exposure, and effect processes; (3) the inputs from LCIA, oceanography, and technology experts; (4) the review of LCIA models to examine which elements of the marine environmental impact pathways are represented or lacking; (5) the definition of research priorities to advance the assessment of the marine technology’s environmental impacts within LCA. We identified three impact categories associated with OAE marine environmental impacts pathways: marine ecotoxicity, marine eutrophication, and ocean acidification. Existing LCIA models only partially capture these pathways and require adaptation for assessing comprehensively OAE. Research priorities include conducting additional experiments on the ecotoxicological and eutrophic effects of OAE deployment in marine environments, and the effect of added alkalinity on a broader range of marine calcifiers. Several of our recommendations are also relevant to enhance marine technologies’ assessments in LCA more broadly, such as improving the ocean’s representation in models, modelling direct release to the marine compartment, and broadening the elementary flows’ coverage for marine eutrophication. Conclusion: We present a methodology to identify marine environmental impact pathways of marine technologies, providing a first phase toward developing CFs. The methodological approach can be applied to other marine technologies, where identifying impact pathways require a multidisciplinary approach that combines the LCA field with oceanography and engineering expertise. Marine and Freshwater Ecology Environmental Engineering LCA LCIA impact assessment impact pathway modelling OAE marine carbon dioxide removal mCDR Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Whereas humanity has long relied on the ocean, growing global consumption, depletion of terrestrial resources, and declining land availability have, in recent decades, led to the diversification, intensification and expansion of human activities into marine environments, a phenomenon termed “the Blue Acceleration” (Jouffray et al. 2020 ). As a result, anthropogenic pressures on marine ecosystems have become more widespread and severe, driving the degradation and, in some cases, collapse of these ecosystems (Halpern et al. 2019 ). Mitigating these stressors and their impacts is thus of utmost importance and require first a clear understanding of their nature and magnitude. Life cycle assessment (LCA) is used to evaluate the environmental performance of marine technologies (Philis et al. 2019 ; Alvarenga et al. 2022 ; Babakhani et al. 2022 ). Here, we define marine technologies as all existing or future technologies designed to be deployed in or on marine environments and that are directly interacting with the ocean. Such technologies are for example mariculture, maritime shipping, offshore wind farms, marine carbon dioxide removal (mCDR) approaches, submarine cables, desalination plants, and deep-sea mining (Jouffray et al. 2020 ). However, critical limitations remain in the LCA methodology to assess impacts of such technologies on marine environments (Woods et al. 2016 ). Life cycle impact assessment (LCIA) method families (e.g., CML, ReCiPe, or TRACI) traditionally focused on impacts on terrestrial and freshwater ecosystems, and while they have begun to address impacts on marine ecosystems over the last decades (Huijbregts et al. 2000 ; Dong et al. 2016 ; Verones et al. 2020 ), these impacts remain underrepresented. Currently, the most common LCIA method families include up to three impact categories assessing marine environmental impacts: marine eutrophication, marine ecotoxicity, and marine acidification, all of which leading to ecosystem damage. These categories are insufficient to capture the full range of marine environmental effects of marine technologies. For instance, seabed damage caused by deep-sea carbon storage (Rastelli et al. 2016 ), biotic resource depletion due to overfishing (Emanuelsson et al. 2014 ), and marine noise pollution from maritime shipping (Chahouri et al. 2022 ) cannot be assessed in these categories. Additionally, LCIA modelling may fail to adequately account for some important considerations of marine environmental impacts caused by marine technologies. For instance, only nitrogen (N) flows are characterised in the marine eutrophication impact category, whereas other nutrients released by these technologies could contribute to marine eutrophication, such as iron (Fe) released by ocean iron fertilisation (Williamson et al. 2012 ). A further limitation in LCIA models is their spatial resolution of the ocean. These models simplify the ocean by only considering coastal seas (Struijs et al. 2009 ) or by only distinguishing coastal seawater and three climate zones (Huijbregts et al. 2000 ; Van Zelm et al. 2009 ), which substantially neglects its spatial heterogeneity and depth dimension. While more recent models better account for this heterogeneity by distinguishing dozens of different large marine ecosystems (LMEs) (Dong et al. 2016 , 2018 ; Cosme et al. 2018 ), these LMEs are constrained to continental shelves (Sherman and Donnelly 2026 ), thus remaining unsuitable for assessing impacts of technologies deployed in the open ocean. To enable more comprehensive evaluations of marine technologies’ environmental performance, it is essential to understand the impact pathways through which these technologies affect marine environments. Additionally, the spatial context in which these processes occur must be considered regionally and by dividing the marine compartment into relevant sub-compartments that reflect the ocean’s spatial variability, as proposed in similar work for marine microplastic impacts (Hajjar et al. 2024 ). Ultimately, integrating these pathways may require the development of new or improved marine impact category indicators that incorporate characterisation factors (CFs) specific to the relevant marine regions and sub-compartments (Delval et al. 2025 ). In this work, we aim to develop a methodological approach that guides LCA practitioners in: (i) identifying marine environmental impact pathways of marine technologies, (ii) understanding which components of these pathways are already covered in current LCIA models, and (iii) determining the research needs to integrate comprehensively these pathways in LCIA. Identifying these pathways represents the basis for developing sub-compartmentalised and regionalised CFs for marine impact categories relevant to marine technologies. Overall, our contribution seeks to improve the representation of marine environmental impacts of marine technologies within LCIA. To showcase its practical relevance, we apply our developed approach to a specific marine technology: ocean alkalinity enhancement (OAE). Interest in mCDR has grown substantially due to its potential to complement deep emissions cuts to meet climate targets, with OAE regarded as one of the most promising options (Smith et al. 2024 ; De Pryck and Boettcher 2024 ; Oschlies et al. 2025 ). This emerging mCDR approach aims to enhance carbon dioxide (CO 2 ) uptake from the atmosphere by the ocean through intentional addition of alkaline materials to seawater (Renforth and Henderson 2017 ; Oschlies et al. 2025 ). While currently being tested at the field level, OAE could enable the removal of billions of tonnes of CO 2 annually at scale (He and Tyka 2023 ; Ringham et al. 2024 ). While two LCA case studies have assessed the environmental impacts of the technology’s supply chain (Foteinis et al. 2022 , 2023 ), its potential effects on marine environments continue to be an active area of research. 2. Method In the LCIA phase, elementary flows (i.e., matter or energy inputs extracted from the environment without previous human processing or outputs released into the environment without subsequent human processing) identified in the life cycle inventory (LCI) phase are translated into environmental impact scores using CFs. These CFs are derived from characterisation models, which are mathematical representations of impact pathways (also called cause-effect chains). Impact pathways describe the mechanisms connecting elementary flows to damages affecting areas of protection (AoP). These AoP in LCA are the natural environment, human health, and resources and ecosystem services (Hauschild and Huijbregts 2015 ). Impact pathways often consist of three types of processes: 1) fate processes, which describe the spatial distribution, residence time, and chemical behaviour of a substance released into the environment; 2) exposure processes, which describe how sensitive receptors (e.g., receptors likely to suffer negative effects, such as marine species and ecosystems) get exposed to the substance; and 3) effect processes, which assess the effects to AoP resulting from this exposure (Woods et al. 2021 ). Here, we aim to develop a methodological approach that allows to qualitatively identify these processes for marine technologies’ marine environmental impacts and inform on knowledge gaps and research priorities for their integration in LCIA models. The starting point to improve assessment of marine environmental impacts in LCIA is often a marine impact category not yet covered by existing LCIA method families, such as ocean acidification (Anderson et al. 2025 ) or marine litter (Hajjar et al. 2025 ). While these efforts are essential for advancing LCA, we, however, opt for an approach that starts at the marine technology level and its elementary flows to ensure that all potential marine environmental impacts associated with that technology are comprehensively identified and integrated into LCIA models. Such an approach would complement existing efforts in LCIA modelling to allow a comprehensive assessment of marine technologies and their marine environmental impacts in LCA. Our approach builds on Woods et al. ( 2021 ), who conducted a similar study on impact pathways for marine plastic litter. They propose a framework that qualitatively identifies the key components of marine litter’s impact pathways, which is an important first step in coordinating future efforts in LCIA development. Their approach also highlights the importance of spatial differentiation and identification of elementary flows. However, the methodology used to construct their framework is not described in detail, limiting its replicability for other technologies or impact categories. Clear methodological guidance is particularly important for impact pathway identification, which typically requires involving experts from multiple backgrounds, such as LCA practitioners, industrial experts, and environmental biologists. For framework development in a multidisciplinary context, the systematic methodology from Richter et al. ( 2024 ) provides valuable insights. Therefore, we adapted the methodological step-by-step approach of Richter et al. ( 2024 ) to the context of environmental impact pathways’ development and combined it with the framework by Woods et al. ( 2021 ) for identifying key components of impact pathways and knowledge gaps for their integration in LCIA. We then applied our developed approach to OAE to evaluate the usefulness of this approach. 3. Results 3.1. Methodological approach for the identification and integration of marine technologies’ marine environmental impacts in life cycle assessment We developed a novel methodological approach that allows LCA practitioners to qualitatively identify the key processes for marine technologies’ marine environmental impact pathways and to determine the current knowledge gaps and research priorities for integrating these pathways in LCIA models. This is an initial phase laying the groundwork for future efforts to build fate, exposure, and effect models from which corresponding fate, exposure, and effect factors and ultimately CFs can be derived. Our methodology (Fig. 1) consists of five steps: (1) selecting relevant oceanography and geochemistry literature on the studied marine technology and its impacts on marine ecosystems; (2) identifying the main components of the marine technology’s marine environmental impact pathways (e.g., the relevant marine elementary flows in the LCI, as well as their associated fate, exposure, and effect processes); (3) revising the identified marine environmental impact pathways through review by experts, with expertise in either LCIA, oceanography, or the studied technology; (4) reviewing the LCIA modelling literature to assess which aspects of the identified marine environmental impact pathways are represented in existing LCIA models and which remain absent; (5) defining research priorities to enhance the assessment of the marine technology within LCIA. 3.2. Application of the methodological approach to ocean alkalinity enhancement We applied the five steps of the developed methodological approach to OAE. A detailed technical description of OAE is given in the supplementary information (SI, Section A). Given our focus on marine environmental impacts, we limited our analysis to impact pathways for substances released into the marine environment by OAE (Fig. 2), and we focused on marine species and ecosystems as the sensitive receptor. Details on impact pathways for humans and structures with instrumental or cultural value to humans, considered sensitive receptors, are provided in the SI (Section B). 3.2.1. Step 1: Selection of relevant literature We reviewed the relevant oceanography and geochemistry literature on OAE and its impacts on marine species and ecosystems, using Scopus and Web of Science. We used a combination of broad search terms, such as ‘ ocean alkalinity enhancement ’, ‘ alkaline mineral ’, and ‘ marine impacts ’, alongside more specific terms related to OAE technologies, including ‘ olivine ’ and ‘ trace metals ’, and our literature review includes publications available up to August 31st, 2025. From the resulting search results (> 200 scientific articles), the most cited or the most relevant articles based on the abstract were selected. Some additional articles were found as cited in the selected literature or recommended by co-authors with expertise in oceanography and OAE. Lastly, one article was suggested by a reviewer during the reviewing process of the manuscript. In total, approximately 75 scientific articles were selected for Step 2. 3.2.2. Step 2: Identification of the marine environment impact pathways From the selected literature, we identified the key elements for the marine environmental impact pathways of OAE. These include elementary flows in the LCI released to the marine environmental compartment, the fate processes, the exposure processes, and the effect processes. From these identified processes, the impact pathways were developed. Identification of marine elementary flows For OAE, the main exchange between the product system and the marine environment is the intentional addition of alkaline material to the ocean to provide carbon dioxide removal (CDR) (NASEM 2022 ; Eisaman et al. 2023 ; Oschlies et al. 2025 ). Depending on the specific OAE approach, alkaline material is typically added to the open ocean at the sea surface or to coastal waters (Renforth and Henderson 2017 ; He and Tyka 2023 ; Lindland et al. 2025 ), and there have been suggestions to directly add it onto marine sediments in coastal areas or offshore (Fuhr et al. 2023 , 2025 ; Dale et al. 2024 ). OAE can use various alkaline materials (Table 1 ). Quicklime (CaO) or hydrated lime (Ca(OH) 2 ) obtained from limestone have been widely studied (Caserini et al. 2022 ; Moras et al. 2022 ; Kowalczyk et al. 2024 ). Other alkaline materials can also be used, such as pulverised silicate minerals (e.g., olivine), pulverised carbonate minerals (e.g., calcite (CaCO 3 )), waste or by-product materials obtained from human activities (e.g., steel slags), or magnesium hydroxide (Mg(OH) 2 ) (Bach et al. 2019 ; Renforth et al. 2022 ; Yang et al. 2023 ). Bases (e.g., sodium hydroxide (NaOH)) isolated from pumped seawater or brine are also potential candidates (Ringham et al. 2024 ; Oschlies et al. 2025 ). Table 1 Types of alkaline materials used in different alkalinity enhancement methods, along with their corresponding deployment (i.e., addition) sites. Alkaline material Method Location References CaO, Ca(OH) 2 Ocean liming Open ocean at the mixed surface layer Oschlies et al. ( 2025 ) Coastal enhanced weathering (CEW) Coastal waters, eventually in wastewater treatment plant outflows Campbell et al. ( 2022 ); Oschlies et al. ( 2025 ) Silicate rocks and minerals (e.g., olivine, dunite, basalt), carbonate rocks and minerals (e.g., CaCO 3 , ikaite), anthropogenic material (e.g., steel slag), Mg(OH) 2 OAE using other alkaline materials Open ocean at the mixed surface layer Bach et al. ( 2019 ); Renforth et al. ( 2022 ); Oschlies et al. ( 2025 ) CEW using other alkaline materials Coastal waters Bach et al. ( 2019 ); Campbell et al. ( 2022 ); Renforth et al. ( 2022 ); Oschlies et al. ( 2025 ) CaCO 3 Enhanced benthic weathering (EBW) Onto marine sediments, coastal or offshore Fuhr et al. ( 2023 , 2025 ); Dale et al. ( 2024 ). Bases (e.g., NaOH) Electrochemical approaches Coastal waters Ringham et al. ( 2024 ); Oschlies et al. ( 2025 ) Additional elementary flows may enter the marine environment through alkaline material addition (Table 2 ). The dissolution of some alkaline materials, such as steel slag or olivine, can release trace metals, with the types and quantities varying depending on the composition and purity of the alkaline mineral (Renforth and Henderson 2017 ; Guo et al. 2024 ). Additionally, essential nutrients and ions may be co-released (Bach et al. 2019 ). Table 2 Potential co-released elements during the dissolution of different alkaline materials. The elements listed are among the most reported in the ocean alkalinity enhancement (OAE) literature; however, this list may not be exhaustive, and other elements could be co-released as new alkaline materials are explored for OAE. Potential co-released element Alkaline material References Trace metals Aluminium (Al) Steel slag Moras et al. ( 2024 b) Cobalt (Co) Olivine, steel slag Moras et al. ( 2024 b); Zhuang et al. ( 2025 ) Chromium (Cr) Olivine, steel slag Bach et al. ( 2019 ); Moras et al. ( 2024 b); Zhuang et al. ( 2025 ) Copper (Cu) Steel slag Moras et al. ( 2024 b) Iron (Fe) CaO, Ca(OH) 2 , Mg(OH) 2 , olivine, steel slag Mayes et al. ( 2008 ); Renforth and Henderson ( 2017 ); Bach et al. ( 2019 ); Dupont and Metian ( 2023 ); Guo et al. ( 2024 ) Manganese (Mn) Steel slag Guo et al. ( 2024 ); Moras et al. ( 2024 b) Molybdenum (Mo) Steel slag Mayes et al. ( 2008 ); Bach et al. ( 2019 ) Lead (Pb) Steel slag Bach et al. ( 2019 ); Moras et al. ( 2024 b) Nickel (Ni) Some silicate rocks and minerals (e.g., olivine, dunite), some carbonate rocks and minerals (e.g., CaCO 3 ), steel slag (Montserrat et al. 2017 ; Bach et al. 2019 ; Flipkens et al. 2021 ; Guo et al. 2024 ; Moras et al. 2024 ) Vanadium (V) Steel slag Mayes et al. ( 2008 ) Zinc (Zn) Steel slag Moras et al. ( 2024 b) Essential nutrients and ions Calcium ion (Ca 2+ ) CaO, Ca(OH) 2 , some carbonate rocks and minerals (e.g., CaCO 3 and ikaite), silicate rocks and minerals, steel slag Bach et al. ( 2019 ); Dupont and Metian ( 2023 ); Eisaman et al. ( 2023 ); Guo et al. ( 2024 ) Dissolved silicate (DSi) CaO, Ca(OH) 2 , Mg(OH) 2 , silicate rocks and minerals, steel slag Renforth and Henderson ( 2017 ); Bach et al. ( 2019 ); Guo et al. ( 2024 , 2025 ) Magnesium ion (Mg 2+ ) CaO, Ca(OH) 2 , carbonate rocks and minerals, Mg(OH) 2 , some silicate rocks and minerals (e.g., olivine, basalt), steel slag Dupont and Metian ( 2023 ); Eisaman et al. ( 2023 ); Guo et al. ( 2024 ) Phosphate (PO 4 3− ) Steel slag Guo et al. ( 2024 , 2025 ) Identification of fate processes Alkaline material and its co-released elements move away from the location of addition through four transport mechanisms: diffusion, advection, mixing, and sinking. Diffusion is the transport of molecules driven by concentration gradients, occurring slowly and at small spatial scales. Transport by ocean circulation includes (i) advection, which redistributes material through current-driven flow, and (ii) turbulent mixing, generated by winds, waves, and shear, which enhances dispersion and dilution. Sinking refers specifically to downward transport to deep ocean layers. These processes can occur in any oceanic zone depending on the location of addition (Bach et al. 2019 ). Another important fate process is the dissolution of the alkaline material. Addition in the open ocean results in dissolution within the mixed surface layer (Kowalczyk et al. 2024 ), whereas addition in coastal waters results in partial dissolution in seawater and in pore waters from coastal sediments (Mendes et al. 2025 ). Lastly, after solid alkaline material settles from the water column into the sediments, it can become buried. When the material is added directly onto sediments, most is buried and remains undissolved (Dale et al. 2024 ). In the SI (Section C), we describe for each fate process its effect on the residence time of the alkaline material and identify the main factors influencing the process. However, alkaline material fate in seawater has been mostly studied in controlled laboratory environments, which do not reflect natural conditions (Meysman and Montserrat 2017 ; Flipkens et al. 2023a ). More in situ experiments are needed to increase our understanding of these fate processes. Identification of exposure processes Marine species and ecosystems are expected to be primarily exposed to the changes in ocean chemistry induced by alkalinity addition, which include an increase in pH and a decrease in seawater partial pressure of CO 2 (Bach et al. 2019 ; Ocean Visions 2022). These chemical changes would be temporary but likely pronounced adjacent to the location of addition, depending on the speed at which seawater mixes and re-equilibrates with the atmosphere (Renforth and Henderson 2017 ; Bach et al. 2019 ; Liu et al. 2025 ). Another exposure process could be via unintended ingestion of alkaline material by grazers and filter feeders, thereby also getting exposed to the potentially contained trace metals and essential nutrients and ions (Harvey 2008 ; NASEM 2022 ; Flipkens et al. 2023b ). In particular, trace metals can biomagnify through the food chain and reach higher trophic levels (Bach et al. 2019 ). Lastly, alkaline material added could increase turbidity, which would affect light availability in the photic zone as well as induce a ‘white water’ phenomenon that could impair predators’ visibility, such as fish and seabirds (NASEM 2022 ), but research on this visual exposure route is lacking. Identification of effect processes Changes in ocean chemistry from added alkalinity could lead to toxic effects on marine species. For instance, the temporary but substantial shift in the carbonate system combined with CO 2 reduction is likely to affect primary producers like phytoplankton with potential cascading effects on entire marine ecosystems (Renforth and Henderson 2017 ). However, recent research shows mostly insignificant effect post-alkalinity addition on pelagic low-trophic-level species (Table 3 ). Overall, these results indicate relatively good physiological tolerance of these species to added alkalinity as well as good community resistance at these trophic levels. Table 3 Ocean alkalinity enhancement experiments on the effects of added alkalinity on marine species and ecosystems. Reference Alkaline material Experiment Species group Effects* Antoni et al. ( 2025 ) Ca(OH) 2 Field, mesocosm Pelagic, bacterial community No effect on community structure under unequilibrated** moderate and intense OAE. Bhaumik et al. ( 2025 ) Ca(OH) 2 Laboratory Pelagic, zooplankton copepods ( Temora longicornis ) Negative effect on respiratory and digestion rates as well as negative effect on phytoplankton prey ( Rhodomonas salina ) availability but positive effect on prey nutritional quality, hence effect on predator considered to be mitigated, under unequilibrated and varying intensity of OAE. Britton et al. ( 2025 ) NaOH Laboratory Benthic, kelp ( Ecklonia radiata ) No effect on physiology across life stages under unequilibrated and varying intensity of OAE, except a negative effect on growth rate on all life stages under unequilibrated intense OAE. Camatti et al. ( 2024 ) Ca(OH) 2 Laboratory Pelagic, copepod ( Acartia tonsa ) No effect on physiology under short-term exposure at unequilibrated moderate OAE. Negative effect on survival rate under long-term exposure at unequilibrated intense OAE. De Castro et al. ( 2025 ) CaO Laboratory Pelagic, phytoplankton and bacterial community Negative effect on development but no effect on community composition for phytoplankton, order-specific effect abundance but no effect on community composition for bacteria under unequilibrated moderate OAE. Delacroix et al. ( 2024 ) Ca(OH) 2 Laboratory Pelagic, phytoplankton ( Tetraselmis suecica ) Negative effect on survival rate under unequilibrated and equilibrated moderate OAE. Mg(OH) 2 No effect on growth and survival rate under unequilibrated and equilibrated moderate OAE. Gately et al. ( 2023 ) Na 2 CO 3 + CaCl 2 H 4 O 2 Laboratory Pelagic, phytoplankton calcium carbonate producer ( Emiliana huxleyi ) and silica producer ( Chaetoceros sp.) No effect on physiology under equilibrated moderate and intense OAE. Goldenberg et al. ( 2024 ) Ca(OH) 2 Field, mesocosm Pelagic and demersal, juvenile fish ( Clupea harengus, Gadus morhua ) No effect on growth and survival rate, positive effect on biomass under unequilibrated intense OAE. Gore et al. ( 2019 ) Na 2 CO 3 Laboratory Benthic, red calcifying algae ( Corallina spp.) Small effect on primary productivity and respiration rates, and photophysiology under unequilibrated moderate OAE. Guo et al. ( 2025 ) NaOH Laboratory, ship-based mesocosm Pelagic, phytoplankton community Small effect on physiology under unequilibrated moderate OAE. Jones et al. ( 2025 ) NaOH Laboratory Benthic, gastropod mollusk ( Phyllaplysia taylori ) and isopod ( Idotea resecata ) Negative effect on survival rate under unequilibrated intense OAE. Kousoulas et al. ( 2025 ) NaOH Laboratory, microcosm Phytoplankton and zooplankton community Effect on community composition and delayed bloom under unequilibrated intense OAE, but considered small compared to climate benefit. NaOH + NaHCO 3 Small effect on community composition under equilibrated moderate OAE. Marín-Samper et al. ( 2024 ) NaHCO 3 + Na 2 CO 3 Field, mesocosm Pelagic, phytoplankton community No effect on community metabolism or composition under equilibrated moderate and intense OAE. Positive effect on primary production under equilibrated moderate OAE. Nocera et al. ( 2025 ) Ca(OH) 2 Laboratory, mesocosm Pelagic, zooplankton community Small effect on species abundance and community structure under short-term exposure at unequilibrated moderate and intense OAE. Oberlander et al. ( 2025 ) NaOH Laboratory Pelagic, phytoplankton ( Thalassiosira pseudonana, Diacronema lutheri ) Small effect on viability and growth rates under short-term exposure at unequilibrated intense OAE. Negative effect on growth rate under long-term exposure at unequilibrated intense OAE. Paul et al. ( 2025 ) NaHCO 3 + Na 2 CO 3 Field, mesocosm Pelagic, phytoplankton community No effect on biomass nor bloom occurrence and magnitude, as well as no effect on most biogeochemical pools under short-term and long-term exposure at equilibrated moderate and intense OAE. Ramírez et al. ( 2025 ) NaHCO 3 + Na 2 CO 3 Field, mesocosm Pelagic, phytoplankton community Small effect on physiology under equilibrated moderate and intense OAE. Sánchez et al. ( 2024 ) NaHCO 3 + Na 2 CO 3 Field, mesocosm Pelagic, zooplankton community Negative effect on reproduction and productivity rates under short-term exposure at equilibrated moderate and intense OAE, probably trophically mediated. Subhas et al. ( 2022 ) NaHCO 3 , Na 2 CO 3 Field, microcosm incubation Pelagic, microbial community Small effect on biological calcification and net primary production rates under equilibrated moderate and intense OAE. Traboni et al. ( 2025 ) Ca(OH) 2 Laboratory, mesocosm Pelagic, plankton community Small effect on community composition and survival rates and no effect on growth and grazing rates under unequilibrated moderate and intense OAE. Xin et al. ( 2024 ) \(\:\text{H}\text{C}{\text{O}}_{3}^{-}+\text{C}{\text{O}}_{3}^{2-}\) Field, mesocosm Pelagic, phytoplankton and microzooplankton community No effect on abundance and biomass under equilibrated moderate and intense OAE. * Effects are noted as small when the study reported them as noticeable but statistically non-significant. Negative effects refer to an adverse response (e.g., reduced survival growth or community productivity), whereas positive effect indicates a beneficial response (e.g., enhanced growth, biomass, or productivity) relative to the control. ** Equilibrated: seawater has reached chemical balance with atmospheric CO 2 . Unequilibrated: seawater is still exchanging CO 2 with atmosphere and has not yet reached balance. Another important effect of OAE-driven ocean chemistry shift and associated pH increase is the counteraction of ocean acidification (Hartmann et al. 2023 ). Mitigating acidification would facilitate the calcification mechanism and thus benefit calcifying organisms (Bach et al. 2019 ). Nevertheless, a minority of calcifying species may experience adverse effects from alkalinity addition (Bednaršek et al. 2025 ). Enhanced calcification from OAE can also trigger unintended calcium carbonate (CaCO 3 ) precipitation, a process that consumes alkalinity and releases CO 2 into surrounding waters (Moras et al. 2022 ). The primary consequence is a reduction in CDR efficiency (Hartmann et al. 2023 ), but it may also exacerbate ocean acidification and cause turbidity (Ringham et al. 2024 ; Jones et al. 2025 ). Additionally, co-released OAE products can cause toxic effects on marine species and ecosystems. While trace metals naturally occur in seawater at low concentrations, they may become harmful when present at elevated levels (Kwong 2024 ). These metals can bioaccumulate in organisms and biomagnify through the food chain (Anandkumar et al. 2018 ; Bach et al. 2019 ). Recent studies have begun to explore these toxic effects (Table 4 ). Beyond trace metals, co-released nutrients and ions may also cause toxic effects when present in high amount. For example, calcium ion (Ca 2+ ) release in quicklime-based OAE can be harmful, as keeping low intracellular Ca 2+ is essential but metabolically costly (Bach et al. 2019 ). Table 4 Ocean alkalinity enhancement experiments on the effects of co-released products on marine species and ecosystems. Reference Experiment Species group Alkaline material Effects Ferderer et al. ( 2024 ) Field, mesocosm Plankton community MgCl 2 ·6H 2 O + Na 2 SiO 3 ·5H 2 O Fertilisation of diatom community due to nutrient release (DSi). Flipkens et al. ( 2023b ) Laboratory Benthic, amphipod ( Gammarus locusta ) Olivine Negative effect on survival, growth, and reproduction rates due to trace metal bioaccumulation (Cr and Ni). Goldenberg et al. ( 2024 ) Field, mesocosm Pelagic and demersal, juvenile fish ( Clupea harengus, Gadus morhua ) Olivine No effect on growth and survival rates due to nutrient release (DSi). Guo et al. ( 2024 ) Laboratory, microcosm Plankton community Steel slag Limited effect on abundance and community composition due to nutrient ( \(\:\text{P}{\text{O}}_{4}^{3-}\) and Si) and trace metal (Mn and Fe) release. Olivine Positive effect on abundance and effect on community composition due to nutrient (DSi) and trace metal (Fe and Ni) release. Guo et al. ( 2025 ) Laboratory, ship-based mesocosm Pelagic, phytoplankton community NaOH No nutrient and limited trace metal release and no effect observed. Steel slag Positive effect on growth rate for nano- and microeukaryotes while negative effect on survival rate in another phytoplankton group ( Prochlorococcus ) due to high trace metal release (Al, Fe, and Mn), overall altering community composition. Nutrient release ( \(\:\text{P}{\text{O}}_{4}^{3-}\) and Si) also observed. Olivine Negative effect on survival rate in some phytoplankton groups ( Prochlorococcus, Synechococcus , and picoeukaryote) due to high trace metal release (Al, Co, Cu, Mn, and Ni). Nutrient release (DSi) also observed. Hutchins et al. ( 2023 ) Laboratory Pelagic and benthic, phytoplankton ( Nitzschia, Ditylum, Emiliana, Trichodesmium, Crocosphaera, Synechococcus ) Olivine Small effect on physiology, positive effect on growth rate for some taxa in specific conditions due to nutrient release (Fe and DSi). Jankowska et al. ( 2024 ) Laboratory Benthic, invertebrate amphipod ( Leptocheirus plumulosus, Eohaustorius estuarius ), polychaete ( Neanthes arenaceodentata, Alitta virens ), bivalve ( Macoma nasuta ) Olivine No effect on survival and growth rates due to trace metal accumulation (Ni). (Li et al. 2024 ) Laboratory Pelagic, phytoplankton ( Gephyrocapsa oceanica, Thalassiosira, pseudonana, Phaeodactylum tricornutum ) Olivine Positive effect on growth for highly silicified diatoms ( T. pseudonana ) due to nutrient release (DSi). Figure 1 Overview of the methodological approach. Adapted from Woods et al. ( 2021 ) and Richter et al. ( 2024 ). The phases of the approach are in dark blue, the required elements for each phase in grey, and the outcome of each phase in light blue. Figure 2 Schematic representation of the general ocean alkalinity enhancement (OAE) life cycle. We look specifically at the marine environmental impacts resulting from alkaline material addition to the ocean. Figure 3 Marine environmental impact pathways of ocean alkalinity enhancement (OAE). Solid arrows indicate established linkages, while dashed arrows represent uncertain or currently under-researched connections based on the present state of knowledge. (Note that a visual exposure process has been proposed in the scientific literature as explained in Section 3.2.2, but it has not yet been linked to any effect processes due to insufficient research.) Co-released products from OAE can also drive eutrophication by enriching the local environment, giving more sensitive phytoplankton species a growth advantage and potentially leading to bloom and alteration of communities’ composition. For instance, diatoms, often limited by dissolved silicate (DSi), can benefit from DSi-based OAE, potentially shifting phytoplankton communities toward diatom dominance (Bach et al. 2019 ; Oschlies et al. 2025 ). Such eutrophication effects have been mainly observed in studies involving steel slag and olivine (Table 4 ). Nonetheless, OAE effects on marine species remain insufficiently understood, prompting scientists to call for further research. This includes studies on higher trophic levels, such as fish (Ferderer et al. 2022 ), and on other species groups, like calcifiers (Sánchez et al. 2024 ), at the community level (Britton et al. 2025 ), and across different marine biomes (Xin et al. 2024 ), including benthic environments (Jones et al. 2025 ). Future experiments should also examine seasonal variability (Ren et al. 2025 ), varying OAE technologies and intensities, and unequilibrated conditions following OAE application (Flipkens et al. 2023b ; Sánchez et al. 2024 ). Long-term studies are particularly important for capturing multi-generational effects (Bhaumik et al. 2025 ). Lastly, more research is needed on the effects of trace metal co-release (Xin et al. 2024 ; Zhuang et al. 2025 ). Marine environmental impact pathways From the information on the LCI marine elementary flows and the fate, exposure, and effect processes, we identified the pathways of OAE’s marine environmental impacts (Fig. 3). The identified pathways can be associated with three marine impact categories: marine ecotoxicity, marine eutrophication, and ocean acidification. The impact pathways for each impact category are presented individually in the SI (Section D). 3.2.3. Step 3: Review of the marine environment impact pathways The impact pathways derived from the literature were subsequently completed and revised through internal review by co-authors with expertise in either LCIA or oceanography and OAE, and through feedback from external OAE experts during a workshop held at the general assembly of the EU-funded SEAO2-CDR (Strategies for the Evaluation and Assessment of Ocean-based Carbon Dioxide Removal) project, a European Union’s Horizon Europe research programme on mCDR approaches, in June 2025. The group of experts was selected based on the relevance of their expertise for this topic, either in LCA ( n = 6 ) or in oceanography and OAE ( n = 8 ). 3.2.4. Step 4: Analysis of the marine environmental impact pathways integration into life cycle impact assessment models We reviewed the LCIA modelling literature to determine which components of the identified OAE marine environmental impact pathways are already represented in existing LCIA models, and which remain unaddressed. This was done for each marine impact category identified as relevant to OAE: marine ecotoxicity, marine eutrophication, and ocean acidification. We examined LCIA method families as implemented in ecoinvent (v3.11), as this is the most widely used LCI database for OAE LCA case studies (Delval et al. 2025 ), as well as other method families not included in ecoinvent and characterisation models that have not yet been incorporated into any LCIA method family. Marine ecotoxicity Marine ecotoxicity refers to the potential damages of toxic substances discharged to marine environments (Carvalho et al. 2024 ). Two LCIA method families in ecoinvent cover this impact category: CML (v4.8) at the midpoint level and ReCiPe (v1.03) at the midpoint and endpoint levels. Two other method families not integrated in ecoinvent exist for marine ecotoxicity at the endpoint level, which are LC-IMPACT and IMPACT World+ (v2.1, expert version, 2024). Lastly, additional characterisation models were developed (e.g., Dong et al. 2016 , 2018 ), but they are not integrated into any LCIA method family. For a detailed overview of how marine ecotoxicity is addressed in LCIA models, please refer to the SI (Section E.I). We identified several gaps in these models that do not allow to properly assess marine ecotoxicity potentially caused by OAE in LCA. A first limitation is that some LCIA models do not characterise all the metals identified in Section 3.2.2 as potential LCI elementary flows released in marine environments by this technology. For example, CML and ReCiPe do not characterise the ecotoxicity of aluminium (Al), Fe, and manganese (Mn), and the model developed by Dong et al. ( 2016 , 2018 ) omits Al, molybdenum (Mo), and vanadium (V). Only the models in LC-IMPACT and IMPACT World+ provide a full coverage of metals potentially co-released by OAE. A second limitation is that marine ecotoxicity models do not account for nutrients or alkaline materials as elementary flows. These substances could potentially cause toxicity in marine species (Renforth and Henderson 2017 ; Bach et al. 2019 ), although this link has not yet been conclusively established. Additionally, the exposure mechanism for alkaline materials would differ fundamentally from those in current marine ecotoxicity models. While current models typically assess toxicity based on the bioavailability of a substance once released in an environment (Hauschild and Huijbregts 2015 ), alkaline materials would primarily cause toxic impacts through changes in ocean chemistry, requiring a different exposure model. Moreover, marine ecotoxicity models often rely on freshwater toxicity data due to the limited availability of data on marine species (Fantke et al. 2018 ), which incorrectly implies a direct correlation between marine and freshwater ecotoxicity (Carvalho et al. 2024 ). Dong et al. ( 2016 , 2018 ) is the only model to rely exclusively on toxicity data for marine species. With respect to marine spatial resolution, the models vary considerably. CML and ReCiPe divide the ocean into only three broad climate zones, which oversimplifies its spatial heterogeneity. Dong et al. ( 2016 , 2018 ) use 64 LMEs, but these are restricted to coastal areas, reducing the applicability of the model to coastal OAE assessments. LC-IMPACT and IMPACT World+ provide the most comprehensive coverage and regionalisation, partitioning the marine environment into nine oceanic and 33 coastal regions (Kounina et al. 2014 ; Carvalho et al. 2024 ). Lastly, marine ecotoxicity models simulate the entry of toxic substances into the marine compartment via discharges from the freshwater compartment (Huijbregts et al. 2000 ; Kounina et al. 2014 ; Dong et al. 2018 ). The reason behind this modelling choice is that marine ecotoxicity impacts typically originate from industrial activities that releases toxic substances into local freshwater systems, which subsequently flow into seawater systems (Dong et al. 2016 ). Conversely, OAE activities may release toxic substances directly into the marine compartment, requiring adapted fate models. Marine eutrophication Marine eutrophication refers to the marine ecosystem response to increased inputs of nutrients or organic matter (Cosme and Hauschild 2017 ). Two LCIA method families in ecoinvent include marine eutrophication as an impact category: EF (v3.1) at the midpoint level and ReCiPe at the midpoint and endpoint levels. Three additional method families not included in ecoinvent also cover marine eutrophication: IMPACT World + at the midpoint and endpoint levels, LC-IMPACT at the endpoint level, and TRACI (v2.2) at the midpoint level. Lastly, one marine eutrophication characterisation model (Henryson et al. 2018 ) is not integrated into any LCIA method family. For a detailed overview of how marine eutrophication is addressed in LCIA models, please refer to the SI (Section E.II). Existing marine eutrophication models only characterise nitrogen (N) compounds as LCI elementary flows to the ocean. This reflects the origin of eutrophication modelling, which was developed to assess eutrophication from N and phosphorus (P) releases associated with fertiliser use, manure application, and fossil fuel combustion (Verones et al. 2020 ). Because marine waters are generally N-limited (EC-JRC 2012 ), LCIA modellers have focused their assessments solely on this nutrient for marine eutrophication (Struijs et al. 2009 ; Cosme et al. 2015 ; Huijbregts et al. 2017 ). However, other substances may also contribute to stimulating primary production, such as P, Fe, or DSi (Cosme et al. 2018 ; Browning and Moore 2023 ). Consequently, current models are unsuitable for comprehensive OAE assessments, where eutrophication impacts may arise from other nutrients (e.g., DSi or Fe). The only exception is the model by Henryson et al. ( 2018 ), which also accounts for P compounds; however, the model is limited to Sweden in its geographical coverage. In terms of ocean’s spatial resolution, most marine eutrophication models are limited to coastal waters (Struijs et al. 2009 ; Cosme and Hauschild 2017 ). This restriction arises because eutrophication from agricultural runoff and fossil fuel combustion generally occurs near emission sources or from discharges from freshwater systems, leading offshore regions to be neglected during model development. Consequently, eutrophication impacts resulting from offshore OAE deployments cannot be captured by these LCIA models. Only IMPACT World+ represents the ocean at a broader scale; however, its globally regionalised model only accounts for elementary flows originating from anthropogenic activities reaching marine waters as air emissions (Roy et al. 2012 ). In contrast, OAE introduces nutrients directly into the ocean at the location of addition. These limitations highlight the need for adapted fate modelling processes to adequately assess OAE-related marine eutrophication impacts. Ocean acidification Ocean acidification refers to the oceanic uptake of anthropogenic CO 2 , lowering seawater pH, reducing carbonate ion concentration, and weakening the ocean’s buffering capacity, collectively impairing calcification and other physiological functions in marine organisms (Orr et al. 2005 ). Although this impact category is not yet included in any method family available in ecoinvent, important progress has been made over the past decade to develop ocean acidification modelling within LCIA. IMPACT World+ includes an ocean acidification impact category and several characterisation models were recently developed (Scherer et al. 2022 ; Anderson et al. 2025 ). For a detailed overview of how ocean acidification is addressed in LCIA models, please refer to the SI (Section E.III). Regarding OAE, characterisation models do not allow to capture its beneficial impact on ocean acidification. Current ocean acidification models focus their characterisation on elementary flows that contribute to ocean acidification (currently CO 2 , carbon monoxide (CO), and methane (CH 4 )), which lead to a decrease in seawater pH. In contrast, OAE counteracts ocean acidification by an impact pathway fundamentally different in LCI elementary flows as well as fate and exposure processes. This marine technology releases alkaline material to seawater, enhancing total alkalinity and buffering capacity, thereby increasing seawater pH and increasing carbonate ion availability. This important difference means that current LCIA models cannot account for the beneficial impacts of OAE to mitigate ocean acidification. 3.2.5. Step 5: Guidance on integrating marine environmental impacts into life cycle impact assessment Comparing OAE marine environmental impact pathways with existing LCIA models revealed key research gaps in OAE assessments within LCA. We formulated recommendations for assessing OAE and outlined research priorities to improve the integration of OAE marine environmental impacts within marine ecotoxicity, marine eutrophication, and ocean acidification impact categories, as well as to enhance the ocean’s spatial resolution in LCIA models. Addressing these research gaps is fundamental to the development of sub-compartmentalised and regionalised CFs for marine impact categories relevant to OAE. These recommendations are summarised in Fig. 4 . Marine ecotoxicity We recommend following the guidance of Carvalho et al. ( 2024 ), who compared existing characterisation models for marine ecotoxicity. The model integrated into the LC-IMPACT method family characterises a broad range of LCI elementary flows and emission compartments, and implements ecotoxicity modelling recommendations from USEtox (Rosenbaum et al. 2008 ). It also offers comprehensive coverage of metals potentially released by OAE and a regionalised marine compartment. IMPACT World+ provides a comparable metal coverage and ocean representation. The midpoint model of Dong et al. ( 2016 , 2018 ) may be appropriate for coastal OAE cases where the alkaline material is known to potentially release one or more of the nine trace metals the model characterises. In contrast, ReCiPe or CML are not recommended, as their underlying models poorly represent metal behaviour (Carvalho et al. 2024 ), and they do not characterise all metals relevant to OAE. Generally, marine ecotoxicity characterisation models vary considerably in spatial coverage, environment flows, and methodological assumptions, which can influence the LCA results. Ideally, two LCIA method families with two different models should be used and the influences on their results compared (Carvalho et al. 2024 ). Improving LCIA method families also requires additional marine toxicity data to derive appropriate effect factors instead of relying on freshwater toxicity data. Importantly, accurate OAE assessment in LCA will require adapted fate models that also represent direct metal releases into the marine environment. Moreover, additional OAE experiments are needed to establish whether nutrient enrichment and increased alkalinity result in ecotoxicological effects on marine species before such processes can be integrated into characterisation models of this impact category. Marine eutrophication For marine eutrophication, LCIA characterisation models should include nutrients beyond N compounds to enable comprehensive assessments of OAE. Broadening nutrient characterisation would improve the overall representation of marine ecosystems, which are not universally N-limited and may exhibit nutrient co-limitation (Cosme et al. 2018 ). However, further experimental research is needed to determine the extent of eutrophic effects caused by elements co-released during OAE before developing adapted fate models. Ocean acidification Adapted characterisation models are required to capture the counteracting impact of OAE on ocean acidification. This modelling effort should start with the development of species sensitivity distributions (SSDs) to quantify species responses to increased alkalinity, elevated pH, and lower dissolved CO 2 , analogous to what has already been done for decreased pH (Azevedo et al. 2015 ; Bednaršek et al. 2025 ; Anderson et al. 2025 ). Such SSDs can only be developed once sufficient experiments are available that evaluate added alkalinity effects from OAE on a broad range of marine calcifying species (e.g., corals, coccolithophores, molluscs, foraminifera, and calcareous algae). Establishing such SSDs would benefit from standardised experiment protocols to ensure comparability across studies. Capturing ecosystem-scale effects from OAE requires, however, complementary modelling frameworks beyond SSD-based characterisation. Marine spatial resolution Ocean representation in LCIA models is often restricted to coastal regions. This reflects the origin of LCA modelling, which assessed human activities occurring primarily on land (Woods et al. 2016 ), with impacts reaching the ocean mainly through runoff into coastal waters (EC-JRC 2010 ; Dong et al. 2016 ; Vea et al. 2024 ). However, with human activities expanding into offshore environments, modelling frameworks must be extended to include these regions. Moreover, the ocean’s spatial heterogeneity must be better represented. Current LCIA models represent the ocean in an overly simplified manner. However, the ocean differs importantly in local conditions, such as primary productivity, temperature, salinity, and biodiversity, both across oceanic regions (e.g., Arctic Ocean and North Pacific Ocean) and oceanic depth zones (e.g., benthic and pelagic) (Piacenza et al. 2015 ; Ninove et al. 2016 ; Liu et al. 2021 ). This spatial variability in marine conditions can lead to different impacts locally on marine environments. Moreover, using an overly simplified marine compartment prevents differentiation between transport processes across marine sub-compartments and their corresponding exposure pathways (Hajjar et al. 2024 ). For OAE assessments, this level of spatial detail is crucial, as the identified OAE-related fate processes vary among sub-compartments and the environmental impact would differ in magnitude depending on the distance to the location of alkaline material addition. To enable accurate assessments, LCIA models should be regionalised, by accounting for different oceanic regions, and sub-compartmentalised, by differentiating across coastal and offshore zones as well as across oceanic depth zones. Similar challenges have already been addressed in microplastics LCIA modelling (Hajjar et al. 2024 ), where a fate framework was proposed that divides the ocean horizontally (from the continental to the global scale) and vertically (with the beach, water surface, the water column, and sediments). This multi-dimensional approach aligns well with the fate processes relevant for OAE and could serve as a basis for developing OAE-specific fate models, and more broadly, for improving the representation of ocean-related impacts in LCIA. 4. Discussion The methodological approach developed in this work enables the identification of marine environmental impact pathways of marine technologies, which requires combining the LCA field with oceanography and engineering expertise. Our approach represents an initial phase toward the development of sub-compartmentalised and regionalised CFs for marine environmental impacts in LCA, which is essential for addressing the current knowledge gaps in LCIA models. Unlike conventional LCIA development, which typically starts from an environmental stressor-endpoint relationship (e.g., ocean acidification leading to biodiversity loss), our approach starts from technology-specific elementary flows released directly into the marine compartment. This bottom-up, technology-driven entry point is uncommon in LCIA development and is particularly necessary for marine technologies, where direct releases bypass terrestrial and atmospheric compartments. We acknowledge several limitations to this approach. The analysis focuses specifically on marine environmental impact pathways, although other sensitive receptors, like humans or structures with instrumental or cultural value (e.g., fisheries zones), may be impacted by marine technologies’ deployment in marine environments. In our application to OAE, the impact pathways for these receptors are briefly addressed in the SI (Section B). Another limitation is that this approach does not address the temporal resolution of marine environmental impacts. LCIA models are usually static, overlooking the temporality of environmental impacts. This issue has been identified for a long time in LCA (Levasseur et al. 2010 ) and remains an active discussion in the LCA community and an ongoing field of research (Fauzi et al. 2019 ). Applied to OAE, the methodological approach highlighted key research needs to enable a more comprehensive assessment of this marine technology within LCA. Among the top research priorities, we identified the need for additional in situ experiments to establish clear causality from added alkalinity and nutrient enrichment to potential ecotoxicological and eutrophication effects. Such experiments should also investigate the impacts of increased alkalinity on a broader range of marine calcifying species. Furthermore, there is a need to develop fate models for OAE that represent elementary flows as being directly released to the ocean, rather than as runoffs from terrestrial, atmospheric, freshwater or coastal systems. These models should also capture the ocean’s spatial heterogeneity more accurately, accounting for both coastal and offshore dynamics, as well as for different oceanic layers. Importantly, several of the research gaps identified, such as a better ocean representation in models, modelling direct release to the marine compartment, and broadening the elementary flows’ coverage for marine eutrophication, remain relevant beyond OAE assessments and addressing them would advance LCA more broadly. Addressing these identified research gaps would allow for the development of more robust CFs. Improving the assessment of OAE’s marine environmental impacts within LCIA would also complement ongoing research efforts that aim to assess the overall environmental performance of OAE with LCA. The primary function of OAE, like other mCDR technologies, is to remove atmospheric CO 2 , and understanding how LCA can appropriately quantify net carbon removal is essential. Although this aspect is not addressed here as the global warming impact category was not found to directly connect to marine species and ecosystems as sensitive receptor, this is an active area of research. Recent case studies notably evaluated how LCA can be used to evaluate the net carbon removal potential of OAE, thereby contributing to reducing global warming (Foteinis et al. 2022 , 2023 ). 5. Conclusion Our paper lays the groundwork for improving LCIA methodologies by providing LCA practitioners with a methodological approach to identify marine environmental impact pathways of marine technologies and determine research priorities for comprehensively integrating these pathways in LCIA models. This methodological approach provides an initial phase toward the development of sub-compartmentalised and regionalised CFs. It includes five steps: (1) selecting oceanography and geochemistry literature on a studied marine technology and its impacts on marine ecosystems; (2) identifying the relevant marine elementary flows in the LCI and the fate, exposure, and effect processes; (3) reviewing by LCIA, oceanography, and technology experts; (4) analysing LCIA models to examine which elements of these marine environmental impact pathways are already represented in these models and which remain unaccounted for; (5) defining research priorities to improve the assessment of the marine technology’s marine environmental impacts within LCA. To evaluate the usefulness of this approach, we applied it to a case study on OAE. While we do not provide CFs due to important existing knowledge gaps, the framework yields five concrete analytical outputs, which together constitute a necessary precondition for any quantitative LCIA modelling of OAE: A complete mapping of marine-relevant LCI elementary flows for OAE (i.e., alkaline materials and co-released trace metals, nutrients, and ions). A technology-specific set of marine fate processes (i.e., diffusion, advection, mixing, sinking, burial, and dissolution). A technology-specific linkage between these marine elementary flows and relevant marine impact categories (i.e., marine ecotoxicity, marine eutrophication, and ocean acidification). A gap matrix showing which LCI marine elementary flows and associated impact categories are covered, and which components remain missing in existing LCIA methods. A prioritised research agenda for CF development identifying what must still be developed. Future research should operationalise the identified marine environmental impact pathways by translating them into quantitative fate, exposure, and effect models. As a next step, provisional CFs could be derived for selected LCI marine elementary flows where data availability is sufficient, allowing exploratory LCIA applications of OAE and providing an empirical basis to test, refine, and prioritise further model development. Furthermore, because the methodological approach is designed for application by LCA practitioners evaluating marine technologies, it supports the systematic inclusion of qualitatively identified environmental concerns, thereby reducing the risk that potentially relevant impacts are excluded solely because quantitative characterisation methods are not yet available, a limitation of many existing marine technology LCAs. Declarations Authors contributions Conceptualisation: M.H.D., P.J.G.H., P.B., N.T.; Methodology: M.H.D., P.J.G.H., P.B., L.S., N.T.; Investigation: M.H.D.; Validation: P.J.G.H., P.B., L.S., P.T.-P., P.G., P.R., N.T.; Visualisation: M.H.D.; Writing: - original draft: M.H.D.; Writing – review & editing: P.J.G.H., P.B., L.S., P.T.-P., P.G., P.R., N.T.. Acknowledgements Mona H. Delval, Patrik J.G. Henriksson, Pablo Trucco-Pignata, Patricia Grasse, Phil Renforth, and Nils Thonemann received funding from the European Union's Horizon Europe research and innovation programme (grant agreement n°101081362-2). Patrik J.G. Henriksson is partially funded by FORMAS CAPS (2023 − 01805) and Inequality and the Biosphere Projects (2020 − 00454), the European Union’s Horizon Europe research and innovation programme (grant agreement no. 101182025). Paul Behrens was supported by a British Academy Global Professorship. Phil Renforth is supported by the Carbon to Sea Initiative. 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Supplementary Files SupplementaryInformation.docx Guidance on integrating marine environmental impacts of marine technologies into life cycle assessment - Application to ocean alkalinity enhancement: Supplementary Information Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9573704","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":632643303,"identity":"8ed775f6-5005-4b3b-b9a8-ceddecdceecd","order_by":0,"name":"Mona H. 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Henriksson","email":"","orcid":"https://orcid.org/0000-0002-3439-623X","institution":"Institute of Environmental Sciences (CML), Leiden University; Stockholm Resilience Centre, Stockholm University","correspondingAuthor":false,"prefix":"","firstName":"Patrik","middleName":"J.G.","lastName":"Henriksson","suffix":""},{"id":632643305,"identity":"f4fa7f29-880c-4580-93e9-8000958c0dd4","order_by":2,"name":"Paul Behrens","email":"","orcid":"https://orcid.org/0000-0002-2935-4799","institution":"Institute of Environmental Sciences (CML), Leiden University; University of Oxford, Oxford Martin School","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Behrens","suffix":""},{"id":632643306,"identity":"29192439-6066-4e0b-86dc-823669ad77fc","order_by":3,"name":"Laura Scherer","email":"","orcid":"https://orcid.org/0000-0002-0194-9942","institution":"Institute of Environmental Sciences (CML), Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Scherer","suffix":""},{"id":632643307,"identity":"aff38ddb-0c00-4fe7-ad2c-9c4988b4315b","order_by":4,"name":"Pablo Trucco-Pignata","email":"","orcid":"https://orcid.org/0000-0003-3996-4600","institution":"National Oceanography Centre","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Trucco-Pignata","suffix":""},{"id":632643308,"identity":"799f942e-ecb4-43d3-ad08-8117a875b66a","order_by":5,"name":"Patricia Grasse","email":"","orcid":"https://orcid.org/0000-0002-1745-4418","institution":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig; GEOMAR Helmholtz Centre for Ocean Research Kiel","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Grasse","suffix":""},{"id":632643309,"identity":"baa13b2a-1ef3-4152-b8c1-fd4cb84a4131","order_by":6,"name":"Phil Renforth","email":"","orcid":"https://orcid.org/0000-0002-1460-9947","institution":"Research Centre for Carbon Solutions, Heriot-Watt University","correspondingAuthor":false,"prefix":"","firstName":"Phil","middleName":"","lastName":"Renforth","suffix":""},{"id":632643310,"identity":"5119a5ef-da98-4c58-9720-434b9e69729a","order_by":7,"name":"Nils Thonemann","email":"","orcid":"https://orcid.org/0000-0001-5966-2656","institution":"Institute of Environmental Sciences (CML), Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Nils","middleName":"","lastName":"Thonemann","suffix":""}],"badges":[],"createdAt":"2026-04-30 07:42:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9573704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9573704/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108841198,"identity":"63613da1-cb2d-47f0-87ba-7b26ecc1c0fc","added_by":"auto","created_at":"2026-05-09 01:07:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":518673,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the methodological approach. Adapted from Woods et al. (2021) and Richter et al. (2024). The phases of the approach are in dark blue, the required elements for each phase in grey, and the outcome of each phase in light blue.\u003c/p\u003e","description":"","filename":"Figure1v2page0001.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9573704/v1/2e0a7180b0dccfa65a42b5ce.jpg"},{"id":108841200,"identity":"cf64ec5a-e4d7-4cff-a61e-f19bf969fb0f","added_by":"auto","created_at":"2026-05-09 01:07:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88166,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the general ocean alkalinity enhancement (OAE) life cycle. We look specifically at the marine environmental impacts resulting from alkaline material addition to the ocean.\u003c/p\u003e","description":"","filename":"Figure2v2page0001.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9573704/v1/304ace2b29051de2dad3c07d.jpg"},{"id":108841197,"identity":"3362be57-0fe6-4937-b354-0e0df229882f","added_by":"auto","created_at":"2026-05-09 01:07:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":518935,"visible":true,"origin":"","legend":"\u003cp\u003eMarine environmental impact pathways of ocean alkalinity enhancement (OAE). Solid arrows indicate established linkages, while dashed arrows represent uncertain or currently under-researched connections based on the present state of knowledge. (Note that a visual exposure process has been proposed in the scientific literature as explained in Section 3.2.2, but it has not yet been linked to any effect processes due to insufficient research.)\u003c/p\u003e","description":"","filename":"Figure3v2page0001.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9573704/v1/2c803e6bd000eb233aa0de0f.jpg"},{"id":108977179,"identity":"8a3cf3df-e837-40c1-8ee4-69f22bf62e28","added_by":"auto","created_at":"2026-05-11 11:30:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":228789,"visible":true,"origin":"","legend":"\u003cp\u003eGuidance for better integration of ocean alkalinity enhancement (OAE)'s marine environmental impacts in life cycle impact assessment (LCIA) models. Inventory refers to the elementary flows in the LCI that were identified to be connected to a particular marine impact category and the LCIA models of this impact category already characterise. Please refer to the SI, Section D, for further details on each process within marine environmental impact pathways.\u003c/p\u003e","description":"","filename":"Figure4page0001.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9573704/v1/5f190e6d9b8122f8fa0fdfa0.jpg"},{"id":108979604,"identity":"69a2d9ca-a51a-411c-90ae-a91092780a26","added_by":"auto","created_at":"2026-05-11 12:00:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2039952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9573704/v1/19851236-1e7e-48d0-8323-44ff076043e2.pdf"},{"id":108841196,"identity":"82607e6e-1c53-4683-abb9-7ead78653b2d","added_by":"auto","created_at":"2026-05-09 01:07:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":482133,"visible":true,"origin":"","legend":"\u003cp\u003eGuidance on integrating marine environmental impacts of marine technologies into life cycle assessment - Application to ocean alkalinity enhancement: Supplementary Information\u003c/p\u003e","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-9573704/v1/9c5bfcd28ed34a5d34693d2c.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Guidance on integrating marine environmental impacts of marine technologies into life cycle assessment — Application to ocean alkalinity enhancement","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWhereas humanity has long relied on the ocean, growing global consumption, depletion of terrestrial resources, and declining land availability have, in recent decades, led to the diversification, intensification and expansion of human activities into marine environments, a phenomenon termed \u0026ldquo;the Blue Acceleration\u0026rdquo; (Jouffray et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a result, anthropogenic pressures on marine ecosystems have become more widespread and severe, driving the degradation and, in some cases, collapse of these ecosystems (Halpern et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Mitigating these stressors and their impacts is thus of utmost importance and require first a clear understanding of their nature and magnitude.\u003c/p\u003e \u003cp\u003eLife cycle assessment (LCA) is used to evaluate the environmental performance of marine technologies (Philis et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alvarenga et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Babakhani et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Here, we define marine technologies as all existing or future technologies designed to be deployed in or on marine environments and that are directly interacting with the ocean. Such technologies are for example mariculture, maritime shipping, offshore wind farms, marine carbon dioxide removal (mCDR) approaches, submarine cables, desalination plants, and deep-sea mining (Jouffray et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, critical limitations remain in the LCA methodology to assess impacts of such technologies on marine environments (Woods et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Life cycle impact assessment (LCIA) method families (e.g., CML, ReCiPe, or TRACI) traditionally focused on impacts on terrestrial and freshwater ecosystems, and while they have begun to address impacts on marine ecosystems over the last decades (Huijbregts et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Dong et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Verones et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), these impacts remain underrepresented. Currently, the most common LCIA method families include up to three impact categories assessing marine environmental impacts: marine eutrophication, marine ecotoxicity, and marine acidification, all of which leading to ecosystem damage. These categories are insufficient to capture the full range of marine environmental effects of marine technologies. For instance, seabed damage caused by deep-sea carbon storage (Rastelli et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), biotic resource depletion due to overfishing (Emanuelsson et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and marine noise pollution from maritime shipping (Chahouri et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) cannot be assessed in these categories. Additionally, LCIA modelling may fail to adequately account for some important considerations of marine environmental impacts caused by marine technologies. For instance, only nitrogen (N) flows are characterised in the marine eutrophication impact category, whereas other nutrients released by these technologies could contribute to marine eutrophication, such as iron (Fe) released by ocean iron fertilisation (Williamson et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA further limitation in LCIA models is their spatial resolution of the ocean. These models simplify the ocean by only considering coastal seas (Struijs et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) or by only distinguishing coastal seawater and three climate zones (Huijbregts et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Van Zelm et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), which substantially neglects its spatial heterogeneity and depth dimension. While more recent models better account for this heterogeneity by distinguishing dozens of different large marine ecosystems (LMEs) (Dong et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Cosme et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), these LMEs are constrained to continental shelves (Sherman and Donnelly \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2026\u003c/span\u003e), thus remaining unsuitable for assessing impacts of technologies deployed in the open ocean.\u003c/p\u003e \u003cp\u003eTo enable more comprehensive evaluations of marine technologies\u0026rsquo; environmental performance, it is essential to understand the impact pathways through which these technologies affect marine environments. Additionally, the spatial context in which these processes occur must be considered regionally and by dividing the marine compartment into relevant sub-compartments that reflect the ocean\u0026rsquo;s spatial variability, as proposed in similar work for marine microplastic impacts (Hajjar et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Ultimately, integrating these pathways may require the development of new or improved marine impact category indicators that incorporate characterisation factors (CFs) specific to the relevant marine regions and sub-compartments (Delval et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this work, we aim to develop a methodological approach that guides LCA practitioners in: (i) identifying marine environmental impact pathways of marine technologies, (ii) understanding which components of these pathways are already covered in current LCIA models, and (iii) determining the research needs to integrate comprehensively these pathways in LCIA. Identifying these pathways represents the basis for developing sub-compartmentalised and regionalised CFs for marine impact categories relevant to marine technologies. Overall, our contribution seeks to improve the representation of marine environmental impacts of marine technologies within LCIA.\u003c/p\u003e \u003cp\u003eTo showcase its practical relevance, we apply our developed approach to a specific marine technology: ocean alkalinity enhancement (OAE). Interest in mCDR has grown substantially due to its potential to complement deep emissions cuts to meet climate targets, with OAE regarded as one of the most promising options (Smith et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; De Pryck and Boettcher \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Oschlies et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This emerging mCDR approach aims to enhance carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) uptake from the atmosphere by the ocean through intentional addition of alkaline materials to seawater (Renforth and Henderson \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Oschlies et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While currently being tested at the field level, OAE could enable the removal of billions of tonnes of CO\u003csub\u003e2\u003c/sub\u003e annually at scale (He and Tyka \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ringham et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While two LCA case studies have assessed the environmental impacts of the technology\u0026rsquo;s supply chain (Foteinis et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), its potential effects on marine environments continue to be an active area of research.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cp\u003eIn the LCIA phase, elementary flows (i.e., matter or energy inputs extracted from the environment without previous human processing or outputs released into the environment without subsequent human processing) identified in the life cycle inventory (LCI) phase are translated into environmental impact scores using CFs. These CFs are derived from characterisation models, which are mathematical representations of impact pathways (also called cause-effect chains). Impact pathways describe the mechanisms connecting elementary flows to damages affecting areas of protection (AoP). These AoP in LCA are the natural environment, human health, and resources and ecosystem services (Hauschild and Huijbregts \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Impact pathways often consist of three types of processes: 1) fate processes, which describe the spatial distribution, residence time, and chemical behaviour of a substance released into the environment; 2) exposure processes, which describe how sensitive receptors (e.g., receptors likely to suffer negative effects, such as marine species and ecosystems) get exposed to the substance; and 3) effect processes, which assess the effects to AoP resulting from this exposure (Woods et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, we aim to develop a methodological approach that allows to qualitatively identify these processes for marine technologies\u0026rsquo; marine environmental impacts and inform on knowledge gaps and research priorities for their integration in LCIA models. The starting point to improve assessment of marine environmental impacts in LCIA is often a marine impact category not yet covered by existing LCIA method families, such as ocean acidification (Anderson et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) or marine litter (Hajjar et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While these efforts are essential for advancing LCA, we, however, opt for an approach that starts at the marine technology level and its elementary flows to ensure that all potential marine environmental impacts associated with that technology are comprehensively identified and integrated into LCIA models. Such an approach would complement existing efforts in LCIA modelling to allow a comprehensive assessment of marine technologies and their marine environmental impacts in LCA.\u003c/p\u003e \u003cp\u003eOur approach builds on Woods et al. (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who conducted a similar study on impact pathways for marine plastic litter. They propose a framework that qualitatively identifies the key components of marine litter\u0026rsquo;s impact pathways, which is an important first step in coordinating future efforts in LCIA development. Their approach also highlights the importance of spatial differentiation and identification of elementary flows. However, the methodology used to construct their framework is not described in detail, limiting its replicability for other technologies or impact categories. Clear methodological guidance is particularly important for impact pathway identification, which typically requires involving experts from multiple backgrounds, such as LCA practitioners, industrial experts, and environmental biologists. For framework development in a multidisciplinary context, the systematic methodology from Richter et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) provides valuable insights. Therefore, we adapted the methodological step-by-step approach of Richter et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to the context of environmental impact pathways\u0026rsquo; development and combined it with the framework by Woods et al. (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for identifying key components of impact pathways and knowledge gaps for their integration in LCIA. We then applied our developed approach to OAE to evaluate the usefulness of this approach.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. Methodological approach for the identification and integration of marine technologies\u0026rsquo; marine environmental impacts in life cycle assessment\u003c/p\u003e \u003cp\u003eWe developed a novel methodological approach that allows LCA practitioners to qualitatively identify the key processes for marine technologies\u0026rsquo; marine environmental impact pathways and to determine the current knowledge gaps and research priorities for integrating these pathways in LCIA models. This is an initial phase laying the groundwork for future efforts to build fate, exposure, and effect models from which corresponding fate, exposure, and effect factors and ultimately CFs can be derived.\u003c/p\u003e \u003cp\u003eOur methodology (Fig.\u0026nbsp;1) consists of five steps: (1) selecting relevant oceanography and geochemistry literature on the studied marine technology and its impacts on marine ecosystems; (2) identifying the main components of the marine technology\u0026rsquo;s marine environmental impact pathways (e.g., the relevant marine elementary flows in the LCI, as well as their associated fate, exposure, and effect processes); (3) revising the identified marine environmental impact pathways through review by experts, with expertise in either LCIA, oceanography, or the studied technology; (4) reviewing the LCIA modelling literature to assess which aspects of the identified marine environmental impact pathways are represented in existing LCIA models and which remain absent; (5) defining research priorities to enhance the assessment of the marine technology within LCIA.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Application of the methodological approach to ocean alkalinity enhancement\u003c/h2\u003e \u003cp\u003eWe applied the five steps of the developed methodological approach to OAE. A detailed technical description of OAE is given in the supplementary information (SI, Section A). Given our focus on marine environmental impacts, we limited our analysis to impact pathways for substances released into the marine environment by OAE (Fig.\u0026nbsp;2), and we focused on marine species and ecosystems as the sensitive receptor. Details on impact pathways for humans and structures with instrumental or cultural value to humans, considered sensitive receptors, are provided in the SI (Section B).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Step 1: Selection of relevant literature\u003c/h2\u003e \u003cp\u003eWe reviewed the relevant oceanography and geochemistry literature on OAE and its impacts on marine species and ecosystems, using Scopus and Web of Science. We used a combination of broad search terms, such as \u0026lsquo;\u003cem\u003eocean alkalinity enhancement\u003c/em\u003e\u0026rsquo;, \u0026lsquo;\u003cem\u003ealkaline mineral\u003c/em\u003e\u0026rsquo;, and \u0026lsquo;\u003cem\u003emarine impacts\u003c/em\u003e\u0026rsquo;, alongside more specific terms related to OAE technologies, including \u0026lsquo;\u003cem\u003eolivine\u003c/em\u003e\u0026rsquo; and \u0026lsquo;\u003cem\u003etrace metals\u003c/em\u003e\u0026rsquo;, and our literature review includes publications available up to August 31st, 2025. From the resulting search results (\u0026gt;\u0026thinsp;200 scientific articles), the most cited or the most relevant articles based on the abstract were selected. Some additional articles were found as cited in the selected literature or recommended by co-authors with expertise in oceanography and OAE. Lastly, one article was suggested by a reviewer during the reviewing process of the manuscript. In total, approximately 75 scientific articles were selected for Step 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Step 2: Identification of the marine environment impact pathways\u003c/h2\u003e \u003cp\u003eFrom the selected literature, we identified the key elements for the marine environmental impact pathways of OAE. These include elementary flows in the LCI released to the marine environmental compartment, the fate processes, the exposure processes, and the effect processes. From these identified processes, the impact pathways were developed.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIdentification of marine elementary flows\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFor OAE, the main exchange between the product system and the marine environment is the intentional addition of alkaline material to the ocean to provide carbon dioxide removal (CDR) (NASEM \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Eisaman et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Oschlies et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Depending on the specific OAE approach, alkaline material is typically added to the open ocean at the sea surface or to coastal waters (Renforth and Henderson \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; He and Tyka \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lindland et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and there have been suggestions to directly add it onto marine sediments in coastal areas or offshore (Fuhr et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dale et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOAE can use various alkaline materials (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Quicklime (CaO) or hydrated lime (Ca(OH)\u003csub\u003e2\u003c/sub\u003e) obtained from limestone have been widely studied (Caserini et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Moras et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kowalczyk et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Other alkaline materials can also be used, such as pulverised silicate minerals (e.g., olivine), pulverised carbonate minerals (e.g., calcite (CaCO\u003csub\u003e3\u003c/sub\u003e)), waste or by-product materials obtained from human activities (e.g., steel slags), or magnesium hydroxide (Mg(OH)\u003csub\u003e2\u003c/sub\u003e) (Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Renforth et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bases (e.g., sodium hydroxide (NaOH)) isolated from pumped seawater or brine are also potential candidates (Ringham et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Oschlies et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTypes of alkaline materials used in different alkalinity enhancement methods, along with their corresponding deployment (i.e., addition) sites.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline material\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCaO, Ca(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOcean liming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpen ocean at the mixed surface layer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOschlies et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoastal enhanced weathering (CEW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoastal waters, eventually in wastewater treatment plant outflows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCampbell et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Oschlies et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSilicate rocks and minerals (e.g., olivine, dunite, basalt), carbonate rocks and minerals (e.g., CaCO\u003csub\u003e3\u003c/sub\u003e, ikaite), anthropogenic material (e.g., steel slag), Mg(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOAE using other alkaline materials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpen ocean at the mixed surface layer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBach et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Renforth et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Oschlies et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCEW using other alkaline materials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoastal waters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBach et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Campbell et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Renforth et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Oschlies et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnhanced benthic weathering (EBW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOnto marine sediments, coastal or offshore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFuhr et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); Dale et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBases (e.g., NaOH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectrochemical approaches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoastal waters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRingham et al. (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Oschlies et al. (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdditional elementary flows may enter the marine environment through alkaline material addition (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The dissolution of some alkaline materials, such as steel slag or olivine, can release trace metals, with the types and quantities varying depending on the composition and purity of the alkaline mineral (Renforth and Henderson \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, essential nutrients and ions may be co-released (Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePotential co-released elements during the dissolution of different alkaline materials. The elements listed are among the most reported in the ocean alkalinity enhancement (OAE) literature; however, this list may not be exhaustive, and other elements could be co-released as new alkaline materials are explored for OAE.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePotential co-released element\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlkaline material\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTrace metals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAluminium (Al)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMoras et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003eb)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCobalt (Co)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlivine, steel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMoras et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003eb); Zhuang et al. (\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromium (Cr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlivine, steel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBach et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Moras et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003eb); Zhuang et al. (\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCopper (Cu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMoras et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003eb)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIron (Fe)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaO, Ca(OH)\u003csub\u003e2\u003c/sub\u003e, Mg(OH)\u003csub\u003e2\u003c/sub\u003e, olivine, steel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMayes et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); Renforth and Henderson (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); Bach et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Dupont and Metian (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Guo et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManganese (Mn)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGuo et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); Moras et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003eb)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolybdenum (Mo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMayes et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); Bach et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLead (Pb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBach et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Moras et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003eb)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNickel (Ni)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSome silicate rocks and minerals (e.g., olivine, dunite), some carbonate rocks and minerals (e.g., CaCO\u003csub\u003e3\u003c/sub\u003e), steel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Montserrat et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Flipkens et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Moras et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVanadium (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMayes et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc (Zn)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMoras et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003eb)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eEssential nutrients and ions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalcium ion (Ca\u003csup\u003e2+\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaO, Ca(OH)\u003csub\u003e2\u003c/sub\u003e, some carbonate rocks and minerals (e.g., CaCO\u003csub\u003e3\u003c/sub\u003e and ikaite), silicate rocks and minerals, steel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBach et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Dupont and Metian (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Eisaman et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Guo et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDissolved silicate (DSi)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaO, Ca(OH)\u003csub\u003e2\u003c/sub\u003e, Mg(OH)\u003csub\u003e2\u003c/sub\u003e, silicate rocks and minerals, steel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRenforth and Henderson (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); Bach et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); Guo et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMagnesium ion (Mg\u003csup\u003e2+\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaO, Ca(OH)\u003csub\u003e2\u003c/sub\u003e, carbonate rocks and minerals, Mg(OH)\u003csub\u003e2\u003c/sub\u003e, some silicate rocks and minerals (e.g., olivine, basalt), steel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDupont and Metian (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Eisaman et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Guo et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphate (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGuo et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eIdentification of fate processes\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAlkaline material and its co-released elements move away from the location of addition through four transport mechanisms: diffusion, advection, mixing, and sinking. Diffusion is the transport of molecules driven by concentration gradients, occurring slowly and at small spatial scales. Transport by ocean circulation includes (i) advection, which redistributes material through current-driven flow, and (ii) turbulent mixing, generated by winds, waves, and shear, which enhances dispersion and dilution. Sinking refers specifically to downward transport to deep ocean layers. These processes can occur in any oceanic zone depending on the location of addition (Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother important fate process is the dissolution of the alkaline material. Addition in the open ocean results in dissolution within the mixed surface layer (Kowalczyk et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), whereas addition in coastal waters results in partial dissolution in seawater and in pore waters from coastal sediments (Mendes et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Lastly, after solid alkaline material settles from the water column into the sediments, it can become buried. When the material is added directly onto sediments, most is buried and remains undissolved (Dale et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the SI (Section C), we describe for each fate process its effect on the residence time of the alkaline material and identify the main factors influencing the process. However, alkaline material fate in seawater has been mostly studied in controlled laboratory environments, which do not reflect natural conditions (Meysman and Montserrat \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Flipkens et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). More \u003cem\u003ein situ\u003c/em\u003e experiments are needed to increase our understanding of these fate processes.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIdentification of exposure processes\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMarine species and ecosystems are expected to be primarily exposed to the changes in ocean chemistry induced by alkalinity addition, which include an increase in pH and a decrease in seawater partial pressure of CO\u003csub\u003e2\u003c/sub\u003e (Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ocean Visions 2022). These chemical changes would be temporary but likely pronounced adjacent to the location of addition, depending on the speed at which seawater mixes and re-equilibrates with the atmosphere (Renforth and Henderson \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother exposure process could be via unintended ingestion of alkaline material by grazers and filter feeders, thereby also getting exposed to the potentially contained trace metals and essential nutrients and ions (Harvey \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; NASEM \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Flipkens et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). In particular, trace metals can biomagnify through the food chain and reach higher trophic levels (Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Lastly, alkaline material added could increase turbidity, which would affect light availability in the photic zone as well as induce a \u0026lsquo;white water\u0026rsquo; phenomenon that could impair predators\u0026rsquo; visibility, such as fish and seabirds (NASEM \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but research on this visual exposure route is lacking.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIdentification of effect processes\u003c/em\u003e \u003c/p\u003e \u003cp\u003eChanges in ocean chemistry from added alkalinity could lead to toxic effects on marine species. For instance, the temporary but substantial shift in the carbonate system combined with CO\u003csub\u003e2\u003c/sub\u003e reduction is likely to affect primary producers like phytoplankton with potential cascading effects on entire marine ecosystems (Renforth and Henderson \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, recent research shows mostly insignificant effect post-alkalinity addition on pelagic low-trophic-level species (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, these results indicate relatively good physiological tolerance of these species to added alkalinity as well as good community resistance at these trophic levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOcean alkalinity enhancement experiments on the effects of added alkalinity on marine species and ecosystems.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlkaline material\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExperiment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecies group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffects*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntoni et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eField, mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, bacterial community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo effect on community structure under unequilibrated** moderate and intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBhaumik et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, zooplankton copepods (\u003cem\u003eTemora longicornis\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative effect on respiratory and digestion rates as well as negative effect on phytoplankton prey (\u003cem\u003eRhodomonas salina\u003c/em\u003e) availability but positive effect on prey nutritional quality, hence effect on predator considered to be mitigated, under unequilibrated and varying intensity of OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBritton et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenthic, kelp (\u003cem\u003eEcklonia radiata\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo effect on physiology across life stages under unequilibrated and varying intensity of OAE, except a negative effect on growth rate on all life stages under unequilibrated intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCamatti et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, copepod (\u003cem\u003eAcartia tonsa\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo effect on physiology under short-term exposure at unequilibrated moderate OAE. Negative effect on survival rate under long-term exposure at unequilibrated intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDe Castro et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, phytoplankton and bacterial community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative effect on development but no effect on community composition for phytoplankton, order-specific effect abundance but no effect on community composition for bacteria under unequilibrated moderate OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelacroix et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, phytoplankton (\u003cem\u003eTetraselmis suecica\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative effect on survival rate under unequilibrated and equilibrated moderate OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMg(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo effect on growth and survival rate under unequilibrated and equilibrated moderate OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGately et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNa\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;CaCl\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e4\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, phytoplankton calcium carbonate producer (\u003cem\u003eEmiliana huxleyi\u003c/em\u003e) and silica producer (\u003cem\u003eChaetoceros\u003c/em\u003e sp.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo effect on physiology under equilibrated moderate and intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoldenberg et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eField, mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic and demersal, juvenile fish (\u003cem\u003eClupea harengus, Gadus morhua\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo effect on growth and survival rate, positive effect on biomass under unequilibrated intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGore et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNa\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenthic, red calcifying algae (\u003cem\u003eCorallina\u003c/em\u003e spp.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall effect on primary productivity and respiration rates, and photophysiology under unequilibrated moderate OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuo et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory, ship-based mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, phytoplankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall effect on physiology under unequilibrated moderate OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJones et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenthic, gastropod mollusk (\u003cem\u003ePhyllaplysia taylori\u003c/em\u003e) and isopod (\u003cem\u003eIdotea resecata\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative effect on survival rate under unequilibrated intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKousoulas et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory, microcosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhytoplankton and zooplankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffect on community composition and delayed bloom under unequilibrated intense OAE, but considered small compared to climate benefit.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaOH\u0026thinsp;+\u0026thinsp;NaHCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall effect on community composition under equilibrated moderate OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMar\u0026iacute;n-Samper et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaHCO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eField, mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, phytoplankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo effect on community metabolism or composition under equilibrated moderate and intense OAE. Positive effect on primary production under equilibrated moderate OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNocera et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory, mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, zooplankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall effect on species abundance and community structure under short-term exposure at unequilibrated moderate and intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOberlander et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, phytoplankton (\u003cem\u003eThalassiosira pseudonana, Diacronema lutheri\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall effect on viability and growth rates under short-term exposure at unequilibrated intense OAE. Negative effect on growth rate under long-term exposure at unequilibrated intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaul et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaHCO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eField, mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, phytoplankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo effect on biomass nor bloom occurrence and magnitude, as well as no effect on most biogeochemical pools under short-term and long-term exposure at equilibrated moderate and intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRam\u0026iacute;rez et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaHCO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eField, mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, phytoplankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall effect on physiology under equilibrated moderate and intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026aacute;nchez et al. (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaHCO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eField, mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, zooplankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative effect on reproduction and productivity rates under short-term exposure at equilibrated moderate and intense OAE, probably trophically mediated.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubhas et al. (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaHCO\u003csub\u003e3\u003c/sub\u003e, Na\u003csub\u003e2\u003c/sub\u003eCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eField, microcosm incubation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, microbial community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall effect on biological calcification and net primary production rates under equilibrated moderate and intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraboni et al. (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa(OH)\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLaboratory, mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, plankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall effect on community composition and survival rates and no effect on growth and grazing rates under unequilibrated moderate and intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXin et al. (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{H}\\text{C}{\\text{O}}_{3}^{-}+\\text{C}{\\text{O}}_{3}^{2-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eField, mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePelagic, phytoplankton and microzooplankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo effect on abundance and biomass under equilibrated moderate and intense OAE.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* \u003cem\u003eEffects are noted as small when the study reported them as noticeable but statistically non-significant. Negative effects refer to an adverse response (e.g., reduced survival growth or community productivity), whereas positive effect indicates a beneficial response (e.g., enhanced growth, biomass, or productivity) relative to the control.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e** Equilibrated: seawater has reached chemical balance with atmospheric CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e. \u003cem\u003eUnequilibrated: seawater is still exchanging CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003ewith atmosphere and has not yet reached balance.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnother important effect of OAE-driven ocean chemistry shift and associated pH increase is the counteraction of ocean acidification (Hartmann et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Mitigating acidification would facilitate the calcification mechanism and thus benefit calcifying organisms (Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nevertheless, a minority of calcifying species may experience adverse effects from alkalinity addition (Bednaršek et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEnhanced calcification from OAE can also trigger unintended calcium carbonate (CaCO\u003csub\u003e3\u003c/sub\u003e) precipitation, a process that consumes alkalinity and releases CO\u003csub\u003e2\u003c/sub\u003e into surrounding waters (Moras et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The primary consequence is a reduction in CDR efficiency (Hartmann et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but it may also exacerbate ocean acidification and cause turbidity (Ringham et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jones et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, co-released OAE products can cause toxic effects on marine species and ecosystems. While trace metals naturally occur in seawater at low concentrations, they may become harmful when present at elevated levels (Kwong \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These metals can bioaccumulate in organisms and biomagnify through the food chain (Anandkumar et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Recent studies have begun to explore these toxic effects (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Beyond trace metals, co-released nutrients and ions may also cause toxic effects when present in high amount. For example, calcium ion (Ca\u003csup\u003e2+\u003c/sup\u003e) release in quicklime-based OAE can be harmful, as keeping low intracellular Ca\u003csup\u003e2+\u003c/sup\u003e is essential but metabolically costly (Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOcean alkalinity enhancement experiments on the effects of co-released products on marine species and ecosystems.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperiment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecies group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlkaline material\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEffects\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFerderer et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eField, mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMgCl\u003csub\u003e2\u003c/sub\u003e\u0026middot;6H\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;+\u0026thinsp;Na\u003csub\u003e2\u003c/sub\u003eSiO\u003csub\u003e3\u003c/sub\u003e\u0026middot;5H\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFertilisation of diatom community due to nutrient release (DSi).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlipkens et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenthic, amphipod (\u003cem\u003eGammarus locusta\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlivine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative effect on survival, growth, and reproduction rates due to trace metal bioaccumulation (Cr and Ni).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoldenberg et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eField, mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePelagic and demersal, juvenile fish (\u003cem\u003eClupea harengus, Gadus morhua\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlivine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo effect on growth and survival rates due to nutrient release (DSi).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuo et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaboratory, microcosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSteel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLimited effect on abundance and community composition due to nutrient (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{P}{\\text{O}}_{4}^{3-}\\)\u003c/span\u003e\u003c/span\u003e and Si) and trace metal (Mn and Fe) release.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlivine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive effect on abundance and effect on community composition due to nutrient (DSi) and trace metal (Fe and Ni) release.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuo et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaboratory, ship-based mesocosm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePelagic, phytoplankton community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNaOH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo nutrient and limited trace metal release and no effect observed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSteel slag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive effect on growth rate for nano- and microeukaryotes while negative effect on survival rate in another phytoplankton group (\u003cem\u003eProchlorococcus\u003c/em\u003e) due to high trace metal release (Al, Fe, and Mn), overall altering community composition. Nutrient release (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{P}{\\text{O}}_{4}^{3-}\\)\u003c/span\u003e\u003c/span\u003e and Si) also observed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlivine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative effect on survival rate in some phytoplankton groups (\u003cem\u003eProchlorococcus, Synechococcus\u003c/em\u003e, and picoeukaryote) due to high trace metal release (Al, Co, Cu, Mn, and Ni). Nutrient release (DSi) also observed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHutchins et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePelagic and benthic, phytoplankton (\u003cem\u003eNitzschia, Ditylum, Emiliana, Trichodesmium, Crocosphaera, Synechococcus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlivine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmall effect on physiology, positive effect on growth rate for some taxa in specific conditions due to nutrient release (Fe and DSi).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJankowska et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenthic, invertebrate amphipod (\u003cem\u003eLeptocheirus plumulosus, Eohaustorius estuarius\u003c/em\u003e), polychaete (\u003cem\u003eNeanthes arenaceodentata, Alitta virens\u003c/em\u003e), bivalve (\u003cem\u003eMacoma nasuta\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlivine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo effect on survival and growth rates due to trace metal accumulation (Ni).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Li et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePelagic, phytoplankton (\u003cem\u003eGephyrocapsa oceanica, Thalassiosira, pseudonana, Phaeodactylum tricornutum\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlivine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePositive effect on growth for highly silicified diatoms (\u003cem\u003eT. pseudonana\u003c/em\u003e) due to nutrient release (DSi).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e \u003cem\u003eOverview of the methodological approach. Adapted from\u003c/em\u003e Woods et al. (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) \u003cem\u003eand\u003c/em\u003e Richter et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003eThe phases of the approach are in dark blue, the required elements for each phase in grey, and the outcome of each phase in light blue.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eFigure\u0026nbsp;2\u003c/b\u003e \u003cem\u003eSchematic representation of the general ocean alkalinity enhancement (OAE) life cycle. We look specifically at the marine environmental impacts resulting from alkaline material addition to the ocean.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eFigure\u0026nbsp;3\u003c/b\u003e \u003cem\u003eMarine environmental impact pathways of ocean alkalinity enhancement (OAE). Solid arrows indicate established linkages, while dashed arrows represent uncertain or currently under-researched connections based on the present state of knowledge. (Note that a visual exposure process has been proposed in the scientific literature as explained in Section 3.2.2, but it has not yet been linked to any effect processes due to insufficient research.)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCo-released products from OAE can also drive eutrophication by enriching the local environment, giving more sensitive phytoplankton species a growth advantage and potentially leading to bloom and alteration of communities\u0026rsquo; composition. For instance, diatoms, often limited by dissolved silicate (DSi), can benefit from DSi-based OAE, potentially shifting phytoplankton communities toward diatom dominance (Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Oschlies et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such eutrophication effects have been mainly observed in studies involving steel slag and olivine (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNonetheless, OAE effects on marine species remain insufficiently understood, prompting scientists to call for further research. This includes studies on higher trophic levels, such as fish (Ferderer et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and on other species groups, like calcifiers (S\u0026aacute;nchez et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), at the community level (Britton et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and across different marine biomes (Xin et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), including benthic environments (Jones et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Future experiments should also examine seasonal variability (Ren et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), varying OAE technologies and intensities, and unequilibrated conditions following OAE application (Flipkens et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; S\u0026aacute;nchez et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Long-term studies are particularly important for capturing multi-generational effects (Bhaumik et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Lastly, more research is needed on the effects of trace metal co-release (Xin et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhuang et al. \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eMarine environmental impact pathways\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFrom the information on the LCI marine elementary flows and the fate, exposure, and effect processes, we identified the pathways of OAE\u0026rsquo;s marine environmental impacts (Fig.\u0026nbsp;3). The identified pathways can be associated with three marine impact categories: marine ecotoxicity, marine eutrophication, and ocean acidification. The impact pathways for each impact category are presented individually in the SI (Section D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Step 3: Review of the marine environment impact pathways\u003c/h2\u003e \u003cp\u003eThe impact pathways derived from the literature were subsequently completed and revised through internal review by co-authors with expertise in either LCIA or oceanography and OAE, and through feedback from external OAE experts during a workshop held at the general assembly of the EU-funded SEAO2-CDR (Strategies for the Evaluation and Assessment of Ocean-based Carbon Dioxide Removal) project, a European Union\u0026rsquo;s Horizon Europe research programme on mCDR approaches, in June 2025. The group of experts was selected based on the relevance of their expertise for this topic, either in LCA (\u003cem\u003en\u0026thinsp;=\u0026thinsp;6\u003c/em\u003e) or in oceanography and OAE (\u003cem\u003en\u0026thinsp;=\u0026thinsp;8\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e3.2.4. Step 4: Analysis of the marine environmental impact pathways integration into life cycle impact assessment models\u003c/p\u003e \u003cp\u003eWe reviewed the LCIA modelling literature to determine which components of the identified OAE marine environmental impact pathways are already represented in existing LCIA models, and which remain unaddressed. This was done for each marine impact category identified as relevant to OAE: marine ecotoxicity, marine eutrophication, and ocean acidification. We examined LCIA method families as implemented in ecoinvent (v3.11), as this is the most widely used LCI database for OAE LCA case studies (Delval et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), as well as other method families not included in ecoinvent and characterisation models that have not yet been incorporated into any LCIA method family.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMarine ecotoxicity\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMarine ecotoxicity refers to the potential damages of toxic substances discharged to marine environments (Carvalho et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Two LCIA method families in ecoinvent cover this impact category: CML (v4.8) at the midpoint level and ReCiPe (v1.03) at the midpoint and endpoint levels. Two other method families not integrated in ecoinvent exist for marine ecotoxicity at the endpoint level, which are LC-IMPACT and IMPACT World+ (v2.1, expert version, 2024). Lastly, additional characterisation models were developed (e.g., Dong et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), but they are not integrated into any LCIA method family. For a detailed overview of how marine ecotoxicity is addressed in LCIA models, please refer to the SI (Section E.I).\u003c/p\u003e \u003cp\u003eWe identified several gaps in these models that do not allow to properly assess marine ecotoxicity potentially caused by OAE in LCA. A first limitation is that some LCIA models do not characterise all the metals identified in Section 3.2.2 as potential LCI elementary flows released in marine environments by this technology. For example, CML and ReCiPe do not characterise the ecotoxicity of aluminium (Al), Fe, and manganese (Mn), and the model developed by Dong et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) omits Al, molybdenum (Mo), and vanadium (V). Only the models in LC-IMPACT and IMPACT World+ provide a full coverage of metals potentially co-released by OAE.\u003c/p\u003e \u003cp\u003eA second limitation is that marine ecotoxicity models do not account for nutrients or alkaline materials as elementary flows. These substances could potentially cause toxicity in marine species (Renforth and Henderson \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Bach et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), although this link has not yet been conclusively established. Additionally, the exposure mechanism for alkaline materials would differ fundamentally from those in current marine ecotoxicity models. While current models typically assess toxicity based on the bioavailability of a substance once released in an environment (Hauschild and Huijbregts \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), alkaline materials would primarily cause toxic impacts through changes in ocean chemistry, requiring a different exposure model.\u003c/p\u003e \u003cp\u003eMoreover, marine ecotoxicity models often rely on freshwater toxicity data due to the limited availability of data on marine species (Fantke et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which incorrectly implies a direct correlation between marine and freshwater ecotoxicity (Carvalho et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Dong et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) is the only model to rely exclusively on toxicity data for marine species.\u003c/p\u003e \u003cp\u003eWith respect to marine spatial resolution, the models vary considerably. CML and ReCiPe divide the ocean into only three broad climate zones, which oversimplifies its spatial heterogeneity. Dong et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) use 64 LMEs, but these are restricted to coastal areas, reducing the applicability of the model to coastal OAE assessments. LC-IMPACT and IMPACT World+ provide the most comprehensive coverage and regionalisation, partitioning the marine environment into nine oceanic and 33 coastal regions (Kounina et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Carvalho et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLastly, marine ecotoxicity models simulate the entry of toxic substances into the marine compartment via discharges from the freshwater compartment (Huijbregts et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kounina et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dong et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The reason behind this modelling choice is that marine ecotoxicity impacts typically originate from industrial activities that releases toxic substances into local freshwater systems, which subsequently flow into seawater systems (Dong et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Conversely, OAE activities may release toxic substances directly into the marine compartment, requiring adapted fate models.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMarine eutrophication\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMarine eutrophication refers to the marine ecosystem response to increased inputs of nutrients or organic matter (Cosme and Hauschild \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Two LCIA method families in ecoinvent include marine eutrophication as an impact category: EF (v3.1) at the midpoint level and ReCiPe at the midpoint and endpoint levels. Three additional method families not included in ecoinvent also cover marine eutrophication: IMPACT World\u0026thinsp;+\u0026thinsp;at the midpoint and endpoint levels, LC-IMPACT at the endpoint level, and TRACI (v2.2) at the midpoint level. Lastly, one marine eutrophication characterisation model (Henryson et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) is not integrated into any LCIA method family. For a detailed overview of how marine eutrophication is addressed in LCIA models, please refer to the SI (Section E.II).\u003c/p\u003e \u003cp\u003eExisting marine eutrophication models only characterise nitrogen (N) compounds as LCI elementary flows to the ocean. This reflects the origin of eutrophication modelling, which was developed to assess eutrophication from N and phosphorus (P) releases associated with fertiliser use, manure application, and fossil fuel combustion (Verones et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Because marine waters are generally N-limited (EC-JRC \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), LCIA modellers have focused their assessments solely on this nutrient for marine eutrophication (Struijs et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cosme et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Huijbregts et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, other substances may also contribute to stimulating primary production, such as P, Fe, or DSi (Cosme et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Browning and Moore \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, current models are unsuitable for comprehensive OAE assessments, where eutrophication impacts may arise from other nutrients (e.g., DSi or Fe). The only exception is the model by Henryson et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which also accounts for P compounds; however, the model is limited to Sweden in its geographical coverage.\u003c/p\u003e \u003cp\u003eIn terms of ocean\u0026rsquo;s spatial resolution, most marine eutrophication models are limited to coastal waters (Struijs et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cosme and Hauschild \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This restriction arises because eutrophication from agricultural runoff and fossil fuel combustion generally occurs near emission sources or from discharges from freshwater systems, leading offshore regions to be neglected during model development. Consequently, eutrophication impacts resulting from offshore OAE deployments cannot be captured by these LCIA models. Only IMPACT World+ represents the ocean at a broader scale; however, its globally regionalised model only accounts for elementary flows originating from anthropogenic activities reaching marine waters as air emissions (Roy et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In contrast, OAE introduces nutrients directly into the ocean at the location of addition. These limitations highlight the need for adapted fate modelling processes to adequately assess OAE-related marine eutrophication impacts.\u003c/p\u003e \u003cp\u003e \u003cem\u003eOcean acidification\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOcean acidification refers to the oceanic uptake of anthropogenic CO\u003csub\u003e2\u003c/sub\u003e, lowering seawater pH, reducing carbonate ion concentration, and weakening the ocean\u0026rsquo;s buffering capacity, collectively impairing calcification and other physiological functions in marine organisms (Orr et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Although this impact category is not yet included in any method family available in ecoinvent, important progress has been made over the past decade to develop ocean acidification modelling within LCIA. IMPACT World+ includes an ocean acidification impact category and several characterisation models were recently developed (Scherer et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Anderson et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For a detailed overview of how ocean acidification is addressed in LCIA models, please refer to the SI (Section E.III).\u003c/p\u003e \u003cp\u003eRegarding OAE, characterisation models do not allow to capture its beneficial impact on ocean acidification. Current ocean acidification models focus their characterisation on elementary flows that contribute to ocean acidification (currently CO\u003csub\u003e2\u003c/sub\u003e, carbon monoxide (CO), and methane (CH\u003csub\u003e4\u003c/sub\u003e)), which lead to a decrease in seawater pH. In contrast, OAE counteracts ocean acidification by an impact pathway fundamentally different in LCI elementary flows as well as fate and exposure processes. This marine technology releases alkaline material to seawater, enhancing total alkalinity and buffering capacity, thereby increasing seawater pH and increasing carbonate ion availability. This important difference means that current LCIA models cannot account for the beneficial impacts of OAE to mitigate ocean acidification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5. Step 5: Guidance on integrating marine environmental impacts into life cycle impact assessment\u003c/h2\u003e \u003cp\u003eComparing OAE marine environmental impact pathways with existing LCIA models revealed key research gaps in OAE assessments within LCA. We formulated recommendations for assessing OAE and outlined research priorities to improve the integration of OAE marine environmental impacts within marine ecotoxicity, marine eutrophication, and ocean acidification impact categories, as well as to enhance the ocean\u0026rsquo;s spatial resolution in LCIA models. Addressing these research gaps is fundamental to the development of sub-compartmentalised and regionalised CFs for marine impact categories relevant to OAE. These recommendations are summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMarine ecotoxicity\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe recommend following the guidance of Carvalho et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who compared existing characterisation models for marine ecotoxicity. The model integrated into the LC-IMPACT method family characterises a broad range of LCI elementary flows and emission compartments, and implements ecotoxicity modelling recommendations from USEtox (Rosenbaum et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It also offers comprehensive coverage of metals potentially released by OAE and a regionalised marine compartment. IMPACT World+ provides a comparable metal coverage and ocean representation. The midpoint model of Dong et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) may be appropriate for coastal OAE cases where the alkaline material is known to potentially release one or more of the nine trace metals the model characterises. In contrast, ReCiPe or CML are not recommended, as their underlying models poorly represent metal behaviour (Carvalho et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and they do not characterise all metals relevant to OAE. Generally, marine ecotoxicity characterisation models vary considerably in spatial coverage, environment flows, and methodological assumptions, which can influence the LCA results. Ideally, two LCIA method families with two different models should be used and the influences on their results compared (Carvalho et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Improving LCIA method families also requires additional marine toxicity data to derive appropriate effect factors instead of relying on freshwater toxicity data.\u003c/p\u003e \u003cp\u003eImportantly, accurate OAE assessment in LCA will require adapted fate models that also represent direct metal releases into the marine environment. Moreover, additional OAE experiments are needed to establish whether nutrient enrichment and increased alkalinity result in ecotoxicological effects on marine species before such processes can be integrated into characterisation models of this impact category.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMarine eutrophication\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFor marine eutrophication, LCIA characterisation models should include nutrients beyond N compounds to enable comprehensive assessments of OAE. Broadening nutrient characterisation would improve the overall representation of marine ecosystems, which are not universally N-limited and may exhibit nutrient co-limitation (Cosme et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, further experimental research is needed to determine the extent of eutrophic effects caused by elements co-released during OAE before developing adapted fate models.\u003c/p\u003e \u003cp\u003e \u003cem\u003eOcean acidification\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAdapted characterisation models are required to capture the counteracting impact of OAE on ocean acidification. This modelling effort should start with the development of species sensitivity distributions (SSDs) to quantify species responses to increased alkalinity, elevated pH, and lower dissolved CO\u003csub\u003e2\u003c/sub\u003e, analogous to what has already been done for decreased pH (Azevedo et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Bednaršek et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Anderson et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such SSDs can only be developed once sufficient experiments are available that evaluate added alkalinity effects from OAE on a broad range of marine calcifying species (e.g., corals, coccolithophores, molluscs, foraminifera, and calcareous algae). Establishing such SSDs would benefit from standardised experiment protocols to ensure comparability across studies. Capturing ecosystem-scale effects from OAE requires, however, complementary modelling frameworks beyond SSD-based characterisation.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMarine spatial resolution\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOcean representation in LCIA models is often restricted to coastal regions. This reflects the origin of LCA modelling, which assessed human activities occurring primarily on land (Woods et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with impacts reaching the ocean mainly through runoff into coastal waters (EC-JRC \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Dong et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vea et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, with human activities expanding into offshore environments, modelling frameworks must be extended to include these regions.\u003c/p\u003e \u003cp\u003eMoreover, the ocean\u0026rsquo;s spatial heterogeneity must be better represented. Current LCIA models represent the ocean in an overly simplified manner. However, the ocean differs importantly in local conditions, such as primary productivity, temperature, salinity, and biodiversity, both across oceanic regions (e.g., Arctic Ocean and North Pacific Ocean) and oceanic depth zones (e.g., benthic and pelagic) (Piacenza et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ninove et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This spatial variability in marine conditions can lead to different impacts locally on marine environments. Moreover, using an overly simplified marine compartment prevents differentiation between transport processes across marine sub-compartments and their corresponding exposure pathways (Hajjar et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For OAE assessments, this level of spatial detail is crucial, as the identified OAE-related fate processes vary among sub-compartments and the environmental impact would differ in magnitude depending on the distance to the location of alkaline material addition.\u003c/p\u003e \u003cp\u003eTo enable accurate assessments, LCIA models should be regionalised, by accounting for different oceanic regions, and sub-compartmentalised, by differentiating across coastal and offshore zones as well as across oceanic depth zones. Similar challenges have already been addressed in microplastics LCIA modelling (Hajjar et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), where a fate framework was proposed that divides the ocean horizontally (from the continental to the global scale) and vertically (with the beach, water surface, the water column, and sediments). This multi-dimensional approach aligns well with the fate processes relevant for OAE and could serve as a basis for developing OAE-specific fate models, and more broadly, for improving the representation of ocean-related impacts in LCIA.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe methodological approach developed in this work enables the identification of marine environmental impact pathways of marine technologies, which requires combining the LCA field with oceanography and engineering expertise. Our approach represents an initial phase toward the development of sub-compartmentalised and regionalised CFs for marine environmental impacts in LCA, which is essential for addressing the current knowledge gaps in LCIA models. Unlike conventional LCIA development, which typically starts from an environmental stressor-endpoint relationship (e.g., ocean acidification leading to biodiversity loss), our approach starts from technology-specific elementary flows released directly into the marine compartment. This bottom-up, technology-driven entry point is uncommon in LCIA development and is particularly necessary for marine technologies, where direct releases bypass terrestrial and atmospheric compartments.\u003c/p\u003e \u003cp\u003eWe acknowledge several limitations to this approach. The analysis focuses specifically on marine environmental impact pathways, although other sensitive receptors, like humans or structures with instrumental or cultural value (e.g., fisheries zones), may be impacted by marine technologies\u0026rsquo; deployment in marine environments. In our application to OAE, the impact pathways for these receptors are briefly addressed in the SI (Section B). Another limitation is that this approach does not address the temporal resolution of marine environmental impacts. LCIA models are usually static, overlooking the temporality of environmental impacts. This issue has been identified for a long time in LCA (Levasseur et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and remains an active discussion in the LCA community and an ongoing field of research (Fauzi et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApplied to OAE, the methodological approach highlighted key research needs to enable a more comprehensive assessment of this marine technology within LCA. Among the top research priorities, we identified the need for additional \u003cem\u003ein situ\u003c/em\u003e experiments to establish clear causality from added alkalinity and nutrient enrichment to potential ecotoxicological and eutrophication effects. Such experiments should also investigate the impacts of increased alkalinity on a broader range of marine calcifying species. Furthermore, there is a need to develop fate models for OAE that represent elementary flows as being directly released to the ocean, rather than as runoffs from terrestrial, atmospheric, freshwater or coastal systems. These models should also capture the ocean\u0026rsquo;s spatial heterogeneity more accurately, accounting for both coastal and offshore dynamics, as well as for different oceanic layers.\u003c/p\u003e \u003cp\u003eImportantly, several of the research gaps identified, such as a better ocean representation in models, modelling direct release to the marine compartment, and broadening the elementary flows\u0026rsquo; coverage for marine eutrophication, remain relevant beyond OAE assessments and addressing them would advance LCA more broadly. Addressing these identified research gaps would allow for the development of more robust CFs.\u003c/p\u003e \u003cp\u003eImproving the assessment of OAE\u0026rsquo;s marine environmental impacts within LCIA would also complement ongoing research efforts that aim to assess the overall environmental performance of OAE with LCA. The primary function of OAE, like other mCDR technologies, is to remove atmospheric CO\u003csub\u003e2\u003c/sub\u003e, and understanding how LCA can appropriately quantify net carbon removal is essential. Although this aspect is not addressed here as the global warming impact category was not found to directly connect to marine species and ecosystems as sensitive receptor, this is an active area of research. Recent case studies notably evaluated how LCA can be used to evaluate the net carbon removal potential of OAE, thereby contributing to reducing global warming (Foteinis et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur paper lays the groundwork for improving LCIA methodologies by providing LCA practitioners with a methodological approach to identify marine environmental impact pathways of marine technologies and determine research priorities for comprehensively integrating these pathways in LCIA models. This methodological approach provides an initial phase toward the development of sub-compartmentalised and regionalised CFs.\u0026nbsp;It includes five steps: (1) selecting oceanography and geochemistry literature on a studied marine technology and its impacts on marine ecosystems; (2) identifying the relevant marine elementary flows in the LCI and the fate, exposure, and effect processes; (3) reviewing by LCIA, oceanography, and technology experts; (4) analysing LCIA models to examine which elements of these marine environmental impact pathways are already represented in these models and which remain unaccounted for; (5) defining research priorities to improve the assessment of the marine technology\u0026rsquo;s marine environmental impacts within LCA.\u003c/p\u003e\n\u003cp\u003eTo evaluate the usefulness of this approach, we applied it to a case study on OAE. While we do not provide CFs due to important existing knowledge gaps, the framework yields five concrete analytical outputs, which together constitute a necessary precondition for any quantitative LCIA modelling of OAE:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eA complete mapping of marine-relevant LCI\u0026nbsp;elementary\u0026nbsp;flows for OAE (i.e., alkaline materials and co-released trace metals, nutrients, and ions).\u003c/li\u003e\n \u003cli\u003eA technology-specific set of marine fate processes (i.e., diffusion, advection, mixing, sinking, burial, and dissolution).\u003c/li\u003e\n \u003cli\u003eA technology-specific linkage between these marine\u0026nbsp;elementary\u0026nbsp;flows and relevant marine impact categories (i.e., marine ecotoxicity, marine eutrophication, and ocean acidification).\u003c/li\u003e\n \u003cli\u003eA gap matrix showing which LCI marine\u0026nbsp;elementary\u0026nbsp;flows and associated impact categories are covered, and which components remain missing in existing LCIA methods.\u003c/li\u003e\n \u003cli\u003eA prioritised research agenda for CF development identifying what must still be developed.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFuture research should operationalise the identified marine environmental impact pathways by translating them into quantitative fate, exposure, and effect models. As a next step, provisional CFs could be derived for selected LCI marine elementary flows where data availability is sufficient, allowing exploratory LCIA applications of OAE and providing an empirical basis to test, refine, and prioritise further model development. Furthermore, because the methodological approach is designed for application by LCA practitioners evaluating marine technologies, it supports the systematic inclusion of qualitatively identified environmental concerns, thereby reducing the risk that potentially relevant impacts are excluded solely because quantitative characterisation methods are not yet available, a limitation of many existing marine technology LCAs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthors contributions\u003c/h2\u003e\n\u003cp\u003eConceptualisation: M.H.D., P.J.G.H., P.B., N.T.; Methodology: M.H.D., P.J.G.H., P.B., L.S., N.T.; Investigation: M.H.D.; Validation: P.J.G.H., P.B., L.S., P.T.-P., P.G., P.R., N.T.; Visualisation: M.H.D.; Writing: - original draft: M.H.D.; Writing \u0026ndash; review \u0026amp; editing: P.J.G.H., P.B., L.S., P.T.-P., P.G., P.R., N.T..\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eMona H. Delval, Patrik J.G. Henriksson, Pablo Trucco-Pignata, Patricia Grasse, Phil Renforth, and Nils Thonemann received funding from the European Union\u0026apos;s Horizon Europe research and innovation programme (grant agreement n\u0026deg;101081362-2). Patrik J.G. Henriksson is partially funded by FORMAS CAPS (2023\u0026thinsp;\u0026minus;\u0026thinsp;01805) and Inequality and the Biosphere Projects (2020\u0026thinsp;\u0026minus;\u0026thinsp;00454), the European Union\u0026rsquo;s Horizon Europe research and innovation programme (grant agreement no. 101182025). Paul Behrens was supported by a British Academy Global Professorship. Phil Renforth is supported by the Carbon to Sea Initiative. The authors would like to thank Louison F.J. Jacoby for his help on the visualisation and Jeroen B. Guin\u0026eacute;e for interesting discussions on LCIA development.\u003c/p\u003e\n\u003ch2\u003eDeclaration of competing interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eAll the data supporting the findings of this study are available within the paper and its Supplementary Information.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlvarenga RAF, Pr\u0026eacute;at N, Duhayon C, Dewulf J (2022) Prospective life cycle assessment of metal commodities obtained from deep-sea polymetallic nodules. J Clean Prod 330:129884. https://doi.org/10.1016/j.jclepro.2021.129884\u003c/li\u003e\n\u003cli\u003eAnandkumar A, Nagarajan R, Prabakaran K, et al (2018) Human health risk assessment and bioaccumulation of trace metals in fish species collected from the Miri coast, Sarawak, Borneo. 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Int J Life Cycle Assess 14:282\u0026ndash;284. https://doi.org/10.1007/s11367-009-0066-8\u003c/li\u003e\n\u003cli\u003eVea EB, Jwaideh M, Richardson K, et al (2024) Enabling comprehensive assessment of marine eutrophication impacts and their evaluation against regional safe operating space. Int J Life Cycle Assess 29:1738\u0026ndash;1755. https://doi.org/10.1007/s11367-024-02311-z\u003c/li\u003e\n\u003cli\u003eVerones F, Hellweg S, Ant\u0026oacute;n A, et al (2020) LC‐IMPACT: A regionalized life cycle damage assessment method. J of Industrial Ecology 24:1201\u0026ndash;1219. https://doi.org/10.1111/jiec.13018\u003c/li\u003e\n\u003cli\u003eWilliamson P, Wallace DWR, Law CS, et al (2012) Ocean fertilization for geoengineering: A review of effectiveness, environmental impacts and emerging governance. Process Saf Environ Prot 90:475\u0026ndash;488. https://doi.org/10.1016/j.psep.2012.10.007\u003c/li\u003e\n\u003cli\u003eWoods JS, Veltman K, Huijbregts MAJ, et al (2016) Towards a meaningful assessment of marine ecological impacts in life cycle assessment (LCA). Environ Int 89\u0026ndash;90:48\u0026ndash;61. https://doi.org/10.1016/j.envint.2015.12.033\u003c/li\u003e\n\u003cli\u003eWoods JS, Verones F, Jolliet O, et al (2021) A framework for the assessment of marine litter impacts in life cycle impact assessment. Ecol Indic 129:107918. https://doi.org/10.1016/j.ecolind.2021.107918\u003c/li\u003e\n\u003cli\u003eXin X, Goldenberg SU, Taucher J, et al (2024) Resilience of phytoplankton and microzooplankton communities under ocean alkalinity enhancement in the oligotrophic ocean. Environ Sci Technol 58:20918\u0026ndash;20930. https://doi.org/10.1021/acs.est.4c09838\u003c/li\u003e\n\u003cli\u003eYang B, Leonard J, Langdon C (2023) Seawater alkalinity enhancement with magnesium hydroxide and its implication for carbon dioxide removal. Mar Chem 253:104251. https://doi.org/10.1016/j.marchem.2023.104251\u003c/li\u003e\n\u003cli\u003eZhuang W, Zhu T, Li F, et al (2025) Potential environmental impacts and management strategies for metal release during ocean alkalinity enhancement using olivine. Environ Sci Technol 59:1091\u0026ndash;1099. https://doi.org/10.1021/acs.est.4c10705 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"a693bd81-108e-4a5a-b5f2-ee946e60dbeb","identifier":"10.13039/100010661","name":"Horizon 2020 Framework Programme","awardNumber":"101081362-2","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Institute of Environmental Sciences (CML), Leiden University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"LCA, LCIA, impact assessment, impact pathway modelling, OAE, marine carbon dioxide removal, mCDR","lastPublishedDoi":"10.21203/rs.3.rs-9573704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9573704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eHuman activities in the ocean are putting growing pressure on marine ecosystems. Life cycle assessment (LCA) is used to assess these activities environmentally but faces limitations in capturing marine impacts. Improving LCA requires a detailed understanding of the marine environment impact pathways of these technologies to develop sub-compartmentalised and regionalised characterisation factors (CFs). We demonstrate how such pathways can be identified, illustrating with ocean alkalinity enhancement (OAE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe build on Woods et al. (2021), who propose a qualitative framework to identify key components of impact pathways, and Richter et al. (2024), who provide guidance on framework development in a multidisciplinary context. We develop a methodological approach that allows to qualitatively identify the marine environmental impact pathways of marine technologies and determine which components are integrated in LCIA models or missing, as an initial phase toward developing CFs for life cycle impact assessment (LCIA). We then apply the approach to OAE as an illustrative case.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults and Discussion: \u003c/strong\u003eOur methodological approach includes: (1) the selection of literature on a studied marine technology and its impacts on marine ecosystems; (2) the identification of the marine elementary flows in the life cycle inventory and the fate, exposure, and effect processes; (3) the inputs from LCIA, oceanography, and technology experts; (4) the review of LCIA models to examine which elements of the marine environmental impact pathways are represented or lacking; (5) the definition of research priorities to advance the assessment of the marine technology’s environmental impacts within LCA.\u003c/p\u003e\n\u003cp\u003eWe identified three impact categories associated with OAE marine environmental impacts pathways: marine ecotoxicity, marine eutrophication, and ocean acidification. Existing LCIA models only partially capture these pathways and require adaptation for assessing comprehensively OAE. Research priorities include conducting additional experiments on the ecotoxicological and eutrophic effects of OAE deployment in marine environments, and the effect of added alkalinity on a broader range of marine calcifiers. Several of our recommendations are also relevant to enhance marine technologies’ assessments in LCA more broadly, such as improving the ocean’s representation in models, modelling direct release to the marine compartment, and broadening the elementary flows’ coverage for marine eutrophication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eWe present a methodology to identify marine environmental impact pathways of marine technologies, providing a first phase toward developing CFs. The methodological approach can be applied to other marine technologies, where identifying impact pathways require a multidisciplinary approach that combines the LCA field with oceanography and engineering expertise.\u003c/p\u003e","manuscriptTitle":"Guidance on integrating marine environmental impacts of marine technologies into life cycle assessment — Application to ocean alkalinity enhancement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 01:06:54","doi":"10.21203/rs.3.rs-9573704/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0241ee3e-c3f2-4c75-80e9-52450dc76863","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67342191,"name":"Marine and Freshwater Ecology"},{"id":67342192,"name":"Environmental Engineering"}],"tags":[],"updatedAt":"2026-05-09T01:06:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 01:06:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9573704","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9573704","identity":"rs-9573704","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}