Cost- and time-efficient ocean modeling method for quantifying carbon dioxide removal using a body of water

Abstract

A cost- and time-efficient ocean modeling method for quantifying the reduction in the atmospheric CO2 burden resulting from CO2 depletion in a body of water is provided, the CO2 depletion being created by alkalinity addition or by some other CDR approach. In preferred embodiments, the method may use one-dimensional (ID) impulse response function (IRE) curves, which preferably may comprise curves of CO2 uptake or CO2 emission reduction (CDRgross) vs time for a given CO2 depletion, achieved for example by a given discharge of alkalinity added uniformly over a short period of time, such as one month. Note that, in the case of alkalinity addition, this alkalinity may be in dissolved or solid form or some mixture of the two. The uptake curves may be generated from a ID vertical diffusivity model, preferably using as input a scalar field which either quantifies the CO2 depletion versus depth and time, or from which this depletion can be directly calculated. In the case of alkalinity addition this is the alkalinity release field (ARE), generated by a ID ARE model, which simulates (in depth and time) the direct release of dissolved alkalinity and / or the alkalinity generated from the dissolution of alkaline particles (feedstock) with a given particle size distribution.

Description

[0001] Utility Patent Application (Non-Provisional)

[0002] TITLE: COST- AND TIME-EFFICIENT OCEAN MODELING METHOD FOR

[0003] QUANTIFYING CARBON DIOXIDE REMOVAL USING A BODY OF WATER

[0004] INVENTOR(S): Stephen Andrew Rackley; Max Daniel Holloway; Gregory Hudson Rau

[0005] CROSS REFERENCE TO RELATED APPLICATIONS

[0006] This application claims priority to and the benefit of the filing date of U.S. Provisional Application No. 63 / 736,034, filed on December 19, 2024, entitled “COST- AND TIMEEFFICIENT OCEAN MODELING METHOD FOR QUANTIFYING CARBON DIOXIDE REMOVAL VIA OCEAN ALKALINITY ENHANCEMENT”, which is hereby incorporated by reference in its entirety.

[0007] BACKGROUND

[0008] It is of interest to reduce the carbon dioxide (CO2) burden in the atmosphere and various methods are being explored to achieve this at meaningful scales, including nature-based methods, such as afforestation, and “engineered” methods, such as direct air capture (DAC). Such methods are collectively called Carbon Dioxide Removal (CDR). For example, ocean alkalinity enhancement (OAE) is a CDR method that accelerates a natural process that reacts alkaline rocks and water with CO2 to convert the CO2 to stable, dissolved, inorganic alkaline forms, that is bicarbonate and carbonate anions, balanced by cations other than H+. These are ultimately transported via streams and rivers to the ocean where they are sequestered from the atmosphere. Engineered OAE can differ from the preceding scheme in that uncarbonated alkalinity can be added to the ocean that then converts dissolved CO2 to bicarbonate and carbonate anions, and reduces the partial pressure of CO2 (PCO2) within seawater such that either CO2 is prevented from escaping to the atmosphere or CO2 is drawn into and stored in the ocean. The chemical effect, after some degree of air-sea gas exchange with the alkalized, Codepleted water has occurred, is a net increase in alkalinity, pH and total dissolved inorganic carbon (DIC) relative to the baseline case of no alkalinity addition.

[0009] Similarly, all other marine CDR (mCDR) methods also operate by causing an initial CO2 or pCO2depletion in seawater which then results in CDR upon partial or complete air-sea gas equilibration. However, the mechanisms and chemical effects of such CDR differ from OAE. In non-OAE cases, CO2 drawdown can be effected by transfer of carbon from the resident ocean CO2 pool to the ocean organic carbon pool (typically via increased marine photosynthesis) or by the physical or chemical removal of CO2 from the ocean and sequestration from the atmosphere. The chemical effects here are to initially reduce DIC and increase pH, with alkalinity being unaffected relative to the counterfactual (i.e. non-mCDR) scenario. Following partial or complete air-sea CO2 equilibration of the preceding water, the DIC rises and the pH declines, with alkalinity remaining unchanged.

[0010] For both OAE and other mCDR methods that cause an initial CO2 or pCC>2 depletion, the measure of CDR achieved is the resulting increase in the total amount of CO2 transferred from the atmosphere to the ocean, or the decrease in the flux of CO2 from the ocean to the atmosphere. The terms CDR and reduction in atmospheric CO2 burden are used interchangeably to denote this phenomenon. The quantity of CDR or reduction in atmospheric CO2 burden can be determined by spatial and temporal integration of the resulting change in air-sea CO2 flux over the affected water body, using a suite of ocean models as described below.

[0011] OAE is recognized to have significant global capacity to perform CDR due to the vast size of the ocean, coupled with the globally massive amount of dissolved inorganic carbon (DIC) already present in seawater. In addition to elevations in alkalinity concentration and pH, the principal measure of the CDR performed by OAE, as described above, is the elevation of DIC from air-sea CO2 flux, relative to the baseline case where OAE is not employed. Said increase in DIC results either from the influx of CO2 into the ocean or the retention of CO2 that would have otherwise degassed to the atmosphere, both relative to a baseline condition. However, the scale of the ocean presents challenges in directly measuring these effects of OAE because the initial reduction in pCO2 (or any chemical effect of OAE), is quickly diluted below the level of present day instrument detection limits, and because of the vast ocean area that needs to be measured over long periods of time (years) in order to quantify the very dispersed, total CDR achieved. For these reasons, it is necessary to estimate the CDR achieved using ocean models, and typically a suite of such models is required to adequately represent the relevant physical and biogeochemical processes, first on a regional scale around the location of alkalinity addition or CO2 depletion (the “near-field model”) and then on a basin or global scale covering the full area of eventual CO2 uptake (the “far-field model”). Constructing and running these models is timeconsuming and costly, and methods are needed that provide an efficient, cost-effective way of conducting such modeling and CDR estimation. Furthermore, while OAE CDR is emphasized herein, by analogy it is understood that the methods described here can be applied to any large body of water in contact with the atmosphere, such as a lake, river, or natural or artificial reservoir, that has been alkalized or otherwise CO2 depleted for purposes of performing CDR. That is, by analogy it is understood that similar modeling methods can be used in CDR applications to the ocean or other water body where said application involves capturing and storing carbon by means of removing the CO2 from the DIC pool via physical, chemical or biological means other than OAE. When the term mCDR is used to denote such a method or process, it is similarly understood that this includes all applicable bodies of water, including lakes, rivers, or natural or artificial reservoirs, and is not intended to limit the description to marine applications. In all cases the effect is to reduce CO2 and pCC>2 in the water such that when in contact with air 1) a transfer of CO2 from air to ocean is initiated or increased, 2) a transfer of CO2 from ocean to air is decreased or stopped, or 3) some combination of 1 and 2, thus, a reduction of the atmospheric CO2 burden is achieved.

[0012] BRIEF SUMMARY OF THE INVENTION

[0013] A cost- and time-efficient ocean modeling method for quantifying carbon dioxide removal resulting from CO2 depletion in a body of water is provided. The embodiment used here to describe the invention assumes that CO2 depletion results from alkalinity addition, but it should be understood that the invention is equally applicable to all methods that initially deplete CO2 in a body of water in order to achieve CDR. In preferred embodiments, the method may use one-dimensional (ID) impulse response function (IRF) curves, which preferably may comprise CO2 uptake curves (CDRgrOss) vs time for a given pCC>2 depletion, arising, in the case of OAE, from a given dose of alkalinity added uniformly over a short period of time, such as one month. Note that this alkalinity may be in dissolved or solid form or some mixture of the two. It should be understood that, in the case of an mCDR intervention resulting in a decrease in the flux of CO2 from the ocean to the atmosphere, the uptake curve will be replaced by a CO2 retention curve. For brevity, the term “uptake curve” is used henceforth to denote both uptake and retention cases. The uptake curves may be generated from a ID vertical diffusivity model, preferably using as input a scalar field - the alkalinity release field (ARF) in the case of OAE, generated by a ID ARF model - which simulates (in depth and time) the direct release of dissolved alkalinity and / or the alkalinity generated from the release of sinking and dissolving alkaline particles (feedstock) with a given particle size distribution. Vertical diffusivity is the term used in physical oceanography to denote the vertical turbulent mixing of a water column, driven by wind, waves, and convection. In the ID vertical diffusivity model, the alkalinity release quantified by the ARF is translated into an equivalent CO2 reduction (which may be denoted the CO2 reduction field, or CRF), using the well understood chemistry of the ocean carbonate system. The CDRgrOss uptake curve represents the difference in atmosphere-to-ocean CO2 flux between the vertical diffusivity model output from a case that includes the CDR intervention and the output from a counterfactual case that excludes the intervention. In embodiments applied to mCDR approaches other than OAE, the CRF input to the ID vertical diffusivity model would be derived using a method specific to the mCDR approach.

[0014] According to another embodiment consistent with the principles of the present invention, a computer implemented method of quantifying the reduction of the CO2 burden in the atmosphere by addition of a CCh-reactive alkaline substance to a water body, such as the ocean, a lake, reservoir, or river, is provided. In some embodiments, the method may include the steps of: instantiating, via one or more processors of a computing platform, an alkalinity release field (ARF) data record in a database, in which the ARF data record includes a ID scalar field of the vertical distribution of the CCh-reactive alkaline substance released into a water body that quantifies, in depth and time, the amount of CCh-reactive alkaline substance released into the water body and particle dissolution of the CCh-reactive alkaline substance; instantiating, via the processor(s), a depletion data record in the database, in which the depletion data record may be calculated from said ARF data record, in which the depletion data record describes the CO2 depletion of water in a ID vertical water column of the water body resulting from the addition of the CCh-reactive alkaline substance, and in which the depletion data record may be calculated using inputs that include: seasonally varying site-specific conditions of temperature and salinity for the water body, and values of at least two carbonate system parameters; instantiating, via the processor(s), a simulation data record in the database, in which the simulation data record simulates dispersion over time of CO2 depleted water within the ID vertical water column using a ID vertical diffusivity model, and in which the simulation data record may be calculated using inputs that comprise the depletion data record that describes CO2 depletion of water in the ID vertical water column resulting from the addition of the CCh-reactive alkaline substance; and instantiating, via the processor(s), a reduction data record in the database, in which the reduction data record describes the reduction of the atmospheric CO2 burden using the ID vertical diffusivity model to produce a ID curve, in which the ID curve may be one of i) a curve of the uptake of CO2 from air to said water body, and ii) a curve of the reduction of CO2 emissions from said water body to air, versus elapsed time from the start of the CCh-reactive alkaline substance addition, and in which the reduction data record may be calculated using inputs that comprise: a site specific time series of wind speed and atmospheric CO2 partial pressure, and an established relationship between CO2 gas exchange velocity and wind speed.

[0015] According to another embodiment consistent with the principles of the present invention, a computer implemented method of quantifying a reduction of the CO2 burden in the atmosphere in contact with a body of water via a reduction of CO2 contained in the water body, in which the reduction of CO2 is achieved by a Carbon Dioxide Removal (CDR) intervention that uses one or more physical, chemical or biological means other than alkalinity addition to said water body is provided. In some embodiments, the method may include the steps of: instantiating, via one or more processor(s) of a computing platform, a depletion data record in a database, in which the depletion data record describes CO2 depletion of water in a ID vertical water column of the water body resulting from said CDR intervention, and in which the depletion data record may be calculated using an intervention-specific calculation routine, and using as input: seasonally varying site-specific conditions of temperature and salinity for the water body, and values of at least two carbonate system parameters from among pH, pCCh, dissolved inorganic carbon (DIC) concentration, alkalinity concentration; instantiating, via the processor(s), a simulation data record in the database, in which the simulation data record simulates dispersion over time of CO2 depleted water within the ID vertical water column using a ID vertical diffusivity model, and in which the simulation data record may be calculated using inputs that include the depletion data record that describes the CO2 depletion of water in the ID vertical water column resulting from the CDR intervention; and instantiating, via the processor(s), a reduction data record in the database, in which the reduction data record describes the reduction of the atmospheric CO2 burden using the ID vertical diffusivity model to produce a ID curve, in which the ID curve may be one of i) a curve of the uptake of CO2 from air to said water body, and ii) a curve of the reduction of water body-to-air emission of CO2, versus elapsed time from the start of the CDR intervention, and in which the reduction data record may be calculated using inputs that comprise: a site specific time series of wind speed and atmospheric CO2 partial pressure, and an established relationship between CO2 gas exchange velocity and wind speed.

[0016] BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Some embodiments of the present invention are illustrated as an example and are not limited by the figures of the accompanying drawings, in which like references may indicate similar elements and in which:

[0018] FIG. 1 - Figure 1 depicts a block diagram of an example cost- and time-efficient ocean modeling method for quantifying carbon dioxide removal via ocean alkalinity enhancement and other mCDR approaches according to various embodiments described herein.

[0019] FIG. 2 - Figure 2 illustrates a block diagram of an example method of generating a ID ARF according to various embodiments described herein.

[0020] FIG. 3 - Figure 3 shows a block diagram of an example method of generating ID vertical diffusivity model output according to various embodiments described herein.

[0021] FIG. 4 - Figure 4 depicts a block diagram of an example method of generating an IRF + dosing profile convolution according to various embodiments described herein. FIG. 5 - Figure 5 depicts a block diagram of an example method of generating a high confidence creditable CDR quantity using a convolution of the IRF uptake curves with a profile of alkalinity dosing or other CO2 depletion activity, project life cycle emissions, and combined uncertainties according to various embodiments described herein.

[0022] FIG. 6 - Figure 6 illustrates an example calculation of the creditable CDR quantity resulting from a specified profile of alkalinity dosing or otherwise generated CO2 depletion in a body of water, on a regular reporting cycle according to various embodiments described herein.

[0023] FIG. 7 - Figure 7 depicts a block diagram of an example of a computer implemented method of quantifying the reduction of the CO2 burden in the atmosphere by addition of a CO2- reactive alkaline substance to a water body, such as the ocean, a lake, reservoir, or river according to various embodiments described herein

[0024] FIG. 8 - Figure 8 illustrates a block diagram of an example of a computer implemented method of quantifying a reduction of the CO2 burden in the atmosphere in contact with a body of water via a reduction of CO2 contained in the water body, in which the reduction of CO2 may be achieved by a Carbon Dioxide Removal (CDR) intervention that uses one or more physical, chemical or biological means other than alkalinity addition to the water body according to various embodiments described herein.

[0025] FIG. 9 - Figure 9 shows an illustrative example of some of the components and computer implemented methods which may be found in a system for quantifying carbon dioxide removal resulting from CO2 depletion in a body of water according to various embodiments described herein.

[0026] FIG. 10 - Figure 10 illustrates a block diagram showing an example of a server which may be used by the system as described in various embodiments herein.

[0027] FIG. 11 - Figure 11 shows a block diagram illustrating an example of a client device which may be used by the system as described in various embodiments herein.

[0028] FIG. 12 - Figure 12 depicts a block diagram illustrating an example of a system database and some example applications, which may function as software rules engines, of a system for quantifying carbon dioxide removal resulting from CO2 depletion in a body of water according to various embodiments described herein.

[0029] DETAILED DESCRIPTION OF THE INVENTION

[0030] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well as the singular forms, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0031] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one having ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0032] In describing the invention, it will be understood that a number of techniques and steps are disclosed. Each of these has individual benefits and each can also be used in conjunction with one or more, or in some cases all, of the other disclosed techniques. Accordingly, for the sake of clarity, this description will refrain from repeating every possible combination of the individual steps in an unnecessary fashion. Nevertheless, the specification and claims should be read with the understanding that such combinations are entirely within the scope of the invention and the claims.

[0033] DEFINITIONS

[0034] As used herein, the terms “computer” and “computing device” refer to a machine, apparatus, or device that is capable of accepting and performing logic operations from software code. The term “application”, "software", "software code", “source code”, “script”, or "computer software" refers to any set of instructions operable to cause a computer to perform an operation. Software code may be operated on by a "rules engine" or processor. Thus, the methods and systems of the present invention may be performed by a computer or computing device having a processor based on instructions received by computer applications and software, with servers and client devices comprising exemplary computing devices.

[0035] The term “client device” as used herein is a type of computer or computing device comprising circuitry and configured to generally perform functions such as recording audio, photos, and videos; displaying or reproducing audio, photos, and videos; storing, retrieving, or manipulation of electronic data; providing electrical communications and network connectivity; or any other similar function. Non-limiting examples of client devices include: personal computers (PCs), workstations, servers, laptops, tablet PCs including the iPad, cell phones including iOS phones made by Apple Inc., Android OS phones, Microsoft OS phones, Blackberry phones, Apple iPads, Anota digital pens, digital music players, or any electronic device capable of running computer software and displaying information to a user, memory cards, other memory storage devices, digital cameras, external battery packs, external charging devices, and the like. Certain types of electronic devices which are portable and easily carried by a person from one location to another may sometimes be referred to as a “portable electronic device” or “portable device”. Some non-limiting examples of portable devices include: cell phones, smartphones, tablet computers, laptop computers, tablets, digital pens, wearable computers such as Apple Watch, other smartwatches, Fitbit, other wearable fitness trackers, Google Glasses, and the like.

[0036] As used herein, the term “computing platform” refers to any combination of hardware and / or software components that is configured to execute instructions and process data, and that includes at least one processor and at least one memory storing instructions executable by the at least one processor, such as a client device, server, other type of computing device, etc. The computing platform may be embodied as a single physical computing device (for example, a server, desktop computer, laptop computer, tablet, smartphone, other client device, embedded system, edge computing node, etc.) or as a logical platform formed by a plurality of physically separate computing devices that interoperate over one or more communication networks. In some embodiments, a computing platform can include or be implemented using virtualized resources provided by a cloud computing, edge computing, grid computing, or other distributed computing environment in which multiple distinct computers, virtual machines, or computing containers collectively provide the one or more processors and associated memory used to perform the described operations.

[0037] In certain implementations, a “plurality of computing platforms” can form a higher-level computing platform, such as a cloud computing platform, edge computing platform, distributed computing system, or hybrid / multi cloud platform, in which each constituent computing platform itself includes at least one processor and memory, and the constituent computing platforms communicate via one or more wired and / or wireless networks to cooperatively execute software components of the disclosed system. The plurality of computing platforms can include, by way of example and not limitation, on premises servers, data center servers, personal computers, mobile devices, virtual machines, and containerized workloads, any of which may be provisioned, scaled, and de provisioned on demand as part of a shared pool of configurable computing resources.

[0038] As used herein, the term “processor of a computing platform” may include a single processor or a plurality of processors that are discrete, multi core, heterogeneous, distributed across multiple computing platforms (e.g., across multiple servers, client devices, other computing devices, etc.), or implemented in virtualized form (for example, as virtual CPUs provided by a cloud or virtual machine environment) and that collectively execute instructions associated with the computing platform. The one or more processors of a computing platform may encompass one processor of one computing device or any combination of processors of one or more computing devices, one or more cooperating computing platforms or multi platform computing platforms, etc., that together execute the relevant instructions or perform the recited processing operations. Multi platform computing platforms can include, by way of example and not limitation, on premises servers, data center servers, personal computers, mobile devices, virtual machines, and containerized workloads, any of which may be provisioned, scaled, and de provisioned on demand as part of a shared pool of configurable computing resources.

[0039] The term “computer readable medium” as used herein refers to any medium that participates in providing instructions to the processor for execution. A computer readable medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical, magnetic disks, and magneto-optical disks, such as the hard disk or the removable media drive. Volatile media includes dynamic memory, such as the main memory. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that make up the bus. Transmission media may also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

[0040] As used herein the term “data network” or “network” shall mean an infrastructure capable of connecting two or more computers such as client devices either using wires or wirelessly allowing them to transmit and receive data. Non-limiting examples of data networks may include the internet or wireless networks or (i.e., a “wireless network”) which may include Wi-Fi and cellular networks. For example, a network may include a local area network (LAN), a wide area network (WAN) (e.g., the Internet), a mobile relay network, a metropolitan area network (MAN), an ad hoc network, a telephone network (e.g., a Public Switched Telephone Network (PSTN)), a cellular network, a Zigbee network, or a voice-over-IP (VoIP) network.

[0041] As used herein, the term “database” shall generally mean a digital collection of data or information. The present invention uses novel methods and processes to store, link, and modify information such digital images and videos and user profile information. For the purposes of the present disclosure, a database may be stored on a remote server and accessed by a client device through the internet (i.e., the database is in the cloud) or alternatively in some embodiments the database may be stored on the client device or remote computer itself (i.e., local storage). A “data store” as used herein may contain or comprise a database (i.e., information and data from a database may be recorded into a medium on a data store). As used in this application, the term “about” or “approximately” refers to a range of values within plus or minus 20% of the specified number. Additionally, as used in this application, the term “substantially” means that the actual value is within about 10% of the actual desired value, particularly within about 5% of the actual desired value and especially within about 1% of the actual desired value of any variable, element or limit set forth herein. As used in this application, the term “CCh-reactive alkaline substance” (sometimes referred to as “alkalinity”) may refer to a homogeneous or heterogeneous alkaline material that may be in solid, liquid, crushed, powdered, granulated, or other physical form. It should be understood that the term “CCh-reactive alkaline substance” includes one or more CC -reactive alkaline substances. Generally, a CCh-reactive alkaline substance is an inorganic, basic (high pH) material that chemically reacts with carbon dioxide (CO2) to form stable carbonates, bicarbonates or some combination of both. Examples of CCh-reactive alkaline substances include oxides, hydroxides, carbonates and silicates of alkali metals (Group 1 elements) and alkali earth metals (Group 2 elements) of the periodic table.

[0042] As used in this application, the term “curve” is synonymous with “graph”. A graph is often called a curve in mathematics, even if it's a straight line, because "curve" refers to any continuous path of points, from wiggly lines and circles to straight lines, representing a function or relation. A line graph (or line chart) specifically connects points with straight segments to show trends, but mathematically, it's a type of curve, and a straight line is just a special, zerocurvature curve.

[0043] A new cost- and time-efficient ocean modeling method for quantifying the reduction of the atmospheric CO2 burden (CDR) resulting from ocean alkalinity enhancement or other methods of initially depleting CO2 in a body of water is discussed herein. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be evident, however, to one skilled in the art (one with ordinary skill in the art) that the present invention may be practiced without these specific details.

[0044] The present disclosure is to be considered as an exemplification of the invention and is not intended to limit the invention to the specific embodiments illustrated by the figures or description below.

[0045] The present invention will now be described by example and through referencing the appended figures representing preferred and alternative embodiments. The embodiments used here to describe the invention wherein CO2 depletion in a water body, such as the ocean, a lake, reservoir, river, etc., results from alkalinity addition via addition of CCh-reactive alkaline substance(s) to the water body, but it is understood that the invention is equally applicable to all CDR methods that deplete CO2 in a body of water, the CO2 depletion created by physical, chemical, or biological means, for example the physical or chemical extraction of CO2 from seawater or the photosynthetic conversion of seawater CO2 to marine biomass.

[0046] FIG. 9 illustrates some physical and software elements of a system for quantifying carbon dioxide removal resulting from CO2 depletion in a body of water (“the system”) 101 which may be used for performing one or more steps of the methods 100, 700, 800, described herein. Data of data and information of the system 101 may be communicated between one or more access points 103 and computing devices and platforms, such as client devices 400, and servers 300, over a data network 105. Client devices 400 and servers 300 may send data to and receive data from the data network 105 through a network connection 104 with an access point 103. The system 101 may include or be in electronic communication with one or more water monitoring systems 112, terrestrial weather sensors 113, and non-terrestrial weather sensors 114 via network connections 104.

[0047] A data store 308 accessible by the server 300 may contain one or more databases, such as a system database 120, which may store data used and generated by the system 101. The data may comprise any information describing one or more alkalinity release field (ARF) data records 121, depletion data records 122, simulation data records 123, reduction data records 124, and environmental data records 125. These inputs can be retrieved by the system 101 at a daily, weekly, monthly, or other time period cycle as real-time ingestion of data is not required.

[0048] In this example, the system 101 comprises at least one client device 400 (but preferably more than two client devices 400) configured to be operated by one or more users. Client devices 400 may include mobile devices, such as laptops, tablet computers, personal digital assistants, smart phones, and the like, that are equipped with a wireless network interface capable of sending data to one or more servers 300 with access to one or more data stores 308 over a network 105 such as a wireless local area network (WLAN). Additionally, client devices 400 may include fixed devices, such as desktops, workstations, and the like, that are equipped with a wireless or wired network interface capable of sending data to one or more servers 300 with access to one or more data stores 308 over a wireless or wired local area network 105. The present invention may be implemented on at least one computing device, such as a client device 400 and / or server 300, programmed to perform one or more of the steps described herein. In some embodiments, more than one client device 400 and / or server 300 may be used, with each being programmed to carry out one or more steps of a method or process described herein.

[0049] The system 101 may comprise or may be in electronic communication with one or more environmental sensors, such as one or more water monitoring systems 112, terrestrial environmental sensors 113, and non-terrestrial environmental sensors 114. Water monitoring systems 112 may include sensor buoys, boat and ship monitoring sensors, and other devices and systems for measuring atmospheric and water body parameters. Terrestrial environmental sensors 113 may comprise weather sensors that are based on or proximate to the surface of the earth, such as weather stations, weather balloons, drones, weather reporting aircraft, etc. Terrestrial environmental sensors 113 may include anemometers, ultrasonic wind sensors, temperature sensors, pressure sensors, precipitation sensors (snow, rain, etc.), and any other earth-based sensor or reporting device or method for measuring atmospheric and water body parameters. Non-terrestrial environmental sensors 114 may comprise environmental sensors that are not based on or proximate to the surface of the earth, such as by being placed on or located on satellites, space stations, and other earth orbiting objects.

[0050] FIG. 9 also shows some software rules engines and components which may be found in a system 101 and which may optionally be configured to run on one or more computing platforms, such as servers 300 and / or client devices 400, according to various embodiments described herein are illustrated. A server 300 and client device 400 may be in wired and / or wireless electronic communication through a network 105 with one or more water monitoring systems 112, terrestrial environmental sensors 113, and non-terrestrial environmental sensors 114. The engines 131, 132, may be in electronic communication so that data may be readily exchanged between the engines 131, 132, and one or more engines 131, 132, may read, write, or otherwise access data in one or more databases 120 of one or more data stores 308.

[0051] In some embodiments, the system 101 may comprise one or more software rules engines or programs, such as one or more communication engines 131 and one or more generation engines 132. In some embodiments, a communication engine 131 and a generation engine 132 may be operated on a single computing platform, such as a single server 300 or a single client device 400. In further embodiments, the system 101 may comprise any number of communication engines 131 and generation engines 132 which may be run on any number of computing platforms, such as any number of servers 300 and / or client devices 400. It should be understood that the functions attributed to the engines 131, 132, described herein are exemplary in nature, and that in alternative embodiments, any function attributed to any engine 131, 132, may be performed by one or more other engines 131, 132, or any other suitable processor logic.

[0052] The system 101 may comprise one or more databases, such as a system database 120, which may be stored on a data store 308 accessible to one or more engines 131, 132. It should be understood that the described structure of the system database 120 (FIG. 12) is exemplary in nature, and that in alternative embodiments, the data contained within the system database 120 may be organized in any other way. In preferred embodiments, a system database 120 may comprise one or more, such as a plurality of alkalinity release field (ARF) data records 121, depletion data records 122, simulation data records 123, reduction data records 124, and environmental data records 125. In some embodiments, an ARF data record 121 preferably may comprise data that includes a ID scalar field of the vertical distribution of the CCh-reactive alkaline substance released into a water body that quantifies, in depth and time, the amount of CCh-reactive alkaline substance released into the water body and particle dissolution of the CCh-reactive alkaline substance used in methods 100, 700, 800. In some embodiments, a depletion data record 122 preferably may comprise data that describes the CO2 depletion of water in a ID vertical water column of the water body resulting from the addition of the CCh-reactive alkaline substance. In some embodiments, a simulation data record 123 preferably may comprise data that simulates dispersion over time of CO2 depleted water within the ID vertical water column using a ID vertical diffusivity model. In some embodiments, a reduction data record 124 preferably may comprise data that describes the reduction of the atmospheric CO2 burden using the ID vertical diffusivity model to produce a ID curve.

[0053] Environmental data records 125 may comprise data describing one or more parameters of water body and / or atmosphere of methods 100, 700, 800. Environmental data records 125 may comprise data that may be obtained from one or more water monitoring systems 112, terrestrial environmental sensors 113, non-terrestrial environmental sensors 114, public databases and websites, such as government databases and websites, private databases and websites, such as company databases and websites, and other suitable sources. Environmental data records 125 may comprise data that may include: the vertical distribution of the CCh-reactive alkaline substance released into a water body that quantifies, in depth and time, the amount of CCh- reactive alkaline substance released into the water body and particle dissolution of the CCh- reactive alkaline substance; seasonally varying site-specific conditions of temperature and salinity for the water body; values of one or more carbonate system parameters (e.g., pH, pCCh, dissolved inorganic carbon (DIC) concentration, alkalinity concentration); site specific time series of wind speed and atmospheric CO2 partial pressure, such as in the atmosphere above a water body; one or more established relationship between CO2 gas exchange velocity and wind speed; particle size distribution (PSD) of one or more CCh-reactive alkaline substances; one or more dissolution rates of one or more CCh-reactive alkaline substances; particle sinking velocity of one or more CCh-reactive alkaline substance for higher drag on non-spherical particles, the non-spherical particles having a degree of non-sphericity, and the degree of non-sphericity assessed based on scanning electron microscopy (SEM) photographs of the particles; vertical diffusivity in a water body above and below a seasonally varying mixed layer depth; one or more 3D regional ocean models; one or more global ocean models; one or more fluid dynamic simulations that model a discharged plume using as inputs the buoyancy and momentum of the discharge, the output of the fluid dynamic simulation that models a discharged plume being vertical distribution of CO2 depletion once the initial forces have dissipated; and data received from one or more water monitoring systems 112, terrestrial environmental sensors 113, and nonterrestrial environmental sensors 114.

[0054] The system 100 may comprise one or more communication engines 131. A communication engine 131 may comprise or function as communication logic stored in a memory 310, 410, which may be executable by the one or more processors 302, 402, of a computing device, such as a server 300 and a client device 400, and / or any other type of computing platform. In some embodiments, a communication engine 131 may be configured to query and / or receive data inputs from one or more data sources, such as water monitoring systems 112, terrestrial environmental sensors 113, non-terrestrial environmental sensors 114, etc., and use those inputs to instantiate one or more environmental data records 125 in the system database 120. In further embodiments, a communication engine 131 may be configured to receive user input and to provide data output to one or more servers 300 and / or client devices 400.

[0055] The system 100 may comprise one or more generation engines 132. A generation engine 132 may comprise or function as generation logic stored in a memory 310, 410, which may be executable by the one or more processors 302, 402, of a server 300, client device 400, and / or any other type of computing platform. In some embodiments, a generation engine 132 may be configured to use one or more environmental data records 125 and one or more steps of methods 100, 700, 800, to generate alkalinity release field (ARF) data records 121, depletion data records 122, simulation data records 123, and reduction data records 124, and to instantiate those data records in the system database 120.

[0056] FIG. 1 illustrates an example of a cost- and time-efficient ocean modeling method for quantifying carbon dioxide removal resulting from ocean alkalinity enhancement (e.g., CO2- reactive alkaline substance addition) or other methods of depleting CO2 in a body of water (“the method”) 100 according to various embodiments. One or more steps of the method 100 may be performed by a communication engine 131 and / or a generation engine 132 which may be executed by one or more processors of a computing platform, such as one or more processors 302 (FIG. 10), processors 402 (FIG. 11), etc.

[0057] In some embodiments, the method 100 may use one-dimensional (ID) impulse response function (IRF) curves or graphs, which preferably may comprise CO2 uptake curves (CDRgrOss) vs time for a given dose of alkalinity (amount of CCh-reactive alkaline substance) added uniformly over a short period of time, such as one month. The curves or graphs may be generated from a ID vertical diffusivity model, using as input a scalar field - the alkalinity release field (ARF), generated by a ID ARF model - which simulates (in depth and time) the release of dissolved alkalinity from the CCh-reactive alkaline substance either directly and / or from sinking and dissolving alkaline feedstock particles with a given particle size distribution. The CDRgross uptake curve represents the difference in atmosphere-to-ocean CO2 flux between the vertical diffusivity model output from a case that includes the CDR intervention and the output from a counterfactual case that excludes the intervention.

[0058] The quantification of CO2 uptake following alkalinity release using IRF curves generated by a 3D global ocean model has been described by Yankovsky et al. (Yankovsky, E., Zhou, M., Tyka, M., Bachman, S., Ho, D., Karspeck, A. and Long, M., 2024. Impulse response functions as a framework for quantifying ocean-based carbon dioxide removal. EGUsphere, 2024, 1-26.) A key feature of the current invention that allows this quantification to be done in a time- and cost-effective manner is the use of a ID model of the CO2 depleted water column, with periodic calibration against a more complex, costly and time-consuming regional or global 3D model, such as that used in the above cited study.

[0059] Since 3D ocean models cannot (at present) directly model the advection, sinking, and dissolution of a broad distribution of CCh-reactive alkaline substance particle sizes, in preferred embodiments where CCh-reactive alkaline substance is released in full or in part as mineral particles, the method 100 may utilize a ID ARF modeling program that may be used to generate a vertical alkalinity release profile that can be used as an input to a ID or 3D ocean model. Preferably, in such cases, the ID ARF modeling program calculates the alkalinity release versus time for a given size distribution of mineral particles of a CCh-reactive alkaline substance as they sink through a ID water column. The program inputs may include the particle composition and size distribution, and parameters describing the dissolution model, Stokes’ velocity correction, and output grid and timestep specification for the receiving model (e.g. a near-field or coastal model constructed using the Regional Ocean Modeling System (ROMS)).

[0060] Key features of the ID ARF model may include:

[0061] The particle size distribution may be represented by multiple size classes, such as approximately 60 sub-classes. For cases assuming non-spherical particles (see below) the representative “particle diameter” for a sub-class may be taken as the particle's major axis length, and dimensional ratios can be specified, for example based on observations from scanning electron microscope (SEM) photographs.

[0062] Particle sinking may be calculated using Stokes’ Law, with an optional particle shape dependent velocity reduction factor applied in cases assuming non-spherical particles. SEM photography can be used to characterize particle shapes, and shape factors can be derived following experimental work, such as that of Bagheri and Bonadonna. (Bagheri, G. and Bonadonna, C., 2016. On the drag of freely falling non-spherical particles. Powder Technology, 301, 526-544.) Particles are preferably assumed to have zero momentum at the point of release.

[0063] Particle dissolution preferably may be calculated using an experimentally derived dissolution rate, e.g., using results of Pokrovsky and Schott for magnesium hydroxide. (Pokrovsky, O.S. and Schott, J., 2004. Experimental study of brucite dissolution and precipitation in aqueous solutions: surface speciation and chemical affinity control. Geochimica et Cosmochimica Acta, 68(1), 31-45.) For spherical particles, the dissolution rate (mol / m2 / s) may be expressed as a radial shrinking velocity. However, mineral dissolution is also observed to occur via step-retreat at the edges of the crystal layers, rather than isotropically, and, in further embodiments, a 2-axis dissolution model could also be used.

[0064] A seabed depth preferably can be specified to limit the maximum depth to which larger particles can sink. This may be applicable if particles are expected to sink to the bed in a region that is shallower than the region of eventual alkalinity dispersal.

[0065] In some embodiments, where alkalinity (the C Ch-reactive alkaline substance) is released as a fully dissolved stream, the ARF will simply be the time series of the quantity of CO2- reactive alkaline substance released at the discharge location.

[0066] In preferred embodiments, the method 100 may utilize a ID vertical diffusivity model that simulates the impact of OAE in a one-dimensional water column. That is, it simulates the dispersion of released alkalinity within a vertical water column, the impact of increased alkalinity on the seawater carbonate system (in particular, reducing the CO2 concentration and partial pressure of CO2 relative to a baseline case), followed by subsequent pCCh equilibration through air-sea gas exchange. This causes either a net atmospheric CO2 uptake by the ocean, or a net reduction in emissions of CO2 from ocean to the atmosphere over a specified time period. The model can be matched to location specific conditions by forcing the air-sea CO2 flux using local wind speed data and by imposing a seasonally varying mixed layer depth (MLD) specific to the region.

[0067] Key features of a ID vertical diffusivity model may include:

[0068] The model represents a vertical water column (e.g., 300 meters deep), divided into grid layers (e.g., 1 meter thick). A notional areal extent of 1000 km2may be used to relate a dosing rate to an alkalinity addition rate in pmol / kg.

[0069] In preferred embodiments, alkalinity dosing can be specified as a simple rate over a fixed dosing period, along with a simple vertical profile (e.g., uniform dosing across the top several layers). Alternatively, an alkalinity release field (ARF(z, t)) representing the spatial and temporal alkalinity release for the specific feedstock can be read in on an hourly or daily basis (e.g. as generated by the ID alkalinity release model). If alkalinity is released from a moving platform, such as a ship, the discharge location will reflect the path of the ship during the dosing period.

[0070] In preferred embodiments, the seawater carbonate system response to increased alkalinity (TA) may be solved, to determine the resulting pCCh depletion relative to baseline, using the approximation reported by Follows et al., and air-sea gas exchange may be parameterized using the Wanninkhof relationship for CO2. (Follows, M.J., Ito, T. and Dutkiewicz, S., 2006. On the solution of the carbonate chemistry system in ocean biogeochemistry models. Ocean Modelling, 12(3-4), 290-301.) (Wanninkhof, R., 2014. Relationship between wind speed and gas exchange over the ocean revisited. Limnology and Oceanography: Methods, 12(6), 351-362.)

[0071] In preferred embodiments, wind speed data (<Uio2>, the expectation value of the square of the wind speed at 10 m elevation) may be calculated by downloading wind speed data on an hourly (or higher) frequency from one or more representative weather stations, or from any other suitable source.

[0072] In preferred embodiments, mixed layer depth (MLD) by month may be sourced from oceanographic studies, such as Cai et al. for the NE US shelf, or downloaded for the region from ocean reanalysis products. (Cai, C., Kwon, Y.O., Chen, Z., and Fratantoni, P., 2021. Mixed layer depth climatology over the northeast US continental shelf (1993-2018). Continental Shelf Research, 231, p.104611.)

[0073] In preferred embodiments, the shallow and deep diffusivities (above and below the MLD) may be set at typical ocean values (e.g., 1.9 10'3m2 / s and 10'4m2 / s respectively).

[0074] In preferred embodiments, the model may run using an outer time loop (e.g., a 1-day timestep) for graphical output, and an inner time loop with a shorter timestep for solving the diffusivity equation. This short inner timestep (e.g., 0.5 hr) will typically be required to maintain stability of the numerical solution of the diffusivity equation.

[0075] In preferred embodiments, the method 100 may include the steps described below which may be used to generate the feedstock’s ARF, the atmospheric CO2 uptake curves, and thence to determine a creditable CDR quantity.

[0076] In some embodiments, the method 100 may include generating a ID ARF in step 10.

[0077] Referring to FIG. 2, some example steps that a method of generating a ID alkalinity release field (ARF) (“the method”) 10 may include are depicted. It should be understood that the steps of method 10 are not limited to the order shown. One or more steps of the method 10 may be performed by a communication engine 131 and / or a generation engine 132 which may be executed by one or more processors of a computing platform, such as one or more processors 302 (FIG. 10), processors 402 (FIG. 11), etc. In some embodiments, a method of generating a ID ARF 10 may comprise preparing a project input data file in step 11. Preferably, a project input data file may be prepared and may comprise one or more of: feedstock composition {wt. fraction, mol. wt., and valency of each reactive alkaline component}, feedstock particle density and particle size distribution, EQOH factor (equivalent OH' ion content of the feedstock i.e., the maximum potential alkalinity contained in the feed that, if dissolved, can consume CO2), max. particle settling depth (e.g., from a near-field 3D model calibration), feedstock dissolution model and rate, seawater density, Stokes’ velocity factors for large and small particles, small vs. large particle diameter threshold, output grid specification (z, t). The ARF can be generated for any specified output grid size and output timestep; default for the ID vertical diffusivity model may be a vertical water column extending from the surface to a depth of 300 m, divided into 300 x Im layers, and a 1-day timestep. The feedstock composition input file will specify the major alkaline components in the feedstock, and will allow a maximum EQOH to be calculated. This will generally need to be corrected to the actual EQOH of the feedstock determined from a full mineralogical analysis and the experimentally measured amount of alkalinity generated from the solid feed within a given time period.

[0078] In step 12, a minute-by-minute dosing profile may be generated. Preferably, this is a sequential file with each record containing the timestamp and the dose rate (kg / min) for the specified minute. For example, the record:

[0079] 2023 10 1 9 30 0.115741E+02 specifies a rate of 11.574 kg feedstock / min for the minute starting at 09:30 on 1st October 2023. This feature of the model allows a fully time-variable dosing profile to be modeled, although it is not strictly necessary when generating the ARF for a uniform, nominal dose.

[0080] In step 13, the ARF for the nominal dose may be generated. The ARF may be a sequential file where each record contains the moles of alkalinity released during a given timestep for each layer in the output grid. For example, the record:

[0081] 25 26792.8594 784.4620 775.9824 767.5488 786.1200 777.1739 768.2784

[0082] 785.4653 776.0577 792.1030 757.1040 772.6388 787.2848 776.9122 790.4000

[0083] 756.0546 792.1729 780.7808 769.4717 458.2839 ... specifies, for output timestep 25, the alkalinity released (mols / timestep) in each layer (only the top 20 layers shown here). The ARF is time-of-year invariant, if not affected by significant seasonal changes in seawater conditions, so that, under such conditions, one ARF output file can be used for all dosing months. In embodiments for mCDR approaches other than OAE, the pCO2depletion will be determined by a comparable method, replacing step 10, specific to the mCDR approach.

[0084] In some embodiments, the method 100 may include generating ID vertical diffusivity model output in step 20.

[0085] Referring to FIG. 3, some example steps that a method of generating ID vertical diffusivity model output (“the method”) 20 may include are depicted. It should be understood that the steps of method 20 are not limited to the order shown. One or more steps of the method 20 may be performed by a communication engine 131 and / or a generation engine 132 which may be executed by one or more processors of a computing platform, such as one or more processors 302 (FIG. 10), processors 402 (FIG. 11), etc.

[0086] In some embodiments, a method of generating ID vertical diffusivity model output 20 may comprise preparing a project input data file in step 21. Preferably, a project input data file may be prepared and may comprise one or more of: representative monthly wind speeds {<Uio2>based on averaging hourly (or higher frequency) data}, monthly average mixed layer depth, shallow and deep diffusivities, and site specific seasonal (or monthly) average seawater T and S.

[0087] In step 22, carbonate system parameters for the site temperature and salinity may be determined. For example, for Halifax, Nova Scotia with fall conditions, suitable parameters are as follows:

[0088] Carbonate system parameters for Halifax at 2.90C

[0089] Btot = 414.469 ! Adjusted to stabilize carb calc

[0090] Ki = 0.649e-6

[0091] K2= 3.376e-10

[0092] Kb = 1.0e-9 ! Adjusted to stabilize carb calc

[0093] Kw= 1.443e-14

[0094] Ko = .0572 pCO2sea = 467.69

[0095] In some embodiments, these parameter values may be calculated using the relationships published in Dickson (2010) (Dickson, A.G., 2010. The carbon dioxide system in seawater: equilibrium chemistry and measurements. Guide to best practices for ocean acidification research and data reporting, 1, 17-40. https: / / www.pmel.noaa.gov / co2 / files / dickson_thecarbondioxidesysteminseawater_equilibriumch emistryandmeasurementsppl7-40.pdf), or alternatively can be back calculated from CO2SYS output. (Lewis, E. and Wallace, D. W. R.: Program Developed for CO2System Calculations, ORNL / CDIAC-105, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, TN, USA, https: / / www.ncei.noaa.gov / access / ocean-carbon-data-system / oceans / CO2SYS / co2rprt.html, 1998. Humphreys, M.P., Lewis, E.R., Sharp, J.D. and Pierrot, D., 2021. PyC02SYS vl. 7: Marine carbonate system calculations in Python. Oceanography, 1-45.) For long-term dosing or discharge the parameters will need to be adjusted for seasonally varying temperature and salinity. The parameters are used to “solve” the seawater carbonate system (i.e., to determine the impact of added alkalinity on the speciation of dissolved inorganic carbon (DIC), including the reduction of pCO2in seawater) using the method described by Follows et al.

[0096] In step 23, a carbonate system parameter (such as the borate constant, Kb) may be finetuned to ensure that pCChsea = pCChair which ensures a zero CDR baseline. In some embodiments, this may be done by setting the feedstock dose to zero and adjusting Kb until the CDR is zero, and is to ensure a zero baseline for project CDR quantification. In some embodiments, when the CDR baseline is pCChsea > pCO2air (such as is usually the case in Halifax, NS) a zero baseline in the ID model may be used to avoid the need to make an explicit counterfactual run. Linearity of the carbonate system response to modest alkalinity increase (or to pCO2reduction generated by other means) ensures that this incurs minimal error.

[0097] In step 24, the ID vertical diffusivity model code may be run for each dosing or discharge month, to write the uptake curve to file on a monthly or other timestep.

[0098] In some embodiments, generating ID vertical diffusivity model output may include step 25, in which any tuning required to match the ID model output to a near-field model output may be identified. Preferably, this tuning may be done by comparing the ID uptake curve(s) with the near-field model results, across an ensemble of sensitivity cases, and identifying which model parameters need to be tuned to minimize any mismatch. For example, at Halifax, this tuning was achieved by specifying a maximum particle settling depth based on the results of a near-field 3D ocean model (as described above). Other sites may need different parameters to be adjusted.

[0099] In some embodiments, the method 100 may include generating a creditable CDR quantity using the IRF + dosing or discharge profile convolution, project life cycle emissions, and combined uncertainties in these quantities in step 30.

[0100] Referring to FIG. 4, some example steps that a method of generating a creditable CDR quantity (“the method”) 30 may include are depicted. It should be understood that the steps of method 30 are not limited to the order shown. One or more steps of the method 30 may be performed by a communication engine 131 and / or a generation engine 132 which may be executed by one or more processors of a computing platform, such as one or more processors 302 (FIG. 10), processors 402 (FIG. 11), etc.

[0101] In some embodiments, a method of generating a creditable CDR quantity 30 may comprise steps 31 through 33 as described below. FIG. 6 illustrates an example calculation of the creditable CDR quantity resulting from a specified dosing or discharge profile on a regular reporting cycle (e.g. monthly or quarterly). Steps 31 to 33, below, describe how the graph is constructed. Step 43 describes how it is used to determine successive creditable CDR volumes.

[0102] In step 31, the weighting factor for each monthly uptake curve may be calculated (i.e., the actual or forecast total feedstock dose or discharge for that month divided by the nominal dose or discharge used to generate the curve, e.g., xy.z tonnes / 500 tonnes).

[0103] In step 32, the overall uptake curve for the full dosing or discharge profile may be determined. Preferably, each monthly IRF uptake curve may be multiplied by its weighting factor and the summation of these “dose weighted monthly uptake curves”, time referenced to the start of dosing or discharge, will generate the overall uptake curve for the full dosing / discharge profile.

[0104] In optional step 33, the overall uptake curve may be compared with the far-field model output across the time horizon (e.g. out to 10 years) to determine any correction needed (e.g. CDRgross. corrected = a x CDRgross.initiai+ b). This correction may be applied for interim crediting reports until it is recalculated at the next far-field update point (see FIG. 6).

[0105] Figure 5 depicts a block diagram of an example method of generating a high confidence creditable CDR quantity using the IRF + dosing / discharge profile convolution, project life cycle emissions, and combined uncertainties (“the method”) 40 according to various embodiments described herein.

[0106] In some embodiments, the method 40 may comprise steps 41 - 43 as described below. One or more steps of the method 40 may be performed by a communication engine 131 and / or a generation engine 132 which may be executed by one or more processors of a computing platform, such as one or more processors 302 (FIG. 10), processors 402 (FIG. 11), etc.

[0107] In step 41, the project’s life-cycle embedded CO2 emissions (ELCA) may be allocated. In preferred embodiments, the project’s life-cycle embedded emissions (ELCA) may be allocated to the 10-year ex ante CDRgross estimated from the far-field model. This is pro-rated to the CDRgross at the end of the reporting period, and subtracted from the CDRgross uptake curve to obtain the corresponding CDRnet. Since total embedded emissions will depend on actual project activity, ELCA and the 10-year allocation preferably may be updated for each reporting period. It is a novel feature of the present invention that this update of the ELCA allocation is taken into account, since the ELCA will change as project activity progresses beyond the initial reporting period.

[0108] In step 42, the l c (one standard deviation) uncertainty band may be determined. In preferred embodiments, the l c uncertainty band may be determined as the envelope of the ensemble of sensitivity cases run using the ID, near- and far-field models, to which should be added the uncertainty in embedded emissions (e.g., by the simple addition of variances). The definition of the cases included in the ensemble will be project and site specific and may change with time.

[0109] In step 43, in preferred embodiments, a value, CDRcred, is calculated to ensure that the CDR quantity claimed has a high confidence (i.e. a high probability that the actual amount of net CDR achieved will be higher than the net CDR claimed). CDRcred may be determined by subtracting an appropriate amount from the mean CDRnet so as to ensure an X% probability that the actual amount of net CDR achieved lies above this value. For example, assuming a normal distribution about the mean of a CDRnet determined using the methods described, subtracting one standard deviation from the mean CDRnet to determine the CDRcred means that there is an 84% probability that the actual amount of net CDR achieved is higher than the CDRcred so chosen.

[0110] For interim reporting periods, between far-field model updates (RP 2, RP n, ... in FIG. 6), the CDRcred quantity at the reporting date may be determined from the overall uptake curve as described above.

[0111] The example of FIG. 6 shows a single short dosing or discharge period within the first reporting period and is representative for early-stage pilot activities. However, the maximum benefit of the method arises for an ongoing operation, since it enables rapid, essentially realtime, low-cost CDR quantification for the full historical dosing or discharge profile. If dosing / discharge has continued during a reporting period, it is a simple matter to generate one or more additional monthly uptake curves and include these in the calculation, with the timeconsuming and costly far-field model updates only being required periodically to ensure that the ID IRF based estimate remains well calibrated.

[0112] FIG. 7 shows a block diagram of an example of a computer implemented method of quantifying the reduction of the CO2 burden in the atmosphere by addition of a CCh-reactive alkaline substance to a water body, such as the ocean, a lake, reservoir, or river (“the method”) 700 according to various embodiments described herein. In some embodiments, the method 700 may be used to improve the functioning of a computing device 300, 400, in performing an estimation of the CDR from addition of a C Ch-reactive alkaline substance to a water body, such as by decreasing the required processing power, decreasing the amount of processing energy used, decreasing the amount of time and computing cycles required, decreasing the amount of memory and data storage required, etc., over existing methods. Existing methods, typically require a suite of such models to adequately represent the relevant physical and biogeochemical processes, first on a regional scale around the location of alkalinity addition or CO2 depletion (the “near-field model”) and then on a basin or global scale covering the full area of eventual CO2 uptake (the “far-field model”). One or more steps of the method 700 may be performed by a communication engine 131 and / or a generation engine 132 which may be executed by one or more processors of a computing platform, such as one or more processors 302 (FIG. 10), processors 402 (FIG. 11), etc., optionally using one more steps of method 100.

[0113] In some embodiments, the method 700 may start 701, and an ARF data record 121 may be instantiated in a system database 120 in step 702. The ARF data record 121 may have a ID scalar field of the vertical distribution of the CCh-reactive alkaline substance released into a water body. A ID scalar field is simply a function that assigns a single number (a scalar, like temperature or height) to every point along a one-dimensional line (like the x-axis), essentially describing how a scalar quantity changes or is distributed in a single direction. In preferred embodiments, the ARF data record 121 comprises a ID scalar field of the vertical distribution of the CCh-reactive alkaline substance released into a water body that quantifies, in depth and time, the amount of CCh-reactive alkaline substance released into the water body and particle dissolution of the CCh-reactive alkaline substance. Preferably, a ID scalar field may be produced quantifying the alkalinity (the CCh-reactive alkaline substance) released into the waterbody (the alkalinity release field or ARF), in which the alkalinity release may be achieved i) directly in dissolved form or ii) as a result of the dissolution of suspended or sinking alkaline particles or iii) a combination of both i and ii. In some embodiments, a particle dissolution may be calculated using as input the particle size distribution (PSD) and the dissolution rate of the feedstock, both determined from laboratory analysis of CCh-reactive alkaline substance feedstock samples. In some embodiments, a reduction factor may be applied to the particle dissolution of the CCh-reactive alkaline substance to adjust particle sinking velocity of the CCh- reactive alkaline substance for higher drag on non-spherical particles, the non-spherical particles having a degree of non-sphericity, and the degree of non- sphericity assessed based on scanning electron microscopy (SEM) photographs of the particles.

[0114] In some embodiments, a depletion data record 122 describing CO2 depletion of water in ID vertical water column of water body resulting from the addition of the CCh-reactive alkaline substance may be instantiated in the system database 120 in step 703. In preferred embodiments, the depletion data record 122 may be calculated from the ARF data record 121, and the depletion data record 122 preferably describes the CO2 depletion of water in a ID vertical water column of the water body resulting from the addition of the CCh-reactive alkaline substance. The depletion data record 122 may be calculated using inputs that comprise: seasonally varying site-specific conditions of temperature and salinity for the water body, and values of at least two carbonate system parameters. The CO2 depletion in the ID vertical water column resulting from the alkalinity addition may be calculated based on the well-established carbonate system chemistry, for example in the case of seawater, using a software system such as CO2SYS, using as input the seasonally varying site specific ocean conditions of temperature and salinity, and using as input values of two or more carbonate system parameters. Preferably, carbonate system parameters may include: pH, pCC , dissolved inorganic carbon (DIC) concentration, and alkalinity concentration.

[0115] In some embodiments, a simulation data record 123 simulating dispersion over time of CO2 depleted water within ID vertical water column using ID vertical diffusivity model may be instantiated in the system database 120 in step 704. In preferred embodiments, the simulation data record 123 simulates dispersion over time of CO2 depleted water within the ID vertical water column using a ID vertical diffusivity model. Preferably, the simulation data record 123 may be calculated using inputs that comprise the depletion data record 122 that describes CO2 depletion of water in the ID vertical water column resulting from the addition of the CO2- reactive alkaline substance. The diffusive dispersion of the CO2 depleted water within the ID vertical water column may be simulated using a ID diffusivity model, using as input the initial CO2 depletion calculated from steps 702 and 703. As an example, inputs used in the calculation of the simulation data record 123 further include vertical diffusivity in the water body above and below a seasonally varying mixed layer depth. In ocean applications, diffusivity values may either be taken as globally representative values or derived from ocean modeling. Mixed layer depth values, per month or season, may be derived from direct measurements or from ocean models.

[0116] In some embodiments, a reduction data record 124 that describes reduction of atmospheric CO2 burden using ID vertical diffusivity model to produce ID curve may be instantiated in the system database 120 in step 705. In preferred embodiments, the reduction data record 124 describes the reduction of the atmospheric CO2 burden using the ID vertical diffusivity model to produce a ID curve, and the ID curve may be one of i) a curve of the uptake of CO2 from air to said water body, and ii) a curve of the reduction of CO2 emissions from said water body to air, versus elapsed time from the start of the C Ch-reactive alkaline substance addition. Preferably, the reduction data record 124 may be calculated using inputs that comprise: a site specific time series of wind speed and atmospheric CO2 partial pressure, and an established relationship between CO2 gas exchange velocity and wind speed.

[0117] In some embodiments, the method 700 may comprise step 706 that may be performed by periodically comparing the ID curve to an equivalent curve produced using a larger scale, more complex 3D model of the water body to ensure that the ID curve matches the curve produced by the more complex model to within an acceptable limit. For example, a larger scale, more complex 3D model of the water body may be a 3D regional ocean model or global ocean model, and a calibration factor or other adjustment is determined to apply to the ID model inputs in order to ensure that the ID uptake curve matches the more complex curve to within an acceptable limit. The correction factor or other adjustment may be applied to any of the inputs of step 702 (for example by limiting the maximum depth to which particles can sink) and / or to step 704 (for example by adjusting the vertical diffusivities or mixed layer depth).

[0118] After steps 705 and / or step 706, the method may finish 707.

[0119] FIG. 8 shows a block diagram of an example of a computer implemented method of quantifying a reduction of the CO2 burden in the atmosphere in contact with a body of water via a reduction of CO2 contained in the water body, in which the reduction of CO2 may be achieved by a Carbon Dioxide Removal (CDR) intervention that uses one or more physical, chemical or biological means other than the addition of a CCh-reactive alkaline substance to the water body (“the method”) 800 according to various embodiments described herein. Example CDR interventions may include: extraction of CO2 from the water body and sequestration of said CO2 from the atmosphere, and the photosynthetic conversion of CO2 residing in a water body to biomass.

[0120] In some embodiments, the method 800 may be used to improve the functioning of a computing device 300, 400, in performing an estimation of the CDR achieved by a Carbon Dioxide Removal (CDR) intervention to a water body, such as by decreasing the required processing power, decreasing the amount of processing energy used, decreasing the amount of time and computing cycles required, decreasing the amount of memory and data storage required, etc., over existing methods. Existing methods, typically require a suite of such models to adequately represent the relevant physical and biogeochemical processes, first on a regional scale around the location of alkalinity addition or CO2 depletion (the “near-field model”) and then on a basin or global scale covering the full area of eventual CO2 uptake (the “far-field model”). One or more steps of the method 800 may be performed by a communication engine 131 and / or a generation engine 132 which may be executed by one or more processors of a computing platform, such as one or more processors 302 (FIG. 10), processors 402 (FIG. 11), etc., optionally using one or more steps of method 100.

[0121] In some embodiments, the method 800 may start 801, and a depletion data record 122 describing CO2 depletion of water in ID vertical water column of water body resulting from the CDR intervention may be instantiated in the system database 120 in step 802. In preferred embodiments, the depletion data record 122 may be calculated using an intervention-specific calculation routine, and may use as input: seasonally varying site-specific conditions of temperature and salinity for the water body, and values of at least two carbonate system parameters from among pH, pCC>2, dissolved inorganic carbon (DIC) concentration, alkalinity concentration. Preferably, the CO2 depletion in the ID vertical water column resulting from the CDR intervention may be determined using an intervention-specific calculation routine, analogous to that described in steps 702 and 703 of method 700 for the case of alkalinity addition. In the case of a discharge to the ocean of a solution whose CO2 has been depleted, the calculation routine may be a fluid dynamic simulation that models the discharged plume using as inputs the buoyancy and momentum of the discharge, the output being the vertical distribution of CO2 depletion once the initial forces have dissipated. For example, inputs used in the calculation of the depletion data record 122 may also include using an intervention-specific calculation routine that comprises a fluid dynamic simulation that models a discharged plume using as inputs the buoyancy and momentum of the discharge, the output of the fluid dynamic simulation that models a discharged plume being vertical distribution of CO2 depletion once the initial forces have dissipated.

[0122] In some embodiments, a simulation data record 123 simulating dispersion over time of CO2 depleted water within ID vertical water column using ID vertical diffusivity model may be instantiated in the system database 120 in step 803. In preferred embodiments, the simulation data record 123 simulates dispersion over time of CO2 depleted water within the ID vertical water column using a ID vertical diffusivity model. Preferably, the simulation data record 123 may be calculated using inputs that comprise the depletion data record 122 that describes CO2 depletion of water in the ID vertical water column resulting from the CDR intervention. The diffusive dispersion of the CO2 depleted water within the ID vertical water column may be simulated using a ID diffusivity model, using as input the initial CO2 depletion calculated from step 802. In preferred embodiments, inputs used in the calculation of the simulation data record 123 may include vertical diffusivity in the water body above and below the seasonally varying mixed layer depth. As an example, inputs used in the calculation of the simulation data record 123 further include vertical diffusivity in the water body, e.g., ocean, above and below a seasonally varying mixed layer depth. In ocean applications, diffusivity values may be taken as globally representative values or derived from ocean modeling. Mixed layer depth values, per month or season, may be derived from direct measurements or from ocean models. Optionally, diffusivity values and mixed layer depths may either be taken as representative of previously measured or estimated values or derived from a more complex modeling of the water body.

[0123] In some embodiments, a reduction data record 124 describing reduction of atmospheric CO2 burden using ID vertical diffusivity model to produce a ID curve may be instantiated in the system database 120 in step 804. In preferred embodiments, the reduction data record 124 describes the reduction of the atmospheric CO2 burden using the ID vertical diffusivity model to produce a ID curve, and the ID curve may be one of i) a curve of the uptake of CO2 from air to said water body, and ii) a curve of the reduction of CO2 emissions from said water body to air, versus elapsed time from the start of the CDR intervention. Preferably, the reduction data record 124 may be calculated using inputs that comprise: a site specific time series of wind speed and atmospheric CO2 partial pressure, and an established relationship between CO2 gas exchange velocity and wind speed. In further embodiments, inputs used in the calculation of the reduction data record 124 may include: a site specific time series of wind speed, partial pressure of CO2 in the atmosphere above the water body, and an established relationship between CO2 gas exchange velocity and wind speed.

[0124] In some embodiments, the method 800 may comprise step 805 that may be performed by periodically comparing the ID curve to an equivalent curve produced using a larger scale, more complex 3D model of the water body to ensure that the ID curve matches the curve produced by the more complex model to within an acceptable limit. For example, a larger scale, more complex 3D model of the water body may be a 3D regional ocean model or global ocean model, and a calibration factor or other adjustment is determined to apply to the ID model inputs in order to ensure that the ID uptake curve matches the more complex curve to within an acceptable limit. The correction factor or other adjustment may be applied to any of the inputs of step 802 (for example by limiting the maximum depth to which particles can sink) and / or to step 803 (for example by adjusting the vertical diffusivities or mixed layer depth).

[0125] After steps 804 and / or step 805, the method 800 may finish 806.

[0126] While some exemplary steps have been provided for steps of the methods described herein, it should be understood to one of ordinary skill in the art that it is not intended herein to mention all the possible alternatives, equivalent forms or ramifications of the invention. It is understood that the terms and steps used herein are merely descriptive, rather than limiting, and that various changes may be made without departing from the spirit or scope of the invention.

[0127] Referring now to FIG. 10, in an exemplary embodiment, a block diagram illustrates a server 300 of which one or more may be used in the system 101 or standalone and which may be a type of computing platform. The server 300 may be a digital computer that, in terms of hardware architecture, generally includes a processor 302, input / output (VO) interfaces 304, a network interface 306, a data store 308, and memory 310. It should be appreciated by those of ordinary skill in the art that FIG. 10 depicts the server 300 in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components (302, 304, 306, 308, and 310) are communicatively coupled via a local interface 312. The local interface 312 may be, for example but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface 312 may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, among many others, to enable communications. Further, the local interface 312 may include address, control, and / or data connections to enable appropriate communications among the aforementioned components.

[0128] The processor 302 is a hardware device for executing software instructions. The processor 302 may be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the server 300, a semiconductor-based microprocessor (in the form of a microchip or chip set), or generally any device for executing software instructions. When the server 300 is in operation, the processor 302 is configured to execute software stored within the memory 310, to communicate data to and from the memory 310, and to generally control operations of the server 300 pursuant to the software instructions. The I / O interfaces 304 may be used to receive user input from and / or for providing system output to one or more devices or components. User input may be provided via, for example, a keyboard, touch pad, and / or a mouse. System output may be provided via a display device and a printer (not shown). I / O interfaces 304 may include, for example, a serial port, a parallel port, a small computer system interface (SCSI), a serial ATA (SATA), a fibre channel, Infiniband, iSCSI, a PCI Express interface (PCI-x), an infrared (IR) interface, a radio frequency (RF) interface, and / or a universal serial bus (USB) interface.

[0129] The network interface 306 may be used to enable the server 300 to communicate on a network, such as the Internet, the data network 105, the enterprise, and the like, etc. The network interface 306 may include, for example, an Ethernet card or adapter (e.g., lOBaseT, Fast Ethernet, Gigabit Ethernet, lOGbE) or a wireless local area network (WLAN) card or adapter (e.g., 802.1 la / b / g / n). The network interface 306 may include address, control, and / or data connections to enable appropriate communications on the network. A data store 308 may be used to store data.

[0130] The data store 308 is a type of memory and may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. Moreover, the data store 308 may incorporate electronic, magnetic, optical, and / or other types of storage media. In one example, the data store 308 may be located internal to the server 300 such as, for example, an internal hard drive connected to the local interface 312 in the server 300. Additionally, in another embodiment, the data store 308 may be located external to the server 300 such as, for example, an external hard drive connected to the I / O interfaces 304 (e.g., SCSI or USB connection). In a further embodiment, the data store 308 may be connected to the server 300 through a network, such as, for example, a network attached file server. The memory 310 may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.), and combinations thereof. Moreover, the memory 310 may incorporate electronic, magnetic, optical, and / or other types of storage media. Note that the memory 310 may have a distributed architecture, where various components are situated remotely from one another, but can be accessed by the processor 302. The software in memory 310 may include one or more software programs, each of which includes an ordered listing of executable instructions for implementing logical functions. The software in the memory 310 may include a suitable operating system (O / S) 314 and one or more programs 320.

[0131] The operating system 314 essentially controls the execution of other computer programs, such as the one or more programs 320, and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The operating system 314 may be, for example Windows NT, Windows 2000, Windows XP, Windows Vista, Windows 7, Windows 8, Windows 10, Windows Server 2003 / 2008 / 2012 / 2016 (all available from Microsoft, Corp, of Redmond, WA), Solaris (available from Sun Microsystems, Inc. of Palo Alto, CA), LINUX (or another UNIX variant) (available from Red Hat of Raleigh, NC and various other vendors), Android and variants thereof (available from Google, Inc. of Mountain View, CA), Apple OS X and variants thereof (available from Apple, Inc. of Cupertino, CA), or the like.

[0132] The one or more programs 320, may be configured to implement the various processes, algorithms, methods, techniques, etc. described herein.

[0133] Referring to FIG. 11, in an exemplary embodiment, a block diagram illustrates a client device 400 of which one or more may be used in the system 101 or the like and which may be a type of computing platform. The client device 400 can be a digital device that, in terms of hardware architecture, generally includes a processor 402, input / output (I / O) interfaces 404, a radio 406, a data store 408, and memory 410. It should be appreciated by those of ordinary skill in the art that FIG. 11 depicts the client device 400 in an oversimplified manner, and a practical embodiment may include additional components and suitably configured processing logic to support known or conventional operating features that are not described in detail herein. The components (402, 404, 406, 408, and 410) are communicatively coupled via a local interface 412. The local interface 412 can be, for example but not limited to, one or more buses or other wired or wireless connections, as is known in the art. The local interface 412 can have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, among many others, to enable communications. Further, the local interface 412 may include address, control, and / or data connections to enable appropriate communications among the aforementioned components.

[0134] The processor 402 is a hardware device for executing software instructions. The processor 402 can be any custom made or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated with the client device 400, a semiconductor-based microprocessor (in the form of a microchip or chip set), or generally any device for executing software instructions. When the client device 400 is in operation, the processor 402 is configured to execute software stored within the memory 410, to communicate data to and from the memory 410, and to generally control operations of the client device 400 pursuant to the software instructions. In an exemplary embodiment, the processor 402 may include a mobile optimized processor such as optimized for power consumption and mobile applications.

[0135] The I / O interfaces 404 can be used to receive data and user input and / or for providing system output. User input can be provided via a plurality of I / O interfaces 404, such as a keypad, a touch screen, a camera, a microphone, a scroll ball, a scroll bar, buttons, bar code scanner, voice recognition, eye gesture, and the like. System output can be provided via a display screen 404A, such as a liquid crystal display (LCD), light emitting diode (LED) display, touch screen display, and the like. The TO interfaces 404 can also include, for example, a global positioning service (GPS) radio, a serial port, a parallel port, a small computer system interface (SCSI), an infrared (IR) interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, and the like. The I / O interfaces 404 can include a graphical user interface (GUI) that enables a user to interact with the client device 400. Additionally, the TO interfaces 404 may be used to output notifications to a user and can include a speaker or other sound emitting device configured to emit audio notifications, a vibrational device configured to vibrate, shake, or produce any other series of rapid and repeated movements to produce haptic notifications, and / or a light emitting diode (LED) or other light emitting element which may be configured to illuminate to provide a visual notification.

[0136] The radio 406 enables wireless communication to an external access device or network. Any number of suitable wireless data communication protocols, techniques, or methodologies can be supported by the radio 406, including, without limitation: RF; IrDA (infrared); Bluetooth; ZigBee (and other variants of the IEEE 802.15 protocol); IEEE 802.11 (any variation); IEEE 802.16 (WiMAX or any other variation); Direct Sequence Spread Spectrum; Frequency Hopping Spread Spectrum; Long Term Evolution (LTE); cellular / wireless / cordless telecommunication protocols (e.g. 3G / 4G, etc.); wireless home network communication protocols; paging network protocols; magnetic induction; satellite data communication protocols; wireless hospital or health care facility network protocols such as those operating in the WMTS bands; GPRS; proprietary wireless data communication protocols such as variants of Wireless USB; and any other protocols for wireless communication.

[0137] The data store 408 may be used to store data and is therefore a type of memory. The data store 408 may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and the like)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, and the like), and combinations thereof. Moreover, the data store 408 may incorporate electronic, magnetic, optical, and / or other types of storage media.

[0138] The memory 410 may include any of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatile memory elements (e.g., ROM, hard drive, etc.), and combinations thereof. Moreover, the memory 410 may incorporate electronic, magnetic, optical, and / or other types of storage media. Note that the memory 410 may have a distributed architecture, where various components are situated remotely from one another, but can be accessed by the processor 402. The software in memory 410 can include one or more software programs 420, each of which includes an ordered listing of executable instructions for implementing logical functions. In the example of FIG. 11, the software in the memory system 410 includes a suitable operating system (O / S) 414 and programs 420.

[0139] The operating system 414 essentially controls the execution of other computer programs, and provides scheduling, input-output control, file and data management, memory management, and communication control and related services. The operating system 414 may be, for example, LINUX (or another UNIX variant), Android (available from Google), Symbian OS, Microsoft Windows CE, Microsoft Windows 7 Mobile, Microsoft Windows 10, iOS (available from Apple, Inc.), webOS (available from Hewlett Packard), Blackberry OS (Available from Research in Motion), and the like.

[0140] The programs 420 may include various applications, add-ons, etc. configured to provide end user functionality with the client device 400. For example, exemplary programs 420 may include, but not limited to, a web browser, social networking applications, streaming media applications, games, mapping and location applications, electronic mail applications, financial applications, and the like. In a typical example, the end user typically uses one or more of the programs 420 along with a network 105 to manipulate information of the system 101.

[0141] It will be appreciated that some exemplary embodiments described herein may include one or more generic or specialized processors (or “processing devices”) such as microprocessors, digital signal processors, customized processors and field programmable gate arrays (FPGAs) and unique stored program instructions (including both software and firmware) that control the one or more processors to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the methods and / or systems described herein. Alternatively, some or all functions may be implemented by a state machine that has no stored program instructions, or in one or more application specific integrated circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic. Of course, a combination of the two approaches may be used. Moreover, some exemplary embodiments may be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer, server, appliance, device, etc. each of which may include a processor to perform methods as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory), a Flash memory, and the like.

[0142] Embodiments of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible program carrier for execution by, or to control the operation of, data processing apparatus. The tangible program carrier can be a propagated signal or a computer readable medium. The propagated signal is an artificially generated signal, e.g., a machine generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a computer. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them.

[0143] A computer program (also known as a program, software, software application, application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

[0144] Additionally, the logic flows and structure block diagrams described in this patent document, which describe particular methods and / or corresponding acts in support of steps and corresponding functions in support of disclosed structural means, may also be utilized to implement corresponding software structures and algorithms, and equivalents thereof. The processes and logic flows described in this specification can be performed by one or more programmable processors (computing device processors) executing one or more computer applications or programs to perform functions by operating on input data and generating output.

[0145] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random-access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, solid state drives, or optical disks. However, a computer need not have such devices.

[0146] Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0147] To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube), light emitting diode (LED) display, or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet.

[0148] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network or the cloud. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client server relationship to each other.

[0149] Further, many embodiments are described in terms of sequences of actions to be performed by, for example, elements of a computing device. It will be recognized that various actions described herein can be performed by specific circuits (e.g., application specific integrated circuits (ASICs)), by program instructions being executed by one or more processors, or by a combination of both. Additionally, these sequences of actions described herein can be considered to be embodied entirely within any form of computer readable storage medium having stored therein a corresponding set of computer instructions that upon execution would cause an associated processor to perform the functionality described herein. Thus, the various aspects of the invention may be embodied in a number of different forms, all of which have been contemplated to be within the scope of the claimed subject matter. In addition, for each of the embodiments described herein, the corresponding form of any such embodiments may be described herein as, for example, “logic configured to” perform the described action.

[0150] The computer system may also include a main memory, such as a random-access memory (RAM) or other dynamic storage device (e.g., dynamic RAM (DRAM), static RAM (SRAM), and synchronous DRAM (SDRAM)), coupled to the bus for storing information and instructions to be executed by processor. In addition, the main memory may be used for storing temporary variables or other intermediate information during the execution of instructions by the processor. The computer system may further include a read only memory (ROM) or other static storage device (e.g., programmable ROM (PROM), erasable PROM (EPROM), and electrically erasable PROM (EEPROM)) coupled to the bus for storing static information and instructions for the processor. The computer system may also include a disk controller coupled to the bus to control one or more storage devices for storing information and instructions, such as a magnetic hard disk, and a removable media drive (e.g., floppy disk drive, read-only compact disc drive, read / write compact disc drive, compact disc jukebox, tape drive, and removable magneto-optical drive). The storage devices may be added to the computer system using an appropriate device interface (e.g., small computer system interface (SCSI), integrated device electronics (IDE), enhanced- IDE (E-IDE), direct memory access (DMA), or ultra-DMA).

[0151] The computer system may also include special purpose logic devices (e.g., application specific integrated circuits (ASICs)) or configurable logic devices (e.g., simple programmable logic devices (SPLDs), complex programmable logic devices (CPLDs), and field programmable gate arrays (FPGAs)).

[0152] The computer system may also include a display controller coupled to the bus to control a display, such as a cathode ray tube (CRT), liquid crystal display (LCD), light emitting diode (LED) display, or any other type of display, for displaying information to a computer user. The computer system may also include input devices, such as a keyboard and a pointing device, for interacting with a computer user and providing information to the processor. Additionally, a touch screen could be employed in conjunction with display. The pointing device, for example, may be a mouse, a trackball, or a pointing stick for communicating direction information and command selections to the processor and for controlling cursor movement on the display. In addition, a printer may provide printed listings of data stored and / or generated by the computer system.

[0153] The computer system performs a portion or all of the processing steps of the invention in response to the processor executing one or more sequences of one or more instructions contained in a memory, such as the main memory. Such instructions may be read into the main memory from another computer readable medium, such as a hard disk or a removable media drive. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in main memory. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

[0154] As stated above, the computer system includes at least one computer readable medium or memory for holding instructions programmed according to the teachings of the invention and for containing data structures, tables, records, or other data described herein. Examples of computer readable media are compact discs, hard disks, floppy disks, tape, magneto-optical disks, PROMs (EPROM, EEPROM, flash EPROM), DRAM, SRAM, SDRAM, or any other magnetic medium, compact discs (e.g., CD-ROM), or any other optical medium, punch cards, paper tape, or other physical medium with patterns of holes, a carrier wave (described below), or any other medium from which a computer can read.

[0155] Stored on any one or on a combination of computer readable media, the present invention includes software for controlling the computer system, for driving a device or devices for implementing the invention, and for enabling the computer system to interact with a human user. Such software may include, but is not limited to, device drivers, operating systems, development tools, and applications software. Such computer readable media further includes the computer program product of the present invention for performing all or a portion (if processing is distributed) of the processing performed in implementing the invention.

[0156] The computer code or software code of the present invention may be any interpretable or executable code mechanism, including but not limited to scripts, interpretable programs, dynamic link libraries (DLLs), Java classes, and complete executable programs. Moreover, parts of the processing of the present invention may be distributed for better performance, reliability, and / or cost.

[0157] Various forms of computer readable media may be involved in carrying out one or more sequences of one or more instructions to processor(s) for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions for implementing all or a portion of the present invention remotely into a dynamic memory and send the instructions over the air (e.g., through a wireless cellular network or WIFI network). A modem local to the computer system may receive the data over the air and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to the bus can receive the data carried in the infrared signal and place the data on the bus. The bus carries the data to the main memory, from which the processor retrieves and executes the instructions. The instructions received by the main memory may optionally be stored on storage device either before or after execution by processor.

[0158] The computer system also includes a communication interface coupled to the bus. The communication interface provides a two-way data communication coupling to a network link that is connected to, for example, a local area network (LAN), or to another communications network such as the Internet. For example, the communication interface may be a network interface card to attach to any packet switched LAN. As another example, the communication interface may be an asymmetrical digital subscriber line (ADSL) card, an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of communications line. Wireless links may also be implemented. In any such implementation, the communication interface sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information. The network link typically provides data communication to the cloud through one or more networks to other data devices. For example, the network link may provide a connection to another computer or remotely located presentation device through a local network (e.g., a LAN) or through equipment operated by a service provider, which provides communication services through a communications network. In preferred embodiments, the local network and the communications network preferably use electrical, electromagnetic, or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link and through the communication interface, which carry the digital data to and from the computer system, are exemplary forms of carrier waves transporting the information. The computer system can transmit and receive data, including program code, through the network(s) and, the network link and the communication interface. Moreover, the network link may provide a connection through a LAN to a client device or client device such as a personal digital assistant (PDA), laptop computer, tablet computer, smartphone, or cellular telephone. The LAN communications network and the other communications networks such as cellular wireless and Wi-Fi networks may use electrical, electromagnetic or optical signals that carry digital data streams. The processor system can transmit notifications and receive data, including program code, through the network(s), the network link and the communication interface.

[0159] Although the present invention has been illustrated and described herein with reference to preferred embodiments and specific examples thereof, it will be readily apparent to those of ordinary skill in the art that other embodiments and examples may perform similar functions and / or achieve like results. All such equivalent embodiments and examples are within the spirit and scope of the present invention, are contemplated thereby, and are intended to be covered by the following claims.

Claims

CLAIMSWhat is claimed is:1) A computer implemented method of quantifying the reduction of the CO2 burden in the atmosphere by addition of a CCh-reactive alkaline substance to a water body, such as the ocean, a lake, reservoir, or river, the method comprising the steps of: a) instantiating, via at least one processor of a computing platform, an alkalinity release field (ARF) data record in a database, wherein the ARF data record comprises a ID scalar field of the vertical distribution of the CCh-reactive alkaline substance released into a water body that quantifies, in depth and time, the amount of CCh-reactive alkaline substance released into the water body and particle dissolution of the CCh-reactive alkaline substance; b) instantiating, via said at least one processor, a depletion data record in the database, wherein the depletion data record is calculated from said ARF data record, wherein the depletion data record describes the CO2 depletion of water in a ID vertical water column of the water body resulting from the addition of the CCh-reactive alkaline substance, and wherein the depletion data record is calculated using inputs that comprise: seasonally varying site-specific conditions of temperature and salinity for the water body, and values of at least two carbonate system parameters; c) instantiating, via said at least one processor, a simulation data record in the database, wherein the simulation data record simulates dispersion over time of CO2 depleted water within the ID vertical water column using a ID vertical diffusivity model, and wherein the simulation data record is calculated using inputs that comprise the depletion data record that describes CO2 depletion of water in the ID vertical water column resulting from the addition of the CO2- reactive alkaline substance; and d) instantiating, via said at least one processor, a reduction data record in the database, wherein the reduction data record describes the reduction of the atmospheric CO2 burden using the ID vertical diffusivity model to produce a ID curve, wherein the ID curve is one of i) a curve of the uptake of CO2 from air to said water body, and ii) a curve of the reduction of CO2 emissions from said water body to air, versus elapsed time from the start of the CCh-reactive alkaline substance addition, and wherein the reduction data record is calculated using inputs that comprise: a site specific time series of wind speed and atmosphericCC>2 partial pressure, and an established relationship between CChgas exchange velocity and wind speed.2) The method of claim 1, further comprising periodically comparing, via said at least one processor, the ID curve to an equivalent curve produced using a larger scale, more complex 3D model of the water body to ensure that the ID curve matches the curve produced by the more complex model to within an acceptable limit.3) The method of claim 2, wherein the larger scale, more complex 3D model of the water body comprises one of a 3D regional ocean model and a global ocean model.4) The method of claim 1, wherein the addition of the CCh-reactive alkaline substance to the water body is performed by at least one of: i) addition to the water body directly in dissolved form, and ii) as a result of the dissolution of suspended or sinking alkaline particles of the CCh-reactive alkaline substance .5) The method of claim 1, wherein the particle dissolution of the CCh-reactive alkaline substance is calculated using as input a particle size distribution (PSD) of the CO2- reactive alkaline substance and a dissolution rate of the CCh-reactive alkaline substance.6) The method of claim 5, wherein a reduction factor is applied in calculating the particle dissolution of the CCh-reactive alkaline substance to adjust particle sinking velocity of the CCh-reactive alkaline substance for higher drag on non-spherical particles, the non- spherical particles having a degree of non-sphericity, and the degree of non-sphericity assessed based on scanning electron microscopy (SEM) photographs of the particles.7) The method of claim 1, wherein the at least two values of the carbonate system parameters are selected from among; pH, pCCh, dissolved inorganic carbon (DIC) concentration, alkalinity concentration.8) The method of claim 1, wherein inputs used in the calculation of the simulation data record further include vertical diffusivity in the water body above and below a seasonally varying mixed layer depth.9) A computer implemented method of quantifying a reduction of the CO2 burden in the atmosphere in contact with a body of water via a reduction of CO2 contained in the water body, said reduction of CO2 achieved by a Carbon Dioxide Removal (CDR) intervention that uses one or more physical, chemical or biological means other than alkalinity addition to said water body, the method comprising the steps of: a) instantiating, via at least one processor of a computing platform, a depletion data record in a database, wherein the depletion data record describes CO2 depletion of water in a ID vertical water column of the water body resulting from said CDR intervention, and wherein the depletion data record is calculated using anintervention-specific calculation routine, and using as input: seasonally varying site-specific conditions of temperature and salinity for the water body, and values of at least two carbonate system parameters from among pH, pCCh, dissolved inorganic carbon (DIC) concentration, alkalinity concentration; b) instantiating, via said at least one processor, a simulation data record in the database, wherein the simulation data record simulates dispersion over time of CO2 depleted water within the ID vertical water column using a ID vertical diffusivity model, and wherein the simulation data record is calculated using inputs that comprise the depletion data record that describes the CO2 depletion of water in the ID vertical water column resulting from the CDR intervention; and c) instantiating, via said at least one processor, a reduction data record in the database, wherein the reduction data record describes the reduction of the atmospheric CO2burden using the ID vertical diffusivity model to produce a ID curve, wherein the ID curve is one of i) a curve of the uptake of CO2 from air to said water body, and ii) a curve of the reduction of water body-to-air emission of CO2, versus elapsed time from the start of the CDR intervention, and wherein the reduction data record is calculated using inputs that comprise: a site specific time series of wind speed, atmospheric CO2 partial pressure, and an established relationship between CO2 gas exchange velocity and wind speed.10) The method of claim 9, further comprising periodically comparing, via said at least one processor, the ID curve to an equivalent curve produced using a larger scale, more complex 3D model of the water body to ensure that the ID curve matches the equivalent curve produced by the more complex model to within an acceptable limit.11) The method of claim 10, wherein the larger scale, more complex 3D model of the water body comprises one of a 3D regional ocean model and a global ocean model.12) The method of claim 11, wherein inputs used in the calculation of the reduction data record further include: a site-specific time series of wind speed, partial pressure of CO2 in the atmosphere above the water body, and an established relationship between CO2 gas exchange velocity and wind speed.13) The method of claim 9, wherein inputs used in the calculation of the simulation data record further include vertical diffusivity in the water body above and below the seasonally varying mixed layer depth.14) The method of claim 9, wherein inputs used in the calculation of the depletion data record further include using an intervention-specific calculation routine that comprises a fluid dynamic simulation that models a discharged plume using as inputs the buoyancyand momentum of the discharge, the output of the fluid dynamic simulation that models a discharged plume being vertical distribution of CO2 depletion once the initial forces have dissipated.

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