Methods to enhance mineralization rates across alkaline waste material by monitoring and optimizing alkalinity, surface roughness, and water content

Abstract

Methods and apparatus for reacting carbon dioxide with an alkaline waste material are provided. The alkalinity, water content and / or surface roughness of the waste material can be monitored during the reaction and appropriate actions undertaken based on the measured parameters to optimize the reaction conditions.

Description

METHODS TO ENHANCE MINERALIZATION RATES ACROSS ALKALINE WASTE MATERIAL BY MONITORING AND OPTIMIZING ALKALINITY, SURFACE ROUGHNESS, AND WATER CONTENTCross-Reference to Related Applications

[0001] This application claims priority to, and the benefit of, United States provisional patent application No. 63 / 622936 filed 19 January 2024, the entirety of which is incorporated herein by reference for all purposes.Technical Field

[0002] Some embodiments relate to methods of removing carbon dioxide (CO2) from the atmosphere. Some embodiments relate to the optimization of reactions occurring between an alkaline waste material and CO2 to enhance or optimize the removal of CO2 by such waste material.Background

[0003] Carbon mineralization is a form of carbon dioxide removal which occurs spontaneously in alkaline waste materials (i.e. , ultramafic mine waste, cement waste). However, the reactions that mineralize CO2 require sufficient labile cations (e.g., Ca2+, Mg2+), water, and CO2 supply to proceed. If any of these parameters are suboptimal then the rate of mineralization will be supressed.

[0004] Annually, the mining and cement industries produce approximately 1.5 Gt of waste material with can be exploited for their labile cations, which readily bind with atmospheric CO2(I. Power et al., 2014). For example, ultramafic mine waste, a product of critical battery metal mining, can contain brucite, a magnesium hydroxide mineral (Mg (OH)2). Rates of brucite dissolution can be two to three orders of magnitude faster than magnesium silicate minerals (e.g., serpentine or olivine) (Lu et al., 2022) and can therefore result in capture of CO2 at time scales of weeks to months instead of years to decades.

[0005] Carbon mineralization occurs when gaseous CO2 dissolves in an alkaline solution with labile cations (e.g., Mg2+) and precipitates carbonate minerals (e.g., magnesite,MgCCh). Carbon mineralization proceeds in three steps, (1) mineral dissolution of alkaline- rich material releases cations into solution (Mg2+, Ca2+) (Eq. 1.1), (2) CC^ gas dissolves into solution and converts to bicarbonate (HCCh') (Eq. 1.2), and (3) bicarbonate reacts with the labile cation (e.g., magnesium) to precipitate carbonate minerals (Eq. 1.3).Release of labile cations: Mg(OH)2-> Mg++ 2OH (1.1)Hydration Mechanism:CO2 mineralization: Mg++ 2HCO3-> MgCO3+ C02+ H20 (1.3)

[0006] The rate of carbon mineralization depends on several physical and chemical properties of the alkaline waste including alkalinity, water content (g HhO / g alkaline solid), and surface roughness. Continuously monitoring and optimizing these parameters presents a pathway to significantly increase the amount of carbon mineralization that occurs in alkaline waste streams. There remains a need to measure and control these parameters at large scale in order to effectively manipulate alkaline waste materials, thereby maximizing carbon mineralization.

[0007] CO2 is absorbed most effectively by alkaline waste at a specific water content. Too little water, and carbon mineralization is limited as water is a reactant needed to form magnesium carbonates (Harrison et al., 2015). Too much water, and CO2 diffusion through material is slowed (Holmes, 2010). The standard method to measure water content in wet solids is loss of weight from oven drying (thermogravimetry), but this technique is time intensive, destructive and requires sample collection and processing in a laboratory. For in- situ water content, the most common instruments used are moisture probes which measure the dielectric properties using time domain reflectometry (TDR) or frequency domain reflectometry (FDR) (Bullock et al., 2004). However, these instruments are not appropriate for use in alkaline waste material due to their high electrical conductivity, which skews moisture readings (Nichol et al., 2003). Furthermore, the reactions that take up CO2 in alkaline tailings occur in the surface layer and rarely penetrate more than a few centimetres(Assima, Larachi, et al., 2013; Bea et al., 2012). These moisture probes provide an average over the length of the prongs which can be > 10cm.

[0008] In order to monitor surficial water content across a large area, the inventors have determined as described further herein that a short-wave infrared (SWIR) hyperspectral camera which produces absorption spectra for each pixel of an image (Barton et al., 2021 ; Dalal & Henry, 1986; Hummel et al., 2001 ; Levy & Johnson, 2021) can be used. SWIR has previously been used in soil sciences to measure water content, pH, total nitrogen, and organic carbon content (Dalal & Henry, 1986; Stenberg et al., 2010) but has yet to be to map water content in alkaline waste materials.

[0009] Monitoring and increasing surface roughness of alkaline waste material can increase CO2 uptake by increasing the reactive surface area of tailings exposed to the atmosphere (surface roughness). The advent of inexpensive 3D modelling software and the increased resolution of digital cameras has made photogrammetry practical for rapid data collection (Rieke-Zapp et al., 2001). Photogrammetry is used to monitor stream channel erosion and has been used extensively in soil science to measure changes in soil structure due to rainfall (Kirby, 1991 ; Rieke-Zapp et al., 2001 ; Taconet & Ciarletti, 2007).

[0010] Both photogrammetry and short-wave infrared spectroscopy are field portable and sensors can be mounted on drones or autonomous rovers (Saari et al., 2017).

[0011] Previous work in the carbon mineralization space has focused on batch dissolution (Harrison et al., 2013b), slurry batch reactor (Harrison et al., 2013a), or unsaturated column injection experiments (Assima, 2012; Assima, Larachi, et al., 2013; Harrison et al., 2015). For high pCC>2 injection experiments, higher water content equates to higher reactivity (Assima, 2012; Harrison et al., 2015). In pCC>2-lean environments, Assima et al., (2013) noted an optimal water content for mineralization rates of around 30% volumetric water content.

[0012] Air-capture experiments using alkaline material have also been performed (Stubbs, 2022, 2023). Stubbs et al., (2022) allowed alkaline minerals to dry from slurry in air and monitored CO2 uptake using dynamic closed chambers. Duplicates of each material were also churned to increased reactive surface area but no effort was made to quantify the increased surface roughness. In all of these studies, ideal water content and surfaceroughness were merely observed with no specific intervention applied to maximize reactivity.

[0013] Patents have been filed using SWIR to measure water content (US10705253, US1 1074447, US11468669) and photogrammetry (US10681856) to measure surface roughness in the agricultural context. However, such methods have not heretofore been used to optimize carbon mineralization of alkaline waste material.

[0014] The foregoing examples of the related art and limitations related thereto are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the drawings.Summary

[0015] The following embodiments and aspects thereof are described and illustrated in conjunction with systems, tools and methods which are meant to be exemplary and illustrative, not limiting in scope. In various embodiments, one or more of the abovedescribed problems have been reduced or eliminated, while other embodiments are directed to other improvements.

[0016] One aspect provides a method of reacting carbon dioxide (CO2) with an alkaline waste material. A reaction mixture of the alkaline waste material is formed, and then alkalinity, water content and surface roughness of the reaction mixture are monitored. In some aspects, the reaction mixture is formed by receiving the alkaline waste material from a source of said alkaline waste material. In some aspects, the source of said alkaline waste material is a mine, a cement kiln, an incinerator, or an industrial plant. In some aspects, the alkaline waste material is ultramafic mine waste containing serpentine, olivine or brucite, for example as obtained from a nickel mine, a diamond mine, a platinum group mine, a chromite mine, or an asbestos mine.

[0017] In some aspects, the step of monitoring the alkalinity of the reaction mixture includes monitoring a pH of the reaction mixture, measuring an instantaneous rate of capture of CO2 by the reaction mixture, for example by measuring CO2 uptake rates using soil gas flux chambers, measuring an amount of time that has elapsed since deposition or last manipulation of the reaction mixture, or measuring eddy covariance. In some aspects, thestep of monitoring the water content of the reaction mixture includes using short wave infrared spectroscopy or short wave infrared spectroscopy hyperspectral imaging. In some aspects, the step of monitoring the surface roughness of the alkaline waste material in the reaction mixture comprises using photogrammetry at a predetermined resolution, for example wherein the photogrammetry is conducted using 3D scanning, or light detection and ranging (Lidar).

[0018] In some aspects, one or more of the pH, water content, or surface roughness of the reaction mixture is adjusted based on an evaluation of one or more of monitoring alkalinity, monitoring water content or monitoring surface roughness, respectively. In some aspects, when it is determined that the water content of the reaction mixture is above a desired water content value, such adjustments can include increasing the surface roughness of the reaction mixture, increasing evaporative surface area of the reaction mixture, or dewatering the reaction mixture. In some aspects, when it is determined that the water content of the reaction mixture is below a desired water content value, such adjustments can include adding water to the reaction mixture or applying a tilling method that promotes overturning to the reaction mixture.

[0019] In some aspects when it is determined that the reaction mixture is too compacted or it is determined that a surface roughness of the reaction mixture is too low by determining that a surface roughness of the mixture is below a desired surface roughness value, such adjustments can include increasing the surface roughness of the reaction mixture or increasing evaporative surface area of the reaction mixture, for example by applying a tilling method that promotes mixing of the reaction mixture.

[0020] In some aspects when it is determined that the reactivity of the reaction mixture at the surface has decreased below a predetermined reactivity decrease level, that a time interval since deposition or last manipulation of the reaction mixture has exceeded a desired manipulation time interval, or that a pH of the alkaline waste material has declined below a predetermined pH value, such adjustments can include removing surface material from the reaction mixture, adding water to the reaction mixture, applying a tilling method that promotes overturning to the reaction mixture or applying a tilling method that increases reactive surface area to the reaction mixture.

[0021] In one aspect, a method for preparing a calibration curve for a rate of CO2 uptake by a material relative to the water content of the material is provided. The method includes providing a plurality of samples of the material having known water contents, obtaining short-wave infrared spectra for each one of the plurality of samples, the short-wave infrared spectra including data for wavelengths between 1800 and 2200 nm, using a convex hull continuum line to connect local maxima of a reflectance spectrum of each one of the obtained short-wave infrared spectra, calculating an area under the convex hull continuum line for wavelengths between 1800 and 2200 nm for each one of the obtained short-wave infrared spectra, and preparing the calibration curve using the known water contents and calculated areas under the curve for each one of the plurality of samples. In some aspects, the calibration curve is further used in any of the methods described above to determine the desired water content value of the reaction mixture.

[0022] In addition to the exemplary aspects and embodiments described above, further aspects and embodiments will become apparent by reference to the drawings and by study of the following detailed descriptions.Brief Description of the Drawings

[0023] Exemplary embodiments are illustrated in referenced figures of the drawings. It is intended that the embodiments and figures disclosed herein are to be considered illustrative rather than restrictive.

[0024] FIG. 1 shows an example embodiment of a method for reacting carbon dioxide with an alkaline waste material.

[0025] FIG. 2 shows an example embodiment of a method for creating a calibration curve for the rate of CO2 uptake by a material relative to the water content of the material using the area between 1800 and 2200 nm to create a calibration curve for each material.

[0026] FIG. 3 shows reflectance spectra behaviour at varying water contents for an ultramafic, serpentine dominant waste material. The reflectance spectra have been normalized so that the continuum (hull) line is 1 .0. This is shown as a black dashed line. The reflectance window of pure water is indicated by a shaded band.

[0027] FIG. 4A shows calibration curves for three alkaline waste materials for the area between the continuum line and the reflectance spectra between 1800 and 2200 nm as a function of gravimetric water content. FIG. 4B shows measured versus expected water content for the three waste materials. Solid black line is the 1 :1 line for expected versus measured water content.

[0028] FIG. 5 shows the relationship between water content and CO2 uptake corrected for surface roughness. Each datapoint (open squares or triangles) is an average of 3 experiments with standard deviations shown.

[0029] FIG. 6 shows CO2 flux time series showing a slurry of waste material and water drying out over 4 days. The dashed line shows the water content and the shaded polygon shows the ideal water content window.

[0030] FIG. 7 shows the relationship between surface roughness and CO2 flux increase above the baseline rate. The three sets of data represent the same material at different water contents.

[0031] FIG. 8 shows the effect of resolution on the relationship between surface roughness and CO2 fluxes for SW-02 waste material. The shaded solid line shows the 1 :1 line for FCO2 increase and surface roughness increase.

[0032] FIG. 9 is time series data showing a slurry material drying over time. The solid black lines shows uptake rates when the material was churned periodically. The dashed line shows the same material allowed to dry with no intervention. Churning is indicated by vertical dashed black lines.

[0033] FIG. 10 shows photos showing pre (left) and post-churning (right). The top photos show the material churned on day 7 of the experiment and the bottom photos show the material churned on day 14.

[0034] FIG. 11 shows a time series showing alkaline waste material reacting over 43 hours. The solid vertical lines indicate when water was added to the waste material and when it was churned (i.e. surface tilled).

[0035] FIG. 12 shows a time series showing alkaline waste material carbonating over 27 days.Description

[0036] Throughout the following description specific details are set forth in order to provide a more thorough understanding to persons skilled in the art. However, well known elements may not have been shown or described in detail to avoid unnecessarily obscuring the disclosure. Accordingly, the description and drawings are to be regarded in an illustrative, rather than a restrictive, sense.

[0037] Alkaline waste materials have the potential to remove significant amounts of CO2 from the atmosphere by converting labile magnesium into stable magnesium-carbonate minerals. The inventors have now determined that the rate at which CO2 is captured from the atmosphere depends on physical and chemical parameters which can be measured and optimized. These parameters include:1. Alkalinity (which may optionally be inferred from time since last manipulation of the material);2. Water content; and3. Surface roughness.

[0038] The manner in which these three parameters affect the rate of mineralization may depend on the grain size and mineralogy of a material. Therefore, in some embodiments, material specific calibration curves are made so that the parameters of alkalinity, water content and / or surface roughness can be optimized based on the values determined for the calibration curves to maximize the rate at which CO2 is captured from the atmosphere for a specific material and grain size. In some embodiments, the grain size of the material is taken into account or noted when generating the calibration curve. In some embodiments, the calibration curve is specific to a specified grain size of the alkaline waste material.Then, these three parameters are measured in real time in the field, using remote monitoring techniques, and compared to the calibration curves. Deviation of the measured parameters from optimal conditions triggers interventions to move the system towards the maximum rate of CO2 capture.

[0039] In some embodiments, a calibration curve for a given material is prepared by evaluating relationships between each of alkalinity, water content and surface roughness and uptake of CO2 by the given material, for example using a suitable apparatus such as aCO2flux chamber. For example, water content and CO2uptake relationships can be determined by measuring the CO2uptake of the given material when mixed to form a plurality of reaction mixtures with varying water contents. Surface roughness and CO2uptake can be evaluated, and the inventors have determined that these parameters exhibit a linear relationship, with the slope of the relationship being dependent on the analysis pixel resolution chosen. In some embodiments, the chosen resolution should result in a 1 :1 relationship and may be unique for each material used. By a 1 :1 relationship, it is meant a relationship in which an increase in surface roughness results in an equivalent increase in CO2uptake as set forth in Equation 2. The relationship between alkalinity and CO2uptake can be evaluated directly by measuring the pH of the reaction mixture or indirectly by measuring the CO2uptake of the given material over a period of time since deposition of the material or since last manipulation (e.g. mixing or tilling) of the material.

[0040] In some embodiments, the physical parameters that control CO2uptake for a given material are remotely monitored during the process. For example, alkalinity can be monitored indirectly using the instantaneous rate of CO2capture or by assessing the time since last manipulation of the material, since direct monitoring of alkalinity may present difficulties on a large scale. Water content can be measured using short wave infrared spectroscopy (SWIR, e.g. by creating a calibration between absorption spectra features and water content, which can be used to estimate water content in a sample of the given material with an unknown water content). Surface roughness can be measured with digital elevation models using photogrammetry at pre-determined resolutions.

[0041] In some embodiments, the parameters of alkalinity, water content and surface roughness are simultaneously and / or continuously monitored across a site containing the alkaline waste material. The parameters of the reaction mixture in which CO2sequestration is taking place can be adjusted based on the determinations made by such monitoring.

[0042] Further, different churning methods may be applied depending on which parameter is limiting and / or suppressing uptake of CO2by the given material. For example, if the material is too wet or compacted, a tilling method focused on mixing could be applied to increase surface roughness and / or assist in dewatering by increasing evaporative surface area. If the material is too dry or is no longer reactive at the surface, a tilling method focused on overturning could be applied to bring up wetter material from depth and / or bring up unreacted material from depth, and / or water can be added to the reaction mixture.

[0043] For example, if water content is slightly too high then CO2 is not able to diffuse into the waste material efficiently and tilling methods aimed at increasing the evaporative surface area and de-watering should be used. If water content is too low, then the mineralization reactions cannot proceed as water is a reactant. In this case, tilling methods that bring up wetter material from depth should be used, or water should be added to the waste material.

[0044] If surface roughness is low, and / or the material has been compacted, CO2 is not able to diffuse into the waste material (e.g., Bea et al). In this case, tilling methods that mix and increase the reactive surface area of the alkaline waste material (i.e., rotary cultivators, rotary tillers, strip tillers, vertical tillers) should be deployed. After an extended period of time, labile cations will become depleted in the surficial material as they are converted to carbonate minerals. If alkalinity has declined then tilling methods that turn over the waste material should be used (i.e., moldboard plow, disk plows, chisel plows). By choosing the ideal tilling method, uptake and energy efficiency can be optimized.

[0045] In some embodiments, the alkaline waste material is a mining waste. In some embodiments, the alkaline waste material is ultramafic mine waste, e.g. containing serpentine, olivine or brucite. Examples of ultramafic mines include nickel mines (komatiite, laterite, Alaskan type intrusions, or the like), diamond mines (kimberlite), platinum group metal mines, chromite mines, asbestos mines or the like. In some embodiments, the alkaline waste material is industrial waste containing high concentrations of Mg(OH)2 or Ca(OH)2, such as cement kiln waste, steel slag, fly ash, or the like, for example obtained from an industrial plant that produces such material.

[0046] In some embodiments, the alkaline waste material is subjected to any desired preparatory treatment and / or treated to produce a desired grain size. In some embodiments, the treatment of the alkaline waste material and the resultant grain size is determined by the nature of the mining or industrial operations that produce the alkaline waste material. In some embodiments, the reaction mixture is received directly from the source that produces the alkaline waste material, e.g. the mine or industrial plant, and the properties of the initial reaction mixture are defined by the form in which the alkaline waste material is received from the source. In some embodiments, the alkaline waste material has, or is processed by a suitable method such as grinding or milling to have, a grain size in the range of between about 10 and about 1000 microns, including any grain size orsubrange of grain sizes within such range, e.g. 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 240, 260, 280, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900 or 950 microns.

[0047] With reference to FIG. 1 , an example embodiment of a method 100 for reacting carbon dioxide with an alkaline waste material is illustrated. At 102, an alkaline waste material is combined with water. At 104, the alkaline waste material is mixed with the water in any suitable manner to form the reaction mixture, for example by wet milling (for example using a ball or semi-autogenous grinding (SAG) mill), using a concrete mixer or concrete truck, or the like. In some embodiments, rather than combining the alkaline waste material with water and mixing, the alkaline waste material is received from its source (e.g. mine, cement kiln, incinerator or industrial plant) as an aqueous mixture that can provide the initial reaction mixture, and steps 102 and 104 do not need to be separately carried out. In some embodiments, the alkaline waste material is deposited as a slurry and then dries gradually until the desired moisture content is reached.

[0048] At 110, various parameters of the reaction mixture are monitored. In some embodiments, the monitoring is periodic. In some embodiments, the periodic monitoring is conducted at an interval of between about 1 and about 24 hours, including any value or subrange therebetween, e.g. 2, 3, 4, 5, 6, 8, 10, 12, 14, 16, 18, 20 or 22 hours. In some embodiments, the monitoring is continuous. In some embodiments, one or more parameters of the reaction mixture are monitored simultaneously.

[0049] In the illustrated embodiment, the parameters that are monitored at 110 are alkalinity at 112 (which may be monitored directly by monitoring pH, and / or indirectly by monitoring the time since the reaction mixture was last manipulated and / or the instantaneous rate of CO2 capture of the reaction mixture), water content at 114 and surface roughness at 116. At 110, monitoring of the monitored parameters is repeated or carried out continuously for as long as needed to allow the reaction mixture to react with carbon dioxide to a desired extent. In some embodiments, only a subset of parameters are monitored at 110, e.g. only one of or only two of alkalinity, water content, and surface roughness. For example, only water content may be monitored at 114 without monitoring alkalinity at 112 or surface roughness at 116 if resources are limited.

[0050] The monitored parameters can be assessed via any suitable technique now known or later developed. In one example embodiment, alkalinity is monitored directly by measuring a pH of the reaction mixture. In one example embodiment, alkalinity is monitored indirectly at 112 by measuring the instantaneous rate of capture, for example by monitoring the change in the rate of CO2 capture by the alkaline waste material over time, for example by measuring CO2 uptake rates using soil gas flux chambers. In one example embodiment, alkalinity is monitored indirectly at 112 by measuring the amount of time that has elapsed since the deposition or last manipulation (e.g. mixing or tilling) of the reaction mixture. In one example embodiment, alkalinity is measured by eddy covariance. Eddy covariance measures the rate of CO2 uptake, which is higher when alkalinity is higher. Eddy covariance can be used where there is a relatively large (e.g. ~500 m2), flat, homogenous area of alkaline waste material.

[0051] In one example embodiment, water content is monitored at 114 using short wave infrared spectroscopy. In one example embodiment, water content is monitored at 114 at scale using hyperspectral imaging, for example by mounting hyperspectral cameras on autonomous unmanned rovers, on drones, or by using satellites. In some embodiments, the unmanned rovers are remotely operated amphibious all terrain vehicles.

[0052] In one example embodiment, surface roughness of the alkaline waste material in the reaction mixture is monitored at 116 using photogrammetry at a predetermined resolution, 3D scanning, or light detection and ranging (Lidar). Such analytical methods can be used to produce digital elevation models (DEMs), which can be used to calculate surface roughness. In one embodiment, as the slope of the linear relationship between roughness and CO2 flux varies according to the grain size of the alkaline waste material, the predetermined resolution for conducting photogrammetry is selected to yield approximately a 1 :1 relationship between surface roughness and CO2 flux. In some embodiments, the instruments used to conduct photogrammetry, 3D scanning, or Lidar are mounted on autonomous unmanned rovers or on drones. In some embodiments, the instruments used to conduct photogrammetry, 3D scanning, or Lidar are mounted on the same autonomous unmanned rover or drone as the hyperspectral camera or cameras used to monitor water content.

[0053] At 120, the monitored parameters can be evaluated, for example by comparing the measured value for each one of the monitored parameters from step 110 to a desired valueor range of desired values for that parameter. If a determination is made at 120 that the monitored parameters each have a desired value or fall within an acceptable range, then at 118 monitoring at 110 can continue. If a determination is made at 120 that one or more of the monitored parameters does not have a desired value or does not fall within an acceptable range, then at 130 one or more process adjustments can be made to move the one or more parameters back towards the desired value or towards the acceptable range.

[0054] For example, at 120, if it is determined that the water content of the reaction mixture measured at 114 is above a desired value or range, then at 130 a tilling method focussed on mixing and / or a dewatering technique can be applied. For example, if it is determined that the water content of the reaction mixture is greater than about 5% to about 25% by weight, including any value or subrange therebetween e.g. 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23 or 24% by weight, a tilling method focused on mixing and / or a dewatering technique can be applied. For example, a tilling method such as using rotary cultivators, rotary tillers, strip tillers or vertical tillers or the like can be applied at 132 to increase surface roughness or to increase evaporative surface area. Alternatively or additionally, the reaction mixture can be dewatered at 140, for example by churning, which may aid in dewatering by increasing the evaporative surface area, by mechanical consolidation (i.e. by compacting wet material to remove water), or in any other suitable manner.

[0055] If at 120 it is determined that the reaction mixture has become compacted, for example if the surface roughness ratio of the reaction mixture approaches 1.0, for example if the surface roughness ratio of the reaction mixture drops below about 1.1 to about 1.5, including any value or subrange therebetween, e.g. 1.2, 1.3 or 1.4, then at 130 a tilling method focussed on mixing can be applied. For example, a tilling method such as using rotary cultivators, rotary tillers, strip tillers or vertical tillers or the like can be applied at 132 to increase surface roughness or to increase evaporative surface area.

[0056] If at 120 it is determined that the reaction mixture is too dry or is no longer reactive at the surface, then at 130 a tilling method focused on overturning such as inverting using a moldboard plow, disk plows or chisel plows or the like can be applied to the reaction mixture at 134. For example, if it is determined that the water content of the reaction mixture is less than about 5% to about 25% by weight, including any value or subrange therebetween e.g. 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23 or 24% by weight, a tillingmethod focused on overturning may be applied and / or more water may be introduced into the reaction mixture. The fact that the reaction mixture is no longer reactive at the surface may indicate that the alkalinity of the reaction mixture has dropped below a predetermined level. The fact that the reaction mixture is no longer reactive at the surface or that the alkalinity of the reaction mixture has dropped below a predetermined level may be inferred from the amount of time that has elapsed since the deposition or last manipulation of the reaction mixture (e.g. the last step of tilling or mixing). For example, after a period of time between about 1 day to about 2 months since the deposition or last manipulation of the reaction mixture, including any value or subrange therebetween e.g. about 2, 3, 4, 5 or 6 days, or about 1 , 2, 3, 4, 5, 6 or 7 weeks, it may be inferred that the alkalinity of the reaction mixture has dropped below a predetermined desired level, and a tilling method focused on overturning may be applied to the reaction mixture. The fact that the reaction mixture is no longer reactive at the surface or that the alkalinity of the reaction mixture has dropped below a predetermined level may be inferred if the instantaneous rate of capture of CO2 by the reaction mixture drops below a predetermined level, e.g. if the instantaneous rate of capture of CO2 by the reaction mixture drops by more than about 50% from an initially measured instantaneous rate of CO2 capture. Without being bound by theory, overturning may bring up wetter alkaline waste material from depth and / or bring up unreacted material from depth. Alternatively and / or additionally, if at 120 it is determined that the alkaline waste material is too dry or is no longer reactive at the surface, then at 130 additional water can be added to the reaction mixture at 136. Alternatively and / or additionally if it is determined at 120 that the alkaline waste material is no longer reactive at the surface, then a tilling method focused on increasing reactive surface area such as use of a rotary cultivator, rotary tiller, strip tiller, vertical tiller or the like can be conducted at 132.

[0057] If at 120 it is determined that the time since deposition or last manipulation is sufficiently high that it may be inferred that the alkalinity of the alkaline waste material has declined, then at 130 alternatively or additionally, surface material can be removed at 144, for example by using an excavator or backhoe and / or fresh material can be brought up from depth, for example by using a tilling method that focuses on overturning such as inverting using a moldboard plow, disk plows or chisel plows or the like at 134.

[0058] If at 120 it is determined based on the monitored alkalinity at 112 that the pH of the alkaline waste material has declined below a predetermined value, for example below a pHof about 7.5 to about 8.5 including any value or subrange therebetween, e.g. 7.6, 7.7, 7.8, 7.9, 8.0, 8.1 , 8.2, 8.3 or 8.4, including for example in some embodiments below a pH of about 8.0, then at 130 surface material can be removed at 144 and / or fresh material can be brought up from depth, for example by using a tilling method that focuses on overturning such as inverting using a moldboard plow, disk plows or chisel plows or the like at 134.

[0059] After the adjustment of any process parameters at 130, at 150 the method returns to monitoring the monitored parameters at 110. This process can be repeated for as long as necessary to react the alkaline waste material with a desired amount of carbon dioxide.

[0060] In some embodiments, the adjustment of process parameters at 130 is conducted based on a machine learning model which has been trained on historical data obtained by carrying out method 100. For example, data pertaining to one, two or all of alkalinity, water content and surface roughness from past implementations of the method can be fed to a machine learning algorithm to train the machine learning model. The machine learning model can be used to predict optimal adjustments to any one, two, or all of alkalinity, water content and surface roughness to optimize the reaction of the carbon dioxide with the alkaline waste material. The machine learning model can be used to control process parameters such as tilling to increase surface roughness or surface area at 132, tilling to overturn material at 134, adding water at 136, dewatering at 140, and / or removing surface material at 144 to optimize one, two or all of alkalinity, water content and surface roughness in the reaction mixture. In some embodiments, the machine learning algorithm can continue to be trained to further optimize the machine learning model as further iterations of method 100 are conducted. In some embodiments, the machine learning model is trained using a specific alkaline waste material and is used to optimize processing of that specific alkaline waste material. In some embodiments, the machine learning model is trained using a specified grain size of the specific alkaline waste material, and is used to optimize processing of that specified grain size of the specific alkaline waste material. In some embodiments, the specific alkaline waste material and the specified grain size are any of the alkaline waste materials and grain sizes disclosed in this specification.

[0061] In some embodiments, the source of carbon dioxide for reacting with the alkaline waste material is ambient (i.e. atmospheric) air.

[0062] Any suitable apparatus can be used to carry out method 100. For example, in some embodiments, cameras or sensors for carrying out photogrammetry such as an appropriate 3D scanner (e.g. HandySCAN™) or a hyperspectral camera including a short range Lidar, and for carrying out SWIR hyperspectral imaging such as an appropriate camera, can be mounted to an autonomous rover or drone to carry out the necessary imaging to monitor water content and / or surface roughness of the reaction mixture.

[0063] In one example embodiment with reference to FIG. 2, a method 200 of preparing a calibration curve reflecting the rate of CO2 uptake by a material relative to the water content of the material using the area between 1800 and 2200 nm in the short-wave infrared spectra for the material is illustrated. At 202, a plurality of samples of the material having varying known water contents are prepared. At 204, short-wave infrared spectra are obtained for each one of the plurality of samples, including data for wavelengths between 1800 and 2200 nm. At 206, a convex hull continuum line is used to connect the local maxima of the reflectance spectrum. At 208, the spectra are detrended relative to the continuum line such that the continuum line represents a reflectance of 1 .0. At 210, the area under the continuum line is calculated for wavelengths between 1800 and 2200 nm. At 212, the calibration curve for the plurality of samples is prepared using the known water contents and determined areas under the continuum line for wavelengths between 1800 and 2200 nm.

[0064] Certain embodiments are further described with reference to the following examples, which are intended to be illustrative and not limiting in nature.1 .0 - Methods1.1 Material Characterization

[0065] A given alkaline waste material is characterized for mineralogy, particle size distribution (PSD), and pH. This characterization will determine base reactivity of the material.

[0066] The pH of mineral wastes was assessed by creating of a slurry consisting of 1-part alkaline waste (1g) and 2-parts deionized (DI) water (2g). Manual pH measurements were taken using an Orion 4-Star A211 pH meter (Thermo Fisher Scientific) after 5 seconds of shaking and after 60s of agitation using a sonicator. To ensure the pH electrode had not drifted, the probe was calibrated using three pH buffers (4.00, 7.00, 10.00), at least daily.

[0067] Thermogravimetric analysis (TGA) is used to quantify brucite content in mineral samples (Assima, Molson, et al., 2013; Turvey et al., 2022; Wynands, 2021). A sample is heated at 10 C / min until it reaches 900 C. Brucite was quantified by measuring the mass lost between 300 and 415 °C, known as the brucite decomposition window, and the technique outlined in Turvey et al., 2022. TGA analysis used a Perkin Elmer TGA 4000 on approximately 50 mg of dry micronized material. Data were analyzed using a MATLAB script to calculate the amount of brucite in the sample (Wynands, 2021). Brucite is typically present in small amounts (less than five weight percent), for which TGA analysis provided a more precise and accurate measure of abundance than x-ray diffraction (Turvey et al., 2022; Wynands, 2021).1.1.3. Particle Size Distribution

[0068] A Mastersizer particle size analyzer (Malvern Panalytical, Malvern, United Kingdom) is used to determine the particle size distribution for a given material. A suspension containing 5% solids was mixed for 10 minutes before an aliquot was drawn for sample analysis. An ultrasonic treatment was applied to the samples to minimize aggregates for between 0 and 2 minutes. The sample is passed through a laser focused beam, and the grains scatter light at an angle that is inversely proportional to their size (Malvern Instruments, 2007). Typically, the P (80) of a material is reported, which represents the sieve mesh size through which 80% of the material will pass.1 .2 CO2Flux

[0069] Measuring the exchange of CO2between the atmosphere and alkaline waste material at high temporal resolution is done using dynamic closed chambers (DCC). DCCs are composed of a 10 cm diameter chamber connected to an infrared-gas analyzer (IRGA).The chamber closes over a 10 cm diameter PVC collar which has been filled with alkaline waste material. This closure creates a sealed environment and isolates a volume of air against the sample. During this time air is circulated between the chamber and the IRGA to create small scale eddies and ensure effectively complete mixing in the chamber headspace. An infrared gas analyzer (IRGA) (LI-8100A, LI-COR Inc., Lincoln, NE) measured CO2 and H2O vapour concentrations at high temporal resolution (1 Hz) and corrected the CO2 concentration for water vapour content, producing a dry CO2 mixing ratio (Cdry).

[0070] These gas concentrations are measured at high temporal resolution, and the resulting rate change in the gas concentration (dC' / dt) is assessed using linear regression. CO2 accumulation or depletion in the chamber headspace can result in concentration gradients, causing a linear regression to underestimate the rate change of gas concentration. To avoid this, the observation length is kept to <120 s. After the chamber lid closes, the first 30 seconds of data are rejected as a dead band (LI-COR, 2015). The CO2 flux is computed using the rate change of dry CO2 from 30s to 120s after chamber lid closure with the following equation (2):where FC02is the CO2 efflux (pmol nT2s'1), V is the volume of the chamber headspace (cm3), S is the soil area (cm2), Pois the initial pressure (kPa), l / l / 0is the initial water vapour mole fraction (mmol mol'1), Tois the initial air temperature (C), R is the universal gas constant (8.314 Pa m3K'1mol'1), and dC' / dt is the rate of change in the CO2 mixing ratio (dry (water corrected) mole fraction) (pmol mol'1s'1) (LI-COR, 2015). Data collected using the DCCs are processed using SoilFluxPro V5.3 (LI-COR Inc.).1 .3. Water Content Monitoring

[0071] SWIR spectroscopy is used to measure surficial water content of alkaline waste material quickly and accurately. A spectrometer measures optical energy reflected or transmitted through a sample in the near-infrared (350 - 2500 nm). When a sample is illuminated by a light source in the spectrometer, specific wavelengths of light are absorbed / reflected due to sub-molecular vibrations of atomic bonds. H2O exhibits unique reflectance features at 1400 nm and 1900 nm (Bowers & Hanks, 1965). Bowers and Hanks (1965) proposed an inverse relationship between percentage reflectance at 1900 nm and water content. For this reason, the reflectance feature of water around 1900 nm was used as a metric for water content in alkaline waste samples.1.3.1. Benchtop SWIR

[0072] Materials are analyzed using the Spectral Evolution OreXpert (Spectral Evolution, MA, USA), a high-resolution instrument that measures the visible near-infrared (VNIR) (350 to 1000 nm) and short-wave infrared (SWIR) spectra (1000 to 2500 nm). The spectrometer is calibrated using a white reference plate prior to beginning sample analysis. Material is loaded into a sample tray and mounted onto a benchtop reflectance probe covered with tinfoil to ensure no ambient light reached the sample. 5W tungsten halogen light is shone on the sample and three spectrometers measure the sample’s reflectance between 350 and 2500 nm. Multiple (three to five) spectra are obtained for each sample to account for possible heterogeneity of water content in the sample. The raw spectra are interpreted using The Spectral Geologist (TSG) software (Spectral Geologist, Belrose, NSW, Australia) which analyses the reflectance spectrum compared to the background shape or curvature (continuum line).

[0073] A convex hull continuum line connects the local maxima of the reflectance spectrum. The continuum line can be visualized as the shape that a rubber band would make if it were stretched over the reflectance spectrum (Mutanga & Skidmore, 2004a). The spectra are detrended relative to the continuum line such that the continuum line represents a reflectance of 1 .0. The area under the continuum line was calculated between 1800 and 2200 nm to take into account the broadening of the limbs of the reflectance feature at higher water contents. By measuring these areas of water reflectance features for samples with known water contents, calibration curves are produced for each material.

[0074] Using the area between 1800 and 2200 nm to create a calibration curve for each material has not been done previously. A combination of reflectance values (Bowers & Hanks, 1965; Dalal & Henry, 1986) and bands (Sudduth & Hummel, 1993) at different wavelengths have been used in the past to estimate moisture content. Other researchers have used the full reflectance spectrum and used multiple linear regression (Hummel et al.,2001) or multivariate partial least squares regression (Bullock et al., 2004) to estimate soil moisture content. Levy and Johnson (2021) used a continuum removed water index between 940and 1646 nm.1.3.2. SWIR Hvoerspectral Imaaina

[0075] Hyperspectral imaging collects a spectrum for each pixel in an image. This is in contrast to the benchtop SWIR, where an average spectrum is collected over the surface that the source light reflects off of. Hyperspectral imaging technology has progressed significantly in the past decade with newer cameras being small and light enough to mount on aerial drones (Barton et al., 2021 ; Levy & Johnson, 2021 ; Saari et al., 2017). By mounting an imaging spectrometer on a vehicle or a drone, water content could be mapped across an entire mine waste facility.1.4. Measuring Surface Roughness

[0076] Carbon mineralization can be limited by the transport of CO2 into the alkaline waste (Harrison et al., 2013a; Rausis et al., 2022; Wilson et al., 2014). If wetted alkaline material surfaces act as sorbent contactors for CO2 capture from air, then it is anticipated that the rate of CO2 capture from air will be dependent on the total area of the wetted mineral surfaces. Here photogrammetry is used to measure surface roughness. Photogrammetry has been used in geomorphology to measure soil erosion and is a preferred technique as it is non-destructive and covers large areas (Kirby, 1991). The impact of data resolution on surface roughness calculations has been noted (Ying et al., 2014), but no standard for choosing a resolution for close range photogrammetry has been proposed.1.4.1. Photogrammetry

[0077] Photogrammetry employs overlapping 2-dimensional images to create a 3- dimensional model of the mineral waste (Rieke-Zapp et al., 2001). A surface area can be calculated if the 3D models are given a scale.

[0078] Using a frame made of 80 / 20 aluminum bars, a Canon EOS Rebel SL3 DSLR Camera with a 55mm lens, and triangulation markers, photographs are taken from multiple angles (~ 8 photos). Raw .CR3 photos are converted to .TIFF files using Adobe Photoshop (Adobe Inc., San Jose, California) as JPEG compression introduces unwanted noise.Converted photos are processed at one of five resolution settings (‘ultrahigh’, ‘high’,‘medium’, ‘low’, ‘ultralow’) using the Agisoft Metashape Professional software (Agisoft LLC, St. Petersburg, Russia). The program creates a 3D digital elevation model, and the distance between triangulation markers is input to give the DEM scale. The area of interest is selected (the 10-cm diameter collar used in chamber measurements), and surface roughness is calculated.

[0079] The surface roughness is the ratio between the actual (measured) surface area (ASA) and the horizontal projected surface area (PSA), where a completely smooth surface would have a value of 1 and a rough surface would have a value of >1 (Bishop et al., 2012; Ye et al., 2021 ; Ying et al., 2014). The ASA is the area measured using photogrammetry, and the PSA is calculated using basic geometry for the area of a rectangle or circle (Eq. 3).A actual surface area) (3) surface roughness-. SR = — - - - -A (projected surface area)

[0080] Surface area change in relation to scale is critical for determining the relationship between surface roughness and reactivity. The inventors propose a scale that results in a 1 :1 relationship between FCO2 increase (flux) and surface roughness increase since reactive surface area (S) is a denominator in Equation 2.Example 2.0 - Results2.1. Material characterization

[0081] Three alkaline waste materials were examined in this example. Serpentine waste 1 (SW-01) contains around 1.8 wt% brucite (Table 1) and is predominantly composed of serpentine [Mg3Si2Os (OH)4] (77.5 wt %) with less amounts of forsterite [Mg2SiC>4] (11.1 wt %). This material has the largest mean grain size of all materials used (Table 1). Serpentine waste 2 (SW-02) contains 1.1 wt % brucite and is dominantly composed of serpentine (86.49 wt %). The third material used was an olivine dominant waste (OW-01) material composed of forsterite (73.1 wt %) with lesser amounts of serpentine (19.5 wt %).Table 1 : Characterization of materials used in this example.2.1. Water Content2.1.1. Short-Wave Infrared Spectroscopy

[0082] An empirical calibration of the relationship between alkaline waste reflectance spectra and water content was obtained by analyzing materials with known water contents using SWIR. Materials were dried and mixed with deionized water to achieve known water content between 0 and 40 % by mass (0fl). The reflectance spectra produced at variable water contents are shown in FIG. 3 with the continuum line normalized to 1.0 (Mutanga & Skidmore, 2004b). The material analyzed in FIG. 3 is a serpentine dominant waste material with lesser amounts of olivine. The dry material (0% water content) shows the diagnostic vibrational reflectance features of serpentine at 1400 and 2350nm due to Mg-OH bonds (Kumar & Rajawat, 2020; Post & Borer, 2000). Serpentine also has a small reflectance feature at 1900nm due to water bonds within the crystal matrix.

[0083] The reflectance of pure water occurs between 1800 and 2200nm (Curcio & Petty, 1951) and is shown as a shaded band in FIG. 3. The area between the continuum line and the reflectance spectra was calculated between 1800 and 2200nm (continuum-removed area, or CRA). A negative correlation between reflectance at 1900nm and water content was found for all three materials (two serpentine and one olivine dominant waste material) and was used to construct calibration curves (FIG. 4A). For all materials, there is a linear relationship between CRA and water content between zero and 25% by mass. Above 25%, the three calibration curves deviate and approach an asymptote. The water content at which each material reaches the plateau is dependent on grain size and base mineralogy. FIG. 4B shows the calibrations being used to measure the same materials with unknown water contents. Error bars on repeated measurements (n = 3) were small, except for one sample with the highest water content (~ 40 %).2.1.2. Relationship between Water Content and CO2 Uptake

[0084] SW-01 was mixed with water until saturation (15.0 wt%) then was allowed to dry out. CO2 uptake and water content were monitored until the material dried out and uptake ceased. SW-02 was mixed with deionized (DI) water to achieve known water contents. Fluxes were then measured after 24 hours using a 10 cm dynamic closed chamber (FIG. 5). This experiment was run in a high humidity (> 90%) and was repeated 3 times to measure reproducibility.

[0085] The presence of water had a significant impact on CO2 uptake rates. At low water content (0%) uptake was modest, 0.4 kg CO2 m'2yr'1for SW-02. As the water content increased, uptake also increased to a maximum of 1.3 kg CO2 m'2yr'1. Above 20% water content, the CO2 uptake decreased rapidly to 0.27 kg CO2 m'2yr'1at 25%. It is assumed that fluxes would remain at this reduced rate as water content increased above 25%. SW- 01 uptake rates exhibited a similar pattern to SW-02. Peak uptake rates occurred at 11 wt % where fluxes reached 2.5 kg CO2 m'2yr'1. For all experiments, the surface roughness was approximately 1 .

[0086] SW-02 was mixed with equal parts water to produce a slurry. This 50% water content slurry was allowed to dry in the lab (~ 30% relative humidity) and fluxes were measured every hour as the material dried (FIG. 6). At high water content (i.e., > 40 %), fluxes are suppressed, bouncing around 0.3 kg CO2 m'2yr'1. As the material dries, CO2 uptake increases until it reaches ~ 22% water content. Fluxes remain steady at 2.3 kg CO2 m'2yr'1until the material drop below the ideal water content range. Below 18% water content, CO2 uptake continues to decline as the material dries. To optimize the reactivity of this material (i.e. SW-02), water content can be kept between 18 and 22 % as shown by FIGs. 5 and 6.2.2. Surface Roughness

[0087] 5 splits of material were mixed to known water content and were then roughened using a spatula. Roughening was done to achieve a gradation in roughness’s from completely flat to as rough as possible across the five collars. FIG. 7 shows the relationship between surface roughness increase and CO2 flux increase for the same material at different water contents. FX001 and FX002 were run at 15% water content, and FX003 was run at 0% water content.

[0088] For all trials the relationship between surface roughness increase and FCO2 increase largely follows a 1 :1 line when the photogrammetry photo resolution is 0.5 mm. FIG. 8 shows an example where the same photos were analyzed at two different resolutions; 0.5 mm and 0.06 mm. At a finer resolution, the slope of a line for the relationship between FCO2 increase and surface roughness increase is 0.34. At 0.5 mm, the slope is 0.95.2.3. Flux Increase by Manipulation

[0089] The following section shows examples where intervention and manipulation of water content, alkalinity, and surface roughness increase CO2 uptake.2.3.1. Example A

[0090] FIG. 9 shows a slurry of alkaline waste material and water (> 50 wt%) that is allowed to dry under ambient conditions. On days 5, 8, 12, and 14, the material was churned using a spatula. For the initial 10 days of measurement, the material was too wet and uptake was minimal (~ 0.5 kg CO2 m'2yr'1). Churning during this time did not have a significant impact on reactivity but may have increased evaporation and aided in dewatering the slurry (FIG. 10, top). After day 10 fluxes increased until reaching a peak on day 17.

[0091] Between day 10 and day 17, churning not only sped up the dewatering process but also increased the reactive surface area of the waste material, further boosting reactivity. As an example, the churning performed on day 14 resulted in a doubling of reactivity from 3.8 kg CO2 m'2yr'1to 7.0 kg CO2 m'2yr'1. This jump in reactivity is due to increased alkalinity of waste porewater at depth equilibrating with the atmosphere. This effect is temporary and the flux decreased after ~ 12 hours to 6.0 kg CO2 m'2yr'1. This new baseline is still 2.2 kg CO2 m'2yr'1higher than immediately before churning. This gain in reactivity is due to increased surface roughness of the waste material (FIG. 10, bottom). Water content at this time was likely suppressing uptake slightly as the ideal water content wasn’t reached until day 17 but churning also helped the material reach this water content faster.

[0092] FIGs. 9 and 10 demonstrate the efficacy of churning when material is too wet and compacted to increase surface roughness and promote evaporation.

[0093] Reactivity can also be increased if material is too dry and water is brought to the surface. In the following experiment dry alkaline waste material was allowed to react under ambient conditions. At hour 10, water was added to the sample and at hour 25 the material was churned. When the material was too dry, the flux was 0 kg CO2 m'2yr'1(FIG. 11). After water was added to the system, the flux increased steadily to 2 kg CO2 m'2yr'1by hour 24. At this point, the material was churned resulting in the flux tripling to 6 kg CO2 m'2yr'1before reaching a plateau at 4.5 kg CO2 m'2yr'1at hour 35. This rate was maintained until the end of the experiment.2.3.3. Example C

[0094] If optimal water content and surface roughness are maintained for alkaline waste material then carbonation will proceed for weeks to months. Over time, rates of uptake will decline as the material surface becomes passivated and labile cations are consumed. At this time, churning to bring up material from depth will be needed to increase uptake rates. FIG. 12 shows wetted alkaline waste material where water was added once a week to keep water content in the optimal range. After equilibration with the atmosphere (first 12 hours), CO2 uptake was constant at 3.5 kg CO2 m'2yr'1for 10 days. After this time, uptake rates declined steadily.3.0 Discussion

[0095] Dissolution of ultramafic minerals (i.e., serpentine, brucite, pentlandite) produces alkalinity, which is then buffered by atmospheric CO2 dissolved in the waste porewater (Eq 1.1). If adequate labile cations (Ca2+, Mg2+), CO2, and porewater are available, then reactions will continue for weeks to months (FIG. 12). After a period of time, labile cations will be depleted in the near surface as they are converted to carbonate minerals, passivated, or are exported as alkalinity (Bea et al., 2012; Stolberg, 2005). Monitoring alkalinity at scale is challenging and instead monitoring instantaneous uptake rates (i.e. the rate at which CO2 is absorbed by the reaction mixture) and time since manipulation is recommended in some embodiments.3.2.2. Water Content

[0096] CO2 is absorbed most effectively by alkaline waste materials at a specific water content. The relationship between water content and CO2 uptake resembles a piecewise linear function (FIG. 5) which reaches a maximum at a specific water content. This maximum is unique to each material and is highly dependent on grain size and base mineralogy. SW-01 reached peak reactivity rates at a lower water content than SW-02. This was likely because SW-01 has a larger grain size than SW-02 with a P (80) of 281 .83 / zm compared to a P (80) of 125 / zm for SW-02 (Table 1).

[0097] Optimal water content in alkaline waste materials is a result of competing processes. When water content is too high (e.g., > 20% for some materials), diffusion of CO2 into waste material is limited as H2O fills the pore space between grains (Holmes, 2010). The diffusion coefficient of CO2 in the gas phase is orders of magnitude higher than in the aqueous phase (Bea et al., 2012). Therefore, the presence of thin films of water surrounding mineral grains results in the ideal conditions for carbon uptake (Holmes, 2010). When water content is too low (i.e., 0%), the reactions that sequester CO2 become inhibited as water is a reactant (Assima, Larachi, et al., 2013; Harrison et al., 2015).

[0098] The optimal water content of waste material can increase uptake three-fold and results from lab experiments shown here highlight the importance of keeping water content within an optimal range. Monitoring water content across an entire tailings facility is not feasible using moisture probes alone, as they are spatially limited. Furthermore, conventional moisture probes measure an average water content across the length of the probes. Short-wave infrared spectroscopy is proposed as a remote sensing technique to measure surficial water content by creating material specific calibration curves (FIGs. 4A, 4B). These calibrations, paired with hyperspectral SWIR cameras, could be used to monitor and map water content across large waste facilities (Levy & Johnson, 2021).3.2.3. Surface Roughness

[0099] The relationship between surface roughness and CO2 uptake is understood to be simpler than for water content. The relationship between surface roughness increase and CO2 flux increase is linear. This relationship is expected to be linear because S is part of the denominator in Eq 2. Substituting the actual surface area for S, which will be higher than the geometric surface area, results in a proportional increase in the flux. The highest increasesin surface roughness in the present experiments were achieved with material at 15% water content compared to dry material. Wet waste material was able to achieve higher surface roughness due to the surface tension of inter-granular water films (Barabasi et al., 1999).

[0100] In order to optimize CO2 uptake across a tailings pile, surface roughness should be optimized as well. Increasing surface roughness too much will increase evaporation, thereby affecting water content, whereas too little surface roughness will limit the contact area between the alkaline waste and the atmosphere. Surface roughness can be monitored using photogrammetry, 3D scanning or Lidar. Monitoring surface roughness across a waste facility can also be used to determine when the material has become compacted.3.33.3.1. Waste Material Too Dry

[0101] If alkaline waste material is too dry, then the reactions that sequester CO2 cannot proceed (FIGs. 5,6). Water is required for each step in the carbon mineralization process; mineral dissolution, formation of bicarbonate, and precipitation of carbonate minerals. In dry conditions, fluxes are near zero (FIG. 11). When water is added, uptake increases as the material reaches its optimal water content.3.3.2. Waste Material Too Wet

[0102] If alkaline waste material is too wet, then CO2 diffusion is limited. The diffusion of CO2 through air is orders of magnitude faster than through water (Bea et al., 2012). By this metric, CO2 diffusion is most effective through the thin films of water that surround mineral grains (Holmes, 2010). This is likely to be an issue at tailings facilities where slurries are typically deposited at approximately 50% water content (Wilson et al., 2014), far above the ideal water content determined from lab experiments. In this case, water content should be reduced to promote CO2 uptake. Dewatering of mine waste can be performed through churning or dewatering. When waste material is oversaturated, churning can be used to speed up evaporation and aid in dewatering by increasing the evaporative surface area (FIG. 9). By reducing the water content of the waste material, the optimal water content of the mine waste can be achieved, increasing uptake dramatically (FIGs. 5, 6).3.3.3. Waste Material is

[0103] Compaction can occur after many wetting and drying cycles or after initial deposition from slurry. This results in decreased permeability of the alkaline waste material, reducing the ingress of CO2 into the subsurface (Bea et al., 2012; Power et al., 2013). In this case, compacted material should be broken up to increase surface roughness and allow CO2 to permeate more easily into the waste material. This will also increase fluxes further by increasing the reactive surface area (FIGs. 7, 9 and 11). In some embodiments, maintaining a surface roughness of at least about 1 .0 to about 1 .5, e.g. at least 1 .2 in some embodiments, is desirable to help ensure that the alkaline waste material has not become too compacted.3.3.4. Alkalinity of Waste Material has Declined

[0104] If high surface roughness and optimal water content are maintained, reactions that consume labile cations will proceed for weeks - months (FIG. 12). After an extended period of time, the labile cations in the near surface material will become depleted and passivated (Assima, 2012; Harrison et al., 2015). Consumption and passivation of labile cations occurs in the near surface and the reactions that sequester CO2 rarely penetrate more than a few centimetres below the surface (Assima, 2012; Bea et al., 2012; Stolberg, 2005). To increase reactivity, surficial material should be removed or fresh material should be brought up from depth (FIG. 11).3.4.

[0105] Researchers in this space to date have noted the importance of water content on CO2 uptake (Assima, 2012; Assima, Larachi, et al., 2013; Harrison et al., 2015; Stubbs, 2022, 2023) and have proposed tilling as a method to increase uptake rates (Stubbs, 2022). These studies do not propose techniques for manipulating these parameters at scale. In the present example, tailored tilling techniques are proposed to keep mineral or mine waste parameters in the optimal zones, thereby maximizing reactivity. The proposed tilling methods are borrowed from agriculture and can easily be adapted for use at mine or other waste sites. Many tilling systems exist that modify, manipulate, and improve soil conditions. These systems have been broadly divided into systems that invert soil and mix soil, both of which can be applied to target a given reactivity suppressor at a site.

[0106] Rotary cultivators, rotary tillers, strip tillers, and vertical tillers are all methods that use rotating tines or blades to break up soil. Typically, these tilling methods are used to cut, lift, mix, and loosen soil (Hillel & Hatfield, 2005) and can be motorized or non-motorized. These methods of tilling, when applied to alkaline waste material can be used to break up surficial crusts and compacted material. This will increase surface roughness across the mine waste. This increased surface roughness will also promote evaporation and aid in tailings dewatering (Munro & Smirk, 2018). Therefore, tillage methods that loosen and mix the alkaline waste should be used when the waste material is too wet or has become compacted.

[0107] Moldboard plow, disk plows, and chisel plows are tilling methods used to fully or partially invert soils. Moldboard and disk plows are designed to cut, lift, and overturn soils using a curved plate or disk (Hillel & Hatfield, 2005). Chisel plowing, in particular, is used for deep tilling to break up, partially invert and partially mix soil while reducing water loss (Hillel & Hatfield, 2005). These methods can be applied to alkaline waste materials and have the effect of bringing up material from depth with higher moisture content and more labile cations. Therefore, these methods of tilling should be used when the waste material is too dry or when reactivity has been depleted.

[0108] Tilling methods used to invert soils, in particular the moldboard plow, require a higher energy to operate compared to the tilling methods used to mix material (Hillel & Hatfield, 2005). By monitoring uptake deficiencies across a site, the appropriate tilling method can be prescribed, increasing the overall energy efficiency of tilling.

[0109] While a number of exemplary aspects and embodiments have been discussed above, those of skill in the art will recognize certain modifications, permutations, additions and sub-combinations thereof. It is therefore intended that the following appended claims and claims hereafter introduced are interpreted to include all such modifications, permutations, additions and sub-combinations as are consistent with the broadest interpretation of the specification as a whole.References

[0110] The following references are of interest with respect to the subject matter described herein. Each of the following references is incorporated by reference herein in its entirety.• Assima, G. P. (2012). CO2 Sequestration in Chrysotile Mining Residues - Implication of Watering and Passivation under Environmental Conditions. Ind. Eng.Chem. Res.• Assima, G. P., Larachi, F., Beaudoin, G., & Molson, J. (2013). Dynamics of carbon dioxide uptake in chrysotile mining residues — Effect of mineralogy and liquid saturation. International Journal of Greenhouse Gas Control, 12, 124-135. https: / / doi.Org / 10.1016 / j.ijggc.2012.10.001• Assima, G. P., Molson, J., & Beaudoin, G. (2013). Accurate and direct quantification of native brucite in serpentine ores — New methodology and implications for CO 2 sequestration by mining residues. Thermochimica Acta, 566, 281-291. https: / / doi.Org / 10.1016 / j. tea.2013.06.006• Barabasi, A.-L., Albert, R., & Schiffer, P. (1999). The physics of sand castles: Maximum angle of stability in wet and dry granular media. Physica A: Statistical Mechanics and Its Applications, 266(1-4), 366-371. https: / / doi.org / 10.1016 / S0378- 4371 (98)00618-9• Barton, I. F., Gabriel, M. J., Lyons-Baral, J., Barton, M. D., Duplessis, L., & Roberts, C. (2021). Extending geometallurgy to the mine scale with hyperspectral imaging: A pilot study using drone- and ground-based scanning. Mining, Metallurgy & Exploration, 38(2), 799-818. https: / / doi.org / 10.1007 / s42461-021-00404-z• Bea, S. A., Wilson, S. A., Mayer, K. U., Dippie, G. M., Power, I. M., & Gamazo, P. (2012). Reactive Transport Modeling of Natural Carbon Sequestration in Ultramafic Mine Tailings. Vadose Zone Journal, 11(2), vzj2011 .0053. https: / / doi.Org / 10.2136 / vzj2011 .0053• Bishop, M. P., James, L. A., Shroder, J. F., & Walsh, S. J. (2012). Geospatial technologies and digital geomorphological mapping: Concepts, issues and research. Geomorphology, 137(1), 5-26. https: / / doi.Org / 10.1016 / j.geomorph.2011.06.027• Bowers, S. A., & Hanks, R. J. (1965). Reflection of radiant energy from soils. Soil Science, 100(2), 130-138. https: / / doi.org / 10.1097 / 00010694-196508000-00009• Bullock, P. R., Li, X., & Leonardi, L. (2004). Near-infrared spectroscopy for soil water determination in small soil volumes. Canadian Journal of Soil Science, 84(3), 333- 338. https: / / doi.org / 10.4141 / S03-090• Curcio, J. A., & Petty, C. C. (1951). The Near Infrared Absorption Spectrum of Liquid Water. Journal of the Optical Society of America, 41(5), 302. https: / / doi.Org / 10.1364 / JOSA.41 .000302• Dalal, R. C., & Henry, R. J. (1986). Simultaneous Determination of Moisture, Organic Carbon, and Total Nitrogen by Near Infrared Reflectance Spectrophotometry. Soil Science Society of America Journal, 50(1), 120-123. https: / / doi.Org / 10.2136 / sssaj1986.03615995005000010023x• Harrison, A. L., Dippie, G. M., Power, I. M., & Mayer, K. U. (2015). Influence of surface passivation and water content on mineral reactions in unsaturated porous media: Implications for brucite carbonation and CO2 sequestration. Geochimica et Cosmochimica Acta, 148, 477-495. https: / / doi.Org / 10.1016 / j.gca.2014.10.020• Harrison, A. L., Power, I., & Dippie, G. (2013a). Strategies for enhancing carbon sequestration in Mg-rich mine tailings. In Proceedings of International Mine ....• Harrison, A. L., Power, I. M., & Dippie, G. M. (2013b). Accelerated Carbonation of Brucite in Mine Tailings for Carbon Sequestration. Environmental Science & Technology, 47 1), 126-134. https: / / doi.org / 10.1021 / es3012854• Hillel, D. I., & Hatfield, J. L. (2005). Encyclopedia of soils in the environment (1st ed). Elsevier / Academic Press.• Holmes, G. J. (2010). A Carbon Dioxide Absorption Performance Evaluation for Capture from Ambient Air. https: / / doi.org / 10.11575 / PRISM / 3443• Hummel, J. W., Sudduth, K. A., & Hollinger, S. E. (2001). Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor. Computers and Electronics in Agriculture, 32(2), 149-165. https: / / doi.Org / 10.1016 / S0168-1699(01 )00163-6• Intergovernmental Panel On Climate Change. (2023). Climate Change 2021 - The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (1st ed.). Cambridge University Press, https: / / doi.org / 10.1017 / 9781009157896• K. A. Sudduth & J. W. Hummel. (1993). Soil Organic Matter, CEC, and Moisture Sensing with a Portable NIR Spectrophotometer. Transactions of the ASAE, 36(6), 1571-1582. https: / / doi.Org / 10.13031 / 2013.28498• Kirby, R. P. (1991). Measurement of Surface Roughness in Desert Terrain By Close Range Photogrammetry. The Photogrammetric Record, 13(78), 855-875. https: / / doi.Org / 10.1111 / j.1477-9730.1991 ,tb00753.x• Kumar, H., & Rajawat, A. S. (2020). Aqueous alteration mapping in Rishabdev ultramafic complex using imaging spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 88(February), 102084. https: / / doi.Org / 10.1016 / j.jag.2020.102084• Levy, J. S., & Johnson, J. T. E. (2021). Remote Soil Moisture Measurement from Drone-Borne Reflectance Spectroscopy: Applications to Hydroperiod Measurement in Desert Playas. Remote Sensing, 13(5), 1035. https: / / doi.org / 10.3390 / rs13051035• LI-COR, Inc. (2015). Using the LI-8100A Soil Gas Flux System. 1-220.• Lu, X., Carroll, K. J., Turvey, C. C., & Dippie, G. M. (2022). Rate and capacity of cation release from ultramafic mine tailings for carbon capture and storage. Applied Geochemistry, 140, 105285. https: / / doi.Org / 10.1016 / j.apgeochem.2022.105285• Malvern Instruments. (2007). Mastersizer 2000 essentials. 98.• Munro, L., & Smirk, D. (2018). How thick is thick enough? 23-34. https: / / d0i.0rg / l 0.36487 / ACG_rep / 1805_01_Munro• Mutanga, O., & Skidmore, A. K. (2004a). Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions. International Journal of Applied Earth Observation and Geoinformation, 5(2), 87-96. https: / / doi.org / 10.1016 / jjag.2004.01.001• Mutanga, O., & Skidmore, A. K. (2004b). Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions. International Journal of Applied Earth Observation and Geoinformation, 5(2), 87-96. https: / / doi.org / 10.1016 / jjag.2004.01.001• Nichol, C., Smith, L., & Beckie, R. (2003). Time domain reflectrometry measurements of water content in coarse waste rock. 40.• Post, J. L., & Borer, L. (2000). High-resolution infrared spectra, physical properties, and micromorphology of serpentines. Applied Clay Science, 76(1-2), 73-85. https: / / doi.Org / 10.1016 / S0169-1317(99)00047-2• Power, I. M., Harrison, A. L., Dippie, G. M., Wilson, S. A., Kelemen, P. B., Hitch, M., & Southam, G. (2013). Carbon mineralization: From natural analogues to engineered systems. Reviews in Mineralogy and Geochemistry, 77(1), 305-360. https: / / d0i.0rg / l 0.2138 / rmg.2O13.77.9• Power, I., McCutcheon, J., Harrison, A., Wilson, S., Dippie, G., Kelly, S., Southam, C., & Southam, G. (2014). Strategizing Carbon-Neutral Mines: A Case for Pilot Projects. Minerals, 4(2), 399-436. https: / / doi.org / 10.3390 / min4020399• Rausis, K., Stubbs, A. R., Power, I. M., & Paulo, C. (2022). Rates of atmospheric CO2 capture using magnesium oxide powder. International Journal of Greenhouse Gas Control, 779(June), 103701. https: / / doi.org / 10.1016 / jjjggc.2022.103701• Rieke-Zapp, D., Wegmann, H., Santel, F., & Nearing, M. (2001). Digital photogrammetry for measuring soil surface roughness. Proceedings of the American Society of Photogrammetry & Remote Sensing 2001 Conference: Gateway to the New Millennium, 1994, 1-8.• Saari, H., Akujarvi, A., Holmlund, C., Ojanen, H., Kaivosoja, J., Nissinen, A., & Niemelainen, O. (2017). VISIBLE, VERY NEAR IR AND SHORT WAVE IR HYPERSPECTRAL DRONE IMAGING SYSTEM FOR AGRICULTURE AND NATURAL WATER APPLICATIONS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3 / W3, 165-170. https: / / d0i.0rg / l 0.5194 / isprs-archives-XLI I-3-W3-165-2017• Stenberg, B., Viscarra Rossel, R. A., Mouazen, A. M., & Wetterlind, J. (2010). Visible and Near Infrared Spectroscopy in Soil Science. Advances in Agronomy, 107(C),163-215. https: / / doi.Org / 10.1016 / S0065-2113(10)07005-7• Stolberg, D. J. (2005). REHABILITATION STUDIES ON TAILINGS STORAGE FACILITIES IN AN ARID HYPERSALINE REGION.• Stubbs, A. R. (2022). Direct measurement of CO2 drawdown in mine wastes and rock powders: Implications for enhanced rock weathering. International Journal of Greenhouse Gas Control.• Stubbs, A. R. (2023). Impact of wet-dry cycles on enhanced rock weathering of brucite, wollastonite, serpentinite and kimberlite: Implications for carbon verification. Chemical Geology.• Taconet, O., & Ciarletti, V. (2007). Estimating soil roughness indices on a ridge-and- furrow surface using stereo photogrammetry. Soil and Tillage Research, 93(1), 64- 76. https: / / doi.Org / 10.1016 / j .still .2006.03.018• Turvey, C. C., Wynands, E. R., & Dippie, G. M. (2022). A new method for rapid brucite quantification using thermogravimetric analysis. Thermochimica Acta, 718, 179366. https: / / doi.Org / 10.1016 / j.tca.2022.179366• Wilson, S. A., Harrison, A. L., Dippie, G. M., Power, I. M., Barker, S. L. L., Ulrich Mayer, K., Fallon, S. J., Raudsepp, M., & Southam, G. (2014). Offsetting of CO2 emissions by air capture in mine tailings at the Mount Keith Nickel Mine, Western Australia: Rates, controls and prospects for carbon neutral mining. International Journal of Greenhouse Gas Control, 25, 121-140. https: / / d0i.0rg / l 0.1016 / j.ijggc.2014.04.002• Wynands, E. (2021). CARBON MINERALIZATION IN ULTRAMAFIC MINE TAILINGS VIA CO2 INJECTION. September.• Ye, J., Xuan, J., Qiao, Y., Zhang, Y., Zhang, H., Xu, J., Liu, X., & Liu, K. (2021). A Simple Analysis of Texture-Induced Friction Reduction Based on Surface Roughness Ratio. Tribology Letters, 69(2), 1-12. https: / / doi.org / 10.1007 / s11249- 021-01440-y• Ying, L. X., Shen, Z. H., Piao, S. L., Liu, Y., & Malanson, G. P. (2014). Terrestrial surface-area increment: The effects of topography, DEM resolution, and algorithm. Physical Geography, 35(4), 297-312. https: / / doi.Org / 10.1080 / 02723646.2014.886923

Claims

CLAIMS:1 . A method of reacting carbon dioxide (CO2) with an alkaline waste material, the method comprising the steps of: forming a reaction mixture of the alkaline waste material; monitoring alkalinity of the reaction mixture; monitoring water content of the reaction mixture; and monitoring surface roughness of the alkaline waste material in the reaction mixture.

2. The method as defined in claim 1 , wherein the step of forming the reaction mixture comprises combining the alkaline waste material with water and mixing the reaction mixture, optionally wherein mixing the reaction mixture comprises wet milling, optionally by use of a ball mill or semi-autogenous grinding (SAG) mill, mixing in a concrete mixer, or mixing in a concrete truck.

3. The method as defined in any one of claims 1 to 2, wherein the step of forming the reaction mixture comprises receiving the alkaline waste material from a source of said alkaline waste material.

4. The method as defined in claim 3, wherein the source of said alkaline waste material comprises a mine, a cement kiln, an incinerator or an industrial plant.

5. The method as defined in any one of claims 1 to 4, wherein the step of monitoring the alkalinity of the reaction mixture comprises monitoring a pH of the reaction mixture, measuring an instantaneous rate of capture of CO2 by the reaction mixture, optionally by measuring CO2 uptake rates using soil gas flux chambers, measuring an amount of time that has elapsed since deposition or last manipulation of the reaction mixture, or measuring eddy covariance.

6. The method as defined in any one of claims 1 to 5, wherein the step of monitoring the water content of the reaction mixture comprises short wave infrared spectroscopy or short wave infrared spectroscopy hyperspectral imaging, optionally wherein a camera used to conduct short wave infrared spectroscopy hyperspectral imaging is mounted to a vehicle or drone.

7. The method as defined in any one of claims 1 to 6, wherein the step of monitoring the surface roughness of the alkaline waste material in the reaction mixture comprises using photogrammetry at a predetermined resolution, optionally wherein the photogrammetry is conducted using 3D scanning, or light detection and ranging (Lidar), optionally wherein an instrument used to conduct photogrammetry is mounted to a vehicle or drone.

8. The method as defined in any one of claims 1 to 7, wherein the steps of monitoring alkalinity, monitoring water content and monitoring surface roughness are conducted continuously.

9. The method as defined in any one of claims 1 to 7, wherein the steps of monitoring alkalinity, monitoring water content and monitoring surface roughness are conducted periodically, optionally wherein a time interval between the periodic monitoring is between about 1 and about 24 hours.

10. The method as defined in any one of claims 1 to 9, wherein the steps of monitoring alkalinity, monitoring water content and monitoring surface roughness are conducted simultaneously.1 1. The method as defined in any one of claims 1 to 10, further comprising adjusting one or more of the pH, water content, or surface roughness of the reaction mixture based on an evaluation of one or more of said steps of monitoring alkalinity, monitoring water content or monitoring surface roughness, respectively.

12. The method as defined in claim 11 , wherein said adjusting step comprises (i) determining that the water content of the reaction mixture is above a desired water content value and (ii) increasing the surface roughness of the reaction mixture, increasing evaporative surface area of the reaction mixture, or dewatering the reaction mixture.

13. The method as defined in claim 12, wherein the desired water content value is between about 5% and about 25% by weight.

14. The method as defined in claim 11 , wherein said adjusting step comprises (i) determining that the reaction mixture is too compacted or determining that a surface roughness of the reaction mixture is too low by determining that a surface roughness of the mixture is below a desired surface roughness value and (ii) increasing the surface roughness of the reaction mixture or increasing evaporative surface area of the reaction mixture.

15. The method as defined in claim 14, wherein the desired surface roughness value is between about 1 .0 and about 1 .5.

16. The method as defined in any one of claims 14 or 15, wherein said increasing the surface roughness of the reaction mixture or said increasing the evaporative surface area of the reaction mixture comprises applying a tilling method that promotes mixing of the reaction mixture.

17. The method as defined in claim 16, wherein the tilling method that promotes mixing of the reaction mixture comprises using a rotary cultivator, a rotary tiller, a strip tiller, or a vertical tiller to mix the reaction mixture.

18. The method as defined in either one of claims 12 or 13, where said dewatering the reaction mixture comprises churning the reaction mixture or carrying out mechanical consolidation.

19. The method as defined in claim 11 , wherein said adjusting step comprises (i) determining that the water content of the reaction mixture is below a desired water content value and (ii) adding water to the reaction mixture or applying a tilling method that promotes overturning to the reaction mixture.

20. The method as defined in claim 19, wherein the desired water content value is between about 5% and about 25% by weight.21 . The method as defined in claim 11 , wherein said adjusting step comprises (i) determining that reactivity of the reaction mixture at the surface has decreased below a predetermined reactivity decrease level or determining that a time interval since deposition or last manipulation of the reaction mixture has exceeded a desiredmanipulation time interval or determining that a pH of the reaction mixture has declined below a predetermined pH value and (ii) removing surface material from the reaction mixture, adding water to the reaction mixture, applying a tilling method that promotes overturning to the reaction mixture or applying a tilling method that increases reactive surface area to the reaction mixture.

22. The method as defined in claim 21 , wherein the predetermined reactivity decrease level is a reduction of about 50% or more from an initially measured instantaneous rate of CO2 capture.

23. The method as defined in either one of claims 21 or 22, wherein the predetermined pH value is about 8.0.

24. The method as defined in any one of claims 21 to 23, wherein the desired manipulation time interval is between about 1 day and about 2 months.

25. The method as defined in any one of claims 19 to 24, wherein said applying a tilling method that promotes overturning to the reaction mixture comprises applying a moldboard plow, a disk plow, or a chisel plow to the reaction mixture.

26. The method as defined in any one of claims 21 to 25, wherein said applying a tilling method that increases reactive surface area to the reaction mixture comprises applying a rotary cultivator, rotary tiller, strip tiller, or vertical tiller.

27. The method as defined in claim 21 , wherein removing surface material from the reaction mixture comprises using an excavator or backhoe to remove the surface material.

28. The method as defined in any one of claims 12 to 13 or 18 to 20, further comprising preparing a calibration curve for the alkaline waste material for a relationship between carbon dioxide uptake and water content of the reaction mixture of the alkaline waste material, wherein the calibration curve is used to provide the desired water content value.

29. The method as defined in any one of claims 14 to 17, further comprising preparing a calibration curve for the alkaline waste material for a relationship between carbondioxide uptake and surface roughness of the alkaline waste material, wherein the calibration curve is used to provide the desired surface roughness value.

30. The method as defined in any one of claims 21 to 24, further comprising preparing a calibration curve for the alkaline waste material for a relationship between carbon dioxide uptake and alkalinity of the reaction mixture of the alkaline waste material, wherein the calibration curve is used to provide the predetermined pH value.31 . The method as defined in any one of claims 28 to 30 or any other claim herein, wherein the calibration curve for the alkaline waste material is specific to a specified grain size of the alkaline waste material, optionally wherein the specified grain size of the alkaline waste material is between about 10 and about 1000 microns.

32. The method as defined in any one of claims 1 to 31 , wherein the alkaline waste material comprises mining waste or industrial waste.

33. The method as defined in claim 32, wherein the mining waste comprises ultramafic mine waste, optionally wherein the ultramafic mine waste contains serpentine, olivine or brucite, and further optionally wherein the ultramafic mine waste is obtained from a nickel mine, a diamond mine, a platinum group mine, a chromite mine, or an asbestos mine, and further optionally wherein the mine processes komatiite, laterite, Alaskan type intrusions, or kimberlite.

34. The method as defined in claim 32, wherein the industrial waste comprises industrial waste comprising high concentrations of Mg(OH)2 or Ca(OH)2, optionally wherein the industrial waste is cement kiln waste, steel slag, or fly ash.

35. The method as defined in any one of claims 1 to 34, wherein the carbon dioxide is supplied from atmospheric air.

36. The method as defined in any one of claims 1 to 35, wherein only one of the alkalinity of the reaction mixture, the water content of the reaction mixture, and the surface roughness of the alkaline waste material in the reaction mixture is monitored, optionally wherein only the water content of the reaction mixture is monitored.

37. The method as defined in any one of claims 1 to 35, wherein only two of the alkalinity of the reaction mixture, the water content of the reaction mixture, and the surface roughness of the alkaline waste material in the reaction mixture are monitored.

38. The method as defined in any one of claims 11 to 37, wherein the step of adjusting one or more of the pH, water content or surface roughness of the reaction mixture is conducted using a machine learning model.

39. An apparatus for carrying out a method as defined in any one of claims 1 to 38, the apparatus comprising an autonomous rover or drone comprising instruments for carrying out short-wave infrared spectroscopy and photogrammetry.

40. A method for preparing a calibration curve for a rate of CO2 uptake by a material relative to the water content of the material, the method comprising the steps of: providing a plurality of samples of the material having known water contents; obtaining short-wave infrared spectra for each one of the plurality of samples, the short-wave infrared spectra including data for wavelengths between 1800 and 2200 nm; using a convex hull continuum line to connect local maxima of a reflectance spectrum of each one of the obtained short-wave infrared spectra; calculating an area under the convex hull continuum line for wavelengths between 1800 and 2200 nm for each one of the obtained short-wave infrared spectra; and preparing the calibration curve using the known water contents and calculated areas under the curve for each one of the plurality of samples.41 . The method as defined in claim 40, wherein the calibration curve is specific to a specified grain size of the material, optionally wherein the specified grain size of the alkaline waste material is between about 10 and about 1000 microns.

42. The method as defined in either one of claims 40 or 41 , wherein the calibration curve is used to provide the desired water content value used in the method as defined in any one of claims 12 to 13 or 18 to 20.