Reduced life cycle climate impact from manure through catalytic methane conversion and carbon dioxide removal

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Methods and technologies to mitigate non-CO 2 GHGs are currently limited, which is a reason for agriculture being categorised as a hard-to-abate sector. This study examines mitigation of GHG emissions from manure storage headspace through oxidisation of CH 4 emissions at low concentrations using a thermal catalytic process with and without subsequent CO 2 capture and storage (CCS). The technology is studied using a combination of process modelling and life cycle assessment at four CH 4 concentrations: 300, 1000, 3000 and 10000 ppmv. The primary energy demand and net climate effect were evaluated, reaching a net climate effect of + 0.10, -0.77, -0.91 and − 0.97 g CO 2 -eq emitted/g CO 2 mitigated, respectively. The wide range of results is mainly influenced by the process energy demand being strongly correlated to the CH 4 concentration. The sensitivity analysis shows that a net negative climate effect can also be achieved at 300 ppmv with access to low emission energy sources. Coupling CCS worsens the net climate effect of the system at all studied CH 4 concentrations, mainly due to the additional energy demand for CO 2 separation. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences/Environmental impact GGR CH4 Methane emissions LCA Manure management Climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction The increase in greenhouse gas (GHG) emissions and climate scenario modelling shows the need for rapidly regressing GHG emissions through the parallel deployment of mitigation efforts and negative emissions across all of sectors 1 . However, some sectors are regarded as more challenging to handle than others. The GHG emissions from the agricultural sector are considered hard-to-abate, as they are predominantly not tied to energy use but rather originate from animals, soils and manure; thus, a renewable energy transition will not impact these emissions 2 . The global climate impact from agri-food systems amounted to 16 Gt carbon dioxide equivalents (CO 2 -eq) in 2022, with over half caused by non-CO 2 GHGs, mainly methane (CH 4 ) and nitrous oxide (N 2 O) 3 . The low concentrations (typically < 1%-vol) and dispersed nature of the emissions makes them difficult to quantify, target and reduce 4 , 5 . This indicates a need for different types of mitigation strategies for GHG emissions in agriculture and that a future net-zero or net-negative emissions balance would require residual emissions being offset through greenhouse gas removal (GGR) 6 . GGR can be achieved through natural (biological) processes as well as engineered (technical) systems, i.e. greenhouse gas removal technologies (GGRTs) 7 . A rather versatile portfolio of GGRTs has been presented, with the majority involving carbon dioxide removal (CDR) 8 . However, second only to CO 2 , CH 4 is so potent of a GHG that it contributes to 25% of total radiative forcing (RF), despite its atmospheric concentration being only 0.5% that of the CO 2 concentration 9 . Due to its high RF and short atmospheric residence time (approximately 12 years), rapid reductions in CH 4 emissions have great potential to impact global warming in the short term 10 . Despite there being a strong case for CH 4 mitigation, it remains poorly covered in climate policy, although it has received increased attention in recent years 11 . A strategy to evade the warming effect of a high GWP gas such as CH 4 is to convert it into a lower GWP gas, such as CO 2 8 . Such research has been put forward, with suggested applications both for the fossil fuel industry 12 and ambient air e.g. 13 , 14 , 15 , although these are at a low technological readiness level 16 . CH 4 gradually oxidises in the atmosphere over time, but this reaction can be initiated by, for example, introducing a catalyst and/or energy addition via light, heat or electricity 17 . CH 4 removal from ambient air would require large air flows due to the low concentration 18 . Removal measures to be implemented in agricultural systems are sparse in the scientific literature so far, despite over half of the global CH 4 emissions originating from agrifood systems 19 . Many CH 4 emissions arise from large poorly constrained areas, such as organic soils, rice fields and wetlands 4 , 20 . Since the higher the concentration of CH 4 , the easier it is to get the CH 4 conversion reaction running 21 , it is preferable to identify a CH 4 point source to increase process efficiency and decrease component size 22 . Possible alternatives of concentrated emission sources include manure storage or stables for ruminants 23 . Livestock cause considerable CH 4 emissions originating from enteric fermentation (~ 70%) and manure (~ 30%) 19 . GHG emissions from manure is the second largest emission source from farms 19 . During manure storage, organic matter in the manure starts to decompose, forming CO 2 under aerobic conditions and CH 4 through anaerobic reactions 24 . The rate of emissions depends on a number of factors, such as organic matter, temperature, moisture and pH 25 , and a future warmer climate would increase CH 4 emissions from manure 26 . Manure degradation can be partially regulated by lowering the pH of the manure through cooling, increasing acidity or covering the source 26 – 28 . The manure could also be subjected to anaerobic digestion, which allows the CH 4 to form under controlled circumstances where it can be collected and utilised for its energy content 29 . Despite this being an efficient utilisation of the waste and lowered climate impact, only around 5% of manure is digested in Sweden, one barrier being the high investment cost of farm-based biogas plants 30 . A potential approach is to treat the air from the manure storage space on-site by oxidising the CH 4 in a reactor containing a catalytic material to facilitate the reaction, as was recently proposed by Sirigina, et al. 21 . The approach of this system is to convert the CH 4 as soon as it is emitted to avoid its warming effect. The resulting CO 2 from the oxidation reaction is biogenic in nature, which can be captured and stored. The most common technologies to capture CO 2 are chemical absorption and adsorption 31 . Geological storage is necessary to achieve long-term removal of the CO 2 from the atmosphere 32 . To achieve sustainable and credible GGR, the applied technologies must result in a net negative emission balance over the system’s lifecycle and long-term storage reliability of the removed emissions 33 . There is a growing number of studies of CDR technologies which emphasise the importance of the LCA methodology to evaluate its effectiveness in delivering negative emissions, suggesting clear variance in effectiveness across technologies (see e.g. review by Rueda, et al. 1 . However, the lifecycle perspective of GGR through the conversion of CH 4 emissions emerging from biogenic sources is still to be covered in the scientific literature. There is also a need for further discussion about scenarios where GGR targeting CH 4 emissions can be considered as a greenhouse gas removal or mitigation strategy. This study aims to contribute with an evaluation of thermal catalytic oxidation as an option for reducing CH 4 emitted from manure storage using a life cycle perspective. The objective of the study was to determine the energy demand and climate efficiency of the proposed technology. Furthermore, the impact of releasing the exhaust gas to the atmosphere after the conversion process was compared with subsequent capture and storage of the CO 2 from the exhaust gas. The study attempted to identify energy and climate related hotspots in the process to provide guidance for future system development. 2 Methodology This study investigated a process technology for thermal catalytic treatment of the CH 4 emitted from manure storage and subsequent CO 2 capture and storage (CCS). The approach was to compare a reference state of conventional unabated manure storage emissions to a scenario where the manure storage space is integrated with the suggested technology: Scenario A: CH 4 -conversion . GHG emissions from manure storage headspace is treated through catalytic CH 4 oxidation and the exhaust gas is released into the atmosphere. Scenario B: Co-removal . GHG emissions from manure storage headspace is treated through catalytic CH 4 oxidation followed by capture and storage of the CO 2 in the outlet gas. The scenarios are explained in greater detail below. The study was performed through a combination of process modelling in Aspen Plus V12 and life cycle assessment (LCA). Both the LCA model and the process model relied largely on generic literature data and are not meant to represent a specific installation or determine an optimal system of production, but rather to provide a representative example based on currently available data. 2.1 Life cycle assessment The study covered each process step involved, from manure storage to the suggested catalytic treatment until the exhaust gas is released or treated for CO 2 capture for storage after the process. Processes occurring upstream of manure storage were excluded as they remain unaffected throughout the scenarios. The system boundary considered was cradle-to-grave, meaning the study included emissions from the entire life cycle of the technical appliances, including the necessary energy and raw materials, transport and processing, manufacturing of machinery, infrastructure and facilities, as well as its end of life. The life cycle inventory (LCI) was established through data collection from the literature and by utilizing the process parameters obtained from process models developed in Aspen (section 2.3), with the resulting data used for the life cycle impact assessment. We used life cycle inventory data on a European level and, if the corresponding data was not available, on a global level. As a final option, country-specific LCI datasets were used. The data was compiled over the three life cycle phases: manufacturing (raw material extraction and processing), operation (plant in operation), and end of life (material waste management). The climate impact and primary energy demand (PED) for the raw material for the process components manufacturing and catalyst was based on data from Ecoinvent v. 3.9.1. End of life for the used materials were included based on Ecoinvent 34 data. $$\:PED=ED*PEF$$ $$\:net\:CE=\frac{{e}_{emitted}-{e}_{mitigated}}{{e}_{mitigated}}.\:$$ ED denotes energy demand, PEF primary energy factor and e GHG emissions in g CO 2 -eq. By this definition, a negative value of the net CE denotes lower system emissions than mitigated emission, whereas a positive value of the net CE is to be interpreted as the system causing more GHG emissions than is being mitigated. As the GHGs emitted from manure are of biological origin, their removal would be categorized as negative emissions, provided that the total removal of GHG emissions is larger than the total GHG emissions emitted to the atmosphere by the processes required for GGR 33 . Compared to the reference state, oxidising CH 4 as soon as it is emitted will help eliminate the large RF which is otherwise exerted during its residence time in the atmosphere. This was shown as an arithmetically negative contribution to the net CE, although the carbon atom is still present in the atmosphere in the CH 4 conversion case. However, the term mitigated was chosen as the assumption that we are in fact removing or mitigating emissions needs a broader system level discussion, which is outside the scope of this article. GWP100 was used as the conversion metric to assess climate impact from GHG emissions as is standard practice in LCA. However, with CH 4 being such a central component of this study, the metric choice was subjected to sensitivity analysis to highlight its implications, by replacing GWP100 with GWP20 and GWP500. GWP values as defined in AR6 are used 36 . To allow this, disaggregated GHG emission data was used when possible. The energy used for operation was solely in the form of electricity. The electricity source was assumed to be a European mix with an emission factor of 239 g CO 2 -eq/kWh 37 and a primary energy factor of 2.3 (Cogen 2017). This was subjected to sensitivity analysis, exchanging the electricity source for natural gas power with 436 g CO 2 -eq/kWh and a primary energy factor of 2.5, and for a more renewable mix represented by the Nordic consumption mix (approximately 50% hydropower, 20% nuclear power, 15% wind and solar power and 15% combined heat and power) with 93.2 g CO 2 -eq/kWh 38 and primary energy factor of 1.7 39 . 2.2 System description Livestock slurry was assumed to be stored on a farm in covered manure storage with a capacity of 5000 m 3 , sufficient for manure from around 200 dairy cows including a grazing period of 3 months 40 . The manure was stored for up to 9 months before being spread on nearby fields as fertilizer. Biological processes degrading the organic matter in the manure form emissions of gases and heat. The CH 4 emission rate was determined based on IPCC guideline data for the average yearly CH 4 emissions from manure from high productivity dairy cattle in a cool, temperate, moist climate Table 10.14; 41 . Based on this, the manure was assumed to emit 0.390 mg of CH 4 (10.5 mg CO 2 -eq) per second. In the reference state, the GHG emissions were released to the atmosphere without any intervention. For this analysis we chose to model four different concentrations of CH 4 (300, 1000, 3000, and 10,000 ppmv), representing conditions which cause low to high CH 4 emissions. This was achieved by adjusting the dilution of CH 4 in four different airflows (6890, 2067, 689 and 207 m 3 /h), respectively. This interval gives information on how the emissions rate relates to the size of the process plant and covers a relevant range of operating conditions. We further assume the share of aerobic vs anaerobic degradation of organic matter in the manure storage was such that the GHG emissions consisted of CO 2 and CH 4 on a 40:60 molar% basis based on Grant, et al. 42 . The CO 2 originating from the manure degeneration (0.71 g/s) entered the process alongside the CO 2 in ambient air, which was set to 417 ppmv. Scenario A: CH 4 conversion In scenario A, the airflow from the covered manure storage headspace was actively ventilated and directed to a catalytic oxidizer where CH 4 was oxidised to CO 2 before release into the atmosphere. Different CH 4 concentrations were achieved by diluting the CH 4 emissions through varying airflow. Scenario B: Co-removal Once the CH 4 had been oxidized, following an identical route as in Scenario A, the residual gas flow entered a carbon capture unit to separate the CO 2 from the rest of the air. Due to the degradation processes in manure forming CO 2 , its concentrations change, to match the amount of CH 4 , creating a 40:60 mix. The CO 2 flows are described in the Supplementary material (SM) (Table B). The separated CO 2 was liquefied and transported to and injected at a geological storage site. According to a previous work on transport and storage for relevant conditions, the captured CO 2 would require 1,2 GJ/tonne CO 2 stored and cause 0.074 tonne CO 2 -eq emissions/tonne CO 2 to be stored 43 . 2.3 Process description and modelling Modelling of the CH 4 conversion unit and co-removal via solid sorbent-based adsorption followed the same methodology as presented in Sirigina, et al. 21 , with some modifications made to fit the application of the present study. The chosen CH 4 concentrations and corresponding gas flows were given as input data (see SM for details). The model output consisted of stream flow rates, energy demand for each process step, as well as the capacity and size of the required equipment. The flow sheet for the methane conversion process is shown in Fig. 1 and Figure K (a) in SM. The outlet stream from the manure storage was directed into the process using a blower. A dehumidifier reduced the inlet moisture content in the stream. The stream was then preheated using the thermal energy of the product stream from the reactor. An electric heater increased the temperature before entering the reactor. The exhaust stream was cooled down after leaving the reactor. For 300 and 1000 ppmv CH 4 , a solid sorbent based vacuum temperature swing adsorption (VTSA)-based process was considered for CO 2 capture. For the cases with a 3000 and 10,000 ppmv CH 4 concentration, a monoethanolamine (MEA) based absorption process was modelled in Aspen Plus and integrated to the CH 4 conversion unit. Process schematics for co-removal based on adsorption (co-removal) and co-removal based on MEA absorption are shown in the SM (Figure K (B and C)). The Aspen Process Economic Analyser (APEA) integrated in Aspen Plus was used to estimate the installed weight of the equipment. The weight of all the equipment except the reactor was obtained from APEA. The methodology for sizing the reactor and the table of heat transfer coefficients used in dimensioning the heat exchangers are presented in the SM. The plant size was inversely related to the CH 4 concentration, as a low concentration entails a larger volume of air being treated to remove the same total amount of CH 4 . Stainless steel 304 was considered as the default material for all the heat exchangers and the reactor in the CH 4 conversion unit. Other installations were assumed to consist of 90% steel and 10% concrete as a best estimate. Plant lifetime was set to 20 years. The catalyst used for CH 4 oxidation is 6.5% Pd/Al 2 O 3 , and a conversion rate of 95% was considered for the analysis. The catalyst amount required for the conversion was estimated based on the kinetic equation provided in Alyani and Smith 44 . For the conversion rate to be even higher, the catalyst amount would increase exponentially. The right amount of catalyst for each CH 4 concentration was calculated based on reaction kinetics to match the amount of CH 4 emitted. The lifetime of the catalyst was set to 10 years, meaning it is exchanged once during the technical lifetime of the plant. The studied catalyst shows reversible inhibition in the presence of water 44 ; hence a dehumidifier is needed. It is plausible that a considerable share of ammonia will dissolve in the condensate from the dehumidifier. However, the effects on the process of remaining ammonia are currently unknown and therefore are not considered. Other gases, such as N 2 O, were assumed to have passed through the system without undergoing any reaction. We assume no gas slip occurred within the treatment process. An inlet temperature of 330°C was considered for the catalytic conversion of methane 21 , 44 . At 300 ppmv, a separate heater was necessary to reach the required reaction temperature, while at the higher CH 4 concentrations, the excess heat from the oxidised CH 4 was enough to sustain the reaction. It was necessary to cool the gas stream after the reaction regardless of whether it was exhausted or if CO 2 was captured. A solid sorbent (APDES-NFC; 3-aminopropylmethyldiethoxysilane - functionalized nanofibrillated cellulose adsorbent) VTSA process was used for the CO 2 capture at the low CH 4 concentrations. The energy demand for CO 2 capture through adsorption (excluding the blower) was set to 11.04 GJ/tonne CO 2 45 . A process based on APDES-NFC was considered due to its similarity with the sorbent used by Climeworks. The case with the maximum productivity was considered and the resulting sorbent requirement was linearly scaled down for the capture of CO 2 , corresponding to the amount in the stream from CH 4 conversion unit. The regeneration temperature was 110°C, capture efficiency was 80% and PED was 2.83 GJ/tonne CO 2 based on Sabatino, et al. 45 . Heat integration was not possible at 300 and 1000 ppmv due to the low quality of waste heat stream from the CH 4 conversion unit. The manufacturing for this case was assumed to equal the DAC plant described by Terlouw, et al. 46 and scaled linearly to match the capture capacity of our system. For 3000 ppmv and 10,000 ppmv CH 4 concentrations, a MEA based absorption process was modelled in Aspen Plus and integrated to CH 4 conversion unit. Heat integration was modelled by using excess heat from the CH 4 reactor for the regeneration of the absorbent. Process schematics for co-removal based on adsorption (co-removal) and co-removal based on MEA absorption are shown in Figure K (B and C). The regeneration temperature was ~ 120°C. The capture efficiency was ~ 89% for the case with 10,000 ppmv CH 4 concentration, while the capture efficiency was ~ 83% for the case with 3000 ppmv CH 4 concentration. It was found from the model that approximately 1.5 kg MEA was required as makeup per tonne of CO 2 capture for the case with 10,000 ppmv CH 4 concentration, while 1.95 kg MEA was required per tonne of CO 2 captured for the case with 3000 ppmv of CH 4 concentration. The amount of MEA required for makeup in our models was similar to the value (1.5 kg) reported in literature 47 . The environmental impact for MEA was obtained from Ecoinvent 34 . The CO 2 capture plant sizing for the MEA absorbent process was done using the Aspen Process economic analyser in the same way as with other installations. 3 Results The output from the process modelling constituted a part of the life cycle inventory and was part of the life cycle PED and CE. Additional results such as specific results from the process model in Aspen can be found in the Supplementary material. As defined in 2.1, a negative net CE denotes lower system emissions than mitigated emissions, whereas a positive net CE is to be interpreted as the system causing more GHG emissions than were being mitigated per functional unit. 3.1 Scenario A: CH 4 conversion The CE for oxidising CH 4 at the four different concentrations is shown in Fig. 2 , displaying the positive and negative climate contribution as well as the net value represented by the difference between the two. The CE was clearly non-linear with an improving net-effect with increasing CH 4 concentrations. A total amount of 0.37 g CH 4 /s (10.3 g CO 2 -eq) was oxidised, and 0.019 g CH 4 /s (0.54 g CO 2 -eq) exited with the waste gas, corresponding to the 95% conversion capacity of the catalyst. In accordance with the definition of the functional unit, this oxidised amount equals − 1.0 g CO 2 -eq. The additional GHG emissions were divided into emissions originating from the manufacturing and those from the operations phase. The results for the end-of-life phase were too low to be clearly displayed in any graphic and were therefore included in the manufacturing phase. The emissions from the operations phase made the predominant contribution (98%, 96%, 93% and 83% at a 300, 1000, 3000 and 10,000 ppmv CH 4 concentration, respectively). However, as the absolute additional emissions decreased for higher CH 4 concentrations, the relative impact of the plant manufacturing increased, corresponding to 2%, 4%, 7% and 17%, respectively (Figure B in SM). The GHG emissions related to the operations phase consisted mainly of energy related emissions. The total PED was 31, 6.3, 2.3 and 0.77 kJ/g CO 2 -eq mitigated from the low to high CH 4 concentrations, respectively. The energy required for operation made up a significant share of the total PED (Figure B in SM). In absolute numbers, it decreased with increasing CH 4 concentration. Lower concentrations of CH 4 led to larger volumes of air being treated to mitigate a given quantity of CO 2 -eq, hence the PED for pressure loss, dehumidifying and heating the air increase. At all four CH 4 concentrations, the blower constitutes the largest share of the PED for operation, followed by the dehumidifier (Figure C in SM). The manufacturing of the dehumidifier was associated with high PED and CE. In the 10,000 ppmv case, this amounted to almost 60% of the PED and 50% of the CE (Figure D in SM). The palladium for the catalyst used to initiate the CH 4 oxidation asserted a high climate impact, and at the lowest concentration, which requires the largest catalyst amount, this constituted almost 65% of the overall climate effect from manufacturing. The recuperator was also demanding to manufacture (Figure D in SM). However, as CH 4 concentrations increased so does the heat discharge from the oxidation process, meaning the size of the recuperator could be significantly decreased. At the lowest concentrations, additional heat was required to reach a high enough temperature for oxidation to initiate, but at 1000 ppmv CH 4 and above, the heat transfer in the recuperator is sufficient to sustain the reaction. 3.2 Scenario B: Co-removal From a life cycle perspective, climate efficiency was worsened when CCS was added (Table 1 ). This is mainly due to the much higher GWP of CH 4 but also due to the additional process steps required for CCS, which slightly increase the PED and cause additional GHG emissions. When summarizing the effects of the oxidized CH 4 with the captured CO 2 , the mitigation equals 12.9, 11.7, 11.5 and 11.4 g CO 2 -eq/s from the lowest to highest CH 4 concentrations, respectively. Table 1 Changes in climate effect (g CO 2 -eq emitted/g CO 2 -eq removed) and PED for co-removal compared to CH 4 conversion. CH 4 concentration (ppmv) 300 1000 3000 10,000 Net climate effect (CH 4 conversion) + 0.11 -0.76 -0.90 -0.97 Manufacturing (CCS) + 0.0057 + 0.013 + 0.0083 + 0.016 Operation (CCS) + 0.011 + 0.13 + 0.057 + 0.00057 Transport and storage (CCS) + 0.015 + 0.011 + 0.0097 + 0.0092 Stored CO 2 -0.21 -0.15 -0.13 -0.13 Net climate effect (co-removal) + 0.14 -0.62 -0.83 -0.94 Change in GWP (absolute) + 0.073 + 0.18 + 0.15 + 0.10 Change in GWP (relative) + 65% + 24% + 17% + 11% Change in PED (relative) + 2% + 51% + 82% + 59% The PED increased due to the added capture unit, mainly due to the energy required for the regeneration of the sorbent. The energy demand from operating CO 2 capture alone was higher than CH 4 conversion in the cases with medium and high CH 4 concentrations. The CO 2 capture process required additional energy use, which was especially visible for the medium concentrations (Fig. 3 ). The PED for the CO 2 capture process was highly dependent of the concentration of both CH 4 and CO 2 . At the highest CH 4 concentration, the energy generated from the CH 4 oxidation reaction could be utilized in the amine regeneration, lowering the need for additional heat. The manufacturing of the CO 2 capture components added a considerable contribution to the overall manufacturing emissions, around 40% for all three cases (Figure G in SM). 3.3 Sensitivity analysis The following results are for the sensitivity analyses performed for the CH 4 conversion scenario. Results for sensitivity analyses for the co-removal scenario can be found in the Supplemental material. 3.3.1 Energy source In a sensitivity analysis, the impact of using different emission factors for electricity was evaluated. The European mix (293 g CO 2 -eq/kWh) used in the main scenario was exchanged for natural gas power (436 g CO 2 -eq/kWh) and a Nordic consumption mix (93.2 g CO 2 -eq/kWh). A comparison shows this had a determining effect on the net CE for the low CH 4 concentration (Fig. 4 ), while the impact is more modest for the higher CH 4 concentration. The primary energy factor differs between the different electricity mixes, which affects the PED. For the natural gas, this leads to a PED increase of between 8.0 and 8.5%; for the Nordic mix, the PED decrease was between 23 and 25%. 3.3.2 Climate metrics A sensitivity analysis for the impact of the choice of climate metric was evaluated, highlighting GWP20 and GWP500 in comparison to the main case which uses GWP100 (Fig. 5 ). The shorter the chosen time horizon for the metric, the better the system performance appears. At the low CH 4 concentration, the system net CE varies greatly depending on the metric used. For the medium and high CH 4 concentrations, the impact is visible but more modest. The GWP reduction for the process was shared by CH 4 and CO 2 . With the use of the different metric time horizons, the relative contribution of each gas varied (Fig. 6 ). The result for PED was also affected as the ascribed value of the functional unit changed with the new GWP. In these two cases, the change would be a 66% decrease in PED per g CO 2 -eq mitigated for GWP20, and a 275% increase for GWP500. 4 Discussion The results confirmed that the CH 4 concentration was the most influential factor for the net CE in the system. It was inversely proportional to the energy demand for operation due to the large volumes of air being treated per gain in reduced climate impact at low concentrations of CH 4 (i.e. the relation between the number of molecules of H 2 O to remove and N 2 molecules to heat compared to CH 4 oxidised). The net CE was positive (Fig. 2 ) at the lowest CH 4 concentration modelled in this study (300 ppmv), i.e. the CH 4 conversion process contributed to global warming from a life cycle perspective much due to the GHG emissions from operation, outweighing the gain from the oxidised CH 4 . However, a sensitivity analysis highlighted the importance of increasing the share of renewable energy sources, as switching to a renewable electricity mix (modelled as the current consumption mix of the Nordic countries) improved the net GWP more than 6 times compared to European mix (Fig. 4 ). This resulted in a net negative CE also for the lowest CH 4 concentration. The impact of the electricity mix was especially clear at the low CH 4 concentration due to its high energy demand. For CH 4 concentrations from 1000 to 10,000 ppmv, a net negative climate effect could be reached even with natural gas-powered electricity, although at worsened net CE. A higher share of renewables also lowered the PED as these energy sources have lower primary energy factors than fossil energy sources. To evaluate and compare the climate impact of emitting or mitigating GHGs, a conversion metric must be chosen. The GWP100 is the most widespread metric used in both policy and science, although it has long been criticised for not fairly representing the actual temperature response, especially underestimating short term effects caused by, for example, CH 4 e.g. 6 , 48 , 49 . The sensitivity analysis of the climate metric (Fig. 5 ) illustrates how a shorter time horizon weights the impact of CH 4 mitigation more than a longer time horizon. GWP20 emphasises the impact of mitigating the short-term effects of CH 4 , resulting in net-negative CE for all three concentrations, both with and without CO 2 capture. GWP500 instead put more emphasis on the long-term impact of CO 2 , giving a clear lowering of the net CE compared to the GWP100 results. Due to the strong warming effect of CH 4 compared to CO 2 , no matter the metric used in this study, avoiding the impact of CH 4 played a decisive role compared to the benefits of also capturing the subsequent CO 2 , ranging from around 50% of the total climate benefit attributed to CH 4 and 50% from CO 2 for GWP500 to 92% for CH 4 and 8% for CO 2 for GWP20 (Fig. 6 ). No matter the concentration and metric, the net CE decreased when CO 2 capture was introduced due to the additional energy (Table 1 ) and material demand compared to sole CH 4 oxidation. However, depending on the studied timescale, the prioritisation between CH 4 and CO 2 could differ, as targeting CH 4 emissions could deliver significant short-term effects. From a longer time perspective, however, the accumulation of CO 2 in the atmosphere is what will largely determine the level the mean global temperature will reach and remain at due to its longer residence time 50 . The study identified the blower, the dehumidifier and the catalyst demand as important hotspots for both energy demand and CE (Fig. 3 ). Since the process energy had such a major impact on the overall result, especially at low concentrations, technological improvements could have a substantial effect on overall performance and feasibility. Energy demand for the blower can be reduced by minimizing pressure drops throughout the process. The development of a less water-inhibited catalyst material with a lower carbon footprint would improve the system CE directly at the manufacturing stage (Figure F & G in SM) and indirectly by reducing or eliminating the need for the energy demanding dehumidification step. Palladium minerals are rare and have wide uses, from catalysts to jewellery, electronics and fuel cells. Hence, like with other metals important for the green transition, there are risks such as rising prices and tightened supply in the future (Andersen et al, 2024). Furthermore, a lower minimum temperature difference in the recuperator could reduce the energy demand, although this would increase the heat transfer area of the recuperator. As part of the PED for operation consists of thermal energy, it may be possible to connect a secondary renewable energy source or waste heat to improve the net CE. All these aspects are especially pronounced at low CH 4 concentrations due to the high energy and raw material demand per CO 2 -eq mitigated. Under practical operating conditions, manure level, outdoor temperature and energy source would have a decisive impact on the net CE. The rate of CH 4 emitted is closely linked to the storage temperature, and the relatively large energy demand for operation constituted a noticeable impact on the net CE. Therefore, one could expect that this type of system would deliver the largest climate benefits if implemented in parts of the world with a warm climate and a substantial renewable electricity supply, and on farms with large herds of livestock and effective manure collection systems. The larger the share of manure collected, the greater the climate benefit that could be achieved for the investment per animal. It is also vital that the entire value chain required for CH 4 oxidation and CO 2 capture and storage can be carried out with a minimum of GHG slip and performed in a climate friendly and energy efficient manner to reach the highest possible total climate benefit and maintain high credibility for the GGR concept. For manure storage, the CH 4 emissions at each moment in time vary based on a number of factors. As this is expected to impact the operation of the machinery as well as system efficiency and subsequent energy use, we chose to conduct this study as a snapshot of a moment in time with static conditions, with the four studied concentrations covering a large interval of conditions. This approach was an attempt to decrease complexity and be able to perform initial evaluation and information gathering on the most important parameters affecting strategies for design and operation of this novel system. Considering the promising net CE presented in this study, it seems reasonable to continue exploring technologies for the conversion of CH 4 with and without subsequent CO 2 capture to analyse its prospects for implementation under different conditions, such as scale, location and CH 4 concentration. Some relevant research topics for future studies could be investigating practical constraints on the equipment and materials, techno-economic analysis, and experimentation to confirm modelling results. There are also options to increase system efficiency and increased systems integration through, for example, carbon capture and utilisation. In the future, more detailed case studies that examine feasibility under certain conditions with more case specific and time-dynamic data for a particular application should be used, whether it be manure storage or some other unabated CH 4 emissions source. The low CH 4 concentrations analysed in this study could allow for the targeting of emission sources where GGR has not been considered previously, such as the agrifood sector, which accounts for over half of overall CH 4 emissions. There is currently no technology to completely eliminate non-CO 2 GHG emissions from agriculture, and improvements in carbon efficiency and emission reduction measures risk being partly counteracted by the increased primary production demand necessary for feeding an increasing world population 51 . The burden on reduced GHG emissions in other sectors would increase unless unabated agricultural emissions could be sequestered or compensated for through additional GGR. Further research on ways to reduce global warming via CH 4 could improve the technology used and identify the most promising areas of implementation to maximise efficiency. 5 Conclusions This study presents a technology capable of delivering climate mitigation by oxidising CH 4 emissions at low concentrations, below the point of ignition, using a thermal catalytic process with and without subsequent CO 2 capture and storage. The results showed that the PED for thermal catalytic CH 4 oxidation was highly dependent on the CH 4 concentration, which cause a slightly positive net climate effect at 300 ppmv CH 4 , while reaching a net negative climate effect at 1000 ppmv CH 4 and above. Hence, the suggested technology was not able to achieve negative emissions at the lowest concentration for the default design and conditions studied. However, the energy source was highly impactful due to the high energy demand for operation. By changing the assumed European electricity mix to the Nordic electricity mix for CH 4 conversion, a net negative climate effect could be achieved for CH 4 conversion at all four studied CH 4 concentrations. Alternatively, changing the climate metrics from GWP100 to GWP20 also resulted in a net negative climate effect at all studied CH 4 concentrations, as the reduced time horizon of the GWP20 metric emphasises the impact of reducing the strong short-term warming effects of CH 4 . From 3000 ppmv CH 4 , the decreasing energy demand in combination with the increasing feasibility of utilising excess heat meant that the impact of oxidising CH 4 resulted in a net negative climate effect even when operated using natural gas and regardless of the chosen climate metric. However, in order to maximise the benefit and credibility of the technology, a low emissions energy source should be pursued, and high energy efficiency is desirable to avoid wasting limited renewable energy resources on GGR. At all CH 4 concentrations, the net climate effect was worsened by adding co-removal of CO 2 from the outlet gas with subsequent storage. This was mainly due to the increased PED for operation and the high GWP of CH 4 , making the avoidance of warming from CH 4 the most pronounced benefit of the proposed technology. While targeting the most concentrated CH 4 emission sources possible is the most energy efficient approach, the study also identified areas where technological development would increase the system climate efficiency. The most prominent climate and energy related hotspots are connected to the blower, dehumidification and catalyst. If these impacts can be lowered by, for example, minimising pressure drops and developing efficient and water-resistant catalysts with low carbon footprints, the increased system efficiency would achieve net negative CE at even lower CH 4 concentrations, thus allowing catalytic conversion of CH 4 to target and efficiently abate a wider range of CH 4 emission sources. Nomenclature CE climate effect CCS Carbon capture and storage CDR Carbon dioxide removal CH 4 Methane CF Carbon footprint CO 2 Carbon dioxide ED Energy demand GGR Greenhouse gas removal GGRT Greenhouse gas removal technology GHG Greenhouse gas GWP Global warming potential kJ Kilo Joule (10 3 joule) LCA Life cycle assessment MJ Mega Joule (10 6 joule) PED Primary energy demand Declarations Acknowledgements This research received funding from The Swedish Energy Agency for the project “Energy efficient negative emissions from agriculture and farming” with grant number 50340-1. The manuscript went through substantial language checking by William Cowley. CRediT authorship contribution statement Emma Bromark : Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft, Writing – review & editing. Sairam Sirigina : Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft, Writing – review & editing. Shareq Mohd Nazir : Funding acquisition, Conceptualization, Supervision, Methodology, Writing – review & editing. Pernilla Tidåker : Conceptualization, Methodology, Writing – review & editing. Åke Nordberg : Conceptualization, Methodology, Writing – review & editing. Per-Anders Hansson : Conceptualization, Funding acquisition, Supervision, Methodology, Writing – review & editing. Emma Bromark had the primary responsibility for the life cycle assessment while Sairam Sirigina had the primary responsibility for the process modelling Data availability Supplementary material associated with this article can be found in the online version. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Rueda, O., Mogollón, J. M., Tukker, A. & Scherer, L. Negative-emissions technology portfolios to meet the 1.5 °C target. 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Joule 5 , 2047-2076, doi:https://doi.org/10.1016/j.joule.2021.05.023 (2021). Terlouw, T., Treyer, K., Bauer, C. & Mazzotti, M. Life Cycle Assessment of Direct Air Carbon Capture and Storage with Low-Carbon Energy Sources. Environ. Sci. Technol. 55 , 11397-11411, doi:10.1021/acs.est.1c03263 (2021). Abanades, J. C., Rubin, E. S. & Anthony, E. J. Sorbent Cost and Performance in CO2 Capture Systems. Industrial & Engineering Chemistry Research 43 , 3462-3466, doi:10.1021/ie049962v (2004). Cain, M. et al. Improved calculation of warming-equivalent emissions for short-lived climate pollutants. npj Climate and Atmospheric Science 2 , 29, doi:10.1038/s41612-019-0086-4 (2019). Nisbet, E. G. et al. Very Strong Atmospheric Methane Growth in the 4 Years 2014–2017: Implications for the Paris Agreement. Global Biogeochemical Cycles 33 , 318-342, doi:https://doi.org/10.1029/2018GB006009 (2019). Allen, M. R. et al. Net Zero: Science, Origins, and Implications. 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Supplementary Files Bromarketal.Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 10 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Sep, 2025 Reviews received at journal 16 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Editor assigned by journal 01 Apr, 2025 Editor invited by journal 01 Apr, 2025 Submission checks completed at journal 29 Mar, 2025 First submitted to journal 28 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6328195","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":442615098,"identity":"c8b38c3b-1b5b-4b17-bb02-c6fb8736ea63","order_by":0,"name":"Emma Bromark","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYLACxgYwxczwgcGCRC2MMxgkSNTCzEOMFn6x04kfGHfck9NtP/vY2OaPRGID/+EDeLVIzs7dLMF4ptjY7Ey6cXJuG1CLRFoCXi0Gt3M3SDC2JSRuO5DGfDi3AaSFxwCvFvvbuZt/gLWcf8Z82ALssPMf8NsinbsNYsuNNOZkBjagFoYcvDoYJG7nbrNIPJNgbHbjGbNhb5uEcZtEGn6H8QO9f+PjjgQ5s/NpzBI//tjI9vMffoDfGhBIQOawEVY/CkbBKBgFo4AQAAAtP0StAfup9gAAAABJRU5ErkJggg==","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Emma","middleName":"","lastName":"Bromark","suffix":""},{"id":442615099,"identity":"6dbbb3ce-cfb3-4036-bc39-9e1be42b53e0","order_by":1,"name":"Devesh Sathya Sri Sairam Sirigina","email":"","orcid":"","institution":"KTH Royal Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Devesh","middleName":"Sathya Sri Sairam","lastName":"Sirigina","suffix":""},{"id":442615100,"identity":"cf44fed3-08e9-4a2c-abf2-668dcf99e720","order_by":2,"name":"Shareq Mohd Nazir","email":"","orcid":"","institution":"KTH Royal Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shareq","middleName":"Mohd","lastName":"Nazir","suffix":""},{"id":442615101,"identity":"e4c71e54-242d-40ee-ab8f-97d40bfa30fe","order_by":3,"name":"Pernilla Tidåker","email":"","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Pernilla","middleName":"","lastName":"Tidåker","suffix":""},{"id":442615102,"identity":"21b52229-23f6-4ce2-bee5-c7b33cd0f4b4","order_by":4,"name":"Åke Nordberg","email":"","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Åke","middleName":"","lastName":"Nordberg","suffix":""},{"id":442615103,"identity":"bf19e70b-e85f-4aca-ab07-188a3a8da78b","order_by":5,"name":"Per-Anders Hansson","email":"","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Per-Anders","middleName":"","lastName":"Hansson","suffix":""}],"badges":[],"createdAt":"2025-03-28 12:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6328195/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6328195/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-27609-2","type":"published","date":"2025-12-10T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81117454,"identity":"c0a323f9-3814-44be-a42d-5a606fca84b9","added_by":"auto","created_at":"2025-04-22 11:58:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":191652,"visible":true,"origin":"","legend":"\u003cp\u003eSystem sketch illustrating the scenarios covered in the study.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6328195/v1/0a3b0797783f0ec741f45b2c.png"},{"id":81116598,"identity":"7e254407-acf0-463a-b6ef-f324081bc7b4","added_by":"auto","created_at":"2025-04-22 11:50:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67869,"visible":true,"origin":"","legend":"\u003cp\u003eThe climate effect of processes necessary for oxidising CH\u003csub\u003e4\u003c/sub\u003e as well as the net effect. The study is designed to remove an equivalent amount of CH\u003csub\u003e4\u003c/sub\u003e in each scenario but at different concentrations, hence the grey bar has the same value for all four studied scenarios.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6328195/v1/69096dd83da2a79280487c55.png"},{"id":81117453,"identity":"1437cb79-86c4-41b4-9d54-ea4f4618e1ad","added_by":"auto","created_at":"2025-04-22 11:58:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82422,"visible":true,"origin":"","legend":"\u003cp\u003eThe share of primary energy demand when operating the CH\u003csub\u003e4\u003c/sub\u003e oxidation and CO\u003csub\u003e2\u003c/sub\u003e capture processes at the four studied CH\u003csub\u003e4\u003c/sub\u003e concentrations, divided between the energy using components.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6328195/v1/e3647b2261241d89b96fb63b.png"},{"id":81116600,"identity":"d2ac40e9-f189-4631-920e-7d0504868049","added_by":"auto","created_at":"2025-04-22 11:50:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":72654,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis for the impact of emissions factor for electricity on the net climate effect for CH\u003csub\u003e4\u003c/sub\u003e conversion at the four studied CH\u003csub\u003e4\u003c/sub\u003e concentrations. The data labels show the relative increase/decrease compared to the main scenario (European mix).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6328195/v1/a5bfdb50c2c15f56a9291d6d.png"},{"id":81116608,"identity":"f24aaa44-17ae-4adb-871b-f45b566c6dad","added_by":"auto","created_at":"2025-04-22 11:50:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":246768,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis highlighting the choice of metric on the net climate effect for CH\u003csub\u003e4\u003c/sub\u003e oxidation at the four modelled CH\u003csub\u003e4\u003c/sub\u003e concentrations. The data labels show the relative increase/decrease compared to the main scenario (GWP100). CH\u003csub\u003e4\u003c/sub\u003e conversion (a) and co-removal (b).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6328195/v1/2505ccaa9a91ecd083ce1e41.png"},{"id":81116602,"identity":"c09e020b-d747-4543-9715-1a57c1df2fe2","added_by":"auto","created_at":"2025-04-22 11:50:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88548,"visible":true,"origin":"","legend":"\u003cp\u003eThe achieved climate benefit of the co-removal is shared by CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e. The figure shows the relative contribution of each gas at the four CH\u003csub\u003e4\u003c/sub\u003e concentrations modelled using different climate metrics.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6328195/v1/2adf2279207ff3f2d5bb901a.png"},{"id":98243499,"identity":"5ae1346a-ce2c-432f-af14-51c5d99e7fb0","added_by":"auto","created_at":"2025-12-15 16:07:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1677884,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6328195/v1/b26bd172-8a7e-47c3-abb7-3a7d350f10d2.pdf"},{"id":81116606,"identity":"84944404-0748-4a2b-b308-717e60d50450","added_by":"auto","created_at":"2025-04-22 11:50:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":616293,"visible":true,"origin":"","legend":"","description":"","filename":"Bromarketal.Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6328195/v1/7d96e484a1207cf366e54bec.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reduced life cycle climate impact from manure through catalytic methane conversion and carbon dioxide removal","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe increase in greenhouse gas (GHG) emissions and climate scenario modelling shows the need for rapidly regressing GHG emissions through the parallel deployment of mitigation efforts and negative emissions across all of sectors \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, some sectors are regarded as more challenging to handle than others. The GHG emissions from the agricultural sector are considered hard-to-abate, as they are predominantly not tied to energy use but rather originate from animals, soils and manure; thus, a renewable energy transition will not impact these emissions \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The global climate impact from agri-food systems amounted to 16 Gt carbon dioxide equivalents (CO\u003csub\u003e2\u003c/sub\u003e-eq) in 2022, with over half caused by non-CO\u003csub\u003e2\u003c/sub\u003e GHGs, mainly methane (CH\u003csub\u003e4\u003c/sub\u003e) and nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The low concentrations (typically\u0026thinsp;\u0026lt;\u0026thinsp;1%-vol) and dispersed nature of the emissions makes them difficult to quantify, target and reduce \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This indicates a need for different types of mitigation strategies for GHG emissions in agriculture and that a future net-zero or net-negative emissions balance would require residual emissions being offset through greenhouse gas removal (GGR) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. GGR can be achieved through natural (biological) processes as well as engineered (technical) systems, i.e. greenhouse gas removal technologies (GGRTs) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. A rather versatile portfolio of GGRTs has been presented, with the majority involving carbon dioxide removal (CDR) \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, second only to CO\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e is so potent of a GHG that it contributes to 25% of total radiative forcing (RF), despite its atmospheric concentration being only 0.5% that of the CO\u003csub\u003e2\u003c/sub\u003e concentration \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Due to its high RF and short atmospheric residence time (approximately 12 years), rapid reductions in CH\u003csub\u003e4\u003c/sub\u003e emissions have great potential to impact global warming in the short term \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Despite there being a strong case for CH\u003csub\u003e4\u003c/sub\u003e mitigation, it remains poorly covered in climate policy, although it has received increased attention in recent years \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. A strategy to evade the warming effect of a high GWP gas such as CH\u003csub\u003e4\u003c/sub\u003e is to convert it into a lower GWP gas, such as CO\u003csub\u003e2\u003c/sub\u003e \u003csup\u003e8\u003c/sup\u003e. Such research has been put forward, with suggested applications both for the fossil fuel industry \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and ambient air e.g. \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, although these are at a low technological readiness level \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. CH\u003csub\u003e4\u003c/sub\u003e gradually oxidises in the atmosphere over time, but this reaction can be initiated by, for example, introducing a catalyst and/or energy addition via light, heat or electricity \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. CH\u003csub\u003e4\u003c/sub\u003e removal from ambient air would require large air flows due to the low concentration \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Removal measures to be implemented in agricultural systems are sparse in the scientific literature so far, despite over half of the global CH\u003csub\u003e4\u003c/sub\u003e emissions originating from agrifood systems \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Many CH\u003csub\u003e4\u003c/sub\u003e emissions arise from large poorly constrained areas, such as organic soils, rice fields and wetlands \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Since the higher the concentration of CH\u003csub\u003e4\u003c/sub\u003e, the easier it is to get the CH\u003csub\u003e4\u003c/sub\u003e conversion reaction running \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, it is preferable to identify a CH\u003csub\u003e4\u003c/sub\u003e point source to increase process efficiency and decrease component size \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Possible alternatives of concentrated emission sources include manure storage or stables for ruminants \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Livestock cause considerable CH\u003csub\u003e4\u003c/sub\u003e emissions originating from enteric fermentation (~\u0026thinsp;70%) and manure (~\u0026thinsp;30%) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. GHG emissions from manure is the second largest emission source from farms \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. During manure storage, organic matter in the manure starts to decompose, forming CO\u003csub\u003e2\u003c/sub\u003e under aerobic conditions and CH\u003csub\u003e4\u003c/sub\u003e through anaerobic reactions \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The rate of emissions depends on a number of factors, such as organic matter, temperature, moisture and pH \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and a future warmer climate would increase CH\u003csub\u003e4\u003c/sub\u003e emissions from manure \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Manure degradation can be partially regulated by lowering the pH of the manure through cooling, increasing acidity or covering the source \u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The manure could also be subjected to anaerobic digestion, which allows the CH\u003csub\u003e4\u003c/sub\u003e to form under controlled circumstances where it can be collected and utilised for its energy content \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Despite this being an efficient utilisation of the waste and lowered climate impact, only around 5% of manure is digested in Sweden, one barrier being the high investment cost of farm-based biogas plants \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. A potential approach is to treat the air from the manure storage space on-site by oxidising the CH\u003csub\u003e4\u003c/sub\u003e in a reactor containing a catalytic material to facilitate the reaction, as was recently proposed by Sirigina, et al. \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The approach of this system is to convert the CH\u003csub\u003e4\u003c/sub\u003e as soon as it is emitted to avoid its warming effect. The resulting CO\u003csub\u003e2\u003c/sub\u003e from the oxidation reaction is biogenic in nature, which can be captured and stored. The most common technologies to capture CO\u003csub\u003e2\u003c/sub\u003e are chemical absorption and adsorption \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Geological storage is necessary to achieve long-term removal of the CO\u003csub\u003e2\u003c/sub\u003e from the atmosphere \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. To achieve sustainable and credible GGR, the applied technologies must result in a net negative emission balance over the system\u0026rsquo;s lifecycle and long-term storage reliability of the removed emissions \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. There is a growing number of studies of CDR technologies which emphasise the importance of the LCA methodology to evaluate its effectiveness in delivering negative emissions, suggesting clear variance in effectiveness across technologies (see e.g. review by Rueda, et al. \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, the lifecycle perspective of GGR through the conversion of CH\u003csub\u003e4\u003c/sub\u003e emissions emerging from biogenic sources is still to be covered in the scientific literature. There is also a need for further discussion about scenarios where GGR targeting CH\u003csub\u003e4\u003c/sub\u003e emissions can be considered as a greenhouse gas removal or mitigation strategy. This study aims to contribute with an evaluation of thermal catalytic oxidation as an option for reducing CH\u003csub\u003e4\u003c/sub\u003e emitted from manure storage using a life cycle perspective. The objective of the study was to determine the energy demand and climate efficiency of the proposed technology. Furthermore, the impact of releasing the exhaust gas to the atmosphere after the conversion process was compared with subsequent capture and storage of the CO\u003csub\u003e2\u003c/sub\u003e from the exhaust gas. The study attempted to identify energy and climate related hotspots in the process to provide guidance for future system development.\u003c/p\u003e"},{"header":"2 Methodology","content":"\u003cp\u003eThis study investigated a process technology for thermal catalytic treatment of the CH\u003csub\u003e4\u003c/sub\u003e emitted from manure storage and subsequent CO\u003csub\u003e2\u003c/sub\u003e capture and storage (CCS). The approach was to compare a reference state of conventional unabated manure storage emissions to a scenario where the manure storage space is integrated with the suggested technology:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eScenario A: \u003cem\u003eCH\u003c/em\u003e\u003csub\u003e\u003cem\u003e4\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-conversion\u003c/em\u003e. GHG emissions from manure storage headspace is treated through catalytic CH\u003csub\u003e4\u003c/sub\u003e oxidation and the exhaust gas is released into the atmosphere.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eScenario B: \u003cem\u003eCo-removal\u003c/em\u003e. GHG emissions from manure storage headspace is treated through catalytic CH\u003csub\u003e4\u003c/sub\u003e oxidation followed by capture and storage of the CO\u003csub\u003e2\u003c/sub\u003e in the outlet gas.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe scenarios are explained in greater detail below. The study was performed through a combination of process modelling in Aspen Plus V12 and life cycle assessment (LCA). Both the LCA model and the process model relied largely on generic literature data and are not meant to represent a specific installation or determine an optimal system of production, but rather to provide a representative example based on currently available data.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Life cycle assessment\u003c/h2\u003e \u003cp\u003eThe study covered each process step involved, from manure storage to the suggested catalytic treatment until the exhaust gas is released or treated for CO\u003csub\u003e2\u003c/sub\u003e capture for storage after the process. Processes occurring upstream of manure storage were excluded as they remain unaffected throughout the scenarios. The system boundary considered was cradle-to-grave, meaning the study included emissions from the entire life cycle of the technical appliances, including the necessary energy and raw materials, transport and processing, manufacturing of machinery, infrastructure and facilities, as well as its end of life. The life cycle inventory (LCI) was established through data collection from the literature and by utilizing the process parameters obtained from process models developed in Aspen (section 2.3), with the resulting data used for the life cycle impact assessment. We used life cycle inventory data on a European level and, if the corresponding data was not available, on a global level. As a final option, country-specific LCI datasets were used. The data was compiled over the three life cycle phases: manufacturing (raw material extraction and processing), operation (plant in operation), and end of life (material waste management). The climate impact and primary energy demand (PED) for the raw material for the process components manufacturing and catalyst was based on data from Ecoinvent v. 3.9.1. End of life for the used materials were included based on Ecoinvent \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e data.\u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:PED=ED*PEF$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:net\\:CE=\\frac{{e}_{emitted}-{e}_{mitigated}}{{e}_{mitigated}}.\\:$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eED\u003c/em\u003e denotes energy demand, \u003cem\u003ePEF\u003c/em\u003e primary energy factor and \u003cem\u003ee\u003c/em\u003e GHG emissions in g CO\u003csub\u003e2\u003c/sub\u003e-eq.\u0026nbsp;By this definition, a negative value of the net CE denotes lower system emissions than mitigated emission, whereas a positive value of the net CE is to be interpreted as the system causing more GHG emissions than is being mitigated. As the GHGs emitted from manure are of biological origin, their removal would be categorized as negative emissions, provided that the total removal of GHG emissions is larger than the total GHG emissions emitted to the atmosphere by the processes required for GGR \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Compared to the reference state, oxidising CH\u003csub\u003e4\u003c/sub\u003e as soon as it is emitted will help eliminate the large RF which is otherwise exerted during its residence time in the atmosphere. This was shown as an arithmetically negative contribution to the net CE, although the carbon atom is still present in the atmosphere in the CH\u003csub\u003e4\u003c/sub\u003e conversion case. However, the term mitigated was chosen as the assumption that we are in fact removing or mitigating emissions needs a broader system level discussion, which is outside the scope of this article.\u003c/p\u003e \u003cp\u003eGWP100 was used as the conversion metric to assess climate impact from GHG emissions as is standard practice in LCA. However, with CH\u003csub\u003e4\u003c/sub\u003e being such a central component of this study, the metric choice was subjected to sensitivity analysis to highlight its implications, by replacing GWP100 with GWP20 and GWP500. GWP values as defined in AR6 are used \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. To allow this, disaggregated GHG emission data was used when possible.\u003c/p\u003e \u003cp\u003eThe energy used for operation was solely in the form of electricity. The electricity source was assumed to be a European mix with an emission factor of 239 g CO\u003csub\u003e2\u003c/sub\u003e-eq/kWh \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e and a primary energy factor of 2.3 (Cogen 2017). This was subjected to sensitivity analysis, exchanging the electricity source for natural gas power with 436 g CO\u003csub\u003e2\u003c/sub\u003e-eq/kWh and a primary energy factor of 2.5, and for a more renewable mix represented by the Nordic consumption mix (approximately 50% hydropower, 20% nuclear power, 15% wind and solar power and 15% combined heat and power) with 93.2 g CO\u003csub\u003e2\u003c/sub\u003e-eq/kWh \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e and primary energy factor of 1.7 \u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 System description\u003c/h2\u003e \u003cp\u003eLivestock slurry was assumed to be stored on a farm in covered manure storage with a capacity of 5000 m\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, sufficient for manure from around 200 dairy cows including a grazing period of 3 months \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The manure was stored for up to 9 months before being spread on nearby fields as fertilizer. Biological processes degrading the organic matter in the manure form emissions of gases and heat. The CH\u003csub\u003e4\u003c/sub\u003e emission rate was determined based on IPCC guideline data for the average yearly CH\u003csub\u003e4\u003c/sub\u003e emissions from manure from high productivity dairy cattle in a cool, temperate, moist climate Table\u0026nbsp;10.14; \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Based on this, the manure was assumed to emit 0.390 mg of CH\u003csub\u003e4\u003c/sub\u003e (10.5 mg CO\u003csub\u003e2\u003c/sub\u003e-eq) per second. In the reference state, the GHG emissions were released to the atmosphere without any intervention. For this analysis we chose to model four different concentrations of CH\u003csub\u003e4\u003c/sub\u003e (300, 1000, 3000, and 10,000 ppmv), representing conditions which cause low to high CH\u003csub\u003e4\u003c/sub\u003e emissions. This was achieved by adjusting the dilution of CH\u003csub\u003e4\u003c/sub\u003e in four different airflows (6890, 2067, 689 and 207 m\u003csup\u003e3\u003c/sup\u003e/h), respectively. This interval gives information on how the emissions rate relates to the size of the process plant and covers a relevant range of operating conditions. We further assume the share of aerobic vs anaerobic degradation of organic matter in the manure storage was such that the GHG emissions consisted of CO\u003csub\u003e2\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e on a 40:60 molar% basis based on Grant, et al. \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The CO\u003csub\u003e2\u003c/sub\u003e originating from the manure degeneration (0.71 g/s) entered the process alongside the CO\u003csub\u003e2\u003c/sub\u003e in ambient air, which was set to 417 ppmv.\u003c/p\u003e \u003cp\u003e \u003cem\u003eScenario A: CH\u003c/em\u003e \u003csub\u003e \u003cem\u003e4\u003c/em\u003e \u003c/sub\u003e \u003cem\u003econversion\u003c/em\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn scenario A, the airflow from the covered manure storage headspace was actively ventilated and directed to a catalytic oxidizer where CH\u003csub\u003e4\u003c/sub\u003e was oxidised to CO\u003csub\u003e2\u003c/sub\u003e before release into the atmosphere. Different CH\u003csub\u003e4\u003c/sub\u003e concentrations were achieved by diluting the CH\u003csub\u003e4\u003c/sub\u003e emissions through varying airflow.\u003c/p\u003e \u003cp\u003e \u003cem\u003eScenario B: Co-removal\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOnce the CH\u003csub\u003e4\u003c/sub\u003e had been oxidized, following an identical route as in Scenario A, the residual gas flow entered a carbon capture unit to separate the CO\u003csub\u003e2\u003c/sub\u003e from the rest of the air. Due to the degradation processes in manure forming CO\u003csub\u003e2\u003c/sub\u003e, its concentrations change, to match the amount of CH\u003csub\u003e4\u003c/sub\u003e, creating a 40:60 mix. The CO\u003csub\u003e2\u003c/sub\u003e flows are described in the Supplementary material (SM) (Table B). The separated CO\u003csub\u003e2\u003c/sub\u003e was liquefied and transported to and injected at a geological storage site. According to a previous work on transport and storage for relevant conditions, the captured CO\u003csub\u003e2\u003c/sub\u003e would require 1,2 GJ/tonne CO\u003csub\u003e2\u003c/sub\u003e stored and cause 0.074 tonne CO\u003csub\u003e2\u003c/sub\u003e-eq emissions/tonne CO\u003csub\u003e2\u003c/sub\u003e to be stored \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Process description and modelling\u003c/h2\u003e \u003cp\u003eModelling of the CH\u003csub\u003e4\u003c/sub\u003e conversion unit and co-removal via solid sorbent-based adsorption followed the same methodology as presented in Sirigina, et al. \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, with some modifications made to fit the application of the present study. The chosen CH\u003csub\u003e4\u003c/sub\u003e concentrations and corresponding gas flows were given as input data (see SM for details). The model output consisted of stream flow rates, energy demand for each process step, as well as the capacity and size of the required equipment. The flow sheet for the methane conversion process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Figure K (a) in SM. The outlet stream from the manure storage was directed into the process using a blower. A dehumidifier reduced the inlet moisture content in the stream. The stream was then preheated using the thermal energy of the product stream from the reactor. An electric heater increased the temperature before entering the reactor. The exhaust stream was cooled down after leaving the reactor. For 300 and 1000 ppmv CH\u003csub\u003e4\u003c/sub\u003e, a solid sorbent based vacuum temperature swing adsorption (VTSA)-based process was considered for CO\u003csub\u003e2\u003c/sub\u003e capture. For the cases with a 3000 and 10,000 ppmv CH\u003csub\u003e4\u003c/sub\u003e concentration, a monoethanolamine (MEA) based absorption process was modelled in Aspen Plus and integrated to the CH\u003csub\u003e4\u003c/sub\u003e conversion unit. Process schematics for co-removal based on adsorption (co-removal) and co-removal based on MEA absorption are shown in the SM (Figure K (B and C)).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Aspen Process Economic Analyser (APEA) integrated in Aspen Plus was used to estimate the installed weight of the equipment. The weight of all the equipment except the reactor was obtained from APEA. The methodology for sizing the reactor and the table of heat transfer coefficients used in dimensioning the heat exchangers are presented in the SM.\u003c/p\u003e \u003cp\u003eThe plant size was inversely related to the CH\u003csub\u003e4\u003c/sub\u003e concentration, as a low concentration entails a larger volume of air being treated to remove the same total amount of CH\u003csub\u003e4\u003c/sub\u003e. Stainless steel 304 was considered as the default material for all the heat exchangers and the reactor in the CH\u003csub\u003e4\u003c/sub\u003e conversion unit. Other installations were assumed to consist of 90% steel and 10% concrete as a best estimate. Plant lifetime was set to 20 years.\u003c/p\u003e \u003cp\u003eThe catalyst used for CH\u003csub\u003e4\u003c/sub\u003e oxidation is 6.5% Pd/Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, and a conversion rate of 95% was considered for the analysis. The catalyst amount required for the conversion was estimated based on the kinetic equation provided in Alyani and Smith \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. For the conversion rate to be even higher, the catalyst amount would increase exponentially. The right amount of catalyst for each CH\u003csub\u003e4\u003c/sub\u003e concentration was calculated based on reaction kinetics to match the amount of CH\u003csub\u003e4\u003c/sub\u003e emitted. The lifetime of the catalyst was set to 10 years, meaning it is exchanged once during the technical lifetime of the plant. The studied catalyst shows reversible inhibition in the presence of water \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e; hence a dehumidifier is needed. It is plausible that a considerable share of ammonia will dissolve in the condensate from the dehumidifier. However, the effects on the process of remaining ammonia are currently unknown and therefore are not considered. Other gases, such as N\u003csub\u003e2\u003c/sub\u003eO, were assumed to have passed through the system without undergoing any reaction. We assume no gas slip occurred within the treatment process. An inlet temperature of 330\u0026deg;C was considered for the catalytic conversion of methane \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. At 300 ppmv, a separate heater was necessary to reach the required reaction temperature, while at the higher CH\u003csub\u003e4\u003c/sub\u003e concentrations, the excess heat from the oxidised CH\u003csub\u003e4\u003c/sub\u003e was enough to sustain the reaction. It was necessary to cool the gas stream after the reaction regardless of whether it was exhausted or if CO\u003csub\u003e2\u003c/sub\u003e was captured.\u003c/p\u003e \u003cp\u003eA solid sorbent (APDES-NFC; 3-aminopropylmethyldiethoxysilane - functionalized nanofibrillated cellulose adsorbent) VTSA process was used for the CO\u003csub\u003e2\u003c/sub\u003e capture at the low CH\u003csub\u003e4\u003c/sub\u003e concentrations. The energy demand for CO\u003csub\u003e2\u003c/sub\u003e capture through adsorption (excluding the blower) was set to 11.04 GJ/tonne CO\u003csub\u003e2\u003c/sub\u003e \u003csup\u003e45\u003c/sup\u003e. A process based on APDES-NFC was considered due to its similarity with the sorbent used by Climeworks. The case with the maximum productivity was considered and the resulting sorbent requirement was linearly scaled down for the capture of CO\u003csub\u003e2\u003c/sub\u003e, corresponding to the amount in the stream from CH\u003csub\u003e4\u003c/sub\u003e conversion unit. The regeneration temperature was 110\u0026deg;C, capture efficiency was 80% and PED was 2.83 GJ/tonne CO\u003csub\u003e2\u003c/sub\u003e based on Sabatino, et al. \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Heat integration was not possible at 300 and 1000 ppmv due to the low quality of waste heat stream from the CH\u003csub\u003e4\u003c/sub\u003e conversion unit. The manufacturing for this case was assumed to equal the DAC plant described by Terlouw, et al. \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and scaled linearly to match the capture capacity of our system.\u003c/p\u003e \u003cp\u003eFor 3000 ppmv and 10,000 ppmv CH\u003csub\u003e4\u003c/sub\u003e concentrations, a MEA based absorption process was modelled in Aspen Plus and integrated to CH\u003csub\u003e4\u003c/sub\u003e conversion unit. Heat integration was modelled by using excess heat from the CH\u003csub\u003e4\u003c/sub\u003e reactor for the regeneration of the absorbent. Process schematics for co-removal based on adsorption (co-removal) and co-removal based on MEA absorption are shown in Figure K (B and C). The regeneration temperature was ~\u0026thinsp;120\u0026deg;C. The capture efficiency was ~\u0026thinsp;89% for the case with 10,000 ppmv CH\u003csub\u003e4\u003c/sub\u003e concentration, while the capture efficiency was ~\u0026thinsp;83% for the case with 3000 ppmv CH\u003csub\u003e4\u003c/sub\u003e concentration. It was found from the model that approximately 1.5 kg MEA was required as makeup per tonne of CO\u003csub\u003e2\u003c/sub\u003e capture for the case with 10,000 ppmv CH\u003csub\u003e4\u003c/sub\u003e concentration, while 1.95 kg MEA was required per tonne of CO\u003csub\u003e2\u003c/sub\u003e captured for the case with 3000 ppmv of CH\u003csub\u003e4\u003c/sub\u003e concentration. The amount of MEA required for makeup in our models was similar to the value (1.5 kg) reported in literature \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The environmental impact for MEA was obtained from Ecoinvent \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The CO\u003csub\u003e2\u003c/sub\u003e capture plant sizing for the MEA absorbent process was done using the Aspen Process economic analyser in the same way as with other installations.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eThe output from the process modelling constituted a part of the life cycle inventory and was part of the life cycle PED and CE. Additional results such as specific results from the process model in Aspen can be found in the Supplementary material. As defined in 2.1, a negative net CE denotes lower system emissions than mitigated emissions, whereas a positive net CE is to be interpreted as the system causing more GHG emissions than were being mitigated per functional unit.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Scenario A: CH\u003csub\u003e4\u003c/sub\u003e conversion\u003c/h2\u003e \u003cp\u003eThe CE for oxidising CH\u003csub\u003e4\u003c/sub\u003e at the four different concentrations is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e, displaying the positive and negative climate contribution as well as the net value represented by the difference between the two. The CE was clearly non-linear with an improving net-effect with increasing CH\u003csub\u003e4\u003c/sub\u003e concentrations. A total amount of 0.37 g CH\u003csub\u003e4\u003c/sub\u003e/s (10.3 g CO\u003csub\u003e2\u003c/sub\u003e-eq) was oxidised, and 0.019 g CH\u003csub\u003e4\u003c/sub\u003e/s (0.54 g CO\u003csub\u003e2\u003c/sub\u003e-eq) exited with the waste gas, corresponding to the 95% conversion capacity of the catalyst. In accordance with the definition of the functional unit, this oxidised amount equals \u0026minus;\u0026thinsp;1.0 g CO\u003csub\u003e2\u003c/sub\u003e-eq.\u003c/p\u003e \u003cp\u003eThe additional GHG emissions were divided into emissions originating from the manufacturing and those from the operations phase. The results for the end-of-life phase were too low to be clearly displayed in any graphic and were therefore included in the manufacturing phase. The emissions from the operations phase made the predominant contribution (98%, 96%, 93% and 83% at a 300, 1000, 3000 and 10,000 ppmv CH\u003csub\u003e4\u003c/sub\u003e concentration, respectively). However, as the absolute additional emissions decreased for higher CH\u003csub\u003e4\u003c/sub\u003e concentrations, the relative impact of the plant manufacturing increased, corresponding to 2%, 4%, 7% and 17%, respectively (Figure B in SM).\u003c/p\u003e \u003cp\u003eThe GHG emissions related to the operations phase consisted mainly of energy related emissions. The total PED was 31, 6.3, 2.3 and 0.77 kJ/g CO\u003csub\u003e2\u003c/sub\u003e-eq mitigated from the low to high CH\u003csub\u003e4\u003c/sub\u003e concentrations, respectively. The energy required for operation made up a significant share of the total PED (Figure B in SM). In absolute numbers, it decreased with increasing CH\u003csub\u003e4\u003c/sub\u003e concentration. Lower concentrations of CH\u003csub\u003e4\u003c/sub\u003e led to larger volumes of air being treated to mitigate a given quantity of CO\u003csub\u003e2\u003c/sub\u003e-eq, hence the PED for pressure loss, dehumidifying and heating the air increase. At all four CH\u003csub\u003e4\u003c/sub\u003e concentrations, the blower constitutes the largest share of the PED for operation, followed by the dehumidifier (Figure C in SM).\u003c/p\u003e \u003cp\u003eThe manufacturing of the dehumidifier was associated with high PED and CE. In the 10,000 ppmv case, this amounted to almost 60% of the PED and 50% of the CE (Figure D in SM). The palladium for the catalyst used to initiate the CH\u003csub\u003e4\u003c/sub\u003e oxidation asserted a high climate impact, and at the lowest concentration, which requires the largest catalyst amount, this constituted almost 65% of the overall climate effect from manufacturing. The recuperator was also demanding to manufacture (Figure D in SM). However, as CH\u003csub\u003e4\u003c/sub\u003e concentrations increased so does the heat discharge from the oxidation process, meaning the size of the recuperator could be significantly decreased. At the lowest concentrations, additional heat was required to reach a high enough temperature for oxidation to initiate, but at 1000 ppmv CH\u003csub\u003e4\u003c/sub\u003e and above, the heat transfer in the recuperator is sufficient to sustain the reaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Scenario B: Co-removal\u003c/h2\u003e \u003cp\u003eFrom a life cycle perspective, climate efficiency was worsened when CCS was added (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This is mainly due to the much higher GWP of CH\u003csub\u003e4\u003c/sub\u003e but also due to the additional process steps required for CCS, which slightly increase the PED and cause additional GHG emissions. When summarizing the effects of the oxidized CH\u003csub\u003e4\u003c/sub\u003e with the captured CO\u003csub\u003e2\u003c/sub\u003e, the mitigation equals 12.9, 11.7, 11.5 and 11.4 g CO\u003csub\u003e2\u003c/sub\u003e-eq/s from the lowest to highest CH\u003csub\u003e4\u003c/sub\u003e concentrations, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in climate effect (g CO\u003csub\u003e2\u003c/sub\u003e-eq emitted/g CO\u003csub\u003e2\u003c/sub\u003e-eq removed) and PED for co-removal compared to CH\u003csub\u003e4\u003c/sub\u003e conversion.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e concentration (ppmv)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10,000\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet climate effect (CH\u003csub\u003e4\u003c/sub\u003e conversion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManufacturing (CCS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.0057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation (CCS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.00057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport and storage (CCS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.0097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;0.0092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStored CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e-0.21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet climate effect (co-removal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;0.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-0.62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChange in GWP (absolute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;0.073\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;0.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;0.15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;0.10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChange in GWP (relative)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;65%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;24%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;17%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;11%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChange in PED (relative)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;2%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;51%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;82%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;59%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe PED increased due to the added capture unit, mainly due to the energy required for the regeneration of the sorbent. The energy demand from operating CO\u003csub\u003e2\u003c/sub\u003e capture alone was higher than CH\u003csub\u003e4\u003c/sub\u003e conversion in the cases with medium and high CH\u003csub\u003e4\u003c/sub\u003e concentrations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe CO\u003csub\u003e2\u003c/sub\u003e capture process required additional energy use, which was especially visible for the medium concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The PED for the CO\u003csub\u003e2\u003c/sub\u003e capture process was highly dependent of the concentration of both CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e. At the highest CH\u003csub\u003e4\u003c/sub\u003e concentration, the energy generated from the CH\u003csub\u003e4\u003c/sub\u003e oxidation reaction could be utilized in the amine regeneration, lowering the need for additional heat.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe manufacturing of the CO\u003csub\u003e2\u003c/sub\u003e capture components added a considerable contribution to the overall manufacturing emissions, around 40% for all three cases (Figure G in SM).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe following results are for the sensitivity analyses performed for the CH\u003csub\u003e4\u003c/sub\u003e conversion scenario. Results for sensitivity analyses for the co-removal scenario can be found in the Supplemental material.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Energy source\u003c/h2\u003e \u003cp\u003eIn a sensitivity analysis, the impact of using different emission factors for electricity was evaluated. The European mix (293 g CO\u003csub\u003e2\u003c/sub\u003e-eq/kWh) used in the main scenario was exchanged for natural gas power (436 g CO\u003csub\u003e2\u003c/sub\u003e-eq/kWh) and a Nordic consumption mix (93.2 g CO\u003csub\u003e2\u003c/sub\u003e-eq/kWh). A comparison shows this had a determining effect on the net CE for the low CH\u003csub\u003e4\u003c/sub\u003e concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e), while the impact is more modest for the higher CH\u003csub\u003e4\u003c/sub\u003e concentration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe primary energy factor differs between the different electricity mixes, which affects the PED. For the natural gas, this leads to a PED increase of between 8.0 and 8.5%; for the Nordic mix, the PED decrease was between 23 and 25%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Climate metrics\u003c/h2\u003e \u003cp\u003eA sensitivity analysis for the impact of the choice of climate metric was evaluated, highlighting GWP20 and GWP500 in comparison to the main case which uses GWP100 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The shorter the chosen time horizon for the metric, the better the system performance appears. At the low CH\u003csub\u003e4\u003c/sub\u003e concentration, the system net CE varies greatly depending on the metric used. For the medium and high CH\u003csub\u003e4\u003c/sub\u003e concentrations, the impact is visible but more modest.\u003c/p\u003e \u003cp\u003eThe GWP reduction for the process was shared by CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e. With the use of the different metric time horizons, the relative contribution of each gas varied (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe result for PED was also affected as the ascribed value of the functional unit changed with the new GWP. In these two cases, the change would be a 66% decrease in PED per g CO\u003csub\u003e2\u003c/sub\u003e-eq mitigated for GWP20, and a 275% increase for GWP500.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe results confirmed that the CH\u003csub\u003e4\u003c/sub\u003e concentration was the most influential factor for the net CE in the system. It was inversely proportional to the energy demand for operation due to the large volumes of air being treated per gain in reduced climate impact at low concentrations of CH\u003csub\u003e4\u003c/sub\u003e (i.e. the relation between the number of molecules of H\u003csub\u003e2\u003c/sub\u003eO to remove and N\u003csub\u003e2\u003c/sub\u003e molecules to heat compared to CH\u003csub\u003e4\u003c/sub\u003e oxidised). The net CE was positive (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e) at the lowest CH\u003csub\u003e4\u003c/sub\u003e concentration modelled in this study (300 ppmv), i.e. the CH\u003csub\u003e4\u003c/sub\u003e conversion process contributed to global warming from a life cycle perspective much due to the GHG emissions from operation, outweighing the gain from the oxidised CH\u003csub\u003e4\u003c/sub\u003e. However, a sensitivity analysis highlighted the importance of increasing the share of renewable energy sources, as switching to a renewable electricity mix (modelled as the current consumption mix of the Nordic countries) improved the net GWP more than 6 times compared to European mix (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This resulted in a net negative CE also for the lowest CH\u003csub\u003e4\u003c/sub\u003e concentration. The impact of the electricity mix was especially clear at the low CH\u003csub\u003e4\u003c/sub\u003e concentration due to its high energy demand. For CH\u003csub\u003e4\u003c/sub\u003e concentrations from 1000 to 10,000 ppmv, a net negative climate effect could be reached even with natural gas-powered electricity, although at worsened net CE. A higher share of renewables also lowered the PED as these energy sources have lower primary energy factors than fossil energy sources.\u003c/p\u003e \u003cp\u003eTo evaluate and compare the climate impact of emitting or mitigating GHGs, a conversion metric must be chosen. The GWP100 is the most widespread metric used in both policy and science, although it has long been criticised for not fairly representing the actual temperature response, especially underestimating short term effects caused by, for example, CH\u003csub\u003e4\u003c/sub\u003e e.g. \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. The sensitivity analysis of the climate metric (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e) illustrates how a shorter time horizon weights the impact of CH\u003csub\u003e4\u003c/sub\u003e mitigation more than a longer time horizon. GWP20 emphasises the impact of mitigating the short-term effects of CH\u003csub\u003e4\u003c/sub\u003e, resulting in net-negative CE for all three concentrations, both with and without CO\u003csub\u003e2\u003c/sub\u003e capture. GWP500 instead put more emphasis on the long-term impact of CO\u003csub\u003e2\u003c/sub\u003e, giving a clear lowering of the net CE compared to the GWP100 results.\u003c/p\u003e \u003cp\u003eDue to the strong warming effect of CH\u003csub\u003e4\u003c/sub\u003e compared to CO\u003csub\u003e2\u003c/sub\u003e, no matter the metric used in this study, avoiding the impact of CH\u003csub\u003e4\u003c/sub\u003e played a decisive role compared to the benefits of also capturing the subsequent CO\u003csub\u003e2\u003c/sub\u003e, ranging from around 50% of the total climate benefit attributed to CH\u003csub\u003e4\u003c/sub\u003e and 50% from CO\u003csub\u003e2\u003c/sub\u003e for GWP500 to 92% for CH\u003csub\u003e4\u003c/sub\u003e and 8% for CO\u003csub\u003e2\u003c/sub\u003e for GWP20 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003e). No matter the concentration and metric, the net CE decreased when CO\u003csub\u003e2\u003c/sub\u003e capture was introduced due to the additional energy (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and material demand compared to sole CH\u003csub\u003e4\u003c/sub\u003e oxidation. However, depending on the studied timescale, the prioritisation between CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e could differ, as targeting CH\u003csub\u003e4\u003c/sub\u003e emissions could deliver significant short-term effects. From a longer time perspective, however, the accumulation of CO\u003csub\u003e2\u003c/sub\u003e in the atmosphere is what will largely determine the level the mean global temperature will reach and remain at due to its longer residence time \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe study identified the blower, the dehumidifier and the catalyst demand as important hotspots for both energy demand and CE (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Since the process energy had such a major impact on the overall result, especially at low concentrations, technological improvements could have a substantial effect on overall performance and feasibility. Energy demand for the blower can be reduced by minimizing pressure drops throughout the process. The development of a less water-inhibited catalyst material with a lower carbon footprint would improve the system CE directly at the manufacturing stage (Figure F \u0026amp; G in SM) and indirectly by reducing or eliminating the need for the energy demanding dehumidification step. Palladium minerals are rare and have wide uses, from catalysts to jewellery, electronics and fuel cells. Hence, like with other metals important for the green transition, there are risks such as rising prices and tightened supply in the future (Andersen et al, 2024). Furthermore, a lower minimum temperature difference in the recuperator could reduce the energy demand, although this would increase the heat transfer area of the recuperator. As part of the PED for operation consists of thermal energy, it may be possible to connect a secondary renewable energy source or waste heat to improve the net CE. All these aspects are especially pronounced at low CH\u003csub\u003e4\u003c/sub\u003e concentrations due to the high energy and raw material demand per CO\u003csub\u003e2\u003c/sub\u003e-eq mitigated.\u003c/p\u003e \u003cp\u003eUnder practical operating conditions, manure level, outdoor temperature and energy source would have a decisive impact on the net CE. The rate of CH\u003csub\u003e4\u003c/sub\u003e emitted is closely linked to the storage temperature, and the relatively large energy demand for operation constituted a noticeable impact on the net CE. Therefore, one could expect that this type of system would deliver the largest climate benefits if implemented in parts of the world with a warm climate and a substantial renewable electricity supply, and on farms with large herds of livestock and effective manure collection systems. The larger the share of manure collected, the greater the climate benefit that could be achieved for the investment per animal. It is also vital that the entire value chain required for CH\u003csub\u003e4\u003c/sub\u003e oxidation and CO\u003csub\u003e2\u003c/sub\u003e capture and storage can be carried out with a minimum of GHG slip and performed in a climate friendly and energy efficient manner to reach the highest possible total climate benefit and maintain high credibility for the GGR concept.\u003c/p\u003e \u003cp\u003eFor manure storage, the CH\u003csub\u003e4\u003c/sub\u003e emissions at each moment in time vary based on a number of factors. As this is expected to impact the operation of the machinery as well as system efficiency and subsequent energy use, we chose to conduct this study as a snapshot of a moment in time with static conditions, with the four studied concentrations covering a large interval of conditions. This approach was an attempt to decrease complexity and be able to perform initial evaluation and information gathering on the most important parameters affecting strategies for design and operation of this novel system. Considering the promising net CE presented in this study, it seems reasonable to continue exploring technologies for the conversion of CH\u003csub\u003e4\u003c/sub\u003e with and without subsequent CO\u003csub\u003e2\u003c/sub\u003e capture to analyse its prospects for implementation under different conditions, such as scale, location and CH\u003csub\u003e4\u003c/sub\u003e concentration. Some relevant research topics for future studies could be investigating practical constraints on the equipment and materials, techno-economic analysis, and experimentation to confirm modelling results. There are also options to increase system efficiency and increased systems integration through, for example, carbon capture and utilisation. In the future, more detailed case studies that examine feasibility under certain conditions with more case specific and time-dynamic data for a particular application should be used, whether it be manure storage or some other unabated CH\u003csub\u003e4\u003c/sub\u003e emissions source.\u003c/p\u003e \u003cp\u003eThe low CH\u003csub\u003e4\u003c/sub\u003e concentrations analysed in this study could allow for the targeting of emission sources where GGR has not been considered previously, such as the agrifood sector, which accounts for over half of overall CH\u003csub\u003e4\u003c/sub\u003e emissions. There is currently no technology to completely eliminate non-CO\u003csub\u003e2\u003c/sub\u003e GHG emissions from agriculture, and improvements in carbon efficiency and emission reduction measures risk being partly counteracted by the increased primary production demand necessary for feeding an increasing world population \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The burden on reduced GHG emissions in other sectors would increase unless unabated agricultural emissions could be sequestered or compensated for through additional GGR. Further research on ways to reduce global warming via CH\u003csub\u003e4\u003c/sub\u003e could improve the technology used and identify the most promising areas of implementation to maximise efficiency.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study presents a technology capable of delivering climate mitigation by oxidising CH\u003csub\u003e4\u003c/sub\u003e emissions at low concentrations, below the point of ignition, using a thermal catalytic process with and without subsequent CO\u003csub\u003e2\u003c/sub\u003e capture and storage. The results showed that the PED for thermal catalytic CH\u003csub\u003e4\u003c/sub\u003e oxidation was highly dependent on the CH\u003csub\u003e4\u003c/sub\u003e concentration, which cause a slightly positive net climate effect at 300 ppmv CH\u003csub\u003e4\u003c/sub\u003e, while reaching a net negative climate effect at 1000 ppmv CH\u003csub\u003e4\u003c/sub\u003e and above. Hence, the suggested technology was not able to achieve negative emissions at the lowest concentration for the default design and conditions studied. However, the energy source was highly impactful due to the high energy demand for operation. By changing the assumed European electricity mix to the Nordic electricity mix for CH\u003csub\u003e4\u003c/sub\u003e conversion, a net negative climate effect could be achieved for CH\u003csub\u003e4\u003c/sub\u003e conversion at all four studied CH\u003csub\u003e4\u003c/sub\u003e concentrations. Alternatively, changing the climate metrics from GWP100 to GWP20 also resulted in a net negative climate effect at all studied CH\u003csub\u003e4\u003c/sub\u003e concentrations, as the reduced time horizon of the GWP20 metric emphasises the impact of reducing the strong short-term warming effects of CH\u003csub\u003e4\u003c/sub\u003e. From 3000 ppmv CH\u003csub\u003e4\u003c/sub\u003e, the decreasing energy demand in combination with the increasing feasibility of utilising excess heat meant that the impact of oxidising CH\u003csub\u003e4\u003c/sub\u003e resulted in a net negative climate effect even when operated using natural gas and regardless of the chosen climate metric. However, in order to maximise the benefit and credibility of the technology, a low emissions energy source should be pursued, and high energy efficiency is desirable to avoid wasting limited renewable energy resources on GGR.\u003c/p\u003e \u003cp\u003eAt all CH\u003csub\u003e4\u003c/sub\u003e concentrations, the net climate effect was worsened by adding co-removal of CO\u003csub\u003e2\u003c/sub\u003e from the outlet gas with subsequent storage. This was mainly due to the increased PED for operation and the high GWP of CH\u003csub\u003e4\u003c/sub\u003e, making the avoidance of warming from CH\u003csub\u003e4\u003c/sub\u003e the most pronounced benefit of the proposed technology.\u003c/p\u003e \u003cp\u003eWhile targeting the most concentrated CH\u003csub\u003e4\u003c/sub\u003e emission sources possible is the most energy efficient approach, the study also identified areas where technological development would increase the system climate efficiency. The most prominent climate and energy related hotspots are connected to the blower, dehumidification and catalyst. If these impacts can be lowered by, for example, minimising pressure drops and developing efficient and water-resistant catalysts with low carbon footprints, the increased system efficiency would achieve net negative CE at even lower CH\u003csub\u003e4\u003c/sub\u003e concentrations, thus allowing catalytic conversion of CH\u003csub\u003e4\u003c/sub\u003e to target and efficiently abate a wider range of CH\u003csub\u003e4\u003c/sub\u003e emission sources.\u003c/p\u003e"},{"header":"Nomenclature","content":"\u003cp\u003eCE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;climate effect\u003c/p\u003e\n\u003cp\u003eCCS\u0026nbsp; \u0026nbsp; \u0026nbsp;Carbon capture and storage\u003c/p\u003e\n\u003cp\u003eCDR\u0026nbsp; \u0026nbsp;\u0026nbsp;Carbon dioxide removal\u003c/p\u003e\n\u003cp\u003eCH\u003csub\u003e4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/sub\u003eMethane\u003c/p\u003e\n\u003cp\u003eCF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Carbon footprint\u003c/p\u003e\n\u003cp\u003eCO\u003csub\u003e2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/sub\u003eCarbon dioxide\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eED\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Energy demand\u003c/p\u003e\n\u003cp\u003eGGR\u0026nbsp; \u0026nbsp;\u0026nbsp;Greenhouse gas removal\u003c/p\u003e\n\u003cp\u003eGGRT\u0026nbsp;\u0026nbsp;Greenhouse gas removal technology\u003c/p\u003e\n\u003cp\u003eGHG\u0026nbsp; \u0026nbsp;\u0026nbsp;Greenhouse gas\u003c/p\u003e\n\u003cp\u003eGWP\u0026nbsp; \u0026nbsp;\u0026nbsp;Global warming potential\u003c/p\u003e\n\u003cp\u003ekJ\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Kilo Joule (10\u003csup\u003e3\u003c/sup\u003e joule)\u003c/p\u003e\n\u003cp\u003eLCA\u0026nbsp; \u0026nbsp; \u0026nbsp;Life cycle assessment\u003c/p\u003e\n\u003cp\u003eMJ\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mega Joule (10\u003csup\u003e6\u003c/sup\u003e joule)\u003c/p\u003e\n\u003cp\u003ePED \u0026nbsp; \u0026nbsp; Primary energy demand\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis research received funding from The Swedish Energy Agency for the project \u0026ldquo;Energy efficient negative emissions from agriculture and farming\u0026rdquo; with grant number 50340-1. The manuscript went through substantial language checking by William Cowley.\u003c/p\u003e\n\u003cp id=\"_Toc185849912\"\u003eCRediT authorship contribution statement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmma Bromark\u003c/strong\u003e: Conceptualization, Methodology, Investigation, Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSairam Sirigina\u003c/strong\u003e: Conceptualization, Methodology, Investigation, Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShareq Mohd Nazir\u003c/strong\u003e: Funding acquisition, Conceptualization, Supervision, Methodology, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePernilla Tid\u0026aring;ker\u003c/strong\u003e: Conceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026Aring;ke Nordberg\u003c/strong\u003e: Conceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePer-Anders Hansson\u003c/strong\u003e: Conceptualization, Funding acquisition, Supervision, Methodology, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eEmma Bromark had the primary responsibility for the life cycle assessment while Sairam Sirigina had the primary responsibility for the process modelling\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eSupplementary material associated with this article can be found in the online version.\u003c/p\u003e\n\u003cp id=\"_Toc185849913\"\u003eDeclaration of competing interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRueda, O., Mogoll\u0026oacute;n, J. 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R.\u003cem\u003e et al.\u003c/em\u003e Net Zero: Science, Origins, and Implications. \u003cem\u003eAnnual Review of Environment and Resources\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 849-887, doi:10.1146/annurev-environ-112320-105050 (2022).\u003c/li\u003e\n\u003cli\u003eTanzer, J. \u0026amp; Hermann, L. in \u003cem\u003eSustainable and Circular Management of Resources and Waste Towards a Green Deal\u003c/em\u003e (eds Majeti Narasimha Vara Prasad \u0026amp; Marzena Smol) 285-296 (Elsevier, 2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"GGR, CH4, Methane emissions, LCA, Manure management, Climate change","lastPublishedDoi":"10.21203/rs.3.rs-6328195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6328195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgri-food systems constitute around one-third of global greenhouse gas (GHG) emissions, with roughly half consisting of non-CO\u003csub\u003e2\u003c/sub\u003e GHGs, mainly methane (CH\u003csub\u003e4\u003c/sub\u003e) and nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO). Methods and technologies to mitigate non-CO\u003csub\u003e2\u003c/sub\u003e GHGs are currently limited, which is a reason for agriculture being categorised as a hard-to-abate sector. This study examines mitigation of GHG emissions from manure storage headspace through oxidisation of CH\u003csub\u003e4\u003c/sub\u003e emissions at low concentrations using a thermal catalytic process with and without subsequent CO\u003csub\u003e2\u003c/sub\u003e capture and storage (CCS). The technology is studied using a combination of process modelling and life cycle assessment at four CH\u003csub\u003e4\u003c/sub\u003e concentrations: 300, 1000, 3000 and 10000 ppmv. The primary energy demand and net climate effect were evaluated, reaching a net climate effect of +\u0026thinsp;0.10, -0.77, -0.91 and \u0026minus;\u0026thinsp;0.97 g CO\u003csub\u003e2\u003c/sub\u003e-eq emitted/g CO\u003csub\u003e2\u003c/sub\u003e mitigated, respectively. The wide range of results is mainly influenced by the process energy demand being strongly correlated to the CH\u003csub\u003e4\u003c/sub\u003e concentration. The sensitivity analysis shows that a net negative climate effect can also be achieved at 300 ppmv with access to low emission energy sources. Coupling CCS worsens the net climate effect of the system at all studied CH\u003csub\u003e4\u003c/sub\u003e concentrations, mainly due to the additional energy demand for CO\u003csub\u003e2\u003c/sub\u003e separation.\u003c/p\u003e","manuscriptTitle":"Reduced life cycle climate impact from manure through catalytic methane conversion and carbon dioxide removal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 11:50:49","doi":"10.21203/rs.3.rs-6328195/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-25T08:03:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-16T08:30:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157467905806925582190264282051268315053","date":"2025-09-04T02:43:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T07:59:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65527327511990678476685742128189496132","date":"2025-04-07T01:48:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T09:20:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-01T09:13:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-01T05:03:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-29T06:58:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-28T12:22:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a27d761-8b8b-4a57-9951-b5473ccf68ec","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47118872,"name":"Physical sciences/Engineering"},{"id":47118873,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2025-12-15T16:00:47+00:00","versionOfRecord":{"articleIdentity":"rs-6328195","link":"https://doi.org/10.1038/s41598-025-27609-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-10 15:57:17","publishedOnDateReadable":"December 10th, 2025"},"versionCreatedAt":"2025-04-22 11:50:49","video":"","vorDoi":"10.1038/s41598-025-27609-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-27609-2","workflowStages":[]},"version":"v1","identity":"rs-6328195","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6328195","identity":"rs-6328195","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}