Monitoring Carbon Sequestration Research Articles

Anomaly detection for geological carbon sequestration monitoring

Geological carbon sequestration (GCS) is a method to reduce the emissions of CO2 into the atmosphere. During GCS operations CO2 is captured from the atmosphere or industrial activities and stored in geological formations for permanent storage. Monitoring is an important element of GCS because it ensures that the stored CO2 remains safely contained in the intended formation during the long term. Additionally, monitoring wells can help to detect CO2 leaks, prompt remediation actions, and provide valuable information to optimize storage by monitoring the behavior of the CO2 over time. In this work, we propose a method for GCS anomaly detection based on an LSTM Autoencoder Neural Network and Isolation Forest. The LSTM-Autoencoder uses the monitor Bottomhole Pressure (BHP) response while CO2 is being injected into a geological structure. To account for the subsurface uncertainty, multiple subsurface model realizations are created, and using reservoir simulation, the multiple monitor BHP are generated to capture the subsurface uncertainty. Anomaly BHP points are detected using the residuals of the LSTM-Autoencoder and Isolation Forest. Additionally, an anomaly score based on the subsurface uncertainty is proposed. Finally, the method robustness is evaluated using point outliers, level shift outliers, and transient shift outliers as anomaly BHP signals. Early detection of abnormal BHP pressure signals can indicate the presence of subsurface fractures, faults, or leaks. Consequently, the correct detection of anomaly points in the pressure signals is of great importance.

Mapping and classification of Liao River Delta coastal wetland based on time series and multi-source GaoFen images using stacking ensemble model

The precise mapping of coastal wetlands holds great significance for monitoring carbon sequestration and storage within coastal ecosystems, particularly in light of climate change and human-induced activities. Time series and multi-source remote sensing data offer distinct advantages in spatial and temporal land use mapping, particularly in wetland systems encompassing various vegetation types. The primary aim of this study was to delineate the spatial distribution of land use within the Liao River Delta (LRD) wetland. This was achieved by employing a stacking ensemble model that integrates time-series GaoFen-1 (GF-1) optical imagery, GaoFen-3 (GF-3) synthetic aperture radar (SAR) imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. The first step involved the application of an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to fuse the GF-1 NDVI and MODIS NDVI datasets, producing time-series NDVI data. Subsequently, the parameters pertaining to vegetation phenology were obtained by employing the threshold method on the time-series NDVI data. We compiled feature datasets encompassing GF-1 spectral bands, indices, phenological parameters, and GF-3 SAR features. A Recursive Feature Elimination and Cross-Validation (RFECV) model was employed to identify and select significant features to mitigate data redundancy. Finally, a stacking ensemble model was constructed by combining five base models [K-Nearest Neighbors (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Machine (LightGBM)] to perform wetland classification. The findings were as follows: (1) ESTARFM was able to successfully fuse GF-1 NDVI and MODIS NDVI data, resulting in spatiotemporal fusion with a coefficient of determination (R2) of 0.85 and a root mean square error (RMSE) of 0.07. (2) the RFECV algorithm was employed to select relevant features in the wetland classification process. Specifically, 75 spectral band features, 89 spectral index features, 13 SAR features, and seven phenological parameters were identified as significant for this task. (3) A stacking ensemble model was constructed using the aforementioned multi-source features. This model exhibited a robust and consistent performance in wetland classification, achieving the highest overall accuracy of 94.33%. Notably, this accuracy improvement ranged from approximately 0.09% to 10.02% compared to the individual base models. Thus, the present study has the potential to be utilized for fine-scale wetland monitoring, thereby offering valuable assistance in the field of wetland environmental research.

Integrating Remote Sensing (RS) and Geographic Information System (GIS) for Carbon Sequestration Monitoring in Tropical Watershed

The watershed has many functions related to ecosystem protection. The existence of vegetation in the watershed can absorb some carbon from the atmosphere through photosynthesis. In fact, this carbon sequestration potential is declining due to vegetated land conversion phenomena. Therefore, monitoring carbon sequestration in this area needs to be done. However, it faces a problem due to the large size of the watershed area in a tropical region, so carbon sequestration cannot be measured by field-measurement. By combining RS and GIS, the carbon sequestration in a large watershed can be measured frequently to solve the problem. This research aims to analyze the dynamic change of carbon sequestration in Upper Bengawan Solo from 2000 until 2020. In this research, carbon sequestration was modelled using the Net Primary Productivity (NPP). NPP was measured by using the CASA method used Landsat Imagery and Meteorological Data from Meteorological Agency of Indonesia (BMKG). The results show that the carbon sequestration in Upper Bengawan Solo over 20 years decreased. The most significant decrease happens in the sub-urban area surrounding the urban area in Surakarta city. It indicates an environmental change in this watershed due to physical or human interference during this period. Meanwhile, by using RS and GIS, the spatial distribution of carbon sequestration change can be known, so it can be used to pinpoint the location which needs prior attention based on the higher level of these changes. This research implies that combining RS and GIS can help carbon sequestration monitoring be quicker and cost-limited.

Geochemical modeling of CO2 sequestration in ultramafic mine wastes from Australia, Canada, and South Africa: Implications for carbon accounting and monitoring

Passive carbonation in ultramafic mine wastes results from the spontaneous reaction of gangue minerals with carbon dioxide (CO2) to form stable carbonate minerals that store CO2. Mines can benefit from unintentional CO2 sequestration to offset greenhouse gas (GHG) emissions. Quantitative X-ray diffraction (QXRD) analysis of numerous samples of mine wastes has typically been used to quantify passive carbonation rates. This analysis yields valuable information about carbon sinks; however, extensive mineralogical assessments are technically demanding and cost-prohibitive for routine carbon accounting. Alternatively, we explored using inverse geochemical modeling to estimate passive carbonation rates and monitor CO2 sequestration in active mines. Water chemistry data, tailings mineralogy, and operational information routinely collected by mines were used as inputs for models. The predictive capabilities of the models were tested for Mount Keith nickel mine (Australia), Diavik diamond mine (Canada) – for which rates were previously determined using QXRD. A new site — Venetia diamond mine (South Africa) — was used to illustrate the potential of geochemical modeling for carbon accounting and as a long-term monitoring tool for CO2 sequestration. Mount Keith models predicted passive carbonation rates (3900 g CO2/m2/yr) consistent with previous QXRD assessments (2400 g CO2/m2/yr; 172 samples) using only two water samples. CO2 removal rates for Diavik were found to be impacted by seasonality and to range between ∼375 and 510 g CO2/m2/yr, showing similarities with previous QXRD rates estimates (313–350 g CO2/m2/yr). With the long-term water chemistry records (2009–2018) available for Venetia mine, we predicted calcite as the main CO2 sink and that ∼14,875 t CO2 were stored in the kimberlite residues impoundments (3.5 km2) over 9 years at a rate of ∼470 g CO2/m2/yr. Moreover, long-term monitoring shows a decline in CO2 removal rates at Venetia, possibly due to changes in kimberlite reactivity and current mine waste disposal practices. Overall, inverse modeling proved that it can be a viable alternative or complement to QXRD techniques for carbon accounting in active mines, substantially reducing the need for mine waste sampling and permitting long-term monitoring. Moreover, the model shows how passive carbonation rates change dynamically with the mine production and waste management practices. Simultaneously, the model provides insights into potential mineral reactions operating in tailings and that regulate carbon sequestration mechanisms. For that reason, this accounting technique has an enormous potential to assist in decision-making for implementing enhanced CO2 sequestration strategies (e.g., enhanced rock weathering) and to support carbon sequestration monitoring, validation, and reporting in active mines.

Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics‐Informed Neural Network Model

AbstractGeophysical monitoring of geologic carbon sequestration is critical for risk assessment during and after carbon dioxide (CO2) injection. Integration of multiple geophysical measurements is a promising approach to achieve high‐resolution reservoir monitoring. However, joint inversion of large geophysical data is challenging due to high computational costs and difficulties in effectively incorporating measurements from different sources and with different resolutions. This study develops a differentiable physics model for large‐scale joint inverse problems with reparameterization of model variables by neural networks and implementation of a differentiable programming approach of the forward model. The proposed physics‐informed neural network model is completely differentiable and thus enables end‐to‐end training with automatic differentiation for multi‐objective optimization by multiphysics data assimilation. The application to the Sleipner benchmark model demonstrates that the proposed method is effective in estimation of reservoir properties from seismic and resistivity data and shows promising results for CO2 storage monitoring. Moreover, the global parameters that are assumed to be uncertain in the rock‐physics model are accurately quantified by integration of a Bayesian neural network.

Using long-term experimental restoration of agroecosystems in Aotearoa New Zealand to improve implementation of Nature-based Solutions for climate change mitigation

Tree planting has long played a major role in the New Zealand Government’s approach to climate mitigation and is increasingly understood as important for climate adaptation. However, large-scale tree planting in Aotearoa New Zealand has been dominated by exotic species. Although there is growing public and expert support for using native species for forest revegetation in farm landscapes, there are two key barriers. First, the lack of ecological and economic data on native species performance in different environmental conditions. Second, policy and market-related mechanisms associated with carbon sequestration, such as the New Zealand Emissions Trading Scheme, favor the continuing use of exotic tree species, especially Pinus radiata, over native species. Consequently, there are strong incentives for exotic forests and insufficient financial support for natives, even when native forest re-establishment is often the preference of landowners, Indigenous peoples, and local communities. The AUT Living Laboratories Program is a long-term, transdisciplinary, experimental restoration research program aimed at addressing scientific, social, and economic knowledge gaps for native revegetation as a Nature-based Solution (NbS) on farmland soils. Here, we present the project design and establishment information from the three experimental restoration sites, which vary in native species composition, planting configuration, and environmental and socio-cultural context. Each site involves partnerships with Indigenous communities, specifically Ngāti Whātua Ōrākei, Ngāti Manuhiri, and Ngāti Pāoa, to value and embed mātauranga Māori as Indigenous knowledge. Monitoring carbon sequestration along with changes in ecological functions and outcomes, including native biodiversity, will be critical to ensure that large-scale tree-planting aligns with the government’s strategies for climate change, native biodiversity, and economic prosperity.

Decarbonisation of the urban built environment through vegetation-based carbon sequestration

The impacts of climate change require a strategic improvement in design decision-making. Leading professionals are aiming for carbon-positive buildings that can achieve carbon sequestration by adding vegetation to buildings. Multiple references and case studies explored in this paper suggest that there is theoretical potential for cities to become carbon sinks by constructing carbon-positive buildings. However, determining effective strategies, and quantifying and monitoring carbon sequestration in buildings, requires a standardised approach so that this carbon sequestration potential can be measurably established. This paper provides two key outputs: firstly, the paper identifies strategies that could shift buildings towards being capable of active carbon sequestration. Secondly, the paper provides a methodological framework with four key considerations that building professionals can use to design for carbon sequestration. These are: understanding the site’s ecological, climatic, cultural and legal context; identifying response, pressure, state and benefits indicators to set carbon sequestration targets; considering site ecosystem functioning and carbon dynamics to strategise carbon sequestration through design; and preparing long-term monitoring, evaluation and management plans. This paper identifies two areas for further investigation: linking manual quantification methods with computer-aided methods; and utilising biomass data and growth models at the landscape, regional, and global levels for carbon sequestration assessment.

Improved geophysical monitoring of carbon sequestration through parameter linkage to reservoir modeling

Predictive reservoir modeling, even if present in the form of only basic hydrogeological model assumptions, is expected to accompany the majority of carbon capture and sequestration monitoring activities. It thus represents a source of prior information about the migration of injected fluids that can benefit geophysical survey planning and ensuing monitoring. Constraining the imaging of geophysical monitoring data with reservoir modeling is preferable over standalone geophysical imaging because of additional complementary hydrogeological information. However, fully coupled hydrogeophysical data inversion for flow-modeling parameters that control saturation predictions is an involved process. Within the context of three-dimensional electromagnetic (EM) inversion of data from borehole-to-surface layouts, we employ a "poor people's" alternative. The approach constrains geophysical inversion parameters through saturation predictions. The coupling is realized through spatially variable lower and upper parameter bounds that scale with gas saturation magnitudes, the latter provided by reservoir modeling. Enhancement of three-dimensional time-lapse plume EM imaging is demonstrated for simulated sequestration into a depleted gas reservoir.

Carbon sequestration scenarios in Portugal: which way to go forward?

Assessing carbon storage and sequestration is key for defining effective conservation actions to mitigate climate change. Forest species changes have direct impacts on carbon stocks and may lead to undesirable climate trade-offs. In this paper, we measure aboveground biomass (AGB) and the impact of forest changes on climate regulation through three land policy scenarios by 2030 in continental Portugal. We found that a High intervention scenario, supported by an important increase in "Other coniferous trees" class, will provide 29.5% more of carbon sequestration, whereas a Low intervention scenario, in which there is a moderate increase in all forest classes, will result in an increase of 5.7%. A business as usual (BAU) scenario, supported by an increase in eucalyptus forests and a decrease in autochthonous species, will decrease carbon sequestration (-2.7%), particularly Lisboa, Algarve and North regions. Economic valuation shows that the High intervention scenario will generate the highest economic outcome for climate regulation by 2030. This study provides a spatial-based methodology for monitoring carbon sequestration and new insights about the impact of policies for Green House Gas (GHG) mitigation, supporting the 2030 Sustainable Development Goals achievement.

Carbon pools of biomass and dead organic matter in typical forest ecosystems of Tibet: A new estimation based on the first forestry carbon sequestration monitoring undertaken in China

AbstractThe forest ecosystems of Tibet function as important carbon sinks. However, previous estimates of carbon budgets have included large uncertainties owing to the limitations of data acquisition and the method of estimation. In this study, IPCC and model estimation methods have been used to calculate all the sectors of carbon stocks in typical forest vegetation of Tibet and explore the biogeographical patterns and potential drivers of these pools. The results showed that the total carbon pools of biomass and dead organic matter in six typical forest ecosystems in Tibet was 457.67 Tg C, of which 10.52–164.85 Tg C (76.53%) was stored in aboveground biomass, 1.87–59.37 Tg C (22.07%) in belowground biomass, 0.01–2.75 Tg C (0.73%) in the forest understory, 0.02–1.57 Tg C (0.53%) in litter, and 0.01–0.27 Tg C (0.14%) in dead wood. These carbon reserves are primarily distributed in mature and over mature forests, comprising 48.89% and 31.58%, respectively. Geographical location and natural conditions affect the carbon sequestration capacity of the Tibetan forest ecosystems that decreases from southeast to northwest. An analysis also suggests that the canopy and tree densities have some effect on the carbon sequestration efficiency in different carbon pools. This is primarily because these two indicators have a greater impact on the growth of trees. In addition, it was found that the forests of Abies fabri and Platycladus orientalis had the highest and lowest degrees of carbon fixation, respectively.

Physico-chemical and dielectric parameters for the monitoring of carbon sequestration in basalt and silica media

Abstract Currently, there are concerns about the safety of carbon sequestration in the geological media. To assuage this concern, scientists and engineers have the tasks to demonstrate fool-proof and comprehensive techniques that can monitor the movement, or otherwise, concentration of the injected CO2 in the subsurface. In this work, well-defined laboratory experiments were used to demonstrate the key physico-chemical characteristics and dielectric parameters that are useful in monitoring carbon sequestration sites. The porous materials used were basalt and silica sand samples to demonstrate the possibility of CO2 injection into different media. To simulate the resident fluids, distilled and brine water samples were used in separate experimentations. Also, the pressures and temperatures were chosen to correspond to different geological depths which are relevant for CO2 injection. The pH, bulk electrical conductivity ( σ b ) and bulk dielectric permittivity ( e b ) of the system were measured for the two different media. On one hand, the decrease in pH was clearly observed in both the basalt and silica sand after the exposure to CO2. On the other hand, σ b and e b increased as CO2 was injected. Our results further revealed a higher ion mobilisation​ potential in basalt medium than that in silica sand. This results in lower pH and higher electrical conductivity in the basalt medium than the silica medium. Thus, a simultaneous measurement of pH, σ b and e b are proposed as a multiparameter approach to monitor CO2 leakage from the storage reservoir. As far as we are aware, this is the first work in the open literature that reports simultaneous dielectric and electrical behaviours of CO2 –water–porous media system for basalt porous medium in connection with carbon sequestration.

Spatial prediction of soil organic carbon using machine learning techniques in western Iran

Estimation of soil organic carbon (SOC) is very useful for accurate monitoring of carbon sequestration. However, there are still significant gaps in the knowledge of SOC reserves in many parts of the world, including western Iran. To partially fill the gap, 865 soil samples were used with 101 auxiliary variables and 5 machine learning (ML) algorithms to digitally map SOC for the plough layer (0–30 cm) at a 90-m resolution in Kurdistan province. Results indicated that the most important auxiliary variables were rainfall (27.09%), valley depth (26.66%), terrain surface texture (23.42%), air temperature (20.18%), channel network base level (16.61%) and terrain vector roughness (14.47%). Results also showed that Random Forests (RF) performed best in predicting the spatial distribution of SOC (RMSE = 0.35% and R2 = 0.60), compared to the other ML algorithms (i.e. Cubist: CU, k-Nearest Neighbor: kNN, Extreme Gradient Boosting: XGBoost and Support Vector Machines: SVM). Furthermore, results estimated the total SOC stocks (SOCS) for the whole study area (~15,208 Tg) and amounts under different land uses. These were bareland (~6 Tg), orchard (~356 Tg), irrigated farming (~782 Tg), forest (~1773 Tg), grassland (~5991 Tg) and dry farming (~6297 Tg). As expected, the SOCS were highest in forest soils (652 g m−2) and lowest in bareland (437 g m−2). This result suggests that the conversion of native land (e.g. Forest) to cultivated land (e.g. Irrigated farming) could lead to significant loss of SOCS and appropriate management of land use could increase SOCS.

Shale Anisotropy Estimation From Logs in Vertical Wells

AbstractAnisotropic elastic parameters for shales are widely needed in seismic imaging, reservoir characterization, and carbon sequestration monitoring. Unlike other elastic parameters such as vertical P and S wave velocities, anisotropy parameters are not measured directly from the acoustic well logs due to the single‐directional nature of a well. We assume that shale anisotropy is induced by thin cracks that are filled with liquid in a background isotropic medium, whose bulk and shear moduli are obtained from the vertically measured P and S wave velocities, density, and porosity from corresponding well logs through a formalized inversion scheme. We show that the estimated anisotropy using the proposed method is consistent with the mineralogy and agrees with the published laboratory measurements. This framework allows us to quantify the uncertainties in the anisotropy parameters estimated from the inversion, which can be used as a measure to evaluate the validity of the chosen rock physics model.

USING LANDSAT SATELLITE IMAGES AND GIS TO ESTIMATE AND MONITOR THE AMOUNT OF ABSORBED CO2 IN DIPTEROCARP FOREST, DAK LAK PROVINCE

Dipterocarp forest is a typical ecosystem in Central Highlands of Vietnam and Dak Lak province. Dipterocarp forest plays significant role about biodiversity values, CO2 absorption and concentration. However, there is a lack of awareness of biological potentials in the local people after years of logging. It is necessary to collect data about forest biomass and carbon stored on maps by space and time in order to estimate and monitor CO2 absorbed in the large area. Hence, using GIS to develop relationships between biomass factor and carbon stock with digital values for monitoring carbon sequestration is important and meaningful in dipterocarp forest ecosystem; this method can generate the necessary database and information in terms of CO2 emission. The research results can create a basis for the dissemination and promotion of payment for environmental services according to REDD+ program.Unsupervised classification into 3-4-5 class and overlap forming combinations were close relationships with TAGTB derived from square plots (30x30m) and achieved confidence level 86.8% which were applied to estimate biomass and forest carbon through Landsat image. In this study, remote sensing and management of data in ArcGIS software through allometric equations model showed dipterocarp forest in Ea Soup and Ea Hleo districts (Dak Lak province) with a total area of 125,404.8 hectares and biomass tanks (above and below ground) of forest trees reaching at 8,156,667.6 tons. Total carbon was stored 4,093,501.1 tons. Total CO2 was absorbed 15,023,149.1 tons.

Towards Providing Solutions to the Air Quality Crisis in the Mexico City Metropolitan Area: Carbon Sequestration by Succulent Species in Green Roofs

INTRODUCTION:In the first months of 2016, the Mexico City Metropolitan Area experienced the worst air pollution crisis in the last decade, prompting drastic short-term solutions by the Mexico City Government and neighboring States. In order to help further the search for long-term sustainable solutions, we felt obliged to immediately release the results of our research regarding the monitoring of carbon sequestration by green roofs. Large-scale naturation, such as the implementation of green roofs, provides a way to partially mitigate the increased carbon dioxide output in urban areas.METHODS:Here, we quantified the carbon sequestration capabilities of two ornamental succulent plant species, Sedum dendroideum and Sedum rubrotinctum, which require low maintenance, and little or no irrigation. To obtain a detailed picture of these plants’ carbon sequestration capabilities, we measured carbon uptake on the Sedum plants by quantifying carbon dioxide exchange and fixation as organic acids, during the day and across the year, on a green roof located in Southern Mexico City.RESULTS:The species displayed their typical CAM photosynthetic metabolism. Moreover, our quantification allowed us to conservatively estimate that a newly planted green roof of Sedum sequesters approximately 180,000,000 ppm of carbon dioxide per year in a green roof of 100 square meters in the short term.DISCUSSION:The patterns of CAM and carbon dioxide sequestration were highly robust to the fluctuations of temperature and precipitation between seasons, and therefore we speculate that carbon sequestration would be comparable in any given year of a newly planted green roof. Older green roof would require regular trimming to mantain their carbon sink properties, but their carbon sequestration capabilities remain to be quantified. Nevertheless, we propose that Sedum green roofs can be part of the long-term solutions to mitigate the air pollution crisis in the Mexico City Metropolitan area, and other “megacities” with marked seasonal drought.

Time and temperature dependency of carbon dioxide triggered metal(loid) mobilization in soil

Assessing the influence of CO2 on soil and aquifer geochemistry is a task of increasing interest when considering risk assessment for geologic carbon sequestration. Leakage and CO2 ascent can lead to soil acidification and mobilization of potentially toxic metals and metalloids due to desorption or dissolution reactions. We studied the CO2 influence on an Fe(III) (oxyhydr)oxide rich, gleyic Fluvisol sampled in close vicinity to a Czech mofette site and compared the short-term CO2 influence in laboratory experiments with observations on long-term influence at the natural site. Six week batch experiments with/without CO2 gas flow at 3 different temperatures and monitoring of liquid phase metal(loid) concentrations revealed two main short-term mobilization processes. Within 1 h to 1 d after CO2 addition, mobilization of weakly adsorbed metal cations occurred due to surface protonation, most pronounced for Mn (2.5–3.3 fold concentration increase, mobilization rates up to 278 ± 18 μg Mn kgsoil−1 d−1) and strongest at low temperatures. However, total metal(loid) mobilization by abiotic desorption was low. After 1–3 d significant Fe mobilization due to microbially-triggered Fe(III) (oxyhydr)oxide dissolution began and continued throughout the experiment (up to 111 ± 24 fold increase or up to 1.9 ± 0.6 mg Fe kgsoil−1 d−1). Rates increased at higher temperature and with a higher content of organic matter. The Fe(III) mineral dissolution was coupled to co-release of incorporated metal(loid)s, shown for As (up to 16 ± 7 fold, 11 ± 8 μg As kgsoil−1 d−1). At high organic matter content, re-immobilization due to resorption reactions could be observed for Cu. The already low pH (4.5–5.0) did not change significantly during Fe(III) reduction due to buffering from sorption and dissolution reactions, but a drop in redox potential (from > +500 mV to minimum +340 ± 20 mV) occurred due to oxygen depletion. We conclude that microbial processes following CO2 induction into a soil can contribute significantly to metal(loid) mobilization, especially at optimal microbial growth conditions (moderate temperature, high organic carbon content) and should be considered for carbon sequestration monitoring and risk assessment.

Evaluación de la rentabilidad económica y captura de carbono en plantaciones de cacao en el plan Chontalpa, Tabasco

En este estudio se evalúo la rentabilidad económica y la captura de Carbono del sistema agroforestal cacao entre tres parcelas de diferentes edades. Se aplico un cuestionario estructurado a 36 productores pertenecientes al poblado C-34 de Huimanguillo, Tabasco. Se evaluaron los egresos e ingresos en términos monetarios de la plantación del cacao. Para la determinación de la captura de Carbono se estudiaron nueve sitios con superficie de tres hectáreas cada uno pertenecientes al poblado C-34 y un sitio ubicado en el Km-21 del campo experimental del Colegio de Postgraduados. Para la cuantificación de Carbono, se utilizo la metodología rápida para la estimación y monitoreo de captura de carbono propuesta por Rendón y Soto, 2007. Para el análisis estadístico se utilizo un análisis ANOVA y una prueba Tukey Kramer. La plantación de cacao de 20 años obtuvo la mayor utilidad con $ 7 653 ± $ 3 921, mientras que la plantación de cacao de 30 años obtuvo la menor utilidad promedio con $ 5 899 ± $ 3 420. El análisis estadístico demostró que no hubo diferencias estadísticas significativas en relación al Carbono capturado en las parcelas evaluadas; Sin embargo, en las plantaciones de cacao de 15 años de edad se obtuvo un valor promedio 100.6 ± 110.5 t/C/ha-1 mientras que en las de 20 años se obtuvo 49.9 ± 14 t/C/ha-1.

Laser ablation molecular isotopic spectrometry of carbon isotopes

Quantitative determination of carbon isotopes using Laser Ablation Molecular Isotopic Spectrometry (LAMIS) is described. Optical emission of diatomic molecules CN and C2 is used in these measurements. Two quantification approaches are presented: empirical calibration of spectra using a set of reference standards and numerical fitting of a simulated spectrum to the experimental one. Formation mechanisms of C2 and CN in laser ablation plasma are briefly reviewed to provide insights for implementation of LAMIS measurements. A simulated spectrum of the 12C2 Swan system was synthesized using four constituents within 473.5–476.5nm. Simulation included three branches of 12C2 (1-0), branches R(0-0) and R(1-1), and branch P(9-8) of 12C2. Spectral positions of the tail lines in R(0-0) and R(1-1) were experimentally measured, since they were not accurately known before. The Swan band (1-0) of the isotopologue 13C12C was also simulated. Fitting to the experimental spectrum yielded the ratio 13C/12C=1.08% in a good agreement with measurements by isotope ratio mass spectrometry. LAMIS promises to be useful in coal, oil and shale exploration, carbon sequestration monitoring, and agronomy studies.

Comparison of Soil Sampling Devices for Soil Bulk Density Determination for Carbon Sequestration Monitoring

Soil bulk density is often used to adjust soil carbon (C) values for changes in soil density because of management differences in C-sequestration studies. Often hand-sampling devices used for soil fertility monitoring may be used for sampling soil to a depth of up to 30 cm. Four sampling devices of differing characteristics that can be used with hand-sampling equipment across full tillage, reduced tillage, and no-tillage to determine soil bulk density were evaluated. There were no significant differences in soil bulk density values between three cylindrical devices, which measured from 19 to 31 mm in diameter. However, a 21 mm × 76 mm rectangular device gave significantly lower bulk density values than the cylindrical devices. Calculation of soil organic carbon (SOC) mass per unit area showed similar results for the cylindrical devices with the rectangular device giving a significantly lower value than the cylindrical devices. Using a cylindrical device on a hand probe in a consistent manner will give satisfactory results when sampling surface soils.

Comparison of Long-Wave Infrared Imaging and Visible/Near-Infrared Imaging of Vegetation for Detecting Leaking ${\rm CO}_2$ Gas

Recent research demonstrated that ${\mmb CO}_{\mmb 2}$ gas leaking from underground can be identified by observing increased stress in overlying vegetation using spectral imaging. This has been accomplished with both visible/near-infrared (Vis/NIR) sunlight reflection and long-wave infrared (LWIR) thermal emission. During a 4-week period in summer 2011, a controlled ${\mmb CO}_{\mmb 2}$ release experiment was conducted in Bozeman, Montana, as part of a study of methods for monitoring carbon sequestration facilities. As part of this experiment, reflective and emissive imagers were deployed together to enable a comparison of these two types of imaging systems for vegetation-based ${\mmb CO}_{\mmb 2}$ leak detection. A linear regression was performed using time as the response variable with red and NIR reflectances, Normalized Difference Vegetation Index (NDVI), and LWIR brightness temperature as predictors. The regression study showed that the reflectance and LWIR brightness temperature data together explained the most variability in the data (96%), equal to the performance of the Vis/NIR reflectance data alone, followed by NDVI alone (90%), and LWIR data alone (44%). Therefore, the two types of imagers contributed in a synergistic fashion, while either method alone was capable of gas detection with increased statistical variability.