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167 results for “greenhouse gas”
Hot spots and hot moments of greenhouse gas emissions in agricultural peatlands
<p>Drained agricultural peatlands occupy only 1% of agricultural land but are estimated to be responsible for approximately one-third of global cropland greenhouse gas emissions. However, recent studies show that greenhouse gas fluxes from agricultural peatlands can vary by orders of magnitude over time. The relationship between these hot moments (individual fluxes with disproportionate impact on annual budgets) of greenhouse gas emissions and individual chamber locations (i.e. hot spots with disproportionate observations of hot moments) is poorly understood but may help elucidate patterns and drivers of high greenhouse gas emissions from agricultural peatland soils. We used continuous chamber-based flux measurements across three land uses (corn, alfalfa, and pasture) to quantify the spatiotemporal patterns of soil greenhouse gas emissions from temperate agricultural peatlands in the Sacramento-San Joaquin Delta of California. We found that the location of hot spots of emissions varied over time and were not consistent across annual timescales. Hot moments of nitrous oxide (N<sub>2</sub>O) and carbon dioxide (CO<sub>2</sub>) fluxes were more evenly distributed across space than methane (CH<sub>4</sub>). In the corn system, hot moments of CH<sub>4</sub> flux were often isolated to a single location but locations were not consistent across years. Spatiotemporal variability in soil moisture, soil oxygen, and temperature helped explain patterns in N<sub>2</sub>O fluxes in the annual corn agroecosystem but was less informative for perennial alfalfa N<sub>2</sub>O fluxes or CH<sub>4</sub> fluxes across ecosystems, potentially due to insufficient spatiotemporal resolution of the associated drivers. Overall, our results do not support the concept of persistent hot spots of soil CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O emissions in these drained agricultural peatlands. Hot moments of high flux events generally varied in space and time and thus required high sample densities. Our results highlight the importance of constraining hot moments and their controls to better quantify ecosystem greenhouse gas budgets.</p>
Mapping and modelling global mobility infrastructure stocks, material flows and their embodied greenhouse gas emissions - Data
<p>Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures. </p><p>The data in this repository show the material stocks contained in global mobility infrastructure networks at the country-level and mapped at 5arcmins, as well as country-level estimates of material flows for maintenance, replacement and expansion of those infrastructures, and the associated GHG emissions from materials production. This repository contains all data as shown in figures of the article, including the GeoTIFF files for figure 3, and the supplementary data file containing full country-level results.</p><p><strong>Data</strong><br>This dataset includes the following data:</p><ul><li>Global maps of material stocks in mobility infrastructure networks at 5 arcmins, separate for all roads, all rail-based infrastructure, as well as in total and per capita</li><li>Global country-level material stock estimates for mobility infrastructures</li><li>Global country-level estimates of material flows and associated GHG emissions for materials production</li><li>Material intensity in mass per area of road (kg/m²) per road type</li><li>Material intensity in mass per area of railway track (kg/m²) per railway type</li><li>Material intensity in mass per area (kg/m²) per bridges and tunnels</li></ul><p>Material intensity factors are available for iron and steel, concrete, asphalt, aggregate (sand & gravel), timber, and other.</p><p><strong>Further information</strong><br>This dataset complements the following scientific article:</p><p>Wiedenhofer, Dominik, André Baumgart, Sarah Matej, Doris Virág, Gerald Kalt, Maud Lanau, Danielle Densley Tingley, u. a. "Mapping and Modelling Global Mobility Infrastructure Stocks, Material Flows and Their Embodied Greenhouse Gas Emissions". <i>Journal of Cleaner Production</i>, November 2023, 139742. <a href="https://doi.org/10.1016/j.jclepro.2023.139742">https://doi.org/10.1016/j.jclepro.2023.139742</a>.</p><p>For further information please see the publication. You can also contact Dominik Wiedenhofer <a href="mailto:dominik.wiedenhofer@boku.ac.at">dominik.wiedenhofer(a)boku.ac.at</a> and visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project: <i>MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</i></p><p><strong>Funding</strong><br>This research was funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p>
Diel greenhouse gas emissions demonstrate a strong response to vegetation patch types in a freshwater wetland
<p>Data supporting submitted research paper. "All_flux_variables.csv" includes all plot data and is organized by the date/time of sample, campaign number, and sample location in rows and data collected as headers in the columns. "Flux_tower_data.csv" are meterological variables include in data analysis collected by a flux tower on sight. All other files are time series data for water pH (n = 2), water temperature (n = 3-5). Refer to the "metadata.csv" for units and descriptions of data in files. </p>
Greenhouse gas fluxes at a agricultural peatland in Southern Finland
<p>Greenhouse gas fluxes were measured during summer and fall of 2024 in an extensively managed agricultural peatland in Holonsuo, Lahti, Finland (61.0025 °N, 25.8214 °E). Greenhouse gas fluxes on four measurement plots were monitored along with soil temperature and water table level. </p> <p>The study site is a peatland drained for agricultural purposes (peat field). Total of four measurement plots were founded at the site. The plots were located diagonally between two ditches. Measurements were carried out approximately every 3-4 weeks from June to September 2024 (Fig1). During the measurement period the field was not used for cultivation. Portable LI-COR Trace Gas Analyzers were the used measurement devices; TG10 for CO<sub>2</sub> and CH<sub>4</sub> and TG20 for N<sub>2</sub>O. Greenhouse gas fluxes were measured using a dark chamber. Volume of the used chamber was 24,16 dm<sup>3</sup> and the chamber was equipped with a fan. Before setting the chamber on the measurement plot the vegetation was cut short each time (approx. 5 cm height). Soil temperature at 5 and 30 cm depth as well as water table level were also measured simultaneously with greenhouse gas measurements.</p> <p>The data set ("Holonsuo_ghg_data.csv") contains the measurement date, water table level (cm below ground), soil temperature at 5 and 30 cm (°C) and greenhouse gas fluxes g m<sup>-2</sup> h<sup>-1</sup>. In Fig1 fluxes are presented as averages of each measurement date (unit mg m<sup>-2</sup> h<sup>-1</sup>).</p>
Carbon sequestration of a forested wetland receiving nutrient inputs - soil, tree and greenhouse gas data
<p><span><span><span><span><span><span><span><span><span><span><span>Here we describe a pilot wetland carbon project located 30 km west of New Orleans where measurements were taken in 2013 and 2018, and applied to the carbon offset methodology, "Restoration of Degraded Deltaic Wetlands of the Mississippi Delta" ("the ACR Methodology") published by the American Carbon Registry (ACR). Baseline emissions were modeled using values derived from scientific literature. Results indicate net sequestration rate of 619,727 tons carbon dioxide equivalent (CO<sub>2</sub>e) over the 40 year project duration, which equates to 16,527 t CO2-e/yr, if wetland greenhouse gases (GHGs) are included, and 200,143 t CO<sub>2</sub>e over 40 years, or 5,003 t CO2-e/yr, if wetland greenhouse gasses were conservatively omitted. A kriging exercise was carried out that modeled the tree and soil pools, which resulted in net sequestration of 723,375 t CO2-e over 40 years (annual mean 18,084 t CO2-e/yr) with greenhouse gases, and 262,472 t CO2-e over 40 years (annual mean rate 6,560 t CO2-e/yr) if greenhouse gases were omitted. Unfortunately, the project was withdrawn, prohibiting the issuance and eventual transaction of carbon credits, due to very large uncertainty estimates mostly associated with GHG emissions and the kriging approach as in situ sampling could not be conducted as required by the methodology.</span></span></span></span></span></span></span></span></span></span></span></p>
Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery
<p><strong>Power Generation Data Set</strong></p> <p>This data set contains imaging data acquired by ESA's Sentinel-2<br> Earth-observing satellite constellation [1] for a sample of power stations that were picked using geographic coordinates <br> provided by the European Pollutant Release and Transfer Register [2]. The images<br> contain scenes of power stations, some of which are actively<br> emitting smoke plumes.</p> <p>This data set was created with the goal to automatically segment plumes, predict the type of fired fuel, predict the rate of power generation and estimate the amount of CO2 emissions, directly from remote sensing images.</p> <p><br> <strong>Description</strong><br> </p> <p>Each image is provided in the GeoTIFF file format, contains a total of 13 bands. Images have either a shape of 120x120 or 300x300 pixels (corresponding to a square area with an edge length of respectively 1.2 km and 3.0 km on the ground)<br> .</p> <p>This repository contains a total of 2131 images. This<br> repository contains a collection of JSON files that hold manual segmentation labels for plumes. Segmentation<br> labels were generated using label-studio [3]. Please note that polygon edge coordinates have to be scaled to fit the images.</p> <p><br> <strong>Content</strong></p> <p>The following files are contained in this repository:</p> <ul> <li>README.md - this file</li> <li>images.zip [2.0GB] - contains 2131 GeoTIFF images</li> <li>segmentation_labels.zip [1.5MB] - contains 2131 JSON files</li> <li>labels.csv [310KB] - contains additional labels for each image: <ul> <li>Generation output rate [4],[5]</li> <li>Country</li> <li>Type of fired fuel</li> <li>Latitude and longitude of the power plant</li> <li>Concurrent weather information (temperature, humidity and wind vector)</li> </ul> </li> </ul> <p> </p> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p> Hanna, J., Mommert, M., Scheibenreif, L., Borth, D.,<br> "Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery",<br> Tackling Climate Change with Machine Learning workshop at NeurIPS 2021.</p> <p>Please refer to this publication for additional information on the data set.</p> <p>The code used for this publication is available at https://github.com/HSG-AIML/RemoteSensingCO2Estimation.</p> <p> </p> <p><br> <strong>Author</strong></p> <p>Joëlle Hanna</p> <p>University of St. Gallen, AIML Lab, School of Computer Science joelle.hanna@unisg.ch</p> <p><br> <strong>References</strong><br> </p> <p>[1]: https://earth.esa.int/web/sentinel/missions/sentinel-2<br> [2]: https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial<br> [3]: https://labelstud.io/<br> [4]: https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show<br> [5]: https://doi.org/10.5281/zenodo.3574566</p>
Data and code for "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act"
<p>For the files and data associated with the Yuan et al. 2022 "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act"</p> <p>Description: Data/code used in energy-economic impacts and health impacts analysis.</p> <p>Directory contents:</p> <p><strong>Energy Economic Impacts</strong></p> <ul> <li><strong>Code </strong>used for producing figures and data tables <ul> <li>'paperFigs_March2022.Rmd' contains the R code used for data analysis and visualization in the paper. (<em>The code runs with R v4.0.0, RStudio v1.4.1106, and the following packages: scales_1.1.1, ggpubr_0.4.0, cowplot_1.1.0, readxl_1.3.1, here_0.1, forcats_0.5.0, stringr_1.4.0, dplyr_1.0.4, purrr_0.3.4, readr_1.3.1, tidyr_1.1.0, tibble_3.0.6, ggplot2_3.3.4, and tidyverse_1.3.0.</em>)</li> <li>'ERL_Figure4.py' contains the Python code used for generating Figure 4 in the paper</li> </ul> </li> <li><strong>Table</strong>: data tables for figures in the paper and supplementary materials</li> <li><strong>Figure</strong>: figures in the paper and supplementary materials</li> <li><strong>Data</strong>: USREP-ReEDS results and data from other sources <ul> <li>'rrpt_subset.csv' contains the portions of the ReEDS output from February 26, 2021 that are necessary to create the figures in the paper.</li> <li>'urpt_subset.csv' contains the portions of the USREP output from February 26, 2021 that are necessary to create the figures in the paper.</li> <li>'urpt_welfare_subset.csv' contains more detailed USREP welfare output from February 26, 2021.</li> <li>'cooper_pop_proj.csv' contains U.S. population projections from the University of Virginia Weldon Cooper Center for Public Service published in 2018.</li> <li>'carbon_price_comparison.csv' contains data from other recent carbon pricing studies, as described in supplementary materials G.</li> </ul> </li> </ul> <p><strong>Health Impacts</strong></p> <ul> <li><strong>analysis</strong>: <ul> <li><strong>lib</strong>: annotated code library, which loads raw data from the root data folder and conducts health impacts analysis</li> <li><strong>data</strong>: outputs <ul> <li><strong>inmap</strong>: spatial inputs/outputs for inmap</li> <li><strong>working</strong>: intermediate procssed output files</li> <li><strong>final</strong>: final health impacts results</li> </ul> </li> </ul> </li> <li><strong>data</strong>: raw data used in analysis <ul> <li><strong>working</strong>: processed intermediate raw data for faster loading in R</li> </ul> </li> </ul>
A comprehensive and synthetic dataset for global, regional and national greenhouse gas emissions by sector 1970-2018 with an extension to 2019
<p>Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO<sub>2</sub> emissions from fossil fuel combustion and industry (FFI), CH<sub>4</sub> emissions, N<sub>2</sub>O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO<sub>2</sub> emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.</p>
Life-cycle greenhouse gas emissions in power generation using palm kernel shell
<p>Although the Japanese feed-in tariff was introduced to expand renewable energy, leading to the expansion of palm kernel shell (PKS) use, the greenhouse gas (GHG) emission reduction effect is evaluated using the limited life-cycle of PKS, focusing on processes after PKS generation point. Therefore, this study aimed to elucidate the life-cycle GHG emissions of power generation using PKS. We targeted two PKS-firing power plants as these are the first two instances of the use of PKS in power plants in Japan. A system boundary was established to cover palm plantation management in Indonesia and Malaysia, as both power plants import PKS from these countries. The GHG emissions were derived from land-use change, palm plantation, oil extraction, PKS transportation, and power plants. Six scenarios were examined for the emissions based on the type of land-use change and the existence of biogas capture in oil extraction. CO<sub>2</sub> emissions from PKS combustion were also calculated by assuming that carbon neutrality was lost because of cultivation abandonment. The GHG emissions in one scenario, where the plantations were replanted and continuously managed and no biogas capture implemented in oil extraction, exhibited an average of 0.134 kg-CO<sub>2</sub>eq/kWh reduction in a plant in Kyushu District, and 0.043 kg-CO<sub>2</sub>eq/kWh reduction in a plant in Shikoku District for liquid natural gas-fired steam power generation, respectively. More than 65% of life-cycle GHG emissions originate from biogas generated during oil extraction; thus, biogas capture is an effective strategy to reduce current emissions. In contrast, in the case of accompanying land-use change or collapse of carbon neutrality, the emissions considerably exceeded those of fossil fuels. These findings indicated that the FIT fails to consider the risk of increased emissions or further substantial emission reductions. Therefore, the feasibility of FIT application to PKS needs to be re-established by evaluating the entire PKS life-cycle. </p>
Agroforestry carbon stocks and greenhouse gas emission rates in central Alberta, Canada
<p>Agroforestry systems (AFS) contribute to carbon (C) sequestration and reduction in greenhouse gas emissions from agricultural lands. However, previously understudied differences among AFS may underestimate their climate change mitigation potential. In this 3-year field study, we assessed various C stocks and greenhouse gas emissions across two common AFS (hedgerows and shelterbelts) and their component land uses: perennial vegetated areas with and without trees (woodland and grassland, respectively), newly planted saplings in grassland, and adjacent annual cropland in central Alberta, Canada. Between 2018 and 2020 (~April–October), nitrous oxide emissions were 89% lower under perennial vegetation relative to the cropland (0.02 and 0.18 g N m−2 year−1, respectively). In 2020, heterotrophic respiration in the woodland was 53% lower in shelterbelts relative to hedgerows (279 and 600 g C m−2 year−1, respectively). Within the woodland, deadwood C stock was particularly important in hedgerows (35 Mg C ha−1 or 7% of ecosystem C) relative to shelterbelts (2 Mg C ha−1 or < 1% of ecosystem C), and likely affected C cycling differences between the woodland types by enhancing soil labile C and microbial biomass in hedgerows. Deadwood C stock was positively correlated with annual heterotrophic respiration and total (to ~100 cm depth) soil organic C, water-soluble organic C, and microbial biomass C. Total ecosystem C was 1.90–2.55 times greater within the woodland than all other land uses, with 176, 234, 237, and 449 Mg C ha−1 found in the cropland, grassland, planted saplings treatment, and woodland, respectively. Shelterbelt and hedgerow woodlands contained 2.09 and 3.03 times more C, respectively, than adjacent cropland. Our findings emphasize the importance of AFS for fostering C sequestration and reducing greenhouse gas emissions and, in particular, retaining hedgerows (legacy woodland) and their associated deadwood across temperate agroecosystems to help mitigate climate change.</p>
Data for meta-analysis of the soil greenhouse gas emissions
<p><span>Exploring the </span><span>responses of greenhouse gases (GHGs) emissions to land use conversion or reversion is significant for taking effective land use measures to alleviate global warming.</span> <span>A global meta-analysis was conducted to analyze the responses of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions to land use conversion or reversion, and determine their temporal evolution, driving factors and potential mechanisms. Our results showed that CH4 and N2O responded positively to land use conversion while CO2 responded negatively to the changes from natural herb and secondary forest to plantation. By comparison, CH4 responded negatively to land use reversion and N2O also showed negative response to the reversion from agricultural land to forest. The conversion of land use weakened the function of natural forest and grassland as CH4 sink and the artificial nitrogen (N) addition for plantation increased N source for N2O release from soil, while the reversion of land use could alleviate them to some degree. Besides, soil carbon would impact CO2 emission for a long time after land use conversion, and secondary forest reached the methane uptake level similar to that of primary forest after over 40 years. N2O responses had negative relationships with time interval under the conversions from forest to plantation, secondary forest and pasture. In addition, meta-regression indicated that CH4 had correlations with several environmental variables, and carbon-nitrogen ratio had contrary relationships with N2O emission responses to land use conversion and reversion.</span> <span>And the importance of driving factors displayed that CO2, CH4 and </span><span>N2O</span><span> response to land use conversion and reversion were easily affected by NH4+ and soil moisture, </span><span>mean annual temperature</span><span> and NO3-, total nitrogen and </span><span>mean annual temperature</span><span>, respectively.</span> <span>This study would provide enlightenment for scientific land management and reducing of GHG emissions.</span></p>
Estimating net carbon balances and greenhouse gas radiative balances of potato and pea crops on a conventional farm in western Canada (Flux and meteorological data)
<p>Data accompanying the paper titled as "Estimating net carbon and greenhouse gas balances of potato and pea crops on a conventional farm in western Canada". Data includes measurements from eddy covariance, chamber, and meteorological sensors. Measurements were mainly conducted in 2018 and 2019, please refer to the paper for the detailed information.</p>
Idiosyncratic phenology of greenhouse gas emissions in a Mediterranean reservoir
<p>Extreme hydrological and thermal regimes characterize the Mediterranean biome and can significantly impact the phenology of greenhouse gas (GHG) emissions in reservoirs. Our study examined the seasonal changes in GHG emissions of a shallow, eutrophic, hardwater reservoir in Spain. We observed distinctive seasonal patterns for each gas. CH<sub>4 </sub>emissions substantially increased during stratification, influenced predominantly by the rise of water temperature and gross primary production and the drop in reservoir mean depth. N<sub>2</sub>O emissions mirrored CH<sub>4</sub>'s seasonal trend, significantly correlating to water temperature, wind speed, and net primary production. Conversely, CO<sub>2 </sub>emissions decreased during stratification and displayed a quadratic, rather than a linear relationship with water temperature -an unexpected deviation from CH<sub>4</sub> and N<sub>2</sub>O emission patterns- likely associated with calcite formation coupled to photosynthesis. This investigation highlights the need to integrate these idiosyncratic patterns into GHG emissions models, enhancing the prediction of global GHG emissions in the global change era.</p>
Data and code for: Influence of atmospheric nitrogen deposition on soil greenhouse gas fluxes from forests in China and the world
<p><span>Since the industrial revolution, greenhouse gas emissions (particularly CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) caused by human activities have accelerated global climate change. To avoid catastrophic transitions in the Earth system, many countries including China have set goals to achieve “net zero emission” (or “carbon neutrality”) by mid-21<sup>st</sup> century. Forestland-related practices are among the most preferred “natural climate solutions”. However, high uncertainties remain in the greenhouse gas fluxes from forest soils, because of the limited capability to observe soil dynamics at a large spatial scale. Meanwhile, forest soil greenhouse gas fluxes are influenced by multiple anthropogenic environmental changes including enhanced atmospheric nitrogen deposition, which further complicates the interactions between forest ecosystem and the atmosphere. During the past half century, simulated nitrogen deposition (or “nitrogen addition”) experiments have been conducted in various forest sites worldwide, founding a basis for quantifying the spatially-varying responses of soil greenhouse gas flux to nitrogen deposition. </span></p> <p><span>In this research, we systematically synthesized global nitrogen addition experiment data from published literature and public databases, using which we explored the responses of the three major greenhouse gases to N input. Derived sensitivity of soil N<sub>2</sub>O emission to N deposition allowed for determining the N saturation (or limitation) status of global forests. Using process-augmented data-driven approach and random forest regression models, we estimated soil greenhouse gas budgets on regional and global levels. On the basis, we quantified the varying effects of N deposition on soil greenhouse gas fluxes in N-limited and N-saturated forests across biomes. </span><span> </span></p> <p><span>The produced global map of N-saturated forests in this research could facilitate studies on carbon and nitrogen cycles and improve forest nitrogen management. The revealed response patterns and response factors of soil greenhouse gases to N input could help improve the structure and parameters of ecosystem models. Furthermore, the localized N<sub>2</sub>O emission factors for 145 countries could be used to reduce the uncertainties in their national greenhouse gas inventories. The “process-augmented data-driven” approach could potentially bridge the gap between site-level manipulative experiments and the demand for regional greenhouse gas budgets, allowing manipulative experiments to play a more important role in global change research. </span></p>
Carbon Dioxide and Methane Flux Meta Analysis, Schaerer et al: Permafrost microbes unleashed: thaw reactors provide timely insights into greenhouse gas feedbacks for climate stewardship
<p>Meta-analysis results and workflow: <strong>Meta-Analysis-Report-V1.pdf</strong> </p> <p>raw data tables for input into meta-analysis:</p> <p><strong>co2_flux_by_layer_temp.csv</strong></p> <p><strong>co2_flux_by_layer_time.csv</strong></p> <p><strong>ch4_flux_by_layer_temp.csv</strong></p> <p><strong>ch4_flux_by_layer_time.csv</strong></p> <p><strong>co2_flux_by_headspace_temp.csv</strong></p> <p>(Data included in these tables was digitized using the R package metaDigitize)</p> <p>****</p> <p>We also attempted to summarize the raw data from 12 studies which is summarized in the <strong><em>Flux_Summary_Report </em></strong>document. we converted all units into mg C / g Soil * d (calculations are included in the <strong><em>co2_meta_analysis</em></strong> spreadsheet). For studies not reporting raw data or data tables (7/12 studies), we estimated the values from the figures manually. This typically resulted in an estimate of the mean flux of several replicates (all studies had 3-10 replicates). We filled in metadata as well as we could based on the information available in the papers, although there were many gaps. This information is summarized in the <strong><em>flux_data_compilation</em> </strong>spreadsheet.</p> <p>Studies in the raw data comparison include: Mackelprang 2011, Waldrop 2010 & 2021, Barbato 2022, Dang 2022, Muller 2018, Monteaux 2020, Dutta 2006, Lee 2012, O'Donnell 2009, Roy Chowdhury 2014, Trubl 2021.</p>
Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production
<p>This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases— US lifecycle inventory database (USLCI), GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real-world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.</p>
Dataset from: Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest Biogeosciences 2019
<p>Dataset used for the manuscript <strong>Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest</strong> in Biogesciences, 2019</p>
Soil greenhouse gas fluxes and associated parameters from forest and oil palm in the SAFE landscape
<b>Description: </b><p>Greenhouse gas fluxes measured by the static chamber method including associated environmental parameters</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/126"><b>Characterising soil microbial communities and measuring associated biogeochemical fluxes</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC HMTF (Research Programme, (NE/K016091/1), <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (79))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3258117">here</a></p><p><b>Files: </b>This consists of 1 file: 3_GHG_jdrewer.xlsx</p><p><b>3_GHG_jdrewer.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>data one off field</b> (described in worksheet Data_one_off)</p><p>Description: Soil and litter parameters</p><p>Number of fields: 14</p><p>Number of data rows: 56</p><p>Fields: </p><ul><li><b>Location</b>: Location measurement was taken (Field type: Location)</li><li><b>site</b>: Location measurement was taken (Field type: ID)</li><li><b>chamber_id</b>: Chamber ID (Field type: ID)</li><li><b>landuse</b>: Land use of location (Field type: Categorical)</li><li><b>pH</b>: Soil pH (Field type: Numeric)</li><li><b>bulk_density</b>: dry weight of soil (Field type: Numeric)</li><li><b>soil_N%</b>: Percentage of soil N (Field type: Numeric)</li><li><b>soil_C%</b>: Percentage of soil C (Field type: Numeric)</li><li><b>litter_N%</b>: Percentage of leaf Nitrogen (Field type: Numeric)</li><li><b>litter_C%</b>: Percentage of leaf Carbon (Field type: Numeric)</li><li><b>C/N_soil</b>: Ratio of soil Carbon: Nitrogen (Field type: Numeric)</li><li><b>Latitude</b>: Latitude of sampling point (Field type: Latitude)</li><li><b>Longitude</b>: Longitude of sampling point (Field type: Longitude)</li><li><b>Elevation</b>: Elevation of sampling point (Field type: Numeric)</li></ul></li><li><p><b>data of repeated measures</b> (described in worksheet Data_repeated_measures)</p><p>Description: Soil greenhouse gas flux data and associated variables</p><p>Number of fields: 14</p><p>Number of data rows: 672</p><p>Fields: </p><ul><li><b>Location</b>: Location measurement was taken (Field type: Location)</li><li><b>site</b>: Location measurement was taken (Field type: ID)</li><li><b>chamber_id</b>: Chamber ID (Field type: ID)</li><li><b>landuse</b>: Land use of location (Field type: Categorical)</li><li><b>date</b>: Date the measurement was taken (Field type: Date)</li><li><b>time</b>: Time the measurement was taken (Field type: Time)</li><li><b>flux_CH4</b>: Soil CH4 flux (Field type: Numeric)</li><li><b>flux_CO2-C</b>: Soil CO2 flux (Field type: Numeric)</li><li><b>flux_N2O-N</b>: Soil N2O flux (Field type: Numeric)</li><li><b>NH4-N</b>: Soil NH4 concentration (Field type: Numeric)</li><li><b>NO3-N</b>: Soil NO3 concentration (Field type: Numeric)</li><li><b>air_temp</b>: Air temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_temp</b>: Soil temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_moisture</b>: Soil moisture around the flux chamber (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2015-01-01 to 2016-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Soil greenhouse gas fluxes along transects from oil palm to riparian forests in the SAFE landscape
<b>Description: </b><p>Riparian greenhouse gas fluxes measured by the static chamber method including associated environmental parameters and river water </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/126"><b>Characterising soil microbial communities and measuring associated biogeochemical fluxes</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC HMTF (Research Programme, (NE/K016091/1), <a href=" http://lombok.nerc-hmtf.info/"> http://lombok.nerc-hmtf.info/</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (Research licence JKM/MBS.1000-2/2 JLD.5 (79))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3258079">here</a></p><p><b>Files: </b>This consists of 1 file: 1_HJ_river_water_riparian.xlsx</p><p><b>1_HJ_river_water_riparian.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>river_water</b> (described in worksheet river_water)</p><p>Description: river water measurments</p><p>Number of fields: 17</p><p>Number of data rows: 63</p><p>Fields: </p><ul><li><b>site</b>: location sample was taken (Field type: Location)</li><li><b>location</b>: habitat (Field type: Categorical)</li><li><b>replicate</b>: water sample replicate number (Field type: Replicate)</li><li><b>sampling_occasion</b>: date of sample collection (Field type: Date)</li><li><b>date</b>: date of sample analysis (Field type: Date)</li><li><b>TDS</b>: Total Desolved Solids (Field type: Numeric)</li><li><b>pH</b>: water pH (Field type: Numeric)</li><li><b>conductivity</b>: water conductivity (Field type: Numeric)</li><li><b>Temp</b>: tempreture of river water (Field type: Numeric)</li><li><b>air_CH4</b>: air concentration of CH4 (Field type: Numeric)</li><li><b>water_CH4</b>: water concentration of CH4 (Field type: Numeric)</li><li><b>air_N2O</b>: air concentration of N2O (Field type: Numeric)</li><li><b>water_N2O</b>: water concentration of N2O (Field type: Numeric)</li><li><b>air_CO2</b>: air concentration of CO2 (Field type: Numeric)</li><li><b>water_CO2</b>: water concentration of CO2 (Field type: Numeric)</li><li><b>NH4-N</b>: concentration of NH4-N in water (Field type: Numeric)</li><li><b>NO3-N</b>: concentration of NO3-N in water (Field type: Numeric)</li></ul></li><li><p><b>data_one_off_field</b> (described in worksheet data_one_off_field)</p><p>Description: soil and littter property measurements</p><p>Number of fields: 12</p><p>Number of data rows: 48</p><p>Fields: </p><ul><li><b>Location</b>: location of chamber (Field type: Location)</li><li><b>chamber_id</b>: chamber ID (Field type: ID)</li><li><b>site</b>: Site ID (Field type: ID)</li><li><b>landuse</b>: land use type (Field type: Categorical)</li><li><b>pH</b>: soil pH (Field type: Numeric)</li><li><b>soil_N</b>: soil nitrogen content (Field type: Numeric)</li><li><b>soil_C</b>: soil carbon content (Field type: Numeric)</li><li><b>litter_N</b>: litter nitrogen content (Field type: Numeric)</li><li><b>litter_C</b>: litter carbon content (Field type: Numeric)</li><li><b>C_N</b>: soil C:N ratio (Field type: Numeric)</li><li><b>Latitude</b>: GPS co-ordinate that the sample was taken (Field type: Latitude)</li><li><b>Longitude</b>: GPS co-ordinate that the sample was taken (Field type: Longitude)</li></ul></li><li><p><b>data_repeated_measures</b> (described in worksheet data_repeated_measures)</p><p>Description: repeated soil measures</p><p>Number of fields: 16</p><p>Number of data rows: 336</p><p>Fields: </p><ul><li><b>chamber_id</b>: Chamber ID (Field type: ID)</li><li><b>site</b>: Site ID (Field type: ID)</li><li><b>landuse</b>: land use type (Field type: Categorical)</li><li><b>sampling_occasion</b>: date of sample collection (Field type: Date)</li><li><b>date</b>: date of sample analysis (Field type: Date)</li><li><b>time</b>: Time the measurement was taken (Field type: Time)</li><li><b>flux_CH4-C</b>: Soil CH4 flux (Field type: Numeric)</li><li><b>flux_CO2-C</b>: Soil CO2 flux (Field type: Numeric)</li><li><b>flux_N2O-N</b>: Soil N2O flux (Field type: Numeric)</li><li><b>NH4-N_H2O</b>: Soil NH4 concentration (Field type: Numeric)</li><li><b>NO3-N_H2O</b>: Soil NO3 concentration (Field type: Numeric)</li><li><b>NH4-N_KCl</b>: Soil NH4 concentration (Field type: Numeric)</li><li><b>NO3-N_KCl</b>: Soil NO3 concentration (Field type: Numeric)</li><li><b>air_temp</b>: Air temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_temp</b>: Soil temperature around the flux chamber (Field type: Numeric)</li><li><b>soil_moisture</b>: Soil moisture around the flux chamber (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2016-11-01 to 2017-11-30</p><p><b>Latitudinal extent: </b>4.3960 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Greenhouse gas and energy fluxes in a boreal peatland forest after clearcutting
<p>This package contains the data used in the research article: "Greenhouse gas and energy fluxes in a boreal peatland forest after clearcutting" published in Biogeosciences journal.</p> <p>Changes in this version:</p> <p>Chamber_data.xlsx is now named Chamber_data_clearcut.xlsx. CO2 fluxes were also corrected.</p> <p>Added daily mean CO2, CH4 and N2O fluxes measured at the control site.</p> <p> </p> <p>Chamber_data_clearcut.xlsx contains the daily mean fluxes of CO2, CH4 and N2O measured with soil chambers at the clearcut site.</p> <p>Chamber_data_control.xlsx contains the daily mean fluxes of CO2, CH4 and N2O measured with soil chambers at the control site.</p> <p>EC_CO2_fluxes.xlsx contains the gapfilled 30-min mean CO2 fluxes (NEE) and its components (GPP and respiration).</p> <p>Energy_fluxes.xlsx contains the gapfilled hourly mean energy fluxes.</p> <p>Meteo_data.xlsx contains the daily means of the meteorological variables used in the study.</p>
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