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118 results for “Combustion”
Overwintering Fires from 2009-2010 Burns near Fairbanks, Alaska: Pre-fire Tree Species Density and Combustion Collected 2023
This dataset contains data from adjacent overwintering and single-season burn sites. For the overwintering fires, we targeted locations that had burned in the summers of 2009, smouldered through the winter months, and reignited in 2010. Adjacent to these overwintering sites, we identified single-season burn sites from within portions of the 2009 fires that were unaffected by overwintering. A total of seven overwintering fire sites and four single-season fire sites were sampled. Data inlcudes within plot measurments of post-fire seedling composition and density, residual SOL, burn depth estimated by black spruce adventitious roots, thaw depth, and pre-fire tree species composition and estimates of combustion. This is one of three packages from this project; this one contains the pre-fire tree species density and combustion data.
Emissions from Residential Wood Combustion in Norway _ 2016
<p>Emissions at 250 m resolution from Residential Wood consumption (RWC) in Norway estimated by the MetVed model (Grythe et al., 2019). The MetVed model relies on several data-sets including dwelling number and type, available residential heating technology, location of RWC pipes/installations and wood consumption (for more detail see Grythe et al., 2019; Atmos. Chem. Phys., 19, 10217–10237, https://doi.org/10.5194/acp-19-10217-2019). </p> <ul> <li>Emissions from Residential Wood Combustion in Norway _ 2016</li> <li>Format: shapefile</li> <li>Pollutants: PM10, PM2.5, PAH, CO, CH4, BC</li> <li>Emission (units): g</li> <li>WC_WE: Wood consumption (dry wood) at the grid (units): kg</li> <li>SSBID: Grid identification number, https://www.ssb.no/natur-og-miljo/geodata (In Norwegian)</li> <li>KOMMUNERNR: Municipality number</li> <li>FYLKE: County number</li> <li>Projection: UTM33</li> </ul> <p> </p>
Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories
<p><strong>Data Access Notice</strong></p> <p>Please note that, at present, the data for a sample of years are provided in this data record due to Zenodo's 50GB data limit. Data for all years 1959-2023 can be accessed via the following link:</p> <p><a href="http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html">http://opendap.uea.ac.uk/opendap/hyrax/greenocean/GridFED/GridFEDv2024.0/contents.html</a></p> <p><strong>Product Description</strong></p> <p>See Jones et al. (2021) for a detailed description of this dataset and the core methods used to produce it. Key details are provided below.</p> <p>GCP-GridFED (version 2024.0) is a gridded fossil emissions dataset that is consistent with the national CO<sub>2</sub> emissions reported by the Global Carbon Project (GCP; <a href="https://www.globalcarbonproject.org/">https://www.globalcarbonproject.org/</a>) in the annual editions of its Global Carbon Budget (Friedlingstein et al., 2023).</p> <p>GCP-GridFEDv2024.0 provides monthly fossil CO<sub>2 </sub>emissions for the period 1959-2023 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO<sub>2</sub> emitted by the oxidation of oil, coal and natural gas, international bunkers, and the calcination of limestone during cement production. The dataset also includes the cement carbonation sink of CO<sub>2</sub>. Note that positive values in GridFED signify a surface-to-atmosphere CO<sub>2 </sub>flux (emissions). Negative values signify an atmosphere-to-surface flux and apply only to the cement carbonation sink.</p> <p>GCP-GridFED also includes gridded uncertainties in CO<sub>2 </sub>emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O<sub>2</sub> uptake based on oxidative ratios for oil, coal and natural gas (see Jones et al., 2021).</p> <p><strong>Core Methodology in Brief</strong></p> <p>GCP-GridFEDv2024.0 was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR v4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2024 global carbon budget (GCP-NAE) for the years 1959-2023 (Friedlingstein et al., 2024). </p> <p>GCP-GridFEDv2024.0 uses a preliminary release of GCP-NAE covering the years 1959-2023 (timestamp 1st August 2024; an update from Andrew and Peters [2023]). The GCP-NAE estimates for year 2023 are based on data available at the timestamp and the estimates are thus expected to differ somewhat from those that will be presented by Friedlingstein et al. (2024), which will adopt updates to GCP-NAE since the timestamp.</p> <p>For full details of the core methodology, see Jones et al. (2021).</p> <p><strong>Changes to the Seasonality of Emissions in GCP-GridFEDv2022.2 onwards</strong></p> <p>The seasonality of emissions (monthly distribution of annual emissions) for the following countries/sources is now based on the seasonality observed in the Carbon Monitor dataset (Liu et al., 2020; Dou et al., 2022): </p> <ul> <li>Austria, Belgium, Brazil, Bulgaria, China, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, India, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Russia, Slovakia, Slovenia, Spain, Sweden, United Kingdom, United States.</li> <li>State or province-level data is used for Brazil, China, Russia, and the United States.</li> <li>This also applies for the Bunker Aviation and Bunker Shipping sectors.</li> </ul> <p>Seasonality is determined in the following ways for those countries/sources:</p> <ul> <li>The seasonality of emissions in 2019-2023 is taken from Carbon Monitor.</li> <li>The seasonality of emissions in all years prior to 2019 is assigned as the average of the seasonality from Carbon Monitor in all years excluding 2020 (due to the impact of COVID-19 on the seasonality of emissions in 2020).</li> </ul> <p>For all countries not listed above and all years 1959-2023, GCP-GridFED adopts the seasonality from EDGAR v4.3.2 (year 2010; Janssens-Maenhout et al., 2019) and applies a small correction based on heating/cooling degree days to account for inter-annual climate variability which effects emissions in some sectors (see Jones et al., 2021).</p> <p><strong>Other New Features of GCP-GridFEDv2024.0</strong></p> <ul> <li>There have been no changes to the functionality of the GridFED code in this update versus the previous update (v2023.1).</li> </ul> <p> </p>
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera I: Site Attribute Data 2022
This dataset contains site characteristics collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Data includes detailed site characteristics collected at the site level. Each site included three 10 m * 2 m plots (A, B, and C) laid in a single 30 m transect (or, where constrained, in parallel).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera II: Tree Inventory Data 2022
This dataset contains tree combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Tree species, diameters (DBH where possible, otherwise BD), condition (living/dead, standing/fallen, etc), and component combustion are recorded for every tree in each 10 m * 2 m plot.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera III: Shrub Inventory Data
This dataset contains shrub combustion measurements collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Shrub species, stem diameters (BD), and component combustion were recorded for every shrub in each 10 m * 2 m plot.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IV: Organic Soil Carbon and Nitrogen Content from Organic Soil Samples 2022
This dataset contains lab-quantified (and some field-measured) characteristics for post-fire residual organic soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in summer and fall of 2022 at UAF and NAU.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera V: Organic Soil Depth 2022
This dataset contains field-measured characteristics for post-fire residual organic soil samples and for additional organic soil depths collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VI: Mineral Soil Sample and pH Data 2022
This dataset contains field- and lab-measured characteristics for post-fire mineral soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in fall of 2022 at NAU.
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VII: Coarse and Fine Woody Debris Inventory 2022
This dataset contains characteristics of coarse woody debris and snags collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera VIII: Seedling Inventory 2022
This dataset contains characteristics of post-fire seedlings and resprouts collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019).
Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IX: metrics derived from All Raw Data Collected Plus Data from Previous Studies on the 2004 Alaska Wildfires Included in Analysis 2022
This data set includes metrics derived from field and lab data collected for deciduous and mixed deciduous-confier plots collected in the summer of 2022 (Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019)), as well as additional data for conifer plots from previous studies of the Taylor Highway Complex (2004), Dall Creek/Yukon Crossing (2004), and Boundary (2004) fires. Those additional data were acquired from: https://www.lter.uaf.edu/d1/d1-detail/id/773 and https://daac.ornl.gov/ABOVE/guides/ABoVE_Plot_Data_Burned_Sites.html. From this complete data set of 333 plots, 311 plots were used in analyses in Black at al. (NCC) paper: "Increased deciduous tree dominance reduces wildfire carbon losses in boreal forests". Plots excluded (from 2022 FiSL data) were poplar-dominated, mixed poplar/conifer dominated, missing soil C data, or conifer-dominated (adventituous root heights were not recorded consistently at sites in 2022 making it impossible to estimate pre-fire conifer stand organic soil C pools for 2022-collected conifer plots). Only 2005-collected conifer plots were used in NCC paper analyses. For all plots, in addition to field/lab derived site characteristics and combustion metrics, post hoc remotely sensed metrics were derived: pre-fire NDVI/EVI-2 trends, 1980-2010 climate normals, and DOB weather metrics.
Dataset for the publication: Molecularly-controlled high swirl combustion system for ethanol/1-octanol dual fuel combustion
<p>This dataset contains the research data featured in the publication "Molecularly-controlled high swirl combustion system for ethanol/1-octanol dual fuel combustion" in Fuel (DOI: 10.1016/j.fuel.2023.128184)</p>
The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland
<p><strong>Submitted data was used to write an article: </strong>Drobniak, A., Jelonek, Z., Mastalerz, M., Jelonek, I., Widziewicz-Rzońca, K., The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland – in preparation.</p> <p> </p> <p><strong>Funding acknowledgments: </strong>The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and the research activities co-financed by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland. </p> <p> </p> <p><strong>Article Abstract:<br></strong></p> <p><span>In the context of the European Union's intensified efforts to curb greenhouse gas emissions and meet climate targets, wood pellets have emerged as a pivotal element in the renewable energy strategy. Yet, biomass pellet combustion has been linked to a range of pollutants impacting air quality and public health. As biomass utilization gains popularity as a fuel for residential heating, it is important to determine this impact and enhance sustainable practices throughout the entire biomass energy production cycle. </span></p> <p><span>This study investigates the intricate dynamics of biomass pellet properties on their combustion emissions, with a specific focus on the differences observed between pellets of woody and non-woody origins. The data reveal a variation in pellet characteristics, especially regarding their ash and fines contents, mechanical durability, and impurity levels, and significant differences in the type and amount of utilization emissions. The results highlight potential health risks posed by the combustion of biomass fuels, particularly non-woody (agro) pellets, due to elevated concentrations of emitted particulate matter (PM), carbon monoxide (CO), nitrogen dioxide (NO<sub>2</sub>), hydrogen sulfide (H<sub>2</sub>S), ammonia (NH<sub>3</sub>), chlorine (Cl<sub>2</sub>), sulfur dioxide (SO<sub>2</sub>), and formaldehyde (HCHO), all surpassing recommended limits.</span></p> <p><span>Moreover, the study reveals that emissions from pellet combustion could be partially predicted by analyzing pellet characteristics. Statistical analysis identified several key variables—including bark content, fines content, mechanical durability, bulk density, heating value, net calorific value, sulfur, and nitrogen content—that impact emissions of CO, NO<sub>2</sub>, H<sub>2</sub>S, SO<sub>2</sub>, HCHO, and respiratory tract irritants. These findings underscore the need for proactive measures, including the implementation of stricter standards for fuel quality and emissions, alongside public education initiatives promoting the cleanest and safest fuels possible. </span></p> <p><strong> </strong></p>
Emission factors and chemical composition of particulate matter from residential biomass combustion
<p>Emission factors and chemical composition of particulate matter from residential biomass combustion.</p>
Estimation of biomass combustion carbon emissions data for 2018 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2018, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2020 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2020, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Estimation of biomass combustion carbon emissions data for 2019 in Africa based on GABAM burned area products.
<p>Estimated biomass combustion carbon emissions data for the African region in 2019, based on the GABAM 30m burned area product.The product is geographically (latitude/longitude) projected with a resolution of 0.00025° (approximately 30 meters) using the WGS84 horizontal datum and the EGM96 vertical datum, and consists of 10° x 10° tiles covering the entire African region.</p>
Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories 1959-2018
<p>GCP-GridFED (version 2019.1) is a gridded fossil emissions dataset that is consistent with the national CO<sub>2</sub> emissions reported by the Global Carbon Project (GCP). GCP-GridFEDv2019.1 provides monthly fossil CO<sub>2 </sub>emissions for the period 1959-2018 at a spatial resolution of 0.1° × 0.1°. The gridded emissions estimates are provided separately for fossil CO<sub>2</sub> emitted by the oxidation of oil, coal and natural gas, with mixed international bunker fuels considered separately, as well as for the calcination of limestone during cement production. GCP-GridFED also includes gridded uncertainties in CO<sub>2 </sub>emission, incorporating differences in uncertainty across emissions sectors and countries, and gridded estimates of corresponding O<sub>2</sub> uptake based on oxidative ratios for oil, coal and natural gas.</p> <p>GCP-GridFED was produced by scaling monthly gridded emissions for the year 2010, from the Emissions Database for Global Atmospheric Research (EDGAR; version 4.3.2; Janssens-Maenhout et al., 2019), to the national annual emissions estimates compiled as part of the 2019 global carbon budget (GCB-NAE) for the years 1959-2018 (Friedlingstein et al., 2019).</p> <p>The data description article is under review.</p>
Dataset supplementing B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)
<p>Dataset presented in B. Ojha, N. Illyaskutty, J. Knoblauch, H. Kohler (2017): High temperature CO/HC gas sensors to optimize firewood combustion in low power fireplaces, Journal of Sensors and Sensor Systems (JSSS), 6, 237–246, 2017 (doi:10.5194/jsss-6-237-2017)</p>
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