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33 results for “Emission Factors”
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>
Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology
<p>This dataset contains direct measurements of biomass burning emission factors for CO<sub>2</sub>, CO, CH<sub>4</sub> and N<sub>2</sub>O. It includes over 4500 EF bag measurements sampled using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems in Brazil, Australia, Botswana, Zambia, South-Africa and Mozambique under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. The table in the included word file explains the individual columns in the excell file. </p> <p> </p>
Changes in the factors influencing forest floor BVOC emissions during forest succession
<p>The files have been uploaded to comply with AGU and journal requirements, particularly the "Open Research" section, which provides links to the data and analytical code necessary for the peer review process. This initiative aims to support transparent and reproducible science. </p> <ul> <li>Data analysis and the ploting of Figure2 in manuscript, along with Figure S1-S3 and Table S1-S4 in supporting information, were conducted using R Studio. The file "Forest floor BVOC emissions_analyses and plots.R" and datasets "ForestFloor.csv", "boxplot_BVOC_ca.csv", "boxplot_BVOC_fi.csv", "boxplot_BVOC_ru.csv", "SamplingSite_1.csv" were utilized for this purpose.</li> <li>To generate Figure 3 in the manuscript, the file "SIMCA 18 for Fig 3.dox" and dataset "ForestFloor.xlsx" were used. The word file provide the the trial software link. </li> <li>For the analysis and ploting of Figure 4 in the manuscript, the file "PLS_PM.R" and dataset "BVOC_PLSR_PM.csv" were employed. </li> <li>The file "For Fig S4.xlsx" was used to create Figure S4 in the supporting information. </li> </ul> <p>Abstract in article</p> <p><span>The boreal forest floor is a crucial source of diverse biogenic volatile organic compounds (BVOCs) emitted into the atmosphere. Climate change is increasing in the frequency of wildfires in the boreal forest, major disturbances with lasting impacts on the ecosystem, particularly the forest floor. Wildfires changed BVOC sources and emissions, influencing aerosol formation during forest succession across various age classes. This study quantified BVOC emissions from the forest floor and characterized microenvironmental conditions, including abiotic factors (air temperature, soil temperature, soil moisture, light intensity) and biotic factors (ground vegetation composition, species coverage, soil respiration). Our objective was to understand how abiotic and biotic factors influence the forest floor BVOC emissions during forest succession. Path models revealed direct influences of ground vegetation composition on isoprene and monoterpene emissions. Sesquiterpene emissions were mainly regulated by abiotic factors, while isoprene and monoterpene emissions were influenced both directly and indirectly by abiotic factors. The indirect impact of abiotic factors was manifested through biotic factors, including vegetation and soil processes. Effect sizes of influencing factors varied across different forest age areas, with temperature exerting a larger impact in earlier burned areas compared to recently burned areas. The influence of soil moisture on BVOC emissions diminished with forest age. Our findings indicated the importance of identifying influencing factors and their relationship with forest floor BVOC emissions during different stages of forest succession for predicting the effect of post-wildfire forest succession on the BVOC emission patterns and, consequently, their impact on climate.<span> </span></span></p>
Assessment of whole-site methane emissions from anaerobic digestion plants: towards establishing emission factors for various plant configurations
<p>This dataset supplements the publication "Assessment of whole-site methane emissions from anaerobic digestion plants: towards establishing emission factors for various plant configurations" by Wechselberger et al. (2025).</p> <p>The dataset contains primary and secondary data underlying the statistical analysis and reported methane emission factors. Emission factors were calculated as described in section 2.3 of the paper. </p> <p>Available files (UTF-8 encoded):</p> <ul> <li>Data.csv (dataset)</li> <li>Glossary.csv (column/variable descriptions of dataset)</li> </ul> <p>The dataset includes plant characteristics and whole-site methane losses of 135 anaerobic digestion plants, covering normal and various other-than-normal operating conditions (155 rows). For statistical analysis, only periods during normal operation and plants with information on the analyzed emission factors and plant characteristics were considered (cf. supplementary information C of the paper). Consequently, the final dataset contained 109 anaerobic digestion plants for statistical analysis on the methane emission factor (% of methane produced) and 28 plants when analyzing the wastewater-specific emission factor (kg methane per population equivalent and year). All but one facility continuously processed feedstock without any post-rotting stages. Plant DE-MH_WP5_1 of the secondary data implemented garage digesters.</p> <p>Data from three plants were collected only after completion of statistical analyses. These data were used to compare methane losses during normal and other-than-normal operating conditions. The respective rows are marked accordingly in the dataset (column “data_collected_after_statistical_analyses”).</p> <p>Version v2 contains the final reference to the publication Wechselberger et al. (2025). The data are the same as in version v1.</p>
#energy_graph on renewable shares in electricity and CO2 emission factors in Australia and Germany
<p>This is the little graph I used for my #energy_graph tweet, including the underlying data, in an Excel file. Here is the tweet: https://twitter.com/WPSchill/status/1464368711817740298?s=20. And here is last year's tweet: https://twitter.com/WPSchill/status/1336633040676720640?s=20</p> <p>I occasionally tweet stuff like this. Follow me, if you like ;) https://twitter.com/WPSchill</p>
Peatland maps and wetland GHG emission factors
<p>The data set includes maps of degraded (~46 Mha globally) and intact peatland (~375 Mha globally) for the year 2015. The spatial resolution is 0.5 degree. The data set also includes IPCC wetland GHG emission factors for degraded and rewetted peatlands.</p> <p>This dataset has been published originally as supplementary data set in </p> <p>Humpenöder, F., Karstens, K., Lotze-Campen, H., Leifeld, J., Menichetti, L., Barthelmes, A., and Popp, A. (2020). Peatland protection and restoration are key for climate change mitigation. Environ. Res. Lett. <em>15</em>, 104093. DOI <a href="https://10.1088/1748-9326/abae2a">10.1088/1748-9326/abae2a</a>.</p> <p> </p>
Emission factors of trace gases and aerosols from wildfire events in central Portugal
<p>The aim of this data set is to provide a comprehensive overview of the chemical composition (trace elements, water-soluble ions and organic compounds) in smoke aerosol particles from some representative wildfires for use in emission inventorying and source apportionment modelling. </p> <p> </p>
Data for Measurement report: Air pollution emission factors of inland river ships under compliance with the 10 parts per million limit for sulfur content in fuel
<p>Since July 1, 2019, China’s domestic diesel fuel has been limited to 10 ppm of sulfur. Hence, to explore the applicability of the “sniffer” method and the distribution and level of inland river ships (IRSs) emission factors (EFs) under this limitation, we installed “sniffer” monitoring equipment, from August 2020 to June 2022, at the Gezhou Dam of the Yangtze River in China and monitored emissions from 8,238 IRSs in total passing through the lock. We partnered with the maritime department to select 100 ships passing through the lock to extract fuel oilsamples for direct fuel sulfur content detection, which determined the true fuel sulfur content of the passing ships. fuel sulfur content.</p> <p>The “sniffer” monitoring equipment included SO<sub>2</sub>, CO<sub>2</sub>, NO, and NO<sub>2</sub> gas sensors, PM<sub>2.5</sub> and PM<sub>10</sub> particulate matter sensors, as well as wind speed, wind direction, temperature, humidity, and pressure sensors.</p>
Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity
<p>Gridded global potentially affected fraction of species (PAF) in 2050 and 2100 per kg GHG for 3 RCPs (2.6, 4.5,8.5) averaged over all species groups. </p> <p>Full method description is available in the article: <a href="https://www.sciencedirect.com/science/article/pii/S092134492300294X">Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity - ScienceDirect</a></p>
Equivalent Black Carbon Emission Factors from Ships Within a Sulfur Emission Control Area
<p>Equivalent black carbon (eBC) emission factors, for ship plumes sampled in the Port of Gothenburg during two measurement campaigns in autumn 2014 and autumn 2015 respectively. </p> <p>Measurement site coordinates: N57.6849, E11.838</p> <p>Equivalent black carbon measured with a Multi Angle Absorption Photometer (MAAP, Thermo Fisher Scientific), wavelength 637 nm. CO2 sampled with a non-dispersive infrared gas analyzer (LI840, LI-COR).</p> <p>Unit (EF_BC): ug (kg fuel)^-1</p> <p>File creator: Stina Ausmeel<br> Contact e-mail: stina.ausmeel@nuclear.lu.se</p>
Improved estimates of carbon dioxide emissions from drained peatlands support a reduction in emission factor
<p>Summary of published carbon dioxide field emission data and their influence factors used for generating Tier 1 emission factor of peat extractions in IPCC 2013 Wetland Supplementary and extra data published after IPCC (2014). </p><p>The dataset is supplementary to the published paper "Improved estimates of carbon dioxide emissions from drained peatlands support a reduction in emission factor" By Hongxing He and Nigel Roulet: He, H., Roulet, N.T. Improved estimates of carbon dioxide emissions from drained peatlands support a reduction in emission factor. <i>Commun Earth Environ</i> <strong>4</strong>, 436 (2023). https://doi.org/10.1038/s43247-023-01091-y. </p><p> </p>
Dataset for the study:"Driving and limiting factors of CH4 and CO2 emissions from coastal brackish-water wetlands in temperate regions"
<p>Dataset used for statistical analysis of the manuscript "Chiapponi, E., Silvestri, S., Zannoni, D., Antonellini, M., and Giambastiani, B. M. S.: Driving and limiting factors of CH<sub>4</sub> and CO<sub>2</sub> emissions from coastal brackish-water wetlands in temperate regions, EGUsphere, https://doi.org/10.5194/egusphere-2023-605, 2023."</p> <p>The dataset include:</p> <ul> <li>CO2 and CH4 fluxes retrived with a portable fluximeter from soils and standing waters</li> <li>environemntal parameters ( T of air and water, Electrical Conductivity (EC), irradiance and water depth </li> </ul> <p>To cite content from this repository: "Chiapponi, E., Silvestri, S., Zannoni, D., Antonellini, M., and Giambastiani, B. M. S.: Dataset for the study:"Driving and limiting factors of CH4 and CO2 emissions from coastal brackish-water wetlands in temperate regions", EGUsphere, 10.5281/zenodo.10390803."</p> <p> </p>
A synthesis of nitric oxide emissions across global fertilized croplands from crop-specific emission factors
<p><span>Nitrogen (N)-fertilizer application to agricultural soils results in substantial emissions of nitric oxide (NO), a key substance in tropospheric chemistry involved in climate forcing and air pollution. </span><span>However, estimates of global cropland NO emissions remain uncertain due to a lack of information on direct NO emission factors (EF<sub>d<sup>s</sup></sub>) of applied N for variours cropping systems at seasonal or annual scales. Here we quantified the crop-specific seasonal and annual-scale NO EF<sub>d<sup>s</sup></sub> through synthesizing </span><span>1094 measurements from 125 field-based studies worldwide. </span><span>The global mean crop-specific seasonal EF<sub>d</sub> was 0.53%, with the highest for vegetables (0.75%). Among cereal crops, the EF<sub>d</sub> of maize (0.45%) or wheat (0.47%) was about three-times higher than for rice (0.12%). At annual scale, the mean EF<sub>d</sub> across all cropping systems was</span><span> 0.58%, with tea plantations having the highest (1.54%). For other cropping systems, the annual-scale EF<sub>d<sup>s</sup></sub> ranged from 0.02% to 1.07%. Besides crop type, also soil organic carbon, total N and pH as well as N fertilizer type were the main factors explaining the variations of NO EF<sub>d<sup>s</sup></sub>. Based on obtained specific EF<sub>d<sup>s</sup></sub> for each crop type, we estimated that NO emissions due to the use of synthetic fertilizers from global croplands are about 0.42–0.62 Tg N yr<sup>−1</sup>. Our budgets are relatively lower if compared to estimates derived by the use of IPCC defaults for NO emissions (0.72–1.66 Tg N yr<sup>−1</sup>) or reported elsewhere (0.67–1.04 Tg N yr<sup>−1</sup>). In our estimates, cash crops (vegetable, tea and orchard), which cover only 9% of the world cropland area, contributed about 31% to total NO emissions from global fertilized croplands. Overall, our meta-analysis provides improved crop-specific NO EF<sub>d<sup>s</sup></sub> reflecting current stage of knowledge. The work also highlights the relative importance of cash crop production as sources for atmospheric NO, i.e., agricultural systems on which mitigation efforts may focus</span><span>.</span></p>
Data Compilation Supporting the Development of gas signatures of smouldering peat wildfire from emission factors
<p>This is the dataset of emission factors used for the paper "Development of gas signatures of smouldering peat wildfires from emission factors" published in 2022 in <em>Journal of the International Association of Wildland Fire</em>.</p> <p>Smouldering peat fires are responsible for regional haze episodes and cause environmental, social and health crisis. Owing to the unique burning characteristics of smouldering peat, identifying and detecting this kind of fire from flaming and among all wildfire fuels remain a challenge. This work explores and develops smouldering peat gas signatures using emission factor (EF) data from the literature. Systematic comparisons and statistical analyses were carried out to investigate the difference and statistical significance of 28 forms of EF combinations created from the 4 most abundant gas species (CO2, CO, CH4 and NH3) from smouldering peat, flaming savanna and grassland, agricultural residue and forest fires.</p>
Supporting Information for 'in situ fire emission factors for Malaysian tropical peatlands with the first investigation of the influence of physicochemical controls on peat fire emission factor variability'
<p>Two files containing:</p> <p>1. Raw mole fraction retrievals from our OP-FTIR spectra (as described in the paper)</p> <p>2. Calculated emission ratios used for the calculation of emission factors (as published in the paper)</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>
China's agricultural greenhouse gas emission intensity and its influencing factors
<p>This dataset includes the agricultural greenhouse gas emissions intensity of China and various factors that affect agricultural greenhouse gas emissions (agricultural patent intensity, agricultural per capita value added, urbanization rate, environmental investment intensity, and urban rural income gap.</p>
Dataset for: Indirect nitrous oxide emission factors of fluvial networks can be predicted by dissolved organic carbon and nitrate from local to global scales
<p>Streams and rivers are important sources of nitrous oxide (N<sub>2</sub>O), a powerful greenhouse gas. Estimating global riverine N<sub>2</sub>O emissions is critical for the assessment of anthropogenic N<sub>2</sub>O emission inventories. The indirect N<sub>2</sub>O emission factor (EF<sub>5r</sub>) model, one of the bottom-up approaches, adopts a fixed EF<sub>5r</sub> value to estimate riverine N<sub>2</sub>O emissions based on IPCC methodology. However, the estimates have considerable uncertainty due to the large spatiotemporal variations in EF<sub>5r</sub> values. Factors regulating EF<sub>5r</sub> are poorly understood at the global scale. Here, we combine 4-year in situ observations across rivers of different land use types in China, with a global meta-analysis over six continents, to explore the spatiotemporal variations and controls on EF<sub>5r</sub> values. Our results show that the EF<sub>5r</sub> values in China and other regions with high N loads are lower than those for regions with lower N loads. Although the global mean EF<sub>5r</sub> value is comparable to the IPCC default value, the global EF<sub>5r</sub> values are highly skewed with large variations, indicating that adopting region-specific EF<sub>5r</sub> values rather than revising the fixed default value is more appropriate for the estimation of regional and global riverine N<sub>2</sub>O emissions. The ratio of dissolved organic carbon to nitrate (DOC/NO<sub>3</sub><sup>-</sup>) and NO<sub>3</sub><sup>-</sup> concentration are identified as the dominant predictors of region-specific EF<sub>5r</sub> values at both regional and global scales because stoichiometry and nutrients strictly regulate denitrification and N<sub>2</sub>O production efficiency in rivers. A multiple linear regression model using DOC/NO<sub>3</sub><sup>-</sup> and NO<sub>3</sub><sup>-</sup> is proposed to predict region-specific EF<sub>5r</sub> values. The good fit of the model associated with easily obtained water quality variables allows its widespread application. This study fills a key knowledge gap in predicting region-specific EF<sub>5r</sub> values at the global scale and provides a pathway to estimate global riverine N<sub>2</sub>O emissions more accurately based on IPCC methodology.</p> <p>This dataset is a global integrated N<sub>2</sub>O dataset including data from 4-year (2017-2020) in situ measurements of six large rivers in China, 3-year (2018-2020) in situ measurements of urban river networks in Beijing of China, and 825 measurements from 70 published papers over six continents. The data includes dissolved N<sub>2</sub>O concentration, biogeochemical (DOC, NO<sub>3</sub><sup>-</sup>, NH<sub>4</sub><sup>+</sup>, temperature, and DO), climatological (climate zones), and geographic (region, location, and land cover) information.</p>
Model data and code supporting "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "
<p>Model data and analysis code supporting the Geoscientific Model Development manuscript "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "</p> <p> </p> <p> </p>
A synthesis of nitric oxide emissions across global fertilized croplands from crop-specific emission factors
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