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60 results for “Emission Inventories”
Gridded HONO emission inventory in mainland China
<p>We produce and provide a HONO emission inventory in mainland China in 2016. The emission inventory have been developed with 1/12 by 1/12 degree spatial resolution. The unit of the emission inventory is t/grid/year. </p>
County-level of particle and gases emission inventory for animal dung burning in the Qinghai–Tibetan Plateau, China
<p><strong>County-level of particle and gases emission inventory for animal dung burning in the Qinghai–Tibetan Plateau, ChinaCounty-level of particle and gases emission inventory for animal dung burning in the Qinghai–Tibetan Plateau, China</strong></p>
China Soil NOx Emission Inventory
<p>A high-resolution emission inventory of soil NO<sub>x</sub> from agricultural areas in China.</p>
A 130-year global inventory of methane emissions from livestock: trends, patterns, and drivers
<p>Livestock contributes approximately one-third of global anthropogenic methane (CH4) emissions. Quantifying the spatial and temporal variations of these emissions is crucial for climate change mitigation. Although country-level information is reported regularly through national inventories and global databases, spatially-explicit quantification of century-long dynamics of CH4 emissions from livestock has been poorly investigated. Using the Tier 2 method adopted from the 2019 Refinement to 2006 IPCC guidelines, we estimated CH4 emissions from global livestock at a spatial resolution of 0.083° (~ 9 km at the equator) during the period 1890−2019. We find that global CH4 emissions from livestock increased from 31.8 [26.5−37.1] (mean [minimum−maximum of 95% confidence interval) Tg CH4 yr-1 in 1890 to 131.7 [109.6−153.7] Tg CH4 yr-1 in 2019, a fourfold increase in the past 130 years. The growth in global CH4 emissions mostly occurred after 1950 and was mainly attributed to the cattle sector. Our estimate shows faster growth in livestock CH4 emissions as compared to the previous Tier 1 estimates and is ~20% higher than the estimate from FAOSTAT for the year 2019. Regionally, South Asia, Brazil, North Africa, China, the United States, Western Europe, and Equatorial Africa shared the majority of the global emissions in the 2010s. South Asia, tropical Africa, and Brazil have dominated the growth in global CH4 emissions from livestock in the recent three decades. Changes in livestock CH4 emissions were primarily associated with changes in population and national income and were also affected by the policy, diet shifts, livestock productivity improvement, and international trade. The new geospatial information on the magnitude and trends of livestock CH4 emissions identifies emission hotspots and spatial-temporal patterns, which will help to guide meaningful CH4 mitigation practices in the livestock sector at both local and global scales.</p>
2019 CEDS posterior ammonia emissions inventory
<p>This posterior inventory is derived from the ammonia emissions inversion method based on 4DEnVar.</p>
Four-dimensional aircraft emission inventory dataset of Landing and take-off cycle in China from 2019 to 2023
<p>Aircraft emissions during landing and takeoff (LTO) having unique three-dimensional spatial characteristics and typical hourly temporal variations. In order to further investigate the adverse effects of aircraft emissions, the adverse effects for aircraft emissions, this study integrated the emission calculation and flight trajectory recognition methods to establish a four-dimensional aircraft emission inventory dataset of China’s LTO cycle (4D-LTO emission inventory dataset) from 2019 to 2023. The dataset has a high spatial-temporal resolution (hourly, 0.03° × 0.03° × 34 height layers).</p> <p>Information for 4D-LTO emission inventory dataset during 2019-2023:</p> <p>Species: NOx.</p> <p>Number of airports included in different years: 2019 (237 airports), 2020 (239 airports), 2021 (248 airports), 2022 (254 airports), 2023 (257 airports).</p> <p>Temporal information: 2019 (8760 hours), 2020 (8784 hours), 2021 (8760 hours), 2022 (8760 hours), 2023 (8760 hours).</p> <p>Spatial information: The horizontal resolution of the 4D-LTO emission inventory is 0.03° × 0.03° with the latitude and longitude range of 3.40°N–53.56°N and 73.44°E–135.09°E, respectively. The altitude resolution was divided into 34 layers from 0 m to 15668 m (0.0 m–38.3 m, 38.3 m–76.7 m, 76.7 m–115.3 m, 115.3 m–154 m, 154 m–231.8 m, 231.8 m–310.3 m, 310.3 m–389.3 m, 389.3 m–469 m, 469 m–549.3 m, 549.3 m–630.3 m, 630.3 m–711.9 m, 711.9 m–794.2 m, 794.2 m–960.7 m, 960.7 m–1130.1 m, 1130.1 m–1302.3 m, 1302.3 m–1477.6 m, 1477.6 m–1656.0 m, 1656.0 m–1929.7 m, 1929.7 m–2211.1 m, 2211.1 m–2599.3 m, 2599.3 m–3107.2 m, 3107.2 m–3643.1 m, 3643.1 m–4210.5 m, 4210.5 m–4813.9 m, 4813.9 m–5458.5 m, 5458.5 m–6151.2 m, 6151.2 m–6900.4 m, 6900.4 m–7717.4 m, 7717.4 m–8617.3 m, 8617.3 m–9621.2 m, 9621.2 m–10759.7 m, 10759.7 m–12080.6 m, 12080.6 m–13664.8 m, 13664.8 m–15668 m.).</p>
Inventory and GHG emissions for Milk Production Systems from systematic review
<p>Inventory table created from a systematic review on LCA of milk production systems in Europe and the emissions from animals and soils and background emissions obtained from the proof of concept.</p>
A 130-year global inventory of methane emissions from livestock: trends, patterns, and drivers
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National Emissions Inventory crosswalk for the InMAP Source-Receptor Matrix (ISRM) dataset
<p>The InMAP Source-Receptor Matrix (ISRM) estimates the air quality impacts of emissions released from any source location in the contiguous United States to any receptor location. Specifically, the values in the ISRM dataset are the change in PM<sub>2.5</sub> concentration (µg m<sup>-3</sup>) in any receptor grid cell per unit of emissions (µg sec<sup>-1</sup>) in any source grid cell. ISRM was created from repeated runs of the Intervention Model for Air Pollution (<a href="http://spatialmodel.com/inmap/">InMAP</a>), isolating the impact of emissions from every grid cell in InMAP and from three emission heights representing ground-level, low-stack, and high-stack emissions. A file of the marginal impacts of emissions from each source location is also included ("marginal_values.csv"), which summarizes the estimates in ISRM by each source grid cell in terms of monetary damages ($ tonne<sup>-1</sup>), increased mortality (deaths tonne<sup>-1</sup>), and population exposure (population*µg m<sup>-3</sup> tonne<sup>-1</sup>).</p> <p>These files join the US EPA National Emissions Inventory to the ISRM to facilitate future analyses. The original ISRM file can be found here: <a href="https://zenodo.org/record/3590127#.Xt0Z4GhKhPb">https://zenodo.org/record/3590127#.Xt0Z4GhKhPb</a></p>
LQ/China's coal methane emission inventory
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Diagnosing uncertainties in global biomass burning emission inventories and their impact on modeled air pollutants
<p>the data and codes I used to plot figures </p>
The high-resolution Global Aviation emissions Inventory based on ADS-B (GAIA) for 2019 - 2021: Low-resolution gridded outputs for 2019 - 2021
<p><strong>The high-resolution Global Aviation emissions Inventory based on ADS-B (GAIA) for 2019 – 2021: Low-resolution gridded outputs for 2019 - 2021</strong></p> <p><strong>Roger Teoh, Zebediah Engberg, Marc Shapiro, Lynnette Dray and Marc E.J. Stettler</strong></p> <p>These files contain the low-resolution monthly sum of the global flight distance flown, fuel consumption, and various pollutants from 2019 to 2021. The data is provided in a 4D grid with spatiotemporal resolution of 0.5° (longitude) x 0.5° (latitude), at altitude intervals of 1000 feet, and at a monthly temporal resolution.</p> <p>The global air traffic activity in GAIA was derived from Spire Aviation data. Non-commercial use and research purposes only.</p> <p>For further details, see README.txt</p> <p><strong>Change log</strong></p> <p>- 6-June-2023: The missing file "2021-08-monthly.nc" has been added and can be found in "Zenodo.zip"</p>
NACP MCI: CO2 Emissions Inventory, Upper Midwest Region, USA., 2007
This data set provides a bottom-up CO2 emissions inventory for the mid-continent region of the United States for the year 2007. The study was undertaken as part of the North American Carbon Program (NACP) Mid-Continent Intensive (MCI) campaign. Emissions for the MCI region were compiled from these resources into nine inventory sources (Table 1):(1) forest biomass and soil carbon, harvested woody products carbon, and agricultural soil carbon from the U.S. Greenhouse Gas (GHG) Inventory (EPA, 2010; Heath et al., 2011);(2) high resolution data on fossil and biofuel CO2 emissions from Vulcan (Gurney et al,. 2009); (3) CO2 uptake by agricultural crops, lateral transport in crop biomass harvest, and livestock CO2 emissions using USDA statistics (West et al., 2011); (4) agricultural residue burning (McCarty et al., 2011);(5) CO2 emissions from landfills (EPA, 2012);(6) and CO2 losses from human respiration using U.S. Census data (West et al., 2009). The CO2 inventory in the MCI region was dominated by fossil fuel combustion, carbon uptake during crop production, carbon export in biomass (commodities) from the region, and to a lesser extent, carbon sinks in forest growth and incorporation of carbon into timber products.
Global Inventory of Methane Emissions from Fuel Exploitation V1 (GFEI_CH4)
This is a global inventory of methane emissions from fuel exploitation (GFEI) created for the NASA Carbon Monitoring System (CMS). The emission sources represented in this dataset include fugitive emission sources from oil, gas, and coal exploitation following IPCC 2006 definitions and are estimated using bottom-up methods. The inventory emissions are based on individual country reports submitted in accordance with the United Nations Framework Convention on Climate Change (UNFCCC). For those countries that do not report, the emissions are estimated following IPCC 2006 methods. Emissions are allocated to infrastructure locations including mines, wells, pipelines, compressor stations, storage facilities, processing plants, and refineries. The purpose of the inventory is to be used as a prior estimate of fuel exploitation emissions in inverse modeling of atmospheric methane observations. GFEI only includes fugitive methane emissions from oil, gas, and coal exploitation activities and does not include any combustion emissions as defined in IPCC 2006 category 1A. The CMS program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. The System uses NASA observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.
On-road vehicle emission inventory and its spatio-temporal variations in North China Plain
<p>The estimated BC, CO, NH<sub>3</sub>, NMVOCs, NO<sub>x</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>, and SO<sub>2</sub> emissions by each vehicle type, fuel type, and national emission standard in 53 cities in North China Plain. (Unit: tons)</p> <p> </p> <p><strong>To cite the data:</strong> Jiang, P., Zhong, X., Li, L., 2020. On-road vehicle emission inventory and its spatio-temporal variations in North China Plain. Environ. Pollut. 267, 115639. https://doi.org/10.1016/j.envpol.2020.115639.</p>
Historical Emission Inventory Database for Shipping
<p><span>The global historical shipping emission inventory dataset covers CO<sub>2</sub> and five key </span><span>atmospheric pollutants (</span><span>NO<em><sub>x</sub></em>, SO<sub>2</sub>, CO, NMVOCs and PM) over the time period from 1970 – 2021 and has been allocated to 0.1</span><span>°×</span><span>0.1</span><span>°</span><span> grids on a bilateral basis.</span><span> </span></p>
Negative Emissions Technologies and Practices Life Cycle Inventories (NEGEM-WP1)
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EMAC/MESSy v2.54.0 model (EMAC) aviation emission inventories as calculated by AirTraf 2.0
<p>The data provided is a ASCII file which contains an aviation emission inventories for one year of a traffic sample over Europe (85 flights) as calculated by AirTraf submodel within the global modelling system EMAC. The submodel AirTraf enabling aircraft trajectory planning is implemented in the chemistry-climate model EMAC with the coupling to the submodel AirTraf. EMAC is a numerical chemistry and climate simulation system that includes submodels describing tropospheric and middle atmosphere processes and their interaction with oceans, land, and influences coming from anthropogenic emissions. It comprises the second version of the Modular Earth Submodel System (MESSy2) to link multi-institutional computer codes, in which the core atmospheric model is the fifth generation European Center Hamburg general circulation model (ECHAM5). We use a horizontal resolution of T42, with 31 hybrid vertical pressure levels up to 10 hPa (~30 km, T42L31ECMWF) and a time step of 20 minutes, with meteorology nudged to ERA5 reanalysis data as boundary conditions. AirTraf allows to calculate aircraft trajectories for given city-pairs with respect to dedicated routing strategies, considering meteorological conditions calculated by ECHAM5 online. Associated with these identified aircraft trajectories, the climate effect from aviation emissions along these trajectories is calculated from the submodel ACCF version 1.0 of EMAC. ACCF employs the aCCFs, that provide spatially and temporally resolved information on the climate effects of aviation emissions to quantify CO<sub>2</sub> and non-CO<sub>2</sub> effects. Specifically, they allow identifying regions of the atmosphere where aviation emissions induce a strong climate effect, e.g. via the formation of warming contrails or the production of radiatively active species like ozone. Thus, using these aCCFs, the estimated climate effect of aviation emissions and their spatial and temporal variability is available for the model domain: Subsequently, this is provided to AirTraf in order to enable not only estimating climate effects for calculated aircraft trajectories, but also planning of climate-optimized flight trajectories. The combination of EMAC/AirTraf/ACCF is applied here to simulate flight trajectories as great circle routes between city pairs (equal to AirClim), considering the variability of synoptic weather patterns in a continuous representation of the global atmosphere, and to quantitatively assess total climate effect and overall performance of flights.</p>
prteixeira/MOVEIM: A detailed bottom-up motor vehicular emission inventory for Manaus city
<p>A detailed bottom-up motor vehicular emission inventory developed for Manaus, the capital of Amazonas (Brazil), based on local information and on the city scale.</p>
WRF-Chem configurations and input data sets for sensitivity tests of emission inventories
<p>WRF-Chem and WPS v4.4 source codes and their configurations with namelist files.</p> <p>Emission inventory data sets (EDGAR-HTAP v2 and v3) for 'anthro_emis' input are included.</p> <p>The KORUS v5 emission data are provided with 'wrfchemi' format.</p> <p>The 'namelist.input' contains physics and chemistry options that are used for WRF-Chem model.</p> <p>The model grid information is available in 'namelist.wps'.</p> <p> </p> <p>Kim, K.-M., Kim, S.-W., Seo, S., Blake, D. R., Cho, S., Crawford, J. H., Emmons, L., Fried, A., Herman, J. R., Hong, J., Jung, J., Pfister, G., Weinheimer, A. J., Woo, J.-H., and Zhang, Q.: Sensitivity of the WRF-Chem v4.4 ozone, formaldehyde, and precursor simulations to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-132, in review, 2023.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.