lair.air.inventories

Emissions Inventories

An emission(s) inventory is an accounting of the amount of pollutants discharged into the atmosphere. An emission inventory usually contains the total emissions for one or more specific greenhouse gases or air pollutants, originating from all source categories in a certain geographical area and within a specified time span, usually a specific year.

Module Attributes

INVENTORY_DIR	Inventory directory
DST_UNITS	Default destination units

Functions

molecular_weight(pollutant)	Calculate the molecular weight of a pollutant.
sum_sectors(data)	Sum emissions from all sectors in the dataset.

Classes

EDGAR(data, pollutant[, src_units, ...])	EDGAR - Emissions Database for Global Atmospheric Research
EDGARv7(pollutant)	EDGAR v7 - Global Greenhouse Gas Emissions
EDGARv8(pollutant[, time_step])	EDGAR v8 - Global Greenhouse Gas Emissions
EPA(data, pollutant[, src_units, time_step, ...])	United States Environmental Protection Agency (EPA) Gridded Methane Emissions
EPAv1([time_step])	EPA Gridded 2012 Methane Emissions
EPAv2([express, scale_by_month])	EPA U.S.
GFEI()	Global Fuel Exploitation Inventory (GFEI)
GFEIv1()	Global Fuel Exploitation Inventory v1
GFEIv2()	Global Fuel Exploitation Inventory v2
Inventory(data, pollutant[, src_units, ...])	Base class for inventories.
MultiModelInventory(data, pollutant[, ...])	Base class for inventories that are multi-model.
Vulcan([time_step, region])	The Vulcan Project
WetCHARTs([model])	WetCHARTs - Wetland Methane Emissions and Uncertainty

lair.air.inventories.INVENTORY_DIR = '/uufs/chpc.utah.edu/common/home/lin-group11/group_data/inventories'

Inventory directory

lair.air.inventories.DST_UNITS: str = 'kg km-2 d-1'

Default destination units

lair.air.inventories.molecular_weight(pollutant: str) → Quantity

Calculate the molecular weight of a pollutant.

Parameters:

pollutantstr

The pollutant.

Returns:

pint.Quantity

The molecular weight.

lair.air.inventories.sum_sectors(data: Dataset) → DataArray

Sum emissions from all sectors in the dataset.

Parameters:

dataxr.Dataset

The inventory data with emissions from multiple sectors as different variables.

Returns:

xr.DataArray

The sum of emissions from all sectors.

class lair.air.inventories.Inventory(data: str | Path | Dataset, pollutant: str, src_units: str | None = None, time_step: str = 'annual', crs: str = 'EPSG:4326', version: str | None = None)

Base class for inventories.

__init__(data: str | Path | Dataset, pollutant: str, src_units: str | None = None, time_step: str = 'annual', crs: str = 'EPSG:4326', version: str | None = None) → None

Initialize the inventory.

Parameters:

datastr | Path | xr.Dataset

The inventory data. If a string or Path, the path to the data. If an xr.Dataset, the data itself. Data should have dimensions of time, lat, lon and each variable should be a different emissions sector. If the source data is not in the correct format, the _process method should be overridden.

pollutantstr

The pollutant.

src_unitsstr, optional

The source units of the data, by default None. If None, the units are extracted from the data attributes.

time_stepstr, optional

The time step of the data, by default ‘annual’.

crsstr, optional

The CRS of the data, by default ‘EPSG:4326’.

versionstr, optional

The version of the inventory, by default None.

get_standard_name() → str

Get the standard name of the inventory.

Returns:

str

The standard name.

get_files() → None | list[Path]

Get the inventory files.

Returns:

None | list[Path]

The inventory files.

property gridcell_area: DataArray

Calculate the grid cell area in km^2.

Returns:

xr.DataArray

The grid cell area.

property absolute_emissions: Dataset

Calculate the absolute emissions (total per gridcell for time step by variable).

Returns:

xr.DataArray

The absolute emissions.

property total_emissions: DataArray

Calculate the total emissions by summation over all variables.

Returns:

xr.DataArray

The total emissions.

property data: Dataset

The inventory data.

Note

pint units have been dequantified and stored in variable attributes.

Returns:

xr.DataArray | xr.Dataset

The inventory data.

quantify() → Dataset

Quantify the data using pint units for each variable.

Returns:

xr.Dataset

The quantified data.

convert_units(dst_units: Any) → Self

Convert the units of the data to the desired output units.

Parameters:

dst_unitsAny

The destination units.

Returns:

xr.Dataset

The data with the units converted.

clip(bbox: tuple[float, float, float, float] | None = None, extent: tuple[float, float, float, float] | None = None, geom: Polygon | None = None, crs: Any = None, **kwargs: Any) → Self

Clip the data to the given bounds. Modifies the data in place.

Input bounds must be in the same CRS as the data.

Note

The result can be slightly different between supplying a geom and a bbox/extent. Clipping with a geom seems to be exclusive of the bounds, while clipping with a bbox/extent seems to be inclusive of the bounds.

Parameters:

bboxtuple[minx, miny, maxx, maxy]

The bounding box to clip the data to.

extenttuple[minx, maxx, miny, maxy]

The extent to clip the data to.

geomshapely.Polygon

The geometry to clip the data to.

crsAny

The CRS of the input geometries. If not provided, the CRS of the data is used.

kwargsAny

Additional keyword arguments to pass to the rioxarray clip method.

Returns:

Inventory

The clipped inventory.

regrid(out_grid: Dataset, method: Literal['conservative', 'conservative_normed'] = 'conservative') → Self

Regrid the data to a new grid. Uses xesmf for regridding.

Note

At present, xesmf only supports regridding lat-lon grids. self.data must be on a lat-lon grid. Possibly could use xesmf.frontend.BaseRegridder to regrid to a generic grid.

Warning

xarray.Dataset.cf.add_bounds is known to have issues, including near the 180th meridian. Care should be taken when using this method, especially with global datasets.

Parameters:

out_gridxr.DataArray

The new grid to resample to. Must be a lat-lon grid.

methodstr, optional

The regridding method, by default ‘conservative’.

Note

Other xesmf regrid methods can be passed, but it is highly encouraged to use a conservative method for fluxes.

Returns:

Inventory

The regridded inventory.

resample(resolution: float | tuple[float, float], regrid_method: Literal['conservative', 'conservative_normed'] = 'conservative') → Self

Resample the data to a new resolution.

Parameters:

resolutionfloat | tuple[x_res, y_res]

The new resolution in degrees. If a single value is provided, the resolution is assumed to be the same in both dimensions.

regrid_methodstr, optional

The regridding method, by default ‘conservative’.

Returns:

Inventory

The resampled inventory.

reproject(resolution: float | tuple[float, float], regrid_method: Literal['conservative', 'conservative_normed'] = 'conservative') → Self

Reproject the data to a lat lon rectilinear grid.

Parameters:

resolutionfloat | tuple[x_res, y_res]

The new resolution in degrees. If a single value is provided, the resolution is assumed to be the same in both dimensions.

regrid_methodstr, optional

The regridding method, by default ‘conservative’.

Returns:

Inventory

The reprojected inventory.

integrate() → DataArray

Integrate the data over the spatial dimensions to get the total emissions per time step.

Returns:

xr.DataArray

The integrated data.

plot(ax: Axes | None = None, time: str | int = 'mean', sector: str | None = None, **kwargs) → Axes

Plot the inventory data.

Parameters:

axmatplotlib.axes.Axes

The axes to plot on.

timestr | int, optional

The time step to plot, by default ‘mean’. If ‘mean’, the mean emissions are plotted. Otherwise pass a string selector or integer index.

sectorstr, optional

The sector to plot, by default None. If None, the total emissions are plotted.

kwargsdict

Additional keyword arguments to pass to xarray’s plot method.

Returns:

matplotlib.axes.Axes

The axes with the plot.

class lair.air.inventories.MultiModelInventory(data: str | Path | Dataset, pollutant: str, src_units: str | None = None, time_step: str = 'annual', crs: str = 'EPSG:4326', version: str | None = None, model: str | None = None)

Base class for inventories that are multi-model.

__init__(data: str | Path | Dataset, pollutant: str, src_units: str | None = None, time_step: str = 'annual', crs: str = 'EPSG:4326', version: str | None = None, model: str | None = None) → None

Initialize the multi-model inventory.

Parameters:

datastr | Path | xr.Dataset

The inventory data. If a string or Path, the path to the data. If an xr.Dataset, the data itself. Data should have dimensions of time, lat, lon and each variable should be a different emissions sector. If the source data is not in the correct format, the _process method should be overridden.

pollutantstr

The pollutant.

src_unitsstr, optional

The source units of the data, by default None. If None, the units are extracted from the data attributes.

time_stepstr, optional

The time step of the data, by default ‘annual’.

crsstr, optional

The CRS of the data, by default ‘EPSG:4326’.

versionstr, optional

The version of the inventory, by default None.

modelstr, optional

The model to select from the multimodel data, by default None. If None, the mean of all models is used.

class lair.air.inventories.EDGAR(data: str | Path | Dataset, pollutant: str, src_units: str | None = None, time_step: str = 'annual', crs: str = 'EPSG:4326', version: str | None = None)

EDGAR - Emissions Database for Global Atmospheric Research

https://edgar.jrc.ec.europa.eu/

EDGAR is a multipurpose, independent, global database of anthropogenic emissions of greenhouse gases and air pollution on Earth. EDGAR provides independent emission estimates compared to what reported by European Member States or by Parties under the United Nations Framework Convention on Climate Change (UNFCCC), using international statistics and a consistent IPCC methodology.

EDGAR provides both emissions as national totals and gridmaps at 0.1 x 0.1 degree resolution at global level, with yearly, monthly and up to hourly data.

get_files() → list[Path]

Recursively get the inventory files for each sector.

Returns:

list[Path]

The inventory files.

get_sector_name(sector: str) → str

Generate a formatted name from the sector description.

Parameters:

sectorstr

The key of the sector in the sectors dictionary.

Returns:

str

The formatted name.

class lair.air.inventories.EDGARv7(pollutant: str)

EDGAR v7 - Global Greenhouse Gas Emissions

https://edgar.jrc.ec.europa.eu/dataset_ghg70

EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database (a collaboration between the European Commission, Joint Research Centre (JRC), the International Energy Agency (IEA), and comprising IEA-EDGAR CO2, EDGAR CH4, EDGAR N2O, EDGAR F-GASES version 7.0, (2022) European Commission, JRC (Datasets).

__init__(pollutant: str) → None

Initialize the EDGAR inventory.

Parameters:

pollutantstr

The pollutant.

class lair.air.inventories.EDGARv8(pollutant: str, time_step: Literal['annual', 'monthly'] = 'annual')

EDGAR v8 - Global Greenhouse Gas Emissions

https://edgar.jrc.ec.europa.eu/dataset_ghg80

EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, a collaboration between the European Commission, Joint Research Centre (JRC), the International Energy Agency (IEA), and comprising IEA-EDGAR CO2, EDGAR CH4, EDGAR N2O, EDGAR F-GASES version 8.0, (2023) European Commission, JRC (Datasets).

__init__(pollutant: str, time_step: Literal['annual', 'monthly'] = 'annual')

Initialize the EDGAR inventory.

Parameters:

pollutantstr

The pollutant.

time_stepLiteral[‘annual’, ‘monthly’], optional

The time step of the data, by default ‘annual’.

class lair.air.inventories.EPA(data: str | Path | Dataset, pollutant: str, src_units: str | None = None, time_step: str = 'annual', crs: str = 'EPSG:4326', version: str | None = None)

United States Environmental Protection Agency (EPA) Gridded Methane Emissions

https://www.epa.gov/ghgemissions/us-gridded-methane-emissions

The gridded EPA U.S. methane greenhouse gas inventory (gridded methane GHGI) includes time series of annual methane emission maps with 0.1° x 0.1° (~ 10 x 10 km) spatial and monthly temporal resolution for the contiguous United State (CONUS). This gridded methane inventory is designed to be consistent with methane emissions from the U.S. EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (U.S. GHGI).

class lair.air.inventories.EPAv1(time_step='Annual')

EPA Gridded 2012 Methane Emissions

https://www.epa.gov/ghgemissions/gridded-2012-methane-emissions

Maasakkers JD, Jacob DJ, Sulprizio MP, Turner AJ, Weitz M, Wirth T, Hight C, DeFigueiredo M, Desai M, Schmeltz R, Hockstad L, Bloom AA, Bowman KW, Jeong S, Fischer ML. Gridded National Inventory of U.S. Methane Emissions. Environ Sci Technol. 2016 Dec 6;50(23):13123-13133. doi: 10.1021/acs.est.6b02878. Epub 2016 Nov 16. PMID: 27934278.

__init__(time_step='Annual') → None

Initialize the EPA inventory.

Parameters:

time_stepstr, optional

The time step of the data, by default ‘Annual’.

class lair.air.inventories.EPAv2(express: bool = False, scale_by_month: bool = False)

EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory

https://zenodo.org/records/8367082

McDuffie, Erin, E., Maasakkers, Joannes, D., Sulprizio, Melissa, P., Chen, C., Schultz, M., Brunelle, L., Thrush, R., Steller, J., Sherry, C., Jacob, Daniel, J., Jeong, S., Irving, B., & Weitz, M. (2023). Gridded EPA U.S. Anthropogenic Methane Greenhouse Gas Inventory (gridded GHGI) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8367082

__init__(express: bool = False, scale_by_month: bool = False) → None

Initialize the EPA inventory.

Parameters:

expressbool, optional

Whether to use the express extension, by default False.

scale_by_monthbool, optional

Whether to scale emissions by month, by default False.

get_monthly_scale_factors() → Dataset

Get the monthly scale factors.

Returns:

xr.Dataset

The monthly scale factors.

class lair.air.inventories.GFEI

Global Fuel Exploitation Inventory (GFEI)

https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HH4EUM

This is the Global Fuel Exploitation Inventory (GFEI) which provides a 0.1 x 0.1 degree grid of methane emissions of fugitive emissions related to oil, gas, and coal activities (IPCC Sector 1B1 and 1B2).

__init__() → None

Initialize the GFEI inventory.

get_files() → list[Path]

Get the inventory files.

Returns:

None | list[Path]

The inventory files.

class lair.air.inventories.GFEIv1

Global Fuel Exploitation Inventory v1

https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HH4EUM&version=1.0

Scarpelli, T. R., Jacob, D. J., Maasakkers, J. D., Sulprizio, M. P., Sheng, J.-X., Rose, K., Romeo, L., Worden, J. R., and Janssens-Maenhout, G.: A global gridded (0.1° × 0.1°) inventory of methane emissions from oil, gas, and coal exploitation based on national reports to the United Nations Framework Convention on Climate Change, Earth Syst. Sci. Data, 12, 563–575, https://doi.org/10.5194/essd-12-563-2020, 2020a.

get_standard_deviations(kind: Literal['relative', 'geometric']) → Dataset

Get the standard deviations.

Parameters:

kindLiteral[‘relative’, ‘geometric’]

The kind of standard deviation to get (‘relative’, ‘geometric’).

Returns:

xr.Dataset

The standard deviations.

class lair.air.inventories.GFEIv2

Global Fuel Exploitation Inventory v2

https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HH4EUM&version=2.0

Scarpelli, T. R., Jacob, D. J., Grossman, S., Lu, X., Qu, Z., Sulprizio, M. P., Zhang, Y., Reuland, F., Gordon, D., and Worden, J. R.: Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: evaluation with inversions of atmospheric methane observations, Atmos. Chem. Phys., 22, 3235–3249, https://doi.org/10.5194/acp-22-3235-2022, 2022.

class lair.air.inventories.Vulcan(time_step: Literal['annual', 'hourly'] = 'annual', region: Literal['US', 'AK'] = 'US')

The Vulcan Project

https://vulcan.rc.nau.edu/ v3: https://daac.ornl.gov/NACP/guides/Vulcan_V3_Annual_Emissions.html

The Vulcan Project quantifies all fossil fuel CO2 emissions for the entire United States at high space- and time-resolution with details on economic sector, fuel, and combustion process. It was created by the research team of Dr. Kevin Robert Gurney at Northern Arizona University.

Gurney, K.R., J. Liang, R. Patarasuk, Y. Song, J. Huang, and G. Roest. 2019. Vulcan: High-Resolution Annual Fossil Fuel CO2 Emissions in USA, 2010-2015, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1741

__init__(time_step: Literal['annual', 'hourly'] = 'annual', region: Literal['US', 'AK'] = 'US') → None

Initialize the Vulcan inventory.

Parameters:

time_stepLiteral[‘annual’, ‘hourly’], optional

The time step of the data, by default ‘annual’.

regionLiteral[‘US’, ‘AK’], optional

The region of the data, by default ‘US’.

get_files(uncertainty='central') → list[Path]

Get the inventory files.

Returns:

None | list[Path]

The inventory files.

reproject(resolution: float | tuple[float, float] = 0.01, regrid_method: Literal['conservative', 'conservative_normed'] = 'conservative', force: bool = False) → Self

Reproject the data to a lat lon rectilinear grid.

Tip

This method is memory intensive and may require a lot of RAM. It is highly recommended to clip the data first.

Parameters:

resolutionfloat | tuple[x_res, y_res]

The new resolution in degrees. If a single value is provided, the resolution is assumed to be the same in both dimensions.

forcebool

Whether to override the clipping requirement

Returns:

Inventory

The reprojected inventory.

class lair.air.inventories.WetCHARTs(model: str | None = None)

WetCHARTs - Wetland Methane Emissions and Uncertainty

https://daac.ornl.gov/CMS/guides/MonthlyWetland_CH4_WetCHARTs.html

This dataset provides global monthly wetland methane (CH4) emissions estimates at 0.5 by 0.5-degree resolution for the period 2001-2019 that were derived from an ensemble of multiple terrestrial biosphere models, wetland extent scenarios, and CH4:C temperature dependencies that encompass the main sources of uncertainty in wetland CH4 emissions. There are 18 model configurations. WetCHARTs v1.3.1 is an updated product of WetCHARTs v1.0 Extended Ensemble.

Bloom, A.A., K.W. Bowman, M. Lee, A.J. Turner, R. Schroeder, J.R. Worden, R.J. Weidner, K.C. McDonald, and D.J. Jacob. 2021. CMS: Global 0.5-deg Wetland Methane Emissions and Uncertainty (WetCHARTs v1.3.1). ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1915

__init__(model: str | None = None)

Initialize the WetCHARTs inventory.

Parameters:

modelstr | None, optional

The model to select, by default None. If None, the mean of all models is used.