{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EDGAR Temporal profiles\n", "\n", "This tutorial demonstrates how to use the auxiliary temporal profiles from edgar.\n", "\n", "The function we will use is :py:func:`emiproc.inventories.edgar.temporal.read_edgar_auxilary_profiles`.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading EDGAR\n", "\n", "Similarly to the EDGAR tutorial, we first load the inventory.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Inventory(EDGARv8)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pathlib import Path\n", "from emiproc.inventories.edgar import download_edgar_files\n", "from emiproc.inventories.edgar import EDGARv8\n", "\n", "year = 2022\n", "\n", "local_dir = Path(\"./edgar\") / str(year)\n", "local_dir.mkdir(exist_ok=True, parents=True)\n", "\n", "# download_edgar_files(local_dir, year=year, substances=[\"CH4\", \"CO2\", \"CO2bio\"])\n", "inv = EDGARv8(\n", " local_dir / \"EDGAR_*.nc\",\n", " year=year,\n", " # Use short names, as this is how they are defined in the axilary table\n", " use_short_category_names=True,\n", ")\n", "inv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Download the auxilliary data\n", "\n", "Now you have to make a manual step to download the auxilliary data from EDGAR website.\n", "\n", "You can find the `auxilliary_tables.rar` file at https://edgar.jrc.ec.europa.eu/dataset_temp_profile \n", "\n", "Download it and extract its files in a local directory.\n", "You should have 4 `.csv` files." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read the profiles" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from emiproc.inventories.edgar.temporal import read_edgar_auxilary_profiles\n", "\n", "path_to_auxiliary_filesdir = Path(\"/home/coli/Data/emiproc/aux_edgar/\")\n", "profiles, indices = read_edgar_auxilary_profiles(\n", " auxiliary_filesdir=path_to_auxiliary_filesdir,\n", " inventory=inv,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What is in the profiles ?\n", "\n", "There are two components to emiproc profiles: \n", "* the `profiles` themselves, which are objects of different temporal profile classes. \n", "* the `indices`, a xarray dataset that tells for each sector/pollutant/gridcell/... which profile to use." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CompositeProfiles(76 profiles from ['HourOfWeekPerMonthProfile', 'WeeklyProfile'])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profiles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have 2 types of profiles:\n", "\n", "Weekly profiles that specify for each weekday how much of emissions are there.\n", "\n", "HourOfWeekPerMonth: which sounds scary, but it means that for each hour of a week\n", "there is a profile value and also for each month this value is different." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "Coordinates:\n",
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       "  * country   (country) object 2kB 'ABW' 'AFG' 'AGO' 'AIA' ... 'PSE' 'ATA' 'ATF'
" ], "text/plain": [ " Size: 45kB\n", "array([[40, 63, 52, ..., 3, 24, 24],\n", " [10, 39, 34, ..., 4, 16, 16],\n", " [40, 63, 52, ..., 3, 24, 24],\n", " ...,\n", " [40, 63, 52, ..., 3, 24, 24],\n", " [40, 63, 52, ..., 3, 24, 24],\n", " [40, 63, 52, ..., 3, 24, 24]], shape=(234, 24))\n", "Coordinates:\n", " * category (category) object 192B 'ENE' 'CHE' ... 'TNR_Aviation_CRS'\n", " * country (country) object 2kB 'ABW' 'AFG' 'AGO' 'AIA' ... 'PSE' 'ATA' 'ATF'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "indices" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the indices, we have a 2 dimensional array with country and category as dimensions.\n", "\n", "This means that each country and emission category can have a different temporal profile." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Set the profiles to the inventory\n", "\n", "Now that we have loaded the profiles, we can set them to the inventory." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "array([[40, 63, 52, ...,  3, 24, 24],\n",
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       "       [40, 63, 52, ...,  3, 24, 24],\n",
       "       ...,\n",
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       "Coordinates:\n",
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       "  * country   (country) object 2kB 'ABW' 'AFG' 'AGO' 'AIA' ... 'PSE' 'ATA' 'ATF'
" ], "text/plain": [ " Size: 45kB\n", "array([[40, 63, 52, ..., 3, 24, 24],\n", " [10, 39, 34, ..., 4, 16, 16],\n", " [40, 63, 52, ..., 3, 24, 24],\n", " ...,\n", " [40, 63, 52, ..., 3, 24, 24],\n", " [40, 63, 52, ..., 3, 24, 24],\n", " [40, 63, 52, ..., 3, 24, 24]], shape=(234, 24))\n", "Coordinates:\n", " * category (category) object 192B 'ENE' 'CHE' ... 'TNR_Aviation_CRS'\n", " * country (country) object 2kB 'ABW' 'AFG' 'AGO' 'AIA' ... 'PSE' 'ATA' 'ATF'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inv.set_profiles(\n", " profiles=profiles,\n", " indexes=indices,\n", ")\n", "inv.t_profiles_indexes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Grouping Categories\n", "\n", "When groupping categories, the profiles are automatically groupped. \n", "\n", "This is done by a weighted average, so that categories with more emissions have more weight in the average during groupping." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Coordinates:\n", " * category (category) object 32B 'agriculture' ... 'transportation'\n", " * country (country) object 2kB 'ABW' 'AFG' 'AGO' 'AIA' ... 'PSE' 'ATA' 'ATF'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from emiproc.inventories.utils import group_categories\n", "\n", "\n", "grouped = group_categories(\n", " inv,\n", " categories_group={\n", " \"agriculture\": [\n", " # Agriculture\n", " \"ENF\", # Enteric fermentation\n", " \"MNM\", # Manure management\n", " \"AWB\", # Agricultural waste burning\n", " \"AGS\", # Agricultural soils\n", " \"N2O\", # Indirect N2O emissions from agriculture\n", " \"IDE\", # Indirect emissions from NOx and NH3\n", " ],\n", " \"industry\": [\n", " \"ENE\", # Power Industry\n", " \"REF_TRF\", # Refineries\n", " \"IND\", # Combustion for manufacturing\n", " \"RCO\", # Energy for buildings\n", " \"PRO_FFF\", # Fuel exploitation\n", " \"NMM\", # Non-metallic minerals production\n", " \"CHE\", # Chemical processes\n", " \"IRO\", # Iron and steel production\n", " \"NFE\", # Non-ferrous metals production\n", " \"NEU\", # Non-energy use of fuels\n", " \"PRU_SOL\", # Solvents and products use\n", " ],\n", " \"waste\": [\n", " \"SWD_LDF\", # Solid waste landfills\n", " \"SWD_INC\", # Solid waste incineration\n", " \"WWT\", # Waste water handling\n", " ],\n", " \"transportation\": [\n", " \"TNR_Aviation_CDS\",\n", " \"TNR_Aviation_CRS\",\n", " \"TNR_Aviation_LTO\",\n", " \"TNR_Aviation_SPS\",\n", " \"TNR_Other\",\n", " \"TNR_Ship\",\n", " \"TRO\", # Road transport\n", " ],\n", " },\n", " ignore_missing=True,\n", ")\n", "grouped.t_profiles_indexes.coords\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the profiles are given for the grouped categories." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Remapping\n", "\n", "The profiles are defined per countries, but the inventory is given on a grid. \n", "If we want to export the inventory with the profiles, we can \n", "remap the profiles onto the grid of the inventory." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we will remap edgar to a coarser grid to speed up the process." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/coli/Documents/Projects/emiproc/emiproc/regrid.py:251: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", "\n", " gdf_weights.geometry_inter.area / gdf_weights.geometry.area\n" ] } ], "source": [ "from emiproc.grids import RegularGrid\n", "from emiproc.regrid import remap_inventory\n", "\n", "\n", "grid = RegularGrid(xmin=-12, xmax=48, ymin=32, ymax=72, nx=60, ny=40)\n", "remapped = remap_inventory(grouped, grid)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "89.6%" ] }, { "data": { "text/plain": [ "Coordinates:\n", " * category (category) object 32B 'agriculture' ... 'transportation'\n", " * cell (cell) int64 19kB 0 1 2 3 4 5 6 ... 2394 2395 2396 2397 2398 2399" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from emiproc.inventories.utils import country_to_cells\n", "\n", "\n", "celled = country_to_cells(remapped)\n", "celled.t_profiles_indexes.coords" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a profile for each cell of the inventory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate time series" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Coordinates:\n", " * substance (substance) ]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Output is kg/year/cell, so we need to convert the units to kg/h/cell\n", "from emiproc.profiles.temporal.constants import N_HOUR_YEAR\n", "\n", "da /= N_HOUR_YEAR\n", "\n", "da.sel(substance=\"CO2\", category=\"transportation\").sum(dim=\"cell\").plot(x='time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There we can see our temporal profile on the domain." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "In this tutorial, we learned how to read EDGAR temporal profiles and combine them to the inventory.\n", "\n", "If you have any questions, please refer to our [support page](https://emiproc.readthedocs.io/en/master/support.html#support)." ] } ], "metadata": { "kernelspec": { "display_name": "pyenv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 2 }