Emissions

Emissions#

Run the tutorial via free cloud platforms:

Learning objectives#

In this notebook we use the cams-global-emission-inventories dataset. The dataset contains anthropogenic and natural emissions. We’ll focus on nitrogen oxides (NOx). These come from several sources: agriculture, ships, power generation, road traffic, industry.

Initial setup#

Before we begin we must prepare our environment. This includes installing the Application Programming Interface (API) of the Climate Data Store (CDS), intalling any other packages not already installed, setting up our CDS API credentials and importing the various Python libraries that we will need.

# Ensure that the cdsapi package is installed
!pip install -q cdsapi

# If you are running this notebook in Colab, uncomment the line below and run this cell.
#!pip install cartopy

Add your ADS API credentials#

To set up your ADS API credentials, please login/register on the ADS, then you will see your unique API key here.

You can add this API key to your current session by replacing ######### in the code cell below with your API key.

import os
os.environ['CDSAPI_URL'] = 'https://ads.atmosphere.copernicus.eu/api'
os.environ['CDSAPI_KEY'] = '###########################################'

Import libraries#

import numpy as np
import xarray as xr
import pandas as pd
import matplotlib.pyplot as plt
import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.colors as mcol
from matplotlib.colors import ListedColormap
import calendar
from zipfile import ZipFile
import warnings
warnings.filterwarnings('ignore')

Here we specify a data directory in which we will download our data and all output files that we will generate:

DATADIR = '.'

Explore and download data#

Visit the download form for the CAMS global emission inventories data. View the parameters in the API script in the following cell and select the corresponding options. For each year there will be a netcdf file inside the zip that will be downloaded. The size of the file for one year is 370 MB. The data is available from year 2000 up to 2020.

At the end of the download form, select “Show API request”. This will reveal a block of code, which should be identical to the code cell below.

Please remember to accept the terms and conditions at the bottom of the download form.

variable = 'nitrogen_oxides'
source = 'anthropogenic'
start_year = '2000'
#years = (np.array('2000', dtype=int) + np.arange(2)).tolist()
years = ['2000', '2020']

import cdsapi
c = cdsapi.Client()

c.retrieve(
    'cams-global-emission-inventories',
    {
        'variable': variable,
        'source': [
            source,
        ],
        'version': 'latest',
        'year': years,
        'format': 'zip',
    },
    f'{DATADIR}/download.zip')

2024-09-17 13:30:02,488 INFO Request ID is 69a335e3-df11-4417-b85e-2815e5ae4c06
2024-09-17 13:30:02,578 INFO status has been updated to accepted
2024-09-17 13:30:04,153 INFO status has been updated to running
2024-09-17 13:30:06,483 INFO status has been updated to successful

'./download.zip'

with ZipFile(f'{DATADIR}/download.zip', 'r') as zipObj:
   # Extract all the contents of zip file in current directory
   zipObj.extractall(path=f'{DATADIR}')

os.remove(f'{DATADIR}/download.zip')

Visualizations#

We plot the data for the sum of emission from all sectors for each month. We define a discrete color map to visualize the values in the map.

dark_red = '#971039'
red = '#D93322'
orange = '#F09235'
dark_yellow = '#F5BE41'
yellow = '#ECD347'
light_yellow = '#FFFF00'
green = '#5AAA5F'
light_green = '#A7D25C'
cyan = '#7EFEE7'
light_blue = '#92D2F9'
blue = '#0B2CA1'
white = '#FFFFFF'
colors = [white, blue, light_blue, cyan, light_green, green, light_yellow, yellow, dark_yellow, orange, red, dark_red]
levels = [4e-18, 4e-17, 4e-16, 4e-15, 4e-14, 4e-13, 4e-12, 4e-11, 4e-10, 4e-9, 4e-8, 4e-7]
#level_ticks = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
emissions_cmap = ListedColormap(colors)

calendar.month_name[1]

'January'

def plot_emissions(da, year, month):
    maplon = [-180,180]
    maplat = [-90,90]
    lonmin = maplon[0]
    lonmax = maplon[1]
    latmin = maplat[0]
    latmax = maplat[1]
    plt.figure(figsize=(20,10))
    ax = plt.axes(projection=ccrs.PlateCarree())
    ax.gridlines(draw_labels=True, linestyle='--')
    ax.add_feature(cartopy.feature.LAND, edgecolor='black')
    ax.coastlines()
    ax.set_extent([lonmin,lonmax,latmin,latmax], crs=ccrs.PlateCarree()) 
    da_plt = da.sel(time = month).plot(ax=ax, add_colorbar=False, cmap=emissions_cmap, levels=levels, vmin=0.0, robust=True) # 'YlOrRd'
    cbar = plt.colorbar(da_plt, fraction=0.025, pad=0.05) 
    cbar.set_label('micrograms')
    cbar.set_ticks(levels)
    cbar.set_ticklabels(levels)
    plt.title('Emissions of NOx from all sectors - {:s} {:s} '.format(calendar.month_name[month + 1], year))

plot_emissions(nox2000_all_sectors_da, '2000', 0)

_images/5ae0ddbe56caebdbbbf1dad89ad823e2b131fecc1abcaf2380beda950683d3f6.png

plot_emissions(nox2020_all_sectors_da, '2020', 0)

_images/3cb65ba34d517b80c63ac425885c1d76465916501f9392328e2e878862a87b04.png

Total flux January-December#

tot_nox_2020 = nox2020_all_sectors_da.sel(time=slice(0,11)).sum(dim='time')
tot_nox_2020.max()

<xarray.DataArray ()> Size: 4B
array(3.1515065e-06, dtype=float32)

def plot_total_emissions(da, year):
    maplon = [-180,180]
    maplat = [-90,90]
    lonmin = maplon[0]
    lonmax = maplon[1]
    latmin = maplat[0]
    latmax = maplat[1]
    plt.figure(figsize=(20,10))
    ax = plt.axes(projection=ccrs.PlateCarree())
    ax.gridlines(draw_labels=True, linestyle='--')
    ax.add_feature(cartopy.feature.OCEAN)
    ax.add_feature(cartopy.feature.LAND, edgecolor='black')
    ax.coastlines()
    ax.set_extent([lonmin,lonmax,latmin,latmax], crs=ccrs.PlateCarree()) 
    da_plt = da.plot(ax=ax, add_colorbar=False, cmap=emissions_cmap, levels=levels, vmin=0.0, robust=True) # 'YlOrRd'
    cbar = plt.colorbar(da_plt, fraction=0.025, pad=0.05) 
    cbar.set_label('micrograms')
    cbar.set_ticks(levels)
    cbar.set_ticklabels(levels)
    plt.title('Emissions of NOx from all sectors - {:s} Jan-Dec.'.format(year))

plot_total_emissions(tot_nox_2020, '2020')

_images/48f17800a5bb5156b694f9441d83f53d5ff27ee7a7fbed0f4806b2dc7efb0a55.png

Total flux for 2000 and 2020#

We calculate the total emissions from all sector for each year

tot_nox_2000 = nox2000_all_sectors_da.sel(time=slice(0,11)).sum()
tot_nox_2000

<xarray.DataArray ()> Size: 4B
array(0.00049035, dtype=float32)

tot_nox_2020 = nox2020_all_sectors_da.sel(time=slice(0,11)).sum()
tot_nox_2020

<xarray.DataArray ()> Size: 4B
array(0.00053885, dtype=float32)

fig, ax = plt.subplots()

tot_emissions = [tot_nox_2000, tot_nox_2020]
bar_labels = ['2000', '2020']
bar_colors = ['tab:red', 'tab:blue']

ax.bar(years, tot_emissions, label=bar_labels, color=bar_colors)

ax.set_ylabel('kg m-2 s-1')
ax.set_title('Total NOx emissions')
#ax.legend(title='Year')

plt.show()

_images/820d8e7b1455932814b032194cb5cff05fa201d4d2f4ab5c3a992d60a8ebcb7b.png

Emissions

Contents

Emissions#

Learning objectives#

Initial setup#

Add your ADS API credentials#

Import libraries#

Explore and download data#

Emissions of NOx from all sectors#

NOx all sectors#

NOx from all sectors in 2000#

NOx from all sectors in 2020#

Visualizations#

Total flux January-December#

Total flux for 2000 and 2020#