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Impact of the Covid-19 Lockdown on Air quality over Europe

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Impact of the Covid-19 Lockdown on Air quality over Europe

copernicus air-quality

RoHub - FAIR Executable Research Object

Anne Fouilloux, Jean Iaquinta

This Jupyter notebook is distributed under MIT License

This Jupyter notebook shows how to discover RELIANCE datacube resources (spatial and temporal search and subsetting) and ROHUB to create Research Object

This notebook shows how to discover and access the Copernicus Atmosphere Monitoring products available in the RELIANCE datacube resources, by using the functionalities provided in the Adam API . The process is structured in 6 steps, including example of data analysis and visualization with the Python libraries installed in the Jupyter environment.

Background

The COVID-19 pandemic has led to significant reductions in economic activity, especially during lockdowns. Several studies has shown that the concentration of nitrogen dioxyde and particulate matter levels have reduced during lockdown events. Reductions in transportation sector emissions are most likely largely responsible for the NO2 anomalies. In this study, we analyze the impact of lockdown events on the air quality using data from Copernicus Atmosphere Monitoring Service.

Python packages

Additional packages need to be installed to run this Jupyter notebook on default EGI notebook environment

pip install adamapi rohub geojson_rewind cmaps cmcrameri seaborn

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Note: you may need to restart the kernel to use updated packages.

Data Management Plan

Authors

  • Make sure you first register to RoHub at https://reliance.rohub.org/.

  • We recommend you use your ORCID identifier to login and register to EOSC services.

  • In the list of authors, add any co-authors using the email address they used when they registered in RoHub.

author_emails = ['annefou@geo.uio.no']

Add the University of Olso as publishers

UiO_organization = {"org_id":"http://www.uio.no/english/", 
                     "display_name": "University of Oslo", 
                     "agent_type": "organization",
                     "ror_identifier":"01xtthb56",
                     "organization_url": "http://www.uio.no/english/"}

list_publishers = [UiO_organization]

list_copyright_holders = [UiO_organization]

Add the funding

  • if your work is not funded set

funded_by = {}

funded_by = {
"grant_id": "101017502",
"grant_Name": "RELIANCE",
"grant_title": "Research Lifecycle Management for Earth Science Communities and Copernicus Users",
"funder_name": "European Commission",
"funder_doi": "10.13039/501100000781",
}

Choose a license for your FAIR digital Object

  • We choose MIT license because we are creating an executable Research Object.

  • To see all the available licenses:

import rohub
licenses = rohub.list_available_licenses()
print(licenses)

license = 'MIT'

Organize my data

  • Define a prefix for my project (you may need to adjust it for your own usage on your infrastructure).

    • input folder where all the data used as input to my Jupyter Notebook is stored (and eventually shared)

    • output folder where all the results to keep are stored

    • tool folder where all the tools, including this Jupyter Notebook will be copied for sharing

  • Create all corresponding folders

import os
import warnings
import pathlib

warnings.filterwarnings('ignore')

WORKDIR_FOLDER = os.path.join(os.environ['HOME'], "datahub/Reliance/Climate")
print("WORKDIR FOLDER: ", WORKDIR_FOLDER)

WORKDIR FOLDER:  /home/jovyan/datahub/Reliance/Climate

INPUT_DATA_DIR = os.path.join(WORKDIR_FOLDER, 'input')
OUTPUT_DATA_DIR = os.path.join(WORKDIR_FOLDER, 'output')
TOOL_DATA_DIR = os.path.join(WORKDIR_FOLDER, 'tool')

list_folders = [INPUT_DATA_DIR, OUTPUT_DATA_DIR, TOOL_DATA_DIR]

for folder in list_folders:
    pathlib.Path(folder).mkdir(parents=True, exist_ok=True)

ADAM API Authentication

The following lines of code will show the personal Adam API-Key of the user and the endpoint currently in use, that provides access to the products in the related catalogue. At the end of the execution, if the authentication process is successfull the personal token and the expiration time should be returned as outputs.

Get ADAM Key from a local file

  • get an account to https://reliance.adamplatform.eu/ (use ORCID to authenticate)

  • get your your ADAM API key (click on your profile after you login and copy “Api Key”)

  • make sure you save your ADAM API key in a file $HOME/adam-key

adam_key = open(os.path.join(os.environ['HOME'],"adam-key")).read().rstrip()

import adamapi as adam
a = adam.Auth()
a.setKey(adam_key)
a.setAdamCore('https://reliance.adamplatform.eu')
a.authorize() 

{'expires_at': '2022-06-01T17:17:15.889Z',
 'access_token': '46927b55192a4f788c74aafe02b453a4',
 'refresh_token': '25cf3329043e4004a4f81e3a17dd213f',
 'expires_in': 3600}

Datasets Discovery

After the authorization, the user can browse the whole catalogue, structured as a JSON object after a pagination process, displaying all the available datasets. This operation can be executed with the getDatasets() function without including any argument. Some lines of code should be added to parse the Json object and extract the names of the datasets.The Json object can be handled as a Python dictionary.

Pre-filter datasets

We will discover all the available datasets in the ADAM platform but will only print elements of interest EU_CAMS e.g. European air quality datasets from Copernicus Atmosphere Monitoring Service

filter_datasets = 'CAMS'

from adamapi import Datasets

datasets = Datasets(a)
catalogue = datasets.getDatasets()

# Extracting the size of the catalogue

total = catalogue['properties']['totalResults']
items = catalogue['properties']['itemsPerPage']
pages = total//items

print('----------------------------------------------------------------------')
print('\033[1m' + 'List of available datasets:')
print ('\033[0m')

# Extracting the list of datasets across the whole catalogue

for i in range(0,pages):
    page = datasets.getDatasets(page = i)
    for element in page['content']: 
        if filter_datasets in element['title'] :
            print(element['title'] + "\033[1m" + " --> datasetId "+ "\033[0m" + "= " + element['datasetId'])

----------------------------------------------------------------------
List of available datasets:

CAMS European air quality forecasts: C2H3NO5 --> datasetId = 69619:EU_CAMS_SURFACE_C2H3NO5_G
CAMS European air quality forecasts: CO --> datasetId = 69620:EU_CAMS_SURFACE_CO_G
EU_CAMS_SURFACE_NH3_G --> datasetId = 69621:EU_CAMS_SURFACE_NH3_G
CAMS European air quality forecasts: NMVOC --> datasetId = 69622:EU_CAMS_SURFACE_NMVOC_G
CAMS European air quality forecasts: NO2 --> datasetId = 69623:EU_CAMS_SURFACE_NO2_G
EU_CAMS_SURFACE_NO_G --> datasetId = 69624:EU_CAMS_SURFACE_NO_G
CAMS European air quality forecasts: O3 --> datasetId = 69625:EU_CAMS_SURFACE_O3_G
CAMS European air quality forecasts: PM10 --> datasetId = 69626:EU_CAMS_SURFACE_PM10_G
CAMS European air quality forecasts: PM25 --> datasetId = 69627:EU_CAMS_SURFACE_PM25_G
CAMS European air quality forecasts: REC --> datasetId = 69628:EU_CAMS_SURFACE_REC_G
CAMS European air quality forecasts: SIA --> datasetId = 69629:EU_CAMS_SURFACE_SIA_G
CAMS European air quality forecasts: SO2 --> datasetId = 69630:EU_CAMS_SURFACE_SO2_G
CAMS European air quality forecasts: TEC --> datasetId = 69631:EU_CAMS_SURFACE_TEC_G

We are interested by 3 different products:

  • Nitrogen Dioxyde (NO2=

  • Particulate matter < 2.5 µm (PM2.5)

  • Ozone concentration (O3)

So we will discover EU_CAMS_SURFACE_NO2_G, EU_CAMS_SURFACE_PM25_G and EU_CAMS_SURFACE_O3_G and print the metadata of each dataset, showing the data provenance.

def get_metadata(datasetID, datasets, verbose=False):
    print('\033[1;34m' + 'Metadata of ' + datasetID + ':')
    print ('\033[0;0m')
    
    paged = datasets.getDatasets(datasetID)
    for i in paged.items():
        print("\033[1m" +  str(i[0]) + "\033[0m" + ': ' + str(i[1]))
    return paged

CAM Surface NO2

datasetID_NO2 = '69623:EU_CAMS_SURFACE_NO2_G'
metadata_NO2 = get_metadata(datasetID_NO2, datasets, verbose=True)

Metadata of 69623:EU_CAMS_SURFACE_NO2_G:

datasetId: 69623:EU_CAMS_SURFACE_NO2_G
creationDate: 2021-07-12T00:00:00Z
dataType: Float32
epsg: 4326
keywords: []
license: {'documentationUrl': '', 'dataProviderName': 'ADS', 'dataProviderUrl': '', 'licenseId': '', 'dataPolicy': '', 'doi': '', 'credits': ''}
maxValue: [1.354510459350422e-07]
minValue: [0.0]
numberOfRecords: 135878
profile: {'profileSchema': 'eo_profile_schema.json', 'name': 'Earth Observation', 'mission': 'CAMS', 'sensor': 'CAMS', 'processingLevel': 'forecast', 'instrument': '', 'platform': ''}
resolutionUnit: degree
temporalResolution: Hourly
unit: 
unitDescription: 
updateDate: 2022-06-01T12:03:26Z
geometry: {'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}
resolutions: [0.1]
anyText: 4326,ESA,Daily,ESA
applications: ['Atmosphere']
datasetManager: govoni@meeo.it
datasetManagerOrganisation: meeo
description: CAMS NITROGEN DIOXIDE
title: CAMS European air quality forecasts: NO2
noDataValue: 0
timeReferenceSystem: UTC
units: µg m-3
unitsDescription: nitrogen dioxide
services: ['MWCS', 'ADAM', 'AdamApi']
technicalManager: govoni@meeo.it
filtersEnabled: {'type': 'object', 'title': 'Dataset filters', 'properties': {'startDate': {'title': 'Start Date', 'type': 'string', 'format': 'date', 'text_rule': 'false', 'ops_only': 'false', 'math_rule': 'false', 'order_rule': 'false', 'pattern': '^d{4}-[01]d-[0-3]d(T[0-2]d:[0-9]d:[0-9]dZ)?$'}, 'endDate': {'title': 'End Date', 'type': 'string', 'format': 'date', 'ops_only': 'false', 'text_rule': 'false', 'math_rule': 'false', 'order_rule': 'false', 'pattern': '^d{4}-[01]d-[0-3]d(T[0-2]d:[0-9]d:[0-9]dZ)?$'}}}
previews: 
startDate: 2018-07-12T00:00:00Z
endDate: 2022-05-31T23:00:00Z
accounting: {'unit': 0.0032}

Particule matter < 2.5 µm (PM2.5)

datasetID_PM2_5 = '69627:EU_CAMS_SURFACE_PM25_G'
metadata_PM2_5 = get_metadata(datasetID_PM2_5, datasets, verbose=True)

Metadata of 69627:EU_CAMS_SURFACE_PM25_G:

datasetId: 69627:EU_CAMS_SURFACE_PM25_G
creationDate: 2021-07-12T03:00:00Z
dataType: Float32
epsg: 4326
keywords: []
license: {'documentationUrl': '', 'dataProviderName': 'ADS', 'dataProviderUrl': '', 'licenseId': '', 'dataPolicy': '', 'doi': '', 'credits': ''}
maxValue: [709.8012084960938]
minValue: [0.0]
numberOfRecords: 33902
profile: {'profileSchema': 'eo_profile_schema.json', 'name': 'Earth Observation', 'mission': 'CAMS', 'sensor': 'CAMS', 'processingLevel': 'forecast', 'instrument': '', 'platform': ''}
resolutionUnit: degree
temporalResolution: Daily
unit: 
unitDescription: 
updateDate: 2022-06-01T12:00:52Z
geometry: {'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}
resolutions: [0.1]
anyText: 4326,ESA,Daily,ESA
applications: ['Atmosphere']
datasetManager: govoni@meeo.it
datasetManagerOrganisation: meeo
description: CAMS SURFACE PARTICULATE METTER D<2.5
title: CAMS European air quality forecasts: PM25
noDataValue: 0
timeReferenceSystem: UTC
units: µg m-3
unitsDescription: PM2.5
services: ['MWCS', 'ADAM', 'AdamApi']
technicalManager: govoni@meeo.it
filtersEnabled: {'type': 'object', 'title': 'Dataset filters', 'properties': {'startDate': {'title': 'Start Date', 'type': 'string', 'format': 'date', 'text_rule': 'false', 'ops_only': 'false', 'math_rule': 'false', 'order_rule': 'false', 'pattern': '^d{4}-[01]d-[0-3]d(T[0-2]d:[0-9]d:[0-9]dZ)?$'}, 'endDate': {'title': 'End Date', 'type': 'string', 'format': 'date', 'ops_only': 'false', 'text_rule': 'false', 'math_rule': 'false', 'order_rule': 'false', 'pattern': '^d{4}-[01]d-[0-3]d(T[0-2]d:[0-9]d:[0-9]dZ)?$'}, 'productId': {'title': 'Product Identifier', 'type': 'string', 'format': 'string', 'text_rule': 'true', 'math_rule': 'false', 'order_rule': 'true', 'ops_only': 'false', 'locked': 'true'}}}
previews: 
startDate: 2018-07-12T00:00:00Z
endDate: 2022-05-31T23:00:00Z
accounting: {'unit': 0.0032}

Surface Ozone (O3)

datasetID_O3 = '69625:EU_CAMS_SURFACE_O3_G'
metadata_O3 = get_metadata(datasetID_O3, datasets, verbose=True)

Metadata of 69625:EU_CAMS_SURFACE_O3_G:

datasetId: 69625:EU_CAMS_SURFACE_O3_G
creationDate: 2021-07-12T02:00:00Z
dataType: Float32
epsg: 4326
keywords: []
license: {'documentationUrl': '', 'dataProviderName': 'ADS', 'dataProviderUrl': '', 'licenseId': '', 'dataPolicy': '', 'doi': '', 'credits': ''}
maxValue: [2.2007016298175586e-07]
minValue: [0.0]
numberOfRecords: 34401
profile: {'profileSchema': 'eo_profile_schema.json', 'name': 'Earth Observation', 'mission': 'CAMS', 'sensor': 'CAMS', 'processingLevel': 'forecast', 'instrument': '', 'platform': ''}
resolutionUnit: degree
temporalResolution: Hourly
unit: 
unitDescription: 
updateDate: 2022-06-01T12:05:11Z
geometry: {'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}
resolutions: [0.1]
anyText: 4326,ESA,Daily,ESA
applications: ['Atmosphere']
datasetManager: govoni@meeo.it
datasetManagerOrganisation: meeo
description: CAMS OZONE
title: CAMS European air quality forecasts: O3
noDataValue: 0
timeReferenceSystem: UTC
units: µg m-3
unitsDescription: ozone
services: ['MWCS', 'ADAM', 'AdamApi']
technicalManager: govoni@meeo.it
filtersEnabled: {'type': 'object', 'title': 'Dataset filters', 'properties': {'startDate': {'title': 'Start Date', 'type': 'string', 'format': 'date', 'text_rule': 'false', 'ops_only': 'false', 'math_rule': 'false', 'order_rule': 'false', 'pattern': '^d{4}-[01]d-[0-3]d(T[0-2]d:[0-9]d:[0-9]dZ)?$'}, 'endDate': {'title': 'End Date', 'type': 'string', 'format': 'date', 'ops_only': 'false', 'text_rule': 'false', 'math_rule': 'false', 'order_rule': 'false', 'pattern': '^d{4}-[01]d-[0-3]d(T[0-2]d:[0-9]d:[0-9]dZ)?$'}}}
previews: 
startDate: 2018-07-12T00:00:00Z
endDate: 2022-05-31T23:00:00Z
accounting: {'unit': 0.0032}

Products Discovery

The products discovery operation related to a specific dataset is implemented in the Adam API with the getProducts() function. A combined spatial and temporal search can be requested by specifying the datasetId for the selected dataset,the geometry argument that specifies the Area Of Interest and a temporal range, defined by startDate and endDate . The geometry must always be defined by a GeoJson object that describes the polygon in the counterclockwise winding order. The optional arguments startIndex and maxRecords can set the list of the results returned as an output. The results of the search are displayed with their metadata and they are sorted starting from the most recent product.

from adamapi import Search

def find_products(a, datasetID, start_date, end_date, geom="", verbose=False):
    search = Search( a )
    if geom != "":
        results = search.getProducts(
            datasetID, 
            geometry= geom_1,
            startDate = start_date,
            endDate = end_date
        )
    else:
        results = search.getProducts(
            datasetID, 
            startDate = start_date,
            endDate = end_date
        )
    
    if verbose:
        # Printing the results
        print('\033[1m' + 'List of available products:')
        print ('\033[0m')
        count = 1
        for i in results['content']:
            print("\033[1;31;1m" + "#" + str(count))
            print ('\033[0m')
            for k in i.items():
                print(str(k[0]) + ': ' + str(k[1]))
            count = count+1
            print('------------------------------------')

Check availability of NO2, PM2.5 and O3 for March 2019 to June 2021 over the whole available area

start_date = '2019-03-01'
end_date = '2021-06-30'

Discover NO2 products

find_products(a, datasetID_NO2, start_date, end_date, verbose=True)

List of available products:

#1

_id: {'$oid': '60fb13a5320b76365f6e153b'}
datasetId: EU_CAMS_SURFACE_NO2_G
productDate: 2021-06-30T23:00:00Z
productId: EU_CAMS_SURFACE_NO2_G_20210630230000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T19:08:21Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e153b', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e153b'}
------------------------------------
#2

_id: {'$oid': '60fb13a5320b76365f6e1540'}
datasetId: EU_CAMS_SURFACE_NO2_G
productDate: 2021-06-30T22:00:00Z
productId: EU_CAMS_SURFACE_NO2_G_20210630220000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T19:08:21Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e1540', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e1540'}
------------------------------------
#3

_id: {'$oid': '60fb13a5320b76365f6e153d'}
datasetId: EU_CAMS_SURFACE_NO2_G
productDate: 2021-06-30T21:00:00Z
productId: EU_CAMS_SURFACE_NO2_G_20210630210000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T19:08:21Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e153d', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e153d'}
------------------------------------
#4

_id: {'$oid': '60fb13a5320b76365f6e1538'}
datasetId: EU_CAMS_SURFACE_NO2_G
productDate: 2021-06-30T20:00:00Z
productId: EU_CAMS_SURFACE_NO2_G_20210630200000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T19:08:21Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e1538', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e1538'}
------------------------------------
#5

_id: {'$oid': '60fb13a5320b76365f6e152b'}
datasetId: EU_CAMS_SURFACE_NO2_G
productDate: 2021-06-30T19:00:00Z
productId: EU_CAMS_SURFACE_NO2_G_20210630190000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T19:08:21Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e152b', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e152b'}
------------------------------------
#6

_id: {'$oid': '60fb13a5320b76365f6e1532'}
datasetId: EU_CAMS_SURFACE_NO2_G
productDate: 2021-06-30T18:00:00Z
productId: EU_CAMS_SURFACE_NO2_G_20210630180000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T19:08:21Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e1532', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e1532'}
------------------------------------
#7

_id: {'$oid': '60fb13a5320b76365f6e152d'}
datasetId: EU_CAMS_SURFACE_NO2_G
productDate: 2021-06-30T17:00:00Z
productId: EU_CAMS_SURFACE_NO2_G_20210630170000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T19:08:21Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e152d', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e152d'}
------------------------------------
#8

_id: {'$oid': '60fb13a5320b76365f6e1527'}
datasetId: EU_CAMS_SURFACE_NO2_G
productDate: 2021-06-30T16:00:00Z
productId: EU_CAMS_SURFACE_NO2_G_20210630160000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T19:08:21Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e1527', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e1527'}
------------------------------------
#9

_id: {'$oid': '60fb13a5320b76365f6e151e'}
datasetId: EU_CAMS_SURFACE_NO2_G
productDate: 2021-06-30T15:00:00Z
productId: EU_CAMS_SURFACE_NO2_G_20210630150000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T19:08:21Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e151e', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e151e'}
------------------------------------
#10

_id: {'$oid': '60fb13a5320b76365f6e151b'}
datasetId: EU_CAMS_SURFACE_NO2_G
productDate: 2021-06-30T14:00:00Z
productId: EU_CAMS_SURFACE_NO2_G_20210630140000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T19:08:21Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e151b', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_NO2_G&_id=60fb13a5320b76365f6e151b'}
------------------------------------

Discover PM2.5 products

find_products(a, datasetID_PM2_5, start_date, end_date, verbose=True)

List of available products:

#1

_id: {'$oid': '60fa9c32320b76365f61fadc'}
datasetId: EU_CAMS_SURFACE_PM25_G
productDate: 2021-06-30T23:00:00Z
productId: EU_CAMS_SURFACE_PM25_G_20210630230000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T10:38:42Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fadc', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fadc'}
------------------------------------
#2

_id: {'$oid': '60fa9c32320b76365f61fadf'}
datasetId: EU_CAMS_SURFACE_PM25_G
productDate: 2021-06-30T22:00:00Z
productId: EU_CAMS_SURFACE_PM25_G_20210630220000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T10:38:42Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fadf', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fadf'}
------------------------------------
#3

_id: {'$oid': '60fa9c32320b76365f61fad7'}
datasetId: EU_CAMS_SURFACE_PM25_G
productDate: 2021-06-30T21:00:00Z
productId: EU_CAMS_SURFACE_PM25_G_20210630210000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T10:38:42Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fad7', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fad7'}
------------------------------------
#4

_id: {'$oid': '60fa9c32320b76365f61fad5'}
datasetId: EU_CAMS_SURFACE_PM25_G
productDate: 2021-06-30T20:00:00Z
productId: EU_CAMS_SURFACE_PM25_G_20210630200000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T10:38:42Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fad5', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fad5'}
------------------------------------
#5

_id: {'$oid': '60fa9c32320b76365f61faca'}
datasetId: EU_CAMS_SURFACE_PM25_G
productDate: 2021-06-30T19:00:00Z
productId: EU_CAMS_SURFACE_PM25_G_20210630190000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T10:38:42Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61faca', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61faca'}
------------------------------------
#6

_id: {'$oid': '60fa9c32320b76365f61face'}
datasetId: EU_CAMS_SURFACE_PM25_G
productDate: 2021-06-30T18:00:00Z
productId: EU_CAMS_SURFACE_PM25_G_20210630180000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T10:38:42Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61face', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61face'}
------------------------------------
#7

_id: {'$oid': '60fa9c32320b76365f61fac2'}
datasetId: EU_CAMS_SURFACE_PM25_G
productDate: 2021-06-30T17:00:00Z
productId: EU_CAMS_SURFACE_PM25_G_20210630170000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T10:38:42Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fac2', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fac2'}
------------------------------------
#8

_id: {'$oid': '60fa9c32320b76365f61fac5'}
datasetId: EU_CAMS_SURFACE_PM25_G
productDate: 2021-06-30T16:00:00Z
productId: EU_CAMS_SURFACE_PM25_G_20210630160000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T10:38:42Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fac5', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fac5'}
------------------------------------
#9

_id: {'$oid': '60fa9c32320b76365f61fabd'}
datasetId: EU_CAMS_SURFACE_PM25_G
productDate: 2021-06-30T15:00:00Z
productId: EU_CAMS_SURFACE_PM25_G_20210630150000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T10:38:42Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fabd', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fabd'}
------------------------------------
#10

_id: {'$oid': '60fa9c32320b76365f61fab7'}
datasetId: EU_CAMS_SURFACE_PM25_G
productDate: 2021-06-30T14:00:00Z
productId: EU_CAMS_SURFACE_PM25_G_20210630140000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-23T10:38:42Z
status: Online
productLink: {'tiff': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/tiff&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fab7', 'png': 'https://reliance-das.adamplatform.eu/wcs?service=WCS&version=2.0.0&request=GetCoverage&format=image/png&coverageId=EU_CAMS_SURFACE_PM25_G&_id=60fa9c32320b76365f61fab7'}
------------------------------------

Discover O3 products

find_products(a, datasetID_O3, start_date, end_date, verbose=True)

List of available products:

#1

_id: {'$oid': '60fb650e320b76365f794fad'}
datasetId: EU_CAMS_SURFACE_O3_G
productDate: 2021-06-30T23:00:00Z
productId: EU_CAMS_SURFACE_O3_G_20210630230000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-24T00:55:41Z
status: Online
------------------------------------
#2

_id: {'$oid': '60fb650d320b76365f794fa5'}
datasetId: EU_CAMS_SURFACE_O3_G
productDate: 2021-06-30T22:00:00Z
productId: EU_CAMS_SURFACE_O3_G_20210630220000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-24T00:55:41Z
status: Online
------------------------------------
#3

_id: {'$oid': '60fb650e320b76365f794fa8'}
datasetId: EU_CAMS_SURFACE_O3_G
productDate: 2021-06-30T21:00:00Z
productId: EU_CAMS_SURFACE_O3_G_20210630210000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-24T00:55:41Z
status: Online
------------------------------------
#4

_id: {'$oid': '60fb650e320b76365f794fab'}
datasetId: EU_CAMS_SURFACE_O3_G
productDate: 2021-06-30T20:00:00Z
productId: EU_CAMS_SURFACE_O3_G_20210630200000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-24T00:55:41Z
status: Online
------------------------------------
#5

_id: {'$oid': '60fb650d320b76365f794f9d'}
datasetId: EU_CAMS_SURFACE_O3_G
productDate: 2021-06-30T19:00:00Z
productId: EU_CAMS_SURFACE_O3_G_20210630190000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-24T00:55:41Z
status: Online
------------------------------------
#6

_id: {'$oid': '60fb650d320b76365f794f94'}
datasetId: EU_CAMS_SURFACE_O3_G
productDate: 2021-06-30T18:00:00Z
productId: EU_CAMS_SURFACE_O3_G_20210630180000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-24T00:55:41Z
status: Online
------------------------------------
#7

_id: {'$oid': '60fb650d320b76365f794f9b'}
datasetId: EU_CAMS_SURFACE_O3_G
productDate: 2021-06-30T17:00:00Z
productId: EU_CAMS_SURFACE_O3_G_20210630170000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-24T00:55:41Z
status: Online
------------------------------------
#8

_id: {'$oid': '60fb650d320b76365f794f97'}
datasetId: EU_CAMS_SURFACE_O3_G
productDate: 2021-06-30T16:00:00Z
productId: EU_CAMS_SURFACE_O3_G_20210630160000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-24T00:55:41Z
status: Online
------------------------------------
#9

_id: {'$oid': '60fb650d320b76365f794f8d'}
datasetId: EU_CAMS_SURFACE_O3_G
productDate: 2021-06-30T15:00:00Z
productId: EU_CAMS_SURFACE_O3_G_20210630150000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-24T00:55:41Z
status: Online
------------------------------------
#10

_id: {'$oid': '60fb650d320b76365f794f86'}
datasetId: EU_CAMS_SURFACE_O3_G
productDate: 2021-06-30T14:00:00Z
productId: EU_CAMS_SURFACE_O3_G_20210630140000.tif
geometry: {'geometries': [{'type': 'Polygon', 'coordinates': [[[-25.000012, 29.999997], [44.999988, 29.999997], [44.999988, 71.999997], [-25.000012, 71.999997], [-25.000012, 29.999997]]]}], 'type': 'GeometryCollection'}
insertDate: 2021-07-24T00:55:41Z
status: Online
------------------------------------

Data Access

After the data discovery operation that retrieves the availability of products in the catalogue, it is possible to access the data with the getData function. Each product in the output list intersects the selected geometry and the following example shows how to access a specific product from the list of results obtained in the previous step. While the datasetId is always a mandatory parameter, for each data access request the getData function needs only one of the following arguments: geometry or productId , that is the value of the _id field in each product metadata. In the case of a spatial and temporal search the geometry must be provided to the function, together with the time range of interest. The output of the getData function is always a .zip file containing the data retrieved with the data access request, providing the spatial subset of the product. The zip file will contain a geotiff file for each of the spatial subsets extracted in the selected time range.

from adamapi import GetData
from geojson_rewind import rewind
import json
import pathlib

Define a function to select a time range, geometry and get data

geom_cam = """{
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "properties": {},
      "geometry": {
        "type": "Polygon",
        "coordinates": [
          [
            [
              -25.0,
              30.0
            ],
            [
              45.0,
              30.0
            ],
            [
              45.0,
              70.0
            ],
            [
              -25.0,
              70.0
            ],
            [
              -25.0,
              30.0
            ]
          ]
        ]
      }
    }
  ]
}"""

Reformat geometry for ADAM API

output = rewind(geom_cam)
geom_dict = json.loads(output)

geom_cam_adam = str(geom_dict['features'][0]['geometry'])

def getZipData(auth, dataset_info):
    if not (pathlib.Path(pathlib.Path(dataset_info['outputFname']).stem).exists() or pathlib.Path(dataset_info['outputFname']).exists()):
        data=GetData(auth)
        image = data.getData(
            datasetId = dataset_info['datasetID'],
            startDate = dataset_info['startDate'],
            endDate = dataset_info['endDate'],
            geometry = dataset_info['geometry'],
            outputFname = dataset_info['outputFname'])

Get NO2 Products for March-June in 2019, 2020 and 2021

%%time

zipfilename_NO2 = {}

for year in ['2019', '2020', '2021']:
    sd = year + '-03-01'
    ed = year + '-06-30'
    zipfilename_NO2[year] = INPUT_DATA_DIR + '/' + 'NO2_EUROPE_ADAMAPI_' + sd + '_' + ed + '.zip'
    
    datasetInfo_NO2 = {
        'datasetID'   : datasetID_NO2,
        'startDate'   : sd,
        'endDate'     : ed,
        'geometry'    : geom_cam_adam, 
        'outputFname' : zipfilename_NO2[year]
        }
    
    getZipData(a, datasetInfo_NO2)

CPU times: user 3.66 ms, sys: 852 µs, total: 4.51 ms
Wall time: 35.9 ms

Get PM2.5 Products for March-June in 2019, 2020 and 2021

%%time

zipfilename_PM2_5 = {}

for year in ['2019', '2020', '2021']:
    sd = year + '-03-01'
    ed = year + '-06-30'
    zipfilename_PM2_5[year] = INPUT_DATA_DIR + '/' + 'PM2_5_EUROPE_ADAMAPI_' + sd + '_' + ed + '.zip'
    
    datasetInfo_PM2_5 = {
        'datasetID'   : datasetID_PM2_5,
        'startDate'   : sd,
        'endDate'     : ed,
        'geometry'    : geom_cam_adam, 
        'outputFname' : zipfilename_PM2_5[year]
        }
    
    getZipData(a, datasetInfo_PM2_5)

CPU times: user 4.84 ms, sys: 1.15 ms, total: 5.99 ms
Wall time: 34.9 ms

Get O3 Products for March-June in 2019, 2020 and 2021

%%time

zipfilename_O3 = {}

for year in ['2019', '2020', '2021']:
    sd = year + '-03-01'
    ed = year + '-06-30'
    zipfilename_O3[year] = INPUT_DATA_DIR + '/' + 'O3_EUROPE_ADAMAPI_' + sd + '_' + ed + '.zip'
    
    datasetInfo_O3 = {
        'datasetID'   : datasetID_O3,
        'startDate'   : sd,
        'endDate'     : ed,
        'geometry'    : geom_cam_adam, 
        'outputFname' : zipfilename_O3[year]
        }

    getZipData(a, datasetInfo_O3)

CPU times: user 0 ns, sys: 6.28 ms, total: 6.28 ms
Wall time: 35 ms

Data Analysis and Visualization

The data retrieved via the Adam API are now available as a zip file that must be unzipped to directly handle the data in a geotiff format. Then with the Python packages provided in the Jupyter environment it is possible to process and visualized the requested product.

Unzip NO2, PM2.5 and O3 data

import zipfile

def unzipData(filename, out_prefix):
    print(os.path.join(out_prefix, pathlib.Path(filename).stem))
    if not (pathlib.Path(os.path.join(out_prefix, pathlib.Path(filename).stem)).exists()):
        with zipfile.ZipFile(filename, 'r') as zip_ref:
            zip_ref.extractall(path = os.path.join(out_prefix, pathlib.Path(filename).stem))

for year in ['2019', '2020', '2021']:
    unzipData(zipfilename_NO2[year], INPUT_DATA_DIR)

/home/jovyan/datahub/Reliance/Climate/input/NO2_EUROPE_ADAMAPI_2019-03-01_2019-06-30
/home/jovyan/datahub/Reliance/Climate/input/NO2_EUROPE_ADAMAPI_2020-03-01_2020-06-30
/home/jovyan/datahub/Reliance/Climate/input/NO2_EUROPE_ADAMAPI_2021-03-01_2021-06-30

for year in ['2019', '2020', '2021']:
    unzipData(zipfilename_PM2_5[year], INPUT_DATA_DIR)

/home/jovyan/datahub/Reliance/Climate/input/PM2_5_EUROPE_ADAMAPI_2019-03-01_2019-06-30
/home/jovyan/datahub/Reliance/Climate/input/PM2_5_EUROPE_ADAMAPI_2020-03-01_2020-06-30
/home/jovyan/datahub/Reliance/Climate/input/PM2_5_EUROPE_ADAMAPI_2021-03-01_2021-06-30

for year in ['2019', '2020', '2021']:
    unzipData(zipfilename_O3[year], INPUT_DATA_DIR)

/home/jovyan/datahub/Reliance/Climate/input/O3_EUROPE_ADAMAPI_2019-03-01_2019-06-30
/home/jovyan/datahub/Reliance/Climate/input/O3_EUROPE_ADAMAPI_2020-03-01_2020-06-30
/home/jovyan/datahub/Reliance/Climate/input/O3_EUROPE_ADAMAPI_2021-03-01_2021-06-30

Read NO2, PM2.5 and O3 data as Xarray

from datetime import datetime
import xarray as xr
import glob

We now read these files using xarray. First, we make a list of all the geotiff files in a given folder. To ensure each raster is labelled correctly with its time, we can use a helper function paths_to_datetimeindex() to extract time information from the file paths we obtained above. We then load and concatenate each dataset along the time dimension using xarray.open_rasterio(), convert the resulting xarray.DataArray to a xarray.Dataset, and give the variable a more useful name.

Our data comes with different grids depending on the time period. For instance, the grid and geographical area for Copernicus Air Quality Analyses before June 12, 2019 is different from data after that date. Therefore, we need to regrid all data before June 12, 2019 to the “new” grid (any time after that date). We use xesmf for regridding.

Utility functions for regridding and reading data into Xarrays

Select the resulting output grid
def read_file(filename, variable, metadata, factor=1):
    tmp = xr.open_rasterio(filename, parse_coordinates=True)
    # Convert our xarray.DataArray into a xarray.Dataset
    tmp = tmp.to_dataset('band')*factor
    # Rename the dimensions to make it CF-convention compliant
    tmp = tmp.rename_dims({'y': 'latitude', 'x':'longitude'})
    # Rename the variable to a more useful name
    tmp = tmp.rename_vars({1: variable, 'y':'latitude', 'x':'longitude'})
    tmp[variable].attrs = {'units' : metadata['units'], 'long_name' : metadata['description']}
    return tmp
pick a recent date for getting the output grid
output_grid = read_file(INPUT_DATA_DIR + '/NO2_EUROPE_ADAMAPI_2021-03-01_2021-06-30/eu_cams_surface_no2_g_2021-06-30t000000.tif', 'NO2', metadata_NO2)
output_grid

<xarray.Dataset>
Dimensions:    (latitude: 400, longitude: 700)
Coordinates:
  * latitude   (latitude) float64 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05
  * longitude  (longitude) float64 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95
Data variables:
    NO2        (latitude, longitude) float32 3.474e-11 4.016e-11 ... 1.052e-10
Attributes:
    transform:      (0.1, 0.0, -25.000012009299, 0.0, -0.1, 69.99999699761337)
    crs:            +init=epsg:4326
    res:            (0.1, 0.1)
    is_tiled:       0
    nodatavals:     (0.0,)
    scales:         (1.0,)
    offsets:        (0.0,)
    AREA_OR_POINT:  Area
Function to get the time from the filename
def paths_to_datetimeindex(paths):
    return  [datetime.strptime(date.split('_')[-1].split('.')[0], '%Y-%m-%dt%f') for date in paths]
Function to read and regrid data
def getData(dirtif, variable, metadata, factor=1, grid_out=None):
    geotiff_list = glob.glob(dirtif)
    # Create variable used for time axis
    time_var = xr.Variable('time', paths_to_datetimeindex(geotiff_list))
    # Load in and concatenate all individual GeoTIFFs
    xarray_list = []
    if grid_out is not None:
        nlats = len(grid_out.latitude.values)
        nlons = len(grid_out.longitude.values)
    for i in geotiff_list:
        tmp = read_file(i, variable, metadata, factor=factor)
        if grid_out is not None:
            print("regridding ", i)
            regridder = xe.Regridder(tmp, grid_out, 'conservative')
            tmp_regrid = regridder(tmp, keep_attrs=True)
            xarray_list.append(tmp_regrid)
        else:
            xarray_list.append(tmp)
    #print(xarray_list[0:2])
    geotiffs_da = xr.concat(xarray_list, dim=time_var)
    return geotiffs_da

Function to plot one single date

import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import cmcrameri.cm as cmc

def plot_one_date(proj_plot, title, varname, vmin, vmax, geotiff_ds, lcmap, prefix_path):
    fig=plt.figure(figsize=(15,10))
    # Define the projection
    
    # We're using cartopy and are plotting in the chosen projection 
    # (see documentation on cartopy)
    ax = plt.subplot(1, 1, 1, projection=proj_plot)
    ax.coastlines(resolution='10m')

    # We need to project our data to the new Mercator projection and for this we use `transform`.
    # we set the original data projection in transform (here PlateCarree)
    # we only plot values greather than 0
    map = geotiff_ds[varname].where(geotiff_ds[varname] > 0).plot(ax=ax, x='longitude', y='latitude', 
                                                                  transform=ccrs.PlateCarree(),
                                                                  vmin=vmin, vmax=vmax,
                                                                  cmap=lcmap, add_colorbar=True)
        
    # Title for plot
    fig.suptitle(title,fontsize = 16, fontweight = 'bold')
    plot_file = prefix_path + '/' + '_'.join(varname) + title.replace(' ', '_') + '.png'
    if os.path.exists(plot_file + '.bak'):
        os.remove(plot_file + '.bak')
    if os.path.exists(plot_file):
        os.rename(plot_file, plot_file + '.bak')  
    fig.savefig(plot_file)

Function to plot several years

def plot_multi_years(proj_plot, title, varname, vmin, vmax, geotiff_dm, lcmap, prefix_path):
    # generate figure
    # Only plot values greater than 0
    p = geotiff_dm[varname].where(geotiff_dm[varname] > 0).plot(x='longitude', y='latitude',transform=ccrs.PlateCarree(),
                                                                subplot_kws={"projection": proj_plot},
                                                                size=8, vmin=vmin, vmax=vmax,
                                                                col='year', col_wrap=3, robust=True,
                                                                cmap=lcmap, add_colorbar=True)

    # We have to set the map's options on all four axes
    for ax,i in zip(p.axes.flat,  geotiff_dm.year.values):
        ax.coastlines()
        ax.set_title(title + ' ' + str(i), fontsize=10)

    plot_file = prefix_path + '/' + '_'.join(varname) + title.replace(' ', '_') + '.png'
    if os.path.exists(plot_file + '.bak'):
        os.remove(plot_file + '.bak')
    if os.path.exists(plot_file):
        os.rename(plot_file, plot_file + '.bak')      
    plt.savefig(plot_file)

Read NO2

NO2_filename = OUTPUT_DATA_DIR + '/' + "NO2_EUROPE_ADAMAPI" + start_date + '_' + end_date + '.nc'

if not (pathlib.Path(NO2_filename).exists()):
    import xesmf as xe
    geotiff_NO2_2019 = getData(INPUT_DATA_DIR + '/NO2_EUROPE_ADAMAPI_2019*/*.tif', 'NO2', metadata_NO2, factor=1.e9, grid_out=output_grid)
    geotiff_NO2_2020 = getData(INPUT_DATA_DIR + '/NO2_EUROPE_ADAMAPI_2020*/*.tif', 'NO2', metadata_NO2, factor=1.e9)
    geotiff_NO2_2021 = getData(INPUT_DATA_DIR + '/NO2_EUROPE_ADAMAPI_2021*/*.tif', 'NO2', metadata_NO2, factor=1.e9)
    geotiff_NO2 = xr.concat([geotiff_NO2_2019, geotiff_NO2_2020, geotiff_NO2_2021], dim='time')
    geotiff_NO2.to_netcdf(NO2_filename)
else:
    geotiff_NO2 = xr.open_mfdataset([NO2_filename])

Plot one date for NO2 (here 1st May 2020 e.g. at the end of the lockdown)

from datetime import datetime
seldate = '2020-05-01'
title = 'Surface NO2 on ' + datetime.strptime(seldate, '%Y-%m-%d').strftime("%-d %B, %Y")
lcmap = cmc.batlowW_r
proj_plot = ccrs.Mercator(central_longitude=12.0)
varname = 'NO2'
vmin = 0
vmax = 20                
plot_one_date(proj_plot, title, varname, vmin, vmax, geotiff_NO2.sel(time=seldate), lcmap, OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_94_0.png

Read PM2.5

PM25_filename = OUTPUT_DATA_DIR + '/' + "PM2_5_EUROPE_ADAMAPI" + start_date + '_' + end_date + '.nc'

if not (pathlib.Path(PM25_filename).exists()):
    geotiff_PM2_5_2019 = getData(INPUT_DATA_DIR + '/PM2_5_EUROPE_ADAMAPI_2019*/*.tif', 'PM2_5', metadata_PM2_5, grid_out=output_grid)
    geotiff_PM2_5_2020 = getData(INPUT_DATA_DIR + '/PM2_5_EUROPE_ADAMAPI_2020*/*.tif', 'PM2_5', metadata_PM2_5)
    geotiff_PM2_5_2021 = getData(INPUT_DATA_DIR + '/PM2_5_EUROPE_ADAMAPI_2021*/*.tif', 'PM2_5', metadata_PM2_5)
    geotiff_PM2_5 = xr.concat([geotiff_PM2_5_2019, geotiff_PM2_5_2020, geotiff_PM2_5_2021], dim='time')
    geotiff_PM2_5.to_netcdf(PM25_filename)
else:
    geotiff_PM2_5 = xr.open_mfdataset([PM25_filename])

Read O3

O3_filename = OUTPUT_DATA_DIR + '/' + "O3_EUROPE_ADAMAPI" + start_date + '_' + end_date + '.nc'

if not (pathlib.Path(O3_filename).exists()):
    geotiff_O3_2019 = getData(INPUT_DATA_DIR + '/O3_EUROPE_ADAMAPI_2019*/*.tif', 'O3', metadata_O3, factor=1.e9, grid_out=output_grid)
    geotiff_O3_2020 = getData(INPUT_DATA_DIR + '/O3_EUROPE_ADAMAPI_2020*/*.tif', 'O3', metadata_O3, factor=1.e9)
    geotiff_O3_2021 = getData(INPUT_DATA_DIR + '/O3_EUROPE_ADAMAPI_2021*/*.tif', 'O3', metadata_O3, factor=1.e9)
    geotiff_O3 = xr.concat([geotiff_O3_2019, geotiff_O3_2020, geotiff_O3_2021], dim='time')
    geotiff_O3.to_netcdf(O3_filename)
else:
    geotiff_O3 = xr.open_mfdataset([O3_filename])

Data analysis - Compute yearly average and maximum for the selected months.

Data analysis for NO2

geotiff_NO2max = geotiff_NO2.groupby('time.year').max('time', keep_attrs=True)
geotiff_NO2max

<xarray.Dataset>
Dimensions:    (year: 3, latitude: 400, longitude: 700)
Coordinates:
  * longitude  (longitude) float64 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95
  * latitude   (latitude) float64 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05
  * year       (year) int64 2019 2020 2021
Data variables:
    NO2        (year, latitude, longitude) float32 dask.array<chunksize=(1, 400, 700), meta=np.ndarray>
Attributes:
    transform:      [  0.1          0.         -25.00001201   0.          -0....
    crs:            +init=epsg:4326
    res:            [0.1 0.1]
    is_tiled:       0
    nodatavals:     0.0
    scales:         1.0
    offsets:        0.0
    AREA_OR_POINT:  Area
    regrid_method:  conservative
geotiff_NO2avg = geotiff_NO2.groupby('time.year').mean('time', keep_attrs=True)
geotiff_NO2avg

<xarray.Dataset>
Dimensions:    (year: 3, latitude: 400, longitude: 700)
Coordinates:
  * longitude  (longitude) float64 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95
  * latitude   (latitude) float64 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05
  * year       (year) int64 2019 2020 2021
Data variables:
    NO2        (year, latitude, longitude) float32 dask.array<chunksize=(1, 400, 700), meta=np.ndarray>
Attributes:
    transform:      [  0.1          0.         -25.00001201   0.          -0....
    crs:            +init=epsg:4326
    res:            [0.1 0.1]
    is_tiled:       0
    nodatavals:     0.0
    scales:         1.0
    offsets:        0.0
    AREA_OR_POINT:  Area
    regrid_method:  conservative

Data analysis for PM2.5

geotiff_PM2_5max = geotiff_PM2_5.groupby('time.year').max('time', keep_attrs=True)
geotiff_PM2_5max

<xarray.Dataset>
Dimensions:    (year: 3, latitude: 400, longitude: 700)
Coordinates:
  * longitude  (longitude) float64 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95
  * latitude   (latitude) float64 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05
  * year       (year) int64 2019 2020 2021
Data variables:
    PM2_5      (year, latitude, longitude) float32 dask.array<chunksize=(1, 400, 700), meta=np.ndarray>
Attributes:
    transform:      [  0.1          0.         -25.00001201   0.          -0....
    crs:            +init=epsg:4326
    res:            [0.1 0.1]
    is_tiled:       0
    nodatavals:     0.0
    scales:         1.0
    offsets:        0.0
    AREA_OR_POINT:  Area
    regrid_method:  conservative
geotiff_PM2_5avg = geotiff_PM2_5.groupby('time.year').mean('time', keep_attrs=True)
geotiff_PM2_5avg

<xarray.Dataset>
Dimensions:    (year: 3, latitude: 400, longitude: 700)
Coordinates:
  * longitude  (longitude) float64 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95
  * latitude   (latitude) float64 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05
  * year       (year) int64 2019 2020 2021
Data variables:
    PM2_5      (year, latitude, longitude) float32 dask.array<chunksize=(1, 400, 700), meta=np.ndarray>
Attributes:
    transform:      [  0.1          0.         -25.00001201   0.          -0....
    crs:            +init=epsg:4326
    res:            [0.1 0.1]
    is_tiled:       0
    nodatavals:     0.0
    scales:         1.0
    offsets:        0.0
    AREA_OR_POINT:  Area
    regrid_method:  conservative

Data analysis for O3

geotiff_O3max = geotiff_O3.groupby('time.year').max('time', keep_attrs=True)
geotiff_O3max

<xarray.Dataset>
Dimensions:    (year: 3, latitude: 400, longitude: 700)
Coordinates:
  * longitude  (longitude) float64 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95
  * latitude   (latitude) float64 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05
  * year       (year) int64 2019 2020 2021
Data variables:
    O3         (year, latitude, longitude) float32 dask.array<chunksize=(1, 400, 700), meta=np.ndarray>
Attributes:
    transform:      [  0.1          0.         -25.00001201   0.          -0....
    crs:            +init=epsg:4326
    res:            [0.1 0.1]
    is_tiled:       0
    nodatavals:     0.0
    scales:         1.0
    offsets:        0.0
    AREA_OR_POINT:  Area
    regrid_method:  conservative
geotiff_O3avg = geotiff_O3.groupby('time.year').mean('time', keep_attrs=True)
geotiff_O3avg

<xarray.Dataset>
Dimensions:    (year: 3, latitude: 400, longitude: 700)
Coordinates:
  * longitude  (longitude) float64 -24.95 -24.85 -24.75 ... 44.75 44.85 44.95
  * latitude   (latitude) float64 69.95 69.85 69.75 69.65 ... 30.25 30.15 30.05
  * year       (year) int64 2019 2020 2021
Data variables:
    O3         (year, latitude, longitude) float32 dask.array<chunksize=(1, 400, 700), meta=np.ndarray>
Attributes:
    transform:      [  0.1          0.         -25.00001201   0.          -0....
    crs:            +init=epsg:4326
    res:            [0.1 0.1]
    is_tiled:       0
    nodatavals:     0.0
    scales:         1.0
    offsets:        0.0
    AREA_OR_POINT:  Area
    regrid_method:  conservative

Visualize average and Maximum values over March-June for 2019, 2020 and 2021

  • We aim at highlighting maritime and terrestrial transport activities before, during and after the first lockdown period in 2020

Maximum NO2

title = 'Maximum Surface NO2 over March-June '
lcmap = cmc.batlowW_r
proj_plot = ccrs.Mercator(central_longitude=12.0)
varname = 'NO2'
vmin = 0
vmax = 80                
plot_multi_years(proj_plot, title, varname, vmin, vmax, geotiff_NO2max, lcmap, OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_113_0.png

Average NO2

title = 'Mean Surface NO2 over March-June '
lcmap = cmc.batlowW_r
proj_plot = ccrs.Mercator(central_longitude=12.0)
varname = 'NO2'
vmin = 0
vmax = 10                
plot_multi_years(proj_plot, title, varname, vmin, vmax, geotiff_NO2avg, lcmap, OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_115_0.png

Maximum PM2.5

title = 'Maximum Surface Particule matter < 2.5 µg m-3 over March-June '
lcmap = cmc.roma_r
proj_plot = ccrs.Mercator(central_longitude=12.0)
varname = 'PM2_5'
vmin = 0
vmax = 50                
plot_multi_years(proj_plot, title, varname, vmin, vmax, geotiff_PM2_5max, lcmap, OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_117_0.png

Average PM2.5

title = 'Mean Surface Particule matter < 2.5 µg m-3 over March-June '
lcmap = cmc.roma_r
proj_plot = ccrs.Mercator(central_longitude=12.0)
varname = 'PM2_5'
vmin = 0
vmax = 15                
plot_multi_years(proj_plot, title, varname, vmin, vmax, geotiff_PM2_5avg, lcmap, OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_119_0.png

Maximum O3

title = 'Maximum Surface Ozone over March-June '
lcmap = 'PiYG'
proj_plot = ccrs.Mercator(central_longitude=12.0)
varname = 'O3'
vmin = 70
vmax = 150                
plot_multi_years(proj_plot, title, varname, vmin, vmax, geotiff_O3max, lcmap, OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_121_0.png

Average O3

title = 'Mean Surface Ozone over March-June '
lcmap = 'PiYG'
proj_plot = ccrs.Mercator(central_longitude=12.0)
varname = 'O3'
vmin = 55
vmax = 100                
plot_multi_years(proj_plot, title, varname, vmin, vmax, geotiff_O3avg, lcmap, OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_123_0.png

Time series of NO2, PM2.5 and O3 at specific locations

Oslo         = {'name': 'Oslo',         'latitude':59.9139, 'longitude':10.7522} 
Stockholm    = {'name': 'Stockholm',    'latitude':59.3293, 'longitude':18.0686} 
Paris        = {'name': 'Paris',        'latitude':48.8566, 'longitude':2.3522} 
Madrid       = {'name': 'Madrid',       'latitude':40.4168, 'longitude':-3.7038} 
London       = {'name': 'London',       'latitude':51.5072, 'longitude':0.1276} 
Milan        = {'name': 'Milan',        'latitude':45.4642, 'longitude':9.1900} 
Moscow       = {'name': 'Moscow',       'latitude':55.7558, 'longitude':37.617} 
Copenhagen   = {'name': 'Copenhagen',   'latitude':55.6761, 'longitude':12.5683} 
Warsaw       = {'name': 'Warsaw',       'latitude':52.2297, 'longitude':21.0122} 
Frankfurt    = {'name': 'Frankfurt',    'latitude':50.1109, 'longitude':8.6821} 
Berlin       = {'name': 'Berlin',       'latitude':52.5200, 'longitude':13.4050} 
NorwegianSea = {'name': 'NorwegianSea', 'latitude':61.0323, 'longitude':1.694} 

locations = [Oslo, Stockholm, Paris, Madrid, London, Milan, Moscow, Copenhagen, Warsaw, Frankfurt, Berlin, NorwegianSea]

Visualize selected locations on a map

def plot_locations(proj_plot, title, varnames, vmins, vmaxs, geotiffs, lcmaps, cities, prefix_path):
    fig = plt.figure(1, figsize=[40,10])
    ax = {}
    
    # Create 3 subplots
    for i in range(1,4):
        # here 1 row, 3 columns and i plot
        ax[i] = plt.subplot(1, 3, i, projection=proj_plot)


    for ax,t,dset,vmin, vmax, lcmap in zip([ax[1], ax[2], ax[3]], varnames, geotiffs, vmins, vmaxs, lcmaps):
        map = dset[t].where(dset[t] > 0).plot(ax=ax, x='longitude', y='latitude', transform=ccrs.PlateCarree(),
                                              vmin=vmin, vmax=vmax, cmap=lcmap, add_colorbar=True)
        
        for l in locations:
            ax.scatter(l['longitude'], l['latitude'], color='red', marker='^', s=500, transform=ccrs.PlateCarree())
        ax.set_title(t, fontsize=25)
        ax.coastlines()
        ax.gridlines()

    # Title for all plots
    fig.suptitle('Copernicus air quality forecasts on ' + title, fontsize=30)
    plot_file = prefix_path + '/' + '_'.join(varnames) + title.replace(' ', '_') + '.png'
    if os.path.exists(plot_file + '.bak'):
        os.remove(plot_file + '.bak')
    if os.path.exists(plot_file):
        os.rename(plot_file, plot_file + '.bak')  
    fig.savefig(plot_file)

%%time

title = '3 years March-June average (2019, 2020 and 2021)'
varnames = ["NO2", "PM2_5", "O3"]
vmins = [0, 0, 55]
vmaxs =  [10, 15, 100 ]
geotiffs = [geotiff_NO2avg.mean('year'), geotiff_PM2_5avg.mean('year'), geotiff_O3avg.mean('year')]
lcmaps = [cmc.batlowW_r, cmc.roma_r,  'PiYG']

proj_plot = ccrs.Mercator(central_longitude=12.0)
plot_locations(proj_plot, title, varnames, vmins, vmaxs, geotiffs, lcmaps, locations, OUTPUT_DATA_DIR)

CPU times: user 6.77 s, sys: 1.41 s, total: 8.18 s
Wall time: 51.3 s
../../_images/air_quality_lockdown_129_1.png

Plot daily averages for all the selected locations

import matplotlib.patches as mpatches
import matplotlib.dates as mdates
import pandas as pd
myFmt = mdates.DateFormatter('%d %B')

def plot_timeseries(variable, df, locations, vmin=None, vmax=None, prefix_path="."):
    ts_file = prefix_path + '/' + variable + '_timeseries_' + '.csv'
    if (pathlib.Path(ts_file).exists()):
        fig = plt.figure(1, figsize=[20,5*len(locations)])
        pdyear = pd.read_csv(ts_file, index_col=0)
        for l,i in zip(locations,range(1,len(locations)+1)):
            ax = plt.subplot(len(locations), 1, i)
            hd = []
            for year, col in zip([2019, 2020, 2021], ['#66c2a5', '#fc8d62', '#8da0cb']):
                dfyear = pdyear.loc[(pdyear['name'] == l['name']) & (pdyear['year'] == year)]
                ts = dfyear[variable].plot(ax=ax,c=col, linestyle='--', alpha=0.3)
                dfyear[variable].rolling(7, center=True).mean().plot(ax=ax,c=col, linewidth=4)
                hd.append(mpatches.Patch(color=col, label=str(year)))
            plt.legend([ax], handles=hd)
            ax.set_title('Copernicus air quality forecasts ' + variable + ' from March to June in ' + l['name'], fontsize=25)
            ax.set_xlabel("")
            ax.xaxis.set_major_formatter(myFmt)
            plt.tight_layout()
    else:
        fig = plt.figure(1, figsize=[20,5*len(locations)])   
        frames = []
        for l,i in zip(locations,range(1,len(locations)+1)):
            ax = plt.subplot(len(locations), 1, i)
            if vmin is not None and vmax is not None:
                plt.ylim(vmin, vmax)
            hd = []
            for year, col in zip([2019, 2020, 2021], ['#66c2a5', '#fc8d62', '#8da0cb']):
                dfyear = df.groupby('time.year')[year].sel(latitude=l['latitude'], longitude=l['longitude'], method='nearest').groupby("time.dayofyear").mean()[variable]
#            dfyear.to_netcdf(variable + '_timeseries_' + l['name'] + '_' + str(year) + '.nc')
                ts = dfyear.plot(ax=ax,c=col, linestyle='--', alpha=0.3)
                dfyear.rolling(dayofyear=7, center=True).mean().plot(ax=ax,c=col, linewidth=4)
                hd.append(mpatches.Patch(color=col, label=str(year)))
            
                dt = dfyear.to_dataframe()
                dt.reset_index(inplace=True)
                dt['year'] = year
                dt['name'] = l['name']
                frames.append(dt)

            plt.legend([ax], handles=hd)
            ax.set_title('Copernicus air quality forecasts ' + variable + ' from March to June in ' + l['name'], fontsize=25)
            ax.set_xlabel("")
            ax.xaxis.set_major_formatter(myFmt)
            plt.tight_layout()
    
        plot_file = prefix_path + '/' + variable + '_rolling_7d' + '.png'
        if os.path.exists(plot_file + '.bak'):
            os.remove(plot_file + '.bak')
        if os.path.exists(plot_file):
            os.rename(plot_file, plot_file + '.bak')  
        fig.savefig(plot_file)
        result = pd.concat(frames)
        result.reset_index(inplace=True)
        result.to_csv(prefix_path + '/' + variable + '_timeseries_' + '.csv', index=False)

plot_timeseries('NO2', geotiff_NO2, locations, prefix_path=OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_132_0.png

PM2.5

plot_timeseries('PM2_5', geotiff_PM2_5, locations, prefix_path=OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_134_0.png

O3

plot_timeseries('O3', geotiff_O3, locations, prefix_path=OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_136_0.png

3 years (2019, 2020 and 2021) March-June average at each selected location

import pandas as pd
import seaborn as sns
sns.set_theme(style="ticks", color_codes=True)

def plot_location_stats(variable, df, locations, prefix_path): 
    ts_file = prefix_path + '/' + variable + '_timeseries_' + '.csv'
    if (pathlib.Path(ts_file).exists()):
        result = pd.read_csv(ts_file, index_col=0)
        ax = sns.catplot(x="name", y=variable, hue="year", kind="bar", data=result, height=7, aspect=2.5)
    else:
        frames = []
        for l,i in zip(locations,range(1,len(locations)+1)):
            dfyear = df.sel(latitude=l['latitude'], longitude=l['longitude'], method='nearest')
            dt = dfyear.to_dataframe()
            dt.reset_index(inplace=True)
            dt['year'] = pd.DatetimeIndex(dt['time']).year
            dt = dt.groupby("year").mean()
            dt['name'] = l['name']
            frames.append(dt)
        result = pd.concat(frames)
        result.reset_index(inplace=True)
        ax = sns.catplot(x="name", y=variable, hue="year", kind="bar", data=result, height=7, aspect=2.5)
    
    ax.fig.suptitle('Total ' + variable + ' averaged (March-June) at selected location')
    plot_file = prefix_path + '/' + 'Total ' + variable + 'at_12_differents_locations' + '.png'
    if os.path.exists(plot_file + '.bak'):
        os.remove(plot_file + '.bak')
    if os.path.exists(plot_file):
        os.rename(plot_file, plot_file + '.bak')  
    plt.savefig(plot_file)
    return result

NO2

stats_loc = plot_location_stats('NO2', geotiff_NO2, locations, OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_141_0.png

PM2.5

stats_loc = plot_location_stats('PM2_5', geotiff_PM2_5, locations, OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_143_0.png

O3

stats_loc = plot_location_stats('O3', geotiff_O3, locations, OUTPUT_DATA_DIR)
../../_images/air_quality_lockdown_145_0.png

Discussion

Maritime routes in the mediteranean areas significantly descreased during the 2020 lockdown, especially from the Suez Canal and along North African coasts. However, these activities are now larger than before covid.

Conclusion

We observed changes for NO2 and for some town PM2.5 but there is no evidence of any sgnificant changes on surface Ozone during the lockdown. A more thorough analysis is required, in particular taking into account meteorological conditions that greatly affect the production of such chemical species.

Creation of research Object in Rohub

The following lines of code will show you how to authenticate to ROHub using your username and password. 
import rohub

Authenticate

  • you need to create two files in your HOME

    • rohub-user: contains your rohub username

    • rohub-pwd: add your password in this file

rohub_user = open(os.path.join(os.environ['HOME'],"rohub-user")).read().rstrip()
rohub_pwd = open(os.path.join(os.environ['HOME'],"rohub-pwd")).read().rstrip()
rohub.login(username=rohub_user, password=rohub_pwd)

Logged successfully as annefou@geo.uio.no.

Creation of a new executable Research Object

ro_title="Impact of the Covid-19 Lockdown on Air quality over Europe using Copernicus Atmosphere Monitoring Service"
ro_research_areas=["Earth sciences"]
ro_description="""This notebook shows how to discover and access the [Copernicus Atmosphere Monitoring](https://ads.atmosphere.copernicus.eu/#!/home) products available 
in the **RELIANCE** datacube resources, by using the functionalities provided in the <font color='blue'> **Adam API** </font>. 

The COVID-19 pandemic has led to significant reductions in economic activity, especially during lockdowns. Several studies has shown that the concentration of nitrogen 
dioxyde and particulate matter levels have reduced during lockdown events. Reductions in transportation sector emissions are most likely largely responsible for the NO2 
anomalies. In this study, we analyze the impact of lockdown events on the air quality using data from [Copernicus Atmosphere Monitoring Service](https://ads.atmosphere.copernicus.eu/).
"""
ro_ros_type="Executable Research Object"
ro_ros_template="Executable Research Object folders structure"

# uncomment the lines below to create a new Research Object
# ro = rohub.ros_create(title=ro_title, research_areas=ro_research_areas, 
#                      description=ro_description, 
#                      use_template=True,
#                      ros_type=ro_ros_type)

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Jupyter notebook comparing CAMS and air quality measurements