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EOSC Future WP6.3 Climate Neutral and Smart Cities

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The EOSC Future project is co-funded by the� European Union Horizon Programme call �INFRAEOSC-03-2020, Grant Agreement 101017536

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Science Project 9, Climate Neutral and Smart Cities

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Partners:

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ESS ERIC (ESS HQ, Sikt)

CESSDA ERIC (SND, CESSDA OFFICE, ADP?)

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IAGOS

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Science Project 9, Climate Neutral and Smart Cities

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Objectives:

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  • Demonstrate that relevant environmental data and data on citizens’ values, attitudes, behavior and involvement can be combined for social, political and scientific analysis

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  • Data available via the EOSC Platform

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  • Do observable indicators related to climate change or local air pollution, have effect on climate change concerns and beliefs?

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  • Local air pollution data could, when combined with other data from the ESS, also be used to explore effects on people’s well-being.

Some research questions

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ESS: Relevant topics, example Round 8

From ESS8 2016

Rotating module D:

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Climate change and energy, including:

attitudes, perceptions and policy preferences.

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�Three pilars �

Indicator selection and data integration

ESS Data

Climate data

Air quality data

Dissemination

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ESS Context lab?

EOSC Platform

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Cross-Domain metadata

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DDI Cross-Domain integration

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Science Project 9, Climate Neutral and Smart Cities

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Concept

Indicator

Indices (computed measures)

Climate change

(observable indicators)

Temperature

Temperature anomaly

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Precipitation

Extreme precipitation days

Wind strength

Storm – extreme wind

Air quality

(classic indicators)

Inhalable particular matter (PM10, PM2,5)

Air Quality

  • Day of interview
  • Week before the interview
  • Month etc.

Nitrogen Dioxide (NO2)

Sulfur Dioxide (SO2)

Ozone (ground –level) (O3)

Identified environmental indicators and indices

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 Data sources for Air Quality and Climate Data

Data source

Data source name

Reference (URL)

Comment

Air quality

European Environmental Agency (EEA)

Air quality  indicator data, by hour

Climate indicators

Copernicus ERA5

Re-analyses climate data, by hour

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Urban region

NUTS level + ESS var Domicile = ‘big city’, ‘suburb’ or ‘outskirt of big city’

N in ESS

Round 8 2016

Stockholm (SE11 Stockholms län)

246

Berlin (DE3 Berlin)

125

Praha (CZ010 Hlavní mesto Praha)

277

Budapest (HU110 Budapest)

291

Wien (AT13 Wien)

398

Madrid (ES30 Comunidad de Madrid)

167

Bruxelles/ Brussels (BE10 Région de Bruxelles-Capitale /Brussels Hoofdstedelijk Gewest)

182

London (UKI London)

143

Oslo (NO01 Oslo og Akershus)?

251

Geographic regions to be covered*

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* Explore inclusion of data from Paris and Munich from ICOS Cities?

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Methods for data integration�

  • Reduce amount of data
    • Climate data cover hourly measures for the

whole world since many decades past

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  • Geomapping
    • NUTS polygons, grids and measurement station geopoints need to be combined
    • Mapping to common projection
    • Use Geostat population data to weight measures within areas

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  • Time
    • Relate all indicators to the timing of the interview
    • Compute variable ‘Date’ to relate events and indices to each other
    • Include data from back in time for normalisation purposes

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  • Indicator production and measures
    • Input from climate and air quality experts to learn about best practices.

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Indicator production, examples EEA�

EEA:

  • The raw data file contains one measure (row) per hour per pollutant per station. Only background stations are included.
  • Firstly, maximum concentration per day per station per pollutant was calculated.
  • Then maximum value per day per region per pollutant.
  • After that, index variables were calculated, based on the EEA Air quality index

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Indicator production, examples ERA5�

ERA5:

  • The file raw data file contains one observation (row) per hour per grid-box (length and width are 0.5 degrees lat/lon).
  • For the regional file, firstly, values were aggregated by day (mean, max, and min values for temperature, sum for precipitation, maximum for wind).
  • Then a population weighted mean per region per day was calculated, based on global human settlement statistics, where the grid-boxes with the largest population were weighted most heavily.
  • After that, rolling average/sum pr week, month (30 days), and year, were calculated based on the daily means.

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Metadata Interoperability�

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Data description:

Use CDI together with DDI-Lifecycle to cover data description

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Workflows:

Use DDI-Cross-Domain Integration (DDI-CDI) Process package to describe our workflows

Examples to be shown in Benjamin’s presentation

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Thanks to�

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Eric Harrison (City University of London)

Bodil Agasøster, Benjamin Beuster, Archana Bidargaddi, Hanna Thome Grieg, Eirik Stavestrand (Sikt)

Arofan Gregory, Joachim Wackerow (Consultants - Sikt) - ESS ERIC

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Iris Alfredsson, Ilse Laze, David Rayner (SND) - CESSDA ERIC

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Hannah Clark - ENVRI FAIR

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Britt Ann Kårstad Høiskar, Miha Markelj, Sverre Solberg and Kjetil Tørseth - NILU

Øystein Godøy, Hans Olav Hygen – Norwegian Centre for Climate Services

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