INSPIRE and Air Quality

Olav Peeters Belgian Interregional Environment Agency (IRCEL – CELINE)

Claudio Maricchiolo

Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA)

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Outline

● INSPIRE and Environmental Monitoring ● INSPIRE and air quality in Italy ● Air quality and human health:

– Why is air quality an issue? – Early warning system – a case from Belgium

● importance of sensor data for modeling ● necessity of cross-boarder exchange of data especially for forecasts

– Environmental Impact Assessment – another case from Belgium ● Modeling: quality of high resolution modeling limited by quality

“external” datasets

INSPIRE: Space & Time

GEOLOGY Hydrography TRANSPORT NETWORKS Buildings POPULATION DISTRIBUTION

LAND COVER Habitats Earth observation … SPATIAL DATA INFRASTRUCTURE

Env. monitoring facilities Time series

Near-real-time data

Inspiring the Environmental Monitoring

SEIS principles

Air quality Water/WISE

Biodiversity/BISE European Data Centers

Marine environment

INSPIRE and Air Quality in Italy

Directive transposition SEIS principles National infrastructure for spatial and env.

monitoring information (INITMA)

Region/EPA network Local authorities/ Policy into action Air implementation pilot

AQD IPR Pilot eENVplus FP7 AQD e-Reporting

Health impact assessment Preservation of cultural heritage

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Loss in statistical life expectancy (PM2.5)

Belgium

Italy

long term health effects of PM2.5

source: IIASA (2012)

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SMOG: increase of premature mortality

Increase of premature mortality (Nawrot et al. 2006) due to short-term exposure to particulate matter (PM10):

PM10 daily mean increased mortality (compared to 30 µg/m³)

70 µg/m³ 5 %

100 µg/m³ 8 %

150 µg/m³ 14 %

200 µg/m³ 19 %

Different info/alert thresholds across the EU – with/without short term measures (examples) :

● Belgium: 70 µg/m³ (daily mean) ● Netherlands: 200 µg/m³ (daily mean) ● France (Nord-Pas-de Calais):

● Info: 50 µg/m³ (running 24-hour mean) ● Alert: 80 µg/m³ (running 24-hour mean)

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Early Warning System

Sometimes working at local level is not sufficient: a PM10 forecast example

PM10 > 70 µg/m³

Based on forecasts In BE speed limits + industrial emissions are lowered during SMOG episodes 2 consecutive days PM10 > 70 µg/m³ (daily mean, average across BE) PM10 Forecast, 24 Jan 2010 from BE was OK… …BUT…

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A SMOG alert was missed!

Measured PM10 : >100 µg/m³

Why did our models underestimate this episode ?

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Long Range transport of Air Pollution

German map available only for day –1 ...

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Impact of lower speed limit (EC)

EC (Elemental Carbon, diesel soot) reductions up to 30%

… probably the most toxic components of PM

Source: VITO - Lefebvre et al. (2009)

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Can INSPIRE be of assistance?

● INSPIRE does provide a useful legal framework ● No INSPIRE compliant web-service fit for exchange real-

time (time-series) sensor data yet – Current implementations of INSPIRE download services are not fit

for the purpose – … but there is a strong candidate (cf Sensor Observation Services

– SOS – see presentation by Arne Bröring et al. - Thursday morning) – This is not impossible via e.g. WFS ... just very, very difficult! – If you succeed to implement something via WFS it will still be less

performant than a SOS

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Sensor observation services (SOS)

http://sos_client.irceline.be

http://sos.irceline.be

Geographic position

Timestamp & measured concentrations (eg 24 hours)

- Efficient transmission of time series

- querriable:

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High resolution models

Quality of modeling is limited by: – Quality of in situ measurements – Representativeness of stations – Quality external datasets:

● CORINE land cover (cf RIO-interpolation tool – big improvement) ● Road network incl. metadata like e.g. the height of bridges, location

of tunnels, etc. ● Emission data (point & line sources) ● Temporal traffic emissions, based on real time traffic data

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Traffic around Antwerp

NO2 concentrations at 08h00 17/01/2012

RIO-ifdm model

Extremely complex traffic problem

You cannot take good decisions without access to quality information

Several scenarios as possible solution:

+ scenarios including public transport and congestion charge

μg/m³

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Environmental Impact Assessment - Air

CORINE Land Cover (2006) RIO-interpolation 4x4km grid

point source emissions (more de- tailed than E-PRTR, incl. smaller sources)

Line source emissions (traffic)

In situ measurements eg. NO2

Tunnels & bridges

Assimilated meteo ECMWF + KMI-RMI

Quality of modelling is limited by quality of emission inventories

- already being used:

Source: OpenStreetMap

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Environmental Impact Assessment - Air

- the height of bridges - temporal traffic emissions, based on real time traffic data (e.g. TomTom congestion data) - regular update of emissions - correct location traffic emissions (exact location of roads is more important for air quality modeling than for traffic management)

- further possible refinement:

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What can INSPIRE help? ● Legal framework for sharing data is useful – but where are

the services with useful data? ● Adequate technical solutions (INSPIRE compliant web-

services) ● Real-time data is still a challenge:

– Transmission of latest measured concentrations in Belgium does not happen in an INSPIRE-compliant manner and would be allot less efficient if it would be INSPIRE-compliant

● Quality of data: INSPIRE is conspicuously quite ● The concept of services and Service Oriented Architecture

(SOA) is very useful

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Example of (cross-border) projects using services:

NL

DE BE

Euregion Meuse-Rhine

IT & BE - development of a reporting service (SEIS & INSPIRE)

Cross-border application of RIO model only possible because of pan-EU dataset CORINE

SOS for transmitting measurements

Air Quality Modeling System through web based services (www.atmosys.eu)

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In Summary

● Environmental monitoring is very important for Air Quality ● We need a performant technical solution for time-series ● Convenience of technical “INSPIRE” solutions is critical for

success ● Using services is very convenient not only when working in

a cross-border setting

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Thank you!

Olav Peeters Belgian Interregional Environment Agency (IRCEL – CELINE)

Claudio Maricchiolo Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA)

Inspire: the connecting framework?

down to earth

GEO/GEOSS GMES/Copernicus services

In-situ data Local Authorities

Research data

bottom-up Env. Monitoring 2.0 Crowd sourcing Citizen science

Putting Data to Work

Input to policy cycle SoE Report Environmental Outlook Data policy Open data Communication Social platform Mobile apps