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