Air quality Monitoring (in the Urban context): modeling, monitoring and using proxy data

Transcription

1 Air quality Monitoring (in the Urban context): modeling, monitoring and using proxy data Olav Peeters Belgian Interregional Environment Agency (IRCEL CELINE)

2 Outline 101: Introduction to IRCEL CELINE ereporting OGC-service we use High resolution AQ models Possible testing locations Important research questions Other (similar) projects 2

3 101: Introduction to IRCEL - CELINE Intergewestelijke Cel voor het Leefmilieu (IRCEL) Cellule Interrégionale de l'environnement (CELINE) Belgische Interregionale Umweltagentur (IRCEL - CELINE) Belgian Interregional Environment Agency (IRCEL - CELINE) Cooperation agreement between three regional agencies Flemish Environment Agency Brussels Environment Walloon Agency for Air and Climate 3

4 101: Introduction to IRCEL CELINE 4

5 101: Introduction to IRCEL CELINE Most important functions of IRCEL - CELINE (air quality): Continuous forecasts (cf SMOG alert) Informing the public (real-time and assessment) National report under the air quality directive (2004/107/EC) Enforcing a common scientific basis between monitoring networks Interregional calibration laboratory Interregional data processing centre (IDPC) - real-time database National Focal Point (Eionet) Compilation GHG inventory 5

6 101: Introduction to IRCEL CELINE Real-time data All major pollutants (incl. BC) Forecasts Information about pollutants Publications Etc. 6

7 101: Introduction to IRCEL CELINE Proto-type new website Replacing static maps with interactive OGC-services 7

8 101: Introduction to IRCEL CELINE Proto-type mobile app using the same OGC-services My office Brussels - annual mean NO2 in (2012) 8

9 101: Introduction to IRCEL CELINE We tweet... 9

10 101: Introduction to IRCEL CELINE and 20 minutes later: 10

11 ereporting (INSPIRE)-services Reporting obligations SOS (UTD-data incl. data quality flags) The general public Incl. validation steps & official approval SOS/WFS (validated and/or aggregated) WMS/WCS (e.g. modeled data) The Commission IPR (2011/850/EU) XML-schema for reporting under the Air Quality directive (2008/50/EC) e.g. SPARQLEndpoint (Linked Data e.g. Plans and Programs ) 11

12 Current situation of AQ-reporting Yearly questionnaire to fulfill reporting obligations of member state under Air Quality Directive 2008/50/EC transmitted as a spreadsheet The problem From the perspective of a national agency: Many different data requests not only from the EEA inconsistencies can occur Very little can be automated time consuming Possible human errors From a European perspective: Time consuming to enter data transmitted per.xls into AirBase Automated validation is more difficult from.xls than XML With new legislation reporting should be INSPIRE compliant From an ethical perspective: The questionnaire is.xls and not e.g. a.ods (OpenDocument Spreadsheet) From an INSPIRE perspective: Finding quality assured, regularly updated data 12

13 Directives impacting on AQD-xsd Exchange of Information Decision 97/101/EC 4th Air Quality Daughter Directive 2004/107/EC Air Quality Directive 2008/50/EC Implementing Provisions for Reporting (IPR) 2011/850/EU XML-schema (AQD-xsd) Guidance document 13

14 IPR under INSPIRE Before: Regional agency Air Quality Questionnaire (.xls) Regional agency Transmission.xls after approval from ministries National Node EEA Regional agency After: Regional agency Regional agency Regional agency Internal dataflows Transmission XML after approval from ministries National Node EEA INSPIRE-compliant transmission (cf 2011/850/EU) 14

15 The dataflows involved (cf Implementing Provisions for Reporting (IPR) 2011/850/EU) INPIRE Data Content Theme III.11.AM Dataset B "zones and agglomerations" III.11.AM Dataset C "assessment regime" III.11.AM Dataset D "assessment methods" III.7. EF Dataset E1a primary validated assessment data measurements III.13 AC Dataset E1b primary validated assessment data modelled III.7. EF Dataset E2a primary up-to-date assessment data measurements III.13 AC Dataset E2b primary up-to-date assessment data modelled III.7. EF Dataset F1a aggregated data - primary validated measurements III.13 AC Dataset F1b aggregated data - primary validated modelled III.7. EF Dataset F2 aggregated data - primary up-to-date measurements III.11.AM Dataset G attainment of environmental objectives III.11.AM Dataset H air quality plans III.11.AM Dataset I source apportionment III.11.AM Dataset J scenario for the attainment year III.11.AM Dataset K "measures" + Dataset A: a header transmitted with every separate submission 15

16 The schema Makes all necessary provisions to use services Has a nested structure so you can use several services nested into each other Makes use of external vocabularies to streamline information (e.g. the pollutant code-list from AirBase, etc.) Uses the INSPIRE-id (unique identifier required by INSPIRE for all features) {ReferenceURI} = {inspireorganisationuri}/{namespace}/{localid} [/{version}] {localid} = Unique ID for the object {namespace} = {countrycode}.{agencycode}.{productcode} {inspireorganisationuri} = {organisationuri}/ {inspiredatasubsection} {organisationuri}: base URI used by the organisation to serve data {inspiredatasubsection} = relative path to data within the organisations URIs e.g. xlink:href=" 16

17 Reporting service INSPIRE compliant 17

18 - Different access rights - Different legally mandated parties -... Validation tool 18

19 Informing the public 19

20 Sensor observation services (SOS) - Efficient transmission of time series Geographic position - querriable: Timestamp & measured concentrations (eg 24 hours) 20

21 SWE-client - new developments REST-api See: - png-graphs (prepared server-side) - GeoJSON (lightweight) - very fast Important for serving SOS data to (and from) mobile clients 52 North workshop in November

22 Modeling RIO-IFDM Immission Frequency Distribution Model In situ measurements eg. NO2 CORINE Land Cover (2006) RIO-interpolation 4x4km grid point source emissions (more detailed than E-PRTR, incl. smaller sources) Assimilated meteo ECMWF + KMI-RMI Quality of modelling is limited by quality of emission inventories Tunnels & bridges Line source emissions (traffic) 22

23 RIO-ifdm Validation transect highway Good results spatial validation (passive samplers) in urban context Another (temporal) validation is planned 23

24 Possible testing locations Antwerp Ghent Brussels With involvement of the relevant regional agency (map = yearly average NO2 Rio-ifdm) Liège Charleroi 24

25 Media attention results PhD on exposure PhD - Evi Dons - Air pollution exposure assessment through personal monitoring and activity-based modeling - September 2013 The University of Hasselt & VITO 25

26 Some important research questions How often does a mobile sensor need to pass by in a street? How can (time aggregated, e.g. hourly) SOS-data be used for calibration? Mobile versus more (smaller, cheaper, autonomous, etc. cf AQMesh) stationary sensors? Mobile data versus proxy data (traffic volume, data via OBD-2-adapter, etc.) Is real-time correction of model via real-time proxy data possible? 26

27 CITI-SENSE 27

28 OpenSense 28

29 Thank you! Olav Peeters Belgian Interregional Environment Agency (IRCEL CELINE) 29