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Analysis of station classification and network design

Analysis of station classification and network design. INERIS (Laure Malherbe, Anthony Ung ), NILU ( Philipp Schneider), RIVM (Frank de Leeuw , Benno Jimmink ). 18th EIONET meeting -Dublin- 25th October 2013. Context.

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Analysis of station classification and network design

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  1. Analysis of station classification and network design INERIS (Laure Malherbe, Anthony Ung), NILU (Philipp Schneider), RIVM (Frank de Leeuw, Benno Jimmink) 18th EIONET meeting -Dublin- 25thOctober 2013

  2. Context (JRC- AQUILA Position Paper on siting criteria and station classification) • An increasing amount of available data on air quality in Europe • Extension of data coverage for all pollutants both in time and space • Much effort dedicated to data QA/QC (e.g. JRC-AQUILA quality programmes) • Information on siting requested in current and future reporting obligations O3 and PM10 monitoring stations for which data have been reported to AirBase AirBase v7, www.eea.europa.eu 18th EIONET meeting, Dublin, 25thOctober 2013

  3. Context (JRC- AQUILA Position Paper on siting criteria and station classification) • ...but still limited information • on the monitoring strategies underlying site selection; • on the fitness for purpose of the selected measurement locations. • An encouragement • to refine existing station classification schemes or develop supplementary ones, • to develop meta-information describing the station surroundings (land use, population density,...) 18th EIONET meeting, Dublin, 25thOctober 2013

  4. Objectives of the study • First part : evaluation of the network design from several angles: • evolution from 1996 • fulfilment of the EUROAIRNET criteria • compliance with the AQD • Second part: a supplementary classification scheme (presented by L. Rouïl at the17th EIONET meeting, 2012): • update the classification according to Joly and Peuch (2012) methodology and check its robustness • analyse the results on the European scale • investigate specific situations NB: study mainly focused on NO2, O3andPM 18th EIONET meeting, Dublin, 25thOctober 2013

  5. Evaluation of the network 18th EIONET meeting, Dublin, 25thOctober 2013

  6. Evolution of the network • Selected years: • 1996: state before the implementation of the Framework AQ Directive (information for the majority of EU15 Members) • 2004: from EU15 to EU 25 • 2007: from EU25 to EU27 • 2011: the most recent year available in AirBase • Considered station categories: 18th EIONET meeting, Dublin, 25thOctober 2013

  7. PM10 1996 2007 2004 2011 18th EIONET meeting, Dublin, 25thOctober 2013

  8. Monitoring criteria • EURO-AIRNET (Larssen et al., 1999): • number of cities to be included in a European representative network: • all large cities (>500,000) • 25% of medium cities (250,000-500,000) • 10% of small cities (50,000-250,000) Minimum requirements for PM (PM10 + PM2.5) • AQ Directive • The number of stations in anagglomeration/zone dependsonpopulation and current AQ status NB: Countries may also use modelling as a supplementary assessment tool ; in that case these numbers may be reduced by up to 50% under the conditions set in Dir. 2008/50/CE, Art. 7 18th EIONET meeting, Dublin, 25thOctober 2013

  9. Monitoring criteria • Application of EUROAIRNET criteria: 18th EIONET meeting, Dublin, 25thOctober 2013

  10. Monitoring criteria • Application of AQD criteria: • Ex: PM monitoring, 702 zones/agglomerationscommon to 1996, 2004, 2007 and 2011. Compliancewith AQD considering the assessment regimes applicable in 2011: • Resultsfor NO2monitoring: 18th EIONET meeting, Dublin, 25thOctober 2013

  11. Supplementary classification according to Joly & Peuch (2012) methodology 18th EIONET meeting, Dublin, 25thOctober 2013

  12. Brief recall of the methodology • Methodology developed in the framework of GMES/MACC program • Objective: establishing a pollutant-specific objective classification of stations based on the temporal variability of the observation data • For each considered pollutant, time series are summarized by eight indicators characterizing the diurnal cycle, the weekend effect and the high frequency variations. • The classification is performed in two stages: • Definition of the classes, from class1 to 10, with a selected set of stations. A linear discriminant analysis is performed so as to best discriminate between rural stations and stations most influenced by human activities (“urban + traffic” sites). • Classification a posteriori of the other stations.  ETC/ACM Technical Paper 2012/17 (2013) 18th EIONET meeting, Dublin, 25thOctober 2013

  13. Update and analysis with AirBase v7 Spatial distribution of the classes • In some countries, all station classes are present. • For some pollutants, other countries only have low or high station classes. • Classification is missing for some stations (criteria not filled, missing data, only daily values reported) O3 NO2 PM10 18th EIONET meeting, Dublin, 25thOctober 2013

  14. Update and analysis with AirBase v7 • More stations have been classified: O3 Monitoring stations not classified last year but classified in this study PM10 NO2 18th EIONET meeting, Dublin, 25thOctober 2013

  15. Update and analysis with AirBase v7 • As in 2012, the classification has been analysed in relation with EoI classification and auxiliary variables (population density, land use) This analysis confirms the robustness of the methodology. Mean and median population density around PM10 measurement stations for each class Distribution of PM10 classes as a function of EoI classification 18th EIONET meeting, Dublin, 25thOctober 2013

  16. Interesting cases • Different types of specific situations have been identified : • stations for which the classification (according to Joly & Peuch, 2012) does not well match the types of area and site provided in AirBase; • stations in specific environments (e.g.: high population density); • stations displaying very different classes according to the measured pollutant. 18th EIONET meeting, Dublin, 25thOctober 2013

  17. Interesting cases: example • Three stations have a population density higher than 20000 inhab./km2 • They are located in the same area: Paris • They are all urban background but do not have the same class. Distribution of population density in each PM10 class 18th EIONET meeting, Dublin, 25thOctober 2013

  18. Interesting cases: example • Berlin agglomeration contains all EoI types of area/station. • It also contains almost all the class numbers, with growing numbers from rural background to traffic sites. • As in Paris, two urban background stations are interesting for study. PM10 classes in Berlin 18th EIONET meeting, Dublin, 25thOctober 2013

  19. General conclusions • Station classification and detailed description of the station surroundings provide helpful support: • to interpret air quality data • to have a better idea of the station representativeness • to select the most relevant sets of stations for trend analysis, model evaluation, data assimilation, air quality mapping, impact studies... • This can be achieved by the joint use of • different classification schemes such as EoI (type of area/type of site) and Joly & Peuch (2012) methodology • auxiliary data such as population density, land cover, emissions... • It is proposed to compile and make such information available to data users. 18th EIONET meeting, Dublin, 25thOctober 2013

  20. Proposed content of the spreadsheet • Station code • Coordinates • Ozone classification (if applicable) • EoI Classification: • Type of area • Type of station • Characteristics of zone (if available) • Classification according to Joly & Peuch methodology (2012): • Class number for O3, NO2 and PM10 • Dominant emission sector(s) (if available) • LAU and NUTS codes (EBM) • Population density within 1 km radius (JRC database and ORNL) • Proportion of each main land cover class within 1 km radius (CORINE) • Other remarks A premilinary Excel file is available => link 18th EIONET meeting, Dublin, 25thOctober 2013

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