M. Houssiau | EIONET AQ | Ljubljana – 5 October 2015 Cross analysis between urban system and air quality Provisional results ETC/Urban Land Soil systems

Objectives of the study Can we identify group of cities with similarities in terms of relevant air quality parameters? What are the main characteristics of these groups of cities? Which are the the cities that could easily evolve/move to another group – would it be a positive trend? The study includes Methodological development Selection of relevant parameters Identification of appropriate spatial units

Data AQ data o O3 – annual mean 8hr daily max o PM10 – nr of days exceeding 50 ug/m3 o NO2 – annual mean Validity >= 75% Time reference Calculation done for the period 2005 – 2006 – 2007 (average over three years) Area Urban and suburban stations Type of stations Traffic

Classification of station Airbase Urban Suburban Rural Urban delineations Urban Audit o Core city o Larger Urban Zone (LUZ) or Functional Urban Area (FUA): a city and its commuting zone Urban morphological zone (UMZ): defined by CORINE land cover classes considered to contribute to the urban tissue and function – set of urban areas laying less than 200m apart

Core – LUZ - 15

Airbase classification vs. Urban audit Number% Stations outside core city and large urban zone Stations inside core city or large urban zone Core city Large Urban Zone61916 Total number of selected monitoring stations

Station classification vs. pollution levels O3 NO2 PM10

Cluster analysis Exclude the monitoring stations outside the city delineation of Urban Audit. Exclude the monitoring stations of rural type. Group urban and suburban monitoring stations according to core city and large urban zone. Cluster 1. PM10 and NO2 are well below the average (35 cities). Cluster 2. low PM10 values. It is differentiated from group 1 because NO2 is on the average (higher concentrations than in group 1) (130 cities) Cluster 3. high levels of O3, low levels of NO2 and PM10. This is the larger group (160 cities). Cluster 4. relatively high values of PM10 (20 cities). Cluster 5. high values of PM10 (20 cities). Cluster 6. high values of PM10 and NO2, but low values of O3 (16 cities).

Clusters mapping

Cross-analysis Exploration of linkages between air quality and characteristics of cities (correlation and variance analysis) o Urban form and distribution, land use o Climate o Socio economic parameters: population, employment, economic sectors o Energy o Waste o Transport o Governance

Climate Positive correlations O3 – Temperature of the warmest month NO2 – Temperature of the warmest month, precipitation PM10 – Temperature of the warmest/coldest month, altitude Warmest month

City form and dynamics Most significant positive correlations NO2 – degree of sealing, compactness, dispersion of LUZ O3 – Percentage of green urban area Most significant negative correlations O3 – degree of sealing, compactness PM10 – degree of sealing, percentage of low density areas Soil sealing

City form and dynamics Clusters 2 and 6 : cities with high degree of soil sealing, relatively compact and low percentage of green urban areas. Clusters 1, 3 and 4 : cities with higher share of green urban areas, with varying degree of soil sealing and tending to be more dispersed cities. Parameters related to city form/structure, together, explain 43% of the differences between the air quality clusters

Other themes Population: no significant correlation Economy sectors Positive correlations: NO2 –number of industrial facilities Negative correlations O3 – number of industrial facilities PM10 – waste generation

Next steps Validation of methodology Interpretation and validation of findings Repeating the exercise for period Thank you !