M. Houssiau | EIONET AQ | Ljubljana – 5 October 2015
Cross analysis between urban system and air
qualityProvisional 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 1352 35
Stations inside core city or large urban zone 2470 65
Core city 1851 48
Large Urban Zone 619 16
Total number of selected monitoring stations 3825 100
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 2011 - 2013
Thank you !
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