m. houssiau | eionet aq | ljubljana – 5 october 2015 cross analysis between urban system and air...

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 !