Chemistry Reference
In-Depth Information
Table 1 Correlation coefficient between gridded PM
10
annual mean values and other parameters
Correlation coefficient
PM
10
36th highest daily mean
0.99
Population density
0.25
Wind speed
0.19
Indicator for valleys
a
0.01
Land use
b
: urban fabric
0.14
Land use
b
: traffic
0.33
Land use
b
: industry
0.12
NO
x
emissions high level sources
0.07
NO
x
emissions low level sources
0.16
NO
x
emissions traffic
0.28
NO
x
emissions total
0.15
PM
10
emissions high level sources
0.07
PM
10
emissions low level sources
0.15
PM
10
emissions traffic
0.26
PM
10
emissions total
0.15
Classification
c
: lowlands
0.18
Classification
c
: mountains
0.11
Classification
c
: urban
0.13
a
From the ASSET project (
http://www.asset-eu.org/
)
b
Corine land cover (
http://www.eea.europa.eu/data-and-maps/data#c12
ΒΌ
corine+land+cover+version+13
)
c
Climate classification map by LANMAP (
http://www.alterra.wur.nl/UK/research/Specialisation+Geo-
coefficient above 0.5. A correlation coefficient of about 0.25 has been calculated
for traffic emissions and population density. A relatively high negative correlation
has been calculated for wind speed and percentage of lowlands. Nevertheless, these
rather low correlation coefficients do not allow a straightforward identification of
critical areas or of thresholds for certain parameters to discriminate between critical
and non-critical areas.
5.2 Selection of a Subset of Critical Areas for Further Analysis
The analysis of the datasets described above has shown that an unambiguous
identification of critical areas allowing for generalisation would require further
more sophisticated tools such as extensive air quality modelling with chemical
transport models and improved comparable datasets.
Nevertheless, the available data have allowed us to identify several areas where
air quality problems are rather severe for specific reasons, and exemplary areas with
problems of a more general nature.
