Noise exposure in EU cities

Noise Exposure in EU cities:
a challenging comparison
G. Licitra and E. Ascari
AIA-DAGA 2013
Conference on Acoustics
EAA Euroregio - 18-21 March 2013

AIA-DAGA 2013 conference on Acoustics
Introdution
 First round of END implementation has been completed,
first action plans has been drawn up;
 Despite an homogeneous reporting system, it’s still
difficult to compare and analyse results;
 In particular, difficulties in comparing maps results and
exposition reflect into different possible actions;
 Indicators and techniques to identify critical and quiet
areas have been attempted but national laws and
requirements are various.

Hot spots identification
 Actions has to be identified for hot spots and critical areas.
 Different scores are defined according local laws and
guidelines; a review of different methods has been
proposed by Probst.
 e.g. Qcity project has proposed a methodology different from the
general linear scoring.
 Hot spot identification is now provided by many softwares,
as it is a relevant issue.
By the way hot spots identification is still a long post-process detailed
activity that often leads to loose contact with concrete actions.
Noise Mapping in the EU: Models and Procedures, CRC Press, 2012 edited by G.LICITRA
From noise maps to critical hot spots: Priorities in action Plans; W. Probst

Group indicator: areal solutions
 The need of an indicator of global noise quality
improvement has been highlighted.
 Lot of studies are now trying to analyse perspectives and
to evaluate effects of mitigation actions along time as an
average over a defined population.
 A group noise indicator called Gden has been proposed
in a lots of previous article to analyse environmental
noise decrement along time and to compare different
locations of a city.
M. Weber; J. Jabben, An indicator for area specific noise impact: Gden
Internoise 2010, Lisbon

Gden for European cities comparison
 Gden could be used also at European level to compare
pollution of different cities because it is simply based
upon population and Lden values, which are available for
all mapped cities.
 Gden values rise with population ni:
 Using it as it is commonly defined would lead to identify
larger cities but not to understand worst polluted cities.

Gden adaptation to European data [1]
 Gden should be scaled on total population N.
where i indicates exposure classes and ni is population in
that class.
 With this renormalization Gden would be equivalent to
average noise exposure of city population.

Gden adaptation to European data [2]
 To apply Gden calculation to EIONET database of
European exposure, average values of Lden should be
assigned also to external classes of exposure;
 This approximation obviously introduces errors which
would be larger for those cities that has an huge number
of people in extreme classes.
Class i Not reported
(<55 dBA)
55-60
dBA
60-65
dBA
65-70
dBA
70-75
dBA
>75
dBA
Ldeni [dBA] 50 57.5 62.5 67.5 72.5 80

Gden use and relation with annoyance
 As already shown in cited study Gden is well related to
highly annoyed (HA)
 Normalized Gden vs total percentage of HA:
Notice that different
mapping methodologies
affect results
We want to try to improve
this relation finding groups
of data within european
exposure dataset.
y = 0,0003e0,0921x
R2
= 0,7475
0%
5%
10%
15%
20%
25%
50 55 60 65 70 75
Gden (dBA)
%HA

Groups within European dataset
 There are large difference reported in shapes of
exposure curves.
 In other papers group of mapping data has been already
highlighted;
 In particular:
 Germany curves are created based on national method of
people distribution;
 England maps are produced all by the same institute (DEFRA)
so are all similar.
 Here some type of curves will be identified according
shape and ratio values between classes.
M. van den Berg, G. Licitra EU-noise maps: Analysis of submitted data and comments
Euronoise 2009, Edinburgh

0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55-60 60-65 65-70 70-75 >75
Lden [dBA]
%ofpopulation
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
55-60 60-65 65-70 70-75 >75
Lden [dBA]
%ofpopulation
 72 monotonic decreasing
 20 curves have less than 50% of population
under 55 dB and more than 15% over 65 dB
 46 curves have more than 50% of population
under 55 dB and less than 15% over 65 dB
(in this group there are 26 German cities)
Exposure shapes [1]
Decreasing >50% Decreasing <50%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
< 55 dBA > 65 dBA
%ofpopulation

0%
10%
20%
30%
40%
50%
60%
70%
55-60 60-65 65-70 70-75 >75
Lden [dBA]
%ofpopulation
0%
10%
20%
30%
40%
50%
60%
70%
55-60 60-65 65-70 70-75 >75
Lden [dBA]
%ofpopulation
Exposure shapes [2]
 45 with a single peak at 60-65 class
 19 curves are England cities and they have more than 45% of population
in peak class
Peak shaped >45% Peak shaped <45%
 All other 26 curves are smoother and
are so distributed within countries:

0%
5%
10%
15%
20%
25%
50,0 55,0 60,0 65,0 70,0 75,0
Gden [dbA]
%HA
decreasing >50%
decreasing <50%
peak shaped >50%
peak shaped <50%
Relation with annoyance within groups
 Gden vs %HA within identified groups
Two groups don’t
follow the trend line

Relation with annoyance for countries
 England and Germany are highlighted
This is a better
agreement that
one considering
whole sample
(R2
=0.74)
Gden(dBA)
%HA

Results & further analysis
 Data shows that an exponential law relates Gden and
%HA;
 Therefore Gden can be used to evaluate health risks in a
specific area/city.
 Following map reports Gden values for European city:
 National cluster can be observed.
 Worst values are highlighted and city names reported.
 Average values for each country

Gden values for European cities

Countries average values

National hot spots
 Since national mapping methods influences Gden results, Gden
could be used at least within region/country to evaluate worst
situation and to assign funds to improve noise average quality.
Gden(dBA) Gden(dBA)

Mapping time evolution
 Gden has been used also to evaluate improvements and
mitigation actions along time;
 It is a good indicator to evaluate also hypothetical
actions and to understand global solution effectiveness;
 It let policy makers understand average situation:
 It is an indicator not only of noise quality but also of
policies quality.
 It could be useful also at local level to evaluate lower
scale solutions and hot spots.
J.Jabben, E.Verheijen, E. Schreurs
Group Noise Exposure level Gden/Gnight; applications to airport noise
Report RIVM 2010 (in Dutch)

Local hot spots identification techniques
 As already said, it is a long detailed activity and often it is
hard to identify immediately worst situation without
completing the whole project;
 A lot of techniques are available and a study to compare
them has been already carried out by authors;
 Here a comparison between Gden and Qcity method is
reported
Notice that in this comparison number of inhabitants has been
corrected for schools and hospital to reflect Italian laws
E.Ascari, C.Chiari,P.Gallo, G.Licitra, D.Palazzuoli
Comparison of methods to identify hot spots in Pisa Municipality
AIA National Conference 2010 (in Italian)

Comparison with Qcity method in Pisa
 Probst proposed a method considering weighted levels
(Ldenw) to reflect annoyance from different sources;
 Noise Score(NS) is assigned based on inhabitants, using
maximum over buildings;
 Then a spatial average of values is carried out to reflect
NS of that area (100 m radius).
( ) )(65L10.max den_w
50)-*(L0.15 den_w
AdBifinhabNS
building
≤∗=
( ) )(65L10.max den_w
57.5)-*(L0.30 den_w
AdBifinhabitNS
building
>∗=

Hot spots according Qcity method

 Gden levels haven’t been weighted in this study because we used
directly total levels (road traffic is the main source).
 Gden resolution depends on extension of aggregation areas available
for each agglomeration, so here Italian census areas are used.
Gden calculation in Pisa
↑ Rijnmond Area (NL) ↑ Pisa (IT)

Hot spots according Gden method

Comparison
G.Licitra, P.Gallo, E.Rossi, G.Brambilla
A novel method to determine multiexposure priority indices tested for Pisa action plan
Applied Acoustics 72(8):6 (2011)
Property Qcity Gden
Resolution High Low
Elaboration time Slow Fast
Simplicity for public and policy makers Low High
Response to developments and local changes Slow Fast
Multiexposure correction introduced Yes To be done

Conclusions
 Gden method is very simple to be implemented and also
to be reported and explained to citizens;
 It is useful to compare different cities and different
countries and to establish common policies within
Europe;
 It is a method that it is robust along time and that allows
comparisons based upon census units which are stable
along time;
 Improvements to Gden indicator to reflects annoyance
due to multi-source exposure could be carried out in
further studies.

Noise exposure in EU cities

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