Citizen Noise Pollution Monitoring

Nicolas
Maisonneuve
Sony Computer Science
Laboratory
Paris, France
maisonneuve@csl.sony.fr
Matthias
Stevens
Vrije Universiteit Brussel,
Programming Technology
Lab
Brussels, Belgium
mstevens@vub.ac.be
Maria E.
Niessen
University of Groningen,
Dept. of Artificial Intelligence
Groningen, The Netherlands
maria@ai.rug.nl
Peter
Hanappe
Sony Computer Science
Laboratory
Paris, France
hanappe@csl.sony.fr
Luc
Steels
Vrije Universiteit Brussel,
Artificial Intelligence
Laboratory
Brussels, Belgium
steels@arti.vub.ac.be




ABSTRACT
In this paper we present a new approach to monitor noise
pollution involving citizens and built upon the notions of
participatory sensing and citizen science. We enable citizens to
measure their personal exposure to noise in their everyday
environment by using GPS-equipped mobile phones as noise
sensors. The geo-localised measures and user-generated meta-data
can be automatically sent and shared online with the public to
contribute to the collective noise mapping of cities. Our prototype,
called NoiseTube, can be found online1.
Categories and Subject Descriptors
H.4 [Information Systems Applications]: Miscellaneous
General Terms
Management, Measurement, Human Factors, Experimentation.
Keywords
Noise pollution, citizen science, sustainability, participatory
sensing, geo-localisation, tagging, mobile phones.
1. INTRODUCTION
Noise pollution is a major problem in urban environments,
affecting human behaviour, well-being, productivity and health
[12]. Excessive noise also has a broader environmental impact, for
instance it can chase animals out of their habitat or alter their
behaviour [39]. According to the green EU paper [12]
Environmental noise, caused by traffic, industrial and
recreational activities is one of the main local environmental
problems in Europe and the source of an increasing number of
complaints from the public . EU experts estimated that 80 million
people suffer from noise levels considered as unacceptable, and
170 million people experience serious annoyance during daytime
in the European Union. Generally however, action to reduce
environmental noise has a lower priority than other environmental
problems such as air and water pollution. With this background,
there is a clear need to manage environmental noise on a national
and local scale. Recognising this as a prime issue, the European
Commission adopted the European Noise Directive [13] requiring
major cities to establish a noise management policy. The first step
is to assess the current noise climate in the city by gathering real-
world data and building noise maps in order to better understand
the problem and support the creation of local action plans.
Numerous international reports (e.g. Principle 10 of the Rio
Declaration on Environment and Development [36]) have

1
NoiseTube website: http://www.noisetube.net
expressed the importance of public participation to move towards
sustainable development. But often participation is only proposed
at the decision making level. Due to the growing influence of Web
2.0 practices [23] - participation, openness and network effect;
people s roles have been transformed from passive consumers of
information into active participants thanks to a democratization of
authoring tools (e.g. wiki s, blogs) and social connection tools
(e.g. social networks). But can we transfer such user-generated
content practices from the digital world to facilitate their adoption
in the real world and environmental context by democratizing
environmental measurement devices and thereby fully opening the
potential of citizen science [26] and community memories [35]?
In this paper we present the NoiseTube project1, which follows a
novel approach to noise pollution monitoring involving the
general public. Taking inspiration from participatory sensing and
using the ubiquitous mobile phone as a platform, our goal is to
investigate how a participatory and people-centric approach to
noise monitoring can be used to create a low-cost, open platform
to measure, annotate and localize noise pollution as it is perceived
by the citizens themselves to inform government officials and the
general public.
Furthermore, as is the case with many issues affecting the
sustainability of urban life, noise pollution cannot be tackled by
policymakers alone. To manage noise pollution in cities one also
needs to consider the behaviour of the citizens themselves. The
first step towards changing such behaviour is to raise awareness.
By involving them in the process of monitoring noise pollution,
we attempt to support the raising of awareness.
In the next section we provide an overview of current and
alternative methods for the assessment of environmental noise.
Then we describe our approach in section 3 and the prototype
NoiseTube platform we are developing in section 4. Further, in
section 5 we discuss the first experiments we have conducted to
assess the credibility of the sensor data. Next, in section 6 we
provide additional background and a discussion. Finally, in
section 7 we conclude this paper.
2. ENVIRONMENTAL NOISE ASSEMENT
2.1 Limitations of the current approach
Nowadays assessments of environmental noise in urban areas are
mainly carried out by officials who collect data at a sparse set of
locations, e.g. close to roads, railways, airports and industrial
estates, by setting up sound level meters during a short period of
time. Propagation models are then used to generate noise maps by
extrapolating local measurements to wider areas. This practice has
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Copyright © 2009. Copyright held by author.

a number of limitations, notably regarding the requirements of the
European Noise Directive [13] - or END for short.
Spatio-temporal data granularity: computational models often
produce results with an unknown error margin, which may lead to
incorrect conclusions regarding caused uncomfort [33]. As stated
by the EU practise guide [14], real data with high granularity in
both time and space is required. However, data collection at
sparse locations hardly scales to meet such requirements.
Furthermore, strategic noise mapping only allows detecting
general noise conditions. How can we monitor unusual local or
short-term noise pollution?
Cost: the cost of such noise mapping campaigns is high due to
need of expertise and human resources, the deployment of
expensive sound level meter devices and the processing effort.
This restricts cities with limited budgets from conducting such
assessments.
People noise exposure assessment: the EU practise guide [14]
requires detailed assessment of the level of noise citizens are
actually exposed to. However, few efforts have been done to
combine noise mapping and population data to assess the noise
exposure of citizens [34].
Indoor noise assessment: current noise mapping only covers
environmental noise, i.e. outdoor noise. However, most people
spend a significant portion of their time indoors and such indoor
exposure is reported in the maps (Fig.1: area in gray with no
information).
2.2 Alternative Approaches
2.2.1 Wireless sensor networks
Recent years have seen an increasing interest in wireless sensor
networks for environmental monitoring [31] and urban sensing
[7]. A wireless sensor network (WSN) is a wireless network
consisting of spatially distributed autonomous devices using
sensors to cooperatively monitor environmental conditions, such
as temperature, sound, air pressure or air quality, at different
locations.
Wireless sensor networks have the potential to revolutionize
environmental assessment, notably with regard to spatio-temporal
granularity. Rather than relying on a limited number of expensive,
accurate, stationary equipment, sensing, a WSN uses large
numbers of cheap, simple sensor devices. Sensors can be directly
embedded into the environment and operate continuously,
enabling a real-time monitoring of environmental phenomena (or
human activities).
A recent example of using WSNs for noise monitoring is
discussed in [32]. In this project noise sensors were placed at
fixed locations in an urban environment. However, it remains
questionable whether this method is cheaper than traditional
approaches for large-scale deployments. Furthermore, the sensors
are static and the way they communicate constrains their
placement to certain topologies. Moreover, the involvement of
citizens is not considered in this project.
2.2.2 Participation of citizens
To implement the requirements of the END [13], involvement of
citizens is key. This is especially important with regards to local
action plans, which often directly affect people living nearby. But
citizens can also contribute in earlier phases, such as during the
actual assessment of noise pollution.
In geography and urban planning there is a trend towards support
for such participation. Under the flag of participatory GIS [6] and
participatory mapping new methodologies are being researched to
better support the participation and involvement of citizens in
projects that are typically tackled using geographical information
systems (GIS), such as the mapping of spatial phenomena or land
use and urban planning.
Some interesting examples in the context of noise pollution
monitoring are [16] and [10, 21]. In the latter project researchers
reached out to citizens concerned with noise pollution in their
neighbourhood. The citizens were trained, coached and equipped
with noise level meters to create noise maps accessible through an
online GIS system.
While such projects focus more on methodologies for reaching
out to citizens and less on technical advances they have equally
inspired our approach.
3. APPROACH
Taking inspiration from wireless sensor networks and the trend
towards participation of citizens in mapping and urban planning,
we have developed a novel approach for the monitoring of urban
noise pollution, based on mobile phones.
Concretely, in the NoiseTube project we intend to use mobile
phones as noise sensors and actively involve the citizens that carry
them by allowing them to provide additional qualitative input
(noise source tagging, annoyance rating, ...).
In the remainder of this section we discuss and motivate this
approach in detail.
3.1 Mobile phone as an Environmental Sensor
The growing popularity of smart phones with significant
computational power, always-on Internet connectivity and
integrated sensors (e.g. microphones, cameras, GPS, motion
sensors) opens the door to a wide range of new applications.
These devices represent a cheap but powerful WSN platform that
is readily available and widely deployed. In this perspective
mobile phones can serve as sensors which are carried by humans
rather than placed at static locations. In addition to carrying
Figure 1. Official noise map of Paris generated
using a computational model and measurements
made at a limited number of locations and
times. Quiet areas are coloured in green while
noisy places are in purple. Gray areas represent
places for which no information is available (e.g.
in buildings).
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