24 research outputs found
Participatory Patterns in an International Air Quality Monitoring Initiative
The issue of sustainability is at the top of the political and societal
agenda, being considered of extreme importance and urgency. Human individual
action impacts the environment both locally (e.g., local air/water quality,
noise disturbance) and globally (e.g., climate change, resource use). Urban
environments represent a crucial example, with an increasing realization that
the most effective way of producing a change is involving the citizens
themselves in monitoring campaigns (a citizen science bottom-up approach). This
is possible by developing novel technologies and IT infrastructures enabling
large citizen participation. Here, in the wider framework of one of the first
such projects, we show results from an international competition where citizens
were involved in mobile air pollution monitoring using low cost sensing
devices, combined with a web-based game to monitor perceived levels of
pollution. Measures of shift in perceptions over the course of the campaign are
provided, together with insights into participatory patterns emerging from this
study. Interesting effects related to inertia and to direct involvement in
measurement activities rather than indirect information exposure are also
highlighted, indicating that direct involvement can enhance learning and
environmental awareness. In the future, this could result in better adoption of
policies towards decreasing pollution.Comment: 17 pages, 6 figures, 1 supplementary fil
Participatory Patterns in an International Air Quality Monitoring Initiative
The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution
Awareness and Learning in Participatory Noise Sensing
The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments
Estimation error.
<p>Difference between estimated and real dB value vs the number of measurements a user has performed.</p
Perception evaluation versus the measured noise level.
<p>The red lines display the average evaluation over the first five measurements of all users; the green lines correspond to the average evaluation over the set of all measures taken by users starting from the 50th one.</p
Overall heatmap.
<p>Worldwide sample density, including all measurements, illustrated as a heatmap (© <i>OpenStreetMap contributors</i> for map data, used and redistributed under the CC-BY-SA licence<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081638#pone.0081638-OSM1" target="_blank">[26]</a>).</p
Tag screen for WideNoise Plus mobile application.
<p>Tag screen for WideNoise Plus mobile application.</p
Perception screen for WideNoise Plus mobile application.
<p>Perception screen for WideNoise Plus mobile application.</p
Noise sample screen for WideNoise Plus mobile application.
<p>Noise sample screen for WideNoise Plus mobile application.</p
Distribution of measured noise levels.
<p>The plot shows the histogram of noise levels for the first measurements performed by users, compared to those performed after some experience is gained (after the 50th measurement).</p