OpenSense 2

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OpenSense II: Sensing the air we breathe

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The OpenSenseII project funded by aims at integrating air quality measurements from heterogeneous mobile and crowdsensed data sources, in order to understand the health impacts of air pollution exposure, and providing high-resolution urban air quality maps.


A major issue for obtaining accurate and high-resolution air pollution maps is the tradeoff between the cost of the measurement device and its accuracy. In OpenSense II we plan to integrate all available data sources starting from the infrastructure developed in the original OpenSense project, together with the high-end static stations of the NABEL network and crowd-sourced data. In addition, we will study the impact of exposure to air pollution on human health and evaluate the potential of crowdsourcing for providing feedback to users.

OpenSense and OpenSenseII in a nutshell

Novel sensing technologies can provide air quality data with unprecedented temporal and spatial resolution. This opens exciting new opportunities for the study of urban air quality and its impact on health. However, as opposed to traditional, expensive, and highly accurate air quality measurements, the use of dense networks based on low-cost sensors is largely unexploited. An important issue for obtaining accurate and spatially highly resolved air pollution data is the trade-off between high cost of accurate air pollution monitoring sensors and the number of such devices required for succinctly monitoring a given geographical area.

Crowdsourcing is a divide-and-conquer technique that has been successfully used for leveraging the intelligence of the crowd (or community) for solving many problems that require community participation (e.g., conducting online polls). Concretely, crowdsourcing can be defined as a participative online activity performed by a group of individuals (or intelligent machines) for mutual benefit between group members or for various other incentives (economic, social recognition, self-esteem, social responsibility), while the crowdsourcer will obtain and utilize the data collected by the user to his/her advantage.

In OpenSense2 we are leveraging the results of the Nano-Tera project OpenSense, particularly on: mobile monitoring of air pollution, sensor and communication platforms, calibration methods, sensor data gathering and visualization, statistical modeling, activity recognition, and personalized health recommendations. By adding the dimension of crowdsourcing and human-centric computation we will study possibilities to incentivize users to make available states based on physical measurements, such as location, motion and pollution, through their mobile personal devices or monitoring assets that they can install in their homes or on their cars.

Using a dispersion model we will compute high-resolution air pollution maps for the cities of Zurich and Lausanne. The model results will provide independent and validated information on air pollutant distributions and will thereby greatly help assess the quality of the sensor data and their suitability to measure city-scale air pollution levels. In addition, we will study concrete applications that measure the impact of long- or medium-term exposure to air pollution on human health and evaluate the potential of crowdsourcing for providing feedbacks to users.

OpenSenseII in the media

  • NZZ: [1]
  • Tagesanzeiger: [2]
  • Blick: [3]
  • Blick am Abend: [4]
  • 20Minuten: [5]
  • ETH News: [6]
  • Tele TOP: [7]
  • BAFU Magazin Umwelt 2/2014 (page 37): [8]