Mobile pollution monitoring helps clear the air

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We are all exposed to pollution as part of our everyday lives. Pollution can be a problem, and even a health risk, particularly in busy, urban areas where there are likely to be large quantities of traffic. Air quality regulations aim to limit exposure to unreasonable or dangerous concentrations of pollutants. However, accurately measuring concentrations over a wide area can be difficult. Existing monitoring equipment tends to be large, expensive and provides results over only a small area. The MESSAGE project was set up to provide a fine-grained data collection and analysis infrastructure using mobile sensors and a distributed, scalable computing platform.

The cost of existing roadside pollution monitoring infrastructure means that the number of monitoring stations deployed within a given area is generally limited. These sites provide information about their immediate area, but urban environments, with many buildings and tightly packed streets, have complex airflow characteristics that affect how pollutants move around the environment. Predicting pollution levels, even over a short distance from a monitoring station, can be very difficult. Instrumenting an environment with static sensors is equally impractical due to the large number of sensors that would be required, the cost of deploying and managing the sensors and the difficulties involved in transferring data to collection points.

MESSAGE (Mobile Environmental Sensing System Across a Grid Environment) uses mobile sensors, which are placed on vehicles and take pollution samples as the vehicles travels through the environment. Initial deployments of the mobile sensor are focussing on  commercial vehicle fleets and public transport vehicles, such as buses. By covering a large area over a period of time, and by linking new data samples with historical information about an area, a picture of the pollution across a geographical area can be constructed. As the number of active sensors is increased, the frequency of readings obtained for a given geographical location can also be increased, which allows a finer-grained and more accurate pollution map to be produced. The network of mobile sensors can be augmented with static sensors in areas that have particularly complex characteristics, or in areas of particular interest. Ultimately, it is possible to envisage a situation where private cars host advanced sensors that can report their readings to a distributed infrastructure in order to maintain a real-time map of pollution.

The ability to capture such detailed information is a significant improvement over current pollution monitoring, but what might this information be used for? There are a number of potential uses, and many more will emerge over time. For example, the availability of a recent or real-time pollution map could enable pedestrians and cyclists to receive advice on the least polluted route from one location to another. Individuals with medical conditions, such as asthma, could receive real-time alerts through a GPS-equipped mobile phone, which would warn them before they entered an area where pollution levels could represent a risk to their condition. In addition to the direct benefit to individuals, councils and similar organisations could also benefit from the pollution data. For example, pollution data could be used to identify pollution hotspots so that they could be mitigated—possibly by changing traffic movements through real-time alteration of traffic signals.

The MESSAGE project is a collaboration between Imperial College London and the Universities of Cambridge, Newcastle, Southampton and Leeds, with support from 19 industrial partners. It has developed a powerful prototype infrastructure to showcase the potential opportunities and benefits of a new system for environmental monitoring and data analysis. Ongoing sensor deployments in Leicester and Gateshead, and recent demonstrations and data-capture exercises in London and Cambridge, have shown the potential offered by the project’s mobile sensing platform.

http://bioinf.ncl.ac.uk/message/