Clean Air in London Project shows the importance of data in combatting air pollution

Clean Air in London Project shows the importance of data in combatting air pollution

As in many other major cities, the air quality in London is pretty poor, resulting in thousands of premature deaths per year. When in 2015 air pollution levels were deemed so bad as to be illegal under EU law, the city decided it was high time to take action, devoting 28 million dollars to bring down pollution significantly in a decade.

The first step to achieve this, was to map the pollution in the city. All pollution that is, and not just the pollution caused by traffic. Luckily, London already has 100 high-fidelity sensors in place. But in order to reveal the factors which cause air pollution to a specific street level, the Clean Air in London Project now wants to distribute thousands of less accurate, but low-cost sensors among committed citizens who are concerned about the quality of the air they breathe.

Engaging the public

The project is an ambitious attempt to source data directly from citizens via an online platform. By engaging the public, London can avoid the expense of installing new sensors and win the flexibility to test specific ideas. In addition, these programs can save scientists and researchers enormous amounts of time and effort. Time and effort that they would otherwise need to pull in this information.

Citizens can be guided to sample locations that are of interest to the researchers: areas where the existing model has the highest uncertainty or where they simply want more information. But the success of the Clean Air in London Project depends on whether or not it can build a community of engaged contributors. In order to succeed, it will have to provide citizens with incentives to collect air quality data on their behalf. By offering some reward if they sample specific locations, for example.

Forecasting and reducing pollution

Of course, finding ways to collect new data is only part of the challenge; the real value for the city comes from what they can do with it. Thanks to machine learning, scientists will be able to understand what causes air pollution to fluctuate across the city. Resulting in a dynamic map that will be able to provide high-resolution forecasts and real-time coverage. And giving government a tool that will allow them to test the impact of specific interventions in minute detail.

The Clean Air in London Project is a great initiative, showing the importance and the utility of data. It is not difficult to imagine the impact a player like DataBroker DAO can have on this evolution and on the development of smart cities in general. By offering these cities a platform that not only lets them acquire the necessary data to outline their policy, but also enables them to recuperate some of the costs by reselling the resulting data to third parties.

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