Keeling Curve

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Project Summary / Description:

open-seneca deploys low-cost mobile air quality sensor networks driven by citizen science. The aim of the initiative is double fold: 1) to empower people with data acquired with low-cost and open-source sensors about their personal exposure to particulate pollution in order to drive behaviour change and 2) to provide high resolution air quality city maps that inform decision-making in tackling air pollution hot spots and complement existent monitoring networks. Raising awareness about personal exposure motivates communities to (a) take action to mitigate its impact on their health, (b) reduce individual greenhouse gas emissions.

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The essence of open-seneca is to empower communities with data about the air they breathe, providing them with the tools they need to act in minimising their exposure to air pollution and reducing GHG emissions. We particularly aim to empower the most vulnerable, often underprivileged, communities to actively fight environmental injustice with data. Our open and collaborative approach, based on open-source hardware, local capacity building and co-creation, and a strong focus on education, enables communities to take ownership of the project in their cities and makes it accessible to everyone, independently from background, age, gender, or race. We have successfully piloted the project working with UN offices, community-driven spaces and associations, and students from universities and schools. In these pilots, we have organised workshops for citizen engagement where a very broad range of citizens were able to build our sensor without the need of any prior knowledge and learn about the effects of air pollution and the importance of individual action, from drivers to children under 10 years old. Additionally, the low-cost nature of the open-seneca sensor enables to deploy monitoring networks in cities in low- and medium-income countries (LMICs) that cannot afford traditional air quality reference stations, and hence provide, for the first time, local air quality data that can help developing locally appropriate policies to tackle air pollution. Moreover, the mobile nature of the sensor allows to monitor all neighborhoods of the city without discrimination, highlighting environmental inequalities that can then be tackled. For example, the results from our pilot in Nairobi show that poorer neighborhoods are the more affected by very high levels of particulate matter (in the process of being published in a high impact, peer-reviewed academic journal). Our approach was able to highlight these inequalities and promote local leadership for change.

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