Air pollution is the single biggest environmental health threat of our time, killing 7 million people and costing the world economy 5 trillion USD per year. Data-driven decision-making remains unfeasible as sensing equipment is expensive, stakeholders lack the necessary knowledge to analyse the data, and suitable interventions are unknown. Breeze deploys its own low-cost air quality sensors that detect all common air pollutants. Thanks to an artificial intelligence-based cloud calibration technology, data gathered by these sensors is comparable to public monitoring networks in quality and accuracy. Based on the gathered data and external data sources a hyperlocal map of air quality is generated. Air quality challenges are matched with a catalogue of more than 3.500 clean air actions and their potential impact in a local implementation is calculated. An artificial intelligence then recommends the most efficient interventions for local challenges, potentially raising the effectiveness of clean air action plans by 10 X. Part of our projects is regularly deploying air quality sensors with local citizens as "clean air champions", enabling them to tell stories about air pollution in their neighborhoods, sharing air quality data with the public through digital means like our citizen portal, as well as through events, and work with governments to develop, implement and track the success of clean air action plans. To facilitate our work with the public, we regularly work with focus groups consisting of our customers (i.e. members of the municipal administration), as well as citizens and environmental NGOs.

Breeze deploys its own low-cost air quality sensors that detect all common air pollutants. Thanks to an artificial intelligence-based cloud calibration technology, data gathered by these sensors are comparable to public monitoring networks in quality and accuracy. Based on the gathered data and external data sources a hyper-local map of air quality is generated. Air quality challenges are matched with a catalogue of more than 3.500 clean air actions and their potential impact in a local implementation is calculated. AI then recommends the most efficient interventions for local challenges, potentially raising the effectiveness of clean air action plans by 10x.