It has been great to be able to work with such a large number of students and share live local air quality data with them. All the schools have seen the benefit in understanding what the data is telling them, and how this can be used to inform decisions about how and when to travel.

“Kathmandu Metropolitan City (KMC) has achieved a historic milestone by launching a real-time air quality monitoring dashboard. City residents can access live air quality updates, location-specific AQI, and an interactive map with a color-coded system ranging from good (green) to hazardous (red). With 18 Clarity air quality sensors installed across the city, the system supports evidence-based policymaking and promotes public awareness to safeguard the health of all—especially youth and vulnerable groups.”

"Clarity’s team helped facilitate our understanding of the air quality space as a whole. We are now checking in with the Clarity team about once a month to touch base about the network and learn how to use the Clarity Dashboard. Having all of our data available in a secure web portal is very useful, especially as we prepare data reports. Brightline wants to make sure we can take all these data streams and analyze them to highlight important air quality issues that the community should know about."

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“We chose Clarity because they provided up-to-date, local, scientifically-backed, and relevant data to make decisions about whether to keep students inside. Because of the geography and demographics in our county, the official air monitoring stations are about an hour away with hills in between, providing irrelevant data. We would have poor air quality and the official stations would be cleared; or the official stations would show heavy particulate levels and we would have clean air."

Having a larger network of sensors where we have a better resolution of where air pollution is worse or better will be beneficial in allowing us to change behaviors, educate people more, or do all sorts of things to improve the health of those people

"We needed high-density data to develop a model that was representative of a real situation on a very local scale – so multiple devices and multiple locations. The Clarity Node-S was ideal for this use case."