Your City can Become a Leader in Air Quality Innovation
By utilizing our sensor’s abilities to detect airborne particulates such as pollen, mold, dust, and microplastics, improve your city’s air quality with consistent data you can rely on. Our particulate identification approach is led by professional aerobiologists, and automated by computer scientists. This approach has resulted in a nove air quality data where hourly reports can be given and detailed species information provided.Utilizing machine learning based on aerobiologist expertise has enabled our algorithms to delineate particles down the species of pollen and mold. Our data has no gaps, and consistently provides accurate and timely information. Know what’s in your air right now.
Healthy Air (Indoors & Out) is our Passion
We are inspired by the initiatives many smart cities and counties have taken to prioritize clean environments in urban settings. Air quality and public health are critically intertwined, and we’re looking to deploy our automated particulate sensors more broadly to assist city plans to become more environmentally conscious. When you can see what’s in the air at every moment, you can better inform policy decisions based on pollution and particulates. Be better able to see the results of efforts to achieve clean air. No need to wonder if certain initiatives are working when you can have access to quantitative data on your air.
Let's discuss possible partnerships to deploy our sensors to provide your municipality with our novel data. Included in partnerships could be IT support for sensors, maintenance products, as well as website widgets to display pollen, mold, dust, and microplastic counts to the public.
- Improve your city’s air quality analysis with consistent data you can rely on.
- No-gap data based on machine learning guided by computer scientists and aerobiologists.
- Real-time, hourly data on any particulates: pollen, mold, dust, microplastics, you name it.
- Better inform policy decisions based off of the best particulate data around.
