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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | University of Washington |
| Country | United States |
| Start Date | Jun 01, 2021 |
| End Date | Dec 31, 2022 |
| Duration | 578 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2131855 |
The broader impact/commercial potential of this I-Corps project is developing an air quality management system which reduces indoor air pollution through heating, ventilation, and air conditioning (HVAC) automation. The proposed project has potential applications in both consumer and enterprise markets. Indoor air quality is a great concern among individuals with respiratory sensitivities and allergies, as well as residents of cities with high levels of air pollution.
An indoor air quality management system could increase the quality of life for these individuals. In addition to consumer applications, the proposed project has potential uses in workplace the importance of mitigating outbreaks of airborne disease. The real-time air quality management solution can be integrated into an existing HVAC system to help mitigate hazardous exposure by detecting and eliminating airborne contaminants.
The project aims to evaluate the commercial potential in saving energy costs, addressing regulatory needs, improving worker productivity, and eliminating airborne virus transmission.
This I-Corps project is a data-driven intervention method that monitors aerosol concentration in real-time and can alert users and systems about elevated levels of airborne contamination. This information can be used to initiate either a manual or automated mitigation response. The proposed system consists of hardware and software components.
The hardware component is a network of sensors that monitor particulate matter (PM), volatile organic compounds, and related variables, such as temperature and humidity. On the software side, web-based analytics software processes the data from the sensors. The sensor data can be aggregated to create a 3-dimensional mapping of the indoor air quality and the particulate matter levels.
The sensor network gathers time- and space-resolved aerosol concentration data using a mesh of low-cost, Internet of Things (IoT) enabled PM sensors to assess the aerosol persistence.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
University of Washington
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