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Ultralow Power Sensor Package for Ground Level Air Pollution Levels from Wildland Fires

Award Information
Agency: Environmental Protection Agency
Branch: N/A
Contract: 68HERD19C0020
Agency Tracking Number: B182A-0004
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 18-NCER-2A
Solicitation Number: 68HE0D18R0010
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-05-01
Award End Date (Contract End Date): 2019-10-31
Small Business Information
8430 Central Avenue Suite C
Newark, CA 94560-3457
United States
DUNS: 803066802
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Melvin Findlay
 Sr. Scientist
 (510) 405-5911
Business Contact
 Edward Stetter
Title: VP/CFO
Phone: (510) 405-5911
Research Institution

Wild fires produce significant air pollution, posing health risks to first responders, residents in nearby areas, and downwind communities. Wildfires are increasing in size and intensity, and the fire season is growing longer. Technologies for measuring air pollutants, including particulates, carbon monoxide, nitrogen dioxide, and carbon dioxide, over the wide range of levels expected in areas downwind of wildland fires are needed.

KWJ proposes to integrate printed gas sensors and particle sensor into a single, <8oz package with the dimensions <4”x5”x1” 10x12.5x2.5cm). In Phase I, we propose using a prototype 7-gas board we have developed in collaboration with Intel, and integrate with Alphasense’s OPC-R1 PM sensor, which is the current state-of-the-art in miniature, optical particle detection. We plan to design a package which can be deployed in a variety of ways: worn by personnel, attached to “javelins” which can be located and relocated around the perimeter by shoving into the ground, on vehicles, and – with the rapid advancement in small UAV capabilities and range – deployed around the fire perimeter on drones. In Phase II we plan to build and test an electrostatic PM sensor, which will measure particles down to 5nm, and use far less power than the optical sensors.

* Information listed above is at the time of submission. *

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