You are here

Developing a robust and scalable calibration approach to low-cost AQ sensing

Award Information
Agency: Department of Commerce
Branch: National Oceanic and Atmospheric Administration
Contract: 1305M218CNRMW0058
Agency Tracking Number: 18-1-099
Amount: $119,990.60
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 8.1.3
Solicitation Number: NOAA-2018-1
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-07-17
Award End Date (Contract End Date): 2019-01-16
Small Business Information
45 Manning Road
Billerica, MA 01821-3976
United States
DUNS: 030817290
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Eben Cross
 Principal Research Scientist
 (978) 663-9500
Business Contact
 Jiri Cistecky
Title: CFO
Phone: (978) 932-0217
Research Institution

TECHNICAL ABSTRACT: Poor air quality represents a major public health risk, contributing to an estimated one of every eight deaths worldwide. Low-cost air quality sensors have developed rapidly over the last few years and offer the opportunity to make air quality monitoring inexpensive and widespread. However, low-cost sensors are currently unable to deliver accurate, reliable data due to a lack of understanding of what parameters impact performance and due to calibration models that inadequately describe sensor behavior under ambient conditions. This proposal will develop improved calibration models for electrochemical sensors utilizing realistically constrained, shortterm laboratory calibration chamber experiments and long-term (24-month) sensor-to-reference co-location field deployments. Various machine learning techniques will be trained and evaluated using sensor and reference data from both calibration chamber experiments and ambient co-located field data. The end result of the Phase I project will be a robust calibration method that clearly identifies the parameters that impact performance over the lifetime of electrochemical sensors. The Phase I effort will provide the empirical foundation from which the optimized machine learning techniques can be adapted in Phase II to provide robust, universal calibration methods for electrochemical sensor systems at scale.SUMMARY OF ANTICIPATED RESULTS: The calibration method for low-cost air quality sensors developed in this project will lead to widely expanded measurements of air quality and to improved understanding and mitigation of air pollution. It will also increase sales of Aerodyne’s low-cost air quality sensor package.

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

US Flag An Official Website of the United States Government