Final Report: Remote Air Quality Reporting (RAQR) Device

EPA Contract Number: 68HERD19C0022
Title: Remote Air Quality Reporting (RAQR) Device
Investigators: Norell, Jeffrey
Small Business: Intellisense Systems, Inc.
EPA Contact: Richards, April
Phase: I
Project Period: May 1, 2019 through October 31, 2019
Project Amount: $99,999
RFA: Small Business Innovative Research (SBIR) PHASE I (2018) RFA Text |  Recipients Lists
Research Category: SBIR - Air Monitoring and Remote Sensing , Small Business Innovation Research (SBIR): Phase 1 (2019) , Small Business Innovation Research (SBIR)

Description:

Intellisense Systems, Inc. (ISI) is advancing the development of a new Remote Air Quality Reporting (RAQR) device to measure and track wildland fire pollutants including carbon monoxide, carbon dioxide, nitrogen dioxide, and particulates, in a small, one-person portable, easy-to-use unit capable of storing and reporting data. As more frequent wildfires are expected to occur near populated areas, persistent air-quality monitoring will become more important. The RAQR system is based on ISI’s existing technology readiness level (TRL)-7/-8 remote meshed sensor technology that can harvest solar power and communicate through radio, cellular, or Iridium, coupled with a new innovative, compact air-quality sensor suite. As RAQR is small, simple to set up, and does not require hardline power, it will enable the end-user to deploy a robust network of air-quality sensors rapidly and at low cost, which can be used to track air quality around densely populated areas at significantly higher resolution than currently possible and can also serve as an early warning system for fire detection.

Summary/Accomplishments (Outputs/Outcomes):

The RAQR device developed and prototyped during Phase I demonstrated the feasibility of a remote, low-power, low-cost device capable of monitoring and reporting air quality data and alerts in near real-time. ISI’s Phase I prototype can measure carbon monoxide, carbon dioxide, nitrogen dioxide, and particulates, as well as humidity and temperature, from a custom compact, easily installed, self-contained sensing module. This integrated sensor suite coupled with ISI’s reporting module can be integrated into a fully autonomous, wireless meshed network with a simple setup with an indefinite lifetime due to low-power sensing technology and solar energy harvesting.

Conclusions:

The RAQR sensing module, when combined with ISI’s mature communications module technology, has the potential to revolutionize wildland fire monitoring, reporting, and user alerts from a low-cost, compact, easily installed system. At approximately 1/10th the cost of legacy, bulky air quality monitoring systems, a RAQR sensing network will greatly increase sensing resolution of areas at risk of wildfire as well as general air pollution at significantly lower installation and sustainment cost compared to the current state of the art. This dense sensing network will allow for earlier detection and alerts of potential fires through increased spatial resolution, as well as provide valuable data to enhance existing weather and smoke modelling. The foundation built during Phase I development will be expanded in Phase II to enhance RAQR’s performance and integration with existing software platforms to increase usability and value to the customer even further. The result of RAQR development will culminate in a product with the same demonstrated success as ISI’s other weather and environmental sensing technologies that have been deployed and are performing in adverse environments in 60+ countries throughout the world.

ISI has reached out to and had discussions with both the National Interagency Fire Center (NIFC) and United States Forest Service (USFS) regarding RAQR and other weather and environmental sensing technologies. The National Forest Service emphasized that early warning tracking is needed and ultimately inclusion into the National Fire Danger Rating System (NFDRS) and risk models are needed. They provided the example of the fires in the Western United States in 2017 and 2018 where a dense underlayer led to significant smoke issues. This is something ISI will explore with either internal funding, SBIR development funding, or through other funding vehicles such as rapid innovation funds (RIFs) in a future effort. The California state government has also had open calls for innovative technologies and ISI will apply to future requests for information (RFIs) as appropriate.

ISI also submitted the RAQR technology to the University of California, Davis (UC Davis) Air Sensors International Conference, which is hosted by the Environmental Protection Agency (EPA) and California Air Resources Board. ISI views this conference as an opportunity to demonstrate and grow ISI’s existing environmental monitoring portfolio by demonstrating our commitment to developing innovative air quality and weather sensors to commercial and government markets. In addition, RAQR will be presented at the 2019 Wildland Fire Canada conference hosted in Ottawa, Canada, under the topic areas of “Connected Sensors” and “Innovated Solutions for Wildfires”. With the primary conference topic being focused on large- and small-scale impacts of wildfire smoke, RAQR provides an objective monitoring tool to support citizen safety and protection.

Located in Southern California, ISI understands the urgent need for rapidly-deployable, low-cost, and dependable air quality sensors for early detection of fires, warnings, and health monitoring in dense urban areas such as the greater Los Angeles area and views commercialization of the RAQR technology as a strategic initiative. ISI has discussed the integration of RAQR’s full suite of air quality data into ClimaCell’s HyperCast forecast software for impact prediction. ClimaCell is a rapidly growing company with an extreme level of accuracy for weather forecasting and has added a new focus on providing a similar service of air quality warning. With limited sensors monitoring these parameters currently in the United States, RAQR will provide dense network data inputs to verify and enhance model prediction to support improved situational awareness.