In this project, the existing CI Rainbow infrastructure in the CI Park is adapted to implement and test an early warning air quality monitoring system that fulfills the requirements of the Wildland Fire Sensors Challenge set by the US Environmental Protection Agency (EPA).

The CI Rainbow infrastructure fundamentally satisfies the data collection and notification requirements of the EPA challenge, and therefore can be extended and adapted to support air quality monitoring. To satisfy the EPA requirements, the following extensions to the framework are necessary:

  • installation of several air quality sensors,
  • adding RaspberryPI-based sensory nodes to interface with the air quality sensors and relay data to the data collection center, and
  • installation of several solar power packs for powering the sensory nodes and the sensors.


In addition, advanced research is being conducted into using Artificial Intelligence to predict air quality in a given area based on the surrounding fires and weather conditions. While not a formal requirement of the EPA challenge, such capability will substantially enhance the monitoring infrastructure.

The challenge calls for air quality sensors with a number of specific capabilities to detect constituents of smoke including particulates, carbon monoxide, ozone, and carbon dioxide. There are numerous sensors available in various price ranges; selecting 3-4 that are suitable would be part of the proposed research. The air quality sensors are often deployed at the locations that are away from the power grid. Therefore, sustainable power supply – such as from solar panels – needs to be considered. The CI Rainbow infrastructure already employs a number of solar-based power packs, but they need some repairs and adjustments. Some of the CI Rainbow equipment has been battered by storms and Santa Ana winds over the last two years, so it needs some replacement parts. Deployment and testing in the field requires several individuals that can configure distant endpoints of communication links as lines of sight must be ensured.

The following are the necessary elements of implementing and deploying an air quality sensory network:

  1. Research and purchase suitable equipment (air quality sensors, RaspberryPIs, and solar packs).
  2. RaspberryPI-based sensory nodes must interface with the air quality sensors. That requires:
    1. provisioning and configuration of the CI Rainbow core software,
    2. a specialized driver that communicates with the specific selected air sensor, and
    3. a JSON data formatter for messages destined for the data collection center.
  3. Extending the CI Rainbow Data Collection server to store the air quality data.
  4. Extending the CI Rainbow Django-based Web app to access and visualize the air quality data. The access will be through a Web browser and from anywhere on the Internet.
  5. Event notification module with a rule-based system that will handle dispatching alerts (e.g., through email, text messaging, phone calls, etc.).
  6. Physical deployment of the sensors.
  7. Repairs to the network and verification of the sustainability (7/24 operation).
  8. Integration and testing of the complete air quality monitoring system including data collection, data access, event notification, and alert dispatching.