Low-cost sensor network for wildfire monitoring and air quality measurements at remote community locations

Abstract

A sensor‑based microsystem for monitoring air quality related to wildfire emissions has been developed for deployment in a distributed network in a remote community. The sensor microsystem is equipped with particulate matter (PM2.5), carbon monoxide, carbon dioxide, ozone, and total volatile organic compound (tVOC) sensors. Root-mean-squared error for PM2.5 of less than 5 ug/m3 has been estimated. An onboard microcontroller-based control system synchronizes sensor data acquisition and communications. The microsystem is designed for operations in forest locations surrounding a remote community where cellular or Wi-Fi signals are not available. Radio communication protocol in mesh networking has been developed and tested in the range of 1–5 km in urban areas with structural barriers and outdoor terrains with vegetation. The air monitoring microsystem is designed for standalone operation with solar power, with a panel size of 50 × 37 cm that can be mounted on treetop configurations. The microcontroller-based architecture is designed for smart monitoring of air quality for optimization of energy usage toward sustainable operations throughout spring to fall, and potentially through winter seasons. System architecture has been developed to integrate several air monitoring microsystems in a network deployment to provide real-time information on potential wildfire status within several tens of thousands of hectares of forest area surrounding a remote community. Implications: This manuscript reports an innovative technology that can be deployed in forest locations surrounding remote communities in the typical northwestern prairie region in Alberta, Canada. These sensor systems will provide real-time data for air quality and wildfire events based on sensor data acquisition from forestlands. The distributed low-cost sensor network will address large gaps in provincial and federal air quality monitoring networks and can potentially be integrated with national data warehouses. These systems will inform policy on public health risk mitigation, emissions monitoring, land use policy, forest management, and climate change policies.