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The Development and Evaluation of Multi-Pollutant Sensor Systems for Measurement of Wildland Fire Smoke
Citation:
Landis, M. The Development and Evaluation of Multi-Pollutant Sensor Systems for Measurement of Wildland Fire Smoke. Virtual NIH Workshop: Getting Smart About Sensors for Disaster Response Research Workshop, NA, July 28 - 29, 2021.
Impact/Purpose:
Sharing experience and results of EPA Wildland Fire Sensor research program to inform NIH discussion on future research funding for sensor development/measurements during distasters including (i) floods and storms; (ii) wildfires, and (iii) industrial disasters and chemical spills.
Description:
Wildland fires can emit substantial amounts of air pollution that may pose a risk to those in proximity (e.g., first responders, nearby residents) as well as downwind populations. Quickly deploying air pollution measurement capabilities in response to incidents has been limited to date by the cost, complexity of implementation, and measurement accuracy. Emerging technologies including miniaturized direct-reading sensors, compact microprocessors, and wireless data communications provide new opportunities to detect air pollution in real time. The U.S. Environmental Protection Agency (EPA) has a desire to advance wildland fire air measurement technology to be easier to deploy, suitable to use for high concentration events, and durable to withstand difficult field conditions, with the ability to report high time resolution data continuously and wirelessly. EPA has encouraged innovation to develop sensor prototypes capable of measuring fine particulate matter (PM2.5), carbon monoxide (CO), carbon dioxide (CO2), and ozone (O3) during wildfire episodes through the Wildland Fire Sensor Challenge, Small Business Innovation Research (SBIR) grants, and internal research projects. The importance of using federal reference method (FRM) versus federal equivalent method (FEM) instruments to evaluate performance in biomass smoke is discussed. Sensor evaluation results including accuracy, precision, linearity, and operability are presented and discussed. Raw solver submitted PM2.5 sensor accuracies of the winners ranged from ~22-53%, while smoke specific EPA regression calibrations improved the accuracies to ~75-93% demonstrating the potential of these systems in providing reasonable accuracies over conditions that are typical during wildland fire events.