Wildland Fire Research: Smoke Measurement
During wildland fire smoke events, people rely on timely public health warnings to help them take appropriate actions. Those warnings rely on accurate and real-time air pollution measurements. While the nation’s established ambient air monitoring network does not cover all locations affected by wildland fire smoke, sensors can help fill those data gaps. However, many air monitoring technologies have not been tested or validated under high smoke concentration conditions, leading to uncertainties about their accuracy and robustness.
On this page:
- Uncrewed Aircraft System for Safe Sampling of Wildfire Smoke
- Performance of Continuous Air Quality Monitors in Smoky Conditions
- Performance of Small Filter-based Air Samplers to Measure Smoke
- Challenge: Wildland Fire Sensors
- Evaluation of Particulate Matter Sensors for Measuring Wildfire Smoke
- Including Sensor Data on the AirNow Fire and Smoke Map
- Wildfire Smoke Air Monitoring Response Technology (WSMART)
- Prescribed Fires vs Wildfires: A Comparative Assessment
- Future Directions
Uncrewed Aircraft System for Safe Sampling of Wildfire Smoke
How can we measure PM2.5 emissions from smoke during either a wildfire or prescribed fire while also keeping people and equipment out of harm’s way? As part of EPA’s wildland fire measurement research, researchers in the Mobile Ambient Smoke Investigation Capability (MASIC) study used an uncrewed aircraft system (“UAS” or “drone”) known as the Kolibri for aerial emission measurements.
Researchers flew sixteen flights and collected a sample for each flight using the Kolibri, plus one ground-based and one background ambient air sample. Researchers identified two key findings: 1) PM2.5 emission factors varied 5-fold with emissions typically decreasing as the combustion efficiency increased, and 2) lower emission factors were measured from the burning of slash piles, compared to burning forest crowns.
This was the first time a UAS was used to sample smoke emissions for a prescribed fire with wildfire-like conditions, and the Kolibri allowed unprecedented access to capture fresh smoke, while minimizing risk to the operators and equipment. This flexibility allows us to better understand how PM2.5 emissions vary with the fuel type (i.e., the materials burned) and fire conditions (i.e., smoldering or flaming).
Wildland fire emission sampling at Fishlake National Forest, Utah using an uncrewed aircraft system (2021)
Performance of Continuous Air Quality Monitors in Smoky Conditions
Continuous PM2.5 air quality monitors are widely used, automated, and provide real-time measurements, including during wildland fire events. However, smoke particles can interfere with how PM2.5 is measured and make it challenging to obtain accurate data. How can we evaluate the use of continuous monitors in smoky conditions?
Researchers in the MASIC study evaluated two continuous PM2.5 air quality monitors against a filter-based PM2.5 air quality monitor designated as a Federal Reference Method (FRM) - and the gold standard in accuracy - in high smoke conditions.
Researchers observed that the T640 had large positive and negative measurement artifacts and should be used with caution. The BAM1020 performs better in smoke and is comparable to the filter-based FRM in smoky conditions.
These findings suggest that additional care should be taken to ensure accuracy of PM2.5 measurements using optical monitors during smoke events. State and local air monitoring agencies can immediately make use of this information to evaluate their monitoring programs.
Performance of Small Filter-based Air Samplers to Measure Smoke
How do smaller sized filter-based air samplers perform relative to regulatory-grade air quality monitors during wildfire smoke events?
Researchers in the MASIC study partnered with the USDA Forest Service Rocky Mountain Research Station to evaluate the performance of three models of commercially-available filter-based PM2.5 samplers against filter-based PM2.5 FRM air quality monitors. All samplers performed well in determining total PM2.5 concentrations with accuracies ranging from 93.1 to 98.2%.
The results provide confidence that small-sized, filter-based samplers can provide scientifically and regulatory relevant PM2.5 concentration data at lower cost and with easier deployment during wildland fire smoke events. The samplers can be used to fill in large spatial gaps in the air quality data and can help evaluate the field performance of other non-regulatory monitors and sensors under real-world smoke conditions.
Challenge: Wildland Fire Sensors
Sensors need to be portable, durable, reliable, wireless, comparable to regulatory monitors, and capable of measuring fine particulate matter (PM2.5), carbon monoxide (CO), ozone (O3), and carbon dioxide (CO2) during wildfire episodes. How can we encourage technological innovations? How can we test the performance of the new technologies?
The Wildland Fire Sensors Challenge was the result of a multi-agency effort to spur innovations in air measurement technology. The Challenge increased awareness of monitoring needs during wildfires and catalyzed the next generation of sensor technology systems for wildland fire applications. These sensors can greatly increase our knowledge of the temporal and spatial variation of smoke and provide information to better protect public health.
Winners of the Wildland Fire Sensors Challenge
Evaluation of Particulate Matter Sensors for Measuring Wildfire Smoke
Ambient air quality is routinely measured by PM2.5 regulatory monitors but they are expensive and require infrastructure and space for proper siting. Public interest in using PM2.5 sensors to provide additional air quality measurements near homes and schools, as well as in more remote areas, is growing. How do these sensors perform relative to regulatory monitors? How do these sensors perform in smoky conditions?
After numerous experiments making measurements with sensors and regulatory monitors side-by-side, researchers developed correction equations specific to each sensor, which allows a sensor’s PM2.5 concentrations to be compared with current smoke monitoring networks. Sensors can be deployed in large numbers to fill in large spatial gaps in monitoring networks near wildfires, which greatly increases our knowledge of the temporal and spatial variation of smoke and helps support public health guidance.
Field Evaluation of Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke (2020)
An analysis of degradation in low-cost particulate matter sensors (2023)
Including Sensor Data on the AirNow Fire and Smoke Map
As PM2.5 sensors become increasingly common across the U.S, can these sensors help communicate air quality information during wildfire smoke events?
Researchers developed a correction equation to improve the comparability of sensor data with regulatory-grade monitors which was critical in being able to display data from PM2.5 sensor networks on EPA’s AirNow Fire and Smoke Map, a collaborative effort with the U.S. Forest Service (USFS). This gives the public valuable and timely air-quality information during smoke episodes, especially where there are spatial and temporal gaps.
EPA’s Technical Approaches for the Sensor Data on the AirNow Fire and Smoke Map
Correction and Accuracy of PurpleAir PM2.5 Measurements for Extreme Wildfire Smoke (2022)
Wildfire Smoke Air Monitoring Response Technology (WSMART)
How can we provide frontline responders with easy-to-use and reliably performing monitoring solutions during wildfire smoke episodes in remote locations?
WSMART sensor loans provide supplemental sensors and mobile monitoring to help fill knowledge gaps in areas affected by wildfire smoke without fixed site monitors. The sensor measurements help assess the exposure of frontline workers and communities to smoke and add observations to inform air quality models.
Over the past few years, the WSMART project has made over 60 loans to eligible participants. EPA researchers loan the monitoring technologies and provide training on the use of the equipment to state, local, and tribal governments. Loans have also been made to Air Resource Advisors (ARAs), technical specialists deployed with wildfire Incident Management Teams to provide smoke expertise.
EPA Science Matters: EPA Scientist Serves as Air Resource Advisor Trainee at the Lookout Fire
EPA Science Matters: EPA Expands Air Monitoring Capabilities to Support Wildfire-Impacted States, Tribes, and Their Frontline Firefighters
Prescribed Fires vs Wildfires: A Comparative Assessment
Prescribed fires can reduce biomass accumulation and thus reduce the risk of catastrophic wildfires. With the expanded use of prescribed fires, what are the trade-offs between the air quality and health impacts of smoke from prescribed fire compared to wildfire?
Predicted concentrations of PM2.5 from prescribed fires were smaller in magnitude and shorter in duration than hypothetical wildfire scenarios or actual wildfires. The CAIF report concluded that well-designed prescribed fires may be able to reduce the size and intensity of future wildfires and ultimately reduce negative air quality and health impacts.
This information provides guidance for multiple levels of government in planning for future land and fire management activities, and to the public and land managers in making more informed decisions, particularly as prescribed fires are increasingly being used to try to reduce the risk of future catastrophic wildfire and improve forest health.
Future Directions
EPA researchers have increased coverage of particulate matter concentration measurements during wildland fire smoke events, supplemented air monitoring in areas affected by wildfire smoke through the WSMART program and used air monitoring instruments to analyze smoke composition and predict where smoke would travel. This work has helped reduce public health risks.
- Future research can help develop and evaluate air quality sensors for pollutants beyond particulate matter.
- With demonstrated state, local, and Tribal interest in supplemental air monitoring, a possible focus is on sensor loan availability and training to support the WSMART program.