NA23OAR0210551

"The impact of wildfires on air quality in the surrounding hundreds of miles has been discussed since the ‘70s. There have been a number of studies investigating the severity of the impact of wildfire smoke on the health of people in affected areas that have shown that hundreds of thousands of people die as a consequence of smoke-tainted air. Many studies focus on the impact of particulate matter even though it is by far not the only harmful component of wildfire smoke. Wildfire smoke is a complex mixture of many different gases in addition to particulate matter. These gases include CO, NO2, SO2, CO2 and O3, all of which have severe health effects when surpassing certain threshold levels. Current available air quality assessment methods are not sufficient to properly warn people of wildfire emission tainted air. Additionally, there are certain areas where the density of air quality monitoring stations is not high enough and models are used to predict air quality. These models are often based on monitoring stations with low measuring rates (one data point every few hours or even less often) resulting in poor accuracy. Developing a low-cost sensor node that can track air quality with nearly comparable measurement performance to the expensive air quality measurement tools will allow for creation of a denser network of monitoring stations and combined with a significantly higher measurement rates (down to 1 value per second is possible with these sensors) would be of significant benefit to understanding the impact of wildfire smoke of air quality. The proposed research, based on prior work ([12], EPA and USDA grants), will test our hypothesis that the low-cost sensor nodes can provide AQ data significantly improved over existing systems at a 10-100 times lower cost."