# Forecasting the Impacts of Prescribed Fires
If the impacts of different emission sources are forecast, then appropriate control measures can be taken to reduce those impacts and achieve better air quality [12]. PB is one emission source that can be controlled more easily than other sources since burn/no-burn decisions are made every day. Through the permitting systems already in place in several Southeastern states, burns can be restricted on imminent poor air quality days and encouraged when meteorological conditions are more favorable. The application of DAQM in this manner may lead to maximized burn capacity while minimizing the impacts on air quality. It can also help to reduce the risk of human exposure to high levels of fire smoke. Current air quality forecasting systems are not equipped with the tools necessary for the dynamic management of PB operations. Typically, prescribed fires are assumed to be county-wide area sources with emissions equal to the average of several past years. The day-specific emissions used in the forecasts are generated by taking the rolling average over a specified period of daily prescribed fire emissions in a particular county for the years included in the average [11]. For example, if the selected averaging period is \(+/-7\) days and the years included are 2011-2015, then for 15 April, all fires in that county are averaged from 8 April to 22 April for 5 years between 2011 and 2015. Such averaging greatly reduces the day-to-day variability compared to the actual prescribed fire emissions. It also smooths out the fires spatially by averaging multiple years of fire emissions over each county. The result is more frequent, but less intense, fires over larger spans, which is not appropriate for an air quality forecasting system to be used in dynamic management. The National Air Quality Forecasting Capability (NAQFC) handles wildfire emissions by tracking satellite-detected ongoing fires that last at least 24 h [13]. Prescribed fires are typically of shorter duration; they burn out in a few hours after their detection. Therefore, prescribed fires cannot be tracked in the same manner as wildfires for air quality forecasting purposes. Estimating prescribed fire emissions from satellites is also problematic as it has been shown that satellites seriously underestimate the burn areas in Southeastern USA [4]. What is needed is a burn activity and impact forecasting system that can provide information well in advance so that burns can be managed through the existing permitting systems. Using a decision tree, we built a model to forecast how much PB activity is going to occur, when and where in Georgia, based on weather and historic burning patterns. Classification and regression trees are one of the simplest and easiest to interpret statistical learning methods that can be built to make a prediction for an observation from predictor variables. Since they split the predictor space into segments that look like the branches of a tree, they are called tree models. In the past, classification and regression trees have been used in the air quality field for various other purposes. Examples include the generation of annual mean concentrations from short episodes [14], model evaluation [15; 16], the prediction of peak ozone or PM\({}_{2.5}\) episodes [17], and air pollution epidemiology [18]. To our knowledge, this is the first time a decision tree has been used to forecast prescribed fire activity. We started using this burn activity model to forecast prescribed fire emissions and their impacts on air quality using an air quality forecasting system. In what follows, the burn activity forecasting decision tree model and the methods used for prescribed fire emissions estimation are described in detail. The air quality forecasting system and its fire impact forecasting capability are reviewed. An evaluation of the burn activity forecasts during the test operation in 2015 and the production operation in 2016 are presented. This evaluation includes comparisons to satellite observations and ground-based accounts of fires.