# Forecasting the Impacts of Prescribed Fires
## 3 Results
### Evaluation of Burn Impact Forecasts  
The overall statistics of the 2016 burn impact forecasts are shown in Figure 7. If the maximum afternoon PM\({}_{2.5}\) concentration on a particular day is in the 95 th percentile of all observed afternoon maximum concentrations during the burn season, then that monitor is most likely impacted by PB. To be sure, burn permits in the county in which the monitor resides as well as the counties surrounding the monitor were reviewed to see if there were any permitted burns that may have affected the observations on that day. The 95 th percentile of all observed afternoon PM\({}_{2.5}\) concentrations was 32 \(\upmu\)g m\({}^{-3}\). On the other hand, the 95 th percentile of all forecast maximum afternoon PM\({}_{2.5}\) concentrations was 43 \(\upmu\)g m\({}^{-3}\). The common threshold for forecast evaluation was set at 32 \(\upmu\)g m\({}^{-3}\) as shown in Figure 7. The precision, recall and \(F1\) score for correctly predicting the PB impacts over 32 \(\upmu\)g m\({}^{-3}\) were equal to 19%, 36%, and 25%, respectively.  
Figure 6: Forecast from county-specific models versus permitted (**a**) daily total burn areas in Georgia; and (**b**) county total burn areas for the January–April 2016 period.  
The number of false positives is approximately three times as large as the number of false negatives in Figure 7, which indicates a general overestimation of the burn impacts. This may be, in part, due to the overestimation of the burn areas for low burn activity and underestimation for high burn activity days. To investigate other possible reasons, we focused on the true positives in Figure 7 and analyzed the relationship between the bias in burn impacts and the bias in burn areas. For the true positives that were associated with the PM\({}_{2.5}\) monitors in Albany (Daugherty County) and Augusta (Richmond County), Georgia, we calculated the burn areas in the county where the monitor resides and in the immediate upwind county (or counties depending on wind direction). The difference between the forecast and permitted burn areas were normalized by the permitted burn area and plotted on the \(x\)-axis in Figure 8. Note that the burn areas were underestimated with the exception of one day, 24 January 2016, in Daugherty County and its upwind county. The difference between the forecast and observed afternoon maximum PM\({}_{2.5}\) concentrations were normalized by the observed concentration and are plotted on the \(y\)-axis in Figure 8. The strong correlation between the two biases (R\({}^{2}\) = 0.77) suggests that the overestimation of the burn impact may be related to an overestimation of the emissions, because the emissions are proportional to burn area. The slope of the linear regression line suggests that this emission overestimation may be around 47% (\(\pm\)23%). The reason for this overestimation may be either an overestimation of the fuel consumption or high biased emission factors. The large intercept (0.96 \(\pm\) 0.16) indicates that there must have been another factor that was not emission related. One potential reason is plume dispersion. The fraction of the actual smoke plumes penetrating into the free troposphere may have been larger than the fraction in our forecasts, which we assumed to be zero, leading to an overestimation of the ground-level PM\({}_{2.5}\) concentrations at the observation sites.  
Figure 7: The 2016 burn impact forecast statistics. The thresholds for burn impacts are drawn at 32 \(\upmu\)g m\({}^{-3}\), which is the 95 th percentile of all observed maximum afternoon PM\({}_{2.5}\) concentrations throughout Georgia between 2 January and 1 May 2016.