# Forecasting the Impacts of Prescribed Fires
## 4 Discussion
The burns that have minimal impact will, therefore, be permissible, while the ones that contribute a lot may have to be denied. For example, suppose burn A contributes almost nothing to the regional peak PM\({}_{2.5}\) concentration. Burn B contributes a little bit more, but still not very much. Burn C contributes still more, and so on, say up through to burn Z. If all burns, A-Z, were permitted, then the region might exceed the NAAQS. However, there might be a point where PM\({}_{2.5}\) concentrations would stop short of the NAAQS, say when only burns A-N were permitted. This approach might maximize the amount of land that could be burned without going over the NAAQS. Burning any more (i.e., burns M-Z) would put the region over the NAAQS, so those burns have to be left to another day. Currently, there is a burn ban that goes into effect on 1 May in 54 counties due to the ozone season. However, there is interest in burning in May and June, and our forecasting system could identify windows of opportunity during those months. Burners that were denied earlier could be encouraged to burn during this period. Because our burn activity forecast is the first of its kind and integrates several ad hoc elements, the burn impact forecasting system has a lot of room for improvement. One area where significant improvements can be made is the assignment of daily burn areas. Using monthly averages instead of an annual average when assigning daily burn areas to each county improved the performance of the burn forecast because of the large variation in burn activity throughout the year. However, the skewness of the burn area distribution suggests that even a monthly average may not be appropriate for capturing the variation. Recall that with monthly averages, we still underestimated high burn activity while overestimating lower activity, leading to low slopes of linear regression lines compared to 1:1 lines. A better approach may be to regress the county total daily burn areas with the same predictors that we currently use for burn/no-burn day classification. The binary decision is not able to capture varying degrees of favorable burn conditions. The burn areas under excellent burn conditions might be much larger than those under marginal conditions. A regression model is better suited than a classification model for covering the spectrum of favorable conditions and associated burn activity. When a sufficient amount of training data becomes available, a regression model should be tested. Also, other approaches such as the random forest model [27] could be tested in the future. Another area of possible improvement is the designation of burn locations within the county. By keeping track of prior permits issued and comparing them to historic burning patterns of individual burners (e.g., burn sizes, frequency, and time of the year), the demand for burning could be forecast more accurately. This may reduce, to a great extent, the need for random assignment of forecast burns to managed lands. Also, by tracking who is most likely to burn and where, accurate burn sizes could be forecast instead of the typical burn sizes that are currently fixed at 120, 80 or 40 ha according to the dominant burner type in each county. Aside from the overestimation of burn areas for low burn activity days, uncertainties in emissions and plume heights may be among the reasons for overestimation of the burn impacts, i.e., the false positives in our forecasts. In addition, it is well known that air quality models like CMAQ typically dilute the plumes, leading to underestimation of the local impacts. Therefore, the rates of overestimation in emissions and/or underestimation of the plume heights may be more severe than our analysis suggests. Our current approach for injecting PB plumes in the vertical layers of CMAQ is based on the assumption that most prescribed burns in Southeastern USA are low intensity fires whose plumes do not penetrate the free troposphere.