# Forecasting the Impacts of Prescribed Fires
## 4 Discussion  
We have developed, to the best of our knowledge, the first dynamic PB impact forecasting system. While previous air quality forecasting systems used prior year averages of PB emissions, this system predicts the PB activity and calculates corresponding PB emissions based on the weather forecast. It predicted active burn days at a county scale with an \(F1\) score larger than 0.50 over most of the Lower Piedmont and Coastal Plain regions of Georgia where more lands are treated with prescribed fire than the rest of the state. The PB forecast overestimated the burn areas on less active burn days while underestimating them on more active days. This may be due to using monthly average burn areas in the forecast whereas actual burn areas vary more frequently due to some days being more conducive to burns than others, as well as the random behavior of the burners. The number of correctly predicted significant smoke impacts at downwind receptors (PM\({}_{2.5}\) concentration larger than 32 \(\mathrm{\SIUnitSymbolMicro g}\) m\({}^{-3}\)) was about six times smaller than the total number of misses and false alarms. However, there was a very strong correlation between the bias in smoke impacts and the bias in burn areas. Therefore, if the bias in forecast burn areas can be reduced, the skill in predicting smoke at downwind receptors will be greatly improved. The current skill of the forecasting system and its potential for improvement opens new avenues for dynamic air quality and cohesive PB management. For example, the forecasting system could be used as a decision support tool, as follows. In Georgia, where a permit is required by law for PB, land managers call the GFC district offices, typically early in the morning, to obtain a permit for a burn later that day. The burn activity and burn impact forecasts provided by our forecasting system could be used by GFC to manage prescribed burns to avoid any potential exceedances through their permitting system. For example, on the morning of 18 January 2016, GFC offices could see on our website that the daily average PM\({}_{2.5}\) concentrations in Thomson County would be as high as 75 \(\mathrm{\SIUnitSymbolMicro g}\) m\({}^{-3}\), with 70 \(\mathrm{\SIUnitSymbolMicro g}\) m\({}^{-3}\) coming from burns throughout Georgia, including 560 ha in Thomas County. Assuming the PB impact is solely from burns in Thomas County, our forecast suggests that each hectare burned will contribute 0.125 \(\mathrm{\SIUnitSymbolMicro g}\) m\({}^{-3}\) to the PM\({}_{2.5}\) concentration. Therefore, to avoid exceeding the national ambient air quality standard (NAAQS) of 35 \(\mathrm{\SIUnitSymbolMicro g}\) m\({}^{-3}\) for daily average PM\({}_{2.5}\), permits in Thomas County should be limited to 240 ha that day. In fact, considering potential contributions from upwind counties, the limit should be set below 240 ha. Clearly, it would be more useful to forecast exactly how many hectares can be burned in each county without violating any air quality standards. Figure 8: Normalized bias in PM\({}_{2.5}\) versus normalized bias in burn areas in upwind counties. The burn areas were generally underestimated. Independent from the bias in burn areas, the PM\({}_{2.5}\) impacts were mostly overestimated. Our current impact forecast considers all burns in the state without distinction of local versus distant burns. Calculating the impacts of burns separately for every county by applying DDM to emissions from 159 counties in Georgia is computationally very demanding. Future research should focus on finding efficient ways of partitioning the total impact calculated by DDM into impacts of the individual counties or, better yet, individual burns upwind. If the PB impact can be broken down by specific burns, it will be seen that some burns contribute little to air quality and exposure while others contribute more. With information on the marginal contribution of each burn to the air quality downwind, the following dynamic management protocol can be employed.