2014 Progress Report: Dynamic Management of Prescribed Burning for Better Air Quality

EPA Grant Number: R835217
Title: Dynamic Management of Prescribed Burning for Better Air Quality
Investigators: Odman, Mehmet Talat , Chan, Daniel , Chang, Michael E. , Hu, Yongtao , Tian, Di
Current Investigators: Odman, Mehmet Talat , Chan, Daniel , Chang, Michael E. , Hu, Yongtao , Tian, Di
Institution: Georgia Institute of Technology , Georgia Environmental Protection Division
EPA Project Officer: Chung, Serena
Project Period: June 1, 2012 through May 31, 2015 (Extended to February 28, 2017)
Project Period Covered by this Report: June 1, 2014 through May 31,2015
Project Amount: $500,000
RFA: Dynamic Air Quality Management (2011) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Air


Prescribed burning (PB) is an important part of land management in the Southeastern United States but also a threat to air quality. If rigid restrictions are imposed on PB because of air quality concerns, ecological and hazard reduction benefits of PB are reduced. Forecast-based dynamic management can both reduce the air quality risks and maximize the amount of lands treated by PB as go/no-go decisions can be made on relatively short notice. The objectives of this project are to:

  • Develop a PB impact prediction system that can be used in forecasting mode using existing forecasting systems, available observational data, and recently developed modeling tools
  • Evaluate the forecasting accuracy of the system under PB influence on air quality
  • Integrate this system into PB management and investigate dynamic management options
  • Assess the benefits of dynamic PB management.

Progress Summary:

The goals of the project have not changed from the original application. Activities during the third year of the project remained focused on the development of operational fire impact forecasting. With the forecasting system we developed in prior years, we forecasted the air quality impacts of prescribed burns in Georgia during the 2015 burn season (January-April 2015). Recall that a prescribed burn forecasting model has been developed last year using meteorological parameters from 18 fire weather monitors in Georgia and exploring the relationships between those parameters and daily burn acreages in the counties where the monitors are located. We revised this model by limiting its predictor variables to circumvent over-modeling and re-trained it with data for a longer period (2010-2014). Because the model used meteorological data from all of the 18 monitors, it was applied statewide to predict whether tomorrow will be a “burn day” in each fire district based on the weather forecast at a central monitor. On a forecasted burn day, the number of burns in the county was calculated by dividing the average daily burn acres to the typical burn size for the predominant type of burners in that county. These burns were randomly distributed to the forested/managed lands over the county. Then, the amount of fuels consumed were estimated using the fuel loads obtained from the Fuel Characteristic Classification System (FCCS) maps and the most recent fuel moisture data from the fire weather network. Fire emissions were then calculated using emission factors characteristic of the Southeastern fuels and distributed between the boundary layer and the lower troposphere. Finally, these fire emissions were input to “HiRes2” air quality forecasting system, which consists of WRF-3.6 and CMAQ-5.0.2 models. HiRes2 provides forecasts of not only air quality (O3 and PM2.5) but also impacts on air quality of sources such as power plants, on-road vehicles and prescribed burns by using the DDM-3D sensitivity analysis method. The forecasted prescribed burn impacts were evaluated using satellite and ground-based observations of fire and smoke. Refinements of the burn impact forecasting system were initiated according to the lessons learnt from the evaluation. 

Future Activities:

Evaluation and refinement of the newly developed PB impact prediction system will continue. The system will be operated in a daily forecasting mode during the 2016 burn season (January-April 2016) and the generated daily burn area and burn impact forecasts will be shared with stakeholders in Georgia. Forecasts as well as hindcasts will be evaluated for incidences when PB impacts are detected by the statewide air quality monitoring network. A new permitting process will be designed that can take advantage of the air quality and PB impact forecasts and manage PB emissions after considering the cumulative impact of all potential burns on regional air quality. Although permits may have to be denied or acreages restricted for best air quality outcomes on some days, burns may be encouraged on other days when there are no imminent concerns. A protocol will be prepared in collaboration with the stakeholders. The effects of the new permitting process will be assessed through simulations with the new prediction system. 

Journal Articles on this Report : 2 Displayed | Download in RIS Format

Other project views: All 19 publications 6 publications in selected types All 4 journal articles
Type Citation Project Document Sources
Journal Article Davis AY, Ottmar R, Liu Y, Goodrick S, Achtemeier G, Gullett B, Aurell J, Stevens W, Greenwald R, Hu Y, Russell A, Hiers JK, Odman MT. Fire emission uncertainties and their effect on smoke dispersion predictions:a case study at Eglin Air Force Base, Florida, USA. International Journal of Wildland Fire 2015;24(2):276-285. R835217 (2012)
R835217 (2013)
R835217 (2014)
R835217 (Final)
  • Abstract: CSIRO-Abstract
  • Journal Article Hu Y, Odman MT, Chang ME, Russell AG. Operational forecasting of source impacts for dynamic air quality management. Atmospheric Environment 2015;116:320-322. R835217 (2014)
    R835217 (Final)
    R833866 (Final)
    R834799 (2015)
    R834799 (2016)
    R834799 (Final)
  • Full-text: ScienceDirect-Full Text HTML
  • Abstract: ScienceDirect-Abstract
  • Other: ScienceDirect-Full Text PDF
  • Supplemental Keywords:

    Silviculture, forest fuels, forecasting, forward sensitivity, simulation 

    Relevant Websites:

    http://sipc.ce.gatech.edu Exit

    https://forecast.ce.gatech.edu/ Exit

    Progress and Final Reports:

    Original Abstract
    2012 Progress Report
    2013 Progress Report
    2015 Progress Report
    Final Report