2013 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
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, 2013 through May 31,2014
Project Amount: $500,000
RFA: Dynamic Air Quality Management (2011) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Air


Objective of Research: Prescribed burning (PB) is an important part of land management in the Southeastern United State 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 second year of the project remained focused on the development of the fire impact forecasting system. The goal of the system is to predict the air quality impacts of prescribed burns, which are being targeted for dynamic management in this project. A prescribed burn forecasting model has been developed using daily burn acreages by county from the Georgia Forestry Commission's burn permit database and regressing them with meteorological parameters from the closest fire weather monitors. The model is trained with 4 years of data (2010‐2013) to forecast whether tomorrow will be a “burn day” when the forecasted burn acreage is a significant fraction of the annual average burn acreage in that county. The counties in Georgia have been categorized in three groups depending on the type of their major burners as single dominant (e.g., military bases), multiple large (e.g., plantations) and various small burners. On a forecasted burn day, average daily acres are assigned to prominent burners according to their burning patterns. For those counties with no dominant burners, the acreage is distributed randomly to forested areas. Once the forecasted burns are distributed spatially, then the emissions can be estimated from fuel loads obtained from satellite imagery and recent fuel moisture data (available from the fire weather network) updated by forecasted meteorology. This method of "bottom‐up" emission estimation from fuel consumption estimates and emission factors continues to be evaluated through comparisons to satellite‐based emission estimates for large prescribed burns and wildfires. The sensitivity of the air quality model predictions to the spatiotemporal allocation of fire emissions has been evaluated. The air quality forecasting system to be used in this research has been updated with the most recent versions of the models such as WRF‐3.6 and CMAQ‐5.0.2 with SAPRC‐07 chemical mechanism. The updated system referred to as “HiRes2” will provide 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 method available in CMAQ‐5.0.2. 

Future Activities:

Testing, evaluation and refinement of the newly developed components of the PB impact prediction system will continue. The system will be operated in a daily forecasting mode during the burn season. Forecasts 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. 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 21 publications 8 publications in selected types All 6 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 Garcia‐Menendez F, Hu Y, Odman MT. Simulating smoke transport from wildland fires with a regional‐scale air quality model: sensitivity to spatiotemporal allocation of fire emissions. Science of The Total Environment 2014;493:544‐553. R835217 (2013)
    R835217 (Final)
  • Abstract from PubMed
  • Full-text: ScienceDirect-Full Text HTML
  • Abstract: ScienceDirect-Abstract
  • Other: ScienceDirect-Full Text PDF
  • Supplemental Keywords:

    Silviculture, forest fuels, forecasting, forward sensitivity, simulation, air quality, prescribed burning, land management

    Relevant Websites:

    Smoke Impact Prediction Center Exit

    HiRes2 Air Quality & Source Impacts Forecasting for Georgia Exit

    Progress and Final Reports:

    Original Abstract
  • 2012 Progress Report
  • 2014 Progress Report
  • 2015 Progress Report
  • Final Report