Air Quality Impacts from Prescribed Burning: Fort Benning Case Study

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Air Quality Impacts from Prescribed Burning Fort
Benning Case Study

• M. Talat Odman
• Georgia Institute of Technology
• School of Civil Environmental Engineering
• Atlanta, GA

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SERDP Project

• Sponsor Strategic Environmental Research
  and Development Program
• Performers Georgia Tech, UGA and Forest Service
• Title Characterization of Emissions
  and Air Quality Modeling for
  Predicting the Impacts of
  Prescribed Burns at DoD Lands
• Technical Objective
• To develop, evaluate and apply a simulation
  system that can accurately predict the impacts of
  prescribed burns on regional PM2.5 and ozone
  levels by
• (1) Improving emission estimates,
• (2) Further developing and coupling advanced
  models,
• (3) Evaluating the models with existing and newly
  collected data, and
• To assess alternative burning strategies
  including no burning and eventual wild fires.

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Air Quality Models
Air quality models (AQMs) are scientifically the
best available tools to estimate the regional
PM2.5 and O3 impacts of forest fires.
Simulated Concentrations
Simulated Impacts
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Reducing Uncertainty

• Accurate emission estimates are needed
• Fuel load and consumption uncertainties limit
  our ability to accurately simulate impacts.
• Emission factors have been developed under
  either laboratory or field conditions. EPAs
  AP-42 Tables are mostly based on studies
  conducted in Western US.
• Recent in situ observations from prescribed
  burns provide better emissions assessments.
• Resolution of fire plumes must be improved
• Given their large domains AQMs have limited
  (horizontal) resolution (4 km).
• In the absence of sub-grid treatments, plumes
  are immediately mixed into the grid cell.
• Sub-grid scale plume models exist but they are
  designed mostly for industrial stacks.
• Also, these plume models have not been coupled
  successfully with AQMs.

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History
In 2003, with SERDP exploratory funding
(CP-1249), we incorporated the Dynamic Adaptive
Grid technique (as well as sensitivity analysis)
in an ozone AQM (no PM capability).
Sensitivity Analysis
Adaptive Grid
Simulation with Air Quality Model
Simulated Fort Benning Fire Plume
Burning Options
Prescribed Burns at DoD Facilities
Impact to Regional Air Quality
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Model Evaluation

• Indicator of success improved agreement of model
  predictions with observations and measurements.
• Observation network is sparse with typical range
  gt 25 km.
• Remote sensing, aircrafts, or ground-based
  (stationary or mobile) techniques can be used to
  chase plumes.
• The range of most ground-based measurements is lt
  2 km.
• There is a gap in data at 10 km range.

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Technical Approach
Emission Estimation
Field Measurements
Model Development Simulations
Feedback (multiple cycles)
Model Evaluation
Burning Strategy Simulations
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Fuel Loading, Consumption

• Assess fuel loads using Fuel Characteristic
  Classification System (FCCS)
• Determine the most representative FCCS fuel bed
  through the use of digital photo series and
  customization methodology for modifying existing
  fuel descriptions
• Compare with National Fire Danger Rating System
  fuel maps and remote sensing data
• Calculate fuel consumption using CONSUME 3.0 and
  Fire Emissions Prediction System (FEPS)
• Input fuel characteristics and conditions,
  lighting patterns, and meteorology to get fuel
  consumption rates by combustion phase

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Emission Factors

• Exploit existing data sets to determine emission
  factors
• Find PM, CO and VOC emission factors for each
  fuel type in Forest Service data set for the
  Southeast
• Enrich with VOC emission factors in Georgia Tech
  data set for Fort Benning
• PNNL project will also provide additional
  emissions data
• Estimate emissions both for flaming and
  smoldering phases
• Weight emission factors over fuel types, combine
  with fuel load and consumption, and estimate
  hourly emissions

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Model Development

• The models to be used in this research are CMAQ
  regional air quality model and Daysmoke local
  plume model.
• Incorporate Dynamic Adaptive Grid methodology in
  CMAQ to improve its grid resolution and
  facilitate its coupling with sub-grid scale
  models.
• Explore the adaptive grid version of MM5 (model
  that provides meteorological inputs to CMAQ)
  recently developed at NCSU as an alternative to
  interpolation.

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Coupling the Models

• Daysmoke can be coupled with CMAQ by injecting
  the particles crossing a wall as emissions into
  the grid cells on the opposite side of the wall.
• Determine where to place this wall using
  Fourier analysis
• Revise Daysmoke and the Adaptive Grid algorithm
  as necessary
• Explore the Particle-in-Grid method for
  modeling the chemistry of Daysmoke particles
  (currently a non-reactive model) before mixing
  into CMAQ grid cells.
• Treat smoldering and nighttime emissions as
  ground-level sources. Here adaptive grid
  resolution will be the major improvement.

Wall
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Field Measurements

• Position 3 mobile teams at predetermined
  locations at or near Fort Benning ( 10 km) the
  morning of the burns according to the burning
  plan and the forecasts (our forecasts with the
  models as well as other local forecasts)
• Each team will be equipped with real-time PM2.5
  and CO monitors.
• Conduct plume sighting (survey) and digital
  photogrammetry from three fire towers at Fort
  Benning. The height and direction of the plumes
  will be determined by triangulation, feature
  tracking and GPS.
• Collect data for 5-8 burns per year over 3 years
  under ideal burn and dispersion conditions.

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Model Evaluation

• Model evaluation will continue throughout this
  project
• Start with data previously collected at Savannah
  River Site and Fort Benning
• Data used in calibrating the model will be
  withheld from evaluation.
• Compare model predictions to historic (range lt 2
  km) and newly collected (range 10 km) data as
  well as to base model predictions
• UCR project at southwestern US will provide
  additional data for evaluation.
• Consider the quality of the data as well as
  spatial and temporal scales of predictions
• Reiterate data collection and model refinement
  steps as necessary

More Desirable
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Model Evaluation

• Whenever there is an opportunity (e.g., February
  28, 2007 smoke event in Atlanta), compare new
  model with base model in terms of agreement with
  regional observations.
• Compute the sensitivity of the model to various
  emission inputs, meteorological inputs (e.g.,
  wind direction), and model parameters (e.g.,
  plume turbulence coefficient).

To appear in May 15 issue of EST Simulation of
Air Quality Impacts from Prescribed Fires on an
Urban Area by Y. Hu, M. T. Odman, M. E. Chang,
W. Jackson, S. Lee, E. S. Edgerton, K. Baumann
and A. G. Russell
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JFSP Project

• Sponsor Joint Fire Science Program
• Performers Georgia Tech and Forest Service
• Title Evaluation of Smoke Models and
  Sensitivity Analysis for Determining their
  Emission Related Uncertainties
• Technical Objective
• Propagate uncertainties in various emission
  parameters to determine the uncertainty in model
  outputs.

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Burning Strategy Simulations

• Determine burning options in consultation with
  land managers, GA Forestry Commission, GA
  Department of Natural Resources and the Forest
  Service.
• Burning Season Simulate equivalent burns
  during each season
• Frequency of burning Using growth models,
  estimate fuel loadings as a function of
  frequency, simulate resulting air quality impact
• Firing Techniques Consider emissions temporal
  profile, plume rise, number of uplift cores
• Leave it to nature (wildfire) option Estimate
  frequency distribution of wildfires. Evaluate
  using recent wildfires in GA/FL.
• Determine impacts using the burn minus no
  burn simulation results.

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Smoke Forecasts

• Continuous air quality forecasting in GA
• http//forecast.ce.gatech.edu

• Coming soon Forecast of the impact of fires
  from 9 counties around Ft. Benning
• GA Muscogee, Chattahoochee, Harris, Talbot,
  Marion, Stewart, Webster
• AL Lee, Russell
• Per ton of emissions from non-urban forests.