Continued improvements of air quality
forecasting through emission adjustments using
surface and satellite data &
Estimating fire emissions: satellite vs. bottom-up
Talat Odman, Yongtao Hu and Ted Russell
School of Civil & Environmental Engineering, Georgia Institute of Technology
With thanks to Pius Lee and the NOAA ARL Forecasting Team
AQAST Meeting, January 15th, 2014
Georgia Institute of Technology
Objective
Improve air quality forecasting accuracy using earth
science products through dynamic adjustments of
emissions inventories and simulation of wildland fire
impacts
– Air quality forecasting is an integral part of air quality
management.
– Current forecasting accuracy calls for improvement.
– Forecasting with 3-D models relies on accuracy of emissions.
– Emission inventories are typically at least 4 years behind
and “growth factors” are outdated.
– Wildland fires are becoming an increasingly important
contributor to PM and ozone.
– Fire is one of the most uncertain emission categories as
multi-year averages of past fires do not represent future
fires.
Georgia Institute of Technology
Hi-Res Forecasting System
Hi-Res Modeling Domains
4-km (123x123)
12-km (123x138)
36-km (148x112)
• Based on SMOKE, WRF and
CMAQ models
• Forecasting ozone and PM2.5
since 2006
• 48-hour forecast at 4-km
resolution for Georgia and
12-km for most of Eastern US
• Used by GA EPD assisting
their AQI forecasts for
Atlanta, Columbus and
Macon
• Potentially useful for other
states
Georgia Institute of Technology
Hi-Res performance during 2006-2013
ozone seasons for Metro Atlanta
Ozone
PM2.5
150
70.0
165
0
0
4-km
4-km
185
75
35.0
1068
60
749
52
0
0.0
75
0
150
70
35
0
Obs.
Obs.
MNB
20%
MNB
-10%
MNE
25%
MNE
32%
Georgia Institute of Technology
Inverse Modeling Approach for Adjusting
Emissions
An emissions and air quality auto-correction system
utilizing near real-time satellite and surface observations
• Minimizes the differences
between forecasted and
observed concentrations (or
AOD)
• With minimum adjustment to
source emissions
• Using contributions of
emission sources calculated by
CMAQ-DDM-3D
– Source contributions can be used
for dynamic air quality
management.(e.g., fires)
Georgia Institute of Technology
Inverse Model Formulation
• Solve for Rj that minimizes 2
DDM-3D calculated sensitivity of
concentration i to source j emissions

J

obs
sim
  ci  ci   Si , j ( R j  1) 

N
total number of obs
 
j 1

2
  
2

i 1
Ciobs



total number of sources
uncertainties
Georgia Institute of Technology
2
weigh for the amount of
change in source strengths


J (ln R ) 2

j


  2
j 1
ln R j



emission adjustment ratio
Off-line tests using “real-time” PM2.5
observations
• Surface PM2.5 data from
six sites in Atlanta
– Direct use of satellite data
(AOD) was problematic
because of much larger
uncertainties compared to
surface data.
– AOD will be “fused” to
PM2.5 concentration fields to
provide “real-time” spatial
patterns.
Kennesaw
Gwinnet
NE Atlanta
Yorkville
Atlanta
West Atlanta Confederate Ave
Douglasville
South DeKalb
Atlanta
Conyers
Fayetteville
Newnan
Georgia Institute of Technology
Walton
McDonough
Peachtree City
SLAMS O3
SLAMS PM2.5
NWS Met
DDM-3D sensitivities calculated for week1: Dec. 1-7, 2013
Shown for select day
Dec. 2, 2013
Obtained emissions
adjustments ratios (Rj)
Source
Area
On-road
Non-road
Dec. 1-7,2013
0.17Georgia Institute
0.83 of Technology
0.85
Point
0.97
PM2.5 Forecasting Performance for week 2: Dec. 08-14, 2013
with emissions adjustments
Dec.11, 2013 PM2.5 Concentration
without emissions adjustments
Dec. 11, 2013 PM2.5 Concentration
Obs (ug/m3)
Sim (ug/m3)
NFE
NFB
8.57
16.57
65%
65%
Emis adjusted
8.45
24%
2%
Dec. 8-14, 2013 4.64
10.04
86%
85%
Emis adjusted
5.62
54%
39%
Dec. 11, 2013
Georgia Institute of Technology
Comparison of Fire Emission Estimates:
Satellite vs. Bottom-up
• Both have roles in improving accuracy of fire impact forecasts:
Satellite for wildfires and bottom-up for prescribed burns.
• Global Biomass Burning Emissions Product (GBBEP) is
currently using Fire Radiative Power from GOES
• Buttom-up estimates use fuel-loads, consumption and emission
factors.
• GBBEP and buttom-up emissions compared for Williams fire, a
200 acre chaparrel fire in California on November 11, 2009
Akagi et al.,
ACP, 2012
Georgia Institute of Technology
Comparison of Emission Estimates:
Williams Fire
• Buttom-up PM2.5 emission
estimates are ~50% larger
than GBBEP emissions
• Aircraft measured aerosol
light scattering, converted to
PM2.5 and compared to
modeled PM2.5
concentrations
Georgia Institute of Technology
Comparison of Modeled PM2.5 to
Aircraft Measurements
• Uncertainties in dispersion modeling (WS, WD, plume height,
etc.) must be reduced to better evaluate emission estimates.
Georgia Institute of Technology
Conclusions
• Dynamic emissions inventory adjustment
dramatically improving PM forecast accuracy in offline testing. On-line testing and implementation
underway
– Large bias in dust emissions in winter corrected
– Improved approach to assimilating AOD and PM
measurements underway
• Bottom-up and satellite-based fire emission estimates
being improved with airborne smoke measurements
– Fire emission contribution forecasts underway for dynamic
prescribed-burn management
Georgia Institute of Technology
Poster
• Davis et al., Nitrogen Deposition (Tiger Team
Project)
Acknowledgements
•
•
•
•
NASA
Georgia EPD
Georgia Forestry Commission
US Forest Service
– Scott Goodrick, Yongqiang Liu, Gary Achtemeier
•
Strategic Environmental Research and
Development Program
• Joint Fire Science Program (JFSP)
• Environmental Protection Agency (EPA)
Georgia Institute of Technology
Georgia Emission Totals (tons/yr)
Georgia Totals (2013 Hi-Res) VOC
NOx
CO
SO2
PM10
PM25
NH3
area-dust
241150
39240
area-others
366497
41790
118093
64613
32450
26965
80896
egu
1439
174136
11689
648564
11863
5977
5
non-egu
32843
49791
76059
60353
15059
10909
3613
non-road
69803
101653
786873
9403
9685
9242
49
on-road (NEI2011)
101360
241964 1084877
1133
10943
8144
4382
Georgia Institute of Technology
DDM-3D sensitivities calculated for week1: Jul. 6-12, 2011
Shown for Jul. 11, 2011
Emission adjustments
ratios (Rj)
Source
Area
On-road
Non-road
Jul. 6-12,2011
Institute of Technology
3.34 Georgia 1.09
1.46
Point
1.10
PM2.5 Forecasting Performance of week2: Jul. 13-19, 2011
with emissions adjustments
Jul.15, 2011 PM2.5 Concentration
without emissions adjustments
Jul. 15, 2011 PM2.5 Concentration
Jul. 15, 2011
Obs (ug/m3) Sim (ug/m3)
NFE
NFB
11.35
3.85
94%
-94%
7.23
50%
-40%
8.67
54%
-44%
14.92
44%
7%
Emis adjusted
Jul. 13-19, 2011
Emis adjusted
14.39
Georgia Institute of Technology