Utilization of Satellite Observation for Improved Air Quality
Simulations
Arastoo Pour Biazar1, Richard T. McNider1, Andrew White1, Daniel Cohan2, Rui Zhang2,
Bright Dornblaser3, Yu-Ling Wu1, Kevin Doty1, Mark Estes3
1.
2.
3.
University of Alabama in Huntsville
Rice University
Texas Commission on Environmental Quality (TCEQ)
The American Thoracic Society 2016 International Conference,
May 13-18, 2016,
Moscone Center, San Francisco, California
Air Quality and Health
 Poor air quality can cause chronic respiratory disease, lung cancer,
cardiovascular disease, skin irritation, headache, and even cause damage to
the brain, nerves, liver, or kidneys (WHO’s 2013 assessment).
 Acoording to WHO, in 2012, air pollution in both cities and rural areas was
estimated to cause 3.7 million premature deaths worldwide. A recent MIT
study estimated 200,000 premature deaths in the U.S. as the result of air
pollution.
 Long-term exposure to air pollutants can cause chronic bronchitis,
permanently damage airway and cause breathing difficulties.
 Both ozone and particulate matter irritate
lungs and airways, worsen asthma
symptoms, and trigger asthma attacks.
 During air pollution episodes, visits
to the emergency room for breathing
problems substantially increases.
Clean Air Act is Working, But More Work to be Done
 The Clean Air Act, requires EPA to set National Ambient Air Quality
Standards (NAAQS) for common air pollutants that are harmful to public
health.
 Primary NAAQS provide public health protection, including protecting the
health of "sensitive" populations such as asthmatics, children, and the
elderly.
 The NAAQS are based on epidemiological and exposure studies which
attempt to find minimum levels of pollutants which can be demonstrably
connected to adverse health effects (morbidity or mortality).
Ozone Standard is Approaching Background Level
 Until 2015 the standard for ozone was 75 ppb for 8-hour averaged
concentration.
 EPA proposed reducing the standard to 60-70 ppb and finally approved 70
ppb for primary and secondary standards.
 With standard approaching the background levels, it is imperative to
understand the impact of natural emissions and long range transport on
ozone concentrations.
Ozone nonattainment based
on 75 ppb standard (>75 ppb)
Ozone nonattainment based
on 65 ppb standard (>65 ppb)
Motivation

Classification: An area is deemed as non-attainment when it exceeds the NAAQS for
a criteria pollutant (O3, NO, SO2, particulate matter) and the state must develop a
State Implementation Plan (SIP) to lower the pollutant levels to meet the NAAQS.

Best Modeling Practice: Model simulations are carried out 1) to establish a baseline
where the model reasonably replicate the episode conditions and the observed
pollutant values for the design period, and 2) to test various emission reduction
scenarios to determine the most efficient strategy for meeting the air quality
standards for the design period.

Cost: Under the Southern Oxidant Study it was estimated that ozone SIP control
decisions involved $5 billion for 6 southeastern states. In Texas the cost of the 2003
SIP for Houston alone was estimated to be over $1 billion. Nationally these SIPs
amount to ten’s of billions in control costs.

Why: Since the decisions rely on the model results, reducing the sources of
uncertainty in the simulations and increasing the confidence in the model results is of
outmost importance to the regulatory agencies.

What: Our objective is to improve the fidelity of the physical and chemical
atmosphere in air quality management decision support tools by employing NASA
science and satellite products.
Our Environmental System Consists of Complex Interactions on Different
Spatial and Temporal Scales
Reducing the Uncertainties in Biogenic Emission Estimates is Critical to
Air Quality Simulations
 Biogenic volatile organic compounds, BVOCs, play a critical role in atmospheric chemistry,
particularly in ozone and particulate matter (PM) formation.
 BVOCs comprise approximately 75%-80% of national VOC emission inventory and are the dominant
summertime source of reactive hydrocarbon In the southeastern United States.
 Reducing uncertainties in biogenic hydrocarbon emissions is a high priority issue for SIP modeling.
NOx + VOC + Sunlight  Ozone
Physical Atmosphere
Atmospheric
dynamics and
microphysics
Chemical Atmosphere
Transport and transformation
of pollutants
Aerosol
Cloud
interaction
Boundary layer development
Natural and antropogenic emissions
Surface removal
Fluxes of heat and
moisture
LSM describing landatmosphere interactions
Photochemistry
and oxidant
formation
Winds, temperature,
moisture, surface
properties and fluxes
 BVOC estimates depend on the amount of
Photosynthetically Active Radiation (PAR) reaching the
canopy and temperature.
 Large uncertainty is caused by the model insolation
estimates. This can be corrected by using satellite-based
PAR in biogenic emission models.
hv
NOx + VOC + hv O3
Biogenic Volatile Organic Compounds (BVOC)
Emissions
BVOC is a function
of radiation and
temperature
T&R
Satellite-Derived Photosynthetically
Active Radiation (PAR)
Based on Stephens
(1978), Joseph (1976),
Pinker and Laszlo
(1992), Frouin and
Pinker (1995)
Satellite-Derived
Insolation
Cloud albedo, surface
albedo, and insolation are
retrieved based on Gautier et
al. (1980), Diak and Gautier
(1983). From GOES visible
channel centered at .65 µm.
SUN
c
h
Inaccurate model cloud
prediction results in
significant under-/overprediction of BVOCs.
Use of satellite cloud
information greatly
improves BVOC
Emission estimates.
Surface
trcld  1.  (albcld  abs cld )
Cloud top
Determined from
satellite IR
temperature
BL OZONE CHEMISTRY
O3 + NO
NO2 + h (<420 nm)
VOC + NOx + h
g
g
-----> NO2 + O2
-----> O3 + NO
-----> O3 + Nitrates
(HNO3, PAN, RONO2)
Satellite-derived insolation and PAR for September 14, 2013, at 19:45 GMT.
Insolation/PAR Evaluation (September 2013)
Spatial Distribution of NMB (normalized mean bias) Against Soil Climate
Analysis Network (SCAN)
WRF
WRF
NMB = 22%
NME = 34%
Satellite
Satellite
NMB = 14%
NME = 27%
13
Performing bias correction
before converting to PAR
UAH PAR
product shows
better
agreement with
SURFRAD
stations for
August 2006
Statistics for 47 TCEQ Sites (for August 2006)
 Satellite cloud assimilation reduced mean bias by 63% and NMB
by 60% over 47 TCEQ sites.
 Due to WRF higher clear sky value, correlation is unchanged.
1. PAR_cntrl:
2. PAR_analytical:
3. PAR_UMD:
4. PAR_UAH:
Base WRF simulation to provide insolation for M EGAN
Base WRF + cloud assimilation from GOES to provide
insolation for M EGAN
Direct use PAR retrievals from UM D, other met inputs
same as case 'PAR_analytical'
Direct use PAR retrievals from UAH, other met inputs
same as case 'PAR_analytical'
OBS_AVE
SIM_AVE
IA
(W/m2)
(W/m2)
WRF cntrl
248.6
299.8
0.95
WRF analytical
248.6
266.8
UAH satellite
248.6
263.6
R
RMSE
MB
MAGE
NMB
NME
(W/m2)
(W/
m2)
(W/m2
)
(%)
(%)
0.91
142.3
53.9
74.7
22.2
30.7
0.95
0.91
143.9
20.3
74.9
8.9
30.7
0.96
0.96
123.2
17.3
71.8
7.5
29.5
Comparing August, 2006, insolation from control WRF simulation (cntrl), UAH WRF simulation
(analytical), and satellite-based (UAH) against 47 radiation monitoring stations in Texas.
Satellite-derived PAR
substantially reduced
isoprene emission
estimates over Texas
(DISCOVER-AQ period)
Domain-wide sum of estimated isoprene (ISOP)
and monoterpene (TERP) emission strength over
Texas area using different PAR inputs in MEGAN
during September 2013.
Case
cntrl
analytical
UAHPAR
OBS_AVE
(ppbV)
0.23
0.23
0.23
SIM_AVE
(ppbV)
0.59
0.61
0.47
Comparison of the spatial pattern of estimated average isoprene
emission rate in MEGAN using different PAR inputs over Texas
domain during September 2013.
IA
R
0.37
0.37
0.41
0.36
0.37
0.40
RMSE
(ppbV)
0.69
0.72
0.69
MB
(ppbV)
0.39
0.42
0.29
MAGE
(ppbV)
0.49
0.51
0.41
NMB
(%)
292
311
225
NME
(%)
326
342
271
Statistics for model isoprene predictions for three cases over 18 TCEQ CAMS sites.
Estimated Emission Difference and Impact on O3 for September 2013
(Satellite - WRF)
ISOP Diff in %
TERP Diff in %
Isoprene emission is more sensitive to PAR input with the highest increase region at Northeast (>30%)
and decrease at the Southwest (> 20%). The relative change for monoterpene emission is modest (-10%
to 5%).
Response for Daily Max 8-hr Average O3 concentrations (September 2013)
O3 (WRF PAR)
Diff O3 (‘UAH’ – ‘WRF’)
NOx
Diff PAR (‘UAH’ – ‘WRF’)
Diff ISOP emission (‘UAH’ – ‘WRF’)
PFT
Maximum daily 8-hr average ozone concentrations (MDA8 O3) for September 1-15, 2013.
Normalized mean bias (NMB) for CMAQ hourly ozone from three simulations at TCEQ sites.
Case
OBS_AVE
SIM_AVE
(ppbV)
(ppbV)
cntrl
30.6
32.7
analytical
30.6
UAHPAR
30.6
R
RMSE
MB
MAGE
NMB
NME
(ppbV)
(ppbV)
(ppbV)
(%)
(%)
0.75
14.5
2.8
11.9
18.2
42.6
32.4
0.76
14.2
2.4
11.6
17.0
41.7
32.5
0.76
14.2
2.5
11.6
17.3
41.8
Most areas impacted by reduction in BVOC are NOx limited, and the reductions are not
enough to make considerable improvement in O3 predictions.
Recap and Concluding Remarks

A new satellite-based PAR was produced and evaluated for this study.

The impact of using satellite PAR on BVOC emission estimates by MEGAN and
consequently on CMAQ simulation during the Texas DISCOVER-AQ Campaign
(September 2013) was examined.

Satellite-based PAR is in reasonable agreement with surface observations and is
able to correct model errors.

For September 2013, using satellite PAR in MEGAN increased isoprene and
monoterpene emission estimates over the east coast but decreased them over the
west coast and Texas.

The impact of PAR inputs on ozone prediction depends on the local NOx/VOC ratio
and is more pronounced over VOC limited regions. In this study, over the VOC
limited regions, the satellite PAR changed surface O3 prediction by 5-8%.

Over east Texas, MEGAN greatly over-estimated isoprene emissions and thereby
the reductions caused by the use of satellite PAR did not significantly affected
ozone predictions.

The large model isoprene over-prediction over east Texas could not be corrected
by the use of satellite PAR.

This study will be repeated using BEIS model.
Acknowledgment
The findings presented here were accomplished
under partial support from NASA Science Mission
Directorate Applied Sciences Program and the Texas
Air Quality Research Program (T-AQRP).
Note the results in this study do not necessarily
reflect policy or science positions by the funding
agencies.