1. 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
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

2. 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.

3. 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).

4. 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 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)

5. 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.

6. Our Environmental System Consists of Complex Interactions on Different Spatial and Temporal Scales

7. 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
Chemical Atmosphere
Transport and transformation of pollutants
Atmospheric dynamics and microphysics
Aerosol Cloud interaction
Photochemistry and oxidant formation
Boundary layer development
Natural and antropogenic emissions
Surface removal
Fluxes of heat and moisture
Winds, temperature, moisture, surface properties and fluxes
LSM describing land-atmosphere interactions

8. BVOC is a function of radiation and temperature
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

9. Satellite-Derived Photosynthetically Active Radiation (PAR)
Based on Stephens (1978), Joseph (1976), Pinker and Laszlo (1992), Frouin and Pinker (1995)

10. 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
Cloud top Determined from satellite IR temperature h
Inaccurate model cloud prediction results in significant under-/over-prediction of BVOCs. Use of satellite cloud information greatly improves BVOC Emission estimates.
BL OZONE CHEMISTRY
O3 + NO > NO2 + O2
NO2 + h (<420 nm) -----> O3 + NO
VOC + NOx + h > O3 + Nitrates
(HNO3, PAN, RONO2) g g
Surface

12. Satellite-derived insolation and PAR for September 14, 2013, at 19:45 GMT.

13. Insolation/PAR Evaluation (September 2013)
Spatial Distribution of NMB (normalized mean bias) Against Soil Climate Analysis Network (SCAN)
WRF
Satellite
WRF
NMB = 22%
NME = 34%
Satellite
NMB = 14%
NME = 27% 13

14. Performing bias correction before converting to PAR

15. UAH PAR product shows better agreement with SURFRAD stations for August 2006

16. 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.
OBS_AVE
SIM_AVE IA R
RMSE MB
MAGE
NMB
NME
(W/m2)
(%)
WRF cntrl
248.6
299.8
0.95
0.91
142.3
53.9
74.7
22.2
30.7
WRF analytical
266.8
143.9
20.3
74.9
8.9
UAH satellite
263.6
0.96
123.2
17.3
71.8
7.5
29.5

17. Comparing August, 2006, insolation from control WRF simulation (cntrl), UAH WRF simulation (analytical), and satellite-based (UAH) against 47 radiation monitoring stations in Texas.

18. 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.
Comparison of the spatial pattern of estimated average isoprene emission rate in MEGAN using different PAR inputs over Texas domain during September 2013.
Statistics for model isoprene predictions for three cases over 18 TCEQ CAMS sites.

19. ISOP Diff in % TERP Diff in %
Estimated Emission Difference and Impact on O3 for September (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%).

20. 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

21. 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 R
RMSE MB
MAGE
NMB
NME
(ppbV)
(%)
cntrl
30.6
32.7
0.75
14.5
2.8
11.9
18.2
42.6
analytical
32.4
0.76
14.2
2.4
11.6
17.0
41.7
UAHPAR
32.5
2.5
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.

22. 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.

23. 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.