RECENT TOPICS OF ATMOSPHERIC RESEARCH
IN THE US
Joshua S. Fu, Ph.D.
1Department of Civil and Environmental Engineering

University of Tennessee, Knoxville, TN, USA
2UT-ORNL Bredesen Center for Interdisciplinary Research and Graduate Study

Energy Science and Engineering Ph.D. Program
3Computer Science and Mathematics Division

Oak Ridge National Laboratory, Oak Ridge, TN, USA

November 16, 2015
Presented at
Symposium Evaporative and Refueling Emissions
Their Effects and prevention techniques
University of Tokyo, Tokyo, Japan

1: Professor
2: Inaugural Professor
3: Joint Appointment Professor - Senior Scientist

Outline• Brief review of current research status in
United States• SOA• Model Development• Multi-scale Simulation• Air Quality, Cost, and Benefit Analysis System• Field campaign and measurements• Examples – UT• Model Development and Applications• HTAP phase2 and MICS-Asia• Arctic Black Carbon

Current Research Status in UnitedStates
1. CMAQ model development
Chemistry updates
Secondary Organic Aerosols updatesTwo-way mode WRF/CMAQ

2. Model Application
3. Air Benefit and Cost and Attainment
Assessment System (ABaCAS)
4. Field Campaigns

1.1 CMAQ model development:
Chemistry updates
Issue: All organic nitrates are lumped into one species in CB05, but they can vary widely in physical andchemical properties. CMAQv5.1 has 7 modeled organic nitrate species (Pleim et al., 2015)

Parameter

Representative values

CMAQ v5.02

CMAQ v5.1 (7 species)

Henry’s Law coef. (M/atm)

0.6 to 40,000

1.7

0.65 to 17,000.

Reaction rate with OH(cm3/molecule-sec)

1.8e-13 to 1.1e-10

1.8e-13

1.1e-12 to 3.3e-11

Alkyl nitrates

PANs

Example (07/10/11) results for NOy
Measurements (Discover AQ)
CMAQv5.02
CB05e51
CB05e51 with hydrolysis

Better characterization of alkyl nitrates can improve predictions of NOy species and organic
nitrogen deposition

1.1 CMAQ model development:
Secondary Organic Aerosol (SOA)
AERO6 module updated• SOA from isoprene updated
 SOA from ISOPRENE + NO3
added Acid catalyzed SOA (AISO3J)
updated to form from IEPOX[Pye et al. 2013 ES&T]• SOA from PAHs (naphthalene)
added [Pye and Pouliot 2012
ES&T]
• SOA from alkanes added (CB05)
or updated (SAPRC) [Pye andPouliot 2012 ES&T]
New AERO6i module• Works with detailed isoprenechemistry (saprc07tic)• Speciated epoxide SOA• SOA from BVOC nitrates (Pye etal., 2015)• SOA from GLY/MGLY uptake onparticles (Pye et al. 2015)

Pleim et al. 2015

1.1 CMAQ model development:
Volatility Basis Set (VBS)(Woody et al., 2014)

1. 1 CMAQ model development:
Volatility Basis Set (continue)
Model performs better with VBS at CSN and IMPROVE sites

1.1 CMAQ model development:
Volatility Basis Set (VBS) (continue)(Woody et al., 2014)

1. 1 CMAQ model development:
Statistical Oxidation Model (continue)
Application over U.S.

SoCAB

Eastern U.S.

Simulation with SOM generally produces more SOA(Jathar et al., 2015)

1.1 CMAQ model development:
Two-way mode WRF/CMAQ
Issue: Simulate the feedbacks of atmospheric pollutants on meteorology
Test Case: California, 2008 (Wong et al., 2011)
upwelling shortwave radiation flux

1.2 Model Application:
Multi-scale modeling: Hemispheric CMAQ
• Hemispheric model needed to provide Initial andBoundary Conditions (IC/BC) for regional modeling– Intercontinental transport impact• Fine scales:– Urban Environments– Linkage with human exposure & health studies– Residual non-attainment

Hemispheric model at 108 km provides IC/BCs for 12 km CONUS with nests to 4 km and 1 km (Xing et al., 2015

1.2 Model Application:
Air quality forecasting
Objective: Provide operational forecasting products to the public
Example: National Air Quality Forecast Capability: Towards prediction of PM2.5(Stajner et al., 2015)

1.2 Model Application:
Wild-fires and climate change
Objective: investigate the potential changes of wild-fire under future climate
condition.
Example: Cause and consequences of climate change: wildfire emissions andtheir air quality impact in the southeastern U.S. (Shankar et al., 2015)

1.2 Model Application:
Fine scale modeling
Objective: fine scale modeling helps to provide more detailed description of air
pollutants, and this information is of great important to assess the human exposuresin urban area.
Example: Comparison of two downscaling techniques, going from 12km to 250m
resolution (Bates et al., 2015).

Atlanta, GA

1.2 Model Application:
Fine scale modeling (continue)

12km resolution

Interpolated with observation+

250m resolution

1.2 Model Application:
Co-benefits due to emission reductions
Objective: estimate the co-benefits due to reduced emission of criteria pollutants

Method:Result (Marjan et al., 2015):

1.2 Model Application:
Energy and emission
Objective: estimate the interaction between energy consumption and emission control
Example: Using an energy system modeling framework to investigate long-termemission trends (Keaveny B., 2014)
Electricity Generation (Petajoules)

Energy model: MARKAL

Emission changes under carbon Tax scenario 2

1.4 Field Campaigns :The DISCOVER-AQ 2013 Texas
campaign(Loughner et al., 2014)

1.4 Field Campaigns :The FRAPPE 2014 campaign(Pickering et al., 2014)

“ABaCAS”: Air Benefit and Cost & Attainment Assessment System
An Integrated AQ Decision Support & Assessment System

Courtesy of Dr. Carey Jang

Developed for “Scientists” and “Policy Makers”

1.3 Air Benefit and Cost and Attainment
Assessment System (ABaCAS) System

Provide Health Impacts andEconomic Benefits Estimate

Provide Real-time Air Quality
Response of Emissions Control

Provide Emissions Control
Cost Analysis and Estimate

Provide Attainment Test/Demo forPM2.5 & O3 Non-attainment Areas

Please visit ABaCAS website
for more information:
http://www.abacas-dss.com

Courtesy of Dr. Carey Jang

“ABaCAS-SE ” (Streamlined Edition”)(Prototype: 2015)
Standalone ABaCAS(Scientists)

Streamlined ABaCAS(Policy-Makers)
ABaCAS Master Script

CoST-CE

RSM-VATSMAT-CE

BenMAPCE
BenMAP-CE

Courtesy of Dr. Carey Jang

ABaCAS-SE

Cost ($$)

CoST-CE
scriptRSM-VATscriptSMAT-CE
scriptBenMAP-CE
scriptAQ Benefit (ug/m3)> 12 ug/m3 (non-attainment)

Attainment (DVs)

Health Benefit ($$)

Benefit ($$)/Cost ($$)

22

U.S.EPA’s Transport Rule: Benefit/Cost & Attainment

EmissionsControl & Cost
PM2.5 & O3

AQ Benefit &Attainment> 12 ug/m3 (non-attainment)

2011
Reduce SO2 & NOx emissions in
28 Eastern U.S.

NA:~502018
Courtesy of Dr. Carey Jang

Health &Economic Benefit

NA: 0

Estimated Annual Number of Health Effects Avoided Due to TR
Health EffectNumber of Cases Avoided

Premature mortality

13,000 to 34,000

Non-fatal heart attacks

15,000

Hospital and emergency department visits

19,000Accute bronchitis

19,000Upper and lower respiratory symptoms

420,000

Aggravated asthma

400,000

Days when people miss work or school

1.8 million

“ABaCAS-SE” (Streamlined Edition, 2014-2015)::
Developed for Policy-Making and Analysis

ABaCAS-SE “YRD 2017 case”:
5~10 minutes to complete a run

ABaCAS-SE

2. Examples - UT
1. Model development and applications
Biomass burning: emission, transport, andimpact over East Asia
Dust storm: model development in WRF/CMAQ
system

2. International collaborative efforts
Task Force on Hemispheric Transport of Air
Pollutants phase2
MICS-Asia
Arctic Black Carbon

2.1 Biomass burning: emission,transport, and impacts over East Asia(1). Develop regional biomass burning emission inputs from
FLAMBE and GFED, compare and evaluate the two emission
inventories Fu et al., 2012, ACP(2). Evaluate impacts of biomass burning on air quality overSoutheast Asia, China, and Taiwan Huang et al., 2013, AE(3). Identify the lee-side trough uplift of biomass burning, andassess the inter-annual variations Dong and Fu, 2015a, b, AE
deep convection?

(4). Assessment of biomass burning aerosols impact on regional
climate In preparation

2.2 Dust storm: model development andapplication over East Asia
Motivation:(1). CMAQ default wind-blown dust scheme significantly
underestimate dust emission (Fu et al., 2014; Xing et al., 2015)(2). No heterogeneous chemistry in CMAQ

Method:(1). Revised parameterization in the microphysical scheme forwind-blown dust emission in CMAQ
(2). Apply source-dependent speciation profile based on local
measurements in the Gobi and Taklaman Deserts(3). Implement dust heterogeneous chemistry

Dust Emission Scheme
Calculation of dust emission flux (F) is based on modified Owen’s equation(Owen et al., 1964; Tong et al., 2015 )

=
F

N

ρ

=i 1 =j 1

gM∑∑ K × A ×× Si × SEP × u* × (u*2 − u*ti , j 2 )Ratio between vertical and horizontal flux

100.134×( clay %) −6 , when : clay % < 20% 
K =0.0002, when : clay % ≥ 20%
Soil erodible potential

SEP = 0.08 × clay % + 1.0 × silt % + 0.12 × sand %Initial friction velocity threshold constant for 13 soil types,
based on filed data from Gillette et al. (1980). The values inoriginal CMAQ double count the impact of soil moisture, andour recent reanalysis of the field data provide new valuesuof*ci , jFriction velocity threshold ( if thefriction velocity exceeds this threshold,there will be dust particles elevated)

u*ti , j = u*ci , j × Z i , j × fi , j
Soil moistureadjusting factor
Surface roughness
adjusting factor

New Friction Velocity Threshold Constant

Cl
ay
Cl
ay

Si
lty

Sa
n

dy

Cl
a

y

Lo
am

Si
lt

Sa
Lo
nd
am
y
S
an
Sa
d
nd
y
Lo
am
Si
lt
Lo
am

1
0.5

Si
lt

Barren_Default

Si
lty

dy
Sa
n

1.5

Barren_Revised

Cl
a

2

Lo
am

0

1
0.5

y
Lo
am
Cl
ay
Lo
am
Cl
ay
Lo
am
Sa
nd
y
Cl
ay
Si
lty
Cl
ay
Cl
ay

Lo
am

0

Si
lty

Cl
a

3 Loamy Sand
2 Sand1 Other

1.5dy

5 Silt Loam
4 Sandy Loam

2

Si
lt

7 Loam6 Silt0San

12 Silt Clay
11 Sandy Clay
10 Clay Loam
9 Silty Clay Loam
8 Sandy Clay Loam

0.5

Sa
Lo
nd
am
y
S
an
Sa
d
nd
y
Lo
am
Si
lt
Lo
am

16 Bedrock
15 Water
14 Organic Material
13 Clay

ShrubGrass_Revised

1

Friction Velocity Threshold Constants (m/s)

Soil Types

ShrubGrass_Default

1.5

3 Barren orSparsely Vegetated

1 Other

Shrubland_Revised

2

5 Shrubland

2 Water

Shrubland_Default

Lo
am
Cl
ay
Lo
am
Cl
ay
Lo
am
Sa
nd
y
Cl
ay
Si
lty
Cl
ay

4 MixedShurbland-Grassland

y
Lo
am
Cl
ay
Lo
am
Cl
ay
Lo
am
Sa
nd
y
Cl
ay
Si
lty
Cl
ay

Initial Friction Velocity Threshold Constants

Lo San
d
am
y
S
an
Sa
d
nd
y
Lo
am
Si
lt
Lo
am

Land Cover Categories

Source-dependent Speciation Profiles
CMAQ default
20% fine
mode

Taklamakan
ASO4
2.49%

AOTHR
50.09%

ANA
3.93%

ANH4
0.00%

ACA
7.92%

AFE
4.69%

AMG
0.00%
AK
3.76%

AH2O
0.54%
AMN
0.11%

ASI
19.38%

ATI
0.28%

AAL
5.68%
AFE
3.35%

APOC
1.07%

Dust
ASO4
2.66%

AAL
5.93%
AH2O
0.54%
AMN
0.08% ATI
0.31%

ANO3
0.16%

APNCOM
0.43%

ACL
1.19%

ASI
20.74%

AOTHR
96.00%

AOTHR
64.38%

AAL
4.99%

AMN
0.00%
ATI
0.00%

AK
0.11%

APNCOM
0.43%

ATI
AMN 0.34%
0.06%
ASO4
0.47%

ANO3
0.08%
ACL
0.09% ANH4
0.19% ANA
0.30%

AOTHR
72.46%

ACA
1.08%
AAL
4.64%

AMG
0.12%

AFE
3.75%

ASI
16.25%

ASI
17.45%

AMN
0.06%

AFE
2.42%

ASI
14.93%

1.08%
APNCOM
0.43%

ANA
2.06% ACA
1.42%

AFE
0.00%

ANO3
0.20%
ACL
0.54%
ANH4
0.35% ANA
1.02%
ACA
AMG
1.79%
0.80%
AK
0.28%
APOC
1.08%

AAL
4.27%

AK
APOC 0.15%

ANO3
0.13%
ACL ANH4
2.36% 0.07%

ANA
0.00%

80%
coarsemode

AOTHR
70.00%

ASO4
2.82%

ANH4
0.00%

AAL
0.00%

ANO3
0.18% ACL
2.42%
ANA
ANH4
2.23%
0.10%
ACA
AMG 2.06%
0.17%

AH2O
0.54%

ACA
0.00%

AMG
0.00%
AK
ASI
0.00%
0.00%

ASO4
0.95%

ASO4
3.55%

AOTHR
55.35%

ANO3
0.02% ACL
0.94%

Gobi

AMN
0.07%

ATI
0.28%

30

ATI
0.36%

AFE
3.05%

AMG
0.82%

AK
0.12%

Heterogeneous Chemistry
Heterogeneous reactions in original CMAQ:N 2O5 + H 2O → 2 HNO3

C2→ HONO + HNO3
2NO2 + H 2O 

13 reactions added into CMAQ in this study:
R1
O3 + dust → products

R8

N 2O5 + dust → 2 NO3−
NO2 + dust → NO3−

C1

R2

OH + dust → products

R9

R3

H 2O2 + dust → products

R10 NO3 + dust → NO3−

R4

CH 3COOH + dust → products

R11 NO3 + dust → HNO3

R5

CH 3OH + dust → products

R12 HO2 + dust → 0.5 H 2O2

R6
R7→ products
HCHO + dust → 0.5 NO3− + 0.5 NOx
HNO3 + dust 

R13 SO2 + dust → SO42−

Reaction Rate:
−1

 rp
4 
K 
Ap
=
+
 D v γ 
g g 
 g

rp : radius of the particle
Dg : diffusion coefficient of gas molecules
Vg : molecular velocity of gas

Υg : uptake coefficient (values from Zhu et al.,2010, ACP)
Ap : particle surface area
31

Results: Dust PM10
Simulated Dust PM10

PM10 Bias vs API

Default

Revised32

Model Improvement – Trace Metals
Model evaluations of fine mode trace metals with Default andRevised speciation profiles at Tazhong, Duolun, and Yulin

Gobi
Taklamakan
DuolunTazhong

K+

PM2.5

Default

Ca2+

Mg2+
RevisedDefault

RevisedDefault

Yulin

RevisedMean Obs (µg/m3)

81.52

Mean Sim (µg/m3)

44.36

0.69

0.12

0.02

0.12

3.06

1.05

Mean Bias (µg/m3)
Normalized MB (%)
Correlation-37.17-45.59
0.67

0.46
208.9
0.42

-0.11
-47.83
0.44

-0.17
-99.8
0.22

-0.07
-36.84
0.51

0.82
36.69
0.22

-1.19
-53.12
0.44

0.23

0.19

Default Profile

2.24

Ca (Dust_Revised)

6

4

2

0

8

CMAQ (Dust_Profile, µg/m3)

CMAQ (Dust_Profile, ug/m3)

CMAQ (Dust_Revised, ug/m3)

Mg (Dust_Revised)400

K (Dust_Profile)Mg (Dust_Profile)Ca (Dust_Profile)K (Dust_Revised)

8

PM2.5

Revised Profile
10

10

6

4

2

2

4

6

8

Observation (ug/m3)

10

240

160

80

0

0
0

PM2.5
320

0

2

4

6

8

10

0

Observation (ug/m3)

80

160

240

Observation (µg/m3)

33

320

400

Case Study: Dust Strom Mar.19-21, 201034

Dust Impact in Two-way WRF/CMAQ
Dust induced Shortwave Radiation Change
Upward @ TOA

Snow cover (NHSNOWM)

Downward @ Surface

Compare with other studies
RegionSouth Korea
East China

HengduanMountains

Tibet Plateau

Taklamkan

MethodCCM3 (Won et al., 2005)
This study
CCM3 (Park et al., 2005)
ADAM (Park et al, 2008)
This study
MFRSR (Ge et al., 2011)
fvGCM (Lau et al., 2006)
WRF-Chem (Chen et al., 2013)This study
RTM, CALIPSO (Kuhlmann andQuaas, 2010)
This study

Dust induced change@ TOA (W/m2)
5 ~ 105 ~ 1010 ~ 20
5 ~ 15-4 ~ 3-2-4-30 ~ 5

Dust induced change@ SFC (W/m2)-20 ~ -10-30 ~ -10-40 ~ -10-40 ~ -20
-60 ~ -20
-20 ~ -10-13
-6
-10 ~ -60-

-60 ~ -90

0~5

-20 ~ -40

35

2.3 MICS-Asia: Preliminary test of boundary
conditions
Motivation: Investigate the impact of lateral boundary conditions from GEOS-Chem,
CHASER, MACC on the prediction of atmospheric chemical pollutants
 Method: dynamic downscaling from global model to regional model (CMAQ v5.0.1)
Summer (JJA)

O3 boundary conditions

Winter (DJF)36

2.4 HTAP: Research Overview•

Objectives
•
•
•
•

•

Quantify source-receptor relationship between different continents
Investigate long-range transport impacts
Examine possible air quality benefit from emission control
Examine model’s capability by comparing H-CMAQ with global models

Method••Model Platform: WRF / Hemispheric CMAQ (H-CMAQ) / Response Surface
Method (RSM)
Source region design• RSM design only need to consider source region definition. The impact from pre-definedsource region could be derived for any receptor region• H-CMAQ is hemispheric scale, so we group the HTAP source regions over north
hemisphere to 7 source regions

HTAP original source regions

RSM source regions

Define 7 source regions over North Hemisphere:
HTAP-source regions
RSM
North America(1). North America
Europe(2). Europe
Rus, Bel, Ukr(3). Russia
South Aisa
(4). South Asia
East Asia(5). East Asia
Southeast Aisa
(6). Southeast Asia
North Africa(7). Africa_MiddleEast
Middle East37

2.4 HTAP: Preliminary results (baseline evaluation)
Surface
SO2Over U.S.

Over Asia
NO2

O3

O3

Generally acceptable, but need further investigation for more variables with more observations.

Column AOD

H-CMAQMODIS
AOD over Sahara wassignificantly
underestimated,because there is no
dust plume rise modulein CMAQv4.7.1. We will
use newly developed
CMAQv5.0.1 for new
baseline simulation.

Model can generally
capture the spring time
long-range transport
from Asia to North
America38

2.4 Arctic Black Carbon: Motivation
Arctic black carbon simulation problems:
Large diversity of modeling BC among different models (Shindell et al., 2008)
Strong underestimation of BC in Arctic (Shindell et al., 2008; Koch et al., 2009)
Improper wet scavenging parameterizations (Bourgeois et al., 2011; Liu et al., 2011)

Major emission source regions for
Arctic black carbon:

USEPA NEI
NPRI

Europe (EMEP)
United States (USEPA NEI)
Canada (NPRI)

EMEP

Russia39

2.4 Arctic Black Carbon: Past Study
Ensemble model simulations of Arctic black carbon

All models strongly
underestimated BC
concentrations in the
ArcticArctic Haze

Shindell et al., 200840

2.4 Arctic Black Carbon: Past Study

wet scavenging
schemes are revised
to improve model
performance
Liu, et al, 201141

2.4 Arctic Black Carbon: Gas Flaring
Russia possess the largest natural
gas reserves of 24% in the world
as of 2009. (Dmitry Volkov, 2008)

Russia is the top 1 gas
flaring country422.4 Arctic Black Carbon: Spatial distribution of gas flaring BC
Gas flare areas (red polygon)
retrieved from satellite (U.S. Air
Force Defense Meteorological
Satellite Program (DMSP)
Operational Linescan System(OLS))
Spatial allocation proxy(contour) nighttime lights
product
Data source: NOAA NGDC

Spatial distribution of gas
flaring BC emission (0.1*0.1
degree)Major gas flaring regions:
Yamal-Nenets
Khanty-Mansiysk

43

2.4 Arctic Black Carbon: Russian Anthropogenic
BC emission
 Residential

5.4%

 Transportation

36.2%

Russian anthropogenic BC= 224 Gg/yr

20.3%

 Industry

Year 2010:

13.1%

25.0%

 Power plants
Gas flaring
Industry

1.1%

Residential
Power plants

Transportation

10.1%
23.6%

37.9%
27.3%

3.3%
2.8%

2.4% 0.8%

Urals
90.7%

44

2.4 Arctic Black Carbon: Modeling domain
CMAQ v5.0.1
Meteorological Input:
WRF V3.5.1

Hemispheric CMAQ（H-CMAQ）

Terrain HT (m)Projection:
Polar
Horizontal Spacing:
180*180 (108 km *108 km)Vertical Spacing:
44 layers

Arctic Circle(north of
66°33′44″ N°)

Gas chemistry:
CB05
Aerosol mechanism:
AERO5
Simulation year:
2010
IC/BC:
GEOS-Chem v9-01-03
45

2.4 Arctic Black Carbon: Observations
AERONET (Russia)

Arctic sites

Moscow

Barrow, USA(55.7 °N, 37.5 °E)(71.3 °N, 156.6 °W)

Zvenigorod

Alert, Canada(55.7 °N, 36.8 °E)(82.5 °N, 62.3 °W)

Yekaterinburg

Zeppelin, Norway(57.0 °N, 59.5 °E)(78.9 °N, 11.9 °E)

Tomsk

Tiksi, Russia(56.5 °N, 85.0 °E)(71.6 °N, 128.9 °E)

Yakutsk(61.7 °N, 129.4 °E)

Ussuriysk(43.7 °N, 132.2 °E)

46

2.4 Arctic Black Carbon: Model Performance
51%

50%

31%

24%

17%

2%

47

2.4 Arctic Black Carbon: Russian BC Emission Impacts

Improvement of modeled BC levels are mainly found during the Arctic Haze periods, i.e.
December – March. More details in: Huang et al., 2014, AAQR; Huang et al., 2015, JGR-Atmos.
48

Pinpoint the transport pathway of gas flaring plumes

1-week backward trajectories (March
17 -19, 2010)

Answers ?????
Possible impact of BC on Arctic sea ice retreat?

2.4 Arctic Black Carbon: Past Study

wet scavenging
schemes are revised
to improve model
performance
Liu, et al, 201151

40 Years of Clean Air Act Progress (1970-2013)
Pollution Down While Economy Up52

Thank you for your attention!