Top-Down Constraints on Emissions: Opportunities and Challenges from Satellite Observations of

Atmospheric CompositionRandall Martin

with contributions from

Shailesh Kharol, Gray O’Byrne, Akhila Padmanabhan, Aaron van Donkelaar

2013 China Emissions Workshop, Beijing

28 June 2013

Lok Lamsal (Dalhousie NASA), Chulkyu Lee (Dalhousie KMA)

Jintai Lin (PKU), Daven Henze (CU Boulder),

Guannan Geng, Qiang Zhang, and Yuxuan Wang (Tsinghua)

Satellite-derived PM2.5 for 2001-2006

van Donkelaar et al., EHP, 2010

Evaluation in North America:r=0.77slope = 1.07N=1057

Outside Canada/USN = 244 (84 non-EU)r = 0.83 (0.83)Slope = 0.86 (0.91)Bias = 1.15 (-2.64) μg/m3

PM2.5 Nearly as Sensitive to Emissions of NOx as to SO2

Kharol et al., GRL, 2013

GEOS-Chem Calculation of Annual PM2.5 Response to 10% Change in Emissions

ΔNOx Emissions ΔSO2 Emissions ΔNH3 Emissions

25%41%34%

ΔPM

2.5 (

ug m

-3)

-0.5

0

1

2

But How Accurate are the Emissions Used in this Calculation?How Accurate are Emissions in General?

What Can We Learn about Emissions from Satellite Observations?

Major Nadir-viewing Space-based Measurements of Tropospheric Trace Gases and Aerosols (Not Exhaustive)

Sensor MOPITT MISR MODIS AIRS SCIA-MACHY

TES OMI CALIOP GOME-2

IASI GOSAT VIIRS TROP-OMI

Platform (launch)

Terra Aqua (1999) ( 2002)

Envisat (2002)

Aura (2004)

Calipso(2006)

MetOp(2006)

IBUKI (2009)

NPP (2011)

Sent-5 Precur (2014)

Typical Res (km)

22x22 18x18 10x10 14 x14

60x30 8x5 >24x13

40x40 80x40 12 x12 11x11 6x6 7x7

Aerosol X X X X X X X X

NO2 X X X X

HCHO X X X X

CO XX X X X X X

Ozone X X X X X X

SO2 X X X X X

NH3 X X

CH4 X X X X X

CO2 X X X X

Solar Backscatter & Thermal Infrared

Close Relationship of NOx and SO2 Emissions With Satellite Tropospheric NO2 and SO2 Columns

Emission

NO NO2

HNO3

lifetime hours

Nitrogen Oxides (NOx) Sulfur Dioxide (SO2)

Emission

SO2

OH, cloudSO42-

day

BOUNDARYLAYER

Satellite

NO/NO2

W ALTITUDE

Tropospheric NO2 column ~ ENOx

Tropospheric SO2 column ~ ESO2

Deposition

Top-Down (Mass Balance) Estimates of NOx & SO2 Emissions

SCIAMACHY Tropospheric NO2 (1015 molec cm-2) NOx emissions (1011 atoms N cm-2 s-1)

Lee et al., 2011

2004-2005

SO2 emissions (1011 atoms N cm-2 s-1)OMI SO2 (1016 molec cm-2)

200649.9 Tg S yr-1

Martin et al., 2006

Lamsal et al., GRL, 2011Streets et al., AE, in press

Application of Satellite Observations for Timely Updates to NOx Emission Inventories

Use GEOS-Chem to Calculate Local Sensitivity of Changes in Trace Gas Column to Changes in Emissions

Forecast Inventory for 2010 Based on Bottom-up for 2005 and Monthly OMI NO2 for 2005-2010

2.5% increase in global emissions

27% increase in Asian emissions

23% decrease in North American emissions

Integration of Top-down Information In Bottom-up ApproachExample Evaluation of Spatial Proxies

Guannan Geng (Tsinghua) et al. in prep

Population, Outdated Road Network Industrial GDP, New Road Network

Complications

Satellite Retrievals

Inverse Modeling

Need to Account for Average Kernel in IR Satellite RetrievalsIASI Provides Some Constraint on NH3 Emissions

Kharol et al., GRL, 2013

Using NH3 emissions from Streets et al. (2003) reduced by 30% following Huang et al. (2012)

with Averaging KernelsTotal Column

Need to Account for Vertical Profile and Atmospheric Scattering (Air Mass Factor; AMF) in UV-Vis Retrievals

dt()

IoIB

EARTH SURFACE

Radiative Transfer Model

Scattering weight t

B

e

I1w

ln)(AMF

)(G

Atmospheric Chemistry Model

“a-priori” Shape factor

2

2

OO

( ) ( ) airS

S

S C

1

TdSw )()(AMF

verticalslantAMF G

Calculate w() as function of:• solar and viewing zenith angle• surface albedo, pressure• cloud pressure, aerosol• OMI O3 column

INDIVIDUALOMI SCENES

SO2 mixing ratio CSO2()

() is temperature dependent cross-section

sigm

a (

)

Local Air Mass Factor and Offset Correction Improves Agreement with Aircraft Observations (INTEX-A and B)

Lee et al., JGR, 2009

SCIAMACHY OMI

Orig: slope = 1.3, r=0.78 New: slope = 1.1, r=0.89

Orig: slope = 1.6, r = 0.71 New: slope = 0.95, r = 0.92

SCIAMACHY OMI

Need to Account for Multiple Effects of Aerosols on UV-Vis Trace Gas Retrievals

Accounting for Aerosol Haze Can Increase R2 (0.720.96) of OMI NO2 vs Ground-based DOAS Observations in China

Jintai Lin (PKU) et al., in prep, ACP

Expected OMI NO2 Retrieval Bias for Snow-Covered ScenesDue to Errors in Accounting for Transient Snow & Ice

O’Byrne et al., JGR, 2010

2original correctedRelative NO Bias

corrected

With CloudFractionThreshold (f < 0.3)

-0.5 0 1.0

All CloudFractions

0.5

Aerosol Retrievals Susceptible to Bias over Bright SurfacesAerosol Optical Depth (AOD) from MODIS and MISR over 2001-2006

MODIS1-2 days for global coverage

(w/o clouds)AOD retrievals at 10 km x 10 kmRequires assumptions about

surface reflectivity

MISR6-9 days for global coverage

(w/o clouds)

AOD retrievals at 18 km x 18 km

Simultaneous retrieval of surface reflectance and aerosol optical properties

0 0.1 0.2 0.3AOD [unitless] van Donkelaar et al., EHP, 2010

Can Remove Biased Data Using Sunphotometer Observations Excluded Retrievals for Land Types with Monthly Error vs AERONET >0.1 or 20%

MODISr = 0.39

(vs. in-situ PM2.5)

MISRr = 0.39

(vs. in-situ PM2.5)

CombinedMODIS/MISR

r = 0.61 (vs. in-situ PM2.5)

0.3

0.25

0.2

0.15

0.1

0.05

0

AO

D [u

nitle

ss]

van Donkelaar et al., EHP, 2010

Δ

Adjoint Reduces Inversion Error vs Mass BalanceTest to Recover 30% Increased NOx Emissions in Four Locations Using

a Week of Synthetic Observations of NO2 Columns

November July

Mass Balance

Adjoint

Inversion – Truth (ΔNOx Emissions molec cm-2 s-1)

Padmanabhan et al., in prep

NME=3x10-3 NME=6x10-3

NME=4x10-4 NME=5x10-4

NME = Normalized Mean Error

How Well Do Models Represent SO2 Lifetime in China?Evaluation of GEOS-Chem SO2 Lifetime vs Calculations

from In Situ Measurements in Eastern USU Maryland Research Flights for Eastern U.S.

2( ) 19 7JJA SO hourst

Hains, Dickerson, et al., 2007

June - August

Mon JJAMon JJA

JJA Mon

C HC H

t t

C is SO2 from EPA Network H is GEOS Mixed Layer Depth

Lee et al., JGR, 2011

Inversion Relies on Relative Error in Bottom-up and Top-down Approaches: Embrace Uncertainty

Need information on uncertainty (σ)

2 2a

2 2a

( )( )

σ σE E F E

J E

Observed Trace Gas

a priori emissions

a posteriori emissions

a priori error observational error

Inverse problem seeks emissions E that minimize cost function J

Errorweighting

A posteriori emissionsE

A Priori NOx Emissions (Ea)Observed NO2 Columns (Ω)

Model F(E) σ σa

Uncertainty in SO2 Retrievals Due to Clouds, Surface Reflectance, SO2 Vertical Profile, and Aerosols

Lee et al., JGR, 2009

Cloud-free Fraction of Scene Cloudy Fraction of Scene

Most Satellites Observe at Specific Times of Day

Requires Attention to the Diurnal Profile of Emissions

Conclusions

• Substantial opportunities and challenges

• Integrate top-down and bottom-up methods & communities

• Account for retrieval assumptions in inversion (e.g. trace gas profile)

• Avoid bias (e.g. aerosol, snow) in satellite data products and algorithms

• Quantify uncertainty in both top-down and bottom-up methods

Acknowledgements:NSERC, Environment Canada