Transcript
Page 1: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Advances in Applying Satellite Remote Sensing to Advances in Applying Satellite Remote Sensing to the AQHIthe AQHI

Randall Martin, Dalhousie and Harvard-Smithsonian

Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie University

Lok Lamsal, Dalhousie U NASA Goddard

45th CMOS Congress, Victoria

7 June 2011

Page 2: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Large Regions Have Insufficient Measurements for AQHI Large Regions Have Insufficient Measurements for AQHI MeasurementMeasurement

Locations of NAPS Sites

Southern Ontario

Page 3: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Major Nadir-viewing Space-based Measurements Major Nadir-viewing Space-based Measurements of AQHI Speciesof AQHI Species

Sensor GOES Imager

MISR MODIS SCIA-MACHY

TES OMI PARASOL CALIOP GOME-2

IASI

Platform (launch)

GOES (varied)

Terra Aqua

(1999) (2002)

Envisat (2002)

Aura

(2004)

PARASOL (2004)

CALIPSO MetOp

(2006)

Equator Crossing

n/a 10:30 1:30 10:00 1:45 1:30 1:30 9:30

Typical Res (km)

4x4 18x18 10x10 60x30 8x5 >24x13 18x16 40x40 80x40 12x12

Global Obs

(w/o clouds)

n/a 7 2 6 n/a 1 1 n/a 1 0.5

Aerosol X X X X X X 1 X

NO2 X X X

Ozone X X X X X

Solar Backscatter, Thermal Infrared, Active

Page 4: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

General Approach to Estimate Surface ConcentrationsGeneral Approach to Estimate Surface Concentrations

NO2 Column

S → Surface Concentration

Ω → Tropospheric column

In Situ

GEOS-Chem

Model Profile

OM

MO S

S

Page 5: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Ground-Level Afternoon NOGround-Level Afternoon NO2 2 Inferred From OMI for 2005-2007 Inferred From OMI for 2005-2007

Lok LamsalNO2 [ppbv]

Page 6: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Ground-Level NOGround-Level NO2 2 Inferred From OMI for 2005 Inferred From OMI for 2005

Temporal Correlation with In Situ Over 2005

×In situ—— OMI

Works in Near-Real-Time!

Values Estimated Using Monthly NO2 Profiles for Different Year (2006)

Insignificant change in results if profiles are daily coincident values from 2005

Lok Lamsal

Page 7: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Aerosol Most Visible over Dark TargetsAerosol Most Visible over Dark Targets

Pollution haze over East Coast Dust off West Africa

Page 8: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Aerosol Optical Depth (AOD) from MODIS and MISR over 2001-2006Aerosol 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 km

Requires 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]

MODISr = 0.40

vs. in-situ PM2.5

MISRr = 0.54

vs. in-situ PM2.5

van Donkelaar et al., EHP, 2010

Page 9: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Agreement With AERONET Varies with Surface Type

9 surface types, defined by monthly mean surface albedo ratios,evaluation against AERONET AOD

MODIS

MISR

Jul

y

van Donkelaar et al., EHP, 2010

Page 10: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Combined AOD from MODIS and MISRCombined AOD from MODIS and MISRRejected Retrievals for Land Types with Monthly Error vs AERONET >0.1 or 20%Rejected Retrievals for Land Types with Monthly Error vs AERONET >0.1 or 20%

MODISr = 0.40

(vs. in-situ PM2.5)

MISRr = 0.54

(vs. in-situ PM2.5)

CombinedMODIS/MISR

r = 0.63 (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

Page 11: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Significant Agreement with Coincident In situ MeasurementsSignificant Agreement with Coincident In situ MeasurementsUsed GEOS-Chem to CalculateUsed GEOS-Chem to Calculate AOD/PMAOD/PM2.52.5 ( (η)η)

SatelliteDerived

In-situ

Sat

ellit

e-D

eriv

ed

[μg/

m3]

In-situ PM2.5 [μg/m3]

Ann

ual M

ean

PM

2.5 [

μg/

m3]

(200

1-20

06)

r

MODIS AOD 0.40

MISR AOD 0.54

Combined AOD 0.63

Combined PM2.5 0.77

van Donkelaar et al., EHP, 2010

Page 12: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Error Sources in Satellite-Derived PMError Sources in Satellite-Derived PM2.52.5

Satellite• Error limited to 0.1 + 20% by

AERONET filter

• Implication for satellite PM2.5

determined by AOD/PM2.5

Model• Affected by aerosol optical

properties, concentrations, vertical profile, relative humidity

• Most sensitive to vertical profile [van Donkelaar et al., 2006]

• Evaluate vs Calipso lidar obs

• Estimate error from bias in profile and AOD ±(1 μg/m3 + 15%)

• Contains 68% (1 SD) of North American data

Sat

ellit

e-D

eriv

ed

[μg/

m3]

In-situ PM2.5 [μg/m3]

van Donkelaar et al., EHP, 2010

Page 13: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

USA Today: Hundreds Dead from Heat, Smog, USA Today: Hundreds Dead from Heat, Smog, Wildfires in MoscowWildfires in Moscow

9 Aug 2010: “Deaths in Moscow have doubled to an average of 700 people a day as the Russian capital is engulfed by poisonous smog from wildfires and a sweltering heat wave, a top health official said Monday.”

MODIS/Aqua: 7 Aug 2010

Page 14: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Spatial and Temporal Variation in Satellite-Based PMSpatial and Temporal Variation in Satellite-Based PM2.52.5

during Moscow 2010 Firesduring Moscow 2010 Fires

van Donkelaar et al., AE, submitted

Page 15: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Satellite-Based PMSatellite-Based PM2.5 2.5 Insensitive to Emission InventoryInsensitive to Emission Inventory

Daily Meteorology More ImportantDaily Meteorology More Important

van Donkelaar et al., AE, submitted

GEOS-Chem Calculation of AOD / PM2.5

Different Emission Inventories

Page 16: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Application of Satellite-based Estimates to Moscow Application of Satellite-based Estimates to Moscow Smoke EventSmoke Event

Before Fires During Fires

van Donkelaar et al., submitted

MODIS-based

In Situ PM2.5

In Situ from PM10

r2 =0.85, slope=1.06

Page 17: Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie

Acknowledgements:Acknowledgements: Environment Canada, Health Canada, NASA Environment Canada, Health Canada, NASA

• Simple Method for Near-Real-Time Estimates of Ground-Level NO2

• Satellite-based PM2.5 Estimate for Long-Term and Extreme Events

Ongoing Work

• Develop daily PM2.5 estimate for Canada

• Improve spatial resolution from 10 km to 3 km

• Evaluate AOD/PM2.5 ratio

Growing Confidence in Application of Satellite Growing Confidence in Application of Satellite Remote Sensing for PMRemote Sensing for PM2.52.5 and NO and NO22


Top Related