Atmospheric Aerosols

R.B. Husar, Washinton University

HTAP Meeting, June 10, 2008, Washington DC

Dimensions of Aerosols:

Particle Size, Composition,

Shape

Dimensions of Gaseous Pollutants:

X, Y, Z, T

Bad News: Aerosol Characterization is Challenging

• PM is characterized by many sensors, sampling methods and tools• Each sensor covers only a fraction of the 6-Dim PM data space.• The 6 D data space is extrapolated from sparse measured data• Or deconvoluted from integral measurements

Satellite-Integral

For integral sensors, the integral samples need to be separated into components

Good NewsOnce the aerosol is characterized, opportunities exists for extracting information about the aerosol sources, transformations, etc

from the data directly.

Phase II HTAP Goal: Integration of Emissions, Models and Observations

Volcanoes

Dust storms Fires

Anthropogenic Pollution

Aerosols are Indicators of Many Earth System Processes Including Human-induced Perturbations

EPA NAAQS PM2.5, O3 Exceptional Event RuleExclusion of data when it is strongly influenced by “exceptional events"

(EE), such as smoke from wildfires or windblown dust or LRTP.

Visibility from Ships, 1938

• Ship observations cataloged in 1938 indicate qualitatively similar pattern to the 1990 AVHRR values

McDonald 1938

AVHRR satellite optical depth data over the oceans Husar et al, 1997.

• The oceanic aerosol pattern is highly regional and and seasonal• The highest oceanic aerosol optical thickness (AOT, 1989-91) is over the tropical regions• The oceanic AOT around N. America, Europe and E. Asia is small compared to Africa and Asia

Continental Surface Visibility (7000+ Human Observers)

Low VisibilityHigh Visibility

Continental Surface Extinction Coefficient Climatology

Dec, Jan, Feb

Jun, Jul, Aug Sep, Oct, Nov

Mar, Apr, May

India

Husar et al, 2000

Fusion of Satellite and Surface Visibility Data

Needed: Reconciliation with Models, Emissions

Vertical Distribution of Aerosols – Space-borne Lidar

• Long rang transport occurs mostly in elevated layers

• Elevated layers mix with BL air

• Cloud interaction is clearly discernable

Winker et., al. 1995

Everglades, FLBig Bend, TX

G. Smoky Mtn.

Sahara Dust - JulyMex. Smoke-May

Emission: GreenModeling: GreenObservations: Green

Emission: YellowModeling: YellowObservations: Yellow

Seasonal Pattern of Fires over N. America

• The number of satellite-fire pixels Jul-Aug (1997-99)• The daily fire counts shows significant day to day fluctuation

Central American

Smoke Plume

Surface PM2.5

Ozone

Kansas Agricultural Smoke, April 12, 2003

Fire Pixels Organics35 ug/m3 max

Ag Fires

SeaWiFS, Refl

Smoke Emission

April 11: 87 T/day

April 10: 1240 T/d

Assuming Mass Extinction Efficiency:

5 m2/g

Emission: RedModeling: YellowObservations: Yellow

Aerosol Nitrate Anomaly – Every 3 Days

Seasonal PM25 by Region

Sulfate-driven Jul-Aug peak

Feb-Mar peak, of unknown

origin

Seasonal Average Fine Dust(VIEWS database, 1992-2002)

• Fine soil concentration is highest in the summer over Mississippi Valley, lowest in the winter• In the spring, high concentrations also exists in the arid Southwest (Arizona and Texas)• Evidently, the summer Mississippi Valley peak is Sahara dust while the Spring peak is from local

(and Asian) sources

Origin of Fine Dust Events over the US

Gobi dust in springSahara in summer

Fine dust events over the US are mainly from intercontinental transport

Supporting Evidence: Transport Analysis

Satellite data (e.g. SeaWiFS) show Sahara Dust reaching Gulf of Mexico and

entering the continent.

The air masses arrive to Big Bend, TX form the east (July) and from the west

(April)

Sahara PM Events over the Eastern US PM10

July 5, 1992

PM10

June 21, 1997

PM10

June 30 1993

Sahara Dust

Sahara Dust

TOMS, July

Aerosol Index

Asian Dust Cloud over N. America

On April 27, the dust cloud arrived in North America.

Regional average PM10 concentrations increased to 65 g/m3

In Washington State, PM10 concentrations exceeded 100 g/m3

Asian Dust 100 g/m3

Hourly PM10

~50% of the variability in springtime PM2.5 in the Western U.S. can be explained by changes in Asian dust (Fischer et al., 2008)

EPISODIC

EPISODIC EPISODIC

… episodic emissions require emission-observation-model integration…

Integration

New Opportunities: Open flow and harvesting of existing data and knowledgeFaster learning through scientific ‘value-chains’ More opportunities to create societal benefits