Aerosol Characterization and the Supporting

Information Infrastructures

R.B. HusarWashington University in St. Louis

Regional Haze Rule: Natural Background by 2064

The goal is to attain natural conditions by 2064;The baseline is established during 2000-2004The first SIP & Natural Condition estimate in 2008;SIP & Natural Condition Revisions every 10 yrs

Natural haze is due to natural windblown dust, biomass smoke and other natural processes

Man-made haze is due industrial activities AND man-perturbed smoke and dust emissions

A fraction of the man-perturbed smoke and dust is assigned to natural by policy decisions

NAAMS: National Ambient Air Monitoring Strategy and NCore

Applications

FASTNET and DataFed pursues several NAAMS recommendations:

• Insightful Measurements – Enhanced real-time data delivery to public– Increase capacity for hazardous air pollutant measurements

– Increase in continuous PM measurements– Support for research grade/technology transfer sites

– Auxiliary [non-EPA] data support

• Multiple pollutant monitoring – Integration of sources, processes, effects

• Incorporate technological advances– Information transfer technologies– Continuous PM monitors– High sensitivity instruments

– Model-monitor integration

FASTNET and DataFedFASTNET (Fast Aerosol Sensing Tools for Natural Event Tracking) an open communal information sharing facility to study aerosol events, including detection, tracking and impact on PM and haze.

The main asset of FASTNET is the community of data analysts, modelers, managers participating in the production of actionable knowledge from data and models

The community is supported by a non-intrusive data integration infrastructure based on Internet standards (web services) and a set of web-tools evolving under the federated data system, DataFed

DataFed is supported by its community and is under the umbrella of the interagency Earth Science Information Partners, ESIP (NASA, NOAA and EPA)

Aerosol Characterization• Atmospheric aerosol system has three extra dimensions (red), compared to gases (blue):

– Spatial dimensions (X, Y, Z) – Temporal Dimensions (T)– Particle size (D)– Particle Composition ( C ) – Particle Shape (S)

• Bad news: The mere characterization of the 7Dim aerosol system is a challenge– Spatially dense network -X, Y, Z(??)– Continuous monitoring (T)– Size segregated sampling (D) – Speciated analysis ( C )– Shape (??)

• Good news: The aerosol system is self-describing. – Once the aerosol is characterized (Speciated monitoring) and multidimensional aerosol data are

organized, (see RPO VIEWS effort), unique opportunities exists for extracting information about the aerosol system (sources, transformations) from the data directly.

• Analysts challenge: Deciphering the handwriting contained in the data – Chemical fingerprinting/source apportionment– Meteorological back-trajectory analysis– Dynamic forward and inverse modeling

Technical Challenge: Characterization

• PM characterization requires many sensors, sampling methods and analysis tools

• Each sensor/method covers only a fraction of the 8-D PM data space.

• Most of the 7-8 Dim PM data space is extrapolated from sparse measured data

• Others sensors integrate over time, space, chemistry, size etc. .

• Example: Satellites, have high spatial resolution but integrate over height H, size D, composition C, shape, and mixture dimensions; these data need de-convolution of the integral measures.

Satellite-Integral

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

Satellite Detection

of

Fire and Smoke

FIRE and Norm. Diff. Veg. Index, NDVI

The ‘Northern’ zone from Alaska to Newfoundland has large fire ‘patches’, evidence of large, contiguous fires.

The ‘Northwestern’ zone (W. Canada, ID, MT, CA) is a mixture of large and small fires

The ‘Southeastern’ fire zone (TX–NC–FL) has a moderate density of uniformly distributed small fires.

The ‘Mexican’ zone over low elevation C America is the most intense fire zone, sharply separated from arid and the lush regions.

Fires are absent in arid low-vegetation areas (yellow).

Fire Zones of North America

Seasonality of Fire

Dec, Jan, Feb is generally fire-free except in Mexico, and W. Canada

Mar, Apr, May is the peak fire season in Mexico and Cuba; fires occur also in Alberta-Manitoba and in OK-MO region

Jun, Jul, Aug is the peak fire season in N. Canada, Alaska and the NW US.

Sep, Oct, Nov is fire over the ‘Northwest’ and the “Southeast’

Seasonal Pattern of Fires over N. America

• The number of ATSR satellite-observed fires peaks in warm season

• Fires are random; onset and smoke amount is unpredictable

MISR Seasonal AOT (MISR Team)Major smoke emission regions by season

Smoke types: blue, yellow, white

Smoke from major fires comes in different colors, e.g. blue, yellow.

The chemical, physical and optical characteristics of smokes are not known

Can the reflectance color be used to classify smokes?

Can column AOT be retrieved for optically thick smoke? Multiple scattering, absoption?

California Smoke 1999 Quebec Smoke 2002

SeaWiFS, TOMS, Surface

Visibility, May 98

Surface ozone depressed under smoke

• Satellite image of color SeaWiFS data, contours of TOMS satellite data (green) and surface extinction coefficient, Bext

• The smoke plume extends from Guatemala to Hudson May in Canada

• The Bext values indicate that the smoke is present at the surface

Hourly PM10 During the Smoke Event

Hourly PM10 concentration pattern at six eastern US locations during May 1998.

Fire Pixels from MODIS, June 25-July 6, 2002

• Several satellite sensor (MODIS, GOES, AVHRR, ATSR…..) detect the location of most fires - DAILY• These ‘fire pixels’ can be used as sensor-based inputs to regional/global models, e.g. NAAPS• However, the quantity of smoke emitted from the from the ‘fire pixels’ can not be estimated well . • Hence, real-time model simulation of smoke transport is limited by the smoke emission estimates

Quebec Fires

Note pixel clusters due to larger fires

Manitoba – Sask. Fires

Note pixel clusters due to larger fires

SE US Fires

Random pixels from small fires

MODIS: The Fine-Scale Picture

The Fires and the Smoke Transport of Smoke from N. Quebec to SE Canada and NE US.

020705MODIS 020706 MODIS

MODIS Land Rapid Response System

020707 MODIS

Haze WebCamsCamNet -Webcam

020707 10:00 Yellow smoke

020706 10:00 Normal bluish haze

Hartford, CT New York, NY Boston, MA

GOES 8 Animation

July 6 animation: low-resolution, high resolution July 7 animation: low resolution, high resolution

GOES 8 and ASOS Visibility July 6, 2002 8:15, 12:15, 16:15 EST

GOES8 20020706_1315 UTC

GOES8 20020706_1315

GOES8 20020706_1715 UTC

GOES8 20020706_2115 UTC

The largest circles correspond to > 100 ug/m3 PM2.5

Smoke Events: Community Websites

• er

Smoke Plumes over the Southeast

• Satellite detection yields the origin and location is the shape of smoke plumes

R 0.68 m

G 0.55 m

B 0.41 m

0.41 m

0.87 m

• The influence of the smoke is to increase the reflectance ant short wavelength (0.4 m)

• At longer wavelength, the aerosol reflectance is insignificant.

Cumulative Seasonal PM2.5 Composition

• PM2.5 chemical components were calculated based on the CIRA methodology

• In addition, the the organics were (tentatively) further separated as Primary Smoke Organics (red) and Remainder organics (purple)

PSO = 20*(K - 0.15*Si – 0.02* Na) Remainder Org = Organics - PSO

• Also, the ‘Unknown’ mass (white area) is the difference between the gravimetrically measured and the chemically reconstructed PM2.5.

• The daily chemical composition was aggregated over the available IMPROVE data range (1988-99) to retain the seasonal structure.

• I order to reduce the noise the daily data were smoothed by a 15-day moving average filter.

Shenandoah

Peripheral Sites: Chemical Mass Balance• Eastern N. America is

surrounded by aerosol source regions such as Sahara and Central America.

• As a consequence, the PM concentration at the ‘edges’ ranges between 4-15 ug/m3; much of it originating outside.

• The chemical composition of the inflow varies by location and season.

• At the Everglades, organics, ‘smoke organics’ and LAC dominate over sulfate and fine dust

• Sahara dust, and smoke from Central America and W. US/Canada are the main contributions to Everglades, FL, and Big Bend, TX.

Badlands(scale 0-15 ug/m3)

Big Bend(scale 0-15 ug/m3)

Voyageurs(scale 0-15 ug/m3)

Acadia

Everglades

Badlands(scale 0-15 ug/m3)

Peripheral Sites: Carbonaceous Mass Balance• At the northern

peripheral sites, Badlands, Voyageurs and Acadia, the organics range from 1.5 to 4 g/m3

• At Big Bend the organics show a spring peak, with a majority of ‘smoke organics’. This indicates biomass smoke origin.

• At the Everglades, the fall peak is due to organics, while ‘smoke organics’ light absorption is present throughout the year.

Big Bend(scale 0-15 ug/m3)

Voyageurs(scale 0-15 ug/m3)

Acadia (scale 0-15 ug/m3)

Everglades (scale 0-15 ug/m3)

Possible Smoke Emission Estimation:Local Smoke Model with Data Assimilation

Emission Model

Land Vegetation

Fire Model

e..g. MM5 winds, plume model

Local Smoke Simulation Model

AOT Aer. Retrieval

Satellite Smoke

Visibility, AIRNOW

Surface Smoke

Assimilated Smoke Pattern

Continuous Smoke Emissions

Assimilated Smoke Emission for Available Data

Fire Pixel, Field Obs

Fire Location

Assimilated Fire Location

Kansas Agricultural Smoke, April 12, 2003

Fire Pixels PM25 Mass, FRM65 ug/m3 max

Organics35 ug/m3 max

Ag Fires

SeaWiFS, Refl SeaWiFS, AOT Col AOT Blue

ASOS Visibility Monitoring System (1200 Sites)

• The Automated Surface Observing System, ASOS; weather every minute.

• The forward scattering (30-500) visibility sensor has a range 17 ft to 30 miles.

• The synoptic visibility data are truncated (<1/4, 1/4,..10+ miles)

• For smoke and haze events (vis. < 10 mile) truncation not a problem

Diurnal Cycle – Surface Bext, April 12, 2003

00 02 04 06

08 10 12 14

16 18 20 22

Night

Day

Night

High Night Bext Low Day Bext

Smoke

FASTNET Event Report: 040705July4Haze, July 6, 2004

July 4, 2004 Aerosol Pulse

Event Summary by the FASTNET CommunityPlease send PPT slides or comments to Erin Robinson or Rudy Husar,

CAPITA

Visit the event discussion forum

July 4, 2004 Aerosol Pulse

• The US-avg. AIRNOW PM25 shows a 3 hr. spike at midnight• In the (airport) ASOS the July 4 spike is conspicuously absent• Thus, the US spike is due to the urban sites affected by smoke

00:00

04:00

08:00

20:00

AIRNOW PM25

AIRNOW PM25

US Hourly Average

ASOS Bext

US Hourly Average

Pulse

No Pulse

Previous work: The July 4th Potassium Spike (Poirot 1998)

• Potassium nitrate is a major component of all fireworks (provides the bang!). • Fine particle K for all IMPROVE data (1988-1997) were averaged for each day of year• The potassium spike on July 5 is 120 ng/m3 compared to 40-60 during the year • The corresponding IMPROVE-average daily fine mass did not show the spike• The K spike is clearly something to consider (and perhaps screen out) in conducting any

analyses using K data

D U S T Update

Global & Local Dust Over N. America

Sahara PM10 Events over Eastern USMuch previous work by Prospero, Cahill, Malm, Scanning the AIRS PM10 and IMPROVE chemical

databases several regional-scale PM10 episodes over the Gulf Coast (> 80 ug/m3) that can be attributed to Sahara.

June 30, 1993

The highest July, Eastern US, 90th percentile PM10 occurs over the Gulf Coast ( > 80 ug/m3)

Sahara dust is the dominant contributor to peak July

PM10 levels.

July 5, 1992

June 21 1997

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

MODIS Rapid Response

FASTNET Event Report: 040219TexMexDust

Texas-Mexico Dust EventFebruary 19, 2004

Contributed by the FASNET Community

Correspondence to R Poirot, R Husar

High Wind Speed – Dust Spatially Correspond

The spatial/temporal correspondence suggests that most visibility loss is due to locally suspended dust, rather than transported dust

Alternatively, suspended dust and ‘high winds’ travel forward at the same speedWind speed animation; Bext animation. (material for model validation?)

PM10 > 10 x PM25During the passage of the dust cloud over El Paso, the PM10 concentration was more than 10 times higher than the PM2.5

AIRNOW PM10 and Pm25 data

PM10 and PM25, El Paso, Feb. 19 2004 - AIRNOW

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Schematic

Link to dust modelers for faster collective learning?

Monte Carlo simulation of dust transport using surface winds (just a toy, 3D winds are essential!)

See animation Note, how sensitive the transport direction is to the source location (according to this toy)