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)