Support by Inter-RPO WG - NESCAUM

1
Fast Aerosol Sensing Tools for Natural Event
Tracking FASTNET
Project Synopsis Haze levels should be reduced
to the natural conditions by 2064. The space,
time, composition features of natural aerosols
are not known This long-term project goal is to
better characterize the natural haze
conditions Focus is on detailed analysis of major
natural events, e.g. forest fires and windblown
dust FASTNET is primarily a tools development
project for data access, archiving and analysis
This, first year pilot project focuses on
demonstrating the feasibility and utility of
approach

• Support by Inter-RPO WG - NESCAUM
• Performed by
• CAPITA Sonoma Technology, Inc

2
Regional Haze Rule Natural Aerosol
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

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

3
Significant Natural Contributions to Haze by RPO
Judged qualitatively based on current surface
and satellite data
WRAP Local Smoke Local Dust Asian Dust
MANE-VU Canada Smoke
VISTAS Local Smoke Sahara Dust
MRPO Local Smoke Canada Smoke Local Dust
CENRAP Local Smoke Mexico/Canada Smoke Local
Dust Sahara Dust

• Natural forest fires and windblown dust are
  judged to be the key contributors to regional
  haze
• The dominant natural sources include locally
  produced and long-range transported smoke and dust

4
Natural Aerosol Features and Event Analysis

• Natural Aerosol Features
• Intense natural event concentrations can be
  much higher than manmade emissions
• Large major natural events frequently have
  global-scale impacts
• Episodic the main impact is on the extreme, not
  on the average concentrations
• Seasonal - dust and smoke events are strongly
  seasonal at any location
• Uncontrollable natural events can seldom be
  suppressed they may be extra-jurisdictional.
• Natural Aerosol Event Analysis
• Much understanding can be gained from the study
  of major natural aerosol events
• Their features are easier to quantify due to the
  intense aerosol signal
• Subsequently, smaller events can be evaluated
  utilizing the gained insights

5
National Ambient Air Monitoring Strategy
(NAAMS)Focus on PM Ozone(Slide for Scheffe)
FASTNET pursues several of the NAAMS
recommendation

• 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
• Multiple pollutant monitoring must be advanced
• AQ is integrated through sources, atmo.
  processes, health/eco effects
• Technological advances must be incorporated
• Information transfer technologies
• Continuous PM monitors
• High sensitivity instruments
• Model-monitor integration

6
Scientific Challenge Description of PM
Particulate matter is complex because of its
multi-dimensionality It takes at leas 8
independent dimensions to describe the PM
concentration pattern

• Gaseous concentration g (X, Y, Z, T)
• Aerosol concentration a (X, Y, Z, T, D, C, F,
  M)
• The aerosol dimensions size D, composition C,
  shape F, and mixing M determine the impact on
  health, and welfare.

7
Technical Challenge Characterization

• PM characterization requires many different
  instruments and analysis tools.
• Each sensor/network covers only a limited
  fraction of the 8-D PM data space.
• Most of the 8D PM pattern is extrapolated from
  sparse measured data.
• Some devices (e.g. single particle electron
  microscopy) measure only a small subset of the
  PM the challenge is extrapolation to larger
  space-time domains.
• Others, like satellites, integrate over height,
  size, composition, shape, and mixture dimensions
  these data need de-convolution of the integral
  measures.

8
Real-Time Aerosol Watch (RAW)

• RAW is an open communal facility to study
  non-industrial (e.g. dust and smoke) aerosol
  events, including detection, tracking and impact
  on PM and haze.
• RAW output will be directly applicable, to public
  health protection, Regional Haze rule, SIP and
  model development as well as toward stimulating
  the scientific community.
• The main asset of RAW is the community of data
  analysts, modelers, managers and others
  participating in the production of actionable
  knowledge from observations, models and human
  reasoning
• The RAW community will be supported by a
  networking infrastructure based on open Internet
  standards (web services) and a set of web-tools
  evolving under the umbrella of Fast Aerosol
  Sensing Tools for Natural Event Tracking
  (FASTNET).
• Initially, FASTNET is composed of the Community
  Website for open community interaction, the
  Analysts Console for diverse data access and the
  Managers Console for AQ management decision
  support.

9
Data Federation Concept and the FASNET Network
Schematic representation of data sharing in a
federated information system. Based on the
premise that providers expose part of their data
(green) to others
Schematics of the value-adding network proposed
for FASTNET Components embedded in the federated
value network
10
Origin of Fine Dust Events over the US
Gobi dust in spring Sahara in summer
Fine dust events over the US are mainly from
intercontinental transport
11
Daily Average Concentration over the US
Sulfate is seasonal with noise Noise is by
synoptic weather
VIEWS Aerosol Chemistry Database

• Dust is seasonal with noise
• Random short spikes added

12
Sahara and Local Dust Apportionment Annual and
July
The Sahara and Local dust was apportioned based
on their respective source profiles.

• The maximum annual Sahara dust contribution is
  about 1 mg.m3
• In Florida, the local and Sahara dust
  contributions are about equal but at Big Bend,
  the Sahara contribution is lt 25.

• In July the Sahara dust contributions are 4-8
  mg.m3
• Throughout the Southeast, the Sahara dust exceeds
  the local source contributions by w wide margin
  (factor of 2-4)

13
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)
14
Seasonal Fine Aerosol Composition, E. US
Smoky Mtn
Upper Buffalo
Everglades, FL
Big Bend, TX
15
Sahara PM10 Events over Eastern US
July 5, 1992
Much previous work by Prospero, Cahill, Malm,
Scanning the AIRS PM10 and IMPROVE chemical
databases several regional-scale PM10 episodes
over the Gulf Coast (gt 80 ug/m3) that can be
attributed to Sahara.
June 30, 1993
June 21 1997

• The highest July, Eastern US, 90th percentile
  PM10 occurs over the Gulf Coast ( gt 80 ug/m3)
• Sahara dust is the dominant contributor to peak
  July PM10 levels.

16
(No Transcript)
17
FASTNET Event Report 040219TexMexDust
Texas-Mexico Dust Event February 19, 2004
Contributed by the FASNET Community Correspondence
to R Poirot, R Husar
MODIS Rapid Response
18
Satellites detect dust most storms in near real
time
The MODIS sensor on AQUA and Terra provides 250m
resolution images of the dust storm Visual
inspection reveals the dust sources at the
beginning of dust streaks.
The NOAA AVHRR sensor highlights the dust by its
IR sensors In the TOMS satellite image, the dust
signal is conspicuously absent too close to the
ground
19
Surface met data from the 1200 station network
documents the strong winds that cause the
windblown dust and resulting low-visibility
regions
20
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 speed
• Wind speed animation Bext animation. (material
  for model validation?)

21
PM10 gt 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
Schematic

• AIRNOW PM10 and Pm25 data

Link to dust modelers for faster collective
learning?
22
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)

23
VIEWS Fine Mass, Sulfate, OC, Dust, 02-07-01
Mass
SO4
OC

• OC

Dust
24
SeaWiFS AOT ASOS FBext, 02-07-01
25
Pattern of Fires over N. America

• The number of ATSR satellite-observed fires peaks
  in warm season
• Fire onset and smoke amount is unpredictable

Fire Pixel Count Western US
North America
26
July 2020 Quebec Smoke Event

• Superposition of ASOS visibility data (NWS) and
  SeaWiFS reflectance data for July 7, 2002

• PM2.5 time series for New England sites. Note the
  high values at White Face Mtn.
• Micropulse Lidar data for July 6 and July 7, 2002
  - intense smoke layer over D.C. at 2km altitude.

27
2002 Quebec Smoke over the Northeast

• Smoke (Organics) and Sulfate concentration data
  from VIEWS integrated database
• DVoy overlay of sulfate and organics during the
  passage of the smoke plume

28
(No Transcript)
29
Please Visit http//datafed.net
30

• NCore Integration
• NOAA/NASA Satellite Global/Continental transport
• Other Networks Deposition, Ecosystems
• Intensive/diagnostic Field Programs
• Longer Term Goal
• Integrated Observation-modeling Complex
• Similar to Meteorological Models (FDDA)
• Model Adjustments Through Obs.
• All in Near Real Time
• Full Model Dims (x, y, z, t, chemistry, size)