Tools and Techniques for Identifying Contributions of PMfine to Regional Haze: Source Apportionment

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Tools and Techniques for Identifying
Contributions of PMfine to Regional Haze
Source Apportionment Techniques,CATT and
FASTNET

• Serpil Kayin, MARAMA
• Rich Poirot, VT DEC

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Tools and Techniques for Identifying
Contributions of PMfine to Regional Haze

• Overview
• Why data analysisRole in SIP planning
• Source Apportionment
• What is it, how does it work
• Interface with modeling inventory development
• SA Work MANE-VU conducted
• CATT and FASTNET DATAFed

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Data Analysis and SIP Planning

• CAAAC and new EPA guidance recommend
  weight-of-evidence demonstration for SIP planning
  and implementation
• WOE Models monitoring data analysis
  inventory review
• Specific data analysis suggested
• Conceptual model (qualitative description)
• Historic trends, meteorologically adjusted
• Transport assessment
• Observation-based methods
• Synthesis of Modeling/Monitoring/EI
• Identify important source sectors or specific
  pollutants
• Assess whether control strategies should be
  national, regional, or local

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Source Apportionment of Ambient Data

• What is source apportionment?
• Mathematical technique for determining
  contributions of various sources to a given
  sample of air
• Produces a sample-specific inventory estimate
• Can be used to develop and validate inventory
  information, track effectiveness of control
  measures
• Can be used for various air pollutants volatile
  organics, semivolatiles, particles, toxics, or a
  combination of these
• Many methods CMB (Chemical Mass Balance), PMF
  (Positive Matrix Factorization), UNMIX

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Source Apportionment of Ambient Data

• SA is a convenient way to extract information on
  pollutant sources from routinely collected
  ambient data (bottom up vs. top down approach)
• EIs generally self-reported, not measured
• EIs often derived from emission factors and
  activity estimates
• SA results allow independent evaluation of
  inventory data and model predictions (especially
  source apportionment results from photochemical
  models)

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Source Apportionment of Ambient Data

• Fingerprints (aka source profiles)
• Chemical patterns of source emissions
• Species should be present in both ambient air and
  in source emissions
• Species should have limited reactivity or similar
  reaction rates.
• Assumptions
• Composition of source emissions is relatively
  constant
• Emissions do not react or selectively deposit
  between source and receptor (mass is conserved)
• Source profiles are linearly independent
• All major sources should be included in the model
  (CMB)

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Example Source Profiles from PMF and Unmix
Modeling at Underhill, VT and Comparison of Daily
Source Contributions, (right) From Polissar et
al. (2001) Poirot al. (2001)
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Source Apportionment of Ambient Data

• Advantages
• Models tolerate deviations in model assumptions
  well (often breaking sources with primary
  secondary precursors into 2 or more source
  components)
• Useful for natural emission sources, others
  with strong flavor (unique chemical
  composition, time series, or spatial origin)
• Can identify previously un-inventoried sources
• Disadvantages
• Usually identifies categories of sources, not
  individual sources
• Retrospective, not predictive
• Identifies only receptor contributions, not mass
  emission rates
• PMF and UNMIX require large number of samples
  (100) CMB can be applied to individual samples

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Source Apportionment of Ambient Data

• What data are available
• PAMS
• PM2.5 speciation data
• Special ozone and PM studies to support
  photochemical models (SEARCH, etc)
• Toxics Network data
• Rural Networks (IMPROVE, CASTNET) and Urban (STN)

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Locations of Recent Northeastern Receptor
Modeling Studies, Conducted by MANE-VU and/or by
Academic Researchers
Early work focused primarily on Rural IMPROVE
sites. More recent analyses based on New Urban
STN speciation data Comparing Urban and Rural
results shows common influences on haze and PM2.5
and Also helps show key Local Urban Sources
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Similarities Differences between Rural Haze
Urban PM 2.5
During Summer, Rural Haze and Urban PM in the
Northeast are Both Dominated by secondary
Sulfates. During the Winter, Local Urban Sources
of Carbon and Nitrate Compounds become Much More
Important
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SA Work MANE-VU conducted

• Battelle Report (May 2002)
• DRI Report (March 2005)
• NESCAUM Report (Tools and Techniques for
  Identifying Contributions to Regional Haze in the
  MANE-VU Region), Appendix B (January 2005)
• CATT and FASTNET web tools for additional SA
  analysis Interpretation (available
  in-progress)

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FASTNET Fast Aerosol Sensing Tools for Natural
Event TrackingCATT Combined Aerosol
Trajectory Tools

• Web-Based Data Acquisition, Visualization,
  Analysis Tools,
• Developed by CAPITA (R. Husar, S. Falke, K.
  Hoijarvi), with significant contributions from R.
  Poirot (VT)
• Funded by the 5 US Regional Planning
  Organizations,
• Managed for MANE-VU by Gary Kleiman Serpil
  Kayin
• Based on Data Architecture Developed with
  previous support from NSF and NASA
• Found at http//datafed.net

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CATT/DATAFed User Instructions and Tutorial

• http//datafed.net
• CATT is under web apps http//datafed.net/projec
  ts/CATT/CATT_Links.htm
• CATT url has resources/discussion and user manual
• Tutorials available
• http//capita.wustl.edu/datafed/tutorial/Tutorial1
  -Basics.htm
• http//capita.wustl.edu/datafed/tutorial/Tutorial2
  -Cursor.htm
• http//capita.wustl.edu/datafed/tutorial/Tutorial2
  -Cursor.htm
• They take a while to download, and need sound
  turned on.
• Also there's the new "user file submittal option"
  described at
• http//capita.wustl.edu/capita/researchareas/CATT/
  Desc-Help/UserView.html

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Datasets Used in FASTNET
Near Real Time Data Integration Delayed Data
Integration Surface Air Quality AIRNOW O3,
PM25 ASOS_STI Visibility, 300
sites METAR Visibility, 1200 sites VIEWS_OL 40
Aerosol Parameters Satellite MODIS_AOT AOT, Idea
Project GASP Reflectance, AOT TOMS Absorption
Indx, Refl. SEAW_US Reflectance, AOT Model
Output NAAPS Dust, Smoke, Sulfate,
AOT WRF Sulfate Fire Data HMS_Fire Fire
Pixels MODIS_Fire Fire Pixels Surface
Meteorology RADAR NEXTRAD SURF_MET Temp, Dewp,
Humidity SURF_WIND Wind vectors ATAD Trajector
y, VIEWS locs.

• Data are accessed from autonomous, distributed
  providers
• DataFed wrappers provide uniform geo-time
  referencing
• Tools allow space/time overlay, comparisons and
  fusion

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A Sample of Datasets Accessible through ESIP
DataFed Mediation Near Real Time ( day)
MODIS Reflectance
MODIS AOT
TOMS Index
MODIS Fire Pix
GOES AOT
GOES 1km Reflec
NEXTRAD Radar
NRL MODEL
NWS Surf Wind, Bext

• It has been demonstrated (project FASTNET) that
  these and other datasets can be accessed,
  repackaged and delivered by AIRNow through
  Consoles

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CATT A Community Tool! Part of an Analysis
Value Chain
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Aerosol Event Catalog Web pages

• Catalog of generic web objects pages, images,
  animations that relate to aerosol events
• Each web object is cataloged by location, time
  and aerosol type.

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Evaluation and Interpretation of Receptor Model
Results by Local Surface Winds or Ensemble
Trajectory Techniques
Sea Salt Source from Both Unmix and PMF Modeling
at Brigantine Since Sea Salt comes from the Sea
(Well, Duh!), It tends to contribute most on days
when man-made pollutants (from inland) are
lowest. But Battelle PMF results suggest sea salt
is increasing over time. Why?
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Receptor Model Results show Sea Salt Source (with
High Na) at NE Sites CATT applied to Entire
IMPROVE Network Shows High Na for the Sea(s)
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Receptor Models indicate a Source of Fine
Windblown Dust from an Unexpected Distant Origin.
Its a Minor Contributor to Haze and PM2.5, but
a good example that Transport Happens
Like Sea Salt, Windblown Dust at Brigantine also
seems to come from the Sea. Weird!
Highest Dust at all Eastern IMPROVE sites comes
from the Sahara Desert
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SA Work MANE-VU conducted
Surface Met and Trajectory Evaluation of Oil
Source Identified by Unmix PMF modeling at
Brigantine, NJ
Trajectory Incremental Probability for Oil Source
Identified by Unmix or PMF modeling at 4 MANE-VU
sites (sources are within region)
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Receptor Model Results show Local Oil Source
(with High Ni) at NE Sites CATT applied to Entire
IMPROVE Network Shows High Ni for East Coast
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Receptor Model Results show Wood Smoke Sources,
which tend to be from Canadian summer Forest
Fires winter Residential Burning in New
England, and more often from Southeastern Fires
in Southern MANE-VU
Quebec Fires of July 2002 were a Big Example of a
Smaller, Common summer influence in New England
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Receptor Model Results show Wood Smoke Sources,
which tend to be from Canadian summer Forest
Fires winter Residential Burning in New
England, and more often from Southeastern Fires
in Southern MANE-VU
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Back Trajectories for All IMPROVE Sites on 7/7/02
Unweighted (top left), color-weighted for OC
(top right), SO4 lower right Cl (lower left)
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Color weighted ATADs for High SO4 (top) Se
(bottom) for 8/12-15/02 Haze event MODIS before
(top) During (bottom)
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Receptor Models indicate a Large source of
Secondary Sulfate (i.e. Coal) at Northeast
Receptor Sites. Same area is upwind of Highest
SO4 throughout the IMPROVE Network. Selenium is a
good Primary Coal Tracer
PMF Sulfate (coal) sources, 7 NE sites SO4 gt 15
ug/m3, all IMPROVE sites Average Upwind Se, all
IMP. sites
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Receptor Models show Secondary Nitrate Source
which appears to be influenced by Midwestern
areas of High Agricultural NO3 Emissions
(at rural IMPROVE sites in MANE-VU and
throughout the East)
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Several Large-Scale Northeastern NO3 Events
observed in recent years, illustrated here by
ASOS visibility data from FASTNET for 2/18-21/04.
Are these Winter NO3 events becoming more
frequent in the Northeast?
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SO4 sources similar for Rural sites like
Shenandoah Urban sites like DC But Urban
areas have larger and different local NO3 sources
NO3 at SHEN
SO4 at SHEN
NO3 at WASH
SO4 at WASH
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Changes in Average Upwind Sulfate (Left) and
Nitrate (Right) from 1992-95 (Top) and 2000-03
(Bottom) averaged for 42 IMPROVE sites
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Modeled Regional Source Impacts for MANE-VU
IMPROVE sites, Summer 2002
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Modeled Regional Source Impacts for MANE-VU
IMPROVE sites, Summer 2002
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• Findings from analysis of speciated aerosol data
  combined with ensemble trajectory evaluations in
  MANE-VU
• Common source categories with impacts on PMfine
  mass concs. visibility impairment in NE sites
• Windblown dust minor contributor to avg. fine
  mass, with highest short term impacts from
  Saharan transport
• Sea Salt minor contributor to fine mass,
  identified at coastal and near coastal sites.
  Significant at best visibility days at Acadia
  Brigantine.
• Oil burning minor contributor to fine mass,
  identified at many sites, within and downwind of
  the NE urban corridor.

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• Findings from analysis of speciated aerosol data
  combined with ensemble trajectory evaluations in
  MANE-VU
• Common source categories with impacts on PMfine
  mass concs. visibility impairment in NE sites
• Ammonium Nitrate a small to moderate
  contributor to avg fine mass, with regional
  influences at rural sites from upwind
  agricultural ammonia-emitting areas, and
  significant local source contributions in urban
  areas.
• Wood Smoke a small to moderate contributor to
  avg fine mass, with contributions higher in rural
  areas, winter peaks in northern areas from
  residential wood burning, occasional large summer
  impacts at all sites from wildfires.

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• Findings from analysis of speciated aerosol data
  combined with ensemble trajectory evaluations in
  MANE-VU
• Common source categories with impacts on PMfine
  mass concs. visibility impairment in NE sites
• Motor Vehicles Secondary Organics a moderate
  to large contributor to avg fine mass, with
  influence from both gasoline diesel vehicles in
  urban areas at forested rural sites, biogenic
  organics are likely to be more important.
• Coal Burning (incl. primary aerosol and
  secondary aerosol formation) the largest mass
  contributing and visibility-impairing source
  category at most sites, with contributions
  primarily from utility and industrial sources in
  western MANE-VU, northern VISTAS and the MRPO
  regions.