Comprehensive Particulate Matter Modeling: A One Atmosphere Approach

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A Hybrid Method for Particulate Matter Source
Apportionment Using A Combined Chemical
Transport and Receptor Model Approach
Yongtao Hu, Sivaraman Balachandran, Jorge
Pachon,Jaemeen Baek, Talat Odman, James A.
Mulholland and Armistead G. Russell School of
Civil and Environmental Engineering, Georgia
Institute of Technology, Atlanta, Georgia
Currently at Currently at IIHR-Hydroscience and
Engineering, University of Iowa. Iowa City, Iowa

10th Annual CMAS Conference, October 25th, 2010
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Objective and Approach

• Develop a source-based approach to integrating
  receptor- and source- oriented modeling of
  particulate matter
• Improve source impact estimates
• Extend impact quantification to more sources
• Expanded spatial and temporal coverage of source
  apportionment
• Provide estimates of uncertainties for spatial
  analysis
• Approach
• CMAQ DDM3D/PM to provide initial source impacts
  and sensitivities
• Use sensitivities to adjust source impacts using
  CMB-type formulation
• Use adjustments and species performance to assess
  uncertainties
• Application
• One month simulation over CONUS
• STN monitors
• Six cities

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Receptor Oriented Modeling (RM)

• RM approaches such as CMB rely on using observed
  concentrations of the PM composition at a
  receptor, along with knowledge of the composition
  of source emissions (source profiles), to solve a
  species balance equation that estimates the
  source impacts. For example CMB species balance
  equations
• Limitations/assumptions/uncertainties
• Emission compositions are constant and known (not
  good for some sources)
• No reactions or differential phase changes (not
  bad for many, but not all, primary compounds)
• Most sources are included (typically only about
  80 of mass is)
• Source compositions are linearly independent of
  each other (co-linearity can be a problem)
• The number of sources is less than or equal to
  chemical species (limitation)

total number of emission sources considered
measured concentration of species i
RMs prediction error to be minimized
emission fraction of species i in total PM2.5
emitted from source j
source js impact on the total PM2.5 concentration
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Source-Oriented Modeling (SM)

• SM approaches using chemical transport models
  (CTMs) follow a first principles approach,
  tracking the emissions, transport, transformation
  and loss of chemical species in the atmosphere to
  simulate ambient concentrations and source
  impacts. For example using DDM3D derived
  sensitivities
• Limitations/uncertainties
• Emissions estimates, Meteorological inputs,
  Missing processes and parameter uncertainties
• Benefits
• Large number of sources, direct link to sources,
  spatial coverage, non-linear chemistry

total number of emission sources that included in
CTM
calculated sensitivity coefficients of species
is concentration to emissions from source j
Simulated concentration for species i
impact from source js emissions outside of the
domain
estimate of source js impact on species is
concentration
impact from source js emissions prior to the
simulation period
total emissions of all tracked pollutants emitted
from source j
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A hybrid approach for particulate matter source
apportionment Combining receptor modeling with
chemical transport modeling
Limited number of sources vs. completeness of
source categories
SMs prediction error to be minimized
Sensitivities to emissions
Sensitivity to BC
Sensitivity to IC
Constraints from source profiles upgraded to
constraints of source-receptor relationship
derived from CTM
We modify the species balance equations which CMB
is based to use outputs of the CTM.
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Hybrid Approach (continued)
The hybrid approach relies on minimizing the
differences (c2) between CTM-calculated and
observed PM2.5 concentrations (including each
PM2.5 component and metals) while considering
estimated uncertainties in both the observations
and source emission rates
where
So,
CTM-simulated base case impact of source j on
species i
to weigh the amount of change in source strengths
total number of sources
total number of species
ratio of adjusted impact from source j to the
base case
a priori uncertainties
Instead of the original CMB solution
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Application

• 2004 MM5-SMOKE-CMAQ-DDM3D simulation
• 36-km grid covering continental United States as
  well as portions of Canada and Mexico.
• Projected VISTAS emissions inventory used as a
  priori inventory.
• First order DDM sensitivity coefficients
  calculated for 32 separate source categories.
• Ambient PM2.5 concentrations apportioned to the
  32 separate sources
• STN, IMPROVE, SEARCH and ASACA networks
• TOT measurements of OC and EC from STN and ASACA
  converted to TOR equivalences.

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PM2.5 monitoring networks
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Hybrid Approach Applied at STN sites

• Major PM2.5 ions and metals measured
• Use reported detection limits and measurement
  uncertainties
• Obtain metals sensitivities to sources
• Split using source specific PM2.5 (unidentified
  portion) sensitivity coefficients and source
  profiles of metals for each of the 32 categories
  assuming that metals remain intact from source to
  receptor.
• Source profiles are assembled from the 84
  profiles compiled by Reff et al. 2009 EST. The
  profiles split PM2.5 emissions to the above 42
  species.

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Choice of ? for Ridge Regression
?N/J42/321.3125 selected
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CMAQ/Hybrid Concentrations
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Initial/Refined (CMAQ/Hybrid) difference (?2Ci)
between simulated and observed PM2.5
concentrations
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Initial and Refined PM2.5 source impacts (in
percentage)
Woodstove
Solvent
Others
Prescribed burn
Other combustion
Nonroad diesel
On-road gasoline
Natural gas combustion
Mineral product
On-road diesel
Fuel oil combustion
Meat cooking
LPG combustion
Dust
Waste burn
Metal product
Coal combustion
Livestock
Biogenic
Aircraft
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Major contributing sources in six cities
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Initial/Refined (CMAQ/Hybrid) Source Impacts
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Initial/Refined (CMAQ/Hybrid) Source Impacts
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Compare with the CMB Results

• CMB apportionment allowed resolution of less than
  10 sources while the hybrid method resolved 32,
  and included total contributions from both
  primary and secondary paths.
• In order to do more specific comparisons, the
  hybrid results are re-grouped to match up with
  the CMB categories by
• (1) splitting the primary and the secondary
  contributions from each hybrid category, using
  the source specific composition profiles and
  assuming that the primary species are inert and
  stick together, and
• (2) merging the hybrid sub-categories that split
  to primary and secondary portions to the major
  categories that match up with the CMB sources.

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Initial/Refined (CMAQ/Hybrid) Source Impacts
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Initial/Refined (CMAQ/Hybrid) Source Impacts
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Initial/Refined (CMAQ/Hybrid) Source Impacts
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Benefits and Future Work

• Hybrid Approach Benefits
• Completeness of sources
• More complete range of sources quantified
• First principles constraints
• Can account for non-linearities and secondary PM
  sources
• Limitations removed, for spatial and temporal
  applications.
• Uncertainty estimation
• Ongoing Work
• Source apportionment at IMPROVE, ASACA and SEARCH
  sites.
• Simulating full year.
• Further uncertainty estimation.
• Additional approach for inverse modeling
• Optimize source compositions.
• Interpolation of source impacts spatially and
  temporally
• Increased resolution

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Acknowledgements

• EPA funding under grants R83362601 and R83386601
• Southern Company and Georgia Power

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