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Comparison of PM Source Apportionment and Sensitivity Analysis in CAMx. Bonyoung Koo, Gary Wilson, Ralph Morris, Greg Yarwood ENVIRON Alan Dunker General Motors R&D Center 8 th Annual CMAS Conference October 19-21, 2009 Chapel Hill, North Carolina. Probing Tools in CAMx.

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comparison of pm source apportionment and sensitivity analysis in camx

Comparison of PM Source Apportionment and Sensitivity Analysis in CAMx

Bonyoung Koo, Gary Wilson, Ralph Morris, Greg Yarwood

ENVIRON

Alan Dunker

General Motors R&D Center

8th Annual CMAS Conference

October 19-21, 2009

Chapel Hill, North Carolina

probing tools in camx
Probing Tools in CAMx
  • Source Apportionment
    • Ozone Source Apportionment Technology (OSAT)
    • Particulate Source Apportionment Technology (PSAT)
    • Reactive Tracer Source Apportionment (RTRAC)
  • Sensitivity Analysis
    • Decoupled Direct Method (DDM) for gas and particulate species
    • Higher-order DDM (HDDM) for gas-phase species
  • Process Analysis
    • Integrated Process Rate (IPR)
    • Integrated Reaction Rate (IRR)
    • Chemical Process Analysis
probing tools in camx1
Probing Tools in CAMx
  • Source Apportionment – Tagged Species
    • Ozone Source Apportionment Technology (OSAT)
    • Particulate Source Apportionment Technology (PSAT)
    • Reactive Tracer Source Apportionment (RTRAC)
  • Sensitivity Analysis
    • Decoupled Direct Method (DDM) for gas and particulate species
    • Higher-order DDM (HDDM) for gas-phase species
  • Process Analysis
    • Integrated Process Rate (IPR)
    • Integrated Reaction Rate (IRR)
    • Chemical Process Analysis
probing tools in camx2
Probing Tools in CAMx
  • Source Apportionment
    • Ozone Source Apportionment Technology (OSAT)
    • Particulate Source Apportionment Technology (PSAT)
    • Reactive Tracer Source Apportionment (RTRAC)
  • Sensitivity Analysis
    • Decoupled Direct Method (DDM) for gas and particulate species
    • Higher-order DDM (HDDM) for gas-phase species
  • Process Analysis
    • Integrated Process Rate (IPR)
    • Integrated Reaction Rate (IRR)
    • Chemical Process Analysis
probing tools in camx3
Probing Tools in CAMx
  • Source Apportionment
    • Ozone Source Apportionment Technology (OSAT)
    • Particulate Source Apportionment Technology (PSAT)
    • Reactive Tracer Source Apportionment (RTRAC)
  • Sensitivity Analysis
    • Decoupled Direct Method (DDM) for gas and particulate species
    • Higher-order DDM (HDDM) for gas-phase species
  • Process Analysis
    • Integrated Process Rate (IPR)
    • Integrated Reaction Rate (IRR)
    • Chemical Process Analysis
brute force method
Brute-Force Method

DCBFM

BFM

Pollutant Concentration

0

E1

E0

Emission

first order sensitivity
First-Order Sensitivity

DCDDM

DCBFM

DDM

BFM

Pollutant Concentration

0

E1

E0

Emission

source apportionment
Source Apportionment

DCDDM

DCBFM

DDM

DCPSAT

BFM

Pollutant Concentration

PSAT

0

E1

E0

Emission

zero out contribution
Zero-Out Contribution

DCDDM

DDM

BFM

Pollutant Concentration

DCPSAT = DCBFM

PSAT

0

E0

Emission

pm modeling episode
PM Modeling Episode
  • February & July from the St. Louis 36-/12-km 2002 PM2.5 SIP modeling
  • Urban & rural receptors:
    • 2 PM2.5 NAAs
    • 6 Federal Class-I areas
  • BFM reductions of 20% and 100% in various emission species from anthropogenic sources
  • PSAT and DDM

Chicago PM2.5 NAA (CNAA), St. Louis PM2.5 NAA (SNAA), Mingo wilderness area (MING), Hercules-Glades wilderness area (HEGL), Upper Buffalo wilderness area (UPBU), Caney Creek wilderness area (CACR), Mammoth Cave national park (MACA), and Sipsey wilderness area (SIPS)

contributions of point source so 2 to pm 2 5 sulfate1
Contributions of Point-Source SO2 to PM2.5 Sulfate

February

Oxidant-limiting effects

July

pm 2 5 sulfate changes due to on road mv emiss reductions1
PM2.5 Sulfate Changes due to On-road MV Emiss Reductions

Indirect effect

February:

Reducing NOx

emissions

Lower acidity

of the aqueous

phase

More SO2

dissolves in the

aqueous phase

More sulfate

produced

Negative Sensitivity

pm 2 5 sulfate changes due to on road mv emiss reductions2
PM2.5 Sulfate Changes due to On-road MV Emiss Reductions

Indirect effect

July:

Reducing NOx

emissions

Less oxidant

available to

oxidize SO2

Further reduction

in sulfate

Positive Sensitivity

pm 2 5 nitrate changes due to on road mv emiss reductions
PM2.5 Nitrate Changes due to On-road MV Emiss Reductions

Less indirect effect

because NOx

dominates on-road

MV emission

summary
Summary
  • 1st-order DDM sensitivities agree well with the BFM model responses to small emission changes (20%)
    • With large emission changes, non-linearity comes into play
    • For SOA and primary PM2.5, the DDM works relatively well even with 100% emission reductions
  • PSAT and zero-out are nearly equivalent in cases with no indirect effect
    • PSAT starts to deviate from the zero-out contribution as indirect effects from limiting reactants or non-primary precursor emissions become important
summary cont
Summary (cont.)
  • Source sensitivity and source apportionment are equivalent for pollutants that are linearly related to emissions; However, when they are different:
    • PSAT is best at apportioning PM pollutants to sources emitting their primary precursors (e.g., sulfate to SO2, nitrate to NOx)
    • DDM sensitivities are more accurate than PSAT in determining the impact of emissions that have indirect effects on secondary PM
    • PSAT works better at estimating the impact of zeroing-out a source while DDM does generally better when a fraction of emissions are eliminated from the source
summary cont1
Summary (cont.)
  • BFM (zero-out) also has limitations:
    • Computationally expensive and subject to numerical noises
    • Sum of the BFM source contributions will not always equal the simulated concentrations in the base case
acknowledgement
Acknowledgement
  • Funded by the Coordinating Research Council

For more details…

Koo, B., G. M. Wilson, R. E. Morris, A. M. Dunker and G. Yarwood.   2009.  

“Comparison of Source Apportionment and Sensitivity Analysis in a Particulate Matter Air Quality Model.” 

Environ. Sci. Technol., 43 (17), pp 6669-6675. 

doi: 10.1021/es9008129